esa-sceva/satcom-chunk-collection huggingface dataset:
## 9 Routing in CRSNs
### CRSN Routing Modules
#### 9.1.2.2 Control Signaling
##### 9.1.2.2.2 Sequence-Based Control Channel Negotiation  
For sequence-based schemes, in [64], CR nodes broadcast their available channels in all of the licensed channels. Time in this case is divided among the same number of available channels, with each channel being assigned a slot at the beginning of the network. Any neighbor having channels in common with the broadcasting neighbor also updates its list. For data transfer, a CR node must wait for the slot corresponding to the channel it has in common with its neighbors. Thus, CR nodes hop along a channel sequence that can differ from that of its neighbor transmitting packets, indicating that it has data to transfer. Once a node pair exchanges the synchronization packets on a common channel, they then decide on a common hopping sequence for the data transfer. The overhead incurred for broadcast and the time needed to establish a link at every instance of data transfer make this scheme too costly for CRSNs. For [20, 65], the process of weighted hopping to secure the control channel is only performed once backup channels are established in case the first one is lost due to extended PU activity. In contrast, [64] proposes an alternative MAC protocol that does not require a common channel for multi-hop CR networks. The time is divided into fixed slots, and all users listen to a channel at the beginning of each slot.
## 3 Method
### Part 2: Spatial Aggregation from GAC Orbit Grid to Regular CM SAF Grid (Level-2b)  
All the variables included in the output of Program Part 1 (Section 3.1) were remapped from their irregular GAC orbit grid to a regular lat-lon grid with a spatial resolution of 0.25\({}^{\circ}\) (1440 \(\times\) 720 grid boxes). The aggregated value was calculated as the average of all contributing GAC/AVHRR pixels in each 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) grid box. The number of pixels contributing to each grid box typically decreases from nadir towards swath edges due to the increasing distance between AVHRR pixel centers in the across-track direction (Figure 5).  
Near the swath edges, about eight AVHRR pixels per grid box are observed. Decreasing the grid box size to, for example, 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) (i.e., decreasing its area by factor 25) would thus introduce large gaps in the orbit and prevent a spatially continuous analysis. There is also a latitudinal effect: towards the poles, a decrease of AVHRR pixels per grid box is observed, due to the decreasing area of a grid box as a consequence of the regular lat-lon projection. Due to the aggregation, some (originally binary) variables, such as cloud cover and cloud phase, become fractional.  
The regular lat-l-on grid causes enormous grid box size distortions towards the poles. To solve this issue, during the remapping, the grid boxes towards both poles (N and S) were systematically merged--in the longitudinal direction--according to their combined area. This does not mean that a true _equal area_ grid was created, but rather that the enormous areal distortions towards both poles were minimized. The concept is similar to what is done operationally in the CERES processing [40; 41]. This nested grid configuration is based on two criteria. First, the number of merged grid boxes per latitude should be an  
Figure 5: Example: Number of GAC pixels contributing to each CM SAF 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) grid box.  
integer (i.e., no half-merged grid boxes), and second, a merged grid box area can never exceed the original grid box area at equator (0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) or 772.8 km\({}^{2}\)). So, when iterating latitudes from equator towards North Pole, merging two neighboring grid boxes can only occur if the combined grid box size decreases below 772.8 km\({}^{2}\), which happens at 60\({}^{\circ}\)N. An illustrative example of the varying grid box width in longitudinal direction is given for Northern Europe in Figure 6, showing the gradient between nested grid boxes sized 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) (South) to nested grid boxes sized 0.25\({}^{\circ}\)\(\times\) 5.0\({}^{\circ}\) (North). To conclude, the output of Part 2 consists of remapped instantaneous albedo (Figure 4).
## 4 Discussion  
### Method Performance  
#### 4.1.1 Imaging Performance  
Imaging performance of the resistivity method implemented is comparable to that of 2-D surface ERT. Detailed subsurface resistivity cross-sections were successfully obtained, on which a wide range of (hydro)geological features were identified with high levels of certainty: (i) fresh basements; (ii) fracturing-weathering profiles, within which clay-poor units (fractured basement, saprock and gravelly-sandy sapolite) could be distinguished from clay-rich units (clayey sapolite, laterite and topsoil); (iii) buried gneiss domes, with a distinction made between core and mantle; (iv) well-developed fault zones, with a determination of the movements of the fault blocks; (v) colluvium; and (vi) deltaic deposits (prodelta, delta front, distributary channel-fill and interdistributary deposits). Resolving this range of features together with general understanding of a specific feature's expected hydrogeological significance (i.e., probable groundwater flow and storage capacities) permits a 2-D cross-sectional characterisation of aquifer potential. Knowledge of the surrounding 3-D geological structure expected permits extrapolation of the aquifer potential into three dimensions, at least locally.  
DOI was in the range of 100 m for all cross-sections with fair minimum sensitivity thresholds for data display. Such DOI is significantly deeper than boreholes equipped with Afridev handpumps, often drilled to around 50 m depth in Malawi [62]. Moreover, it is deeper than most fractured/weathered basement aquifers in tropical environments. Hence, survey characterization covers the depths of predominant interest for assessing aquifer potential typically drilled, but also would allow investigation of Malawi's largely untapped deeper groundwater resources at >50-100 m depths, especially some of the thicker alluvial aquifer units in, for instance, the Shire River Basin (1976, 1977).  
Although not attempted in this study, both vertical and horizontal resolutions of the cross-sections may be adapted to the subsurface configuration and imaging objectives by modifying the spacing of the sounding mid-points and by adding/removing/shifting individual resistivity measurements. Simulations with synthetic resistivity models may be run with R2 to predict the response of the acquisition system to expected subsurface configurations; based on this response, the acquisition settings may be optimised. The versatility of the methodology to allow optimal characterization and adaptive investigation of local aquifer potential is hence high.
## 3 Results and Discussions
### Peak Strength
In the range of 100-200 kPa, the slopes of strength envelopes of reinforced sand were larger than those of unreinforced sand, indicating that the strength increments induced by fibers had a positive correlation with normal pressure. However, in the range of 200-400 kPa, the strength envelopes of reinforced sand were almost parallel to those of unreinforced sand. In other words, the strength increments induced by fibers tended to be constant with the increase in normal pressure. Increasing the peak strength of unreinforced sand with a constant fiber-induced strength increment led to a decreasing peak strength ratio with the increase in normal pressure, as shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{ \begin{tabular}{c} **Normal** \\ **Pressure**, **(kPa)** \\ \end{tabular}  }} & \multicolumn{4}{c}{**Fiber Content, 0.04\%**} & \multicolumn{4}{c}{**Fiber Content, 0.20\%**} & \multicolumn{4}{c}{**Fiber Content, 0.40\%**} \\  & **60\({}^{\circ}\)** & **90\({}^{\circ}\)** & **120\({}^{\circ}\)** & **60\({}^{\circ}\)** & **90\({}^{\circ}\)** & **120\({}^{\circ}\)** & **60\({}^{\circ}\)** & **90\({}^{\circ}\)** & **120\({}^{\circ}\)** \\ \hline \(100\) & \(1.08\) & \(1.07\)Similar phenomena were observed from different tests by other researchers [4; 12; 16; 17; 18]. It is generally believed that these phenomena are related to the different failure modes of fibers. When the normal pressure was relatively low, the pullout failure was predominant, and the frictional resistance was proportional to normal pressure. With the increase in normal pressure, the yield and break failure of fibers gradually played the dominant role; hence, the strength increments tended to be irrelevant to the normal pressure. The threshold of normal pressure that divides the two failure modes is known as the critical confining stress. Nevertheless, obvious breakage of fiber strips was not observed in the direct shear tests of this study, indicating that the current test results cannot be well explained using the above analysis. The exact reasons why the fiber-induced strength increment did not continuously increase with normal pressure in pullout failure mode is not entirely clear due to the lack of more evidence and further research. It is inferred that the interactions between fiber strips and sand particles inhibited the continuous increase in frictional resistance under high normal pressures. According to the mechanisms of frictional resistance generation, one possible reason was that the stress orthogonal to the fiber surface, which was transferred from the sand matrix, did not increase proportionately with the increase in normal pressure subjected to the top of sand specimens, influenced by the interactions between fibers and sand particles.
## 3 Deep Learning Architecture and Signal Reconstruction
### U-Net  
Similarly, the U-net architecture was implemented in the small (2,046,545 parameters) and big (16,992,849 parameters) versions and with both Sigmoid and ReLU activation output functions. For all of these models, the loss was a mean squared error (MSE) [23], and each model was trained for 100 epochs with a batch size of 32 and an early stopping callback after five epochs if no improvement of the MSE on the validation set was observed. The diagram showing the simplified architecture of one of the tested U-net models is shown in Figure 3.
# Multi-Objective Electric Vehicles Scheduling Using Elitist Non-Dominated Sorting Genetic Algorithm
## 2 Optimization Problem and Methodology
### Hybrid Evolutionary Algorithm
#### 2.3.1 Decision Variables  
In summary, this NSGA-II algorithm considers the following variables:  
* \(V_{i(t)}\) and \(q_{ij(t)}\)--Voltage and power flows defined by AC optimal power flow (AC OPF)
* \(P_{(ES,t)}\) and \(Q_{(ES,t)}\)--Active and reactive power supplied by external suppliers
* \(P_{(DC,t)}\), \(Q_{(DC,t)}\) and \(P_{GCP(DC,t)}\)--Active and reactive power produced by DG units
* \(P_{(SNS,t)}\)--Active power of service not supplied. The SNS can be positive in case of load curtailment or negative in case of generation curtailment
* \(P_{(ESS,t)}\)--Active power charged in electric storage systems (negative values represent power discharge)
* \(P_{(EV,t)}\)--Active power charged in electric vehicles (negative values represent power discharge)
* \(P_{DR(L,t)}\)--Load demand curtailment under demand response programs  
A numerical algorithm is used to determine the AC optimal power flow for a radial network [PERSON] et al. (2017). In this study, it is assumed that the VPP operates in a radial distribution grid, which turns the said numerical algorithm suitable for this situation. This algorithm determines the \(V_{i(t)}\), \(q_{ij(t)}\) and power losses from the knowledge of the other variables. This process is run before each individual being evaluated for the two functions composing the multi-objective function.  
Figure 2: Flowchart of the nondominated sorting genetic algorithm (NSGA-II) algorithm.
## 1 Introduction  
Since the advent of chirped-pulse amplification (CPA) technology, ultrafast laser amplification has experienced a revolution [1]. Output peak powers currently reach multi-petawatts (PW) and will be pursued to 100 PW in the future [2, 3, 4, 5, 6]. Due to the limitations of compressor grating and a large crystal size, it is more competitive to improve the peak power by reducing the pulse duration than by increasing the pulse energy. However, gain narrowing significantly reduces bandwidth during the amplification process, and thus it is difficult to achieve a shorter pulse duration.  
Shorter pulse durations in CPA regenerative amplifiers have been demonstrated by reducing gain narrowing in two ways: intra-cavity and extra-cavity. For the intra-cavity way, spectral filters such as etalons [7], birefringent plates [8, 9, 10, 7], special dielectric-coated mirrors [11], special dielectric-coated filters [12], acoustic-optic programmable dispersion filters (AOPDF) [13], optical rotatory dispersion spectral filters [14, 15], polarization-encoded chirped pulse amplification [16, 17] and modified polarization-encoded chirped pulse amplification [18] were used to compensate the gain narrowing effect. However, these intra-cavity spectral filters introduce high energy loss, increase the round-trip numbers of the seed beam in the amplification cavity and decrease the output energy. For the birefringent plates, [PERSON] et al. [9, 10] designed a birefringent plate considering the two parameters of incident angle and rotation angle, which made the design complex. The incident angle to the birefringent plate was between 10\({}^{\circ}\) to 20\({}^{\circ}\), without a coating, which introduced large energy loss. For the extra-cavity way, special dielectric-coated filters or AOPDF were used to pre-compensate the gain narrowing effect. [PERSON] et al. inserted an AOPDF before the amplifier, and the output spectra were broadened from 35 nm to 75 nm [19]. [PERSON] et al. made a specially coated filter with a transmission of \(\sim\)40% near 730-830 nm [9]. It was inserted into the grating stretcher and before the amplifier, and the output spectra were broadened from 18 nm to 28 nm. The AOPDF device is expensive, and the special dielectric-coated filter with fixed spectral coating is difficult to optimize for the output spectra.  
Here, we demonstrate extra-cavity gain compensation with a waveplate; a fused-silica multi-order triple-wavelength waveplate (WP) was designed and placed at the exit of the oscillator. Since the incident angle to the WP was roughly normal and the WP was coated with anti-reflection, it led to low energy loss of 50%. As a result, the output spectra of the regenerative amplifier were broadened from 25 nm to 40 nm, and the compressed pulse was 33.2 fs with 1.5 mJ of output energy. Then, the amplified pulses were used for cross polarized wave (XPW) generation, and the generated XPW spectra were broadened from 45 nm without WP to 68 nm with WP, with a conversion efficiency of 15%.
# Worldwide Connectivity for the Internet of Things Through LoRaWAN
## 1 Introduction
NetTrotter is deploying the SigFox\({}^{\text{TM}}\) network in Italy, but Italian customers see only the SigFox logo and services, and all the back-end systems are run by SigFox France. In this way, they are creating a global unique network for the customer, and the roaming issue becomes purely a matter of sharing the revenues between the different local companies and the mother company in France. As a consequence, the \"true\" roaming for LPWANs can be seen--at the time of writing--only in LoRaWAN\({}^{\text{TM}}\), since the LoRaWAN\({}^{\text{TM}}\) operators are independent one another and LoRaWAN\({}^{\text{TM}}\) end nodes do not have any SIM. The rest of the paper is organized as follows. Section 2 provides an introductory overview of LoRaWAN\({}^{\text{TM}}\) networks. Section 3 describes the different functional elements in the LoRaWAN\({}^{\text{TM}}\) back-end. In Section 4, we give an example of a very important procedure, i.e., the way an end device joins a LoRaWAN\({}^{\text{TM}}\) network; by providing such an example we want to highlight the interaction of the various functional elements in LoRaWAN\({}^{\text{TM}}\) networks. Then, the rationale of LoRaWAN\({}^{\text{TM}}\) back-end architecture with the objective of global roaming for massive IoT is discussed in Section 5. Finally, in Section 6 we draw the conclusions of this work.
## 1 Introduction
The differential current is used as input for the wavelet transform to analyze faults [14; 15; 16; 17; 18; 19; 20; 21]. The boundary wavelet transform is implemented in the differential protection of power transformers to distinguish internal faults from other disturbances. The method is designed for real-time applications and implemented in a digital signal processor for real-time analysis [14]. The algorithm distinguishes between internal faults and inrush currents in power transformers. Fault currents are analyzed by using the DWT to evaluate their remarkable characteristic values, and the highest values produced by the total wavelet correlation matrix are used to identify inrush and internal fault currents in power transformers. The results are validated with an experimental test setup [18]. Moreover, the algorithm can identify fault occurrence with the continuous wavelet transform (CWT) and improve conventional current differential protection methods in the presence of current transformer (CT) saturation [20]. A spectrum of wavelets has been used for the prediction of winding insulation defects in transformers [21]. From the literature review, it can be seen that the wavelet transform methodology has been widely used in power system fault analysis. However, many studies only focus on transmission lines or transformers, and few have combined the two components when analyzing systems. Most of the references use wavelets to detect and identify fault types in electrical systems, but they do not analyze the mother wavelet [1; 20; 21; 22; 1; 23; 14; 15; 16; 17; 18; 19; 20; 21]. Additionally, there are research articles that compare mother wavelets [18; 19; 22; 23]. They study the comparative use of 16 different wavelets for fault classification in overhead transmission line systems. However, it is revealed that the Db4 wavelet completely satisfies the fault classification algorithm [23]. Another point is that the result from a simulation that has been widely used may not correctly represent the actual system in the real world due to some parameter and factor that has been simplified, which might affect the result. In terms of wavelet transform application, the research using wavelets to detect and discriminate fault types on power systems might not take effects from different mother wavelets into consideration when evaluating the performance of the algorithm. Thus, this paper presents the effect of mother wavelets on the performance of fault classification algorithms in the power system. The result in terms of accuracy between different mother wavelets was evaluated by using an experimental setup modeled after the actual system with a transmission line connected to the power transformer. The paper is divided into six sections as follows. The second section provides the fundamentals and theory of the DWT. The experimental setup and system characteristics are presented in Section 3. The details of the fault classification algorithm used in this study are contained in Section 4. The results and conclusions of the study are summarized in Sections 5 and 6, respectively.
# Defect Processes in Halogen Doped SnO2
## 3 Results  
### Structural Properties  
SnO\({}_{2}\) has a rutile structure and it belongs to the space group of P4\({}_{2}\)/mmm (space group no 136) of the tetragonal system. The calculated lattice parameters of the unit cell are a = b = 4.717 A and c = 3.189 A (\(\alpha\) = \(\beta\) = \(\gamma\) = 90\({}^{\circ}\)), in good agreement with the experimental results (a = b = 4.737 A and c = 3.186 A) [40]. In Figure 1a-h, the geometry of doped SnO\({}_{2}\) structures is presented for the examined dopants (F, Cl, Br, and I) in substitutional and interstitial positions, where in each case the local distortion of the lattice is shown. The respective lattice parameters are presented in Table 1. An increase of cell dimensions due to the halogen incorporation in the SnO\({}_{2}\) lattice is observed, as the substitution of oxygen with a larger atom (all halogens have a larger atomic radius than oxygen) will distort the lattice, increasing total volume, more so in the case of interstitial doping. It is interesting to note that in the case of iodine at an interstitial position, I\({}_{\rm{I}}\), the cell increases in volume as both a and b increase, but the c parameter decreases. The anisotropic changes in the lattice parameters upon the introduction of the I atom is due to the reduction in the c-direction of the Sn-Ii bond length (3.31 A) as compared to the Sn-I bond before our optimization (3.40 A).  
Figure 1: _Cont._
# The Development of a Spaceborne SAR Based on a Reflector Antenna
## 4 Challenges and Development Trends in Reflector Antenna-Based SAR Technology
### Risks for Reflector Antenna-Based SAR  
#### On-Orbit Deployment Accuracy and Structural Stability  
The on-orbit deployment process for reflector antennas (especially large deployable reflectors) is highly complex, involving numerous deployment joints and mechanisms (e.g., numerous rods and nodes in framework antennas, rib joints in radial rib antennas). Fully simulating the zero-gravity, thermal cycling, and other space environments on the ground for comprehensive validation is extremely challenging. Post-deployment, the achieved surface accuracy may deviate from the ideal paraboloid (due to thermal distortion, material creep, uneven tension distribution, etc.), leading to focal point deviation (defocusing). This causes antenna pattern degradation, including main beam broadening, gain reduction, and increased sidelobe levels. Ultimately, this manifests in SAR imagery as blurring, ghosting, and other severe quality issues. For instance, the Qilu-1 satellite experienced imaging performance degradation attributed to reflector surface deformation.