esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep-Sea Underwater Cooperative Operation of
###### Abstract
Therefore, it is difficult to satisfy the high-level autonomy and intelligence requirements expected of the unmanned submersibles in complex underwater environments. As a result, operational modes that rely solely on AUV and AUG clusters often fail to make optimal decisions rapidly in command and decision-making. In most missions, AUVs and AUGs cannot completely erase the status of \"human in the mission loop\". Therefore, in future underwater operations, it is necessary to consider the cooperative operation of manned/unmanned submersibles to overcome the limitations of pure unmanned submersible swarms. Compared to pure unmanned submersibles, the parallel interaction and integration of human intelligence, and machine intelligence in manned/unmanned submersible cooperation is conducive to realize two-way complementation between manned and unmanned submersibles. Therefore, the manned/unmanned submersible cooperation can result in improved performance when dealing with complex tasks. There are fewer deep-sea manned submersibles worldwide compared to unmanned submersibles (AUVs, ROVs, ARVs, and AUGs). Among them, only eight deep operational manned submersibles, namely America's New Alvin (6500 m), France's Nautile (6000 m), Russia's MIR-1 and MIR-2 (6000 m), Japan's Shinkai6500 (6500 m), and China's Jiaolong (7000 m), Shenhai Yongshi (4500 m), and Fendouzhe (11,000 m) [21], can dive to a depth equal to or more than 4500 m.  
Therefore, there has been little research conducted on the cooperative operation of deep-sea manned/unmanned submersibles. Unlike the widely used AUV and AUGclusters, there are few instances of cooperative operation of deep-sea manned/unmanned submersibles. Manned submersibles and AUVs conduct dives in a time-segmented manner, that is, when the manned submersible dives, the AUV does not dive; when the AUV dives, the manned submersible does not dive. Therefore, manned submersibles and AUVs have not yet achieved simultaneous diving and cooperative operation. This also restricts the improvement of the operation efficiency of the whole voyage. In this study, we focus mainly on the cooperative operation between manned submersibles and AUVs, which can significantly improve the efficiency of scientific investigations, resource surveys, and search and rescue operations. The remainder of this paper is structured as follows: Section 2 provides an overview of cooperative operation of manned/unmanned submersibles. Section 3 describes the underwater cooperative operation modes of manned submersibles and AUVs for different application scenarios in the deep sea. Section 4 focuses on the support system. In Section 5, we analyze the relative efficiency of several different scenarios, and finally, Section 6 provides the conclusions of this study.
## 3 Proposed Protocol
The main center is located as per network requirements, for example if this system is working in a sports stadium, the center would be located in the control room. The RREP packets always look for direct link nodes to forward the data packets. Figure 5 shows the RREP process among UAV-to-UAV nodes. Algorithm 2 shows the data communication among UAV-to-UAV nodes by using RREQ packets. ```
1.Initializing
2.CULAV: The candidate UAV node
3.RREQ: Route Request Packet
4.If (UAV (RREQ)= CULAV) then
5.Drop RREQ
6.else
7.If UAV destination (RREQ)= CULAV) then
8.Route Selected
9.else
10.Wait for a random time and repeat the process
11.Record (RREQ-CULAV)
12.Add RREQ
13.Rebroadcast (RREQ)
14.endif if process
15.end process
```  
**Algorithm 2****(UAV-to-UAV)**  
The RREQ packet contains the source UAV and Destination UAV IDs, REEQ ID, and lifetime, UAV latitude, longitude, and Altitude information. To handle the broadcast storm issues in the network, the proposed mechanism uses the UAV-ID with an RREQ message, if the same ID is received then the UAV node ignores that ID. The Route Reply (RREP) messages are broadcast from the direct UAV node to acknowledge the receiving status to the source UAV node. After this process, the next step is the routing decision where the nodes share the route information for some time interval. After this time, all the RREQ messages are dropped.
# Deep Learning of GNSS Acquisition
###### Abstract  
Signal acquisition is a crucial step in Global Navigation Satellite System (GNSS) receivers, which is typically solved by maximizing the so-called Cross-Ambiguity Function (CAF) as a hypothesis testing problem. This article proposes to use deep learning models to perform such acquisition, whereby the CAF is fed to a data-driven classifier that outputs binary class posteriors. The class posteriors are used to compute a Bayesian hypothesis test to statistically decide the presence or absence of a GNSS signal. The versatility and computational affordability of the proposed method are addressed by splitting the CAF into smaller overlapping sections, which are fed to a bank of parallel classifiers whose probabilistic results are optimally fused to provide a so-called probability ratio map from which acquisition is decided. Additionally, the article shows how noncoherent integration schemes are enabled through optimal data fusion, with the goal of increasing the resulting classifier accuracy. The article provides simulation results showing that the proposed data-driven method outperforms current CAF maximization strategies, enabling enhanced acquisition at medium-to-high carrier-to-noise density ratios.  
GNSS acquisition; machine learning; deep learning; data fusion +
Footnote †: journal: Sensors
## 4 Experimental Validation
### Real Scene Experimental Test  
The simulation experiments have proved that the proposed algorithm has good performance and the localization time and error distance are greatly reduced, so it can be significantly shown that the improved AMCL algorithm has better superiority. Therefore, the real effectiveness of the improved algorithm is verified, and the experiments are carried out and analyzed in real scenarios. Figure 21a,b shows the front and side view of the AGV used in the experiment. Figure 21c shows the experimental site used in the experiment, and Figure 21d shows the QR code labels pasted in the experimental site.  
Real experimental scenarios are established and tested using the improved AMCL algorithm to obtain valid data for analyzing the reliability of the algorithm.  
The map generated using the SLAM technique is shown in Figure 22, where the thin green line from 0 to 1 is the trajectory of the AGV. The round-trip is one lap, and the data of the AGV travelling ten laps are saved and imported into MATLAB2021B for analysis, which proves the validity of the proposed algorithm by comparing the data with the positional error of AGV arriving at the target point, the maximal corrective angle, the speed fluctuation, and line acceleration.  
Figure 19: Particle distribution and position error: (**a**) particle distributions generated by the AMCL algorithm and improved algorithms on trajectories; (**b**) AMCL and improved AMCL algorithm for processing information in case of kidnapping.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parametric** & **Algorithm** & **Value** \\ \hline Starting particle & AMCL & 1500 \\ Time from start to target & Improved AMCL & 1500 \\ Time from start to target & AMCL & 12.59 s \\ (10-turn average) & Improved AMCL & 7.42 s \\ Offset maximum distance & AMCL & \((-18.79,22.38)\) cm \\ (both sides) & Improved AMCL & \((-4.79,6.82)\) cm \\ Deviation distance to target point & AMCL & 27.36 cm \\ (10-turn average) & Improved AMCL & 4.81 cm \\ Dissolution of kidnapping recovery time & AMCL & 2.2 s \\ (10-turn average) & Improved AMCL & 0.39 s \\ \hline \hline \end{tabular}
\end{table}
Table 4: AMCL and improved AMCL fusion QR code algorithm comparison.  
Figure 20: Comparison between the data of the simulation experiment: (**a**) distance offset on both sides when travelling; (**b**) relationship between error and time; (**c**) line speed fluctuation during scanning; (**d**) angular velocity fluctuations during sweeping.
## 4 Results and Discussion
### Mean Score Ranking Technique
Furthermore, based on clients' feedback, 'enhance safety performance parameters' has MS value of 4.41 and is ranked first. 'Improve collaboration between stakeholders' has MS value of 3.60 and is ranked third. The MS value for 'increase stability and efficiency' is 3.77, and it is ranked second. According to the findings, the common CSFs among all respondents are to 'enhance safety performance parameters', 'enhance collaboration between stakeholders', and 'increase stability and efficiency'. Perhaps the most unexpected aspect of the results is the comparatively low rank of'reduced claims and litigation risks', which has MS value of 2.56. In fact, there is growing evidence that'reduced claims and litigation risks' are of major importance in implementing BIM and sustainability practices in the built environment [106].
## 2 Antenna Simulation for LEO Downlink Analysis
The receiving antenna is designed following ITU-R S.508-6 regulations to have side lobe levels (SLLs) of less than the required mask. The actual radiation patterns of the receiving and transmitting antennas are used, in order to more accurately estimate the data link budget when interference is strong in the side lobe of the receiving antenna.
## 3 Case Study: Campulung Town Hall Building
### Energy Analysis--A Pre-Intervention, Non-Invasive, Multicriterial Study
#### 3.3.3 Results from the Thermographic Study on the Building's Envelope  
A thermographic study was also undertaken on the Campulung Town Hall, specifically examining the north facade of the structure. We have used a TESTO 872s thermal imaging camera to conduct thermographic observations of the Town Hall of Campulung. The weather conditions at the time of measurement were overcast, with a morning temperature of around 3 \({}^{\circ}\)C. The prevailing conditions were optimal for performing thermal imaging  
Figure 8: Results regarding the degree of relative humidity and temperature obtained from the equipment located in an office room facing the south facade of the building.  
as they effectively reduced the influence of solar radiation on the thermal measurements, enabling more-precise identification of heat losses from the building's exterior. This study employed thermal imaging technology to detect regions experiencing heat losses, thus highlighting the energy efficiency problems of the building's exterior and better proving the necessity of implementing solutions. The outcomes of this assessment are crucial for planning future conservation and restoration endeavors, especially due to the Town Hall's historical significance and unique architectural requirements. The heat loss through the windows was particularly significant, with temperatures reaching up to 14.1 \({}^{\circ}\)C (see Figure 9). The significant difference in temperature clearly demonstrates that the windows are vulnerable areas in the building's envelope. The outdated timber-framed windows of this historic structure are evidently insufficient in mitigating energy dissipation.  
The thermographic research highlights a significant issue of energy inefficiency at the Campulung Town Hall, mainly caused by outdated construction materials and deteriorating architectural components. By replacing or updating the historical windows with thermally efficient alternatives that preserve the building's beauty and historical signifi  
Figure 9: Multiple thermal images of the north facade of the building.  
cance, energy consumption can be reduced, and the sustainability of the property can be improved. This short thermographic study provides essential evidence to support the need for investing in energy efficiency measures at the envelope level.
## 3 Key Techniques and Methods
### Convolutional Neural Network (CNN)  
CNN is a kind of feedforward neural network. The neurons in CNN can respond to the specific region in an image to extract features, which makes it outstanding in processing large unstructured data. A convolutional neural network includes convolutional layer, pooling layer, and fully connected layer  
There are three critical concepts in CNN, namely receptive field, parameter sharing, and pooling layers. [PERSON] and [PERSON] [32] explained them in detail. The receptive field is a square region. It is a local subset of the neurons that the kernel connected to. The size of this square is the receptive field. Neurons of the same kernel should get the same pattern of the image regardless of their positions. As a result, the parameters should be shared by all the neurons of the same kernel. This concept is called parameter sharing. Pooling layers are also connected to a square region of the previous layer. However, polling layers are different from the convolutional layers. They are not determined by the weights or bias in the learning process, and the result just depends on the input. The max pooling is the common type in CNN. The maximum value that the neurons return is taken as the feature of the image. The average pooling can be interpreted in a similar way. Pooling reduces the complexity and dimensions of the feature map and improves the result to lead to less overfitting. At the same time, the features can keep translation invariance after pooling, which means if there are some translations, such as rotation, scale, distortion, in images, the pooling features are also effective.  
As shown in Figure 4, the sizes of the image and convolutional layers are 5 \(\times\) 5 and 3 \(\times\) 3, respectively. There are nine parameters in the convolutional layer, namely the weight matrix. The nine parameters mean nine neurons. According to the sizes of image and kernel, the output is going to be a 3 \(\times\) 3 matrix, which is called the feature map. In the first step, the neurons were connected to the receptive field on the image; then it slides to the next region by one stride in the second step, as shown in Figure 4b. The computation was processed in each neuro as follows.  
\[f(x)=act\left(\sum\limits_{i,j}^{n}\theta_{(n-i)(n-j)}x_{ij}+b\right), \tag{3}\]  
where \(f(x)\) was the output, \(act\) is the activation function, \(\theta\) is the weight matrix, \(x_{ij}\) is the input, \(b\) is the bias. The softmax activation function is selected in this research.
## 5 Results and Discussion
### The Results of the Calibration Flight
#### 5.1.2 Results of the Improved Coherent Noise Suppression Method  
For the same calibration flight data, the results using the ICNS method are shown in Figures 10 and 11. The blue dot and black solid line represent the compensated field before and after the ICNS method, respectively. In Figure 11, the multi-frequency point interference fields were mostly eliminated by the proposed method. However, some of the interference fields were still not eliminated, which may have originated from the random motion of the carrier.  
Figure 8: The pre-processing and post-processing results of the calibration flight data using AIC method.  
Figure 9: The time–frequency diagrams of the pre-processing (**a**) and post-processing (**b**) data using AIC method.  
By comparing the results in Figures 9 and 11, it can be seen that the ICNS method had a better suppression effect on the vibration interference magnetic field of the helicopter platform than the common AIC method if the two magnetometers were used. In order to further prove the effectiveness of the ICNS method proposed in this paper, Section 5.2 presents a simulation target design for the low-frequency magnetic field detection experiment to verify the effectiveness of this method.
## 5 Numerical Results
Simulation Scenarios and Sensitivity Analysis: Four scenarios (100%, 70%, 50%, and 30% resource availability) were tested to assess robustness. At 30% capacity, the model-maintained compliance with AVA-mandated deadlines (\(\leq\)90 days), with the end-to-end time increasing marginally to 30,396 min (\(\approx\)63.3 days). Stakeholder coordination, particularly between the Academic Affairs Office and the Evaluation Committees, proved critical; a 10% reduction in committee availability increased the comparative evaluation time by 14%, underscoring the need for dynamic prioritization protocols. \begin{table}
\begin{tabular}{c c c c c} \hline  & **Academic Office** & **Academic Office** & **Academic Office** & **Academic Office** \\  & **Availability 100\%** & **Availability 70\%** & **Availability 50\%** & **Availability 30\%** \\ \hline Payment.Average Time in System & 30,040.28 & 30,035.54 & 30,271.33 & 30,396.00 \\ \hline Academic office.Utilization \% & 9.13 & 39.02 & 58.77 & 78.37 \\ \hline Payment.Number Completed & 7.743 & 7.62 & 7.42 & 6.92 \\ \hline Payment.St Dev of & 7699.28 & 7610.38 & 7786.02 & 7672.78 \\ \hline UAC.Utilization \% & 1.56 & 1.54 & 1.51 & 1.45 \\ \hline Director.Utilization \% & 1.14 & 1.12 & 1.09 & 1.04 \\ \hline UAC secretariat.Utilization \% & 0.82 & 0.81 & 0.79 & 0.76 \\ \hline Evaluation committee.Utilization \% & 1.00 & 0.99 & 0.96 & 0.93 \\ \hline complete.Number Completed & 8.54 & 8.40 & 8.11 & 7.68 \\ \hline complete.Average Time in System & 26,571.84 & 26,666.97 & 26,698.03 & 26,977.67 \\ \hline complete.St Dev & 7278.39 & 7336.01 & 7352.78 & 7388.09 \\ \hline Pubblication.Average Time in System & 6049.56 & 6046.87 & 6084.51 & 6154.35 \\ \hline Comparative.Average Time in System & 12,299.29 & 12,333.06 & 12,390.46 & 12,495.81 \\ \hline Assignment.Average Time in System & 17,718.40 & 17,819.91 & 17,888.62 & 18,175.05 \\ \hline Pubblication.St Dev & 4318.22 & 4308.95 & 4376.37 & 4346.37 \\ \hline Comparative.St Dev & 4400.03 & 4380.01 & 4446.31 & 4460.89 \\ \hline Assignment.St Dev & 5865.32 & 5916.77 & 6020.02 & 6027.21 \\ \hline \end{tabular}
\end{table} 
Table 2: Numerical results. Validation with Bonita BPM: The integration of Bonita BPM validated the simulation results, automating approval workflows and reducing manual interventions by 65%. Digital tools eliminated redundancies in document routing (e.g., contract formalization), cutting the average handling time by 50%. While the model assumed idealized stakeholder adherence, real-world cultural resistance and regulatory updates may affect scalability. Future work should incorporate machine learning to dynamically adjust resource allocation in response to fluctuating demand. These results affirm that simulation-driven BPR can harmonize efficiency and compliance in HEIs, providing a replicable blueprint for bureaucratic environments.
# A GeoNode-Based Platform for an Effective Exploitation of Advanced DInSAR Measurements
###### Abstract  
This work presents the development of an efficient tool for managing, visualizing, analysing, and integrating with other data sources, the deformation time-series obtained by applying the advanced differential interferometric synthetic aperture radar (DInSAR) techniques. To implement such a tool we extend the functionalities of GeoNode, which is a web-based platform providing an open source framework based on the Open Geospatial Consortium (OGC) standards, that allows development of Geospatial Information Systems (GIS) and Spatial Data Infrastructures (SDI). In particular, our efforts have been dedicated to enable the GeoNode platform to effectively analyze and visualize the spatio/temporal characteristics of the DInSAR deformation time-series and their related products. Moreover, the implemented multi-thread based new functionalities allow us to efficiently upload and update large data volumes of the available DInSAR results into a dedicated geodatabase. The examples we present, based on Sentinel-1 DInSAR results relevant to Italy, demonstrate the effectiveness of the extended version of the GeoNode platform.  
Keywords:SDI; GeoNode; WebGIS; DInSAR; SBAS +
Footnote †: journal:
###### Abstract
[26; 27] added lignin fibers into loess, and their results showed that the lignin fibers significantly improved the strength, thermal conductivity, thermal diffusivity, and specific heat of the loess. [PERSON] et al. [28] also studied the improvement effect of lignin fibers on the strength and deformation characteristics of red clay through a series of experimental tests and a scanning electron microscope test. The results showed that lignin fibers can improve the strength of red clay to a certain extent. [PERSON] et al. [29] showed that lignin fiber-fly ash-treated expansive soils had greater strength and water stability. [PERSON] et al. [30] studied the effects of freeze-thawing on lignin fiber-treated expansive soils, and the results showed that lignin fibers can effectively improve the freeze-thaw resistance of expansive soils. Previous studies have focused on the general effect of lignin fiber on different kinds of soils. The effectiveness of lignin fiber improvement has been confirmed, but a certain gap in strength compared to traditional lime-modified soil has also been observed. According to existing studies, the optimal dosage of traditional amendments such as lime for expansive soils is generally 4-6% [31; 32]. Although the strength of this amendment method has improved, the carbon emissions and environmental pollution to the surrounding soil caused by high dosages should not be underestimated. Considering both environmental friendliness and improvement effects, the lignin fiber and lime composite improvement method could be a potential approach. However, there is limited study in this area. Furthermore, the microscopic mechanism of lignin fiber treatment is still not clear. This study aims to explore a novel method for soil improvement which involves the use of lignin fiber (LF) from the paper industry combined with lime to enhance the properties of expansive soils. Lime is currently a common material for improving expansive soils, but its environmental impact has prompted a search for more sustainable alternatives. This paper proposes the partial replacement of lime with lignin fibers to reduce the environmental load and investigates the potential effects of this composite material on the mechanical properties and microstructure of the soil. The uniqueness of this method lies in its use of industrial byproducts for soil stabilization, which not only lowers treatment costs but also enhances environmental sustainability. By optimizing the ratio of lignin fibers to lime, this study provides an effective approach to the sustainable improvement of expansive soils, utilizing industrial waste and enhancing the eco-friendliness and economic benefits of soil improvement materials.