esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
\"Vehicular Ad-hoc Networks\" (VANETs): As an active research area in the field of wireless sensor networks, they ensure road safety by exchanging alert messages about unexpected events in a decentralized manner. One of the significant challenges in the design of an efficient dissemination protocol for VANETs is the broadcast storm problem, owing to the large number of rebroadcasts. A generic solution to prevent the broadcast storm problem is to cluster the vehicles based on topology, density, distance, speed, or location in such a manner that only a fewer number of vehicles will rebroadcast the alert message to the next group. However, the selection of cluster heads and gateways of the clusters are the key factors that need to be optimized in order to limit the number of rebroadcasts. Hence, to address the aforementioned issues, this paper presents a novel distributed algorithm CDS_SC: Connected Dominating Set and Set Cover for cluster formation that employs a dominating set to choose cluster heads and set covering to select cluster gateways. The CDS_SC is unique among state-of-the-art algorithms, as it relies on local neighborhood information and constructs clusters incrementally. Hence, the proposed method can be implemented in a distributed manner as an event-triggered protocol. Also, the stability of cluster formation is increased along with a reduction in rebroadcasting by allowing a cluster head to be passive when all its cluster members can receive the message from the gateway vehicles. The simulation was carried out in dense, average, and sparse traffic scenarios by varying the number of vehicles injected per second per lane. Besides, the speed of each individual vehicle in each scenario was varied to test the degree of cohesion between vehicles with different speeds. The simulation results confirmed that the proposed algorithm achieved 99% to 100% reachability of alert messages with only 6% to 10% of rebroadcasting vehicles in average and dense traffic scenarios.  
Keywords:vehicular ad-hoc network; intelligent transportation systems; cluster-based information dissemination protocol; message dissemination; connected dominating set; set cover problem +
Footnote †: journal: Sensors
# Exact Method for Generating Strategy-Solvable Sudoku Clues
## 5 Two Improvements
### Incremental Approach  
Our method requires to fix a maximum step size, \(K\), in advance. Since it has a significant impact on constraint size, \(K\) needs to be as small as possible. However, for a small \(K\), our method may return false clues. To see this, let us take a look at Figure 3. Suppose that the left grid is an initial state and the right grid is a state in step \(K\). The right grid is not yet completed, but no constraint has been violated up to step \(K\). Since there is no further step, our method returns the initial grid, even if only a naked single strategy is allowed. Notice that if the right grid was in step \(K-1\) or less, our method would reject the initial grid (because the constraints for final states are violated).  
We propose a practical method using a smaller \(K\) while keeping the exactness. A basic idea is to repeatedly apply our original method while incrementing \(K\) from an initial number \(K_{min}\) untilexceeds a sufficiently large number \(K_{max}\). Algorithm 1 is a pseudo code for the method. Notice that once constraints become unsatisfiable, constraints with any lager maximum step size must be unsatisfiable.  
```
for\(K=K_{min}\) to \(K_{max}\)do  Generate constraints for a given SSC instance with maximum step \(K\). if the set of constraints is unsatisfiable then  return UNSAT elseif the grid in step \(K\) is completed then  return Sudoku clues in the initial grid. endif endfor
```  
**Algorithm 1** Incremental approach for the strategy-solvable Sudoku clues (SSC)
# Radar Voxel Fusion for 3D Object Detection
## 4 Results
### Ablation Studies  
This section evaluates additional training configurations of our proposed RVF network to measure the influence of the proposed training strategies. Table 3 shows an overview of the results.  
To study the effect of the introduced yaw loss, we measure the Average Orientation Error (AOE) as introduced by nuScenes. The novel loss reduces the orientation error by about 40% from an AOE of 0.5716 with the old loss to an AOE of 0.3468 for the RVF-Net. At the same time, our novel yaw loss increases the AP score of RVF-Net by 4.1 percent. Even though the orientation of the predicted bounding boxes does not directly impact the AP calculation, the simpler regression for the novel loss also implicitly increases the performance for the additional regression targets.  
Data augmentation has a significant positive impact on the AP score.  
Contrary to the literature results, the combined IoU and distance threshold decreases the network performance in comparison to a simple IoU threshold configuration. It is up to further studies to find the reason for this empirical finding.  
We have performed additional experiments with 10 lidar sweeps as the input data. While the sweep accumulation for static objects is not problematic since we compensate for ego-motion, the point clouds of moving objects are heavily blurred when considering 10 sweeps of data, as the motion of other vehicles cannot be compensated. Nonetheless, the detection performance increases slightly for the RVF-Net sensor input.  
For a speed comparison, we have also started a training with non-sparse convolutions. However, this configuration could not be trained on our machine since the non-sparse network is too large and triggers an out-of-memory (OOM) error.
## 5 Conclusions  
The results of the seismic response analysis, which was conducted to confirm the working condition of the bridge restrainer, show that the bridge restrainer did not work when assuming the breaking of all fixed supports. Further, the cases wherein the bridge restrainer worked were identified by considering breaking of the pier base as an unexpected situation in bridge design, which was reported to be a largely influential factor. Furthermore, there is a risk of bridge collapse when the bridge restrainer is installed between girders, even though the relative displacement between the superstructure and substructure does not reach the working gap of the bridge restrainer. However, the bridge restrainer could prevent bridge collapse when it is installed between the pier and the girder, although the working load exceeded the allowable design load. Some findings obtained from this study are as follows:  
1. In the existing Japanese design of bridge restrainers, the design load is determined only by using the uniform dead reaction load of the bridge, regardless of the setting location. However, in view of the dynamic influence of a massive earthquake, there is the potential for a large cable reaction force that exceeds the design load, and the actual load changes significantly in each setting position. Therefore, it is believed that the design of the bridge restrainer plays a central role in the fail-safe system.
2. This paper proposes a reasonable design method for bridge restrainers that employ seismic response analysis to determine the design load and the SGA for obtaining the optimal construction member. The results of a trial design using the proposed method indicate that the combination of members selected as the quasi-optimal solution differed according to the assumed unexpected situation and the applicable object function. Therefore, the designer should thoroughly examine the assumed condition of the target bridge.
3. The validity of the quasi-optimal solution obtained from the results of the trial design was proven because the obtained quasi-optimal solution agreed with the optimal solution obtained by a round-robin simulation employing the single-mass model.  
This paper deals only with the basic specifications of steel girders. Therefore, reliability improvement is necessary, and the applicability of the proposed method should be verified by conducting more trial designs for various types of bridges. By doing so, it is thought that the merits of this method, using GA, will become more apparent.  
Conceptualization, H.T. and Y.Y.; methodology, Y.Y.; software, C.L.; validation, H.T., Y.Y. and C.L.; formal analysis, Y.Y.; investigation, Y.Y.; writing--original draft preparation, Y.Y.; writing--review and editing, H.T.; visualization, Y.Y.; supervision, H.T.; project administration, H.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# A Random Forest-Based Precipitation Detection Algorithm for FY-3C/3D MWTS2 over Oceanic Regions
## 2 Data and Methods
### GEO-LEO Satellite Image Fusion Algorithm  
The FY-2F satellite has a geostationary orbit, whereas NOAA-19 has a sun-synchronous orbit. The optical paths and observation time of the VISSR and AMSU-A modules onboard these two different satellites for the same target are not exactly the same, and the projections of their detection pixels on the ground are also different. To make the observations of the VISSR and AMSU-A more comparable, the VISSR pixels and AMSU-A pixels are also paired; that is, they are matched to the same pixels and share the same observation time within a certain threshold range.
# Semantic Segmentation of Aerial Imagery Using U-Net with Self-Attention and Separable Convolutions
## 3 Proposed Method
### Proposed Model
#### 3.4.5 Final Predictive Layer  
Culminating in the final layer, a \(1\times 1\) convolutional operation with softmax activation consolidates the learned features into a pixel-wise segmentation map. This top-level synthesis of information encapsulates the model's predictive capabilities, offering a nuanced delineation of diverse classes within the satellite imagery.
## 3 DCS Scheme Families
### Tug-of-War  
In _Tug-of-War (ToW)_[27], the authors propose a data-centric mechanism where queries and events meet at a point that is selected based on the relative frequencies of events and queries. ToW operates in two modes referred to as _write-one-query-all_ and _write-all-query-one_. The former allows a sensor to store an event in the nearest mirror image but queries need to be disseminated to all mirror images. Conversely, in the latter operation node events must be stored in all mirror images to facilitate a query node that disseminates the query to the nearest mirror image. ToW takes its motivation from the Structured Replication (SR) mechanism in GHT. In SR, to alleviate a node's load, a detected event is stored in the nearest mirror image. This mechanism alleviates the storage cost but as a query node has no idea which image node may have the data, the query node needs to query all of the images and this increases the query cost. Like SR-GHT, a home node and a set of \(\mathcal{A}^{r}-1\) mirror images are assigned in ToW for each event class \(c\) and here \(r\) is referred to as the system resolution. The mode a sensor node will operate depends on \(r\). The system resolution is determined based on the relative query frequency and events detected for a particular class of event. To minimize the communication cost, ToW adjusts the rendezvous point on the fly on an optimal basis. With a given system resolution, nodes define the mode of operation while in the selected mode nodes need to find an optimal value of \(r\) in order to minimize the communication cost. In _write-one-query-all_ mode, the total communication cost \(C_{w_{1}Q_{all}}\) per unit interval is defined by:  
\[C_{w_{1}Q_{all}}\cong C_{c}+2C_{c}\] \[=f_{c}\cdot k\cdot\overline{\delta}\sqrt{n}\Big{/}\Big{/}\Big{/} \Big{/}\Big{/}\Big{/}\Big{/}\Big{/}\Big{/}\Big{/}\Big{(}2^{\prime}-\frac{1- \overline{\delta}}{2^{\prime}}\Big{)} \tag{3}\]  
By using an optimal \(r\), the cost of ToW in this mode per event is defined by:  
\[C_{w_{1}Q_{all}}\cong 2\sqrt{2k\overline{\  
selecting a more adaptive number of replicas. ToW is based on a geometric replication formula that calculates the number of replicas, \(N_{r}=d^{d}\), where \(d\) is the network-depth. Thus, the main drawback of ToW is its inflexibility in selecting the number of replicas, which are limited to 1, 4, 16, 64, and so on. In contrast, QAR calculates the number of replicas as \(N_{r}=d^{2}\) allowing the number of replicas to grow in a quadratic fashion. Furthermore, QAR provides a mathematical model that can find the optimal number of rendezvous nodes based on the ratio of consumption and production traffic.
# Multi-Level Elasticity for Wide-Area Data Streaming Systems: A Reinforcement Learning Approach
## 3 System Model and Problem Definition
### Infrastructure Model  
For the execution, a DSP application needs to be deployed on computing resources, which will host and execute the operators. In a large-scale environment, multiple cloud data centers and fog micro-data centers provide computing resources on-demand. A DSP system can be deployed over multiple data centers to exploit the geographic distribution of the infrastructure, and acquire resources closer to data sources and consumers. We indicate a data center or a micro-data center as a deployment region (or region, for short). A _region_ contains computing resources that are close to each other, meaning  
Figure 1: Model of a DSP application and representation of the multi-level elasticity problem: the application operators (i.e., vertices of the application DAG) should be conveniently replicated at run-time, to satisfy performance requirements, and deployed over an elastic set of computing resources.  
that they can exchange data using local area networks, which result in negligible communication delay. Computing resources belonging to different regions exchange data using wide area networks, with not negligible communication delay.  
We assume that a region contains virtual computing resources (i.e., either virtual machines or container running on physical machines), which can be acquired and released as needed, thus enabling _infrastructure elasticity_. For the sake of simplicity, in this paper, we assume that within the same region the acquired computing resources are homogeneous. Nevertheless, the proposed approach can be extended to consider the case of heterogeneous computing resources within the same region, accounting for different prices and capacities. Each region allows acquiring and release computing resources as needed; nevertheless, in accordance with the limits imposed by nowadays cloud service providers, we assume that the number of virtual resources that can be acquired in a certain time period is limited (e.g., at most 50 new virtual machines can be acquired at once). Regions well model nowadays wide-area distributed computing infrastructures: for example, the large-scale computing infrastructure of a cloud provider can be modeled as a multitude of regions, where each region includes resources of a specific availability zone within a single data center. Considering fog/edge computing scenarios, regions can also model geographically distributed micro-data centers, each one composed by a set of servers placed at network edges.
# An Algorithm to Generate a Weighted Network Voronoi Diagram Based on Improved PCNN
## 3 Improved PCNN
### Structure of the Improved PCNN  
The principle of the improved PCNN can be summarized in the following three aspects: (1) all points in the point cluster and all nodes in the road network are considered as the neurons, in which the points in the point cluster are treated as initial neurons; (2) the initial neurons will generate an auto-wave simultaneously, and the auto-wave transmits along the road segments from a neuron to another at a certain speed; (3) the transmission speed of the auto-wave on a certain road segment is determined by the importance of its initial neuron and the attributes of the passed road segments.  
The structure of the improved model is shown in Figure 3, and it can be represented with the following equations.  
\[E_{p_{m}}[n+1]=\left\{\begin{aligned} V_{E}&\text{if }Y_{p}[n]=1\\ \min(S[n]+W_{qp}[n],E_{p}[n])&\text{if }Y_{p}[n]=0,Y_{q}[n]=1 \text{ and }p\in R_{q}\\ E_{p}[n]&\text{otherwise}\end{aligned}\right. \tag{8}\]  
\[S[n+1]=S[n]+\Delta S[n] \tag{9}\]  
\[Y_{p}[n+1]=\left\{\begin{aligned} 1&\text{if }S[n+1]\geq E_{p}[n+1]\\ 0&\text{otherwise}\end{aligned}\right. \tag{10}\]  
\[W_{qp}[n+1]=\left\{\begin{aligned} V_{E}&\text{if }Y_{p}[n+1]=1 \text{ and }p\in R_{q}\\ W_{qp}[n]&\text{otherwise}\end{aligned}\right. \tag{11}\]  
where \(n\) is the iteration number and \(E_{p}\) stands for the dynamic threshold of neuron \(p\). \(V_{E}\) represents a constant with a large value. \(\triangle S\) is the speed of the current auto-wave. \(S\) is the distance between the initial neuron and the current iteration. \(Y_{P}\) is determined by the comparison between the current wavelength \(S\) and the threshold \(E_{p}\). \(W_{pq}\) represents the connection weight between neuron \(p\) and neuron \(q\). \(R_{q}\) is the neighbor set of the neuron \(q\).
## 3 Materials and Methods
### Bidirectional Temporal Convolutional Networks  
TCN comprises three primary components: causal convolution, dilated convolution and residual connections [19]. These components can amalgamate the strengths of CNN and RNN. Nevertheless, traditional TCN solely conducts forward convolution operations on input sequences, thereby capturing only the forward data features of traffic sequences while disregarding potential hidden features in the reverse direction. Consequently, this article introduces bidirectional convolution to capture bidirectional data features [20]. Compared to TCN, BiTCN enhances the feature fusion effect on the bi-directional time-series information of network traffic by processing past and future data separately through  
Figure 2: The whole process of the CPO-BiTCN-BiLSTM-SA model.  
two temporal convolutional networks, which enables the model to consider both forward and backward information in the time series, as well as capture the dependencies in the time series more comprehensively. The architecture of the bidirectional dilated causal convolutional is depicted in Figure 3.  
The causal property of causal convolution dictates that an input traffic sequence \((x_{0},x_{1},\dots,x_{\tau})\) traverses through the network to produce outputs \((y_{0},y_{1},\dots,y_{\tau})\), with each output at any given time step being dependent on the inputs preceding the current time point. To address the network's limitations in capturing long-range dependency information in traffic data, inflated convolution is introduced. This allows for the acquisition of a larger receptive field with fewer layers, thus facilitating the efficient extraction of deeper information from local to global scales, thereby enhancing the model's expressive capacity. Simultaneously, this approach renders the network more lightweight and enhances computational efficiency. When dealing with one-dimensional data \([x_{0},x_{1},\dots,x_{\tau}]\), the output value of the hidden layer at time step \(t\) was depicted, as shown in Equation (1).  
\[h(t)=\sum_{i=0}^{5-1}f(i)\cdot x_{t-d\cdot i}, \tag{1}\]  
where \(f(i)\) denotes the convolution kernel element at position \(i\), \(S\) represents the size of the convolution kernel, and \(d\) the expansion factor.  
To circumvent the problem of losing important original features during the information extraction process, residual connectivity was introduced, which can improve the stability of the model in the process of dealing with long sequential traffic data, thus accelerating the response and convergence of the deep network, as well as mitigating the issue of vanishing gradients. Furthermore, we integrated dropout and batch normalization techniques to prevent model overfitting and to expedite training. The structure of the residual connection is illustrated in Figure 4.  
BiTCN addresses the limitations of traditional TCN, which solely extract unidirectional traffic information, by enhancing the extraction of traffic sequence features.  
Figure 3: Bidirectional inflated causal convolutional network structure.
## 1 Introduction  
Aero-engine casing is a kind of thin-walled rotary part for which service stability is critical to service safety and engine reliability. Severe deformation often occurs in the machining of an aero-engine casing part due to the influence of both initial residual stress and machining residual stress, which negatively impacts the performance and service life of an aero-engine [1, 2]. As deformation force is an important physical quantity associated with deformation, its monitoring can provide useful information for deformation control.  
With the material being removed during machining, the balance of residual stress is broken, which leads to part deformation. While under the fastened state, the deformation of a part is restricted within the limits of the fixtures, and the residual stress is balanced by the clamping force. The component in the clamping force resulting from the unbalanced residual stress is commonly referred to as the deformation force, which can be used to represent the state of unbalanced residual stress inside the workpiece [3]. Due to the structural characteristics of aero-engine casings, the most useful deformation force for deformation control of an aero-engine casing is in the radial direction. By integrating force sensors into the fixture, the deformation force can be monitored. The radial deformation force of the aero-engine casing can be monitored by introducing force sensors in the radial auxiliary supports.  
Past research results notwithstanding, the accurate acquisition and monitoring of deformation force during the machining process of an aero-engine casing remains a challenging problem. As the auxiliary supports of aero-engine casing fixtures acting on the sidewalls can only provide a one-way support force, the part and fixtures may be separated from each other during machining, which makes monitoring the deformation force impossible. In this paper, aiming to prevent the fixture and the part from separating, a deformation force monitoring method for aero-engine casing based on the idea of applying a pre-support force is proposed. However, as the deformation cannot be determined in advance, the deformation force monitoring method based on relying on a pre-support force is faced with the issues of low monitoring success rate and the clamping deformation that affects the machining accuracy. To solve the above problems, this paper offers the following two innovations: (1) a set of sophisticated clamping devices with force sensors are specifically developed; and (2) the pre-supporting force is decided by an equivalent flexibility and a probabilistic model which predicts the subsequent deformation force based on the preceding deformation forces. In addition, considering the modeling and measurement errors, fusing filtering algorithms to neural networks [4; 5; 6] can remove data noise and restore the original data, which can improve the performance of the model. Therefore, the Kalman filter algorithm is applied to reduce the modeling and measurement errors.
# Remote Radio Frequency Sensing Based on 5G New Radio Positioning Reference Signals
## 5 Results
### Reflection Gain Impact on PNFR Level  
In this section, the impact of reflection gain on the peak-to-noise floor ratio is measured. For this purpose, a radar target from Table 4 was chosen (with a velocity of 32.53 m/s and a range of 213.42 [m]), and simulations were performed with three different levels of reflection gain: \(\frac{0.01*(1+1)}{\sqrt{2}}\), \(\frac{0.005*(1+1)}{\sqrt{2}}\), and \(\frac{0.001*(1+1)}{\sqrt{2}}\). The legend of the figures is supplemented with the suffix \(\mathrm{G}=\mathrm{X}\), where \(\mathrm{X}\) denotes the amplitude of the reflection gain for which the PNFR was measured.