esa-sceva/satcom-chunk-collection huggingface dataset:
# Use of Granulated Rubber Tyre Waste as Lightweight Backfill Material for Retaining Walls
## 2 Properties of Rubber Aggregates
### Conventional Material Mixtures  
The main reason for soil modification is for the improvement of the properties of the soil, such as raising its bearing capacity and reducing settlement and lateral deformations. Moreover, the addition of rubber granulates must ensure the correct performance of the mixture, based on the requirements and characteristics needed. Certain studies focus on the behaviour of conventional rubber/arid mixtures in the quest to improve the resulting properties.  
Studies on rubber/sand mixtures show that the compressibility of the mixtures rises with the increasing rubber content [39]. The compressibility properties are the same for tests performed under drained or undrained conditions. It should be borne in mind that the grading and type of rubber fail to affect the compression or swelling ratios of the mixtures [40]. Moreover, the void ratio decreases with an increasing tyre-chip content up to a maximum of 30-40%, at which point it then starts to increase [41].  
The results obtained in the unconfined compressive strength (UCS) test indicate that the total compression and plastic deformations undergo an increase as the rubber content increases in the rubber/sand mixtures by up to 50%; above this percentage, no significant results are appreciated [27]. It can be observed that the compressibility characteristics remain the same for the tests performed under drained and undrained conditions [28].  
According to the analysis of the samples for the parameters of shear strength (friction angle) and deformability (void ratio), the rubber/sand mixtures show optimum substitution percentages of approximately 28 to 30% [41]. [PERSON] and [PERSON] [35] characterised mixtures of shredded tyres and sand by varying tyre-chip contents using large-scale direct shear tests; the results highlight the improvement in shear strength. The best performance was obtained at low to intermediate confining pressures.  
Mixtures of rubber and clay, whereby clay is substituted with rubber in small amounts (less than 30%), have been shown to decrease the maximum dry unit weight and the corresponding optimum moisture content. Positive dilatancy has been observed during shearing, especially at low vertical pressures [42].  
The substitution of conventional aggregates with rubber granulates in brick manufacturing yields benefits in the insulating properties of these elements. Percentages fluctuate between 5% and 11%, depending on the amount of rubber used [43]. The average values of compressive, flexural, and ultimate stresses are inversely proportional to the percentage of rubber crumb substitution. The strengths decrease drastically when the level of rubber replacement increases.
# On-Board Flickering Pixel Dynamic Suppression Method Based on Multi-Feature Fusion
## 2 Methodology
### Visual Feature Extraction Based on Facet Model
Subsequently, the estimation error of the facet model could be determined based on the following criterions as  
\[Dev(i,j)=\frac{\sum_{m=-1}^{1}\sum_{n=-1}^{1}\sqrt{\left[I(m+i,n+j)-f_{ facet}(m+i,n+j)\right]^{2}}}{\sum_{m=-1}^{1}\sum_{n=-1}^{1}\sqrt{\left[I(m+i,n+j)- mean(pic)\right]^{2}}}, \tag{6}\]  
where \(Dev(i,j)\) is the estimation error of the facet model while \(mean(pic)\) represents the mean of the current image. Note that the denominator of Equation (6) is used in the normalization of the estimation error for easy threshold comparison. Finally, we set up a visual feature model of blind pixels (VFBP model) and define \(\text{Th}_{\text{IM}}\) as the threshold of estimation error. The results of matching VFBP model are expressed by \(X_{IM}(i,j)\), which is written as  
\[X_{IM}(i,j)=\left\{ \begin{array}{ll}1,&Dev(i,j)<\text{Th}_{\text{IM}}\\ 0,&Dev(i,j)\geq\text{Th}_{\text{IM}}\end{array} \right., \tag{7}\]  
If \(Dev(i,j)\) is smaller than \(\text{Th}_{\text{IM}}\), \(X_{IM}(i,j)\) will be assigned to 0, which means this potential blind pixel might be an IRPT. On the contrary, \(X_{IM}(i,j)\) will be set to 1, which means this pixel might be a blind pixel. In this paper, \(\text{Th}_{\text{IM}}\) is set to 0.30. According to the change of practical optical system, this threshold needs to be adjusted.
# A Novel Real-Time PV Error Handling Exploiting Evolutionary-Based Optimization
## 3 Methodology
### Overall Architecture  
The overall methodology, as depicted in Figure 8, details the process of comparing and validating actual values with predicted values in the context of PV system modeling and optimization. The essential parameters guiding this model are thoughtfully cataloged in Table 3, offering a comprehensive overview of the variables involved in the comparison process. All crucial parameters, including \(I_{gen}\), \(V_{gen}\), \(P_{gen}\), \(I_{act}\), \(V_{act}\), and \(P_{act}\), play a distinct role in the validation procedure. \(I_{gen}\) and \(V_{gen}\) represent the output current and voltage, respectively, calculated from the genetic algorithm, providing predicted values for the installed PV system. Similarly, \(P_{gen}\) signifies the output power generated by the PV system, as determined by the genetic algorithm. On the other side, \(I_{act}\), \(V_{act}\), and \(P_{act}\) denote the measured values of output current, voltage, and power from the PV system meters, offering a real-world reference for the system's performance.  
Figure 8: PV error-handling conceptual architecture.  
The comprehensive procedure unfolds as follows: leveraging data from sensors and meters and exploiting the single-diode model, the GA produces the predicted values (\(V_{gen}\), \(I_{gen}\), \(P_{gen}\)). Simultaneously, the actual output voltage, current, and power (\(V_{act}\), \(I_{act}\), \(P_{act}\)) are retrieved from the meters embedded within the PV system. These actual and predicted values then undergo a comparison through the PV error-handling algorithm, as explicated in the preceding section. This algorithm systematically assesses the deviation between actual and predicted values, considering predefined thresholds for error, typically set experimentally to enhance accuracy. The outcome of this analysis provides invaluable insights into the efficacy of the genetic algorithm in modeling and optimizing the PV system, allowing researchers and engineers to refine and improve the accuracy of their models.  
The overall procedure is described in Algorithm 3.  
```
1:\(I_{gen}\), \(V_{gen}\), \(P_{gen}\), \(I_{act}\), \(V_{act}\), \(P_{act}\)
2:functionRealTimeErrorHandling(\(I_{gen}\), \(V_{gen}\), \(P_{gen}\), \(I_{act}\), \(V_{act}\), \(P_{act}\))
3:for each parameter param in \(\{I,V,P\}\)do
4:\(error\leftarrow\text{abs}(param_{gen}-param_{act})\)\(\triangleright\) Calculate absolute error
5:if\(error\) exceeds predefined threshold then
6:Alert: High deviation in \(param\) values
7:endif
8:endfor
9:Return: Analysis results and insights
10:endfunction
```  
**Algorithm 3** Real-time PV Error Handling
# Multi-Object Tracking with Predictive Information Fusion and Adaptive Measurement Noise
## 2 Materials and Methods
### Adaptive Measurement Noise (AMN)
 \tag{14}\]  
As the state vector is modified, the covariance matrices \(Q\) and \(R\) in the KF also need to be adjusted. DeepSORT [5] suggests using information from the state vector for this purpose. Here, \(Q\) is the process noise covariance matrix, reflecting the uncertainty of the model state as it evolves over time. \(R\) is the measurement noise covariance matrix, indicating the potential uncertainties and errors that may occur during measurement. Calculations reference Equations (15) and (16), where \(w_{k-1}\) represents the posterior estimate of the width state in the previous time step, and \(w_{k}^{-}\) denotes the prior estimate of the width state at the current time step, with the remaining variables defined similarly, and \(\sigma_{p}=0.05\), \(\sigma_{v}=0.00625\), and \(\sigma_{m}=0.05\). \[Q=\text{diag} \begin{bmatrix}(\sigma_{p}w_{k-1})^{2},(\sigma_{p}h_{k-1})^{2},( \sigma_{p}w_{k-1})^{2},(\sigma_{p}h_{k-1})^{2},\\ (\sigma_{v}w_{k-1})^{2},(\sigma_{v}h_{k-1})^{2},(\sigma_{v}w_{k-1})^{2},(\sigma _{v}h_{k-1})^{2}\end{bmatrix}  \tag{15}\]  
\[R=[(\sigma_{m}w_{k}^{-})^{2},(\sigma_{m}h_{k}^{-})^{2},(\sigma_{m}w_{k}^{-})^{ 2},(\sigma_{m}h_{k}^{-})^{2}] \tag{16}\]  
We observed that during the update process of the KF, the fixed measurement noise covariance \(R\) does not accurately reflect the measurement uncertainty, which impacts the model's precision. [PERSON] [23] addressed the influence of detection quality on the KF update by proposing the NSA-KF method, which utilizes detection confidence \(c\) to reflect noise during measurement, as shown in Equation (17). However, the NSA method only considers detection confidence in a simplistic manner. While it effectively compensates for measurement noise in cases of foreground occlusion, its performance regarding measurement noise caused by overlapping objects is less satisfactory. Therefore, we proposed a method that allows the measurement noise covariance \(R\) to adaptively adjust based on overlap information, reflecting the uncertainty in the observations. This method is referred to as adaptive measurement noise (AMN), as shown in Equation (18). We used the overlap rate \(o\) as a state to reflect the overlapping phenomena of tracked objectsand combined it with the NSA method to more accurately represent the observation quality and uncertainty of the objects, with the calculation of \(o\), as described in Equation (3). \[R_{new}=(1-c)R \tag{17}\]  
\[R_{new}=\frac{1-c}{1-o}R \tag{18}\]
## 3 Passive Systems
### Field-passive Fluids in Energy Dissipating Systems  
#### 3.2.1 Passive Dampers  
Damping is the most effective method to reduce unwanted vibrations in cases where the system is excited close to its natural frequency. There are two main types of viscoelastic dampers: viscoelastic liquid damper, having for instance a container filled with a viscoelastic liquid; viscoelastic solid damper, which consists for example in a sandwich structure of steel plates intercalated with layers of a certain viscoelastic polymer [161; 162]. For the same reasons exposed above, the attention here is focus exclusively on viscoelastic liquid dampers.  
Viscoelastic liquid dampers generally consist of a moving piston or plate within an open container filled with some viscoelastic fluid. They may be used to solve very different vibration problems. A typical example is the damping of operational vibrations in an industrial piping system Figure 4. Another example is the damping of shock-like forces induced, e.g., by a large forge hammer. In this case, the damping device needs to absorb a significant amount of energy in a very short time, so that the system comes to rest as quickly as possible allowing the start of the next operation of the hammer with minimum recovery time [163]. Since the 1980s, such viscoelastic fluid dampers havebeen also used in civil engineering applications like seismic and vibration isolation of buildings and as energy dissipation devices for bridges and buildings [164]. [PERSON] [165] made an exhaustive list of references where viscoelastic dampers have been used in the control of vibration and noise.  
The use of STFs in dampers or shock absorbers has not experienced yet such a wider use as the VEFs. The first publication regarding the use of STFs in dampers or shock absorbers appeared in the 1990s [166] and since then few additional works have been published [167; 168; 169].
# Competition with the Industrial IoT: A Service-Dominant Logic Approach
## 1 Introduction  
In the dynamically shifting business environment, the integration of technological advancements with strategic partnerships among competitors, particularly within the European Union [1], has emerged as a critical strategy for securing competitive advantage, especially for small- and medium-sized enterprises (SMEs) [2, 3]. This phenomenon, known as coopetition, marries the seemingly contradictory forces of cooperation and competition and has significantly captured the attention of management scholars [4].  
The rise of the Industrial Internet of Things (IIoT) offers unparalleled opportunities for businesses to boost operational efficiency and foster innovation [5]. Ongoing developments in artificial intelligence (AI) and machine learning (ML) underscore the transformative potential of the IIoT [5]. When integrated with interoperability and accessibility, these technologies are crucial to boosting market responsiveness and facilitating value co-creation [6]. This is particularly evident through coopetition among manufacturing small- and medium-sized enterprises (SMEs) [7, 8]. However, despite these advancements, there needs to be more solutions oriented towards leveraging the IIoT for cooperation networks, particularly among SMEs. A study by the BCG Henderson Institute, featured in the MIT Sloan Management Review, reveals a concerning 85% failure rate of coopetition networks in the shortterm, with success rates not exceeding 15% in the long term, predominantly in networks led by dominant firms like Microsoft and Amazon [9]. This highlights a significant research gap in understanding and facilitating successful coopetition networks for SMEs, which need to merge corporations' resources and market dominance.  
Addressing this gap, this research aims to explore IIoT systems' multiple levels of interoperability and accessibility within SME networks and design an IIoT system for facilitating coopetition. Adopting the value co-creation perspective of Service-Dominant Logic (S-D Logic) [10], this study aims to develop an IoT artefact that empowers SME coopetition networks, fostering an ecosystem that encourages collaborative innovation and mutual value creation. By utilizing the Design Science Research (DSR) methodology, this research seeks to bridge the theoretical insights of S-D Logic with practical applications in the IIoT, specifically targeting SME coopetition [11].  
This paper is structured to first explore the synergies and challenges of integrating coopetition networks with the IIoT through the lens of S-D Logic. After laying the theoretical groundwork, it discusses the methodology employed and details the design and development of the IIoT system, Cockpit4.0+, which is specifically tailored to facilitate coopetition among stone industry SMEs. The paper then presents a preliminary acceptance evaluation of the Cockpit4.0+ prototype, followed by an experimental pilot test. It concludes with a comprehensive discussion that synthesizes the findings, explores their broader implications, and proposes directions for future research.
# Electromagnetic Field-Aware Radio Resource Management for 5G and Beyond: A Survey
## 5 Challenges and Open Research Topics
### Open Research Topics
#### 5.2.1 Joint Optimization of Energy Efficiency and EMF Constraints  
Optimizing both energy efficiency (EE) and EMF exposure requires advanced algorithms that balance network performance and regulatory safety limits [72]. RIS deployments, user association, multiplexing/modulation techniques, cooperative transmissions, power control, and beamforming can be leveraged to develop solutions that achieve sustainable and safe 5G operations.
# Development and Field Testing of a Wireless Data Relay System for Amphibious Drones
## 3 Results  
As described herein, the results of the system stability evaluations for flight characteristics and water quality data are presented concisely as core processes of the development of the amphibious-drone-based system for surface water quality monitoring. Subsequently, we present detailed results of field tests of the wireless data relay using two amphibious drone systems.
## 3 Results
### Observed Correlation of Rainfall, GNSS-IWV, and CAPE at the GNSS Station Locations
#### 3.1.4 Relation between Rainfall and CAPE  
Next, we analysed the relation between rainfall and CAPE. Previous research based on the idealized parcel theory suggested a power-law relationship between rainfall and CAPE with an exponent \(\beta=0.5\) (Equation (4)) [4; 26]. The Q-Q plot indicates that below the 10 th percentile rainfall and CAPE do not follow the same distribution within the assumed log-linear relation at both stations (Figure 7A,B).  
We indicate that there is a power-law relationship between logarithmically binned CAPE and median rainfall of each bin (Figure 8A,B).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model (TUCU)** & **RMSE** & **R-Squared** \\ \hline _exponential_ & 1.3 & _0.17_ \\ power & 10 & 0.13 \\ gaussian & 9 & 0.07 \\ polynomial & 11 & 0.09 \\ \hline
**Model (CATA)** & **RMSE** & **R-Squared** \\ \hline _exponential_ & 1.56 & _0.15_ \\ power & 11 & 0.10 \\ gaussian & 10 & 0.08 \\ polynomial & 11 & 0.09 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparing the quality of the fits for rainfall vs. GNSS-IWV based on RMSE and R-squared values for TUCU (top) and CATA (bottom) for different fitting models. The best-fit results are marked in italic font.  
Figure 6: Quantile–Quantile plot on a \(log_{e}\) scale of rainfall vs. GNSS-integrated water vapour shows that most of the data are well correlated when the \(log_{e}\)(TRMM rainfall) vs. GNSS-IWV is considered in both stations (**A**) for TUCU and (**B**) for CATA. Below the 10 th percentile, rainfall and GNSS-IWV do not follow an identical distribution at both stations. Black dashed lines indicate the rainfall percentiles (the 10 th and 90 th percentiles).
# Mitigating the Scintillation Effect on GNSS Signals Using MP and ROTI
###### Abstract  
Ionospheric scintillation is one of the main error sources of Global Navigation Satellite System (GNSS) positioning. The presence of scintillation may result in cycle slips, measurement errors or even losses of lock on satellites, eventually leading to complete failure of positioning. Typically, scintillation parameters S4 and \(\sigma_{\phi}\) are used to characterize amplitude and phase scintillation, respectively. However, the scintillation parameters can only be generated from data with a frequency of at least 1 Hz. Rate of change of total electron content index (ROTI) is often used as a proxy for scintillation parameters, which can be obtained from 1/30 Hz data. However, previous research has shown the inefficiency of ROTI to represent scintillation. Therefore, the multipath parameter (MP) has been proposed as another proxy for scintillation parameters, which can also be obtained from 1/30 Hz data. In this paper, both MP and ROTI (standard parameters) were used to mitigate scintillation effects on precise point positioning (PPP). To evaluate the effectiveness of MP and ROTI in mitigating scintillation effects, S4 and \(\sigma_{\phi}\) were also used for comparison and validation. Three strategies are proposed: (1) remove all observations from the satellite that is most affected by scintillation; (2) remove the scintillation-affected observations; (3) weight the measurement noise matrix in the Kalman Filter (KF) process. The results show that the observation removal and weighting strategies are considerably more effective than the satellite removal strategy. The results also show that the improvement of PPP outputs reaches 93.1% and the performance of standard parameters is comparable to that of scintillation parameters in the observation removal and weighting strategies.  
Keywords:GNSS; scintillation parameters; mitigation; multipath; ROTI +
Footnote †: journal: Remote Sens.
# Solid-State Foaming Process Optimization for the Production of Shape Memory Polymer Composite Foam
## 1 Introduction  
Shape memory materials are stimuli-responsive materials with the ability to change their shape, after having been quasi-plastically distorted upon the application of an external stimulus. The stimulus is mostly heat, as reported by [PERSON] et al. [1]. After designing shape memory polymer snap fits triggered by heating, [PERSON] et al. [2] conducted further development on heat triggered snap fits and fastening methods for the assembly/disassembly of products. Inadvertent triggering was the main obstacle for spreading of such joining methods. [PERSON] et al. [3] investigated various triggering methods, such as biological and magnetic triggers, as well as provided a non-comprehensive list of various triggers, such as physical- and chemical-based triggers.  
The most common shape memory materials are shape memory alloys (SMAs) and shape memory polymers (SMPs). SMPs are distinguished from SMAs by unique properties, such as their light weight, ease of processing, low cost, and high shape recovery abilities. The shape memory effect can be observed in heat-activated SMPs when thermo-mechanical cycles are performed. These thermo-mechanical cycles consist of three main steps. First, the SMP is processed into a permanent shape, then it is heated up beyond glass transition temperature (\(T_{g}\)) and deformed. Next, it is cooled to reach its temporary shape. After that, it is heated to glass transition temperature (\(T_{g}\)) to trigger the shape recovery process, which returns it to its original permanent shape. The thermo-mechanical cycles are shown in Figure 1.  
Consequently, SMPs are used widely for different applications, such as being light actuators, structural parts of a reduced size during transport, and as expandable/deployable structures. Furthermore, they can be used as hydrogen gas storage. [PERSON] et al. [4] created enclosed cells in cross-linked PVC foam and elastomeric polyurethane foam to store hydrogen gas; the tested foams did not show better volumetric capacity over metallic foams. However, they are preferred due to the permeability of hydrogen gas.