esa-sceva/satcom-chunk-collection huggingface dataset:
# The Machine-to-Everything (M2X) Economy: Business Enactments, Collaborations, and e-Goverance
## 5 Discussion
### Digital Smart Contracts  
While human-to-human business enactments are governed by oral, or written contracts, they are not applicable to the highly-automated, machine-driven and human-focused M2X Economy. First, human-centered oral and written contracts are difficult to process even for smart machines [1]. Second, traditional contracts [48] are often under-specified and do not provide sufficient details about the actual transaction processes as well as about the parties obligations and rights [23; 34]. Third, they do not allow for extensive automation, scale badly and lack a computerized transaction protocol [49]. Fourth, efficient and automated means of conflict-resolution are missing [1; 23].  
While we propose the utilization of electronic smart contracts to address the issues above, one may argue that a cloud-based online shop for services of the M2X Economy would be sufficient, e.g., Amazon's web shop proves to scale well and even partially automates business enactments. Still, such types of business enactments suffer from transparency issues which complicate--or even prevent and sabotage--conflict-resolution mechanisms. Especially the unequal power relations between a single entity and the service-offering cloud shop prevent fair markets and business enactments.  
In contrast, smart contracts allow for the automated, consistent, transparent, and auditable enactment of contracts by a network of mutually distrusting nodes where no arbitration of a trusted authority is required [24; 50; 51]. As a result, allowing for fact tracking, non-repudiation, auditability, and tamper-resistant storage of information in a distributed multi-stakeholder setting. In case of any conflicts, pre-defined rollback mechanisms are applied as described in [23].  
Finally, Amazon-resembling service provision promotes lock-in effects, and obstructs much needed interoperability and openness of the M2X ecosystem as discussed in the subsequent Section 5.2. Neither traditional contracts, nor a cloud-hosted shop-resembling service provisions, allow for dynamic, P2P- (even local) ad-hoc enactments.
# Road to Efficiency: V2V Enabled Intelligent Transportation System
## 4 V2V Communication Architectures
### Hybrid Cellular and Ad-Hoc Network-Based V2V  
C-V2X technology is a hybrid approach to cellular communication that uses existing infrastructure for direct communication between vehicles and roadside infrastructure [4; 17]. It enables direct peer-to-peer communication without reliance on cellular infrastructure, allowing high-bandwidth communication for real-time data exchange and low-latency responses. In areas with limited or congested cellular coverage, vehicles switch to ad-hoc mesh networking, providing a decentralized communication infrastructure [67]. This  
Figure 5: Blockchain-based Intelligent Internet Vehicles Architecture [66].  
infrastructure allows for dynamic mode switching between cellular communication and ad-hoc mesh networking based on factors like signal strength, traffic density, and application requirements. The hybrid architecture benefits from wide coverage and high data rates, particularly in urban and suburban areas. It allows smooth handover between cellular and ad-hoc modes, ensuring uninterrupted communication during mode transitions. The ad-hoc mesh network is self-organizing, allowing vehicles to dynamically join and leave the network as they move, ensuring continuous communication even in changing traffic patterns [68].  
Mesh routing protocols like Ad-hoc On-demand Distance Vector (AODV) and Dynamic Source Routing (DSR) are used for efficient message routing within a mesh network, enabling quick communication paths. Hybrid cellular and ad-hoc networks use end-to-end encryption for confidentiality and integrity, preventing unauthorized access. Robust authentication mechanisms validate vehicle identities in both modes. In urban areas with dense cellular coverage, cellular communication is used for traffic signal coordination and collision avoidance. In rural or remote areas, ad-hoc mesh networking is used for safety and traffic coordination. The system adapts to dynamic traffic scenarios by selecting the most suitable communication mode based on real-time conditions.  
Figure 6 illustrates the hybrid cellular and ad-hoc network-based V2V communication system, which demonstrates the integration of both cellular and ad-hoc communication. The V2V contact network in which vehicles fitted with the said high-end communication gear can directly communicate with other similar gadgets is relevant in that it can allow for faster exchange of data that is relevant in real-time matters such as accident occurrence and flow of traffic information. Likewise, the feature of updating from a distance, as well as the cellular networking through the cellular towers, is also used for the networking in the given figure. To depict the integration of these two channels of communication and the direction of data, the figure uses arrows and signal waves.
## 2 Design
### Principles of Optical Generation  
The principle of excitation is based on the well-known phenomenon of thermal excitation. The light absorbed by the particle results in rapid heating and a subsequent stress that excites vibrations in the particle. In general, the particle heating is not uniform but our particles are small compared with the optical wavelength and the contribution to the thermalisation of the hot electrons that are initially excited means that it is reasonable to assume uniform heating. In the case of larger particles, these assumptions might not be valid.  
To maximise the efficiency of generation, the particle needs to be a strong absorber of the pump light, which can be achieved by using an appropriate pump wavelength, particle material and, to a lesser extent, particle size. Figure 1b shows the absorption cross-section for a 150 nm gold nanoparticle as a function of wavelength. In this case, the absorption around 530 nm is strongly determined by the main plasmon resonance and largely independent of the size and the optical properties of the external media. In our set-up, the pump wavelength was fixed at 415 nm, which provided good absorption for all particle sizes and was not strongly absorbed by the surrounding media.
# Intelligent Ship Scheduling and Path Planning Method for Maritime Emergency Rescue
## 2 Intelligent Ship Action Planning Method for Ocean Targets
### Principle of Algorithm
When an endpoint is completely covered, the direction will be converted, and the conversion Angle formula is as follows:  
\[\theta^{r}=\tan^{-1}\left(\frac{Y_{B}^{r}}{|X_{B}^{r}-X_{A}^{r}|}\right) \tag{3}\]  
where \(\theta^{r}\) represents the conversion Angle under the regional coordinate system; \(Y_{B}^{r}\) represents the ordinate of region endpoint \(B\) in the regional coordinate system. \(X_{A}^{r}\) and \(X_{B}^{r}\) represent the horizontal coordinates of the region endpoints \(A\) and \(B\) in the regional coordinate system, respectively.
# Impacts of Diversity Technique Application in Order to Reduce the Fading in IEEE 802.15.4 Networks
## 3 BER in the Case of an SC Combiner
### BER in the Case of SC Combiners for Rayleigh Fading Channels
\tag{16}\]  
After substituting the integral solutions (12), (13), (15) and (16) in (10), the final expression for the BER of the transceiver of the IEEE 802.15.4 network operating in an environment with AWGN and with a signal impacted by Rayleigh fading, previously passed through an SC combiner with two branches, is obtained:  
\[ \begin{array}{c}BER_{Zigbee,SC,Ray}=\frac{1}{\sqrt{\pi}\gamma_{0}T}\bigg{[}G _{2,2}^{1,2}\bigg{(}\frac{2}{T\gamma_{0}}\bigg{|}\begin{array}{c}0,-\frac{1} {2}\\ 0,-1\end{array}\bigg{)}+G_{2,2}^{1,2}\bigg{(}\frac{1}{T\gamma_{0}}\bigg{|} \begin{array}{c}0,-\frac{1}{2}\\ 0,-1\end{array}\bigg{)}\bigg{]}\\ -\frac{1}{4\pi}\sum\limits_{n=0}^{\infty}\frac{(-1)^{n}}{(T\gamma_{0})^{n+1}n!} (2^{n}+1)G_{3,3}^{2,2}\bigg{(}1\bigg{|}\begin{array}{c}-n,-n-\frac{1}{2},1\\ 0,\frac{1}{2},-n-1\end{array}\bigg{)}.\end{array}  \tag{17}\]
## 2 Materials and Methods
### Bark Image Classification  
Prior research [9] demonstrated that Convolutional Neural Networks (CNNs) are effective for bark image classification. We evaluated three models: EfficientNet-B3 [10], ResNet-50 [11], and MobileNet-V3-small [12]. For this paper, we focus solely on EfficientNet-B3, which achieved the highest overall accuracy of 83.21% in our previous work [9]. Its balance of computational efficiency and high performance makes it ideal for analyzing how environmental factors influence classification accuracy.  
We compared the experimental design of CBDS_Small with that of the Central-Bark Dataset from our previous work [9]. While CBDS encompassed 25 species across 19,147 images from 4697 trees, CBDS_Small is deliberately smaller, with 252 images from 9 trees of 3 species. This focused approach isolates and analyzes the impact of environmental variables--time of day, bark moisture, and cardinal direction--on classification accuracy, allowing us to attribute performance differences to these specific factors rather than dataset scale or model architecture.  
The model was trained using the Adam optimizer [13] with a learning rate of 0.01 and a cross-entropy loss function, standard choices for optimizing deep learning models in image classification tasks. To enhance robustness and mitigate overfitting, we applied data augmentation techniques, including random image rotation, horizontal and vertical flipping, and grayscale transformations, which simulate natural variations in image capture. All models were trained on 224 \(\times\) 224-pixel input images with a mini-batch size of 32, a configuration optimized for computational efficiency and model convergence. The validation strategy, stratified five-fold cross-validation with tree-level splitting, remains consistent with our previous work. During testing, test images were center-cropped and rescaled to the required dimensions to evaluate the dataset's baseline performance under standardized conditions.
# Narrowband Internet of Things (NB-IoT):
## 2 Narrowband-IoT Standard and Releases
### Release 15 Enhancements
#### 2.3.1 Latency Reduction  
In Release 15, NB-IoT supports new features to further reduce the transmission delay as well as to further reduce the power consumption dissipated during long transmission requirements.  
In this regard, the NB-IoT UE is now able to support the physical layer Scheduling Request (SR) which is a special physical layer message to request the network to send the access grant (DCI format 0) so that the UE can transmit the uplink data. Also, NB-IoT uses a wake-up (Wu) signal to wake up the main receiver. This signal is transmitted in idle mode only when the UE is required to decode the physical downlink control channel in paging occasions. Therefore, power consumption reductionwith the wake-up signal technique is larger when the UE wakes up from deep sleep more frequently (i.e., for shorter DRX/eDRX cycles). Also, significant power consumption reduction is achieved even when a common wake-up signal is used for a group of UEs. Quick RRC release and early data transmission during random access channel (RACH) procedure are supported to reduce the UE transmission latency and hence power consumption.
## 4 Application to Power Electronics  
### Presentation of the Application  
This paper illustrates the proposed approach with the sizing of an aeronautic power channel presented in Figure 10. It is composed of an autotransformer, a rectifier system defined by two three-phase diode rectifiers in parallel, two interphase inductances, and a harmonic filter.  
In Figure 10:  
* (_Va, Vb, Vc_) = _Vs_ is an AC three-phase voltage source;
* _Vdc_ is the channel output voltage.  
The electrical system for this application is presented Figure 11.  
Figure 11: Electrical circuit of the aircraft electrical power channel (reproduced with permission from [11]).  
Figure 10: Overview of the aircraft electrical power channel (reproduced with permission from [11]).  
In Figure 11:  
* (a,b,c) corresponds to each of the three-phase of the voltage source.
* \(f\) is the frequency of Vs;
* _Vdc, iL5, iL6, iL8, iL9, iL11, iL12, iL15, iL16, iL17_ are state variables;
* Currents and voltages of diodes D1 to D12 are state outputs, such as the channel input currents _iL2, iL3_.  
Given that the application operates solely through natural switching, its behavior remains inherently unpredictable, thereby complicating the modeling and optimization processes necessary for effective sizing. Consequently, the analytical model of the application cannot be established without any a priori on the model. The sizing was approached using various methodologies outlined in [11], specifically by addressing a mono-objective optimization problem, as follows:  
* A time-domain simulator (Saber) was employed, in conjunction with a stochastic particle swarm optimization (PSO) algorithm, successfully resolving the optimization challenge within a time span of 2 days for a single run;
* SQP was utilized, alongside a finite difference method, to compute the gradients. An analytical model was crafted to ascertain the frequency-domain characteristics, albeit with a priori knowledge regarding operational modes. This necessitated that the sizing was conducted based on these modes, consequently narrowing the solution space. This optimization problem was also resolved in 2 h for one run.  
For the determination of capacitances and inductances, analytical models were developed as described in [11], leveraging the geometrical parameters of the aircraft's power channel components. However, these models are not detailed within this paper, which primarily focuses on the dynamic model and the time-domain simulations. It is worth noting that the component models have been thoroughly validated, as demonstrated in [47], with simulation models being benchmarked against established reference simulators, such as Saber [47].
## 3 Results
### Optical Properties  
The use of the UV-vis absorption spectrum, a straightforward evaluation technique for CQD products, is presented in Figure 4. This characterization was carried out within a wavelength range of 200-600 nm. The UV-vis absorption spectrum exhibits strong and sharp absorption bands at 283 nm, likely attributed to the \(\pi\)-\(\pi^{\star}\) transition of the C=C bond. Additionally, a weak and broad band at 335 nm is observed, corresponding to the n-\(\pi^{\star}\) transition in the C=O bond [34]. This outcome confirms the formation of carbon nanoparticles following the reaction, aligning with reported experimental results [35; 36]. Additionally, the results demonstrate that CQDs exhibit blue fluorescence properties under UV light at 254 nm (inset in Figure 4), which is attributed to the quantum confinement effect and edge defects [34]. In orange juice, compounds such as citric acid, sucrose, and glucose are present [37]. Plasma generated at this interface produces a dense population of electrons that interact with the sample solution, leading to the dissociation of solution precursors and the production of C2 and CH species [38]. These species facilitate carbon nucleation and carbon dot formation.  
The normalized photoluminescence (PL) spectrum of CQDs is presented in Figure 4. The maximum PL emission wavelength of CQDs was recorded at an excitation wavelength of 300 nm. The PL peak fitting in Figure 4 illustrates the PL spectrum composed of two peaks: (I) the main peak centered at 430.3 nm (green line curve) with a full-width half maximum (FWHM) of 94 nm and (II) another shoulder peak at 511 nm (wine line curve) with an FWHM of 73 nm, representing the emission centers denoting the surface states of the samples. CQDs exhibit multiple emission centers due to the presence of different surface chemical groups on CQDs [39]. The distribution of emission centers on CQDs can be inferred from their PL spectra.
# Application of Soil Moisture Active Passive (SMAP) Satellite Data in Seismic Response Assessment
## 3 Results
### Integration of \(\Delta SM_{SMAP}\) with Seismic Records
#### 3.3.5 Meinong, Taiwan  
On 6 February 2016, southern Taiwan was struck by the Meinong earthquake. The \(\Delta SM_{SMAP}\) map represents the soil moisture increase after this earthquake (see Figure 15a). This increase can be attributed to the earthquake impacts due to the absence of precipitation in its SMAP event window. GEER field reconnaissance teams reported a large number of surface manifestations of liquefaction in the surveyed areas, all of which were located in regions with \(MDI>0\). Grids with positive values of \(\Delta SM_{SMAP}\), corresponding to the USGS liquefaction hazard map, increase the possibility of liquefaction occurrence in regions not inspected by GEER teams [80].  
Figure 14: Damage proxy map of Accumoli, Italy 2016 earthquake, identifying areas in which considerable deformations occurred, overlayed on seismic records and \(\Delta SM_{SMAP}\) (9 km \(\times\) 9 km) in grids with \(MDI>0\). The DPM surface changes range from minor (yellow pixels) to major (red pixels) for potential damage.
## 2 Materials and Methods
### Implementation of automated processing facility  
To make the daily processed vegetation products easily available to a broad range of potential end-users, a dynamic web mapping service (WMS) was developed. In addition, to provide 'near' real time products, the different steps of the procedure were implemented as automated processing chain. The  
\begin{table}
\begin{tabular}{l c c c c c} \hline
**Biome** & **Potential** & \multirow{2}{*}{
\begin{tabular}{c} **Tmin\({}_{\text{min}}\)1** \\ **(\(\mathbf{\text{\text{\text{\text{\text{\text{\textasci  
whole procedure consists of different modules (Figure 1) and the object-oriented programming language python ([[http://www.python.org](http://www.python.org)]([http://www.python.org](http://www.python.org))) was adopted to link the activities for the different modules. The following processing steps are carried out in the automated processing chain:  
* The MODIS surface reflectance product (MOD09 QC) is downloaded from the USGS Land Process DAAC data pool on a daily basis. The data pool provides direct ftp access to the most recent MODIS products ([ftp://e4 ftl01u.ecs.nasa.gov/MOLA/MYD09 GQ.005](ftp://e4 ftl01u.ecs.nasa.gov/MOLA/MYD09 GQ.005)). The MODIS Reprojection tool is used to clip and reproject the images and the GDAL tool is used to convert the images from GeoTiff format to ASCII raster;
* Masking cloud contaminated pixels by checking MODIS band quality (band 4) or thresholds of red band (band 2) and calculation of NDVI (equation 2a) as proxy for FPAR as 250 m ASCII raster;
* Meteorological data are requested from the KNMI SWE server on a daily basis using the SOS GetObservation operation. After processing of the data, observations for 16 stations are interpolated using Thiessen polygons, resulting in 250 m ASCII rasters for PAR, S\({}_{\rm{Tmin}}\) and S\({}_{\rm{VPD}}\);
* Potential LUE derived from the aggregated biome map is stored as static grid file (ASCII raster) with 250 m resolution;
* Combining the different intermediate products, a per pixel calculation is made for all vegetation covering pixels according to equation 3, resulting in the final GPP product;
* The final mapping products are stored as ASCII raster and made available through a WMS. We used the Open Source platform UMN Mapserver ([[http://mapserver.gis.umn.edu/](http://mapserver.gis.umn.edu/)]([http://mapserver.gis.umn.edu/](http://mapserver.gis.umn.edu/))) together with p.mapper ([[http://www.pmapper.net](http://www.pmapper.net)]([http://www.pmapper.net](http://www.pmapper.net))) for implementation of the WMS. The Mapserver platform serves as common gate interface which supports a whole range of OGC and ISO standards. The p.mapper framework provides a suite of standardized functionality for viewing, query and processing of spatial data.  
The automated processing chain has been implemented for the surface area of the Netherlands. In this paper results for vegetation productivity for the year 2008 between January 1 and December 31 are presented. The dynamic web mapping service will be continued after this date.
# Effect of Non-Homogeneous Soil Characteristics on Substation Grounding-Grid Performances: A Review
## 3 Effect of Horizontal Three-Layer Soil Characteristics on Grounding System Performances
### Apparent Soil Resistivity and Reflection Factor (K)
As previously stated, analyzing the performance of a grounding system in a horizontal three-layer soil structure is more complex and difficult than in a two-layer soil structure. There is no comprehensive and extensive study on the performance of grounding systems in horizontal three-layer soil constructions. In next Sections 3.2 and 3.3, a few relevant works on the influence of grounding design parameters, such as grounding electrode types and vertical rod length in horizontal three-layer soil structures will be discussed.
## 4 Conclusions  
In conclusion, we generated two-color \(e\)-beams in LWFA experiments and realized the hybrid of laser-plasma wakefield acceleration. When the plasma length was increased to be larger than the laser pump depletion length, one \(e\)-beam was still accelerated to 640.7 MeV with 1.1% FWHM energy spread and the other \(e\)-beam lost its energy rapidly to 230.5 MeV with 50.3% FWHM energy spread. These results were verified using 2D PIC simulations, and the simulations confirmed that after the laser pump depleted, the acceleration mode changed from LWFA to PWFA. Thereafter, the PWFA dominated the electron acceleration and enhanced the energy of the second \(e\)-beam. Moreover, the second \(e\)-beam could achieve a narrow energy spread because the energy chirp compensation further compressed its energy spread in the PWFA stage. This scheme of \(e\)-beam generation is useful not only for realizing a small-scale PWFA experimental facility but also to boost electron energy after dephasing or laser depletion in LWFA. The narrow energy-spread \(e\)-beams will lead to the realization of monoenergetic \(\gamma\)-rays.  
Conceptualization, Y.W. and C.Y.; Methodology, Y.W., C.Y., Z.Q. and W.W.; Validation, C.Y., W.W. and J.L.; Investigation, Y.W.; Software: Y.W.; Experiments manipulation, Y.W., C.Y., Z.Q., W.W., Z.Z., R.Q., K.F., L.K., Y.C. and C.W.; Experiments source, X.Y. and Y.L.; Funding acquisition, C.Y., W.W., J.L., R.L. and Z.X.; Writing--Original Draft Preparation, Y.W.; Writing--Review & Editing, C.Y., Z.Q. and J.L.  
This work was supported by the National Natural Science Foundation of China (Grant Nos. 11127901, 11425418, 11875065), Shanghai Sailing Program (Grant No. 18 YF1426000), Strategic Priority Research Program (B) (Grant No. XDB16), Key Laboratory for Laser Plasmas of Shanghai Jiao Tong University and State Key Laboratory Program of the Chinese Ministry of Science and Technology.  
The authors declare no conflict of interest.