esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Implementing L2S-M in a Cloud-Native Platform for Intra-Site Connectivity: K8s Case
### L2S-M Information and Lses  
L2S-M has been released as a publicly available open-source code that can be used in most K8s distributions [45]. This solution has received interest from the research community, as it has been used in multiple European research projects in different contexts: LABYRINTH [47], focused on providing security functions using UAVs, using L2S-M to enable communications between the aircrafts; and the FISHY project [48], focused on providing a coordinates framework for cyber-resilient supply chain systems, where L2S-M is used as the basis of the main platform built to deploy the functionalities used in the project, the FISHY reference framework (FRF).  
As it was described in previous paragraphs, current NFV orchestrators, like the well-known Open Source MANO (OSM) [28], have limited support for the deployment of NFV cloud functions using K8s since there is no native way to create virtual networks able to interconnect several VNFs in the clusters. However, our ongoing work includes the definition of a new feature to be included in the codebase of OSM to enable the deployment of network functions in K8s clusters. This feature, named \"Connectivity among CNFs  
Figure 7: Deleting a virtual network in L2S-M.  
using SDN\", has already been approved and it is currently in the design phase in direct collaboration with the OSM community. The details of this feature can be seen in the official OSM site [49]. In this regard, we will briefly describe the steps that will be performed to add a K8s cluster in the OSM ecosystem using L2S-M, as well as the deployment of a NS using virtual networks within the cluster:  
* At the cluster (VIM) registration time, the OSM user selects that the data plane used in the CNFs communications is provided by L2S-M. Then, the user defines the resource definitions (i.e.,.yaml templates or Helm [50] charts) in order to tell the orchestrator how to build and manage these networking resources within the cluster.
* When a new network is deployed using the orchestrator, the resource orchestrator component (RO) of OSM takes the values and configuration parameters of each VL in the descriptor and translates them into the parameters used in the L2S-M virtual networks. After this process, the orchestrator contacts the K8s cluster and follows the flow seen in Figure 4.
* Once the VLs have been processed, then the orchestrator proceeds to add the corresponding K8s annotations to each VNF to add the VLs associated with them, and start their deployment within the K8s cluster (as seen in Figure 5), finalising the deployment of the NS using L2S-M as the data plane networking solution in the cluster.
###### Abstract  
The modern agriculture system based on open-field crops requires a lot of energy and resources in terms of soil, water, and chemicals. Vertical farming (VF) systems could be a viable alternative for some types of cultivation that are receiving interest thanks to their high modularity, optimized water and nutrients use, and LEDs employment as an energy-efficient light source. However, VF design and installation are expensive and require well-tailored optimization depending on the specific crop to increase its competitiveness. This work analyzed the effects of different combinations of NPK (nitrogen-phosphorus-potassium) slow-release fertilizers and LED-based light recipes on the growth of baby leaf lettuce (_Lactuca sativa_ L.), taking advantage of the Design of Experiments (DoE) methodology. The type of slow-release fertilizer, its quantity measured as the number of aggregates from 0 to 6, and the type of light recipe were considered as input factors, and their possible influence on the growth of lettuce (in terms of morphological parameters) in a controlled indoor farming system was measured. Results suggest that using higher fertilizer inputs equal to six aggregates leads to an increase of average leaf area equal to 46% (from 13.00 cm\({}^{2}\) to 19.00 cm\({}^{2}\)), while the fresh weight of lettuce increases by 65% (from 1.79 g to 2.96 g). However, the height of plants also depends on the combination of the light recipes. In particular, the separate coupling of higher inputs of two fertilizers and light recipes leads to an increase in the height of lettuce equal to 33% (from 6.00 cm to 8.00 cm).  
keywords: lettuce; light emitting diode; Design of Experiments; vertical farming; slow-release fertilizers; NPK; mathematical models; indoor farming +
Footnote †: journal: Applied sciences
## 3 Results
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Treatment**} & **TOC** & **MOC** & **POC** \\ \cline{2-4}  & \multicolumn{3}{c}{**g kg\({}^{-1}\)**} \\ \hline \hline \multicolumn{4}{c}{P\({}_{2}\)O\({}_{5}\) doses (kg ha\({}^{-1}\)) (P)} \\ \hline
0 & 18.1 & 14.7 & 3.4 \\
30 & 20.0 & 17.3 & 2.7 \\
60 & 19.4 & 16.9 & 2.5 \\
120 & 17.8 & 14.9 & 2.9 \\
240 & 21.1 & 18.6 & 2.5 \\ \hline \hline \multicolumn{4}{c}{Inoculation (I)} \\ \hline Without & 19.6 & 17.7 a & 3.7 a \\ With & 19.0 & 15.3 b & 1.9 b \\ \hline Test—P & 0.003 ** & 0.006 ** & 0.683 \({}^{\text{ns}}\) \\ Test—I & 0.174 \({}^{\text{ns}}\) & 0.001 ** & 0.003 ** \\ Test—P \(\times\) I & 0.214 \({}^{\text{ns}}\) & 0.477 \({}^{\text{ns}}\) & 0.765 \({}^{\text{ns}}\) \\ \hline CV (\%) & 4.91 & 7.57 & 36.91 \\ \hline \hline \end{tabular}  
Means followed by different letters in the column for different treatments differed by the LSD post-hoc test at the 5% probability level. **: significant at 1% probability. ns: not significant. CV: coefficient of variation; POC: carbon stock particulate; MOC: carbon stock mineral; TOC: total organic carbon. Regression equation: TOC = 8 \(\times\) 10\({}^{-5}\)x\({}^{2}\) – 0.0122x + 19.129, R\({}^{2}\) = 0.4565. Regression equation: MOC = 0.0103x + 15.558, R\({}^{2}\) = 0.3436.  
\end{table} 
Table 25: Mean values for carbon stock in particulate (POC), mineral (MOC), and total organic carbon (TOC) fractions in the 0.10–0.20 m soil layer in a no-tillage system, considering the residual effect of phosphate fertilization and inoculation of preceding grasses with _A. brasilense_. Selvria/MS, 2023/2024. Finally, the inoculation of grain-producing grasses led to a greater accumulation of carbon fractions and, consequently, organic matter in the soil. The residual effect of P\({}_{2}\)O\({}_{5}\) doses resulted in an increase in the concentration of this nutrient in the soil, in accordance with the increase in the residual dose. However, it did not result in differences in the dry matter production of Paiaguas grass or in soybean or sorghum grain yield. On the other hand, for the dry matter production of Piata grass, higher productivity was observed with higher residual P\({}_{2}\)O\({}_{5}\) doses, as well as for labile, moderately labile, and inorganic phosphorus levels. For enzymatic activities and soil biological attributes, the residual phosphorus doses in the soil did not result in significant differences. Regarding carbon stock and different soil organic matter fractions, higher values were obtained in areas with the highest residual phosphorus doses. However, technologies and management practices, such as those presented in the current study, can contribute to more resilient and sustainable agricultural practices, with increased productivity and reduced pressure on production areas. This is important in the light of future scenarios, where there will be increased global demand for food and products from agriculture, in a highly fertilizer-dependent agricultural system.
# A Flexible FPGA-Based Stochastic Decoder for 5G LDPC Codes
## 6 Implementation Results and Discussion
### Results
#### 6.2.3 Processing Throughput  
The proposed architecture requires a reduction in hardware resources, although it performs with a considerably lower processing throughput expense. This lower throughput of stochastic decoders is due to the high number of decoding cycles (DCs) needed to reach a correct code word. Due to the high number of DCs and the partially parallel decoder architecture, the problem is made worse by the fact that each DC requires many clock cycles, which reduces decoding throughput. The SD architecture requires a maximum of 620 DCs per frame, and each decoding cycle necessitates 68 clock cycles for code length \(N=3808\). Table 5 compares both designs and concludes that the processing throughput of the SD design is about 38% lower than that of the min-sum design. Table 6 demonstrates that an increase in the code rate results in a concurrent increase in the processing throughput.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Standard** & **5G** & **5G** \\ \hline Code length & 3808 & 3808 \\ \hline Base code rate & \(1/3\) & \(1/3\) \\ \hline Sub-matrix size & 56 & 56 \\ \hline Implementation & Kintex-7 FPGA & Kintex-7 FPGA \\ \hline Decoding algorithm & Stochastic decoding & Min–Sum \\ \hline Scheduling & Column-layered & Row-layered \\ \hline No. of interconnects & 34,729 & 138,916 \\ \hline Intrinsic message width & 8-bit & 4-bit \\ \hline Extrinsic message width & 1-bit serial & 4-bit \\ \hline LUTs & 8278 & 12,962 \\ \hline Slice registers & 1767 & 2041 \\ \hline DCs or Itrs & \(\approx\)620 DCs & 15 \\ \hline Avg. throughput & \(\approx\)953 Mbps & 1.5 Gbps \\ \hline \(E_{b}/N_{o}\) at BER = \(10^{-6}\) & 2.65 dB & 2.57 dB \\ \hline \hline \end{tabular}
\end{table}
Table 5: FPGA implementation results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Standard** & **5G** & **5G** \\ \hline Code length & 3808 & 3808 \\ \hline Base code rate & \(1/3\) & \(1/3\) \\ \hline Sub-matrix size & 56 & 56 \\ \hline Implementation & Kintex-7 FPGA & Kintex-7 FPGA \\ \hline Decoding algorithm & Stochastic decoding & Min–Sum \\ \hline Scheduling & Column-layered & Row-layered \\ \hline No. of interconnects & 34,729 & 138,916 \\ \hline Intrinsic message width & 8-bit & 4-bit \\ \hline Extrinsic message width & 1-bit serial & 4-bit \\ \hline LUTs & 8278 & 12,962 \\ \hline Slice registers & 1767 & 2041 \\ \hline DCs or Itrs & \(\approx\)620 DCs & 15 \\ \hline Avg. throughput & \(\approx\)953 Mbps & 1.5 Gbps \\ \hline \(E_{b}/N_{o}\) at BER = \(10^{-6}\) & 2.65 dB & 2.57 dB \\ \hline \hline \end{tabular}
\end{table}
Table 5: FPGA implementation results.
## 3 Results and Discussion
### Discussion
#### 3.3.2 Statistic on Pseudorange Residuals  
This last sub-section no longer proposes a focus on a specific area but proposes a statistic on pseudorange residuals in order to assess the statistics of NLoS occurrence and impact in urban areas. The red line in the left-hand part of Figure 11 represents the probability density function (pdf) of the pseudorange residuals marked as LoS by the camera, and the red line in the right-hand part of Figure 11 represents the pdf of the pseudorange residuals marked as NLoS by the camera. All these residuals are computed from the EKF PVT. Both pdfs represented by red lines in Figure 11 are obtained by fitting the corresponding histograms represented in blue in Figure 11. In clear sky conditions and thermal noise only, the pseudorange residuals' distribution should be Gaussian. The pdf in the left-hand part of Figure 11 looks Gaussian or at least symmetrical, tending to indicate that for those data, the MC processor was able to remove the multipath components. However, the distribution on the right-hand part is clearly asymmetric.  
The residuals larger than 8.6 m (3\(\sigma\) of the \"clean\" distribution, represented by a vertical line on the left panel in Figure 11) are considered to be due to NLoS conditions. They represent around 2% of the whole distribution. Moreover, 70% of these residuals are marked as NLoS by the camera, and 100% of the residuals greater than 29 m are marked as NLoS by the camera, which represents 0.13% of the measurements.  
Areas for improvement may exist both regarding the image processing and the GNSS processing:  
* Propose a criterion or a set of criteria in order to decide which data to exclude or to overweight in case the camera flags too many data as NLoS;
* Detect small and close \"obstacles\" (such as lamp poles) in order not to mark the related area as NLoS;
* Improve classification of vegetation;
* Feedback loop and additional criteria for adaptively tuning the image processing parameters regarding the threshold used on the probability map.  
Figure 11: A pdf of the “clean” (**left**) and of the “excluded/overweighted” (**right**) pseudorange residuals for the whole track. The pdfs are represented with red lines and are obtained by fitting the corresponding histograms represented in blue.
## 5 Results and Discussions  
In this section, the energy, exergy, and economic performances of the proposed sCO\({}_{2}\)/LiBr-H\({}_{2}\)O AHP system are analyzed. Performance comparisons are conducted between the sCO\({}_{2}\)/LiBr-H\({}_{2}\)O AHP system with a single sCO\({}_{2}\) system, a sCO\({}_{2}\)/DH system, and a sCO\({}_{2}\)/ammonia-water AHP system. Parametric studies are carried out to find out the influences of some key parameters on the system performance indicators, including _COPP_, _COHP_, \(\eta_{ex}\), and _ctotal_, as defined in Section 3.3. Then, the particle swarm optimization (PSO) algorithm is adopted to obtain the system optimal operation conditions. The main input parameters and assumptions are listed in Table 9. By using these input conditions, the parameters of system state points are calculated and summarized in Tables 10-12 for the sCO\({}_{2}\)/LiBr-H\({}_{2}\)O AHP system, sCO\({}_{2}\)/ammonia-water AHP system and sCO\({}_{2}\)/DH system, respectively. The following Table 9 shows the main input conditions for the simulation. Table 10 shows the thermodynamic properties and costs of exergy streams for the sCO\({}_{2}\)/LiBr-H\({}_{2}\)O AHP system. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Present Results** & **Published Results** \\ \hline \(y_{A2}\) & 0.481 & 0.481 \\ \(y_{A7}\) & 0.335 & 0.335 \\ \(q_{A4}\) & 0.023 & 0.024 \\ circulation ratio & 4.554 & 4.555 \\ COHP & 1.615 & 1.612 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison between the simulated results and the published data for ammonia-water based AHP. \begin{table}
\begin{tabular}{c c} \hline \hline
**Items** & **Values** \\ \hline \(T_{0}\) (\({}^{\circ}\)C) & 25 \\ \(P_{0}\) (MPa) & 0.101325 \\ \(Q_{\text{core}}\) (MW) & 600 1  \\ \(T_{\text{core}}\) (\({}^{\circ}\)C) & 800 1  \\ \(T_{1}\) (\({}^{\circ}\)C) & 35 1  \\ \(P_{1}\) (MPa) & 7.4 1  \\ \(\eta_{\text{mcom}}\) \& \(\eta_{\text{recom}}\) & 0.85 1  \\ \(\eta_{\text{lur}}\) & 0.86 1  \\ \(\varepsilon_{\text{HTR}}\) \& \(\varepsilon_{\text{LTR}}\) & 0.86 1  \\ \(\Delta T_{\text{DHE,end}}\) (\({}^{\circ}\)C) & 5 1  \\ \(T_{\text{H6}}\) (\({}^{\circ}\)C) & 60 1  \\ \(T_{\text{H1}}\) (\({}^{\circ}\)C) & 45 1  \\ \(P_{\text{u}set}\) (MPa) & 1.0 1  \\ Fuel cost (\$/MWh) & 7.4 1  \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The main input conditions for the simulation.
###### Abstract  
The development of integrated, compact, and multifunctional photonic circuits is crucial in increasing the capacity of all-optical signal processing for communications, data management, and microsystems. Plasmonics brings compactness to numerous photonic functions, but its integration into circuits is not straightforward due to insertion losses and poor mode matching. The purpose of this article is to detail the integration strategies of plasmonic structures on dielectric waveguides, and to show through some examples the variety and the application prospect of integrated plasmonic functions.  
plasmonics; photonic integrated circuits; surface plasmon; nanotweezers; couplers; strong coupling; magneto-plasmonics; nanoantennas +
Footnote †: journal: _Sci._
# Review of Image Forensic Techniques Based on Deep Learning
## 2 Image Forensic Techniques  
### Passive Forensics  
Passive image forgery detection techniques can be classified as conventional manual feature-based and deep learning-based. The conventional detection algorithms for copy-move forgery detection (CMFD) are: discrete cosine transform (DCT) [14], discrete wavelet transform (DWT) [15], polar complex exponential transform (PCET) [16], scale-invariant feature transform (SIFT) [17], speeded up robust feature (SURF) [18], etc. The conventional detection algorithms for splicing tamper detection are: inconsistency detection by color filter array (CFA) interpolation [19], and inconsistency detection by noise features [20]. However, these conventional detection methods have the drawbacks of low generalization, poor robustness, and low detection accuracy. Deep learning-based detection algorithms,which take advantage of autonomous learning features, solve the above problems of conventional algorithms. In this survey, we mainly review forgery detection algorithms based on deep learning.
# Experimental Evaluation of Thermal Performance and Durability of Thermally-Enhanced Concretes
## 2 Materials and Methods
### Test Methods
#### 2.2.1 Thermal Properties
While thermal conductivity represents the heat transfer through a specific type of material with unit area and has a unit temperature gradient regardless of the thickness of the material, the thermal transmission coefficient, called the U-value, is the heat loss through a given thickness of a material and a relative simple way to estimate energy efficiency of each material. Since thermal conductivity only considers conduction and does not include convection or radiation, this cannot reflect real heat movement or losses in buildings and houses. To exactly estimate heat loss, taking account radiation and convection, thermal transmission should be calculated and defined as the insulation management criteria within building regulations in Korea [28]. There are several available methods to measure the thermal transmission of composite building components with variable thermal conductivities in the steady-state condition: the most well-known testing method is the guarded hot box or calibrated hot box method (CHB), consisting of cold and hot chambers and a specimen mounting panel [18; 19]. Thus, in order to evaluate a realistic thermal performance of building components (such as a wall), the calibrated hot box method (CHB) was used in this study. Figure 1 shows a schematic plan of the calibrated hot box used in this study. The temperature difference between cold and hot boxes across a specimen is established as 20 \({}^{\circ}\)C and has been maintained until the constant heat balance is reached in the specimen. Figure 1: Schematic view of calibrated hot box. In this study, both QTM and GHP were implemented to gather thermal conductivity values, while CHB was conducted to measure the thermal transmission values. The surface of the concrete brick specimen was polished before applying QTM. The measurements by QTM were averaged at four different locations on the surface at ambient temperature. For GHP [15], all specimens were oven-dried in a ventilated oven at 100 \({}^{\circ}\)C until the change of mass was less than 0.2% to minimize moisture effects in the concrete. A large-scale specimen with 1500 \(\times\) 1500 \(\times\) 220 mm was installed in the calibrated hot box apparatus, which is presented in Figure 2, in accordance of ASTM C1363 and ISO 8990 [18; 19]. The temperature of the hot and cold boxes was set at 20 and 0 \({}^{\circ}\)C, respectively. This testing continued to maintain a constant heat flow across a specimen and measured the heat flow and surface temperatures of both sides of it.
## 3 Validation
### Significant Wave Height Validation  
Time series of significant wave height observations (black solid dots) and significant wave height simulations (red solid lines) in the Taiwan Strait during the passage of typhoons Soudelor and Dujuan are shown in Figures 6 and 7, respectively.  
Figure 6: Time series of observed wave height (solid dots) and simulated wave height (red solid lines) in the Taiwan Strait during the passage of Typhoon Soudelor. (**a**) Buoy No.2, (**b**) Buoy No.3, (**c**) Buoy No.4, (**d**) Buoy No.5, (**e**) Buoy Gulei, (**f**) Buoy Douweigang, (**g**) Buoy Huangqi, (**h**) Buoy Beishuang.  
This study used the typhoon wind fields without consideration of the background wind, which means the wind fields were not very accurate when the typhoons were distant. The wave simulations for the periods when the typhoons were distant and after the second landfall were not very accurate. The simulation of significant wave height in the northern part of the Taiwan Strait (No. 5) is  
Figure 7: Time series of observed wave height (solid dots) and simulated wave height (red solid lines) in the Taiwan Strait during the passage of typhoons Dujuan. (**a**) Buoy No.2, (**b**) Buoy No.3, (**c**) Buoy No.4, (**d**) Buoy No.5, (**e**) Buoy Gulei, (**f**) Buoy Douweigang, (**g**) Buoy Huangqi, (**h**) Buoy Beishuang.  
larger, while the simulation of significant wave heights in the central part of the Taiwan Strait is more accurate. The maximum significant wave height in the Taiwan Strait was at buoy No. 2, i.e., up to 6.7 m. During 28-29 September, the measured significant wave height at the buoys in the middle of the Taiwan Strait had an obvious peak. The measured maximum significant wave height at the buoys offshore of Fujian Province was at Huangqi buoy, i.e., 3.2 m. The measured significant wave height of the offshore buoys has obvious tidal periodic oscillation, and the simulation of the significant wave height under the influence of the tidal current and water level is closer to the measured one, exhibiting obvious tidal periodic oscillation.
## Appendix A Ground-Based Direct Validation per Biome Type  
* C3S PROBA-V SA v1.0 Figure 17: Direct validation per biome type (**a**) Forest, (**b**) Crops, (**c**) grass/shrublands, and (**d**) desert) of the MCD43A3 C6 blue-sky albedo best quality pixels versus the ground measurements (RMs) at 20 GBOV stations during the 2014–2018 period. The continuous black lines correspond to the 1:1 lines and the dashed lines correspond to the optimal (GCOS uncertainty requirement), target (C3S KPI) and predefined threshold levels. The red lines correspond to the Major Axis Regression (MAR).
# Reliable Semantic Communication System Enabled by Knowledge Graph
## 2 Related Work
### Performance Metrics
#### 2.2.1 BLEU Score  
BLEU is currently the most commonly used metric in text evaluation [37]. It evaluates the similarity by counting the number of the same n-grams between transmitted and received texts, where n-gram means \(n\) consecutive words in the text. The formula can be expressed as  
\[\log\text{BLEU}=\min\biggl{(}1-\frac{l_{s}}{\bar{l}_{s}},0\biggr{)}+\sum_{n=1} ^{N}\omega_{n}\log p_{n} \tag{1}\]where \(s\) and \(\hat{s}\) denote the transmitted sentence and restored sentence, respectively. \(l_{s}\) and \(l_{\hat{s}}\) are the lengths of the transmitted sentences \(s\) and restored sentence \(\hat{s}\), respectively. \(\omega_{n}\) represents the weight of n-grams, and \(p_{n}\) denotes the precision of n-grams.