esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Efficient Clustering Multi-Hop Routing Protocol in a UWSN
## 4 Performance Evaluation
### Simulation Environment  
The network scenario consists of dynamic nodes deployed sparsely in a three-dimensional environment. The water current causes node movements, which change the topology rapidly. The sink is located at the center of the surface, as shown in Figure 1. The simulation parameters are shown in Table 3. According to the sensed data, the node can generate one or two packets for transmission. The transmission range of each node varies from 150 to 200 m [10; 12]. The underwater sensor nodes are mobile, with the current at 1-3 m/s, which causes changes in the network deployment [10]. In the study, the network deployment was changed after every 100 rounds in order to evaluate the network's mobility. The transmission range of nodes was varied in order to evaluate different network scenarios.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Protocols** & **Parameters to Select the Cluster Head** & **Network** & **Assisted Node** & **Energy** \\ \hline LEACH [5] & Random value & 2D & None & Yes \\ DBR [7] & Depth-based, holding time of packets & 3D & None & Yes \\ EERBLC [9] & Residual energy, random value, & & \\ number of layers & 3D & Two sinks & Yes \\ C-LEACH [10] & Distance, location of the node & 2D & Control node & Yes \\ MLCEE [12] & Fitness value, small Hop\_ID and low number of layers & 3D & Two sinks & Yes \\ TCEB [13] & Residual energy, number of neighbors, path loss & 2D & None & Yes \\ EOCA [14] & Number of neighbor nodes, residual energy, distance to & & \\  & the sink node & & & \\ EULC [15] & Residual energy, distance to & & & \\ S-BEAR [16] & Distance to the sink & 2D & None & Yes \\ CVBF [18] & Residual energy, vector-based routing & 3D & Virtual sink & Yes \\ EECMR & Residual energy, duration of the cluster head, & & & \\  & distance to the sink & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Qualitative comparison of clustering protocols.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Parameters** & **Value** \\ \hline Network deployment area & 500 \(\times\) 500 \(\times\) 500 m \\ Number of nodes & 50 to 450 \\ Generated packets at each node & 1 or 2 data packets \\ Transmission range & 150 m, 200 m \\ Acoustic frequency & 30 kHz \\ Transmit power & 2 W \\ Receive power & 0.1 W \\ Initial node energy & 5 Joules \\ Number of sink nodes & 1 \\ Data packet size & 200 bits \\ Rounds & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.  
An example of a network after the implementation of EECRM is shown in Figure 3. Each node is represented as a CM, CH, or CR. The sink is located at the surface, which is represented as a red star in the figure. In order to easily visualize the cluster, we deployed 50 nodes in a volume of 100 \(\times\) 100 \(\times\) 100 m. We assumed that the nodes nearest to the sink would become the main forwarder for other nodes, the nodes at the seabed would select the cluster to join, and some nodes at the border of two depth layers would act as the relay which connects the cluster head of the deeper cluster to the sink.
# A Comprehensive Survey on Knowledge-Defined Networking
## 9 Conclusions, Recommendations, and Future Research  
This paper presents a comprehensive survey of knowledge-defined networking. We first presented its high-level architecture and compared KDN with SDN and traditional networking. The functions, protocols, sub-planes, models, interfaces, languages, etc. of each plane were presented with real-world examples from the existing literature. Moreover, we identified practical applications of KDN in diverse domains such as traffic engineering, network management, security, big data, cloud computing, network virtualization, and business. Among these applications, traffic engineering, network management, and security stand out as the three most dominant applications in KDN, having many applications under various sub-categories. For instance, generic application traffic engineering has routing, load-balancing, and QoS provisioning as specific applications. Furthermore, we discussed benefits, limitations, and design guidelines for KDN. Finally, we also discussed some ongoing efforts by several parties to standardize KDN in order to make it more interoperable. As far as academic implications, this research provides a comprehensive survey and a tutorial for knowledge-defined networking. This research can be used as a very useful reference for future academicians during the innovation of new concepts related to knowledge-defined networking in different application domains. Furthermore, this work can be used to identify poorly researched or developed domains in KDN and perform more research in those areas. Additionally, this research will pave the way to point out and highlight challenges in implementing KDN and how to overcome them, which researchers can further investigate to mitigate the challenges. Based on this survey, the following recommendations can be provided for knowledge-defined networking:  
* Attention from experts is recommended to make sure KDN systems collect large quantities of high-quality data in terms of relevancy, completeness, unbiasedness, consistency, and non-outdatedness;
* In instances where legacy systems cannot be directly converted to pure KDN, a hybrid KDN approach that implements a blend of legacy and pure KDN is recommended;
* In order to secure data, knowledge, and machine learning models, robust data protection techniques such as access control, encryption, and intrusion detection systems should be used. Furthermore, for secure data, knowledge, and model sharing, KDN systems can integrate distributed secure blockchain technology;
* It is recommended to implement interoperable APIs to improve the interoperability of KDN systems that may use different standards to implement the KDN;
* Distributed knowledge generation (federated learning) and dissemination are recommended to improve the scalability of KDN systems;
* In order to improve the reliability of KDN systems, the machine learning models are recommended to be trained using high-quality data and tested thoroughly, evaluating performance evaluation metrics such as accuracy, error, F1-score, etc., before deployment in the network;
# An Improved CNN Model for Within-Project Software Defect Prediction
Specifically, we first compiled source code to ASTs and followed existing strategies to select them and form token vectors such that the token vectors act as a summarization of source files. Then, we converted the code vector strings into integers, which were then sent to our CNN model as input. Our CNN model comprised a word embedding layer, three convolutional layers, three max-pooling layers, four dense layers, and three dropout layers. Finally, the generated features produced by the CNN model were fed into a logistic regression classifier to predict whether a source file was buggy. We first evaluated our method on the Simplified PROMISE Source Code (SPSC) dataset, which was used in a paper by [PERSON] [24], to confirm that our CNN model was comparable to or better than existing state-of-the-art models in terms of F-measure (also known as F1 score). Then, we evaluated our model on the PROMISE Source Code (PSC) dataset, which was derived from the PROMISE repository [30], to target AST-based features in terms of F-measure, G-measure, and Matthews correlation coefficient (MCC). The experimental results indicated that our model was comparable to the existing CNNmodel, and outperformed various traditional machine learning models including the random forest, naive Bayes, logistic gegression, RBF network and decision tree models for WPDP. Furthermore, we discovered that different hyperparameter settings of a CNN model could generate similar average results for all projects but is markedly different in terms of individual project or version, which we referred to as hyperparameter instability. We concluded that hyperparameter instability may present a threat and an opportunity for deep learning models on defect prediction. Our study made the following contributions:  
* We proposed an improved CNN model for better generalization and capability of capturing global bug patterns. The model learned semantic features extracted from a program's AST for defect prediction. * We built and published a dataset named PSC, which targeted AST-based features from the PROMISE repository based on five principles. The PSC dataset was larger than the SPSC dataset, and we excluded versions for which source code could not be found, or the labeled CSV file did not match the source code. * We performed two experiments to validate that the CNN model could outperform the state-of-the-art machine learning models for WPDP. The first experiment demonstrated the validity of our improved model, while the second experiment validated the performance of our improved CNN model as compared with other machine learning models. * We performed a hyperparameter search, which took dense layer numbers, dense layer nodes, kernel size, and stride step into consideration to uncover empirical findings. * We concluded that hyperparameter instability may be a threat and an opportunity for deep learning models on defect prediction. The discovery was based on experimental data and targeted deep learning models like CNN.
# Research on Clock Synchronization of Data Acquisition Based on NoC
## 3 The Proposed Design Clock Synchronization Scheme
### Improved White Rabbit Protocol  
The traditional White Rabbit clock synchronization protocol is mainly used for clock synchronization of off-chip independent systems, and it transmits the reference clock and clock message information via Ethernet. Both the transmission frame format and the link delay is complex [28; 29].  
In this paper, the clock synchronization of multiple clock domains on the NoC is carried out, and the clock message information is transmitted inside the chip. Therefore, the White Rabbit protocol is improved and applied in the data collection based on the NoC.  
Firstly, in the traditional White Rabbit protocol, Sync-E transmits the clock by embedding the clock into the data link. But in this paper, the clock is synchronized inside a chip, and the clock can be transmitted only through the internal physical circuit, so the Sync-E technology is omitted.  
In this paper, since the clock message information is only transmitted within the chip, the PTP message can be simplified, as shown in Figure 8. The simplified PTP format is shown in Table 1.  
Figure 8: PTP message composition. PTP message contains three parts, namely, PTP header, PTP body, and suffix.  
Figure 7: Message transmission link delay. T1 and T3 are the time when the corresponding node sends a message, T2 and T4 are the time when the corresponding node receives information. There are delays t1, t2, t3, t4, t5, and t6 in the link of switched messages, where the transmission delays of messages in the same link are much the same. However, since the forwarding node is dealing with the receiving and sending of multiple messages from the slave node, it needs to wait for the arbitration output, so t2 is not necessarily equal to t5.  
The bit width of each message is 34 bits, in which the first 3 bits are the clock message type encoding, the last 8 bits are the address of the source node and destination node of the message, and the other 23 bits are the timestamp information. In addition, the destination node addresses of the sync and follow_up packets are set to 4'b1111, and messages are sent to each slave node simultaneously by broadcasting. In addition, various delay calculations in the Ethernet transmission link are reduced.  
The DDMTD used in this paper is consistent with the traditional White Rabbit protocol and needs no improvements.
# A Time Integration Method Based on Galerkin Weak Form for Nonlinear Structural Dynamics
## 1 Introduction
[PERSON] has developed a framework for constructing a direct integration method based on weighted residuals of the governing equations [33]. [PERSON] [34] has proposed a direct integration method based on Galerkin weak form for linear dynamics, named the GW method. [PERSON] and [PERSON] have formed a one-step time FEM by setting acceleration, velocity, and displacement to be linear, quadratic, and cubic Taylor polynomials, respectively, which has been applied to both linear [29] and nonlinear [35] structural dynamic problems. [PERSON] has proposed a time FEM with inherent energy conservation based on [PERSON]'s canonical equations combined with the continuous Galerkin (cG) method [36], which has been successfully applied to nonlinear elastodynamics [37]. Different from the traditional Galerkin FEMs with continuous state variables in the time domain, [PERSON] and [PERSON] [38] and [PERSON] and [PERSON] [39], have proposed the discontinuous Galerkin (dG) FEM for time-dependent problems by using discontinuous trial and test functions. The transition variables between two elements are discontinuous, based on which a step-by-step integration method with energy decay [40] was established. Up to now, the development of a reliable and effective numerical algorithm for nonlinear dynamic equations is still a core research subject in structural dynamics [5; 10]. In the present paper, a time integration method based on Galerkin method for solving linear/nonlinear IVPs is proposed and presented. A set of implicit time integration schemes have been derived with time elements of degree \(m=1,2,3\) being used. By means of the Newton-Raphson method, the computational iterative procedure is formed, and then the accuracy and numerical stability of the algorithm are studied and analyzed, after which an integration scheme with controllable numerical damping is constructed for linear elements. Typical numerical examples, including the simple pendulum, a long span planar skeletal structure, and a cantilever beam, are solved to test the characteristics of the proposed method against some other representative time integration schemes including the trapezoidal rule, [PERSON] method (\(\gamma=1/2\), \(\beta=1/6\)) [1], [PERSON] \(\theta\)-method (\(\theta=1.4\)) [41], and [PERSON] method (\(\gamma=1/2\)) [5]. The results demonstrate the merits of the proposed method in terms of both stability and efficiency. In the following sections of the paper, these contents will be presented one by one.
## 4 Algorithm Design
### Greedy-Based Local Search  
There is a deviation in the position of the ABS, so Equation (23) does not yield a unique solution. The set \(F_{t}\) of this group of circles is  
\[F_{t}=\{f_{k}(x,y,z)|k\in N,\frac{(x_{k}x+y_{k}y+z_{k}z)^{2}}{R_{k}^{2}(x_{k}^{ 2}+y_{k}^{2}+z_{k}^{2})(x^{2}+y^{2}+z^{2})}=1\}, \tag{27}\]  
where \(rc=[x_{k},y_{k},z_{k}]\) are the center coordinates and \(N\) is the number of circles. Considering the offset of the ABS position, the possible set of offset circles is  
\[F_{t}^{\prime}=\{f_{k}^{\prime}|k\in N^{+},f_{k}^{\prime}(x,y,z)=f_{k}(x- \delta_{x},y-\delta_{y},z-\delta_{z})\}, \tag{28}\]  
Use \(Fxy_{t}\), \(Fyz_{t}\), and \(Fxz_{t}\) to represent the projection equations of \(F_{t}\) on the x-y, y-z, x-z plane, and then calculate the similarity of projection between \(F_{t}\) and \(F_{t}^{\prime}\), where the similarity is expressed by the arithmetic mean of the overlapping areas of the projections:  
\[\rho_{k}=\frac{1}{3k}\sum_{i=1}^{k}(\rho 1_{k,i}+\rho 2_{k,i}+ \rho 3_{k,i}), \tag{29a}\] \[\rho 1_{k,i}=\int_{x_{1i}}^{x_{2,i}}|Fxy_{t,i}-Fxy_{t}|\,dx,\] (29b) \[\rho 2_{k,i}=\int_{y_{1,i}}^{y_{2,i}}|Fyz_{t,i}-Fyz_{t}|\,dy,\] (29c) \[\rho 3_{k,i}=\int_{z_{1,i}}^{z_{2,i}}|Fxz_{t,i}-Fxz_{t}|\,dz, \tag{29d}\]  
where the upper and lower bounds of the integral are the intersection points between the projections. Furthermore, the weight of each ring can be expressed as  
\[\omega_{k}=\frac{\omega_{k}^{\prime}}{\sum_{j=1}^{N}\omega_{j}^{ \prime}}, \tag{30}\] \[\omega_{k}^{\prime}=1-\frac{1}{1+\exp^{\frac{\rho_{k}-\rho max}{ \rho max}}}, \tag{31}\]  
where the closer \(\omega_{k}\) is to 0, the more similar the ring is to the result calculated using the wrong position, that is to say, the greater the possibility of error. Therefore, we assign small weights to reduce the impact in subsequent calculations, so as to select the optimal method in a single iteration. Then, based on the movement speed of the UAV, use the center of circle \(rc_{k}=[x_{k},y_{k},z_{k}]\) obtained in Formula (32) multiplied by \(\omega_{k}\) and get coordinates:  
\[rc^{\prime}=\sum_{k=1}^{N}(\omega_{k}r\dot{c}_{k})+\mathbf{v}_{\mathbf{c}\mathbf{ t}}\Delta t+\kappa_{k,t}, \tag{32}\]  
Combine the coordinates with the AP's SRP to form a vector \(\mathbf{r}\):  
\[\mathbf{r}=[a,d], \tag{33}\] \[\mathbf{r}^{\prime}=rc^{\prime}-C_{0,c},\] (34) \[\alpha=\arccos\frac{\mathbf{p}_{\mathbf{c}\mathbf{t}}\dot{\mathbf{ r}}^{\prime}}{|\mathbf{p}_{\mathbf{c}\mathbf{t}}||\dot{\mathbf{r}}^{\prime}|},\] (35) \[d=|\mathbf{r}^{\prime}|, \tag{36}\]  
where \(\mathbf{r}\) is the next adjustment direction and step size of the UAV numbered \(c\), so the encoding for it becomes  
\[C(c)=\mathbf{p}_{\mathbf{c}\mathbf{t}}+\mathbf{r}^{\prime}, \tag{37}\]  
So far, a greedy search for a single UAV is completed.
# A Testbed for GNSS-Based Positioning and Navigation Technologies in Smart Cities:
## 6 Test Campaigns and Results
### DGNSS-Based Cooperative Positioning  
Concerning CPS and CPA, baseline computation tests were initially performed to check the consistency of the information propagated by the smartphones through the network [54]. Two test-campaigns have been performed afterwards by relying on different networks and infrastructure:  
* Turin-to-Dublin (TRN-DUB) co-located agents (i.e., Politecnico di Torino Campus, Turin, Italt) and remote _testbed_ running on an AWS(tm) (Dublin, Ireland), trough 4G/LTE cellular connectivity;
* Barcelona-to-Barcelona (BRC-BRC) co-located agents and _testbed_ (i.e., Universitat Autonoma de Barcelona (UAB) campus, Barcelona, Spain), trough 4G/LTE cellular connectivity.
## 5 Conclusions  
1. Lateral environmental wind generates a yaw force, yaw moment, and roll moment. These forces and moments are minimal at lower wind speeds (less than 3 m/s) but become more noticeable and cannot be ignored at higher wind speeds (greater than 4.5 m/s). However, the yaw force, yaw moment, and roll moment generated by the athlete are dominant, while the influence of the skis is minimal. Additionally, lateral wind affects the lift, drag, and pitch moment of the athlete. The impact is minimal at lower wind speeds but becomes more evident and cannot be neglected at higher wind speeds, although it has almost no effect on the skis.
2. Under the conditions of asymmetric postures, the multi-body system experiences a noticeable yaw force, yaw moment, and roll moment, while also significantly affecting the lift, drag, lift-to-drag ratio, and pitch moment of the system. Among these effects, the adverse impact of roll rotation is generally more significant than that of yaw rotation, given the same rotation angle.
3. The multi-body system exhibits yaw self-stabilization and roll self-stabilization phenomena, which can provide a solution for maintaining flight stability. This can be achieved by adopting an appropriate yaw rotation angle and/or roll rotation angle to partially or even completely counteract the adverse effects of lateral environmental wind.
4. This study, utilizing CFD numerical simulations, presents the first investigation into the mechanisms underlying the effects of unfavorable conditions on the aerodynamic characteristics and stability of the flight phase in ski jumping. It provides important scientific guidance for training athletes in achieving stable flight and enhancing their sport performance. Furthermore, it facilitates the formulation of effective technical requirements to improve flight stability and offers valuable support for real-time prediction and decision making during competitions.  
Formal analysis, Q.H.; Methodology, Q.H. and W.T.; Writing--Original Draft Preparation, Q.H. and W.T.; Writing--Review and Editing, Q.H., W.T. and Y.L.; Funding Acquisition, Q.H. and Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 11932013 and 11802068, and the National key research and development plan project of China, grant number 2018 YFF0300500.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 4 Discussion
This means that the complete recovery-time point can be evaluated differently depending on the monitoring indicators chosen. The NBR\({}_{2.1}\) and NBR\({}_{1.6}\) values exceeded the levels of the reference area in 2017, but NBR\({}_{2.1}\) reached the reference level earlier than NBR\({}_{1.6}\). Given that the 2.1 \(\upmu\)m wavelength is more sensitive to water at low moisture levels than the 1.6 \(\upmu\)m wavelength [54], NBR\({}_{2.1}\) might be able to detect the recovery of moisture conditions in a forest earlier. On the other hand, the NDVI almost reached the reference level in 2017, but it did not exceed the level during our analysis period. Therefore, the complete recovery-time point indicated by the NBR was faster than that of the NDVI. This result is consistent with the results of previous studies [16]. [PERSON] [44] compared two camera images taken immediately after and six years after the East Coast fire, and a lot of the area had been recovered by vegetation, but canopy height was still low. Although surface moisture conditions are generally preserved under vegetation cover, the complete recovery of vegetation biomass and health conditions will be achieved after the recovery of the moisture conditions in the forest. [PERSON] [55] showed that the complete recovery of vegetation after a severe forest fire required more than 20 years. Immediately after the East Coast fire, GPP was reduced to an extent dependent on the severity of the fire. This was consistent with the result of a previous study [56]. The GPP in the burned areas reached reference levels in 2004. This was the fastest recovery among NBR\({}_{2.1}\), NBR\({}_{1.6}\), and NDVI. Further, after 2015, the GPP in burned areas was much higher than the GPP in the reference area, particularly in areas with high fire severity. This result could have been caused by technical and ecological factors. First, the fraction of absorbed photosynthetically active radiation (fAPAR), estimated based on the relationship with NDVI, is an important parameter for calculating MODIS GPP. However, the recovery pattern shown by the NDVI was different than that of the GPP. We did not test the GPP algorithm in this study, but [PERSON] [11] argued that satellite-based GPP can be influenced by canopy structure. Second, the GPP of young forests after disturbances, such as forest fire, shows dramatic growth, while GPP in older forests slightly declines [57; 58].