esa-sceva/satcom-chunk-collection huggingface dataset:
# Retrieval of Cloud Ice Water Path from FY-3F MWTS and MWHS
## 2 Satellite Data and ERA5 Data
### Cross-Calibration
Figure 1 displays scatter plots of observed brightness temperatures from corresponding channels of MWHS-II and MHS before and after correction using the double-difference method. Figure 1a,b shows the scatter plots of MWHS-II brightness temperatures before correction against MHS in corresponding channels, while Figure 1c,d illustrates the same after correction. It can be observed that, as FY-3F and METOP-C are satellites in similar orbits, the observed brightness temperatures from corresponding channels exhibit high consistency. Following correction using the double-difference method, the correlation between the brightness temperatures from the two satellites improves significantly, with reductions in both RMSE and MAE, thereby enhancing the accuracy of IWP retrieval. Figure 1: Scatter plots of observed brightness temperatures from corresponding channels of MWHS-II and MHS before and after applying the double-difference correction, based on data from 15–16 June 2024: panels (**a**,**c**) depict scatter plots for the 89 GHz channel of MWHS-II before and after correction, while panels (**b**,**d**) represent scatter plots for the 166 GHz channel of MWHS-II before and after correction. Similarly, Figure 2 presents scatter plots of observed brightness temperatures from corresponding channels of MWTS-III and AMSU-A before and after correction using a radiative transfer simulation system. Figure 2a,b depicts the brightness temperatures of MWTS-III before correction against AMSU-A, while Figure 2c,d illustrates the same after correction. As in Figure 1, the observed brightness temperatures from corresponding channels of MWTS-III and AMSU-A demonstrate high consistency. Post-correction, the correlation improves, with noticeable reductions in RMSE and MAE. These results indicate that the double-difference method effectively reduces discrepancies in observational data between the FY-3F satellite and its counterparts, enhancing data reliability.
# Assessing the Long-Term Evolution of Abandoned Salinized Farmland via Temporal Remote Sensing Data
## 5 Discussion
The hardened soil layer reduces the capillary effect of soil water and decreases the efficiency of the dry drainage system. The tug-of-war between the efficiency of salt drainage and the evaporation capability of the abandoned salinized farmland creates a long-term salt balance in the dry drainage system [3]. In other words, the soil in the abandoned salinized farmland is difficult to improve due to the dry drainage effect, which has resulted in a thousand-year-old codependent relationship between the cropping system and the dry drainage system in Hetao. The results show that the area of abandoned salinized farmlands has declined steadily in recent decades. The continuous decrease in the area of abandoned salinized farmland weakens the codependent relationship between the cropping system and the dry drainage system. There were three main reasons for the decline: (1) drainage facilities and water-saving irrigation techniques reduced the groundwater table [43]; (2) the inlet salt was reduced while the well-irrigation facilities were set up to pump groundwater for irrigation; (3) the introduced salt-tolerant crops that could grow in the salt-affected field not only lowered the area of the abandoned salinized farmland but also removed salt from the field [44]. The desalinization progress relaxed the tight connection inside the dry drainage system, where the deeper water table staggered the evaporation capability of the fallow area, and the shrinking abandoned saline area reduced the scale of the system. The soil salt content of wasteland decreased as the salt was leached by rainfall, while less salt was transferred into the area. The salt of the cropping area was directly leached into the deeper groundwater with irrigation and rainfall. This provided a moment to reutilize abandoned salinized farmland after artificial leaching campaigns. However, the risk of salinization bounce back still exists due to the rising groundwater table during rainy years. Therefore, a leaching project should be thoroughly designed and conducted step by step after evaluating the environmental conditions and the future climate.
# Toward SDM-Based Submarine Optical Networks: A Review of Their Evolution and Upcoming Trends
## 4 Submarine Amplifiers
### SDM Technology-Cladding Pumping EDFA (MC-EDFA)  
SDM technology dictates hardware integration and energy saving especially in undersea environments where submarine amplifiers are deployed. The development of SDM-based amplifiers is mandatory for the further deployment of SDM. A cladding-pumping MC-EDFA can successfully meet these requirements and reduce the number of the pump lasers connected with the spatial channels, compared to individually core-pumped amplifiers. Many demonstrations and experiments have already been done, boosting the development of cladding-pumping amplifiers.  
In [39] an optical gain greater than 15 dB with a noise figure smaller than 5.5 dB was achieved with the implementation of a double-cladding MC-EDFA. In [40], the authors presented both a C-band and an L-band 7-core EDFA with gains of 18.0 dB and 13.1 dB and NF of 6.1 dB and 5.8 dB, respectively, showing that the core-absorption enhancement is effective to increase output power of cladding-pumped multi-core EDFAs.  
Figure 22: Pumping methods for MC-EDFA. (**a**) Core pumping. (**b**) Cladding pumping. (**c**) Hybrid pumping.  
[PERSON], [PERSON] et al. in [41] demonstrated a 108 Tb/s transmission over 120 km by employing a 7-core cladding-pumping EDFA using hybrid modulation, bringing optimization to the data throughput and achieving a net spectral efficiency of 39.27 bit/s/Hz. A 19-core cladding pumped EDFA has also been reported for first time, achieving optical signal amplification over 1500 km in [42]. Authors in [43] demonstrated a 0.715 Pb/s transmission over a distance of 2009 km, equivalent to a throughput-distance product of 1.44 Eb/s \(\times\) km which is the largest throughput \(\times\) distance product achieved for demonstrations using SDM-based amplifiers.  
In [44], a 32-core amplifier has been used to demonstrate 1 Pb/s transmission over 205 km, achieving high-aggregate spectral efficiency of 217.6 b/s/Hz. Another 32-core demonstration counted a greater than 17-dB gain and a 6.5 dB NF by using MC-EYDFA. A distance of over 1850 km resulted in power consumption benefits as the number of cores are scaled in MC-EYDFAs, as presented in [45].  
A schematic of an SDM EDFA depicting all sections of amplification process is presented in Figure 23.
## 6 Results
### Performance of SRFG  
The evaluation for the propagation criterion was measured with the introduced metric of criterion fraction. Criterion fraction is defined as: \(\frac{m}{k}\), where \(m\) is the number of bits in the input variable kept constant to get the propagation criterion of degree \(k\). For the SRFG function generator as the output functions are balanced always, the value of \(k=\frac{n}{2}\) and maximum number of bits we can keep constant is also \(\frac{n}{2}\). These values give the upper bound of criterion fraction as 1.  
In Figure 4, we showed the results of the propagation criterion for SRFG. Figure 4a shows the result by varying the number of variables and number of bits of each variable. Similarly, Figure 4b shows the result by varying the number of expression length and number of bits of each variable. It shows that more number of variables are providing more propagation; however, with the increasing number of bits propagation follows a steady output of 0.8 approximately. In both the cases, the results achieve more than 80% propagation criterion effect which reflects the strictly avalanche effect.  
The correlation immunity characteristics were plotted in Figure 5. Figure 5a shows the result by varying the bits and number of variables from 2 to 5. It notifies that increasing number of bits is able to generate more immunity. Figure 5b shows the result by varying the bits from 16 to 2048 and expression length from 2 to 10. We have noticed a new feature in both the cases that irrespective of the expression length and number of variables, SRFG is capable of producing 100% correlation immune functions with and after 128 bits. This makes the SRFG efficient and suitable for cryptographic algorithms.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Metrics** & **Values** \\ \hline No. of variables (N) & 2 to 5 \\ No. of bits (n) & 16,32, 64, 128, 256, 512, 1024 and 2048 \\ No. of Expressions (L) & 2 to 10 \\ Boolean functions & AND, XOR, NOT, OR \\ Sample size & 800 \\ Sample technique & Random \\ \hline \hline \end{tabular}
\end{table}
Table 1: Metric set-up.  
Figure 4: Propagation criterion. (a) Varying number of bits and variables; (b) Varying number of bits and expression length.  
The algebraic immunity of the SRFG generated functions are always at \(\frac{n}{2}\) irrespective of the number of bits and number of expression length and therefore these parameters is not shown graphically explicitly.
# Estimating Destination of Bus Trips Considering Trip Type Characteristics
## 5 Results  
### Analysis Overview  
We conducted an analysis utilizing the transportation card data of the Seoul metropolitan area, in which there is approximately no missing alighting tag information, to apply the proposed trip type classification and alighting stop estimation methods and examine the accuracy. The time range of the utilized data was one year in 2020. For the validation of the alighting stop estimation methodology developed in this study, we utilized one-month (April 2020) data of the Seoul metropolitan sphere (Seoul, Gyeonggi Province, and Incheon) where the non-tagged alighting ratio was low. As the non-tagged alighting ratio is very low in the case of subways, we solely selected the bus use data as the estimation-target. First, we built complete data (data with complete boarding-alighting records) for the accuracy validation of the alighting stop estimation method and deleted all alighting information from the data. Then, we tested the model's accuracy and identified the valid data ratio through the alighting stop estimation in each stage considering the trip type based on the data. Furthermore, because the travel patterns may be significantly different between each day of the week, we also performed the validation for each day of the week.
## Nomenclature  
**Acronyms**  
AC & Alternating Current  
AI & Artificial Intelligence  
ANN & Artificial Neural Network  
BESS & Battery Energy Storage System  
DC & Direct Current  
DT & Digital Twin  
EMT & Energy Management Tool  
GIS & Geographic Information System  
HES & Hybrid Energy System  
HOMER & Hybrid Optimization of Multiple Energy Resources  
HY4 RES & Hybrid for Renewable Energy Solutions  
IoT & Internet of Things  
MRE & Marine Renewable Energy  
MSE & Mean Square Error  
NARX & Non-Linear Autoregressive Model Exogenous  
NL-GRG & Non-Linear Generalized Reduced Gradient  
NNTS & Neural Network Time Series  
PAT & Pump-as-Turbine  
PED & Positive Energy District  
PERS & Port Environmental Review System  
PHS & Pumped Hydropower Storage  
PV & Photovoltaic  
UWF & Urban Waterfront  
Variables  
\(B_{e}^{i}\) & Batteries for energy needs [kWh]  
\(B_{p}^{i}\) & Feasible battery energy [kWh]  
\(COV_{Demand}\) & Percentage of covered demand by the hybrid module [%]  
\(E\) & Energy required (W)  
\(E_{e}^{i}\) & Energy need [kWh]  
\(E_{-}^{i}\) & Energy deficit in [kWh]  
\(E_{+}^{i}\) & Energy surplus in [kWh]  
\(E_{n}^{i}\) & Energy need [kWh]  
\(E_{revenues}\) & Energy savings (kWh),  
\(f_{PV}\) & Derating factor of solar PV  
\(g\) & Gravity acceleration [9.81 m/s\({}^{2}\)]  
\(G_{T}\) & Incident solar irradiance [kW/m\({}^{2}\)]  
\(G_{T,STC}\) & Incident solar irradiance under standard test conditions [kW/m\({}^{2}\)]  
\(h_{net}\) & Effective head [m]  
\(H\) & Head (m)  
\(H\) & Pump head [m]  
\(H^{i}\) & Hydropower generated [kWh]  
\(H_{nend}^{i}\) & Required hydropower [kWh]  
\(H_{t}\) & Average turbine head [m]  
\(N\) & Number of years
## 5 Performance Evaluation
### Experimental Results
#### 5.2.2 Kafka Broker Virtual Network Function Experiment
For this reason, given the isolation issues affecting containers, the test with the Kafka broker VNF running in a Docker container on the bare metal was not considered. The results are reported in Table 4. It is clear that our approach is not only the fastest at starting a new VNF, making it a great candidate for adoption in a serverless approach, but it is also more efficient than the baseline competitors. In fact, in order to handle the same volume of traffic, it is the one that requires the least amount of CPU capacity. Clearly, using Docker containers within a VM involves additional overhead, which is balanced, in operation, by the ability to flexibly allocate the VM's computing power to different functions implemented with different containers. However, isolation is only granted at the VM boundary. Our approach maintains the same flexibility, but also guarantees isolation at the microVM level, thus presenting itself as a good candidate for the implementation of \begin{table}
\begin{tabular}{l l} \hline
**Deployment Model for Kafka Broker VNF** & **Amount of CPU Power** \\ \hline VM & 0.045 \\ Docker in a VM & 0.054 \\ Kata + Firecracker & 0.034 \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison of the ability of different deployment models to handle 1 Mbps of Kafka traffic. Figure 11: Batch write latency (in ms) for a Kafka broker VNF running in a Kata container on (blue) a QEMU hypervisor, and (red) a Firecracker hypervisor. CNF, as in the right side of Figure 4. From a numerical viewpoint, the results show that our VNF requires about 0.86 CPU occupancy to handle 25 Mbps, whereas the other approaches would require more than 1 CPU core occupancy. To sum up, it is possible to say that the solution of Kata + Firecracker has a lower resource footprint than the alternative deployment solution based on a classic QEMU hypervisor, in terms of storage and memory. They have similar CPU consumption levels, which essentially depend on the VNF and not on the underlying virtualization solution. In addition, data plane performance is fully aligned to the baseline solution, based on QEMU, and it is capable of satisfying the requirement of most IoT applications, with a significant speed increase in terms of boot time, which is a major bonus.
## 1 Introduction
### Network Packet and Flow Datasets
Note that some protocols use partially encrypted messaging, where typically the initial exchange is in plaintext. These variations in use will be clearly reflected in payload entropy values.
# Linked Open Government Data: Still a Viable Option for Sharing and Integrating Public Data?
## 6 What Factors Are Holding back the Spread of LOGD?
### LOGD Data Friction
#### 6.2.1 Technical Dimension
The ability to _construct SPARQL queries_ becomes paramount when deploying and interlinking datasets [33]. RDF itself poses challenges, requiring substantial effort to capture knowledge from data owners and domain specialists, and transform the data into a suitable RDF format based on appropriate vocabularies [35]. Government datasets managed by different departments and agencies present significant challenges in ensuring data quality, consistency, and standardization, which can hinder the adoption of LOGD [68]. Several key issues highlight these challenges. As noted by [PERSON] et al. [62], when assessing the uptake and quality of linked datasets belonging to several European portals indexed by the European Data Portal, there is a problem of _vocabulary dereferenceability_: the datasets analyzed referred to more than 3000 proprietary vocabularies but over than 95% of them were non-dereferenceable, undermining efforts to establish standardized and consistent terminologies. [PERSON] [78] emphasizes the _tension between volume and quality_ in big OGD, pointing out that it is difficult, also economically, to guarantee satisfactory quality when the main priority is to open up large amounts of (linked) data, even at the risk of publishing incorrect, obsolete, and non-integrated data. [PERSON] et al. [77] observe that the open world perspective underlying LD, according to which anyone can say anything about anything, leads to challenges such as _inconsistencies in data_ and _contrasting views_ arising from different sources about the same entity, raising concerns about timeliness, completeness, and accuracy. Apart from the already mentioned problems with the quality of links, [PERSON] et al. [33] note that _lack of linking_ in published datasets compromises overall data quality and has a global impact on the reproducibility of LOGD processes. Furthermore, despite the authority of data providers, datasets are often not uniformly accurate or consistent, as noted in [87], where inconsistencies between source systems, often due to _different domain foci_, increase the complexity of data integration. Acknowledging the significance of enhancing data availability, quality, and understandability, [PERSON] et al. [69] underscore these factors as essential prerequisites for the successful implementation of LOGD. In the same vein, [PERSON] et al. [88] highlight that the inconsistency of content in OGD poses a significant challenge to LD integration.
## 5 Challenges and Future Research Trends
### Adaptation to Dynamic Environments  
A fixed channel estimation model may no longer maintain high accuracy in dynamic wireless communication environments. If the model is retrained to adapt, it will consume significant time and computational resources. This approach fails to meet the real-time and low-cost requirements. Therefore, research on adaptive model design can be strengthened in future work. Such a design would enable the model to adjust its parameters and structure autonomously, adapting to complex and changing environments for stable and high-performance channel estimation.
# Research on High-Efficiency Routing Protocols for HWSNs Based on Deep Reinforcement Learning
## 5 Algorithm Simulation and Result Analysis
### Simulation Experiment of Energy Consumption with Varying Node Numbers in HWSNs  
In various scenarios of HWSNs operations, the number of nodes typically changes according to different application requirements. If the proposed algorithm exhibits good energy efficiency optimization performance across different node counts in HWSNs, it indicates the strong adaptability of the algorithm. As illustrated in Figure 11, to validate the performance of the HDQN algorithm in energy efficiency optimization with varying node numbers, it is compared with five other algorithms. The total network energy consumption of various algorithms is analyzed as the number of nodes changes. The number of nodes in the HWSNs is set to 100, 200, and 300, with all six algorithms running for 700 rounds under identical initial parameters. The proposed HDQN algorithm consistently ranks among the top in terms of total remaining energy across all three node counts. This observation indicates that the HDQN algorithm effectively reduces network energy consumption in scenarios with varying numbers of HWSNs nodes, demonstrating its robust adaptability and energy efficiency optimization capabilities.  
Table 6 presents the total remaining energy of various energy efficiency optimization algorithms after 700 rounds of operation in scenarios with different numbers of heterogeneous sensor nodes. As depicted in Table 6, the HDQN algorithm exhibits superior performance compared to the second-ranked algorithm across node counts of 100, 200, and 300, achieving energy consumption reductions of 257.2 mJ, 944.7 mJ, and 1071.3 mJ, respectively. The corresponding percentage improvements stand at 4.04%, 8.19%, and 6.46%. This suggests that the HDQN algorithm effectively optimizes energy efficiency in  
Figure 11: Simulation experiment of remaining energy with varying node numbers in HWSNs.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & \multicolumn{3}{c}{**Energy Consumption After 700 Rounds (mJ)**} \\ \cline{2-4}
**Algorithm** & **BS Location (100,** & **BS Location (150,** & **BS Location (200,** \\  & **100)** & **150)** & **200)** \\ \hline EECHR & 37,810.5 & 56,742.1 & 73,782.6 \\
2L-HMGEAR & 39,879.7 & 59,101.76 & 78,013.3 \\ NCOGA & 40,342.9 & 57,986 & 75,961.7 \\ DEEC & 40,308.8 & 61,311.8 & 79,485.2 \\ SEP & 35,630.9 & 52,684.2 & 65,241.9 \\ HDQN & 35,373.6 & 51,975 & 63,849.3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Simulation data of energy consumption with varying BS positions in HWSNs (unit: mJ).  
HWSNs across varying numbers of sensor nodes, demonstrating its robust performance and adaptability to different deployment scenarios.
## 3 Results and Discussion
### Bio-Medical Potential of CPL-Rx
#### 3.5.2 Antioxidative Activity  
Various natural polysaccharides exhibit antioxidative or radical scavenging potential [4; 34]. Therefore, we aimed to examine the CPL-Rx using three antioxidant tests, i.e., TEAC, DPPH\({}^{\bullet}\) radical scavenging activity, and oxygen radical absorbance capacity assays. The results were compared with the activity of well-known and highly active antioxidant molecules (Trolox or ascorbic acid) and presented as equivalents of the standards (Table 4). All protocols revealed high potential of the samples. In the Trolox equivalent antioxidant capacity (TEAC) assay, the activity of the rose root crude polysaccharide was found to be 0.302 \(\pm\) 0.01 mmol/g of dry extract (DE). The result of the DPPH\({}^{\bullet}\) radical scavenging assay was presented as the half maximal effective concentration (EC\({}_{50}\) value) as well as Trolox (TE) and ascorbic acid equivalents (AsAE). The ability to scavenge DPPH\({}^{\bullet}\) radical (TE = 0.092 \(\pm\) 0.01 mmol/g DE) was found to be lower than the activity against ABTS\({}^{\bullet+}\) radical cation (used in the TEAC protocol).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Sample** & **Concentration** & **COX-1 Inhibition** & **COX-2 Inhibition** & **HYAL Inhibition** \\  & **(mg/mL)** & **(\%)** & **(\%)** & **(IC\({}_{50}\); ug/mL)** \\ \hline CPL-Rx & 0.25 & n.a. & n.a. \\ CPL-Rx & 0.5 & n.a. & n.a. \\ CPL-Rx & 0.75 & 4.67 \(\pm\) 0.26 & 6.96 \(\pm\) 0.36 \\ CPL-Rx & 1 & 10.64 \(\pm\) 0.59 & 8.19 \(\pm\) 0.39 \\ CPL-Rx & 2 & 18.91 \(\pm\) 0.83 & 45.64 \(\pm\) 1.95 \\ CPL-Rx & 5 & 23.26 \(\pm\) 0.88 & 71.98 \(\pm\) 1.82 \\ ASA & 1 mM & 65.26 \(\pm\) 1.84 & 75.03 \(\pm\) 4.07 & - \\ EGCG & - & - & - & 280.50 \(\pm\) 3.52 \\ \hline \end{tabular}  
Positive controls: acetylsalicylic acid (ASA)—for the COX inhibitory assay; epigallocatechin gallate (EGCG)—for the HYAL inhibitory assay; Abbreviation: n.a.—no activity observed.  
\end{table}
Table 3: Inhibition of COX-1, COX-2, and hyaluronidase (Hyal) activity in the presence of _R. rugosa_ root crude polysaccharide (CPL-Rx).  
ORAC is a somewhat more advanced and accurate assay since it engages peroxy radicals and takes into account antioxidant reaction kinetics [35]. It was demonstrated that CPL-Rx was an efficient peroxy radical scavenger (0.134 \(\pm\) 0.10 mmol Trolox/g DE).  
Our results are in agreement with previous studies of the antioxidant potential of rose polysaccharides [12; 34; 36].