esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this research, the modeling and possible real-world implementation of a dynamic lane assignment (DLA) system on one of the most congested corridors in the city of Zagreb-Vukovar Street was explored. The first phase of a DLA concept was developed through the integration of collected real-time traffic data, detailed demand analysis, and microsimulation using the PTV VISSIM tool with Python to address uneven peak-hour traffic demand. The main goal is to optimize traffic flow without physical infrastructure expansion.  
The simulation model was calibrated, and the model with the proposed DLA concept was developed. In the first phase, the middle lane acts as a dynamic lane, adjusting the direction according to traffic demand. The simulation results confirmed that DLA can significantly improve the performance of the traffic network when applied at targeted corridors. The most notable improvements in evaluation parameters were achieved at the middle intersection of the corridor where the DLA concept was implemented (Vukovar Street-Nova Cesta Street). Vehicle delay was reduced by over 58%, queue length was decreased by 19%, and CO emissions dropped by 46%, respectively. These results demonstrate that DLA can reallocate the capacity in traffic networks to match variable traffic demand.  
On the other hand, the DLA implementation had a direct impact on the edge intersections and delay, and emissions slightly increased due to downstream prioritization. Network-wide coordination and careful scenario planning to avoid displacing the congestion are essential.  
Future research will focus on expanding the scope of DLA implementation but also on implementing the DLA variable in smaller time frames and with greater frequency. Also, this first phase of DLA implementation is planned for real-world implementation at the observed corridor, and the impacts on real traffic will be observed and analyzed. Additionally, simulations will be extended to cover full-day traffic variations and multi-peak scenarios.  
Overall, this research offers valuable insights for urban traffic planners and policymakers who focus on improving traffic efficiency and sustainability in dense urban environments using Intelligent Transport System services.  
Conceptualization, M.V. and L.D.; Methodology, M.V. and L.D.; Formal analysis, L.D. and M.M.; Investigation, M.V. and L.D.; Resources, M.V.; Data curation, L.D. and M.M.; Writing--Original draft, M.V.; Writing--Review and editing, M.V. and L.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The collected and analyzed data for the current research are available from the corresponding author upon reasonable request.  
The author [PERSON] was employed by the company Prometis Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.
## 2 Materials and Methods
### Data Analysis  
Data were expressed as means \(\pm\) standard deviation (SD). The SHR was calculated in stroke patients on the paretic and the non-paretic sides. The Spearman coefficient was used to evaluate the correlation between the clinical FMA scores and instrumental data (maximum flexion and maximum abduction of the humerus; scapular PR, TI, UD in maximum flexion and maximum abduction of the humerus; and the SHR for PR, TI, UD at 30\({}^{\circ}\), 60\({}^{\circ}\), 90\({}^{\circ}\), and 120\({}^{\circ}\) of flexion and abduction) for the 7 patients' paretic and non-paretic arms. The Wilcoxon non-parametric test, calculated using a Monte Carlo method for small samples, was used to evaluate the correlation between the paretic and non-paretic arm for the instrumental data. Data from 25 healthy controls, with a mean age of 37 \(\pm\) 11 years [29], were reported as a reference for readers and compared with the stroke patients' data only qualitatively because it was not possible to proceed with statistical analysis. A statistical consultant from our institute performed a statistical analysis using the statistical package for social sciences (SPSS) software, version 15.0 (SPSS Inc., Chicago, IL, USA). A \(p\)-value of \(<\) 0.05 was considered significant for all analyses.
# Immuno-PET for Glioma Imaging: An Update
## 4 Targets for Immuno-PET
### Vegf
Following this, the same research group conducted a preliminary investigation to explore the accessibility of bevacizumab to tumors in children with DIPGs [42]. The study involved measuring the tumor uptake of [89 Zr]-bevacizumab and determining the optimal time for PET imaging. Seven children with DIPG participated, including one with spinal cord metastases and another with disease extension to the facial nerve. All subjects had previously undergone radiation therapy in conjunction with gemcitabine or temozolomide. PET scans yielded positive results in five cases and negative results in two. Tumor uptake of the tracer varied among patients, with tumor SUVs ranging from 1.0 to 5.3 at 72 h and from 1.0 to 6.7 at 144 h post-injection. In the case of spinal cord metastases, the PET scan was positive in all metastatic locations. A biodistribution study revealed that tracer uptake was highest in the liver, followed by the kidneys, spleen, lungs, and vertebrae, resulting in an effective dose of 0.9 mSv/MBq. Notably, four out of five tumors exhibited significant [89 Zr]-bevacizumab uptake only within contrast-enhanced areas on MRI, indicating substantial variability in tracer uptake among tumors and suggesting differences in local VEGF expression. While these findings were promising and supported the potential utility of immuno-PET with [89 Zr]-bevacizumab for assessing VEGF expression in DIPGs in vivo, the limited number of enrolled patients and the absence of pathological validation underscore the need for further studies to establish more robust confirmation.
# Object Detection in Remote Sensing Images by Combining Feature Enhancement and Hybrid Attention
## 3 Methods
### Feature Enhancement Fusion Network  
The existing methods usually just aggregate hierarchical feature maps between high- and low-level output layers, which do not take full advantage of the fused feature information. In comparison, the proposed FEFN considers deeply the current fused layers and further enhances the receptive fields of the fused feature maps by using three sub-networks, which contain different dilation rates and different numbers of dilation convolution. This operation can reduce semantic differences between the features of different layers and extract multi-scale features on different perceptual fields, which enhances multi-scale feature representation.  
The FEFN module shown in Figure 3 first uses a \(1\times 1\) convolutional layer to adjust the number of channels for the high-level features \(Conv_{n}\). Then, the adjusted result is upsampled through bilinear interpolation, and the refined high-level features \(Conv_{n1}\) are gotten. Then, the refined high-level features implement element-wise addition with low-level features \(Conv_{n-1}\) and get the first fused feature map \(P\). The feature map \(P\) is followed by three sub-networks. In each sub-network, the dilation convolution with different dilation rates and different convolution numbers operates. Finally, the outputs of sub-networks \(P_{1}\), \(P_{2}\), \(P_{3}\) are concatenated in channel dimension to support the effective fusion of contextual information. The enhanced fused feature map \(Conv^{\prime}_{n-1}\) can be obtained as follows:  
\[P_{1}=f_{1}(P),\ P_{2}=f_{1}(f_{2}(P)),\ P_{3}=f_{1}(f_{2}(f_{2}(P))) \tag{2}\]  
\[Conv^{\prime}_{n-1}=f_{concat}(P_{1},P_{2},P_{3}) \tag{3}\]  
where \(f_{n}\) represents the dilation convolution with dilation rate \(n\), and \(f_{concat}\) denotes a concatenation operation.  
With the same computational complexity as a standard convolution, dilated convolution inserts \"holes\" between pixels in convolutional kernels to maintain the resolution and receptive field of the network [29]. By changing the dilation rate and convolutional number, we can obtain sub-networks with different receptive fields, which can be used to capture multi-scale context information and improve the detection performance of objects with large-scale variations. The three sub-networks with different groups of dilation convolution layers further consider the current fused feature \(P\) thoroughly.  
Furthermore, through the fusion of three sub-networks containing different dilation convolution, the FEFN module enhances the object information with different receptive fields. This approach captures high-level semantic features while retaining more low-level detailed features, and considers the fused feature maps from different layers. Therefore, FEFN utilizes different scales, different receptive fields, and thus obtains more discriminative and robust features, which facilitates multi-scale object detection.  
Figure 3: Feature enhancement fusion network.
# Student's t Kernel-Based Maximum Correntropy Criterion Extended Kalman Filter for GPS Navigation
###### Abstract  
Global Navigation Satellite System (GNSS) receivers may produce measurement outliers in real-world applications owing to various circumstances, including poor signal quality, multipath effects, data loss, satellite signal loss, or electromagnetic interference. This can lead to a noise distribution that is non-Gaussian heavy-tailed, affecting the effectiveness of satellite navigation filters. This paper presents a robust Extended Kalman Filter (EKF) based on the Maximum Correntropy Criterion with a Student's t kernel (STMCCEKF) for GPS navigation under non-Gaussian noise. Unlike traditional EKF and Gaussian-kernel MC-CEKF, the proposed method enhances robustness by leveraging the heavy-tailed Student's t kernel, which effectively suppresses outliers and dynamic observation noise. A fixed-point iterative algorithm is used for state update, and a new posterior error covariance expression is derived. The simulation results demonstrate that STMCCEKF outperforms conventional filters in positioning accuracy and robustness, particularly in environments with impulsive noise and multipath interference. The Student's t-distribution kernel efficiently mitigates heavy-tailed non-Gaussian noise, while it adaptively adjusts process and measurement noise covariances, leading to improved estimation performance. A detailed explanation of several key concepts along with practical examples are discussed to aid in understanding and applying the Global Positioning System (GPS) navigation filter. By integrating cutting-edge reinforcement learning with robust statistical approaches, this work advances adaptive signal processing and estimation, offering a significant contribution to the field.  
GPS; Maximum Correntropy Criterion; non-Gaussian noise; extended Kalman filter; outlier; Student's t kernel +
Footnote †: journal: Applied Sciences  
1
## 2 Related Work  
Recent studies have shown the effectiveness of machine learning-based prediction modeling in supporting code optimization, parallelism mapping, task scheduling, and processor resource allocation [10]. Moreover, predicting running times and memory footprint is important for estimating the cost of execution and better managing resources at runtime [11]. For instance, in-memory data processing frameworks like Spark can benefit from the informed co-location of tasks [10]. In fact, if too many applications or tasks are assigned to a computing node, such that the memory used on the host exceeds the available  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Meaning** \\ \hline \(\mathcal{T}=\{t_{1},t_{2},\ldots,t_{n}\}\) & Set of tasks. \\ \(\mathcal{A}\subseteq(\mathcal{T}\times\mathcal{T})=\{a_{1},\ldots,a_{m}\}\) & Dependencies. \(a_{i}=(t_{i},t_{j}),t_{i}\in\mathcal{T},t_{j}\in\mathcal{T}\). \\ \(d_{t}\) & Description of the dataset processed by task \(t\). \\ \(\mathcal{W}=(\mathcal{T},\mathcal{A})\) & Workflow. \\ \(\mathcal{N}^{in}(t)=\{t^{\prime}\in\mathcal{T}\mid(t^{\prime},t)\in\mathcal{A}\}\) & In-neighborhood of task \(t\). \\ \(\mathcal{N}^{out}(t)=\{t^{\prime}\in\mathcal{T}\mid(t,t^{\prime})\in\mathcal{A}\}\) & Out-neighborhood of task \(t\). \\ \(\mathcal{M}\) & Regression prediction model. \\ \(\mathcal{S}=\langle s_{1},\ldots,s_{k}\rangle\) & List of stages. \(s_{i}\subseteq\mathcal{T}\mid(t_{x}\parallel t_{y})\forall t_{x},t_{y}\in s_{i}\). \\ \(C\) & Maximum amount of memory available for a computing node. \\ \(C_{s}=C-\sum\limits_{t\in s}\mathcal{M}.predict\_mem(t,d_{t})\) & Residual capacity of a stage \(s\). \\ \hline \hline \end{tabular}
\end{table}
Table 1: Meaning of the main symbols.  
one, memory paging to disk (i.e., swapping), data spilling to disk in Spark, or OOM errors can occur with consequential drops in performance.  
Our work focused on improving the performance of a Spark application using machine learning-based techniques. The challenge is to effectively schedule tasks in a data-intensive workflow for improving resource usage and application performance, by inferring the resource demand of each task, in terms of memory occupancy and time.  
State-of-the-art techniques aimed at improving the performance of data-intensive applications can be divided into two main categories: analytical-based and machine learning-based. For each category, the main proposed solutions and their differences with respect to our technique are discussed.
# Range-Free Localization Approaches Based on Intelligent Swarm Optimization for Internet of Things
## 3 Proposed Localization Algorithms
### CSO-Based Enhanced DV-Hop Algorithm
```
1:Input: n nodes; k anchors; communication range R
2:
3: Network topology distribution: square random; H-shaped; O-shaped; W-shaped
4: for i = 1 to n do
5: for j = 1 to n do
6: Distance calculation
7:\(d_{ij}=\sqrt{(x_{i}-x_{j})^{2}+(y_{i}-y_{j})^{2}}\)
8: and initialize hop-count \(H_{i,j}=0\);
9: if\(d_{ij}<=R\) then
10:\(H_{i,j}=1\);
11: else if \(i==j\) then
12:\(H_{i,j}=1\);
13: else
14:\(H_{i,j}=inf\);
15: end if
16: end for
17: end for
18:
19: Creation of the Matrix of hop count between anchors based on the path algorithm;
20: Creation of distance matrix between anchors based on the path algorithm;
21:
22: for i = 1 to k do
23: Average hop size distance calculation \(AHSize_{i}\) per anchor node based on Equation (1);
24: end for
25:
26: for i = 1 to k do
27:\(W_{i}\) weighted values calculation per anchor nodes based on Equation (18);
28: end for
29:
30: New corrected average hop size distance \(AHSize_{new}\) according to Equation (17);
31: for i = 1 to k do
32:\(d_{ijk}=AHSize_{new}\times H_{i,k}\) from \(i\)-th anchor to \(k\)-th unknown node;
33: end for
34:
35: Determine the position of the unknown node using a 2D-hyperbolic based on Equation (23);
36:
37: CSO parameters initialization and using the coordinate of nodes calculated according to
38: 2D-hyperbolic;
39: Evaluate the chickens' fitness values;
40: Made iteratively an update of the positions according to Equations (24), (26), and (29);
41: Stop the iteration when the goals are reached;
42:Output: the best locations of unknown nodes. ```  
**Algorithm 1**Pseudocode of the Proposed Algorithms
# An Approach to Dynamic Sensing Data Fusion
## 3 Sensor Data Acquisition System
### Sensing Data Acquisition
The desired values are measured by as little sampling timeinterval as possible. \"Improvement (%)\" means (deviation to the desired value in old method - deviation to the desired value in new method) / deviation to the desired value in new method * 100 %. The efficiency of all stages of experiments was not significantly reduced under the premise of improving the experimental accuracy, as shown in Figure 8.  
In Figure 8, the average sampling interval of the dynamic sampling method is 200/16 = 12.5, which is larger than that of the fixed sampling method. This reduces the processing time and data transmission time of the processor and improves the average efficiency. The experimental effects (improvement) of the heading, distance to the destination waypoints, lateral deviation distance, and longitude and latitude experiments were the best, achieving over 300% \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline \multirow{2}{*}{**Experimental Content**} & \multicolumn{2}{c}{**Fixed Acquisition Interval**} & \multicolumn{2}{c}{**Dynamic TimeInterval**} & \multirow{2}{*}{**Improvement (\%)**} \\ \cline{2-2} \cline{4-6}  & **Sampling Interval (ms)** & & & **Deviation to the Desired Value** & \\ \hline
**Heading experiment** & 10 & 512.0 & Dynamic & 125.0 & 309.6 \\
**Speed experiment** & 10 & 51.4 & Dynamic & 41.2 & 24.8 \\
**GPS altitude** & 10 & 37.6 & Dynamic & 27.8 & 35.3 \\
**Barometer height** & 10 & 41.7 & Dynamic & 39.2 & 6.4 \\
**Distance to waypoint** & 10 & 682.7 & Dynamic & 122.8 & 455.9 \\
** Lateral deviation** & 10 & 75.4 & Dynamic & 23.4 & 222.2 \\
**Distance experiment** & & & & & \\
**Locating identification** & 10 & 450 & Dynamic & 180 & 150.0 \\
**Number of satellites** & 10 & 0 & Dynamic & 0 & 0 \\
**Control mode** & 10 & 0 & Dynamic & 0 & 0 \\
**Reception types** & 10 & 0 & Dynamic & 0 & 0 \\
**Reception** & 10 & 0 & Dynamic & 0 & 0 \\
**identification** & 10 & 0 & Dynamic & 0 & 0 \\
**Automatic photography** & & & & & \\
**Cycling route** & 10 & 0 & Dynamic & 0 & 0 \\ identification & 10 & 0 & Dynamic & 0 & 0 \\
**Flight pattern** & 10 & 0 & Dynamic & 0 & 0 \\
**Types of taking off and landing** & & & & & \\
**Longitude and latitude** & 10 & 5.8 & Dynamic & 1.2 & 383.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between the collection algorithms. Figure 8: Efficiency comparison. improvement on average. The effects of the speed, GPS altitude, barometric height, and locating identification experiments were next, achieving about 20% improvement on average.
# A Dynamic Surface Gateway Placement Scheme for Mobile Underwater Networks +
## 4 PADP Algorithm Description
### Objective Function 2: Reduce Average End-To-End Delay  
End-to-end delay is another critical performance measure in UWSNs. Therefore, a second objective function is introduced. This objective function must also satisfy the constraints given above. End-to-end delay is the sum of the per-hop delays over the entire path from the source to destination, where packets are respectively generated and received. There are three primary types of delay: queuing and channel access delays, transmission delay, and propagation delay. Since queuing and channel access delay are more related to MAC protocol, which is beyond the scope of this work, we mainly consider the transmission and propagation delay. Due to the low bandwidth and propagation speedin UWSNs, the transmission and propagation delays are much longer compared to wireless sensor network on the ground.  
To reduce the average end-to-end delay, which may be computed as the average end-to-end latency for each packet from its source to destination, a gateway deployment should cover sensor nodes in Layer 1 that commonly experiences high traffic as shown in Figure 2. This approach will reduce the number of hops for a large proportion of packets, subsequently decreasing the average end-to-end delay. The rationale behind this approach is that one cannot be certain of the underlying sensor node topology. Some sensor nodes in the first layer may have more connections to sensor nodes in a lower layer than others. Layer 1 nodes with a larger number of connections are more likely to experience higher volumes of traffic. Therefore, covering sensor nodes experiencing high traffic will reduce the average end-to-end delay of all packets.  
PADP monitors traffic volumes during the previous \(\psi\) seconds and assigns a weight of \(\omega\), to each sensor node \(i\) in Layer 1:  
\[\omega_{i}=\frac{\eta_{i}}{\sum_{k=1}^{m}\eta_{k}}\]  
where \(\eta_{i}\) represents the number of packets received by sensor node \(i\) during the last \(\psi\) seconds.  
The objective function to minimize average end-to-end delay is:  
\[\max_{C_{q}}\bigg{(}\sum_{k=0}^{n}\sum_{i=0}^{m}\max_{q}\omega_{i}Y_{iqk}\bigg{)}, \tag{15}\]  
Combine the above two equations, the linearization process is similar to the 4.6 Linearization, so it can be referred to the method above to achieve the optimization for reducing average end-to-end delay. In addition, we can solve this optimization problem with no changes to the constraints generates a solution for the deployment with reduced the average end-to-end delay.
# Spatial Distribution and Monitoring of Land Subsidence Using Sentinel-1 SAR Data in Java, Indonesia
## 3 Results  
The results indicate that several areas experience deformation, as shown in Figure 2. The green area shows relatively no changes in the LOS, while the yellowish to red areas show the elongation of the LOS during the data acquisition period, which is assumed to be highly correlated with LS. It has been identified that 10 locations experience LS. From the west to east part of the Java, based on the administrative region, the areas that have a significant signature of LS are (a) Serang*, (b) Greater Jakarta (including West Jakarta, North Jakarta, East Jakarta, South Jakarta, Centra Jakarta, Tangerang City, and Bekasi), (c) Cianjur*, (d) Bandung*, (e) Cirebon, (f) Brebes and Tegal, (g) Pekalongan, (h) Greater Semarang (including Kendal, Semarang and Demak), (i) Surabaya, and (j) Sidoardjo. Meanwhile, subtle signatures of LS were found in Subang and Pamanukan, Indramayu, and Nganjuk. Several new locations that experience LS have been found in this study, in addition to the regions reported in the previous study [8; 37]. The regions indicated with an asterisk are not on the northern coast of Java, while almost no LS is detected in the southern part of Java.
# Evaluating V2X-Based Vehicle Control under Unreliable Network Conditions, Focusing on Safety Risk
## 3 Methodology
### Simulation Environment  
Our simulation environment could be separated into five modules as in Figure 2. The first one is the vehicle dynamic module, which is a six-dimensions-of-freedom (6-DOF)  
Figure 1: Control algorithm.  
electric vehicle model that has one engine on the rear axle. This complex model was chosen to support a realistic vehicle simulation and reliable conclusions. The second module contains the vehicle control function, while the third module is responsible for the scenario initialization and actor control.  
The fourth module implements the DSRC network physical layer and the data transmission between vehicles. In this module, we can perform the network degradation by manipulating the additional noise for our simulation. In the last module, we have implemented a simple network monitoring algorithm to be able to check the network state throughout the simulation. As a major contribution of our research, we have developed a new control concept to deal with the uncertainty caused by the degraded communication and to reduce the risk posed by wireless communication. This includes the use of the introduced SRI function, which allows the system to respond to changes in network quality.