esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Principals of Routing Protocols and Their Breakdowns
### Energy Aware Multi-Path Routing Protocols
#### 3.1.8 Cuwsn [31]  
In [31], the authors introduced a cluster-based underwater wireless sensor network (CUWSN), a grid-based clustering system. A single cluster is generated for each grid, and the cluster head is selected based on residual energy. A specified node knows the position of all other nodes, and it is called the coordinator node. Coordinator nodes help communicate within the cluster and data transmission to the sink. Due to the over-reliance on the coordinator nodes, the battery is likely to die out faster than other nodes, causing communication disruption from the node to the sink.  
* Comparison  
In [30], the authors used a special node as cluster head, so there is no election phase here, but in [31], the cluster heads are elected based on residual energy. In EERU-CA, the nodes are distributed in clusters, whereas in CUWSN, each node consists of only one cluster. Both protocols use single-hop communication to communicate within the cluster and multi-hop for communicating with other clusters. Table 5 shows the comparison of the protocols. In Table 4, we compare the cluster-based routing protocols.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Protocol** & **Hello Packet** & **Localization** & **Simulation Tool** & **Number of Sink** & **System Model** & **Forwarding Node Selection** \\ \hline EH-ARCUN & Not used & Not needed & MATLAB & Multiple, moving sink & Harvesting based & Maximum energy \\ Multi-hop ARQ & Not used & Not needed & Not given & Not given & Vertical layering & L2 protocol \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of hybrid routing protocols.  
* Depth-based routing protocols:  
In UWSNs, depth-based routing is a popular routing technique. The selection of a forwarding node in depth-based routing depends on the node's depth. The network topology follows the hierarchical method. The forwarder selected by the source is a node with a depth level that is always lower than the source node's present position; this indicates that the selected forwarder is a node closer to the sink node. The following section addresses the energy-efficient depth-based routing protocols of UWSN.
# Experimental Study of Vibration Isolation Characteristics of a Geometric Anti-Spring Isolator
## 3 Mathematical Simulation
As the excitation amplitude increases, the peak area of transmissibility curve deflects to the right and the peak of curve increases (Figure 6b). The nonlinearity of the geometric anti-spring isolator enhances with the increment of excitation amplitude. However, the transmissibility of the isolator in the isolation area for different excitation amplitude does not change too much. When the geometric parameter is 0.92 and the excitation amplitude is 0.01, the transmissibility peak first decreases and then increases with the increment of the load weight (Figure 6c). When the dimensionless load weight is 1.75 (the rated working load), the resonance frequency reaches to the minimum value. The increment of the resonance frequency of the geometric anti-spring isolator is slow when reducing the load but fast when increasing the load. As shown in Figure 6d, with increasing of the damping, the peak of the transmissibility curve reduces and the nonlinearity is weakened. The attenuation rate of the transmissibility curve in the isolation area does not change when the damping is not too large. So providing appropriate damping will be profitable to weaken the nonlinearity and retain the good vibration isolation performance of the isolator. The imperfection of load weight will largely change the nonlinearity characteristic of the geometric anti-spring isolator. Figure 7 shows the transmissibility curves of the geometric anti-spring isolator for  
Figure 6: The mathematical simulation results. (**a**) Transmissibility for different nonlinear geometric parameters at the rated load. (**b**) Transmissibility for different excitation displacement amplitudes. (**c**) Transmissibility for different load weights. (**d**) Transmissibility for different damping ratios. different load weights when the nonlinear geometric parameter is 0.92. When the load weight is 1.55 or 1.96, the transmissibility curve deflects toward the right, nearly the same as the situation for the rated load. However, when the load weight is 1.34 or 2.08, the transmissibility curve deflects to the left when the excitation amplitude is small. When the excitation amplitude is large, the transmissibility deflects to the right again. The isolator shows a softening-type stiffness characteristic when the excitation level is low and a hardening-type stiffness characteristic appears when the excitation level is gradually enhanced.
# Design and Implementation of an Indoor Localization System Based on RSSI in IEEE 802.11 ax
## 5 Conclusions  
The design, construction, and implementation of an indoor localization system based on RSSI in IEEE 802.11 ax was carried out in this research work, utilizing a wireless sensor network. An accuracy of approximately two meters was achieved, and the expectations were met, while the feasibility of RSSI-based localization was confirmed. However, it was noted that RSSI-based distance estimation and unknown coordinate determination were significantly influenced by obstacles and interference, and the system's accuracy was degraded. Three main localization methods were highlighted, and distinct advantages and limitations were characterized for each. Trilateration was utilized as an efficient technique for calculating the coordinates based on known distances between nodes, and an error of 1.9758 m relative to the actual position of the unknown node was achieved. However, high sensitivity to interference and obstacles was observed, and its precision was compromised. The Min-Max method was evaluated for its resilience to such interference, and a slightly lower error of 1.5966 m was yielded. Robustness in challenging environments was demonstrated, but a slight sacrifice in its accuracy along the x-axis compared to that of trilateration was observed. The best performance was achieved by the Maximum Likelihood method, with an error of 1.4433 m relative to the actual position of the unknown node. It was identified as particularly effective in scenarios involving multiple nodes and complex environments, with the highest accuracy among the three methods offered. However, greater computational resources were required, which limited its suitability for applications where the processing power was a constraint. This method was found to be the most accurate and reliable for RSSI-based indoor localization, particularly in environments with significant interference. Optimization and comprehensive test bench evaluations were emphasized as essential to ensure a consistent performance under varying conditions.  
Not applicable.
## 3 System Description
Subsequently, during the second time slot, the received signals \(Y_{12}\) and \(Y_{22}\) at \(R_{x1}\) and \(R_{x2}\), respectively, can be written as:  
\[Y_{12}=-H_{11}S_{2}^{*}+H_{12}S_{1}^{*}+N_{12} \tag{5}\]  
\[Y_{22}=-H_{21}S_{2}^{*}+H_{22}S_{1}^{*}+N_{22} \tag{6}\]  
where \(N_{12}\) and \(N_{22}\) denote the noise signals at \(R_{x1}\) and \(R_{x2}\) during the second time slot. Thus, the transmitted OFDM symbols can be estimated using a Maximum Ratio Combiner (MRC) as follows:  
\[\hat{S_{1}}=H_{11}^{*}Y_{11}+H_{12}Y_{12}^{*}+H_{21}^{*}Y_{21}+H_{22}Y_{22}^{*} \tag{7}\]  
\[\hat{S_{2}}=H_{12}^{*}Y_{11}-H_{11}Y_{12}^{*}+H_{22}^{*}Y_{21}-H_{21}Y_{22}^{*} \tag{8}\]  
where \(Y_{12}^{*}\) and \(Y_{22}^{*}\) are the complex conjugates of \(Y_{12}\) and \(Y_{22}\), respectively, while \(H_{11}^{*}\), \(H_{21}^{*}\), \(H_{12}^{*}\), \(H_{12}^{*}\), \(H_{22}^{*}\) are the complex conjugates of \(H_{11}\), \(H_{21}\), \(H_{12}\), \(H_{22}\), respectively. Finally, the STBC decoder decodes the received complex-valued symbols and is denoted as \(Y_{dstbc}\). The instantaneous SNR is estimated using the OFDM symbols \(Y_{dstbc}\). This value is fed to the transmitter via a feedback channel, as illustrated in Figure 1, and used for selecting the modulation level of \(M\)-PSK. The SNR thresholds' criteria for the modulation level selection are presented in Section 4.3.
# Modeling and Predicting Carbon and Water Fluxes Using Data-Driven Techniques in a Forest Ecosystem
## 3 Results
### GPP Modeling Using the Machine Learning Methods

Table 3: Model performance rankings of the data-driven models for carbon and water fluxes for the testing period. ANN model among the training, validation and testing periods. The over- and under-estimation of the ANN model are clearly demonstrated in the figure. As shown from Figure 3a, the simulated values of daily GPP consistently follow the measured ones, while the under-estimation of the ANN model is clearly seen in the peaks, especially during the validation and testing periods. Figure 3: Eddy covariance-measured and ANN model-simulated daily carbon and water fluxes in the whole period from 2004 to 2009. (**a**) GPP; (**b**) \(R\); (**c**) NEE; and (**d**) ET. Figure 2: Comparisons of daily GPP measured by eddy covariance technique and predicted by data-driven models for the testing period. (**a**) ANN model; (**b**) GRNN model; (**c**) ELM model; (**d**) ANFIS model; (**e**) SVM model; and (**f**) GMDH model. Furthermore, the comparisons of annual total carbon and water fluxes measured by eddy covariance technique and predicted by different data-driven models in the whole period are shown in Figure 4. As shown in Figure 4a, there is a slight difference among the applied models, which are able to reproduce most of the observed annual GPP. However, in the prediction period (2009), annual total GPP measured by the eddy covariance technique is under-estimated by 10% to 14%.
## 1 Introduction
In this paper, flexibility in both supply and demand is analyzed by means of detailed monitoring data of PowerMatching City (PMC), which is a residential smart grid pilot which got realized in the year 2007 in the City of Groningen in the Netherlands [23]. This pilot includes 22 households (HH) with PV systems and different configurations of their energy systems with \(\upmu\)-CHP, smart hybrid heat pumps (SHHP), and also smart appliances, as illustrated in Figure 1a and detailed in Table 1[24]. An energy management software, PowerMatcher, has been used to operate power flows on this pilot [25]. Figure 1: PowerMatching City: (**a**) scheme of the system, (**b**) energy consumption (blue line), energy production (red line), and power flow (blue area) for a household with photovoltaic (PV) systems and Micro-combined heat and power (\(\upmu\)CHP) jointly producing energy, in the winter of 2012. Negative power flows indicate that power is fed to the grid. Our analysis will quantify the electricity consumption of households, purchase of power from the grid, and feed-in of electricity from households to the grid, as well as PV and \(\upmu\)-CHP energy production during 2012, in order to analyze self-sufficiency of residential electricity networks with PV systems in combination with other distributed energy systems. The shiftable load is also quantified to put this analysis in a more complete perspective. In recent EU reports, one of the most dense areas in terms of the investment in smart grids was the Netherlands, and the peak year of the investment was 2012 [26]. The investigated final phase (phase 2) of the pilot duration is January 2012 through January 2015, and the available data for our research was limited to January 2012 until January 2013. This paper is structured as follows. The pilot configuration, energy tariffs, the data, the data processing methods, data quality, and equations applied to determine energy indicators are presented in Section 2. The results are presented in Section 3 and discussed in Section 4. The paper is completed by conclusions presented in Section 5.
## 4 Video Application Based on Deep Reinforcement Learning Algorithm
### Deep Reinforcement Learning Algorithm Based on Value Function
Because the state space is very complex, the traditional DQN algorithm has difficulty effectively approximating the Q-value function to obtain the optimal strategy. The author combined the deep double Q-network (DDQN) and the duel DQN algorithm to find the optimal strategy to solve the above problems. The effectiveness of the proposed scheme is proved by the simulation results of different system parameters. [PERSON] et al. [76] considered an edge content delivery network with vehicle nodes, and proposed using the DDQN model to solve the problem of content migration. The DQN model defined the state space as the priority information of the content and the delivery status of the content, the actions are defined as migrating, caching and deleting content and the reward is defined as the access delay. This model reduces content access latency by 70% compared with traditional policies. Aiming to minimize the user cost, [PERSON] et al. [77] studied the user-centered content delivery problem with service delay constraints in vehicle networking. The optimal content delivery strategy problem is modeled as an MDP which defines the state space as service time, vehicle location information, content cache status and signal strength; the action is defined as whether the vehicle obtains content delivery from the RSU or cloud and the cost is defined as the cost paid by the vehicle for content. The DDQN algorithm is proposed to solve the above problems, and can achieve the best performance while minimizing the cost.
###### Abstract  
The emergence and spread of Internet of Things (IoT) technologies along with the edge computing paradigm has led to an increase in the computational load on sensor end-devices. These devices are now expected to provide high-level information instead of just raw sensor measurements. Therefore, the processing tasks must share the processor time with the communication tasks, and both of them may have strict timing constraints. In this work, we present an empirical study, from the edge computing perspective, of the process management carried out by an IoT Operating System (OS), showing the cross-influence between the processing and communication tasks in end-devices. We have conducted multiple tests in two real scenarios with a specific OS and a set of wireless protocols. In these tests, we have varied the processing and communication tasks timing parameters, as well as their assigned priority levels. The results obtained from these tests demonstrate that there is a close relationship between the characteristics of the processing tasks and the communication performance, especially when the processing computational load is high. In addition, these results also show that the computational load is not the only factor responsible for the communication performance degradation, as the relationship between the processing tasks and the communication protocols timing parameters also plays a role. These conclusions should be taken into account for future OS and protocol developments.  
operating system; IoT; process management; wireless communications; network stack Article
## 2 Experimental Program
### Results and Discussion
#### 2.3.3 Steel Reinforcement Strain  
Figure 6 and Table 3 show the results of load and reinforcement strain, \(\epsilon_{s}\). For both specimens, the strains at points S1 and S4 did not exhibit a significant change until the failure of the specimen. At loading positions S2 and S3, the steel reinforcement strains of the integral specimen were similar to that at S1 and S4 before the yield load was reached, and they increased rapidly from the yield load to the ultimate load. Additionally, for the modular specimen, the steel reinforcement strains at S2 and S3 were not significantly different from the strains at S1 and S4. The strains at S2 and S4 in the modular specimen exhibited similar behavior to those in the integral specimen until the yield load was reached, but there was no rapid increase in the strains after the yield load was reached [26]. Thus, the steel reinforcement strains of the integral specimen at loading positions S2 and S3 after the yield load was reached were greater than that of the modular specimen. Therefore, it is estimated that most of the steel reinforcement deformation of the modular specimen occurred at the discontinuity interface of the joint, considering that the crack at the discontinuity was 327% larger than the central crack of the integral specimen and that the initial failure pattern occurred at the joint.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Measured Strain of Integral Specim**} & \multicolumn{3}{c}{**Measured Strain of Modular Specimen**} \\  & \multicolumn{3}{c}{**(\(\chi 10^{-6}\) mm/mm)**} & \multicolumn{3}{c}{**(\(\chi 10^{-6}\) mm/mm)**} \\ \cline{2-7}  & **At Service** & **At Yield** & **At Ultimate** & **At Service** & **At Yield** & **At Ultimate** \\  & **Load** & **Load** & **Load** & **Load** & **Load** & **Load** \\ \hline S1 (500 mm) & 39 & 91 & 124 & 45 & 65 & 82 \\ S2 (1000 mm) & 43 & 161 & 1642 & 111 & 150 & 204 \\ S3 (2000 mm) & 107 & 271 & 1956 & 144 & 186 & 259 \\ S4 (2500 mm) & 50 & 112 & 181 & 46 & 66 & 94 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Measured strain at location S1, S2, S3, and S4 of integral and modular specimen.  
Figure 6: Reinforcement strain of the integral and modular specimens: (**a**) S1; (**b**) S2; (**c**) S3; (**d**) S4.
# Blockchain and IoT Convergence--A Systematic Survey on Technologies, Protocols and Security
## 5 Distributed Ledgers
### Consensus
#### 5.2.1 Pow  
The first algorithm ever used to reach a global consensus was the Proof of Work (Pow) [26]. The PoW has long been the most widely used method to achieve consensus within Blockchain architectures [27]. PoW, by searching for hash functions whose difficulty depends on the computational power of the entire network, makes it difficult to build a valid block and connect it to a Blockchain. Modifying a block requires the revalidation of the block itself, plus the revalidation of all subsequent blocks. The older the block to modify, the greater the number of validations needed. If you consider that only a few miners or groups of miners can generate new blocks every 10 min, even the change  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Attack** & **Cloud** & **RFID** & **NFC** & **Blockchain** \\ \hline Wrapping & X & & & & \\ Eavesdropping & X & X & X & & \\ Flooding & X & & & & \\ Stealing Account & X & & & & \\ MITTM & X & X & X & & \\ Browser & X & & & & \\ Reflection & X & & & & \\ Session Hijacking & X & & & & \\ Replay & X & X & X & & \\ Brute force & X & & & & \\ DoS/DDoS & X & X & X & & \\ Skimming & & X & & & \\ Phishing & X & & X & X & \\ User tracking & & & X & & \\ Spoofing & X & & X & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vulnerability comparison.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **PoW** & **PoS** & **DPoS** & **pBFT** \\ \hline Type & Permissionless & Both & Both & Permissioned \\ Scalability & Medium & High & High & Low \\ Finality & Probabilistic & Probabilistic & Probabilistic & Deterministic \\ Transaction/s & Low & High & High & High \\ \hline \hline \end{tabular}
\end{table}
Table 2: General comparison between Consensus mechanism.  
of the newly validated block is extremely expensive. This difficulty protects the chain of blocks from tampering; the longer the chain of blocks, the more difficult it is to reverse any previously recorded transaction. To manipulate the block chain, an attacker must have more than 50% of the entire PoW-based network's computing power. Although PoW provides an elegant solution for the global consensus of distributed ledgers, it has several inherent drawbacks first and foremost the electricity consumption required. This is of primary importance especially when thinking in terms of IoT where energy efficiency, computing power and installed memory have limits to be taken into account.
## 2 Overall System Design
### System Architecture and Functional Module Division  
The solar radiation simulation module is designed to reconstruct the spectral, spatial, and irradiance characteristics of solar interference during Sun-transit events. It integrates a tunable laser source, spectral shaping filter assembly, beam homogenization optics, collimation system, and a programmable attenuation unit. After emission, the laser beam is processed through a homogenizing fiber and a beam-flattening structure to generate a uniform far-field profile. A software-controlled attenuation module enables continuous power adjustment from 10% to 100% (max = 130 mW), covering the irradiance levels corresponding to different Sun-transit phases. Additionally, a precision rotation stage is included to simulate incident angle variations within \(\pm 3^{\circ}\), representing the dynamic angular relationship between the Sun and the communication terminal.  
The signal beam generation module provides ultra-weak narrowband laser output at 1550 nm, emulating satellite terminal transmission under strong solar background conditions. This module consists of a narrow-linewidth 1550 nm laser source, and a motorized translation stage. By adjusting the optical alignment and beam collimation via precise displacement control, the system simulates variable beam divergence angles. Combined with a fiber attenuator, this enables signal power adjustment at the 10\({}^{-10}\) W level, supporting evaluations of signal detectability under varying background intensities.  
The two optical beams are combined via a semi-reflective beam splitter to form a shared propagation path, enabling spatial co-alignment of the solar background and signal beams. This configuration facilitates controlled reconstruction of the optical interference environment encountered during Sun-transit events, as illustrated in Figure 1, and provides a unified physical input condition for subsequent anti-interference capability testing.  
Figure 1: Schematic diagram of the system.( The red text annotations indicate the key technologies indicators and performance requirements of the system).