esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Location-Based Interactive Model
### Location-Based On-Demand Sensing Model  
Based on the proposed model above, we propose a location-based on-demand sensing model to enable the cloud to interact with WSNs on demand based on mobile users' location. In particular, this model schedules a sensing data report of sensors based on demand and location of mobile users. When there is no request from mobile users, inactive transmission mode is set to sensors to save energy. When there is a mobile user request from a specific area, only sensors in the area belonging to the user's region of interest are activated to provide sensing services. As sensors are activated on demand, specific requirements of applications and users can be triggered.  
The model exploits the cloud's capability to trigger sensing in sensor nodes on demand. We assume that in mobile computing applications, the cloud has the ability to track location of users and knows when a user needs sensing data as well as at which location (i.e., by tracking using GPS or various location tracking schemes [14; 15; 16]), we propose to use the cloud as a coordinator to control scheduling of physical sensor nodes on demand. In particular, based on requirements of applications and current location of a mobile user, the cloud makes a scheduling request to a sensor network near the user to serve. The detail of the on-demand location-based scheduling scheme (Algorithm 1) is presented below.  
```
Step 1: cloud \(c->\) current location of mobile user \(\mu\) Step 2: cloud \(c->\) allocates a set of virtual sensors and reversely map to a set of physical sensor nodes \(\xi_{\mu^{\textit{current}}}\) which matches with interest of the application and the user, using the above functions Step 3: cloud \(c->\) makes a schedule for the sensor nodes based on current location and requirements of the mobile user Step 5: cloud \(c->\) sends a scheduling request to a corresponding base station \(BS\) Step 6: \(BS->\) broadcasts the scheduling request to corresponding sensors. Step 7: nodes that receive the scheduling request set their own schedule based on the request. Step 8: when the user moves out of the area, cloud \(c\) sends a request to cancel the scheduling request for the set of sensors.
```  
**Algorithm 1** On demand location-based scheduling scheme for WSNs  
In our algorithm, if a node is set to be active, its parent nodes on the route to the cloud through the sink must be active too to enable the node forward sensing data. It does mean that the scheduling algorithm above has to maintain a connected graph of active sensors and the sink. We assume that locations of sensors are known and managed by the cloud. Based on location information, request packets can be forwarded from the sink to sensors efficiently using region and location-based routing protocols [17; 18; 19].
# Large Language Models Meet Next-Generation Networking Technologies: A Review
## 2 Background
### Network Intelligence
* _Network Management_: Efficient network management is critical for the ongoing operation and security of a network. This process involves ongoing monitoring, optimization, and security protection of the network. Manual management can be inefficient, with a high risk of human error, and it requires constant adaptation to changing network conditions. Intelligent systems can autonomously manage monitoring, optimization, and security tasks, adapting in real time to network changes and reducing the need for manual interventions. For example, AI-driven systems can enhance network security by automatically detecting and responding to threats, allowing for proactive threat mitigation. Additionally, AI models can continuously adapt security protocols by learning from past incidents, improving its ability to prevent and counter new, evolving threats, thus ensuring more robust and adaptive network protection. AI methods are often viewed as a black box by application developers and network administrators. However, deploying AI models to address networking issues involves numerous practical challenges. We identify four main practical problems associated with implementing AI models in real-world network intelligence:  
* _Complex Network Configurations_: AI models can help automate and simplify the configuration of complex networks, but accurately translating high-level management policies into network commands can still be a challenge. * _Heterogeneous Infrastructure Management_: Network provisioning often involves intricate configurations and updates tightly coupled with underlying heterogeneous and diverse infrastructure, necessitating an abstraction layer for network operators to manage network parameters independently of the underlying infrastructure [41]. Traditional AI algorithms struggle to navigate and integrate with such diverse systems, making effective management challenging. * _Unstructured Log and Network Data_: Networks generate vast amounts of unstructured log and operational data, making it difficult to extract actionable insights. These unstructured data complicate troubleshooting, diagnostics, and overall network management, presenting a significant challenge for traditional AI techniques that rely on clear, structured inputs to function effectively. * _Dynamic Network Environment_: Conventional AI systems often rely on static, rule-based learning models, which do not adapt well to dynamic network environments. These systems struggle to continuously interpret and adjust to changes in network intents, such as user complaints or system alerts. This limitation results in less accurate and effective responses, preventing the network from staying aligned with evolving objectives and conditions.
# Enhancing Smart Agriculture by Implementing Digital Twins: A Comprehensive Review
## 2 The Definition of Digital Twins  
The fundamental concept of a DT, which involves a connected virtual representation of a physical object or system [6], appears to be relatively straightforward. However, its application and implementation can differ significantly across various domains and industries.  
Although it is quite clear that in addition to their core purpose of modelling real-world systems, DTs are designed to empower individuals to make informed business decisions with tangible impacts on the physical realm, a clear definition of the DT concept may be helpful to distinguish it from related concepts such as simulation, modelling, and data analytics. By defining its scope, characteristics, and capabilities, researchers and practitioners can better identify the specific features and requirements that make a system a true DT.
# Biogenic Nanoparticles: Synthesis, Characterisation and Applications
## 2 Biogenic Synthesis of Nanoparticles
### Characterisation of Nanoparticles
#### 2.5.1 Geometry  
Size and shape determine the unique properties of nanoparticles to be effective for respective applications. At scales less than 100 nm, advanced equipment with excellent magnification and resolution are utilised to examine the size and shape of nanostructures. Two of the most widely used nanoscale imaging techniques are high-resolution transmission electron microscopy (HRTEM), and field-emission scanning electron microscopy (FeSEM), which can image and identify nanoparticle atomic composition. TEM uses a beam of electrons to illuminate the nanoparticles and can reveal scale, shape, aggregation state and morphology detail. An example Transmission electron microscopy (TEM) image is shown in Figure 2, showing gold nanoparticles in an average size of 20.93 nm.
## 5 Control Architecture  
As shown in Figure 9, the proposed control architecture is divided in two levels, the _Decisional level_, and the _Executive level_. The _Decisional level_ includes:  
* The graphical user interface (_GUI_), through which the user sets the robot's goals and the task. In addition, the user can control the robot, observe its status and view the information from the sensors.
* The _Supervisor_ is responsible for observing the robot status and take the needed decisions when an internal error occurs.
* The _Task manager_ is a finite state machine that breaks down the main objective into small tasks.  
On the other hand, the _Executive level_ includes:  
* _Hardware_: It is composed of motors, drivers, etc.
* Four _Motor controllers_, which consequently move the motors according to the desired position, velocity, and speed.
* The _Robot model (Kinematic and dynamic)_ is composed by the models described previously. According to the desired trajectory, the motor state is calculated.
* The _Path planner_ takes into account the goal task to generate the trajectory.
* The _SLAM_ system makes the simultaneous location and mapping of the environment. The reconstruction will be used by the path planner to generate the trajectory.  
Figure 9: Control architecture of the robot.
## 7 Trends and Open Research Lines  
The need for agile and effective solutions to real-world transportation problems is becoming increasingly urgent, particularly in the face of uncertainty and dynamic conditions. To address this challenge, richer versions of vehicle routing problems can be developed, incorporating inventory management decisions, multiple depots, facility location, time windows, and external dynamic conditions. One promising approach is to use a fuzzy simheuristic algorithm that can adapt to changing conditions and provide robust solutions. Another important trend is to investigate a higher level of integration between the fuzzy component, the simulation component, and the metaheuristic component, also including a reinforcement learning component in order to increase the capacity of the resulting approach to deal with both uncertainty and dynamic environments.  
The application of fuzzy simheuristics is also likely to expand to more complex systems, such as integrated L&T networks, which require advanced simulation approaches like discrete event or agent-based models. In order to speed up the computation and improve the solution quality, enhanced strategies to identify promising solutions can be employed, such as surrogate models that quickly estimate the statistics that the simulation provides or models based on reinforcement learning that can be used to adjust different parameters, such as the number of runs required to obtain precise estimates [23]. Fuzzy simheuristics can be applied to a wide range of fields beyond transportation and logistics, such as telecommunications, finance, healthcare systems, and smart cities. Furthermore, this approach can be integrated with machine learning and learnheuristics to address optimization problems with dynamic inputs. Multipopulation and agent-oriented fuzzy simheuristics are other promising areas of research that can enhance the efficiency of these algorithms in solving complex and stochastic combinatorial optimization problems. Overall, these trends and research directions provide exciting opportunities for future developments in fuzzy simheuristics.
# Flexible UHF RFID Tag for Blood Tubes Monitoring
## 3 Design of the Tag Antenna
### Design Considerations  
For the design of a suitable tag antenna for blood tube labeling, some initial considerations should be taken. First, the tag should be flexible and low-cost. Second, the thickness of the tag antenna substrate should be as thin as possible, and it should also be adhesive, so it can be attached easily to the blood tube. Finally, the design should be attractive, and for this, the general recommendation is that the size of the tag should be small, so as not to cover the full surface of the blood tube. In the case of the Becton Dickinson (BD) company, three different sizes of blood tubes are available. The tube with largest volume (10 mL) has a height of 100 mm and an inner diameter of 16 mm, the medium volume tube (7 mL) has 100 mm and 13 mm, and the height and the inner diameter of the smallest volume tube (5 mL) is 75 mm and 13 mm, respectively. All blood tubes have the same PET wall thickness of 1 mm.  
Taking into account these initial considerations, a compact and low-profile RFID tag antenna has been designed to operate from 865 MHz to 868 MHz. The elements and dimensions used for the tag design were: an adhesive copper sheet with a thickness of 35 \(\upmu\)m, an adhesive Kapton polyimide substrate (3M\({}^{\text{TM}}\) Kapton polyimide film tape 5413 amber) with a thickness of 70 \(\upmu\)m, the smallest blood tube size (75 mm \(\times\) 13 mm) with a PET wall thickness of 1 mm, and the NXP UCODE G2 XL IC chip with a TSSOP8 package. This IC chip has a power threshold sensitivity of \(-17\) dBm, a complex impedance \(Z_{c}\) = 16 - \(j\) 156 \(\Omega\) and a die size (length \(\times\) width) of 0.31 mm \(\times\) 0.51 mm. In this work, we have used the Kapton polyimide [26], since it is flexible and thin. In addition, as shown in Section 2, it has a real permittivity similar to the PET tube and has a low dielectric loss factor.  
While most of the commercial tags are of inductive type, and are designed using the T-matched technique, we have found that such designs lead to small reading ranges and low radiation efficiencies when they operate attached to clinic tubes, in the proximity of blood. Due to the high relative permittivity value of the blood, capacitive antennas experience a good size reduction, and can be adapted to the size and curvature required by clinic tubes. Consequently, our design approach started with a basic capacitive dipole antenna, and some bending was applied to fit the tag in the curvature and space available in clinic tubes. In addition, these conforming operations were applied trying to maximize the amount of copper area in the capacitive pads. This is better from the point of view of losses, since then induced surface currents can flow across larger areas.
## 4 Events Depending on a Boolean Signal  
Table 1 presents the definition of two sets of five events generated through the evolution analysis of Boolean signals, being the former set composed of \(Up\), \(Down\), \(UpOrDown\), \(UpDown\), and \(DownUp\) (which updates the definitions already proposed in [12]) and the latter set constrained by the verification of conditions to fire the transition and composed of \(UpTrigger\), \(DownTrigger\), \(UpOrDownTrigger\), \(UpDownTrigger\), and \(DownUpTrigger\). The timings associated with the generation of the events were previously presented in Figure 2.  
Figure 6 presents the models that generate the referred events. Each model is executed concurrently with the specification model where the respective event is used. In Figure 6b,d,f,h, it is assumed that those events are associated with one specific transition (a reference to \(\ell\)3 is used in this figure).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Expression** \\ \hline \(Up(a)\) & \(a_{n-1}=0\wedge a_{n}=1\) \\ \(Down(a)\) & \(a_{n-1}=1\wedge a_{n}=0\) \\ \(UpOrDown(a)\) & \((a_{n-1}=0\wedge a_{n}=1)\vee(a_{n-1}=1\wedge a_{n}=0)\) \\ \(UpDown(a)\) & \(a_{m}=0\wedge a_{n-1}=1\wedge a_{n}=0\) with \(m<(n-1)\) \\ \(DownUp(a)\) & \(a_{m}=1\wedge a_{n-1}=0\wedge a_{n}=1\) with \(m<(n-1)\) \\ \(UpTrigger(a)\) & \((a_{n-1}=0\wedge a_{n}=1)\) \\  & if and while receptive transition is enabled and ready \\ \(DownTrigger(a)\) & \((a_{n-1}=1\wedge a_{n}=0)\) \\ \(UpOrDownTrigger(a)\) & \((a_{n-1}=0\wedge a_{n}=1)\vee(a_{n-1}=1\wedge a_{n}=0)\) \\  & if and while receptive transition is enabled and ready \\ \(UpDownTrigger(a)\) & \((a_{m}=0\wedge a_{n-1}=1\wedge a_{n}=0\) with \(m<(n-1)\)) \\  & if and while receptive transition is enabled and ready \\ \(DownUpTrigger(a)\) & \((a_{m}=1\wedge a_{n-1}=0\wedge a_{n}=1\) with \(m<(n-1)\)) \\  & if and while receptive transition is enabled and ready \\  & if and while receptive transition is enabled and ready \\ \hline \hline \end{tabular}
\end{table}
Table 1: Definition of events associated with Boolean signal \(a\).  
Figure 5: Modeling the \(Up\) and \(UpTrigger\) events: (**a**) the signal behavior to detect, (**b**) the main model with a transition \(t1\) associated with an \(Up\) event and transition \(t3\) with two input places, one guard and an associated \(UpTrigger\) event, (**c**) the sub-model for the \(Up\) event and (**d**) the sub-model for the \(UpTrigger\) event.
# Assessment of Heavy Metal Distributions in Sand Beaches in the Maltese Islands
## 3 Results and Discussion
### Environmental and Health Risk Assessment
#### 3.2.2 Potential Ecological Risk Assessment  
Values for the geo-accumulation index, I\({}_{\text{geo}}\), calculated for each of the heavy metals assessed across all bays, were negative indicating that the beaches analysed are uncontaminated from heavy metals. The ecological risk factor, E\({}_{i}\), shows that a potential ecological risk may originate from Cu and Pb concentrations upon comparison with the background levels associated with these elements. Such results are tabulated in Table 10.  
With reference to range values outlined in Table 4, Cu tends to cause a considerable potential ecological risk, while Pb poses a moderate potential ecological risk. E\({}_{i}\) values computed for Mn and Zn are low and thus these elements present a low ecological risk. Figure 5 illustrates the distributions of the monomial potential ecological risk factor computed per bay studied. It can be observed that Ballut Reserve Bay, Marsalform Bay, and Rinella Bay have the highest risk factor for Cu; Marsalform Bay, Riviera Bay, and Golena Bay (equal percentage risk) have the highest risk for Mn; Ballut Reserve Bay, Rinella Bay, and Marsalform Bay and Mistra Bay (equal percentage risk) have the highest risk factor for Pb; and Riviera Bay, and Rinella Bay and Marsalform Bay (equal percentage  
Figure 4: Pie charts illustrating the Hazard Index (HI) for children across bays reviewed in Malta and Gozo.  
risk) have the highest risk factor for Zn content. This shows that the beaches with a higher overall potential ecological risk were Ballut Reserve Bay, Marsalform Bay, and Rinella Bay. Marsalform Bay is located in the north-east part of Gozo, while Ballut Reserve Bay and Rinella Bay are located in the south-east region of Malta. This confirms that both lithogenic and anthropogenic sources affect the elemental concentrations reviewed, since some of the bays outlined are located in the same direction as the prevalent winds on the islands and most are highly frequented beaches.
# Multi-Backup Beams for Instantaneous Link Recovery in mmWave Communications
## 5 Performance Evaluation
### Beam Recovery Times  
One major performance metric for beam recovery schemes is the beam recovery time. It is defined as the overall time period that is required to determine an alternative link direction once the direct link of the main beam is blocked. Namely, it is the period required to determine the new best beamforming and combining vectors, and their principal directions at the MS and BS. Figure 7 shows the recovery times at the MS \(T_{rec}^{\text{MS}}\) (likewise for the BS, \(T_{rec}^{\text{BS}}\)). The proposed scheme achieves instantaneous recovery times without the requirement for beam scanning or resetting the access schemes when a link is blocked. The only time required here is the PSS transmission duration of the beam vectors (i.e., 200 \(\upmu\)s). Overall, the scheme here promotes the feasibility of SA mmWaves with ultra-low recovery times, thereby realizing low latency requirements.
## 3 Chip Layout and Post-Layout Simulation Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Parameters** & [18] & [19] & [20] & [21] & [22] & **This Work** \\ \hline \multirow{4}{*}{APD} & CMOS technology (nm) & 180 & 130 & 180 & 180 & 180 & 180 \\  & Type & Off chip & Off chip & Off chip & Off chip & On chip \\  & C\({}_{\text{pd}}\) (pF) & 1 & 2 & 3 & 1.2 & 0.5 & 0.5 \\  & Responsivity (A/W) & 32 & 32 & N/A & 50 & 2.72 & 4.16 \\  & Wavelength (nm) & N/A & N/A & N/A & 905 & 850 & 850 \\  & Max. TZ gain (dB\(\Omega\)) & 106 & 78 & 100 & 87 & 95.1 & 67 \\  & Gain control & Yes & Yes & Yes & Yes & No & Yes \\  & Bandwidth (MHz) & 150 & 640 & 180 & 230 & 608 & 830 \\  & Min. detectable current (\(\upmu\)App) & 0.5 & 5.6 & 5.0 & 0.6 & 3.4 & 1.0 \\  & Max. detectable current (mA\({}_{\text{PP}}\)) & 1.0 & 9.0 & 2.0 & 5.0 & N/A & 5.5 \\  & Linearity & Linear & Linear & N/A & Linear & N/A & Linear \({}^{\text{\text{\text{\text{\text{\text{\text{\text{\text{\power. Ref. [20] suggested an eight-channel receiver array that operated with two modes, i.e., either a parallel mode or a selectable mode to save power dissipation. In addition, a p-i-n photodiode was exploited for the high-gain mode while an APD was employed for the low-gain mode. However, utilizing these two photodetectors increased the implementation costs and might cause difficulties when integrating with a PC board. Moreover, the dynamic range was considerably narrower when compared to that of other works. Ref. [21] demonstrated a dual-mode AFE preamplifier which could be configured as a charge-sensitive amplifier (CSA) or as a gated active load-assisted (GALA) TIA, depending upon the intensity of the input current, thereby extending the dynamic range to 96 dB. Yet, the bandwidth shrunk down to 98 MHz in CSA mode from 230 MHz in GALA-TIA. Also, the power dissipation was still large owing to the dual-mode operations. On the contrary, this work shows competent performance despite the comparatively lower responsivity of the on-chip CMOS APDs and the lower transimpedance gain characteristics. In particular, this sensor can achieve a wide dynamic range with the aid of the proposed concurrent AGC methodology, and also provide very low power dissipation and small core area characteristics.
## 3 Results
The simulation experiments conducted in Section 2.6 of this paper demonstrated that the data source error was within 10 cm, fulfilling the requirements for the parameter inversion in the probability integration method of mining subsidence. Consequently, the fused data were suitable for mining subsidence parameter inversion. When compared with mining subsidence parameter inversion using UAV data alone, the parameter inversion based on fused data showed higher accuracy in monitoring the subsidence basin edge. This improvement is conducive to achieving more accurate parameter inversion. The combined monitoring approach of InSAR and UAV was utilized to determine the subsidence value at the monitoring point. The BFGS algorithm was employed in conjunction with data fusion results, geological mining conditions of the study area, and the parameters of the working face. The probability integration method was then applied to invert the projected parameters of the working face of Banji 110,801 for ten iterations, and the average value was adopted as the final inversion parameter. Table 5 presents the final inversion results, encompassing the following parameters: subsidence coefficient (q = 1.18), tangent of major influence angle (tan\(\beta\) = 2.05), mining influence propagation angle (\(\theta_{0}\) = 87.67), and deviation of inflection point (s\({}_{1}\) = 40.34, s\({}_{2}\) = 79.26, s\({}_{3}\) = 20.17, and s\({}_{4}\) = \(-\)70.42). The subsidence prediction at each monitoring point was conducted using the probability integration method, based on the inversion parameters mentioned above. A comparison was made between the predicted results and the outcomes of the leveling survey, as depicted in Table 5. The analysis revealed a root-mean-square error of 138.74 mm, indicating superior accuracy compared with the results obtained solely through  
Figure 8: Comparison of fusion data and level data on dip line. UAV-technology-based monitoring. However, the topography of the mine area introduces a systematic function model error during the fitting process with the probabilistic integral model. Consequently, this leads to a reduction in the accuracy of the fit compared with the simulation experiments.