esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this study, we analyzed the LUCs and the dynamic variations in ESV over the past 20 years in the study area. The findings indicate that built-up land has continuously expanded, while the areas of farmland, forest land, and grassland have steadily decreased. Overall, the ESV in the study area showed a declining trend, decreasing to CNY 359.295 billion in 2020, a reduction of CNY 23.79 billion compared to 2000. The spatial distribution of ESV exhibited a clear east-high, west-low pattern, with high-value areas predominantly located in regions around Tai Lake and surrounding forests, while low-value areas were mainly found in the western regions, consisting of farmland and built-up land. Notably, areas with moderate and high ESV in the eastern part have gradually been replaced by areas of lower ESV, reflecting the impact of urbanization on the ecological environment.  
The Moran's I index showed a negative correlation between land use and ESV, particularly in the low-to-high transition patterns in arable land and forest areas and the high-to-low transition in built-up and water areas. Scenario predictions based on different future development paths showed that under the NDS scenario, the areas of farmland, forest land, and grassland generally decreased, whereas the EPS scenario indicated an increase in these areas. The FPS scenario predicted the largest increase in farmland area,while the CDS scenario aligned well with the relevant policy requirements, particularly emphasizing the protection of farmland and ecological land.  
In conclusion, the results of this study highlight the interplay between urbanization and ecological protection, underscoring the importance of rational land use planning and policy interventions. These findings provide crucial insights for future regional land resource management and the protection of ecosystem service values, especially in guiding sustainable urban development and ecological security.  
Conceptualization, S.H., Q.M. and H.J.; data curation, S.H., Q.M. and H.J.; funding acquisition, S.H.; methodology, Q.M.; software, S.H.; supervision, W.W.; validation, S.Z.; visualization, X.L.; writing--original draft, Q.M.; writing--review and editing, S.H., H.J. and Q.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Scientific and Technological Research Project of Henan Province (Grant No. 232102210043).  
Not applicable.  
The original findings of the research are encompassed within the paper.  
The contributors have no conflicts of interest to disclose.
# Effect of Primary Spruce Lamella Aging on the Bending Characteristics of Glulam Beams
###### Abstract  
Glued laminated (glulam) beams are used in the roofs, ceilings and walls of buildings as well as in bridges and towers. At present, with the limitation of tree harvesting, the production of glulam beams from recycled wood sources is implemented with the proviso that their mechanical properties and resistance to pests, fire and weathering will not be aggravated. This work deals with the primary effect of aging Norway spruce wood (_Pica abies_ Karst. L.) lamellas on the moduli of rupture (MOR) and elasticity (MOE) in bending of three-layer glulam beams composed of sound and aged lamellas and polyurethane (PUR) glue. Three methods of lamella aging were used: (A) natural, lasting 60 years in the form of roof trusses with a greater or lesser degree of bio-attack by woodworm (_Anobium punctatum_ De Geer); (B) artificial, caused by increased temperatures from 160 to 220 \({}^{\circ}\)C for 4 h; (C) artificial, caused by 2% water solutions of inorganic preservatives, namely, CuSO\({}_{4}\times\) SH\({}_{2}\)O, ZnCl\({}_{2}\), H\({}_{3}\)BO\({}_{3}\) or (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\), for 28 days. The lowest MOR values were determined for glulam beams in which all three lamellas or two surface lamellas had a greater degree of bio-attack (60.5 MPa, a decrease of 25.9%) or were exposed to primary aging at 220 \({}^{\circ}\)C (62.6 MPa, a decrease of 23.3%). On the contrary, the exposure of lamellas to 160 or 180 \({}^{\circ}\)C did not significantly influence the MOR of beams (76.0-82.7 MPa, an average decrease of 1.6%). The MOE of glulam beams ranged from 7540 to 10,432 MPa without an obvious influence of the method of lamella aging or their location in the beams. Linear correlations between the MOR or MOE of glulam beams and the shear strength (\(\sigma\)) of gluel joints, if both composite types consisted of similarly aged lamellas, were only slightly significant or insignificant.  
Keywords:Norway spruce wood; lamellas; natural and artificial aging; polyurethane glue; glulam; modulus of rupture; modulus of elasticity; shear strength +
Footnote †: journal: Applied Sciences
# Aerospace
## 10 Technology Development and Mission Infusion  
Several key technologies enabling ADEPT drag-modulation aerocapture have already been tested in flight and ground facilities. The SR-1 sounding rocket flight test demonstrated successful exo-atmospheric deployment of ADEPT from its stowed configuration and aerodynamic stability without active control through Mach 0.8 [34]. The 3D-woven carbon fabric, which serves as the ADEPT thermal protection system, and the structural surface have been tested in arc-jet facilities at over 200 W/cm\({}^{2}\) and 8 kPa [54]. The dynamics of the drag skirt separation event have been studied using both CFD simulations and experimental tests [55]. The experimental ballistic range tests demonstrated clean separation of the drag skirt from the center body for a scaled solid flight model in hypersonic free flight [56]. A future sounding rocket experiment similar to the SR-1 test can potentially demonstrate the ADEPT drag skirt separation in flight at supersonic speed. An end-to-end low-cost technology demonstration of the ADEPT drag-modulation aerocapture system can be performed at Earth using a Geostationary Transfer Orbit (GTO) rideshare mission [39]. The next step would be to add a single satellite as a secondary payload to a Mars/Venus mission and demonstrate aerocapture at a planetary target. The successful demonstration of SmallSat aerocapture at Mars/Venus will establish it as a proven technology and eventually pave the way for large Flagship-class aerocapture missions to Uranus and Neptune [57; 58; 59; 60].
When combined with DSSS with a spreading gain of 4, the bit error ratio (BER) performance is nearly identical to interference-free scenarios, highlighting the robustness of the proposed method. These findings confirm that the proposed technique is a practical and effective solution for NBI cancellation in SC-FDE systems. Furthermore, the results suggest that the proposed technique can be effectively applied to mobile and satellite communication systems, where robust interference cancellation is critical for maintaining receiver sensitivity in challenging environments. Its ability to minimize distortion of the transmitted signal makes it particularly advantageous for communication scenarios with high sensitivity requirements. The structure of this paper is as follows. Section 2 explains the SC-FDE communication system model. Section 3 presents the interference cancellation method using FFT nulling, and Section 4 describes the proposed fine frequency shift technique. In Section 5, the simulation results are shown, and the conclusions are drawn in Section 6.
## Materials and Methods
### Surface Water Storage (SWS) Estimation
#### 2.4.2 WSE Observed from Jason-2, Sentinel-3 A/B and Cryosat-2  
The rapid development of satellite radar altimetry technology provides an additional means for monitoring lake/reservoir changes. Jason-2 [55] is a continuation of the TOPEX/Poseidon [56] and Jason-1 missions [33], which was launched in June 2008. Jason-2 is a high-precision radar altimeter that operates in the Ku-band (13.6 GHz) and C-band (5.3 GHz) frequencies with a repeat cycle of 10 days, resulting in a ground footprint ranging from 2 to 4 km, depending on the surface roughness.  
CryoSat-2, launched by the European Space Agency in 2010, is equipped with a state-of-the-art synthetic aperture radar altimeter [38; 57], with a ground footprint of only about 300 m in the orbital direction and an orbital ground spacing of approximately 7.5 km. CryoSat-2 operates in a drift orbit mode, allowing it to monitor a greater number of inland water bodies, such as rivers, lakes, and reservoirs.  
Sentinel-3 mission is not only equipped with a SAR altimeter but the dual-satellite constellation (Sentinel-3A and 3B) also greatly improves the spatial coverage, allowing for a balance of both spatial and temporal resolutions, making it highly beneficial for monitoring inland water bodies [58; 59]. Additionally, the Open-Loop Tracking Command (OLTC) [60; 61] equipped on Sentinel-3 drives is less affected by topographical effects compared to CryoSat-2 and Jason-2 and holds significant promise for the reliable observation of inland water bodies, particularly medium-sized (approximately 300 m wide) or smaller rivers.  
The WSE time series in the study was constructed using data from three different satellite missions, Jason-2 (2010-2016), Sentinel-3 (2016-2019), and Cryosat-2 (2010-2019). Note that system errors between different altimetry tasks must be eliminated when constructing a WSE time series. Detailed data processing was described by [61; 62; 63].
## 1 Introduction
In order to deal with incomplete resource aggregation, a multi-layer optimization model of energy trading strategy for VPPs is constructed based on hybrid game theory among SES, VPP, PVP alliance, and the outer power grid, including the master-slave game model of SES and VPP, the master-slave game model of VPP and PVP alliance, and the cooperative game model embedded in the PVP alliance. In our proposed energy trading strategy for VPPs, the decision variables include the electricity purchase and sale price between SES and VPP, the electricity purchase and sale price between VPP and PVP alliance, and the purchased heat and the electricity transaction quantity among SES, VPP, PVP alliance, and the outer power grid. 2. The abovementioned energy trading strategy for VPP with incomplete resource aggregation includes many market entities within cooperative games. It cannot be directly solved by traditional optimization algorithms because the proposed mathematical model belongs to the nonconvex nonlinear model. In this paper, novel hybrid game theory-based optimization algorithms have also been proposed and discussed to obtain the optimal energy trading strategy. KKT conditions were used to solve the master-slave game model between VPP and PVP alliance, Dichotomy was used to solve the master-slave game model between SES and VPP, and ADMM was used to solve the cooperative game among members in the PVP alliance. The remainder of this work is organized as follows. Section 2 presents the whole system configuration with incomplete resource aggregation, market rules, and the general problem for the optimal energy trading strategy of VPP. Section 3 develops the novel hybrid game theory-based optimization for the energy trading strategy of VPP, followed by the detailed multi-level game model for our proposed energy trading strategy, and the corresponding whole-solving process using KKT, dichotomy, and ADMM algorithms. Results are presented and discussed in Section 4 to show the effectiveness of the proposed approach. Finally, the concluding remarks are given in Section 5.
## 4 Performance Evaluation and Analysis
### Simulation Setup
Next is the traditional energy-saving routing (GreenSR) algorithm, which conserves energy by optimizing topology and considering satellite battery levels. It initializes a subgraph containing links with non-zero demand, calculating the shortest paths and costs. For nodes outside the subgraph, it determines their activation status based on cost impact, forming an energy-efficient topology that prioritizes nodes with higher battery levels. Finally, the simple genetic algorithm (SGA) is used to optimize the topology and select dormant satellites for energy conservation of satellite batteries by means of the conventional genetic algorithm. In contrast, the GreenRP algorithm not only considers the formation of energy-saving topologies but also introduces a link-level dormancy mechanism, differing from GreenSR's focus on satellite unit dormancy and GreenLP's emphasis on inter-satellite links. Additionally, GreenRP also considers both the network's lifespan and service quality, whereas GreenSR focuses only on the network's lifespan. The simulation results are analyzed in detail below.
## 4 Performance Analysis and Evaluation
### Energy Consumption analysis of IP-WSN  
For energy consumption analysis, we consider an IP-WSN with densely deployed IP sensing devices. The network consists of two types of IP sensing device: fully functional IP sensing devices (IP-FFD) and reduced functional IP sensing devices (IP-RFD). IP-FFD holds the complete 6 LoWPAN protocol stack and performs the routing functions. IP-RFD performs the sensing functions and forwards the data to the IP-FFD. We have followed the energy consumption model used in [2,24-27]. Table 2 represents the used parameter values in the energy consumption model. Here, E\({}^{\text{tx}}\) and E\({}^{\text{rx}}\) is the distance-independent amount of energy consumed by the transmitter and receiver electronics and the digital processing of each. Here \(\alpha\) and \(\beta\) are path loss exponent (\(2<\alpha<5\)) and a constant [J/bit m\({}^{2}\)], r is a transmission range. L\({}_{\text{ctrl}}\) is the length of control packets in bits, L\({}_{\text{E}}\) is the energy needed by the transmitter device to transmit or receive a packet, and T is the time period between two consecutive topological changes of the IP-WSN. \(nffd_{p}(d_{i})\) indicates the number of fully functional neighboring node for a path p and range d:  
\[E=E^{\alpha}+\beta*d^{\alpha}+E^{\alpha}\]  
Since same type of transmitting and receiving device is concerned in IP-WSN:  
\[E^{\alpha}=E^{\alpha}=E_{dec}\]  
\[E=2*E_{dec}+\beta*d^{\alpha}\]  
\[C_{i}^{ctrl}(r)=[L_{ctrl}*\beta*r^{\alpha}+(n_{i}(r)+1)*L_{ctrl}*L_{E}]\frac{1} {T}\]  
\[C_{i}^{\text{inf}}(p)=[\sum_{i=1,j=2}^{N}(nffd_{p}(d_{i})+1)*L_{data}*\beta d_{ i,j}^{\alpha}+(n_{p}(d_{i})+1)*L_{data}]*L_{E}\]  
\[C_{i}^{total}(p)=\sum_{i=1}^{N}[C_{i}^{ctrl}(r)]+C_{i}^{\text{inf}}(p)\]  
So energy consumed by MIPv6 scheme can be calculated by the following mathematical derivation. Here \(E_{i}^{mipv6}\) indicates energy consumption by the MIPv6 scheme:  
\[E_{i}^{mipv6}=SC_{i}^{mipv6}*C_{i}^{total}(p)\]  
\begin{table}
\begin{tabular}{l l} \hline
**Parameter** & **Value** \\ \hline
**No of IP-WSN Node (N)** & 25-120 \\
**Network Area (A)** & 120 \(\times\) 120 M \\
**Node density (p)** & 0.00173\(\times\)0.00833 \\
**Initial Energy** & 2 J \\ \hline
**Transmit/Receive electronics (L\({}_{\text{E}}\))** & 50 nJ bit\({}^{-1}\)m\({}^{2}\) \\
**Transmission Power** & 5.85 \(\times\) 10\({}^{-5}\) W \\
**Number of SMAG** & 1–10 \\
**Transmission range (r)** & 25 m \\
**Packet size** & 2 KB \\ \hline \end{tabular}
\end{table}
Table 2: Parameter values.  
Energy consumed by PMIPv6 scheme can be calculated by the following mathematical derivation. Here \(E_{i}^{pmipv6}\)indicates energy consumption by PMIPv6 scheme:  
\[E_{i}^{pmipv6}=SC_{i}^{pmipv6}*C_{i}^{total}(p)\]  
Finally energy consumed by SPMIPv6 scheme can be calculated in the same way. Here \(E_{i}^{pmipv6}\) indicates energy consumption by SPMIPv6 scheme:  
\[E_{i}^{pmipv6}=SC_{i}^{pmipv6}*C_{i}^{total}(p)\]
# A Review on Anaerobic Digestion of Lignocellulosic Wastes: Pretreatments and Operational Conditions
## 2 Overview of AD Process
### Solid-State (ss) and Wet-State Anaerobic Digestion
#### 2.4.1 Wet-State AD  
In wet digestion processes, the feedstock is slurried with the addition of water to the mixture in order to be within the desired level (TS less than 15%), and normally this can be achieved by adding water either by recirculation of the liquid effluent fraction or by codigestion with wetter wastes. The latter is more preferable, as it provides a chance to mix various waste streams, such as sewage sludge or manure and OFMSW [32; 33; 34; 35]. The less complicated mechanical equipment needed and the opportunity to reduce the inhibitory effect of some substances as they become diluted by water are considered advantages when applying this process. On the other hand, complicated pre-treatment, high consumption of water and energy for heating, and the reduction of working volume caused by the sedimentation of inert materials remain as obstacles in this process [36; 37; 38]. Continuous stirredtank reactors (CSTR) are generally used in wet digestion processes, in which mechanical mixers or a combination of mechanical mixing and biogas injection are employed [37].
# Application of Artificial Neural Networks in Assessing Mining Subsidence Risk
## 4 Analysis and Discussion
### Mine Subsidence Grade  
Analysis A is used to predict whether subsidence can occur for the given factors, but it always includes the possibility of incorrect prediction as well. Analysis B is necessary to back up the results of analysis A by assessing the MSR index to indicate the extent of risk for subsidence at the location in question.  
Finally, in this study, the grade of MSR was classified into the three categories, level 0 (L0, low risk), level 1 (L1, moderate risk), and level 2 (L2, high risk), by combining the results of analyses A and B, as indicated in Figure 16. In this diagram, the MSR index for deciding L0 or L1 is determined considering the average values of the MSR index presented in Figure 15.  
The subsidence risk grades determined by the diagram for the training data and the validation data are presented in Figure 17. For the training data of coal mines, only one of the 82 locations where subsidence actually occurred was predicted to be safe, which means that the correct prediction percentage for the training data is 98.8% (=81/82 subsidence locations). For the other types of mines, the correct prediction percentage was 98.5% (=66/67 subsidence locations). For the validation data,  
Figure 16: Subsidence risk grade diagram.  
Figure 15: Range of MSR index: (**a**) coal mines (**b**) other types of mines.  
the correct prediction percentage was 90% (=9/10 subsidence locations) and 100% (=7/7 subsidence locations) for the coal mines and the other types of mines, respectively.  
The overall procedure for deciding subsidence risk grade is summarized in Figure 18.
## 2 Theoretical Background
### Lightweight Cryptography for MQTT  
Lightweight cryptography has gained prominence as a practical solution for addressing the limitations of resource-constrained IoT devices, including limited processing power,  
Figure 1: MQTT protocol.  
restricted memory, and energy constraints. Lightweight cryptography refers to encryption techniques designed to minimize resource usage while maintaining acceptable levels of security [4]. Algorithms such as ChaCha20, Salsa20, SIMON, SPECK, and TinyAES are particularly well-suited for edge environments due to their reduced key scheduling complexity, smaller code footprint, and lower computational requirements. These schemes often trade off marginal cryptographic strength or feature sets in exchange for practical performance on constrained platforms.  
Authenticated encryption schemes [4], particularly AEAD (Authenticated Encryption with Associated Data) modes like AES-GCM and ChaCha20-Poly1305, are critical in such applications as they ensure both confidentiality and message integrity. The choice of cipher must balance security requirements with device capabilities, especially when continuous data transmission is involved, such as in real-time patient monitoring scenarios.
## 3 Preliminaries
### Problem Definition  
The PIG is required to detect the existence of the elbow and T-junction, as well as accurately estimate their parameters given the limited processing power and real-time constraint. For the estimation of elbow parameters, since the \(X_{E}\) is aligned with \(X_{S}\), we only need to estimate the direction represented by \(\theta_{E}\), elbow radius \(R_{E}\), distance from the camera \(\Delta x_{E}\), and elbow length \(l_{E}\) to define the elbow, as shown in Figure 2. For the estimation of T-junction parameters, the direction \(\theta_{T}\), the branch radius \(r_{B}\), the distance from the camera \(\Delta x_{T}\), and T-junction length \(l_{T}\) are needed to define the T-junction, as shown in Figure 3a. Here, we assume that the elbow pipe radius \(r_{E}\) and T-junction main pipe radius \(r_{T}\) are equal to the straight pipe radius. Note that, if they are significantly different, the thickness change estimation should be used and is out of the scope of this paper.
## 6 Implementation and Application of the PORTAL-DOORS System
### Specific Use Cases for the PORTAL-DOORS System  
The original PORTAL-DOORS blueprint paper [1] discussed the following use cases:  
* Assisting with organization of the 'bioinformatics resourceome' and the description, discovery and use of resources for e-science and e-medicine in health care and life sciences (see [1] Sec. III).
* Cataloguing resources for biomedical computing (see [1] Sec. IV and VIII).
* Cataloguing patents and trademarks and relating them to products and services for e-business (see [1] Sec. IX).
* Assisting with semantic search, decision support and knowledge management applications in translational research and drug discovery for personalized medicine (see [1] Sec. XI).  
More detailed descriptions of examples in the context of biomedical translational research include the following use cases of PORTAL-DOORS as an information-seeking support system for:  
* Pharmacogenomic molecular imaging [9].  
* PET and SPECT brain imaging [40].  
Although originally conceived and described in the context of health care and life sciences, the diversity of possible use cases for PORTAL-DOORS remains as universal as the diversity of possible use cases for IRIS-DNS.
## 4 Test Results
### Preliminary Test  
Before improving the accuracy in the position domain using the FKP correction, a preliminary test was conducted on the range domain and the test data were analyzed through post-processing. The data were collected for an hour (2-3 pm in local time) on 24 September 2013. As the FKP RS, YANP station was selected which is 70 km east of INCH station as user. As analyzed previously, the divergence-free hatch-filtered (N = 1000) double-differenced (DD) measurements were used and (7) can be divided into two cases such as the residual error before applying the FKP corrections as \(\alpha\) and that after applying them as \(\beta\).  
\[\begin{split}\alpha&=_{u}\Delta_{i}{}^{i}\
abla^{j} \rho-\left(\overline{R}^{i}\cdot_{u}\Delta_{\tau}\vec{e}^{i}-\overline{R}^{j} \cdot_{u}\Delta_{\tau}\vec{e}^{i}-\overline{R}_{u}\cdot\vec{e}^{i}_{u}+\overline {R}_{u}\cdot\vec{e}^{i}_{u}+\overline{R}_{u}\cdot\vec{e}^{i}_{u}-\overline{R} _{u}\cdot\vec{e}^{j}_{u}\right)\\ \beta&=\alpha-\left({}^{i}\
abla^{j}\delta T_{FKP} \rightarrow_{r}\Delta_{u}{}^{i}\
abla^{j}T^{i}_{\text{model}}\right)+\left(- ^{i}\
abla^{j}\delta I_{FKP}\right)\end{split} \tag{9}\]  
As shown in Figure 7, the residual errors decreased because of the positive effect of the FKP corrections. In addition, most of the range errors of each satellite were reduced by more than 0.1 m and the maximum reduced error is approximately 0.35 m on PRN #8 (the smallest elevation angle) as listed in Table 3. This is because the smaller the elevation angle, the higher is the error due to the spatial decorrelation [27].  
Figure 6: Reference Stations operated by NGII in Korea.  
Figure 7: (**a**) DD residual errors without FKP correction; and (**b**) DD residual errors with FKP correction.