esa-sceva/satcom-chunk-collection huggingface dataset:
# Precoding for RIS-Assisted Multi-User MIMO-DQSM Transmission Systems
## 6 Results and Discussion
### BER Performance  
For simulations, all systems are using a normalized transmission energy per user, i.e., \(\mathbb{E}[\parallel\mathbf{t}_{\mathbf{x}}{}^{\text{H}}\mathbf{t}_{\mathbf{x}} \parallel]=K\), the same number of Tx/Rx antennas, and the same SE. Also, all systems are using similar precoding strategies and the optimal ML detection criterion alike. In order to carry out fair comparisons, all systems are compared considering MUI. The channel uses a spatial correlation coefficient of \(\rho=0.7\) in the BS and the RIS/relays. Tables 4 and 5 show the configuration and the QAM constellation utilized for SE = 8 bpcu/user and SE = 12 bpcu/user, respectively. Note that systems using spatial modulation require lower-order QAM constellations, while the conventional system requires higher-order QAM constellations to achieve the desired SE. All simulations were carried out using MATLAB SA 2015(r).  
As shown in Figures 8 and 9, when the correlated fading channel is considered, the RIS-MU-DQSM-II and the AF-MU-DQSM schemes are affected by 7 dB to 15 dB approximately. Under this scenario, the conventional MU-MIMO-Smux system is affected only by 5 dB. This result can be explained by the reflecting mirrors or the relaying antennas affected by the spatial correlation. On the other hand, the proposed RIS-MU-DQSM-I was only affected by 2-3 dB. It means that the ZF technique is robust under the correlated fading scenario. Note that users who are free of MUI (marked as RIS-MU-DQSM-I\({}^{\intercal}\)) have a similar performance compared to the conventional MU-MIMO-Smux system.  
Figure 6: BER Performance comparison for a SE of 8 bpcu/user, L = 2, 4 users, and the uncorrelated fading channel.  
Figure 7: BER performance comparison for a SE of 12 bpcu/user, L = 4, 8 users, and the uncorrelated fading channel.  
BER performance that the other users under MUI. This fact shows the robustness of the BD technique used to avoid interference in the second hop. It is worth noting that when the proposed schemes are using a reduced number of mirrors in the RIS they have similar BER performance than the conventional MU-MIMO-SMux scheme. However, when an increased number of mirrors are used, the proposed RIS-MU-DQSM-II, clearly outperforms both schemes used as reference.  
Figure 8: BER performance comparison for a SE of 8 bpcu/user, L = 2, 4 users, and a correlation factor of \(\rho=0.7\).  
Figure 9: BER performance comparison for a SE of 12 bpcu/user, L = 4, 8 users, and a correlation factor of \(\rho=0.7\).
# Optimizing Routing Protocol Design for Long-Range Distributed Multi-Hop Networks
## 4 Load-Balancing Routing Optimization Scheme Design  
The load-balancing routing optimization strategy comprehensively considers communication delay, link reliability, node occupancy rate, and node load rate to plan transmission paths that meet the constraint conditions for different service data types in a LoRa distributed multi-hop network. The load-balancing routing optimization strategy is shown in Figure 6.  
Figure 5: MPR node selection at different network scales.  
The specific optimization strategy is as follows: Initially, the gateway broadcasts a routing request to obtain information such as the location of the end nodes, link information, end node occupancy rate, and end node load status within the network. Next, it obtains the service type of the end nodes and, at the same time, calculates the node weight matrix based on the end node location information to determine the service priority. Then, it determines the SF that meets the service type for end nodes of different service priorities under constraint conditions, and plasma the transmission path for each end node [23; 24]. Afterward, from a global optimum perspective, the node load balance degree and the average node load rate are minimized, and the link with the lowest load cost is selected as the path to reach the aggregation node. Finally, the routing optimization ends.
# Sensor Observation Service API for Providing Gridded Climate Data to Agricultural Applications
## 2 Basic Concepts and Related Information
### Data Source  
NARO developed a daily Agro-meteorological Grid Square Data system (AmGSD) [1]. AmGSD seamlessly combines three different data produced by the Japan Meteorological Agency (JMA), including data acquires from the Automated Meteorological Data Acquisition System (AMeDAS), nine-day numerical weather prediction and daily climatic normal value [6]. AmGSD provides meteorological dataset at approximately 1-km resolution from 1980 to the present, and new weather data are updated every day. The data are available for the whole of Japan in six areas, as shown in Figure 1.  
Each grid contains nine daily meteorological variables, which are average air temperature, maximum air temperature, minimum air temperature, average relative humidity, sunshine duration, global solar radiation, downward long-wave radiation, precipitation and average wind speed. AmGSD uses Japanese third order mesh code that has an eight-digit integer defined by Japanese Industrial Standard JIS X0410. Land use information for each grid is provided by the National Land Information Division, MILT of Japan [7].  
The data are encoded in Network Common Data Form (NetCDF). Each file contains one year of daily data of a specific meteorological variable. The file is separately stored in the \"Area/Year/Item\" directory structure on their server. For example, maximum air temperature of Area 1 in the year 1980 is kept in /Area1/1980/AMD_Area1_TMP_max.nc. Metadata are recorded separately from data. For example, land use information is kept in the GEODATA NetCDF file in a separate directory.  
Due to the complicated hierarchical structure and internal defined structure, it is not convenient for ordinary scientists and programmers themselves to access AmGSD and convert the data into point-based climate data.  
NARO provides AMD_Tools for connecting and retrieving data from AmGSD. AMD_Tools is written in the Python programming language and available at NARO's homepage [1]. Even if AMD_Tools is provided, a user still needs an extra program for calling the AMD_Tools, as well as understanding the structure of the data and the coverage of each area.
## Nomenclature  
* _Symbols_ a parameter for the Gumbel function axis (\({}^{\circ}\)C) b parameter for the form of the Gumbel function (\({}^{\circ}\)C) C heat capacity (J\(\cdot\)K\({}^{-1}\)) \(c_{S}\) specific heat capacity when PCM is in the solid state (J\(\cdot\)kg\({}^{-1}\cdot\)K\({}^{-1}\)) \(c_{L}\) specific heat capacity when PCM is in the liquid state (J\(\cdot\)kg\({}^{-1}\cdot\)K\({}^{-1}\)) \(\mathbf{e}\) thickness (m) \(f_{liq}\) liquid fraction of the hybrid model \(f_{liq,bin}\) liquid fraction of the binary model \(f_{\mathrm{G}}\) Gumbel function \(f_{obj}\) objective function \(\mathrm{R_{c}}\) Thermal contact resistance h specific enthalpy (J\(\cdot\)kg\({}^{-1}\)) \(\mathrm{L_{A}}\) latent heat (J\(\cdot\)kg\({}^{-1}\)) \(\mathrm{m}\) mass (kg) n mesh number \(Q\) energy transferred (J) R resistance (m\({}^{2}\cdot\)K\(\cdot\)W\({}^{-1}\)) S surface area (m\({}^{2}\)) T temperature (\({}^{\circ}\)C) t time (s) \(U\) uncertainty _Greek symbols_ \(\rho\) density (kg.m\({}^{-3}\)) \(\phi\) heat flux (W) \(\psi\) latent heat distribution coefficients between the binary function and the Gumbel function \(\lambda\) thermal conductivity _Subscripts_ A melting of pure substance cal calculation exp experimentation left left side of the sample LP left plate M end of melting m during the melting phase mod acquisition module right side of the sample RP right plate s during the solidification phase
## 2 Methodology
### Spatial-Temporal Fusion and Calculation  
After the proposal of the temporal sequence methods TRC and RIA and the single-frame method GELCM, Spatial-Temporal Fusion Detection can be defined as:  
\[\text{STFD}(x)=(\text{GELCM}(x)+\text{RIA}(x))\times\text{imdilate}(\text{TRC}( x),\text{se}), \tag{10}\]  
where \(\text{imdilate}(\cdot)\) represents the dilation operation, and se denotes the structuring element used for dilation. This study observes that the GELCM and RIA demonstrate high precision in capturing the centroids of targets, while TRC performs less effectively in this aspect. To address this, we apply a dilation operation to TRC and multiply its result with the sum of the GELCM and RIA.  
Furthermore, TRC and the GELCM demonstrate strong detection capabilities in complex backgrounds, while RIA, which relies on energy accumulation, is comparatively less effective in such conditions. Although TRC is less sensitive to dim targets, the GELCM and RIA effectively address this limitation. Additionally, TRC and RIA primarily utilize temporal information within image sequences, whereas the GELCM focuses on spatial information. Together, these three methods enable robust detection of dim and small targets in diverse and challenging scenarios. By leveraging target energy accumulation and regional contrast, the three methods delineate temporal target profiles through gradient-based analysis. Crucially, TRC detects targets under varying background intensities while collaborating with RIA and the GELCM to suppress clutter via spatiotemporal synergy. Their pointwise multiplication further filters background interference, enabling STFD to enhance detection performance.  
In this study, the parameters DT, DB, and \(N\) are configured based on the target's motion speed, while \(l\) is determined by the target's size. For example, in scenarios where the target moves at a speed of 1-3 pixels per frame, DT is set to 3, DB to 6, and \(N\) to 7. Since infrared small targets typically have a small size, \(l\) can be directly set to a value between 1 and 3.
# [PERSON] on Korean Block Ciphers
## 4 Evaluation
### Korean Block Ciphers in Quantum Gates  
Korean block ciphers, such as CHAM, LEA, and HIGHT, are based on the ARX architecture. However, the primitive operations for each block cipher are totally different, in terms of word size and the number of operations. In Table 6, the quantum resource estimates for each block cipher, and the higher cost is shown in red and the lower cost is shown in green. The detailed comparison is as follows.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Block Cipher** & **Plaintext (bit)** & **Key (bit)** & **Qubits Used** & **Toffoli Gates** & **CNOT Gates** & **X Gates** \\ \hline \multirow{3}{*}{CHAM} & 64 & 128 & 196 & 2400 & 12,285 & 240 \\  & 128 & 128 & 268 & 4960 & 26,885 & 240 \\  & 128 & 256 & 396 & 5952 & 32,277 & 304 \\ \hline \multirow{3}{*}{HIGHT} & 64 & 128 & 201 & 6272 & 20,523 & 4 \\ \cline{2-6}  & 128 & 128 & 385 & 10,416 & 28,080 & 68 \\ \cline{1-1} \cline{2-6}  & 128 & 192 & 513 & 15,624 & 39,816 & 100 \\ \cline{1-1} \cline{2-6}  & 128 & 256 & 641 & 17,856 & 45,504 & 130 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison between Korean block ciphers in terms of quantum resources.  
Figure 14: Quantum circuit for SPECK-32/64.
# Resource Assignment Algorithms for Autonomous Mobile Robots with Task Offloading
## 5 Simulation Results and Discussion
### Variable Scenarios
The fixed parameter values are \(l_{m}=33\,\text{ms}\), \(\gamma_{m}=1/2\) CPU, \(\ell_{i}=111\,\text{ms}\), while the bit rates produced by the AVs are random samples from a Gaussian distribution with mean \(25\,\text{Mbit/s}\), standard deviation \(7\,\text{Mbit/s}\), and bounded in the \([15,35]\) Mbit/s interval (samples outside the interval are assigned the value of the nearest limit). The UL access rates are uniformly distributed in \([0,120]\) Mbit/s, and the wired delays are uniformly distributed in \([5,150]\) ms. BS connections are considered absent if the UL bit rate is lower than \(25\,\text{Mbit/s}\), and DC connections are not kept if the delay is higher than \(60\,\text{ms}\). Even in this case, 100 random configurations are generated, and the results are averaged. With these parameters, we obtain, on average, \(\tilde{N}_{\text{BS}}=53.9\) and \(\tilde{N}_{\text{DC}}=12.8\). Figure 16 shows the variation in the average total latency as the number of calls increases up to \(N_{\text{call}}=10^{6}\) for the GA and SM algorithms. Initially, SM underperforms with respect to GA, up until \(10^{4}\) calls; after this point, SM improves the KPI and achieves its minimum before \(2\times 10^{4}\) calls. GA, on the other hand, achieves a slightly lower KPI after this point, but it needs almost two orders of magnitude more calls. Figure 16: Scenario _40/60/20_variable_: achieved KPI as the number of calls varies. Figure 15: Scenario _10/25/5_variable_: (**a**) Average number of calls. (**b**) Running time.
# Monitoring of Liquid Viscosity for Viscous Dampers through a Wireless Impedance Measurement System
## 4 Experimental Results
### Viscous Damper Viscosity Index (VI) Based on RMSD  
From the above results, it can be seen that the damping fluid viscosity can be monitored qualitatively by the impedance signal. In order to quantitatively represent the relationship between viscosity and the real impedance, this paper proposes a definition of the damping fluid viscosity damper viscosity index (VI) based on the root mean square error (RMSD).  
\[\text{RMSD}=\sqrt{\frac{\sum\limits_{i=1}^{n}\left(Z_{1}(\omega_{i})-Z_{0}( \omega_{i})\right)^{2}}{\sum\limits_{i=1}^{n}\left(Z_{0}(\omega_{i})\right)^{2}}} \tag{3}\]  
In Equation (3), n denotes the frequency points in the selected sweep range; \(i\) is the \(i\)-th frequency point in the sweep range; \(Z_{0}(\omega_{i})\) is the impedance in the initial state of the structure at the \(i\)-th frequency point; \(Z_{1}(\omega_{i})\) is the impedance after the lossy viscosity change of the structure at the i-th frequency point. The results of RMSD calculation are listed in Table 2.  
\[\text{VI}=\frac{\text{RMSD}^{\text{E}}-\text{RMSD}^{\text{L}}}{\text{RMSD}^{ \text{H}}-\text{RMSD}^{\text{L}}} \tag{4}\]  
In Equation (4), \(\text{RMSD}^{\text{H}}\) is the RMSD at 5,000,000 cSt and \(\text{RMSD}^{\text{L}}\) is the RMSD at 5000 cSt. \(\text{RMSD}^{\text{E}}\) is the RMSD at other measured damping fluid viscosities.  
From Figure 14, it can be seen that the viscosity monitoring index is 1 when the viscosity of the damping fluid is 5,000,000 cSt. As the viscosity of the damping fluid  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Viscosity of** & \multirow{2}{*}{**RMSD**} & \multicolumn{2}{c}{**Viscosity of**} \\
**Damping Fluid (cSt)** & & **Damping Fluid (cSt)** & **RMSD** \\ \hline
5000 & 0.0958 & 200,000 & 0.0412 \\
10,000 & 0.0814 & 500,000 & 0.0307 \\
20,000 & 0.0709 & 1,000,000 & 0.0201 \\
50,000 & 0.0584 & 2,000,000 & 0.0134 \\
100,000 & 0.0498 & 5,000,000 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Calculation results of RMSD for different damping fluids.  
Figure 13: Impedance real part resistance of damping fluid with different viscosity.  
decreases, the VI decreases, and reaches 0 when the viscosity of the damping fluid is 5000 cSt. Figure 15 shows the experimental results of three replicate tests. The experimental results show that when the viscosity of the damping fluid changes, the test results of the wireless impedance measurement system are relatively stable. The VI based on the RMSD proposed in this paper can well judge the change of the fluid viscosity. This verifies the reliability and stability of the VI. Thus, the RMSD-based VI can track changes in the damping fluid viscosity.  
Figure 14: Viscosity index of different viscosity damping fluids.  
Figure 15: Normalized viscosity monitoring index for three replicated experiments.
## 3 Results and Discussion
### AlBA/TA Hydrogel Thread as a Tissue Suturing Application  
Sutures exert a pressure on the tissue at the site of application. This can lead to a reduced recovery and inflammation [26; 27]. In contrast, hydrogels swell by means of body moisture and exhibit elasticity (Figure 4a). To verify this, AlBA/TA hydrogel threads were used to suture the skin of rats during testing to confirm whether the suture works efficiently and whether the skin was patched even after swelling. First, the dorsal skin of a rat was sutured seven times, after which PBS (1\(\times\)) was sprayed to swell the thread for 10 min. Even after full swelling of the threads, no mechanical cracks in the swollen hydrogels were observed, and the tissue was well sealed (Figure 4b). In addition, to measure the fixing strength of the tissue, the maximum force value without breaking was determined for two edges of rat skin sutured with two stitches using a two-layer thread and was found to be 0.5 \(\pm\) 0.1 N (Figure 4c). In addition, we checked whether the thread showed adhesion to the tissue when in a swollen state (hydrogel). To achieve this, threads were arranged between the two pieces of rat skin and after applying slight pressure a 20 mm plate, it was lifted at a speed of 5 mm/min, and the adherence between the fixed tissue parts was observed (Figure 4d). Therefore, it was concluded that the AlBA/TA hydrogel thread could fulfill a useful role in tissue suturing. For further discussion, the polysaccharide thread-based suturing can face challenges in sealing a variety of tissues, ranging from extremely soft liver tissues to highly elastic cardiac/vascular tissues against uncontrolled hemorrhage. To completely replace existing threads composed of biodegradable poly (lactic-co-glycolic acid), the mechanical toughness of the polysaccharide hydrogel fibers should be enhanced.
# Additive Manufacturing of Bone Scaffolds Using PolyJet and Stereolithography Techniques
## 2 Materials and Methods
### Permeability Analysis of 3D-Printed Scaffold Structures  
#### 2.2.1 Surface Energy Calculation  
The surface energy and contact angle determine the hydrophobic properties of a material. Higher hydrophobicity for a scaffold material surface would result in increased permeability. The experimentally measured permeability could be affected by higher contact angles resulting from the surface energy of the material used in the development  
Figure 1: The 3D CAD models of the scaffold structures with hexagonal (H) and cubic (C) pore geometries: (**a**) isometric view and (**c**) front view of scaffold structure with hexagonal pore shape; (**e**) optical microscopic image of a 3D-printed scaffold with hexagonal pore shape; (**b**) isometric view and (**d**) front view of scaffold structure with cubic pore shape; (**f**) optical microscopic image of a 3D-printed scaffold pore of scaffold structure with cubic pore shape.  
of 3D scaffold structures. In order to analyze the effects of the contact angle and surface energy of the printed scaffolds on the fluid permeability through the pores, an FTA 200 dynamic contact angle analyzer was used to determine the contact angle using tap water, 15% glycerol water solution, and 20% glycerol water solution as test liquids.  
The sessile drop method was used to measure the contact angle. For this purpose, 5 mm high scaffold structures were modeled and printed using the Polylet and uSLA printing techniques. The height of all scaffold structures shown in Figures 1 and 2 was 15 mm. The maximum working height of the FTA 200 dynamic contact angle analyzer was also 15 mm; therefore, for the complete formation of hanging drop and correct angle measurements, separate scaffold structures measuring 5 mm in height were printed, while keeping the rest of the dimensions the same. The scaffold structures were placed on a horizontal stage (in built direction) and 3 uL of the probe liquid was dropped on the surface of the scaffold using a microsyringe with a flow rate of 1 uL/s. Each reported value reflects an average of at least three measurements at three different positions on the 3D-printed sample surface. The measured contact angle was used for approximate surface energy values, along with the surface tension, polar, and dispersive components of each test liquids. Different models have been used for surface energy analysis, such as the Zisman critical wetting tension method, [PERSON] method, [PERSON] method, [PERSON] geometric mean method, [PERSON] harmonic mean method, and [PERSON] acid-base theory [37]. Each model has certain limitations in terms of the angle limits and the number of test liquids used. In this study, the Zisman critical wetting tension model was used, which requires two test liquids and provides good estimations of the surface energy [37].
## 3 Results and Analysis
### Long-Term Trends for Forest Ecosystem Status from 2010 to 2020
#### 3.2.2 Long-Term Variation Trend in EQI (KEQI) from 2010 to 2020  
Figure 7 shows the spatial distribution of KEQI and a statistical histogram of KEQI for each country over the forest ecosystem in the Lancang-Mekong region from 2010 to 2020. The overall percentage increase for five countries in the Lancang-Mekong region was 48.95%, the percentage decrease was 45.84% and the percentage unchanged was 5.21%. The  
Figure 6: Spatial distribution of long-term variation trends for vegetation evaluation indicators in the Lancang–Mekong region from 2010 to 2020. (**a**) Differences in annual average LAI; (**b**) differences in annual maximum FVC, (**c**) differences in annual cumulative GPP (gC/m\({}^{2}\)/yr), and (**d**) summary of statistics for three vegetation evaluation indicators in five countries. The color classification of red, yellow, cyan, and blue is based on each indicator in (**a–c**), respectively.  
areas located in western Myanmar, central Thailand, and northeastern Vietnam showed an improvement trend in KEQI, suggesting positive developments for the health of the forest ecosystem. However, Cambodia demonstrated a significantly deteriorated trend in KEQI, indicating a decline in the overall health and quality of the forest ecosystem (Figure 7a). Among them, the forest health of Cambodia significantly deteriorated from 2010 to 2020, with the highest proportion of decreased areas at 60.34%, followed by Laos at 52.98%, Myanmar at 44.14%, Thailand at 36.24%, and Vietnam at 31.22% (Figure 7b). The proportion of areas in which forest health improved in Vietnam, Thailand, and Myanmar (61.78%, 55.67%, and 47.98%) was greater than that of the areas in which it decreased (33.63%, 39.08%, and 47.02%).  
Focusing on the significant deterioration in ecosystem quality in Cambodia from 2010 to 2020 (Figure 7), the regional deforestation in Cambodia was monitored using Chinese Gaofen no. 1 satellite (GF-1) images before 2015 and 2020 (Figure 8). Previous research has suggested that this deterioration is likely due to the deforestation of areas with high forest coverage [35; 36; 37]. Figure 8a,b illustrates the regional deforestation (central point located in 104.206\({}^{\circ}\) E, 14.003\({}^{\circ}\) N) caused by urbanization in Khoueng Oddar Meanchey, a province in northwestern Cambodia and Figure 8c,d illustrates the regional deforestation (central point located in 104.672\({}^{\circ}\) E, 12.676\({}^{\circ}\) N) in Khoueng Kampong Thom, a province in the central part of Cambodia. In Figure 8a,c, the forest is distributed densely in these areas; however, it had been replaced by cities, farmland, and bare land by the year 2020 or 2021 in Figure 8b,d.