esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion and Conclusions  
In summary, we have presented a broad overview of longitudinal electron acceleration in radially polarized laser beams and pulses. The potential of this acceleration scheme has been emphasized by several authors within the last 20 years. However, it is only until the recent developments in high-power infrared laser sources at the ALLS facility that direct acceleration of electrons at rest by the longitudinal electric component has been experimentally confirmed. The theoretical background presented here suggests that experiments were conducted just at the threshold for producing attosecond electron pulses.  
The main limitation of the first ALLS experiment was the conversion efficiency of the fundamental Gaussian mode to the radially polarized mode. The actual mode converter limited the amount of energy per pulse to half a mJ. For the next set of experiments, few-mJ RPLPs with only 2 to 3 optical cycles will be produced. Avenues to produce RPLPs on the 200 TW beamline are also considered. These improvements will surely bring the future electron acceleration experiments into the sub-cycle regime.  
It was shown that the static analysis presented in Section 3 provides good physical insight into the process of longitudinal electron acceleration in RPLBs. However, it does not provide accurate predictions. Dynamical analyses, like that presented in Section 4, are essential for a good understanding of the conditions in which experiments are done. The next generation of numerical analyses will have to take into account tight focusing, ultrashort pulse duration, ionization dynamics, and particle correlations. This can be done to a good extent with particle-in-cell codes (see, for example [19]). However, we emphasize the fact that modelling light-matter interaction processes in high-pressure gases and solid-density targets may require special numerical tools to correctly predict the dynamics [82].  
In conclusion, attosecond electron diffraction is likely to be achieved at the INRS in a near future. Improvements to both theory and experimental procedures are still needed. Measuring and characterizing attosecond electron pulses remains, probably, the biggest challenge.
# Evaluating the Impact of Pre-Configured Uplink Resources in Narrowband IoT
## 2 NB-IoT
### Operational States  
In the context of NB-IoT, RRC states play a critical role in managing the communication behavior of UE within the cellular network [8]. NB-IoT RRC states dictate the UE's operational modes, influencing power consumption, latency, and responsiveness. NB-IoT defines two main RRC states, RRC_CONNECTED and RRC_IDLE, as shown in Figure 2. The RRC_IDLE state represents the state of minimal activity, where the UE conserves power by only periodically listening for paging signals while waiting for potential downlink (DL) data or network commands. On the other hand, the RRC_CONNECTED state signifies an active communication phase, enabling two-way data exchange between the UE and the network. While in RRC_CONNECTED, the UE is more responsive but also consumes more power since it is in what is denoted as the transmission + cDRX state in Figure 1.  
The energy efficiency of NB-IoT is strongly based on energy-saving mechanisms embedded within the RRC states, defined by 3 GPP release 13. Among these mechanisms, the connected discontinuous reception (cDRX), the extended discontinuous reception (eDRX), and the power saving mode (PSM) are crucial for minimizing energy consumption while maintaining connectivity [8; 14], as shown in Figure 2. cDRX and eDRX modes strategically manage the UE's reception cycles, allowing it to periodically wake up to listen for incoming signals while spending the majority of time in a low-power dormant state. These mechanisms balance the trade-off between responsiveness and power efficiency. However, PSM is designed mainly for applications with infrequent communication needs. It allows the UE to detach from the network and enter an extended sleep mode, significantly conserving energy. The UE re-establishes communication upon awakening, effectively reducing the network signaling overhead. These energy-saving mechanisms are integrated into NB-IoT's RRC states, contributing to the technology's ability to support diverse IoT applications with varying energy constraints.  
Based on these improvements, subsequent 3 GPP releases, such as release 15 [23] and release 16 [24] focused on mitigating signaling overhead and power consumption through innovative solutions such as UE-based EDT and PURs. These solutions include CP and UP CIoT Evolved Packet System (EPS) enhancements. Unlike the conventional five-step default RAP [25], illustrated in Figure 2, the EDT approach introduces a different method. In this approach, the UE signals its intention to transmit small data during Msg 3. On the other hand, PURs further streamline the process by enabling direct transmission in Msg 3, without the need for Msg 1 and Msg 2. These innovations mark significant strides in optimizing the NB-IoT handling of small data exchanges and are discussed in more detail below.
# Scenario-Based Emergency Material Scheduling Using V2X Communications
## SEMS Method
### SEMS Assumptions and Model  
In the SEMS model, we discuss a scheduling problem of multiple supply depots, disaster depots, commodities and transport modes for logistics management of relief commodities. Transport modes include road transport and air transport. Additionally, the model considers damage to the road. The SEMS network is shown as Figure 2.
## 4 Simulations and Results
### Case 2  
In this case, the same primary noise as in Case 1 is adopted in the simulation. The secondary path \(S(z)\) is suddenly changed in the middle of the adaptive process (\(n=\) 50,000). The frequency responses of the original and changed path are shown in Figure 4a,b, respectively. The parameters of the three methods are set as follows. In Method 1, \(k=0.2\), \(\mu_{i}=0.0002\) for all i, and \(\mu_{s}=0.004\). In Method 2, \(\mu_{i}=0.0002\) for all i, \(\mu_{s}=0.02\), and \(k=0.2\). The parameters used in the proposed method are \(\mu_{i}=0.0002\) for all i, \(\mu_{s}=0.02\), \(\gamma=0.9988\), \(\epsilon=0.0023\), \(\lambda_{max}=0.9\), and the value of the forgetting factor is initially set as \(\lambda(1)=0.4\). Similar comparisons to those conducted for Case 1 are provided in Figures 9-12. From these figures, we see that the proposed method can not only yield excellent performances similar to those in Case 1 but also accurately track the change in the secondary path. Therefore, the proposed method again performs well and outperforms Method 1 and Method 2 in terms of the variable secondary path.  
Figure 11: Comparison between the modeling errors \(\triangle S(n)\) of Method 1, Method 2, and the proposed method for the variable acoustic path \(S(z)\) shown in Figure 4.  
Figure 12: Mean variable forgetting factor of the proposed method for the variable acoustic path \(S(z)\) shown in Figure 4.  
Figure 10: Comparison between the residual errors of Method 1, Method 2, and the proposed method for the variable acoustic path \(S(z)\) shown in Figure 4. **(a)** Mean square error \(E[e^{2}(n)]\). **(b)** Mean square error \(E[e^{2}(n)]\) obtained by averaging the square values of 50 past residue errors.
## 3 Results
### Indices for Phytoplankton Bloom Characterization  
#### 3.2.1 Phytoplankton Intensity Index (PII)  
The Phytoplankton Intensity Index is a non-normalized index that is solely based on chl-a. Higher values correspond to more extensive blooms. Here, the PII values ranged from 0 to 1256 (Figure 8). Nine bays had a PII value of 0 during the time period under study. There is considerable variation between the index values in different years. The highest mean values were calculated for Matsalu Bay (PII = 740), Haapsalu Bay (691), and Parnu Bay (619). Similarly, the highest values were estimated in Haapsalu Bay (1256), Matsalu Bay (1171), and Parnu Bay (807) (Figure 9). It is likely that due to the nature of the bays, the results indicate phytoplankton blooms in the areas but not necessarily cyanobacterial biomass.
## 4 Results and Discussion
### Simulation results
It is worth mentioning that the distinction between the net load and net generation scenarios takes into consideration the net power in the hybrid AC/DC distribution network as a whole, not only the LV AC network. Additionally, the local ESS incorporated in the ST has also responded to the frequency deviations observed in the MV AC network using the \(P(f_{MV})\) droop. As shown in Figure 22, the local ESS provides an effective contribution in terms of active power for all considered scenarios. It is also possible to observe that the response of the local ESS does not depend on the load scenario in the hybrid distribution network. This behavior was expected since the local ESS was set to react only as a function of the \(P(f_{MV})\) droop. The overall active power-frequency support provided by the ST to the main power system for all the considered scenarios is obtained from the sum of all responses depicted in Figures 20-22. The disaggregated contribution per type of resource is shown in Figure 23, where it is possible to observe the global contribution of non-controllable loads, controllable PV generation, controllable EV charging and energy storage capacity available in the hybrid distribution network plus local ESS. For the net load scenarios, the total active power consumed by the ST increases from approximately 330 to approximately 660 kW during the response to the over-frequency event and decreases to approximately 100 kW during the response to the under-frequency case. For the net generation scenarios, the total active power injected by the ST increases from approximately 240 to approximately 460 kW during the response to the under-frequency event but reaches an active power consumption of roughly 120 kW during the response to the over-frequency case. Figure 21: Active power consumption variation in the AC LV network due to load voltage sensitivity (dotted area), controllable energy storage (dashed area) and controllable PV generation (white area). Positive values correspond to power consumption. Figure 22: Active power variation in the local ESS. Positive values represent power consumption (ESS charging). Positive values correspond to power consumption.
## 1 Introduction
Optical satellite images have been largely used to generate topographic maps [7; 18], and SAR images have also been moderately applied in the waterline extraction method [19]. This uses a time series of satellite images taken during different tidal periods to map intertidal topography. The heights of the waterlines can be measured from in situ data or marine hydraulic models, and a gridded DEM is created using spatial interpolation techniques [20]. Although waterline extraction is simple and fast, it is sensitive to noise and the edge location is not accurate with only a limited number of remote sensing images [15; 21]. Although this method is still commonly used, DEM generation is constrained between the highest and lowest level of acquired images and provides contour lines without the representation of small scale topographic features, such as localized tidal tributaries [7; 22]. Using remote sensing data to generate tidal flat topography only provides information about the selected regions and times when the satellite images were acquired [23]. Thus, the use of an insufficient number of images in the waterline extraction method may lead to inaccuracies [2]. In this paper, we propose the pixel-based statistical analysis of intertidal classification and topography using multitemporal Sentinel-1 backscattering coefficients. Specifically, this study (1) distinguishes the tidal flats from land and ocean via the Jenks Natural Breaks (JNB) classification using Sentinel-1 SAR data; (2) estimates intertidal topography from a multitemporal SAR backscattering logistic model on a pixel-by-pixel basis; and (3) evaluates our Sentinel-1 intertidal topography against airborne Lidar and waterline-based Landsat TM/ETM+ Digital Elevation Models (DEMs). This study attempts to determine the feasibility of our innovative statistical analysis for intertidal classification and topography to monitor the near real-time spatiotemporal distribution changes of tidal flats through continuous and stable SAR data collection on local and regional scales.
## 4 Results and Discussion  
### Fracture Network Modelling  
The fracture density of the borehole was determined using two methods, the fractal dimension and the P10 parameter. Both approaches are based on the BHTV data and therefore include all planes detected by BHTV, including faults, veins, joints and bedding planes. The average fracture density in KSF is 4.7 m\({}^{-1}\) and in BCF is 2.6 m\({}^{-1}\), based on the number of fractures per metre (P10). In the BCF, fracture density is highest near the border of the two formations at \(\sim\)400 m, below which fracture density decreases significantly (1 m\({}^{-1}\)), after which fracture density increases steadily with depth (Figure 4A).  
There is no significant difference in the two formations' fractal dimension (D); the average D value is 1.53. The highest peak in D values is in the BCF at 500 m (Figure 4B). The average length exponent E is \(-\)0.80, varying from \(-\)0.24 to \(-\)1.48, and F is 10.0.  
The orientation of the bedding and fracture planes are slightly different in the two formations. In the sandstone formation, the dip direction of the planes is mainly NE; a smaller group of planes has the dip direction SW. In the claystone, the dip direction of the plains is mainly E, and there are also two groups with different orientations whose dip direction is W and NW (Figure 5). This difference was taken into account during the modelling of the fracture system.  
The fracture network was simulated in a 150 \(\times\) 150 \(\times\) 845 m cuboid around the borehole. The presence of interconnected subsystems can characterise the simulated fracture networks. Based on ten runs, two typical geometries were distinguished. In the first case, the whole fracture system defines one communicating group (Figure 6A), while in the other case, there are two connected subgroups in the fracture network. The boundary of the two subgroups is located in the BCF at 670 m (Figure 6B).  
Figure 4: (**A**) Fracture density log expressed with the number of fractures per metre (P10 parameter); (**B**) Fractal dimension log of the well, determined at every 50 m, with 25 m overlap; (**C**) Aperture coefficient log along the well; (**D**) Effective porosity log of the well. Dashed lines indicate the border between the KSF and the BCF.  
Figure 5: Lower hemisphere stereographic projection of the poles of the bedding and fracture planes of the KSF (**A**) and the BCF (**B**) based on BHTV data on Schmidt net. The isolines denote a 2% increase in frequency.
# Study of a Synchronization System for Distributed Inverters Conceived for FPGA Devices
###### Abstract  
In a multiple parallel-connected inverters system, limiting the circulating current phenomenon is mandatory since it may influence efficiency and reliability. In this paper, a new control method aimed at this purpose and conceived to be implemented on a Field Programmable Gate Array (FPGA) device is presented. Each of the inverters, connected in parallel, is conceived to be equipped with an FPGA that controls the Pulse-Width Modulation (PWM) waveform without intercommunication with the others. The hardware implemented is the same for every inverter, therefore, the addition of a new module does not require redesign, enhancing system modularity. The system has been simulated in a Simulink environment. To study its behavior and to improve the control method, simulations with two parallel-connected inverters have been firstly conducted, then additional simulations have been performed with increasing complexity to demonstrate the quality of the algorithm. The results prove the ability of the method proposed to limit the circulating currents to negligible values.  
smart grid; FPGA; parallel inverters; circulating current +
Footnote †: journal: Applied System
## 2 Models and Definitions
### Network Coverage Rate  
The network coverage rate, which reflects the coverage degree of an underwater sensor network covering a monitoring area or targets, is also a primary standard to evaluate a node-deployment algorithm. This standard is defined as the ratio of the effective area, the number of UWSN covers of the targets to the entire area of the target region or all target numbers by using the \(Cor\) expression; the formula is expressed as follows [26]:  
\[Cor=\frac{V_{covered}}{V_{region}} \tag{6}\]  
where \(V_{covered}\) is the volume of the monitoring area covered by active nodes and \(V_{region}\) is the volume of the total monitoring area.
## 3 Modeling, Simulation and Analysis
### Modelling and Simulation of the Metasurface
Figure 6: Comparison between the quasi-non-diffractive and conventional vortex waves. The (**a1–g1**) normalized E-field amplitude and (**a2–g2**) spiral phase of the quasi-non-diffractive vortex wave. The (**h1–n1**) normalized E-field amplitude and (**h2–n2**) spiral phase of the conventional vortex wave. The simulated distributions of the E-field amplitude on the x-z planes for the quasi-non-diffractive and conventional vortex waves are plotted in Figure 8, respectively. As for the quasi-non-diffractive vortex wave in the x-z cross-section, the distance between the two main lobes of the divergence beams maintains a value of 0.6\(\lambda\) (9 mm) within the non-diffractive range. As a contrast, the distance between the two main lobes of a conventional vortex wave is already 3.47\(\lambda\) (52 mm) at the distance of 9.8\(\lambda\) (146.95 mm) away from the metasurface, which exhibits a faster divergence rate than the former. It can be seen from the comparison that the divergence property of the vortex wave is significantly suppressed in the non-diffractive distance. Figure 7: The **(a1–e1)** normalized E-field amplitude and **(a2–e2)** spiral phase distributions of the quasi-non-diffractive vortex wave generated from the proposed metasurface at a height of 10\(\lambda\), 15\(\lambda\), 20\(\lambda\), 25\(\lambda\) and 30\(\lambda\) above the metasurface, respectively, where \(\lambda\) is 15 mm. Similarly, the **(f1–j1)** normalized E-field amplitude and **(f2–j2)** spiral phase distributions of the conventional vortex wave are shown.
## 2 Jamming Attack Models
### Description of the Proposed Jamming Attack Models  
Based on the foregoing discussion and recognizing both, the jammer's transmitted RF power and the knowledge/ignorance of the target network's communication protocols to the jammer, we propose the following jamming attack models, which are in fact, the derivatives of the models proposed by [PERSON] _et al._[1], redefined to suit the information warfare requirements:  
1. _Constant Jammer with Normal Power (CON)_ is a constant jammer with transmitted RF power comparable with the average RF transmitted power of the target WSN.
2. _Constant Jammer with High Power (COH)_ is a constant jammer with high transmitted RF power.
3. _Deceptive Jammer with Normal Power (DECN)_ is a deceptive jammer with transmitted RF power comparable with the average RF transmitted power of the target WSN.
4. _Deceptive Jammer with High Power (DECH)_ is a deceptive jammer with high transmitted RF power.
5. _Random Jammer Imitating CON, (RACN)_.
6. _Random Jammer Imitating COH, (RACH)_.
7. _Random Jammer Imitating DECN, (RADECN)_.
8. _Random Jammer Imitating DECH, (RADECH)_.
9. _Reactive Jammer with Normal Power (REN)_ is a reactive jammer with transmitted RF power comparable with the average RF transmitted power of the target WSN.
10. _Reactive Jammer with High Power (REH)_ is a reactive jammer with high transmitted RF power.
## 1 Introduction
Conceptual design is a fundamental milestone at the initial phase of any product development process, helping the early assessment of optimal design solution with regard to other crucial factors such as manufacturing, time-to-market, performance, testing and cost [1; 13; 14]. Therefore, the designers always come up with several design solutions in the concept design phase and after a meticulous evaluation, select the best one. According to literature [51; 52], around 80% of the product quality/cost ratio can be determined by the end of the initial design phase and therefore, the initial design phase plays a vital role. To bolster the initial design phase, numerous design tools have been developed to expedite the design process. In addition, the latest technological advancements in the field of computer, programming, artificial intelligence, CAD tools (i.e., integrated in many commercial software) and numerical solvers have further enhanced the design process with regard to fast computations and complex simulations [53; 54; 55]. For the last few decades, topology optimization (TO) has been extensively studied together with generative design (GD) as superior conceptual design methodologies [1; 13; 14; 42; 56]. Despite this, TO is usually seen as a specific structural optimization tool instead of a way to define concepts. On the contrary, GD has the advantage of simultaneously considering many materials and manufacturing methods for a single simulation, which reduces the simulation time. In addition, GD provides many design solutions as a combination of different structures and materials in a single simulation, while TO provides a single design solution. Furthermore, TO can implement manufacturing constraints to drive the final shape to a feasible manufacturing, while GD uses manufacturing methods and provides much better optimized shape for manufacturing, and therefore requires little or no remodeling. On the other hand, TO is useful in higher mass reduction and fast computation. However, both TO and GD have numerous applications inside automotive, aerospace, civil, and medical engineering [1; 13; 14; 42; 56; 57; 58; 59; 60; 61]. This paper investigates TO as a concept design tool in mechanical engineering, providing guidelines to assess its input from a list of requirements so that preliminary sketches may be provided. The paper aims to extend its assessment from the methodological point of view to foster its adoption in the creative definition of innovative components, constrained by mechanical criteria, at the early stage of design. In Section 2, the methodological part is provided in terms of mathematical formulation and computational set-up (Section 2.1) of the proposed TO approach, and the design workflow and guidelines (Section 2.2). Then, in Section 3, a case study is provided to show an example of how carrying out the requirements translations into TO input conditions and, in Section 4, results are discussed to compare it with a standard design workflow. Finally, in Section 5, conclusions are outlined.