esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
High-efficiency of all-fiber lasers can be reached by using FBGs for the selection and tuning of the generated laser lines in single laser emission. However, in order to obtain equal output powers of the simultaneously generated laser lines in dual-wavelength laser generation, the use of a method to balance the intra-cavity losses is required. In this regard, from the experimental results the Hi-Bi FOLM has been demonstrated as a reliable optical device for the fine intra-cavity losses adjustment. The wavelength displacement of the Hi-Bi FOLM by temperature variations on the Hi-Bi fiber stands for a straightforward and non-arbitrary method for the gain balancing of both generated laser lines in dual-wavelength fiber lasers instead of polarization controllers which are commonly used for this purpose.  
On the other hand, from the obtained results it can be noticed that DCFs allow the increase the output power in the development of single-mode fiber lasers by using cladding-pumping technique with a multi-mode pump source. The use of DCFs in fiber lasers is an attractive option for replacement of bulk solid-state lasers in order to reach high power laser emission. However, in case of all-fiber lasers design, the high power provided is frequently restricted to the optical power handled by the fiber elements which form the cavity. Moreover, although the high ytterbium co-doping in EYDCF reduces the erbium ion pairs formation, the phenomenon occurs. As a consequence, SQS pulses are present in EYDCF fiber lasers. Regardless of the EYDCF length, under low pump power conditions the self-pulsations are determined by the relaxations oscillations. In addition, the EYDCF acts as saturable absorber due to the weakly pumped section of the EYDCF. When pump power is increased, the relaxation oscillations are amplified to a faster rate leading to the increase of pulses repetition rate, which is typical characteristic of passively Q-switched lasers. As the pump power is increased, the saturable absorption is reduced due to the increase of gain uniformity along the EYDCF, reaching a limit in which CW laser operation is obtained. As a consequence, in case of the proposed configuration when pump power is increased, the full repetition of the SQS laser operation depends on the EYDCF length [21]. In order to avoid possible damages in fiber systems caused by high energy SQS pulses generated at low pump power when a CW laser source is required or otherwise to establish possible applications of the SQS laser source with a wide repetition rate, the existence of SQS pulses in double-clad-fiber-based lasers has to be studied establishing operation limits, the conditions which contribute to their generation and the mechanisms to suppress or increase their operation according with the laser application.  
Figure 7: Characterization of the SQS laser pulses as a function of the pump power: (**a**) measured average power and repetition rate; (**b**) estimated pulse energy and measured pulse duration; (**c**) estimated pulses peak power.
# Drag and Attitude Control for the Next Generation Gravity Mission
## 2 NGGM Objectives, Measurement Technique, and Fundamental Observables
### The Satellite-To-Satellite Tracking Technique and the NGGM Scenario
Finally, taking into account altitude constraints together with engineering requirements, the proposed sets of orbits in Table 3 of ref. [23] guarantee an excellent homogeneity and sampling, including a matching ground-track longitude shift for the two pairs. The parameters of a suitable choice of orbits for the NGGM Bender constellation are provided in Table 2. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **First Pair (Polar Pair)** & **Second Pair (Inclined Pair)** \\ \hline Mean orbit altitude & \(h_{1}=492\) km & \(h_{2}=396\) km \\ Orbit inclination & \(i_{1}=89^{\circ}\) & \(i_{2}=65^{\circ}\) \\ Ground track sub-cycles & 5, 26, 31 days & 5, 13, 18, 31 days \\ Ground track homogeneity 1 & \(h_{l}=1.397\) & \(h_{l}=1.172\) \\ Ground track shift in longitude & \(\Delta(\text{Lon})=-0.790^{\circ}\) & \(\Delta(\text{Lon})=-1.499^{\circ}\) \\ after the shorter sub-cycle & \(\Delta(\text{Lon})=-0.790^{\circ}\) & \(\Delta(\text{Lon})=-1.499^{\circ}\) \\ \hline \hline \end{tabular} \({}^{\dagger}\) Defined as the ratio of the largest and the smallest difference between adjacent ascending equator crossings.  
\end{table} 
Table 2: Orbital parameters of a Bender constellation suitable for the NGGM. Figure 2: Arrangement of two satellite pairs (S1-S2, S3-S4) in “Bender Constellation”. The selection of the inter-satellite distance depends on the spatial wavelengths of the gravity field to be retrieved, and on the orbit altitude [25]. Given the mission objectives and altitude range of the NGGM constellation, a reference inter-satellite distance \(d\) = 220 km (i.e., the same average distance of the GFO satellites) has been preliminarily identified for NGGM within the Phase A study.
# Photonic Technology for Precision Metrology
## 3 Some Practical Aspects of High-Resolution Measurements of Absorption Spectra
Some well-known equations are needed to explain this. Absorption gas sensors use the effect of the absorption of radiation by molecules, the wavelength \((\lambda)\) of which is matched to their resonance frequency, the so-called absorption band. The radiation \(I(\lambda)\) passing through the layer of gas (\(L\)) with the absorption coefficient \(\alpha(\lambda)\) is attenuated according to the Beer-Lambert law:  
\[(\lambda)=I_{0}(\lambda)e^{-\alpha(\lambda)L}, \tag{2}\]  
where \(I_{0}(\lambda)\) is the initial intensity, before the absorbing layer. Absorbance (\(Abs\)) can be used to define the quantity characterizing the absorption of radiation, which is described by the following formula:  
\[Abs=\alpha(\lambda)L=\sigma(\lambda)NL=ln\bigg{(}\frac{I_{0}(\lambda)}{I( \lambda)}\bigg{)}, \tag{3}\]  
Figure 3: Exemplary N\({}_{2}\)O absorption spectra observed in the vapors obtained from a 2 mg PrNQ sample at a temperature of 50 to 400 \({}^{\circ}\)C. The measurement was carried out at the following conditions: resolution 0.5 cm\({}^{-1}\), 2.4 m gas cell, 660 mbar, beam splitter KBr, and liquid nitrogen cooled MCT detector. where \(\sigma(\lambda)\) denotes the absorption cross section in cm\({}^{2}\), and \(N\) is the absorber concentration in cm\({}^{-3}\). In the case of the inaccurate matching of the laser wavelength to the absorption line or using not single-mode laser, then the absorption coefficient does not possess the same value. This is illustrated in Figure 4. For the sake of simplicity, the same spectral full widths in the half maximum (_FWHM\({}_{1}\) = FWHM\({}_{2}\)_) of the radiation sources were assumed. Figure 4a shows that, for the first source, small changes in the wavelength cause significant changes in the absorbance (\(\Delta Abs_{1}\)). On the other hand, in the case of source 2, these changes are much smaller (\(\Delta Abs_{2}<<\Delta Abs_{1}\)). It follows that such an arrangement is much less sensitive to the shape of the absorption line. As the absorber concentration increases, the shape of the absorption line changes (its amplitude and width increase). This results in the non-linear response of the absorption sensor with source 1 (Figure 4b).
# The Effects of Fertilization on the Growth and Physiological Characteristics of _Ginkgo biloba_ L.
## Results
### Effects of Fertilizer on Leaf N, P, and K Contents  
In Yellow Sea Forest Park, N and P contents in leaves showed significant differences between levels of single N fertilizer application (\(p<0.05\)), while K content was not affected by N fertilizer application (Figure 1; \(p>0.05\)). N and K contents showed no significant response to single P fertilizer (\(p>0.05\)), while P content showed a significant difference (Figure 2; \(p<0.05\)). N and K contents showed significant differences between the different amounts of single K fertilizer application (\(p<0.05\)), while P content was not affected by K fertilizer application (Figure 3; \(p>0.05\)). After two years of continuous fertilization, a single N fertilizer application of 600 g-tree\({}^{-1}\) resulted in an increase in N content greater than 16.4% and an increase in P content greater than 19.9% compared with unfertil plots (CK\({}_{1}\)). Meanwhile, a single P fertilizer application of 800 g-tree\({}^{-1}\) resulted in an increase in P content greater than 23.8% compared with unfertilized plots (CK\({}_{1}\)). A single K fertilizer application of 200 g-tree\({}^{-1}\) resulted in an increase in N content greater than 12.2% and an increase in K content greater than 21.8% compared with unfertilized plots (CK\({}_{1}\)). Therefore, single N fertilizer treatment promoted _Ginkgo_ N and P content significantly, single P fertilizer treatment only promoted _Ginkgo_ P content significantly, and the promotion effect of single K fertilizer treatment on N and K content were significant. Additionally, orthogonal tests in EXP.2 showed that combined N, P, and K fertilizer treatment had a significant promoting effect on N, P, and K content (Figure 4; \(p<0.05\)). After two years of continuous fertilization, Treatment 9 resulted in an increase in N content greater than 13.4% compared with unfertilized plots (CK\({}_{2}\)). Treatment 6 resulted in an increase in P content greater than 21.5% compared with unfertilized plots (CK\({}_{2}\)). Treatment 7 resulted in an increase in K content greater than 16.7% compared with unfertilized plots (CK\({}_{2}\)).  
Figure 1: Effects of single N fertilizer application on _G. biloba_ leaf N, P, and K contents in EXP.1.  
Figure 2: Effects of single P fertilizer application on _G. biloba_ leaf N, P, and K contents in EXP.1.
###### Abstract  
With the vast quantity of GPS data that have been collected from location-based social networks, Point-of-Interest (POI) recommendation aims to predict users' next locations by learning from their historical check-in trajectories. While Graph Neural Network (GNN)-based models have shown promising results in this field, they typically construct single-layer graphs that fail to capture the hierarchical nature of human mobility patterns. To address this limitation, we propose a novel Hierarchical Graph Learning (HGL) framework that models POI relationships at multiple scales. Specifically, we construct a three-level graph structure: a base-level graph capturing direct POI transitions, a region-level graph modeling area-based interactions through spatio-temporal clustering, and a global-level graph representing category-based patterns. To effectively utilize this hierarchical structure, we design a cross-layer information propagation mechanism that enables bidirectional message passing between different levels, allowing the model to capture both fine-grained POI interactions and coarse-grained mobility patterns. Compared to traditional models, our hierarchical structure improves cold-start robustness and achieves superior performance on real-world datasets. While the incorporation of multi-layer attention and clustering introduces moderate computational overhead, the cost remains acceptable for offline recommendation contexts.  
n
# Recent Progress and Challenges Regarding Magnetic-Based Nanoparticles for Targeted Drug Delivery
## 5 Monitoring of Magnetic-Based Nanocarriers Interacting with Cells in Biological Systems
The direct technique for investigating free radicals, including ROS, is electron spin resonance. As the production of ROS predominantly takes place in mitochondria, employing this method provides a valuable opportunity to monitor interactions between different molecules, including nanoparticles and cells. The ESR method investigated the interaction between magnetic nanoparticles functionalized with spin labels with yeast and cancer cells. 2,2,6,6-Tetramethylpiperidine 1-oxyl (TEMPO) and 4-Hydroxy-2,2,6,6-tetramethylpiperidine-1-oxyl (4-Hydroxy-TEMPO, TEMPO) spin probes can interact with free radicals and are considered potential scavengers for these radicals [133; 134; 135]. Based on alterations in the shape and intensity of the ESR signal, it is possible to differentiate such molecules (whether free or attached to magnetite nanoparticles) located outside a cell, within the cell membrane, and inside a cell. Figure 9 shows changes in the structure and intensity of ESR spectra of a TEMPOI spin label attached to magnetite-based NPs loaded with doxorubicin (Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)@SiNHDOX@Dextran-TEMPOL) [134]. The nanoparticles incubated with cells were monitored over time by analyzing changes in ESR spectra. The ESR signals from TEMPOI spin labels vary based on the nanoparticles' location within the cell. The primary advantage of this method, in contrast to those mentioned earlier, lies in its capability to observe and monitor both the dynamic and static aspects of the endocytosis process, including the entry of nanoparticles into the cells. An additional benefit of this method is that the samples do not necessitate any specific preparation. Spin labels can be broadly applied to various types of nanomaterials, and the study of endocytosis using the ESR technique is ongoing, including its application in a free form without nanomaterials [137].
## 3 Validation Cases
### CYGNSS and Ancillary Data  
CYGNSS is a National Aeronautics and Space Administration (NASA) mission consisting of eight low-Earth orbit satellites for GNSS-R [7]. Each satellite has two off-nadir antennas for science measurements and a zenith antenna for navigation and receiving the direct GPS signal. CYGNSS satellites generate DDMs from GPS L1 signals. The mission was originally designed to estimate wind speed over the ocean. However, after launch, it has been utilized for other applications, including soil moisture estimation. In this work, we used the CYGNSS L1 v3.1 product [29].  
The one-arc-second SRTM DEM [30] was used in the IGOT model as \(\zeta_{\text{DEM}}\). Furthermore, the DEM gradient was calculated using the method presented in [31], which calculates the gradient of each pixel in the DEM by fitting a plane using a window of a specific size. This method was used to reduce the noise in the DEM gradient. The window size in this work has units of DEM samples, each of which is approximately 30 m in extent.
# MedChain: Efficient Healthcare Data Sharing via Blockchain
## 3 MedChain Model
### Preliminaries
#### 3.2.1 Healthcare Data  
Healthcare data describes a patient's health state, in the form of electronic healthcare record, e.g., a CT image, or data stream collected by a medical sensor, e.g., ECG signal. Each healthcare record/data stream is assigned a unique ID (_DID_). A data stream is a time series record, which maps each sampled value to a time label. A data stream is cut and saved into multiple data chunks. One chunk records the sampled value within a time interval. Thus, each chunk has a start time and an end time labeling the range of the data chunk. All data chunks of the same data stream share the same _DID_, but different chunk indexes.  
Figure 3: Modules of a super peer.  
Figure 2: MedChain architecture.  
The derived information from healthcare data can be classified into immutable information and mutable information. The immutable information maintains the security, integrity, and authenticity of data, which includes the identity of the related users, the digest of the data, the endorsement (e.g., digital signature) from the data provider, and the data collection range of a stream. The mutable information facilitates data query and access, which includes the description, the tags, and the network location (i.e., URL) of the actual data.  
For healthcare data, the identity of the data, the network location, and the start time, and end time of the chunk for data stream belong to the privacy of a patient, as well as the actual data. Thus, privacy protection is needed in MedChain to disassociate _DID_, data location, and the actual data with data owner (i.e., patient).
## 7 Conclusions  
In conclusion, we proposed an architecture for the next generation of smart roadside infrastructure, where communications between infrastructure devices (TFCs, TMSs, etc.) rely on TSN and are managed by a centralized control plane. The routing and scheduling assignment problem was addressed using a multi-agent DRL-based online solution that is flexible, scalable, and reliable. Service channel management supports multiple service operators and operates automatically, without requiring intervention from network or service managers. TSN is poised to play a crucial role in converging diverse networks into a single technology (IP over Ethernet), and its integration with 5G networks will further enhance flexibility and scalability. Future research involves assuming full 5G-TSN integration to implement V2I communications, potentially involving multiple TSN control planes. Additionally, addressing the coexistence of best-effort and time-critical traffic will be an important next step.  
Figure 9: Larger scenario used for evaluating models’ scalability.  
**Author Contributions:** Conceptualization, S.G.-C., C.R.d.M., C.C.-P. and S.S.; methodology, S.G.-C., C.R.d.M., C.C.-P. and S.S.; software, S.G.-C. and C.R.d.M.; validation, S.G.-C., C.R.d.M., C.C.-P. and S.S.; formal analysis, S.G.-C. and C.R.d.M.; investigation, S.G.-C., C.R.d.M., C.C.-P. and S.S.; resources, C.C.-P. and S.S.; data curation, S.G.-C. and C.R.d.M.; writing--original draft preparation, S.G.-C.; writing--review and editing, S.G.-C., C.R.d.M., C.C.-P. and S.S.; visualization, S.G.-C.; supervision, C.C.-P. and S.S.; project administration, C.C.-P. and S.S.; funding acquisition, C.C.-P. and S.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Spanish Ministry of Economic Affairs and Digital Transformation and the European Union--NextGenerationEU, in the framework of the Recovery Plan, Transformation and Resilience (PTRR) (Call UNICO I+D 5G 2021, ref. number TSI-063000-2021-15- 6 GSSMART-EZ), as well as by the Agencia Estatal de Investigacion of Ministerio de Ciencia e Innovacion of Spain under project PID2022-137329 OB-C41/MCIN/AEI/10.13039/50110001103.  
**Institutional Review Board Statement:** Not applicable  
**Informed Consent Statement:** Not applicable  
**Data Availability Statement:** The software presented in this study is publicly available in a GitHub repository: [[https://github.com/sergigarciaacanton/AI_MARL_CNC_Implementation](https://github.com/sergigarciaacanton/AI_MARL_CNC_Implementation)]([https://github.com/sergigarciaacanton/AI_MARL_CNC_Implementation](https://github.com/sergigarciaacanton/AI_MARL_CNC_Implementation)) (accessed on 24 December 2024).  
**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.
## 4 Proposed Framework
### Layer 3--Indexing and Search  
In the third layer, or indexing and search, the main component is Elasticsearch. Elasticsearch is a high-performance [38] indexing and search engine. Its use as a repository for storing system calls allows for high availability of data, in addition to enabling searches through useful metadata for filtering, aggregation, sorting, etc. [33]. In addition to containing the indexes created by Logstash representing the datasets, Elasticsearch integrates with the layer 4 machine learning module, with the layer 5 Kibana tool, and is also used to store the results of anomaly analyses. Thus, its operation takes place through the provision of data from queries to datasets or anomaly detection results, or simultaneously indexing new datasets and anomaly detection results by the machine learning module. For each index representing a dataset in the processing flow described in Figure 4, after its analysis, a new index is created in Elasticsearch with the anomaly score corresponding to the windows of system calls parsed by the machine learning module [33]. system calls windows consist of a data structure that stores sequences of system calls of multiple sizes and is a common approach for analyzing system calls [33]. Several operations can be done in Elasticsearch for manipulation and storage of datasets in conjunction with Kibana.
# Personalized Privacy Protection Based on Space Grid in Mobile Crowdsensing
## 4 Personalized Privacy Protection
### Experimental Results
#### 5.3.2 Impact of Personalized Grid Structure on System Performance  
We conducted a comparison of the communication cost between the PAG structure and the AG structure in task broadcasting. Using a set of 100 task coordinates, along with the AG spatial index and the PAG spatial index, we generated the broadcast areas \(GR_{AG}\) and \(GR_{PAG}\) according to Algorithm 3, while Table 4 provides the interpretation of the symbols used in Algorithm 3. Subsequently, we initiated broadcasting within the designated broadcast areas. Following this, we calculated the average number of notified workers (\(ANW\)) and the number of propagation hops (\(n_{\mathit{hops}}\)) for the task broadcast. The specific results are presented in Tables 5 and 6.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Definition** \\ \hline \(c_{i}\) & the last unit with excessive utility in the broadcast area \\ \hline \(c_{i^{\prime}}\) & subunit of \(c_{i}\) obtained according to Algorithm 3 \\ \hline \(w_{\mathit{required}}\) & the noise count of the subunit is required \\ \hline \(w_{c_{i}}\) & noise count of \(c_{i}\) \\ \hline _percentile_ & percentage of subarea in the secondary grid \\ \hline \hline \end{tabular}
\end{table}
Table 4: Symbols required for Algorithm 3.  
Figure 4: Values of averageErr under different spatial partition strategies.