esa-sceva/satcom-chunk-collection huggingface dataset:
# Revolutionizing Urban Pest Management with Sensor Fusion and Precision Fumigation Robotics
## 1 Introduction
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Aspect** & \multicolumn{2}{c}{**Existing Methods**} & \multicolumn{2}{c}{**Proposed Work**} \\ \hline Technology Used & \(\bullet\) & Manual fumigation, breeding infertile mosquitoes & \(\bullet\) & Sensor fusion (LiDAR, depth camera with YOLOv8, IMU) \\  & \(\bullet\) & Biogents sentinel trap, light traps, ovitraps, and gravitraps & \(\bullet\) & 3D-LiDAR for mapping and navigation \\ \hline Primary Limitations & \(\bullet\) & Overex to chemicals affecting health & \(\bullet\) & Requires sophisticated technology and initial setup \\  & \(\bullet\) & Inefficiency in high mosquito densities & \(\bullet\) & \(\bullet\) \\  & \(\bullet\) & Limited field of view (2D-LiDAR) & & \\ \hline Efficiency & \(\begin{array}{l}\text{Varies significantly with manual fumigation}\\ \text{and trap effectiveness}\end{array}\) & \(\begin{array}{l}\text{High efficiency due to automated, precise}\\ \text{detection and fumigation of hotspots}\end{array}\) \\ \hline Health Impact & Potential health risks due to chemical exposure & \(\begin{array}{l}\text{Reduced risk to human operators by}\\ \text{automating chemical fumigation}\end{array}\) \\ \hline Environmental Impact & Potential for chemical dispersal affecting non-target areas & \(\begin{array}{l}\text{Focused application of chemicals, reducing}\\ \text{environmental footprint}\end{array}\) \\ \hline Navigation and Mapping & Limited in non-open areas like indoor environments or dense urban settings & \(\begin{array}{l}\text{Advanced navigation using 3D-LiDAR and}\\ \text{LIO-SAM algorithm, improving accuracy in}\\ \text{complex environments}\end{array}\) \\ \hline Hotspot Identification & Relies heavily on manual inspection and stationary traps & \(\begin{array}{l}\text{Automated real-time identification and}\\ \text{remapping, increasing responsiveness to}\\ \text{changing conditions}\end{array}\) \\ \hline Operational Strategy & Static with periodic manual adjustments & \(\begin{array}{l}\text{Dynamic, with ongoing adjustments based on}\\ \text{real-time data collection}\end{array}\) \\ \hline Cost & \(\begin{array}{l}\text{Lower initial cost but higher due to labor and}\\ \text{repeated interventions}\end{array}\) & \(\begin{array}{l}\text{Higher initial investment but lower ongoing}\\ \text{costs due to automation}\end{array}\) \\ \hline Adaptability & Limited adaptability to new breeding grounds without manual intervention & \(\begin{array}{l}\text{High adaptability with continuous learning}\\ \text{and updating capabilities}\end{array}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison table contrasting the existing methods with the proposed work. hotspots in real time. This adaptation enables more effective data collection and targeting of potential breeding hotspots.
# Gray Image Denoising Based on Array Stochastic Resonance and Improved Whale Optimization Algorithm
## 1 Introduction
Compared with other traditional algorithms, it has a simpler principle and a stronger optimization ability. However, the algorithm still has many shortcomings, such as the linear and random parameters set in the algorithm, which means the optimization of the algorithm is limited, and the poor diversity of the algorithm population will lead to the problem of local optimal. In view of the above problems, the author of this paper proposed an improved whale optimization algorithm (IWOA) [28] in the previous research, and applied the algorithm to the diagnosis of bearing fault signal, which proved that the algorithm has fast convergence speed, good optimization ability and stability, and has good application in weak signal detection. Under the above research background, this paper combines intelligent optimization algorithm with array stochastic resonance for the first time, and proposes a gray image denoising method based on improved whale optimization algorithm and parameter adaptive array stochastic resonance. Firstly, the noisy gray image is converted into one-dimension aperiodic binary pulse amplitude modulation (BPAM) signal by scanning, coding and modulating. Then, the traditional whale optimization algorithm is improved in the initial solution distribution, global search ability and population diversity generalization. The IWOA algorithm is applied to select the structural parameters of the SR. Finally, the denoised one-dimensional aperiodic signal is demodulated, decoded and reverse scanned to obtain the denoised image. Experimental results show that the proposed algorithm has an obvious effect on removing strong noise interference in image information. Comparing with the classical image denoising algorithm and the array SR algorithm with fixed parameters, the proposed algorithm has higher image quality, better visual effects and a higher peak signal-to-noise ratio. The remaining chapters of this paper are arranged as follows: in this study, the theoretical analysis of array stochastic resonance is introduced in Section 2, and the theoretical analysis of improved whale optimization algorithm is introduced in Section 3. In Section 4,the above two theories are combined to propose the algorithm proposed in this study. Then, in Section 5 of the article, an experimental demonstration is conducted and the experimental results are explained. Finally, in the sixth section, a summary of the full text is made.
## 3 Methodology
### Multiscale Mixed-Graph Convolution
#### 3.3.2 Multiscale Features Fusion  
Similarly to previous works [12; 18], we partition the human skeleton into three scales, joints, limbs, and body parts, as illustrated in Figure 2 (the blue dashed lines represent the connectivity of some nodes). The connectivity between joints at each of these scales are represented by three adjacency matrices: \(\mathbf{A}_{\mathrm{s1}}\), \(\mathbf{A}_{\mathrm{s2}}\), and \(\mathbf{A}_{\mathrm{s3}}\). Furthermore, to facilitate information transmission and fusion across different scales, we draw inspiration from the feature pyramid structure [40] by concatenating features from various scales and by subsequently extracting the features once more. As illustrated in Figure 1b, the feature pyramid structure is incorporated into Kinematic Net/Dynamics Net. Input data \(\mathbf{X}_{in}^{k}\) or \(\mathbf{X}_{in}^{d}\) is first fed into three distinct scales of SS-MGCN blocks, respectively. After processing at Scale 1 (joint scale), the extracted features are concatenated with the features at Scale 2 (limb scale), effectively blending fine-grained joint information with intermediate limb information. This new combined representation then feeds into another SS-MGCN block. The same process recurs between Scale 2 and Scale 3 (body part scale): the second set of outputs is concatenated with those from Scale 3 to combine limb and coarse body-part insights. Finally, the last Concat block produces the network's overall pose/motion embedding \(\mathbf{X}_{out}^{k}\in\mathbb{R}^{C_{out}\times T\times V}\) or \(\mathbf{X}_{out}^{d}\in\mathbb{R}^{C_{out}\times T\times V}\), fusing features from all scales.
## 1 Introduction
[PERSON] [10] showed that the hybrid model produced better precision and recall than a classic ARIMA when forecasting monthly rates of complex social events, especially under abrupt change and nonlinear dynamics. [PERSON] and [PERSON] [27] also found that hybrid models work well for urban risk prediction, since they blend both sequence and nonlinear patterns found in time series data. Similar results have come up in studies focused on Europe, showing that these models can adapt to regional crime risk, even when data limitations or operational conditions differ [28]. Research also highlights the need for model evaluation and selection to be transparent and repeatable. There is a push for better validation, including nested cross-validation, tuning model settings, and using multiple performance metrics for well-grounded conclusions [29]. Visualization tools--such as time series breakdowns, confusion matrices, or variable importance charts--have become common and help explain findings to both technical and non-technical audiences [30]. The impact of hybrid and multiclass models in crime analytics extends beyond prediction. They are now used for operational policing, city planning, and policy action [4; 31]. Studies from North America and Europe have reported clear drops in crime and less wasted resources once these models help set patrol routes or plan police schedules [4; 32]. Hybrid ARIMAANN and ensemble machine learning models are both scalable and flexible, which makes them a good fit for local and regional authorities who need to base their actions on solid data in complex and changing settings. There are still some challenges. Data often come from very different sources--police reports, social media, or sensors--which can be messy or incomplete. Models must also be interpretable, so decisions can be defended in court or during official checks [33, 34]. In Romania, recent improvements in data quality mean it is now possible to apply these advanced models to full regional datasets, but this has not been explored enough. Our study fills this gap, offering a clear, empirical comparison between hybrid ARIMA-ANN and classic multiclass classifiers for monthly crime risk stratification, and sets up a method that can be used in future studies in Romania or similar settings.
## 3 Results and Discussion
### FGD Through Zeolite 13X
#### 3.3.3 13X Characterization
The enhanced stability of the trapping of the SO\({}_{2}\) derivatives adsorbed within the microporosities of the zeolite may explain why 13X [75 \({}^{\circ}\)C\(\_\)45 RH%] appears to retain a greater quantity of S-based compounds than 13X [75 \({}^{\circ}\)C\(\_\)30 RH%].
# Energy Efficiency Perspectives of PMR Networks +
## 5 Technical and Economic Considerations on Energy Efficiency of Next Generation PMR Systems
### Business Models for Mobile Broadband PMR
#### 5.1.1 Contract Services through an Existing Mobile Network Operator  
In this model, the public safety agency simply contracts data subscriptions with an MNO to provide mobile broadband services. Public safety users and consumers share the same spectrum and network. The public safety entity pays a consistent, predictable periodic fee for network access, usually a function of some known factor, such as the number of end users, devices or usage. This arrangement is relatively inexpensive if traffic, the number of users and subscription fees can be low, and fast if MNO LTE service already is available. CAPEX concerns only applications and terminals, which could remain significant if a large number is required. OPEX consists mainly of monthly fees for using the MNO service, and is proportional to the number of users and usage volume. Challenges for the MNO model include no control over four critical requirements: coverage (usually very poor in sparsely populated areas), availability, prioritization and resilience. Typically, little or no support exists for mission-critical features, and gaps in coverage can occur where the population density is low, such as in rural and isolated areas. For mission-critical needs, these issues might be addressed through stringent service level agreements (SLAs) to assure such features as priority access or network redundancy in case of an outage, which may significantly increase the subscription fee. In addition, most MNOs have a monthly data cap and additional fees for excess usage, which can significantly impact OPEX.
## 4 Experiment
### Pose Estimation Experiments in Indoor and Outdoor Environments
#### 4.4.2 Metrics  
This experiment adopts the widely used evaluation metric introduced by the SuperGlue [35] method, which assesses algorithm performance by calculating the area under the cumulative error distribution curve at various thresholds (Area Under Curve, AUC). Pose error is defined as the maximum of the translation vector and the angular error of the rotation vector between the predicted and true poses. Specifically, at thresholds of 5\({}^{\circ}\), 10\({}^{\circ}\), and 20\({}^{\circ}\), the AUC values corresponding to each threshold are computed using the Receiver Operating Characteristic (ROC) curve, providing a comprehensive evaluation of the proposed algorithm's performance.
# Formulation Strategies to Improve Oral Bioavailability of Ellagic Acid
## 4 Formulation Strategies for Improving EA Oral Bioavailability
### Lipid-Based Carriers  
#### 4.8.1 Solid Lipid Nanoparticles (SLNs)  
SLNs are colloidal delivery systems based on a high melting point lipid core coated by surfactants. In recent years, SLNs have been considered to be promising delivery systems due to the ease of manufacturing processes, scale up capability, biocompatibility and also biodegradability of formulation constituents [86]. SLNs also have some disadvantages, such as low drug loading efficiency and the possibility of drug expulsion due to its crystallization during storage [87]. Besides the initial burst release of drug which usually occurs represent an additional concern with these formulations [88]. These systems were proposed as carriers for improving the anticancer activity of EA against prostate cancer by [PERSON] et al. [89]. EA-loaded SLNs were prepared by the homogenization method as follows. EA was added after dissolution in DMSO to the melted lipid phase, consisting of Precirol\({}^{\circledR}\) and Tween 80, then the aqueous phase with poloxamer 407 was added dropwise at the same temperature under high speed homogenization. Finally, the obtained nanoemulsion was cooled for NP solidification. Tween 80 seemed to increase EA encapsulation, while the poloxamer at higher concentrations yielded unsuitable sizes. The optimal formulation showed an average diameter around 100 nm with a narrow size distribution (PDI = 0.28) and a negative zeta potential (\(\zeta\) = \(-\)20 mV). The EE was about 89% and DL 36%. Release studies performed at pH 7.4 demonstrated a noteworthy initial burst effect due to the drug present on or close to the surface, followed by a sustained release phase. Like polymer-based particulate systems, SLNs often lack uniformity of distribution of the drug in the nanostructure, which can explain the initial burst effect. In vitro biological studies revealed that EA loaded in SLNs was more efficacious than free EA on PC3 human prostate cancer cells, being the IC50 values of 61 and 82 uM, respectively.
# A Blockchain Network Communication Architecture Based on Information-Centric Networking
## 1 Introduction
While maintaining compatibility with P2P networks, it leverages ICN multicast techniques to provide relay acceleration services. Compared to prior studies, ICN nodes hosting blockchain applications can fully utilize their network node capabilities to optimize network topology and broadcast protocols using the delay information of the enhanced resolution system and leveraging ICN multicast capabilities. Specifically, in terms of network topology, ICN nodes classify network domains based on the delay information provided by the enhanced resolution system, and establish the domain gateway based on data flow forwarding dependencies. This approach facilitates the construction of a hierarchical and structured relay network topology. Regarding the broadcast protocol, ICN nodes employ ICN multicast to achieve parallel broadcasting. Upon receiving a message, an ICN node rebroadcasts it to P2P nodes, thereby reducing the overall broadcast latency and bandwidth consumption in the network. The main contributions of this paper are as follows:  
1. We propose BLOCK-ICN, an ICN-based blockchain network communication architecture compatible with IP networks that enables the deployment of blockchain applications on ICN nodes with computing and storage capabilities. While ensuring compatibility with existing P2P networks, BLOCK-ICN utilizes ICN multicast to provide relay acceleration services. 2. We construct a hierarchical structured ICN relay network topology based on BLOCK-ICN. It utilizes delay information provided by the enhanced resolution system to partition network domains, and selects a domain gateway based on data flow forwarding dependencies, to optimize network topology alignment. Based on this topology, a parallel tree-based broadcast method leveraging ICN multicast is proposed, to reduce network broadcast latency and bandwidth consumption. 3. We implemented a simulation of BLOCK-ICN using SimBlock. Experimental results demonstrated that, in a network with 16,000 nodes, compared to the Bitcoin network, when ICN nodes constituted 1% of the total nodes, the broadcast latency for blocks to reach 90% and 50% of the network was reduced by 25% and 33.2%, respectively, while maintaining good network scalability. The remainder of this paper is structured as follows: Section 2 reviews existing blockchain network communication approaches and ICN-based blockchain research. Section 4 presents the design of the proposed BLOCK-ICN architecture. Section 5 provides a theoretical analysis of the proposed approach. Section 6 details the simulation experiments. Section 7 discusses future research directions. Finally, Section 8 summarizes the conclusions of this study.
# Efficient and Secure Management of Medical Data Sharing Based on Blockchain Technology
## 5 Experiments
### Software and Hardware Environment  
In the experiment, a total of three hosts on Alibaba Cloud were used, all of which were equipped with Ubuntu 20.04 systems, 64G memory, 500G hard drives, of which the machine deployed the Orderer cluster had 32 cores, while the other two had 16 cores. The network bandwidth was 1 Gbps. The software used included Docker v20, Hyperledger Fabric 2.4, Fabric-CA 1.5, caliper 0.5, Jmeter 5.6.3, Golang 1.8+, shell script, and Nodejs. Among them, Caliper, Jmeter, and Nodejs were used to implement throughput testing for the blockchain network built, while the rest were used for blockchain network construction. Finally, seven organizations were deployed on three machines, including one regulatory organization, one medical cloud organization, and five hospital organizations. Three peer nodes were set up in each organization. The distribution of organizations among the three machines is shown in Figure 7.
# W-Band 4 th Order Waveguide Filter Based on Double Layer SU8 Microfabrication
## 2 Materials and Methods
### Extraction of Coupling Coefficients  
In the process of filter synthesis, it is necessary to convert the coupling coefficients to physical dimensions, which is described in [15]. There are many ways to extract the coupling coefficients, such as the weak coupling method, group delay method, and so on. The extraction methods of the inter-cavity coupling coefficients all use the eigenmode method. The coupling coefficient between adjacent resonators is calculated by the formula \(k=({e^{2}}-{f_{\text{m}}}^{2})/({e^{2}}+{f_{\text{m}}}^{2})\), and then the size of the coupling diaphragm between adjacent resonators is determined, as shown in Figure 2a. The external quality factor \(Q_{\text{ext}}\) is extracted using the weak coupling method. The boundary of resonator 1 will be affected, resulting in a shift of the resonant frequency. At this time, it is necessary to compensate for this effect by fine-tuning the size of the resonant cavity 1, as shown in Figure 2b.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Ref.** & \(f_{\text{0}}\)**, **GHz** & **Resonators** & **Technology** & **Thickness, \(\upmu\)m** & **Thickness Error, \(\upmu\)m** & **Steepness** \\ \hline [5] & 220–321 & Rectangular, TE\({}_{101}\) & SU-8 & 432 & about 10–15 & * \\ [6] & 293.2 & Rectangular, TE\({}_{101}\) & SU-8 & 432 & about 10–15 & * \\ [7] & 100 & Rectangular, TE\({}_{101}\) & SU-8 & 635 & about 15 & max of \(1^{\circ}\) \\ [8] & 671 & Rectangular, TE\({}_{101}\) & SU-8 & 191 ** & * & 0.6\({}^{\circ}\) \\ [9] & 100 & Rectangular, TE\({}_{201}\) & SU-8 & 1270 & 5 & 1.3\({}^{\circ}\) \\ [10] & 100 & Rectangular, TE\({}_{102}\) & TE\({}_{301}\) & SU-8 & 1270 & * & 1–2\({}^{\circ}\) \\ [11] & 400 & elliptic cavity, TM\({}_{110}\) & DRIE & 280 & 5 & 1.46\({}^{\circ}\) \\ [12] & 298.6 & Rectangular, TE\({}_{101}\) & SU-8 & 432 & about 10–15 & * \\ [13] & 450 & Rectangular, TE\({}_{101}\) & DRIE & 30/275 & equipment accuracy & * \\ [14] & 270 & Elliptic, quasi-TM\({}_{110}\) & DRIE & 30/275 & equipment accuracy & about 3\({}^{\circ}\) \\ This work & 90 & Cylindrical, TM\({}_{010}\) & SU-8 & 250/500 & 4 & 1\({}^{\circ}\) \\ \hline \end{tabular} \({}^{\star}\) It is not mentioned in the article. ** Multiple single-layer SU-8 chips.  
\end{table}
Table 1: Recently published waveguide filter operating at frequencies above 90 GHz.  
Figure 1: (**a**) The designed 4 th BPF, (**b**) Top view and side view of the SU8 chip, (**c**) Main critical dimensions in microns are: \(r_{1}=1233.5\), \(r_{2}=1188.9\), \(r_{3}=401.4\), \(r_{4}=498.2\), \(w_{1}=1127.2\), \(l_{a}=1631\), \(l_{b}=736.2\), \(d_{0}=91.9\), \(d_{1}=2492.6\), \(r_{g}=0.2\), \(t=500\), \(t_{1}=250\), (**d**) the design of pressing block I, and (**e**) the design of pressing block II and an exploded view when assembled. (The orange line is the label line and not the structural part).  
Figure 2: Model and coupling analysis of the cylindrical resonators: (**a**) the relation of inner-mode coupling k versus the coupling irises’ dimensions; (**b**) extract Qe’s S\({}_{21}\) curve and model.
## 4 Overview of Research Projects on AI/ML for Communications and Networking
### Europe--H2020 Research Framework  
The recently published European vision for the 6G ecosystem suggests an \"AI everywhere\" principle, where AI is infused throughout the network, can be traded as a service, and is ultimately the key enabler to intertwine the human, physical and digital worlds. AI shall eventually enable a self-contained wireless ecosystem with zero-touch and automated decision-making processes, where massive data collection, distributed computation, and ML inference inter-work to meet stringent network performance targets and offer an immersive experience to subscribers [110].  
The EU Horizon 2020 program has already put a strong emphasis on the research of AI/ML-empowered wireless networks [111]. In the latest call 5G-PPP Phase-3, there are in total nine topics, with most of the funded projects scrutinising AI/ML-based solutions for communications and networking in their objectives [112]. The nine project themes are as follows: coordination and support actions, infrastructure projects (5 GENESIS), advanced 5G validation trials across multiple vertical industries (5 GROWTH), long-term evolution (ARIADNE), automotive (5G-CARMEN), 5G core technologies innovation projects, 5G for connected and automated mobility, innovation for verticals with third-party services, and smart connectivity beyond 5G (the flagship Hexa-X project). Inside the parentheses, we include representative projects from each theme that are elaborated shortly below.  
In a nutshell, most of the grants under the 5G-PPP Phase-3 call apply AI/ML for dynamic network slicing and closed-loop automation at the edge and core networks. Furthermore, the near-real-time RIC and the software-based control of reflective intelligent surfaces (RIS) constitute the lion's share of research activities in the RAN. More research outputs regarding the real-time RIC are available under the Windmill project within the EU H2020 Marie Sklodowska-Curie framework innovative training networks (ITN) [113].