esa-sceva/satcom-chunk-collection huggingface dataset:
# Role of Radio Telescopes in Space Debris Monitoring: Current Insights and Future Directions
## 4 Radio Telescope Measurements with Illuminators of Opportunity
### Lofar  
The Netherlands Institute for Radio Astronomy, ASTRON, developed and built the LOw-Frequency ARray for radio astronomy (LOFAR), an international network of radio telescopes. LOFAR began operations in 2012 and currently comprises 52 independent stations. Of these, ASTRON manages 38 stations, whereas the remaining 14 are international stations situated across Europe [87; 88]. Earlier research conducted by a group of researchers at the Warsaw University of Technology (Poland) explored the application of LOFAR radio telescopes as passive radar receivers [89]. Initially, the focus was on the possibility of detecting airplanes, but the research was expanded to include the observation of space objects traveling in LEO. The research team carried out experimental trials to validate the concept, utilizing a LOFAR station as the receiver and commercial digital audio broadcasting (DAB) transmitters as illuminators of opportunity for aerial object detection [90; 91].  
In a previous study, researchers employed the LOFAR station PL610 in Borowiec, Poland, and a proximate DVB-T transmitter to detect ISS [92; 93]. The utilization of DVB-T offers the advantage of providing a broader bandwidth and potentially more precise range measurements compared with previously utilized DAB+ transmitters [93]. The cross-ambiguity function (CAF) calculated in [92] is displayed in Figure 16, showing its appearance both prior to and following the implementation of Direct Path Interference (DPI) and clutter cancellation techniques. Figure 16a clearly shows the presence of DPI and clutter elements. In contrast, Figure 16b illustrates the successful removal of these interferences, demonstrating the efficacy of the cancellation method. This enhancement results in a clearer CAF, which allows for a more precise identification of target signals in the vicinity of the zero bistatic range and velocity. This enabled the detection of weak signals from distant objects. The successful detection of ISS across a wide range highlights the capability of passive radar systems for space surveillance [94; 95]. Recent studies have further demonstrated the potential of this approach by detecting Starlink satellites [96], which have significantly lower radar cross-sections than the ISS, showcasing the feasibility of exploiting the existing radio astronomy infrastructure to enhance space situational awareness.
## 5 Simulations and Comparison
### Vehicle Models and Test Conditions
#### 5.1.3 Comparison with Another Controller
Note however that the mean kinetic energy is comparable for both controllers. Similar observation can be made for the Kambara vehicle (but the mean kinetic energy is slightly larger for the proposed algorithm). An additional test was done for velocity disturbances \(V_{x}=0.4\) m/s, \(V_{y}=-0.5\) m/s. In this case, it was found that the results are much worse, which means significantly larger tracking errors (according to the assumed indexes) and a significant increase in average kinetic energy (mostly more than doubled, with the exception of the cycloid trajectory for Kambara). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **SIRENE** & **Kambara** & **Kambara** \\  & & **Linear t.** & **Linear t.** & **Cycloid t.** \\ \hline
**Index** & & **C. Dist.** & **C. Dist.** & **C. Dist.** \\ \hline MIA & \(x_{e}^{E}\) & 0.6669 & 0.5464 & 0.0953 \\  & \(y_{e}^{E}\) & 0.6032 & 0.4637 & 0.2962 \\  & \(\psi_{e}\) & 0.4111 & 0.3167 & 0.1988 \\ MIAC & \(\tau_{u}\) & 0.0117 & 0.3051 & 0.3627 \\  & \(\tau_{r}\) & 0.0196 & 0.0117 & 0.0246 \\ RMS & \(||e||\) & 1.2537 & 0.9804 & 0.4147 \\ KE & \(K_{m}\) & 252.74 & 16.377 & 24.452 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance for SIRENE and Kambara. Figure 12: Simulation results for Kambara, CL controller and cycloid trajectory: (**a**) desired and realized trajectory; (**b**) position errors; (**c**) position error states; (**d**) velocity error states; (**e**) velocities; (**f**) applied force and torque.
# Assisting Drivers at Stop Signs in a Connected Vehicle Environment
## 4 Assisting Drivers around Stop Sign Intersection  
In this section, first, we summarize the driving rules and the priorities of passing through the stop sign intersections. Then, the successive steps of the intelligent proposed driving assistance protocol are presented in detail. The main assumption of the proposed protocol is using vehicular network technology to communicate among traveling vehicles and installed infrastructure (i.e., stop signs). Wireless transceivers are assumed to be equipped in these entities over the network. Moreover, cameras, GPS receivers, digital maps, and wireless transceivers are assumed to be installed on intelligent traveling vehicles in this protocol. These types of equipment are responsible of gathering the traffic data of the investigated area of interest. Algorithm 1 systematically illustrates the steps of the proposed protocol.  
```
Data:\(V_{i}\): traveling vehicle; \(V_{i}\).Loc: location of \(V_{i}\); \(V_{i}\).RL: relative location of \(V_{i}\) on which input traffic flow; \(Int_{T}\): intersection topology; \(InF\): input traffic flow; \(Int_{Stop}\): input traffic flow with a stop sign; \(Road_{M}\): main road \(InF\) with no stop sign; \(R_{M}\).2D: two-directional main road, \(R_{M}\).1D: one-directional main road.
1 Configures \(Int_{T}\);
2 Extracts all \(InF\)s and determines \(Int_{Stop}\)s;
3 Checks \(V_{i}\).Loc and \(V_{i}\).RL;
4 if\(V_{i}\) is located on \(In_{Stop}\)then
5\(V_{i}\) should come to complete STOP;
6 else
7 if\(V_{i}\) is located on \(R_{M}\).2 Dthen
8 if\(V_{i}\) wants to turn LEFTthen
9\(V_{i}\) should come to complete STOP;
10\(V_{i}\) should wait for a safe GAP;
11 else
12\(V_{i}\) can proceed directly through \(Int_{T}\);
13
14 end if
15 else
16\(V_{i}\) can proceed directly through \(Int_{T}\)]
17
18 end if
19
20 end for
```  
**Algorithm 1**Driver's assistance protocol around stop sign road intersections
# MBMQA: A Multicriteria-Aware Routing Approach for the IoT 5G Network Based on D2D Communication
## 3 System Model
### Back-Pressure Routing
#### 3.1.2 Link Scheduling  
In the second stage, a set of links are selected to be activated simultaneously among all non-conflicting links in the network. For each TTI (transmission time interval), the transmission rates allocated to the maximal weight of link \((i,j)\) will be set, while the optimal commodity (data stored in a backlog queue of \(i\) node that is destined for \(j\) node) for any link will be solely transmitted. As a result, the back-pressure max weight schedule could be described as follows:  
\[\mu_{(i,j)}^{f_{c}}(t)=\max\sum_{i\in N}\sum_{j\in N}\mu_{(i,j)}(t)\times W_{ (i,j)}(t) \tag{3}\]  
For each link \((i,j)\in E\), a transmission rate \(\mu_{(i,j)}^{f_{c}}(t)\) is given to the corresponding flow \(f_{c}\), while the flow is referred to the flow selected for the maximal weight of link \((i,j)\) during the transmission.
## 1 Introduction
Additionally, in the task of processing graph-structured data, GNNs often encounter the issue of over-smoothing [13] when trying to increase depth. This results in overly similar feature representations between different nodes, reducing the model's discriminative ability and expressiveness. To tackle this problem, a skip-knowledge module needs to be introduced into the SSP-AA model. This module allows the model to flexibly select and integrate features from different layers, greatly enhancing the expressiveness of node representations. It enables the SSP-AA model to demonstrate higher prediction accuracy and efficiency when handling large-scale graph data tasks, thus bringing about further innovation and development in the field of graph structure prediction. In the task of handling large-scale graph-structured data, existing methods present numerous challenges and shortcomings. They include the excessive overhead of model training and the inability to assign suitable weights to nodes, resulting in a limited capacity of the model to capture information in the graph structure. Additionally, the issue of over-smoothing arises with the increasing depth of graph neural networks and leads to overly similar node feature representations. All these reduce the efficiency and predictive accuracy of the model, posing significant challenges for prediction tasks oriented toward large-scale graph-structured data. Therefore, this paper proposes an adaptive attention-based method for sparse subgraph prediction based on the GraphSAGE model (SSP-AA), which aims to effectively process sparse subgraph data while maintaining high predictive performance. The contributions of this paper are as follows:  
1. First of all, the SSP-AA model incorporates an adaptive attention mechanism to enhance its ability to handle graph-structured data. This approach addresses the limitations of the existing model, specifically in terms of adaptive weight allocation when aggregating neighbor node features;
2. Moreover, integrating a jumping knowledge module addresses the over-smoothing problem that frequently occurs with increasing depth in GNNs. The jumping knowledge module allows the model to flexibly select and combine features across different layers, enhancing the expressive power of node representations;
3. Finally, utilizing sparse subgraphs helps to decrease the complexity of the graph structure, retaining crucial node information in the graph while reducing computational overhead in prediction tasks and classification tasks involving large-scale graph-structured data.
## Materials and Methods
### Characterization  
Surface morphologies of AAO templates and polymer nanostructures were observed using scanning electron microscopy (SEM, JSM-6510; Jeol Ltd., Tokyo, Japan and TM-1000; Hitachi High-Technologies Corp., Tokyo, Japan) and field emission scanning electron microscopy (FE-SEM, SU-8000; Hitachi High-Technologies Corp., Tokyo, Japan). The surface chemical property of the polymer blends films were analyzed by attenuated total reflection (ATR, AIM-8800 Infrared Microscope; Shimadzu Corp., Kyoto, Japan). The wavenumber range of 3200-3600 cm\({}^{-1}\) was selected for investigation due to differences in the chemical structures between PS and PVA (the presence of a hydroxyl group). Thermal transition behavior of polymers were evaluated using modulated differential scanning calorimetry (M-DSC, DSC Q200; TA Instruments Inc., New Castle, DE, USA). A contact angle meter (DM 500; Kyowa Interface Science Co., Ltd., Saitama, Japan) was used to measure the surface interactions between molten polymers and AAO surfaces, and between water droplets and polymer surfaces. The friction coefficient of polymer surfaces at the macroscale was evaluated by a friction and wear tester (EFM-III-F; Orientec Co., Ltd., Tokyo, Japan) using stainless steel materials. The friction test conditions were evaluated using an applied force of between 21-40 N with the rotational speed of 10 rpm for 30 s. All of the tests were performed at ambient conditions (25 \({}^{\circ}\)C, relative humidity 35%). The surface mechanical properties at the nanoscale were evaluated by nanoindenter (G200; Agilent Technologies Inc., Santa Clara, CA, USA).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Materials** & **Temperature (\({}^{\circ}\)C)** & **Pressure (MPa)** & **Press Time (min)** & **Obtained** \\  & & & & **Nanostructures** \\ \hline PS\({}^{1}\) & 160–220 & 5 & 30 & Nanopillars \\ PS\({}^{2}\) & 160–220 & 5 & 30 & Nanopillars \\ PS\({}^{1}\)/PVA & 120–160 & 5 & 30 & Nanopillars \\ (90/10 wt\%) & & & & 15–30 & Porous Nanopillars \\ PS\({}^{1}\)/PVA & & & & 60–120 & Nanofragments \\ PS\({}^{1}\)/PVA & & & & \\ (70/30 wt\%) & 120–180 & 5 & 15–120 & Nanopoapods \\ \hline \hline \end{tabular}
\end{table}
Table 2: Imprinting conditions.  
Figure 1: Fabrication of one-dimensional nanostructures by thermal nanoimprint.
## 3 Results and Discussion
### Quantitative Evaluations  
As shown in Table 2, CLSTM yielded the lowest accuracy and the highest variance. The poor performance of CLSTM can be attributed to the fact that it simply switches the processes within LSTM to convolutions for 2D data, rather than being properly tuned for image segmentation. The accuracy values of the other models are high and similar, and the variance of BDULSTM is the smallest among all models, demonstrating that its performance is the most stable. The variance further decreases for U-Net-LSTM and BDULSTM when increasing the number of sequence images, indicating that increasing the number of sequence images is beneficial for accuracy. CLSTM achieved the greatest precision of 0.98. However, it had an extremely poor recall rate. This indicates that CLSTM is under-segmented and loses a portion of the soil pore region, resulting in worse performance than U-Net. As U-Net-LSTM and BDULSTM progressed from three sequence images to seven sequence images, their precision decreased, indicating that larger pore structures were obtained. Correspondingly, their recall increased following the addition of sequence images. This indicates that adding more sequence images causes these models to correct for omissions in segmenting pores and to classify more regions as pores. The F\({}_{1}\)-score, which is a combined precision and recall evaluation indicator, is the most comprehensive measure of segmentation effect. The F\({}_{1}\)-score of CLSTM was the lowest, indicating that its segmentation effect was the worst. BDULSTM slightly outperformed U-Net-LSTM, and after increasing from three to seven sequence images its F\({}_{1}\)-score increased significantly, further demonstrating that increasing the number of sequence images is effective up to a certain point. In general, CLSTM is not suitable for directly segmenting images. U-Net performed well in general, but it had some limitations. Additionally, U-Net-LSTM is beneficial for U-Net optimization. All indicators of BDULSTM were the best and exhibited the smallest variance, indicating that it has strong robustness and adaptability for identifying pores and solid particles in complex soils.
# Design and Implementation of a 2D MIMO OCC System Based on Deep Learning
## 3 Description of the Proposed System Architecture
### Transmitter  
In order to ensure reliable data transmission across a network, it is necessary to optimize the payload of the data for transmission. The optimization technique commonly involves the encoding of data into a format that is understandable by computers. The process of encoding data involves converting information into digital signals, which are represented by binary numbers, specifically \"0\" and \"1\". The encoding procedure entails mapping these numerals to digital signals transmitted over a communication channel  
Figure 1: Reference architecture of OCC for a system-based 2D OOK-MIMO and deep learning technique using an LED array  
using various patterns of voltage or current levels. LEDs are employed for the purpose of visually representing digital signals during the transmission process. In the context of this system, the representation of a binary digit \"0\" is achieved by deactivating the corresponding LED, and a binary digit \"1\" is displayed by activating the LED. The utilization of this encoding process is vital to ensuring precise and reliable data transmission over a network. The utilization of digital signals enables enhanced resistance against noise and various forms of interference, thereby increasing the likelihood that transmitted data will be received accurately.  
The subsequent phase involves the addition of the sequence number (SN). The SN is employed within a communication system to distinguish between the received data packets at the receiver. Moreover, the SN allows the cameras on the receiving side to identify missing packets in the event of oversampling. The SN designates the payload and contains sequence information for every data packet. The duration of the SN can be modified in accordance with the conditions and requirements of the system. Channel conditions determine the SN length, but it is possible to shorten it to improve system performance. The 2D MIMO technique was implemented in this research using an SN length of 2 bits. The decision to use this specific length for the SN was made by considering the trade-off between system performance and computational complexity.  
The preamble, which is the first segment of the symbol frame, serves the essential purpose of synchronizing and identifying the start of a frame. This enables the receiver to decode the data efficiently. The presence of any fault in the preamble section of a frame results in the transmission of a defective bit, thereby making the data unobtainable by the receiver. On the other hand, a well-constructed preamble enables the extraction of information contained within the frame, thereby enhancing the accuracy and reliability of transmission. This preamble facilitates the straightforward detection of image frame initiation.
# A Novel Algorithm to Optimize the Energy Consumption Using IoT and Based on Ant Colony Algorithm
## 2 Literature Review
### Optimization Approach in IoT Routing  
In 2015, [PERSON] et al. proposed an energy-efficient clustering scheme for nodes' lifespan in the IoT network with separate nodes. A suitable clustering algorithm for node classification can increase grid energy efficiency. However, clustering requires additional overhead, such as selecting and determining the cluster head and cluster construction. This paper proposes a new regional energy-conscious clustering method using separate nodes called REAC-IN. In REAC-IN, cluster heads are selected based on weight. The weight is determined based on each sensor's static energy and the average regional energy of all sensors in each cluster. Improperly distributed clustering algorithms can separate nodes from clusters. These disconnected nodes communicate with the sink by consuming excessive amounts of energy. The average regional energy and the distance among the sensors and the sink are used to determine whether the isolated node sends its data to the threaded node in the previous node or the sink. This process is performed to increase the network's lifespans. The simulation results of the current study showed that REAC-IN optimizes other clustering algorithms [27].  
In 2015, [PERSON] et al. introduced a clustering algorithm and fault tolerance and energy-efficient routing algorithm for wireless sensor networks. Energy conversion and fault tolerance are two significant issues in deploying a wireless sensor network. The design of routing and clustering algorithms for large-scale wireless sensor networks should address these issues for long-term network operation. This paper proposes distributed clustering, and routing algorithms commonly referred to as Distributed Clustering and Routings (DFCRs). It has been further proven that this algorithm has optimal energy consumption and fault tolerance. The DFCR algorithm uses the distributed time retrieval of distributed sensor nodes due to a sudden error in the cluster vertices. This algorithm considers the sensor nodes without cluster heads in its communication domain. The researchers performed extensive experimental tests on the proposed algorithm using various network scenarios. Then, the experimental results are compared with the existing algorithms to show the algorithm's performance power in terms of various metrics and achieve the desired results [28].
## 6 Prediction of Field Strength by Construction Log Data and Future Perspectives  
### Relationship between the W/C Ratio and Stabilized Soil Strength  
There are many proposed formulas to predict the laboratory and field strengths [17; 18; 19; 20; 21; 22]. Among them, one of the key parameters, the W/C ratio, is introduced, which is defined as the total weight of water contained in the soil and binder slurry against the weight of the binder. The ratio is also expressed by the water content of soil, binder content, and water to binder ratio.  
\[\begin{array}{c}W/C=(W_{us}+W_{wc})/W_{c}\\ =(w/aw+w/c)\end{array} \tag{6}\]  
where \(aw\) = binder content, \(w\) = water content of soil, \(w/c\) = water to binder ratio of binder slurry, \(W/C\) = total water to binder ratio, \(W_{c}\) = dry weight of binder (kg), \(W_{wc}\) = weight of water contained in binder slurry (kg), and \(W_{us}\) = weight of water contained in soil (kg).  
Figure 11: Stabilized soil strength distribution within the column [16].  
Figure 12: Effect of atomized cement slurry injection on penetration in the hard layer [16].  
Figure 10: Mixing machine injecting atomized cement slurry [16].  
Figure 13 shows an example of the relationship between the _W/C_ ratio and laboratory stabilized soil strength at 28 days of curing, \(q_{u28}\), for various soils [23]. Furnace slag cement type B was used as a binder in the laboratory mix tests, where the cement dosage and the _w/c_ ratio of cement slurry were changed to cover a wide range of _W/C_ ratios. In the figure, test cases of the _w/c_ ratio of 100% are shown, where the original soils are classified into three groups depending on their fines content, _Fc_: 0 to 30%, 30 to 70%, and 70 to 100%. The strength rapidly decreased with increasing _W/C_ ratio irrespective of the fine content. It was also found that the strength became larger when the fine content became large. A similar relationship has been found in various soils and binders stabilized by the deep mixing method [24] and pneumatic flow mixing method [25]. The stabilized soil strength can be formulated as Equation (7):  
\[q_{u}=a\times(W/C)^{b} \tag{7}\]  
where \(a\) = parameter (kPa), \(b\) = parameter, and \(q_{u}\) = unconfined compressive strength (kPa).  
The parameters \(a\) and \(b\) are influenced by the fines content, _Fc_, where the value of \(a\) increases as _Fc_ increases, and the value of \(b\) is negative and its absolute value decreases as _Fc_ increases.
# Virtual Pseudonym-Changing and Dynamic Grouping Policy for Privacy Preservation in VANETs
## 3 Models and Goals
### Goals  
The main concern of this article is to protect the location privacy of vehicles in a vehicular network. We set the following goals in this research work.  
1. Construction of dynamic grouping of vehicles at diverse nature of road network.
2. Virtual pseudonym change scheme in case of lower vehicle traffic.
3. Protection of a vehicle location information while querying to LBS.
4. Reduce the impact of privacy on VANETs applications.
5. Create uncertainty for an adversary to link the pseudonyms of a vehicle at different locations.