esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
[33], an adaptive enhanced weighted clustering algorithm (AEWCA) was proposed, which optimizes the WCA by taking into account the optimal node degree and the distance between neighboring nodes when selecting the cluster head, together with the reliability of inter-node links and the energy consumption of nodes. This algorithm achieves the goals of balancing network energy consumption, extending network lifetime, and making combat time longer. In ref. [34], an improved weighted and location-based clustering algorithm (IWCA) was proposed, which uses a location-based K-means++ clustering algorithm to set up the initial UAV clusters and considers the remaining energy, node degree, relative mobility, and average distance as the cluster head selection principle. This scheme significantly improves performance, such as group delivery rate, network lifetime, cluster head replacement rate, and energy consumption. In ref. [35], a new routing scheme called the weighted cluster S-UAV routing scheme was proposed, which selects the cluster head and cluster members for each cluster based on a new weighted formula based on distance, speed, and reward index. This scheme reduces the total time delay of the network. In ref. [36], a dynamic weighted clustering algorithm (DWCA-DCH) was proposed, which selects the cluster heads by considering communication quality and remaining energy. This calculation balances communication efficiency and lifecycle, improving the survivability and stability of UAV cluster self-organizing networks. The improved weighted clustering algorithm mentioned above can significantly extend network lifespan and improve communication efficiency by dynamically adjusting cluster size or position constraints. But the simulation task environment in the article is relatively ideal and does not focus on anti-interference issues. In highly dynamic UAV emergency-rescue scenarios subject to frequent interference, once the cluster head or critical link encounters interference, communication within the cluster may be completely paralyzed, resulting in delayed or erroneous rescue instructions, posing a serious threat to task safety. Therefore, it is necessary to introduce anti-interference mechanisms in weighted clustering algorithms and optimize them specifically for interference risks.
# Research on Energy Harvesting Mechanism and Low Power Technology in Wireless Sensor Networks
## 3 Methods  
To enhance the energy efficiency and longevity of the network, we implemented two strategies for the WSN, as illustrated in Figure 1. Firstly, we employed energy harvesting technology to convert renewable sources like solar and wind energy into electrical energy, which effectively supplements the power supply of the nodes. Secondly, we utilized neighbor discovery technology to decrease the operational time of the nodes, along with data fusion technology to reduce the volume of data transmission. These measures were aimed at minimizing energy consumption and prolonging the network's usage period.
# Localization of GNSS Spoofing Interference Source Based on a Moving Array Antenna
### Related Work
The article proposes a moving array antenna-based method aiming to address the issue of locating GNSS spoofing interference sources. This method can be implemented on the widely used hardware platforms of GNSS nulling antenna [26], enabling it to possess simultaneous capabilities of anti-interference and deception interference detection and localization.
## 2 Materials and Methods
### Characterization
#### 2.3.4 Deicing Test  
The deicing capability of the 4-layer circuit was tested. First, two circuits were coated with an approximately 2.5 mm thick ice layer by freezing deionized water at \(-16\)\({}^{\circ}\)C for 2 h. Then, one of the circuits (heating circuit) was connected to the power source at 200 V, whereas the other circuit acted as a reference (reference circuit). The deicing time was measured by IR thermography by registering the temperature on specimen surface. A FLIR E50 thermal camera was pointing over the specimen surface placed in a vertical position to improve water removal.
## 4 5G and V2X Deployment Projects
### 5g-Mobix  
The 5G-MOBIX initiative, an EU-funded project conducted from 2018 to 2022, brought together 60 partners from 13 countries to develop and evaluate automated vehicle functionalities using 5G technology. The project focused on two cross-border corridors and six urban trial sites, aiming to assess the benefits of 5G for connected and automated mobility (CAM) applications. The trials conducted as part of 5G-MOBIX allowed for the assessment of various CAM applications, including cooperative overtake, highway lane merging, truck platooning, remote driving, and vehicle quality of service support. These applications were tested in real cross-border environments, utilizing multi-access edge computing (MEC) infrastructures and exploring the interworking of multi-operator deployments. The project aimed to gain technical insights and identify new markets for CAM applications and edge service developers.  
The trials considered diverse traffic; network coverage; service demand conditions; and legal, business, and social aspects at each site. They evaluated the performance of different protocols, scenarios, and 5G features, addressing challenges related to cross-border functionality, telecommunication infrastructure, application security, privacy, and regulations. The project took a holistic approach to evaluation, considering technical aspects, impact factors, and alternative approaches to solutions. The deployment of trial sites in 5G-MOBIX provided the opportunity to test various configurations; assess interoperability between systems and components; and gain insights, lessons learned, and best practices. The results and specifications from these trials significantly contributed to the research and development of 5G for CAM. Mobile operators benefited from cooperation with neighboring operators and road operators, gaining valuable insights for future trials and enhancing their understanding of limitations, roaming, and cross-network interconnection.  
The project's findings on 5G performance in connected and automated vehicles (CAVs) operations offered a realistic assessment of technology capabilities and demands. This information, along with the specifications of the 5G-augmented vehicle and roadside infrastructure architecture, guided further development and deployment in the automotive industry and transport sector. In addition to technical aspects, 5G-MOBIX also examined standards, regulations, and cost considerations associated with 5G roll-out. The analysis highlighted gaps in standardization, policies related to autonomous vehicles, infrastructure requirements, roaming charges, data privacy, security, and the costs associated with 5G deployment. The project aimed to bridge these gaps and provide valuable insights for stakeholders involved in the development and implementation of 5G for CAM services and associated business models [90].
## 3 Results and Discussion
### Electrical Parameters
Nevertheless, the variability of the Cp and Cs parameters as a function of frequency changes (f) is much greater than for the Z and G parameters. The observed lack of clear similarities or differences between single-flower honeys may result from the fact that the honeys tested were fresh, unheated, and unadulterated with sucrose, as indicated by the values of physicochemical parameters, and therefore they adopted characteristic values of similar dielectric parameters. As honeys are subject to changes during storage, with water evaporation and changes in chemical composition, these differences may become more visible. Monitoring electrical capacity enables assessment of whether the honey is fresh or already showing signs of degradation [44].
## 4 Results
### Sensitivity to \(\alpha\)  
Another important factor to be analyzed when dealing with the assessment of the performance of a Q-learning algorithm is represented by the learning rate \(\alpha\). As for the discount factor, a sweep over three different values has been performed, specifically 0.1, 0.5 and 0.9. For the present analysis, the best combination between reward function and discount factor has been selected: R1 with \(\gamma\) = 0.9. The boost exploration strategy has also been maintained.  
In Table 6, the results obtained by the control agent are reported for each DM. As for previous experiments, CLUST3 appears to be the hardest driving environment to be solved when moving from the original combination of R1, \(\gamma\) = 0.9 and \(\alpha\) = 0.9. Moreover, unexpected results can be obtained on different DMs when drastically changing the agent configuration. As an example, a very low learning factor can be capable of outperforming even the most common values (e.g., 0.9) on specific DMs. Specifically, the lowest rFC is exploited by Q-learning with \(\alpha\) = 0.1 on CLUST2 while a comparable rFC with respect to \(\alpha\) = 0.9 is obtained on CLUST1. As far as \(\alpha\) = 0.5 is concerned, such a configuration appears to be not optimal in minimizing the FC whereas it is capable of achieving the best CS results for CLUST1, CLUST2 and CLUST4.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(\gamma\)} & \multirow{2}{*}{**DM**} & \multicolumn{8}{c}{_SoC\({}_{f}\) (-)_} & \multicolumn{8}{c}{_FC (g)_} \\ \cline{2-13}  & & _DP_ & _R1_ & _R2a_ & _R2b_ & _R2c_ & _R3_ & _DP_ & _R1_ & _R2a_ & _R2b_ & _R2c_ & _R3_ \\ \hline \multirow{9}{*}{0.999} & CLUST1 & 0.6000 & 0.5904 & - & - & - & 0.6011 & 246.6 & 244.47 & - & - & - & 293.15 \\  & CLUST2 & 0.6000 & 0.6000 & - & - & - & 0.6049 & 197.2 & 263.46 & - & - & - & 266.82 \\  & CLUST3 & 0.6000 & - & - & - & - & 0.5795 & 152.6 & - & - & - & - & 185.51 \\  & CLUST4 & 0.6000 & 0.5974 & - & - & - & 0.5982 & 53.9 & 93.81 & - & - & - & 95.23 \\ \cline{1-1} \cline{2-13}  & \multicolumn{8}{c}{rFC (g)} & \multicolumn{8}{c}{\(\Delta\)_FC (\%)_} \\ \cline{1-1} \cline{2-13}  & CLUST1 & 246.6 & 258.27 & - & - & - & 293.15 & - & 4.73 & - & - & - & 18.88 \\ \cline{1-1} \cline{2-13}  & CLUST2 & 197.2 & 263.46 & - & - & - & 266.82 & - & 33.60 & - & - & - & 35.30 \\ \cline{1-1} \cline{2-13}  & CLUST3 & 152.6 & - & - & - & 214.99 & - & - & - & - & - & - & 40.88 \\ \cline{1-1} \cline{2-13}  & CLUST4 & 53.9 & 97.59 & - & - & - & 97.86 & - & 81.06 & - & - & - & 81.56 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Reward shaping analysis considering \(\gamma\) = 0.999.  
Since the learning rate generally determines the speed of the Q-table update, a non-trivial response of the agent has been proved for throughout the present test cases. In fact, substantial variation can be obtained in the behavior of the agent when a reduction in the \(\alpha\) value is introduced in the Q-learning setup. Nevertheless, as a general outcome of several research works in the literature, a higher value leads to more robust RL controllers for HEVs.
## 2 Materials and Methods
### Studied Radiation Shielding Parameters
\[B(E,X)=1+\frac{b-1}{K-1}\ (K^{x}-1)\ for\ K\
eq 1 \tag{6}\]  
\[B(E,X)=1+(b-1)x\ for\ K=1 \tag{7}\]  
\[K(E,x)=cx^{a}+\frac{d\ \tanh\frac{x}{\chi_{k}-2}-\tanh(-2)1-\tanh(-2)}{1-\tanh(-2) }\ for\ x\leq 40\ mfp \tag{8}\]  
In the above equations:  
\(E\): Incoming photon energy;  
\(X\): Penetration depth in millimetres per second;  
\(B\): Accumulation factor at 1 millimetre per second;  
\(K\): Photon-dose multiplication factor. The projected range is a critical parameter for analysing the shielding and interaction of nuclear radiation with different objects at different protons and alpha kinetic energies. The predicted range provides an explanation for the mean value of the depth at which alpha and proton particles begin to slow down and come to rest. The stopping power of a vehicle is defined as the quantity of energy lost over a certain distance. The mass stop power of a system is the amount of energy lost per unit of density. For the computation of ion deposition data in ion irradiation materials, the Stopping and Range of Ions in Materials (SRIM) equation was utilized, and Monte Carlo simulations were utilized for the computation of data using the SRIM equation. According to the findings of this study, attenuation characteristics of the glass produced were tested against charged particles, such as alpha and proton, with the results measured in terms of alpha/proton mass stopping power and alpha/proton projected range [15]. The last term to discuss is \"effective neutron removal\" (\(R\)), which refers to the process of removing neutrons from the material in a timely way. Following the definition of the cross-defined (or un-scattered) cross-section definition, the total amount of nuclear energy generated in a fission event from the first (or fast) neutron is equal to the average anticipated quantity of nuclear energy generated in a fission event. Calculating the R values of a material may be accomplished using the following Equation (9):  
\[\Sigma_{R/\rho}=\sum w_{i}\Big{(}\Sigma_{R/\rho}\Big{)} \tag{9}\]  
In Equation (9), \(\Sigma_{R/\rho}\) is mass removal cross-section (cm\({}^{2}\)/g), and \(w_{i}\) is partial density (g/cm\({}^{3}\)) of the \(i\)th element [16].
## 2 Related Work
### Hop-Based Energy Efficient Routing  
In hop-based routing, the metric of (optimal) hop number or corresponding individual distance is treated as the primary factor in order to achieve energy efficiency during routing process. It can be seen that the factor of hop number or hop distance is not specifically proposed and addressed by most of the energy efficient routing protocols above, when in fact, it has very important impact on many network metrics like energy consumption, latency, routing overhead, interference, _etc._[21].  
The authors in [22] present some pioneering work by studying different energy models under general wireless network environment. The authors in [6, 14] consider different energy consumption for source and intermediate node and each cluster head uses direct transmission under their small scale network environment. The authors in [2] study selection of transmission manner from probability point of view. They present a probability of \(P_{i}\) to transmit data through multi-hop manner and a probability \((1-P_{i})\) to transmit through single hop manner to sink node. The authors in [3] also study the energy consumption under both single hop and multi-hop transmission manners. They claim that the preference of multi-hop routing to single hop routing depends on source to sink distance and reception cost. The author in [4] proposes a Multihop/Direct Forwarding scheme to split data traffic into two branches by using either direct or multihop transmission to achieve good energy efficiency and network lifetime performance. In our previous work [5], we propose a Hop-based Energy Aware Routing (HEAR) algorithm which can largely reduce energy consumption as well as prolong network lifetime. However, the hotspot problem cannot be avoided since we try to minimize the total energy consumption during certain route rather than to let sensor nodes consume their energy at similar rate.  
Even though the above-mentioned energy efficient routing protocols or algorithms can improve energy efficiency and prolong network lifetime to some degree, they cannot effectively overcome the hotspot problem which is tightly related with energy and traffic models. In this paper, we propose a Distance-based Energy Aware Routing (DEAR) algorithm which can effectively alleviate the hotspot problem based on the theoretical deduction and analysis of relevant models.
## 3 Discussion
Our study found that the levels of uEVs p-AQP2(S256) were significantly higher in patients with diabetes than in healthy controls, which may be a compensatory mechanism to reabsorb water and mitigate the increased plasma osmolality caused by hyperglycemia. In contrast, uEVs p-AQP2(S256) was significantly decreased in patients with DN, which may be related to decreased renal function and impaired urine concentration in patients with DN. Persistent hyperglycemia can lead to insulin resistance in type 2 diabetes, and previous studies have demonstrated that GSK-3B is strongly associated with the development of insulin resistance [43]. Our study found that uEVs p-GSK3B(Y216) were significantly increased in diabetic patients compared with healthy controls, consistent with previous findings. GSK-3B is also closely related to the cell signaling pathway involved in renal injury repair and regeneration and mediates podocyte damage [44]. Our study found that uEVs p-GSK3B(Y216) were significantly increased in patients with DN compared with diabetic patients. Moreover, in the kidney tissue of a mouse model of DN, both p-AQP2(S256) and p-GSK3B(Y216) were similarly altered, indicating their important roles in the progression of DN and their potential as biomarkers for the diagnosis of DN. Strict inclusion and exclusion criteria were set for subjects in the discovery phase of biomarkers to ensure homogeneity within the group and contribute to the screening of ideal biomarkers. Our study included patients with a confirmed history of diabetes for less than two years and a normal blood glucose one year before the diagnosis of DM to exclude patients with pathological changes of DN but no clinical manifestations. The enrolled patients with DN had pathological grades III-IV. Although strict enrollment criteria ensure intragroup homogeneity and facilitate the discovery of ideal biomarkers, these markers must be further validated in heterogeneous populations. The utilization of spot urine samples yields reproducible data and suggests the potential use of uEVs to diagnose renal disease [6]. However, sample preparation for uEVs poses additional challenges to the accuracy of MS-based targeted quantitation. Our study provides a new strategy for the isolation and identification of hundreds of phosphopeptides from uEVs, indicating their potential as liquid biopsies for the treatment of kidney disease. p-AQP2(S256) and p-GSK3\(\beta\)(Y216) are potential biomarkers for the diagnosis of DN, but further validation in a larger heterogeneous cohort of patients with DN is required.
# Moment-Based Stochastic Analysis of a Bistable Energy Harvester with Matching Network
## 7 Conclusions  
Ambient dispersed energy is a potentially unlimited energy source that can be exploited for supplying power to the next generation of wireless connected IoT electronic devices. In particular, parasitic mechanical vibrations are very promising for their relatively high power density and they widespread presence.  
In this work, we studied a bi-stable piezoelectric energy harvester for parasitic ambient mechanical vibrations scavenging. We showed that the application of matching network, interposed between the piezoelectric transducer and the electrical load, increases the harvested power and the power conversion efficiency by more than 30 times in the weak noise limit, and by more than five times for medium-strong noise intensity.  
The matching network is designed to reduce the impedance mismatch between the mechanical and the electrical domains of the harvester. With respect to other previously proposed solutions based on power factor correction, the new setup not only reduces the reactive power reflected by the load to the source but also maximizes the average power absorbed by the load.  
To properly optimize the matching network, we developed a novel design methodology. For a linear harvester, the optimal values of the matching network elements can be determined analytically applying stochastic analysis methods and solving the resulting Lyapunov equation. For a nonlinear harvester, we developed a technique based on finding an approximate solution for a nonlinear Lyapunov equation applying the Gaussian moment  
Figure 7: Relative error for the root mean square output voltage (red squares), and the power efficiency (blue circles), between theoretical predictions obtained using Gaussian moment closure method and full numerical simulations versus the noise intensity.  
closure method. The proposed methodology represents a significant improvement for the design of nonlinear systems, for which few techniques are currently available.  
Although the design methodology is best suited for weak noise intensity, we verified through numerical simulations that the application of the optimized matching network offers a significant boost in the power performance of the energy harvester, even for the medium-high intensity of ambient mechanical vibrations.  
Conceptualization, K.S., M.B., F.L.T. and F.B.; Methodology, K.S., M.B., F.L.T. and F.B.; Software, K.S., M.B. and F.B.; Validation, K.S., M.B., F.L.T. and F.B.; Formal analysis, M.B., F.L.T. and F.B.; Investigation, K.S.; Writing--original draft, M.B. and F.B.; Writing--review & editing, M.B., F.L.T. and F.B.; Supervision, M.B. and F.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data is contained within the article.  
This research has been partially conducted within the Italian inter university PhD program in Sustainable Development and Climate Change.  
The authors declare no conflict of interest.
## 3 Results
### Energy Consumption  
Tank A11 (traditional) operated for a total of 32 pumping-over cycles during the first 6 days of the process, with an overall pumping-over system operating time of 9.2 h. Additionally, it performed 12 pumping-over cycles with a total duration of 2.3 h. In both cases, the power consumption remained nearly constant at approximately 2.85 kW for the pump and 0.65 kW for the spreader.  
In tank A21, the AMI system was activated 26 times during the first 4 days and 16 times in the final phase, resulting in total compressor operating times of 18.8 min and 5.1 min, respectively, with a nearly constant power consumption of approximately 28.7 kW.  
Table 2 summarizes the energy consumption for the two systems: the traditional pumping-over system and the AMI system.  
These findings are confirmed by the results related to energy consumption. In fact, in the AMI system, the electricity consumption of the centralized compressor during fermentation is 11.4 kWh, which is approximately 25% of the energy consumption of the pumping-over unit: 40.3 kWh (Table 2). This is due to the methodology adopted for breaking the cap in the AMI system, where only 23.9 min of compressor operation were sufficient to disintegrate a solid mass that, in an 800 hL fermenter, can exceed a thickness of 1.5 m. Therefore, the installed system does not suffer from the use of an oversized machine for its operation, as its interventions are limited to very short periods.  
These results are consistent with those obtained in previous studies conducted on Negramaro and Primitivo grapes [22; 26], which have skins with different consistency compared to the Syrah cultivar. They tend to confirm that, even with caps composed of skins of intermediate consistency, the AMI system proves to be more efficient and sustainable than the traditional pumping-over system, with energy consumption nearly four-times lower and, consequently, with a lower environmental impact.  
In terms of energy consumption, the AMI system proves to be superior to the pumping-over system, consuming nearly four-times-less energy. This, in turn, leads to a lower environmental impact.