esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Blockchain, Smart Contracts, and Emerging Technologies  
#### 3.4.1 Main Trends  
The adoption of blockchain and smart contract technologies has revolutionized the management of DERs, particularly in enabling secure, transparent, and efficient energy transactions. These technologies have been widely applied in P2P energy trading systems, which decentralize energy markets by eliminating the need for intermediaries and automating transactions [61; 62]. Blockchain's inherent features--immutability, transparency, and decentralization--align perfectly with the goals of modern energy systems [63]. A hierarchical blockchain framework, for instance, has been utilized within microgrids to integrate distributed control with energy trading [64]. By combining public and private blockchain systems, this approach ensures high throughput for trading while maintaining system stability and security against false data injection attacks [19].  
One prominent trend is the use of blockchain-based smart contracts to automate energy transactions and enforce rules defined by market participants. For example, a blockchain-enabled system in Singapore incorporated smart contracts to manage energy exchanges among residential, commercial, and industrial users. By modeling inter-sectorial interactions with non-cooperative game theory and considering renewable generation uncertainties through receding horizon optimization, the system flattened electricity procurement schedules and improved the overall market efficiency [65]. Artificial intelligence has also been integrated with blockchain to enhance the system performance and security. A notable example is a secure energy trading platform for residential homes that combines blockchain with an Improved Sparse Neural Network (ISNN). This system introduced a novel Proof-of-Computational-Closeness (PoCC) consensus protocol, which optimized computational costs and provided robust defenses against Sybil attacks. The ISNN further accelerated error convergence rates and minimized data transmission times, proving scalable and efficient for large-scale implementations [66].  
Another innovative application is the use of blockchain to manage Virtual Power Plants (VPPs) composed of small-scale prosumers. These decentralized systems aggregate energy resources through public blockchains and self-enforcing smart contracts, which automate energy contributions, delivery tracking, and remuneration processes. The experimental results demonstrated that such systems operate effectively on public blockchains with manageable computational overheads, offering a scalable solution for energy aggregation [67]. Blockchain's role in secure grid management extends beyond trading. A hierarchical blockchain model demonstrated its capability to safeguard distributed control systems within microgrids. By employing Proof of Authority (PoA) and smart contracts to regulate control parameters like active power and frequency, this model ensured a robust performance under adverse conditions while improving the social welfare of microgrid participants through double-auction pricing mechanisms [19].
# 1-D Convolutional Neural Network-Based Models for Cooperative Spectrum Sensing
## 2 Related Works
An essential feature of our models is their privacy-centric approach. Instead of transmitting raw data, which may contain sensitive information, the models send only the model weights as a vector to the fusion center as the signals are processed at the SU level. This vector representation preserves the privacy of the SU devices while still enabling effective cooperative spectrum sensing. This not only accelerates the detection time but also optimizes the computational resources, making our designs more efficient for resource-constrained SU devices. In contrast to conventional cooperative spectrum sensing approaches that may involve transmitting raw data or detailed information, our vectorized model weight transmission minimizes information exchange, reducing the risk of privacy breaches. This streamlined communication contributes to the models' rapidity and computational efficiency. By leveraging the strengths of 1 DCNN and LSTM/BLSTM, we aim to enhance the performance of our CSS system. It is crucial to emphasize that while we have taken inspiration from the mentioned references, our proposed hyper-parameters and network architectures are distinct and carefully designed to cater to the specific challenges encountered in the CSS of the proposed scenario. Specifically, we propose vectorizing multi-antenna data into a one-dimensional format, which can facilitate processing and analysis using machine learning models. This approach can also improve the memory efficiency by significantly reducing the memory overhead and decreasing the memory footprint compared to artificially expanding the data to higher dimensions. This memory efficiency arises from data preprocessing techniques and the model architecture. The presence of fewer parameters means that less memory is required to store the model's weights and intermediate activations during training, resulting in lower memory usage during forward and backward passes in the training process. This advantage is particularly significant when working with limited computational resources, making it a practical choice, especially in resource-constrained environments.
## 5 Simulation Results
### First Case Study  
Suppose the MG working in the islanding operation and the load demanded is \(1+j\) 0.7 pu. If the capacity of DG\({}_{2}\) is twice as big as the capacity of DG\({}_{1}\), it is expected that the power extracted from DG\({}_{2}\) is twice as big as the one from DG\({}_{1}\). This means DG\({}_{2}\) supplies 0.67 pu and DG\({}_{1}\) supplies 0.33 load. If DG\({}_{2}\) consists of two similar units, it is supposed that until \(t=1\) s, only one unit of DG\({}_{2}\) and full capacity of DG\({}_{1}\) is perched in the circuit. Therefore, active and reactive powers distribute between them optimally and equally. Then, at \(t=1\) s both units of DG\({}_{2}\) perch in the circuit and it operates with full capacity. Therefore, as explained before, DG\({}_{2}\) supplies load, twice as much as DG\({}_{1}\) does. This situation continues to hold until \(t=2\) s. By this time, \(1.25+j\) 0.6 pu load is added to the existing one in the circuit and power distribution continues as the previous state. At the time \(t=3\) s, another load fluctuation happens and due to necessity of frequency and voltage control, active load decreases by \(j\) 0.25 pu and reactive load increases by \(j\) 0.55 pu. Active and reactive powers of DG units and frequency control diagrams are shown in Figures 11 and 12.  
The sum of total active and reactive powers is shown in Figure 11a,b. Clearly, the proposed controller has been able to track the reference signal properly. Figure 11c,d illustrate active and reactive powers of the source. From Figure 12a, it can be observed that at load fluctuation time, frequency has low swing, but it returns to its final and steady value. Figure 12b,c illustrates the voltages of these sources in \(dq\) frame. Stability of \(d\)-axis voltage and \(q\)-axis voltage remaining at zero suggest the proper performance of the controller.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline AC grid voltage (\(V_{AC}\)) & 380 Vrms \\ AC grid frequency (\(f_{AC}\)) & 50 Hz \\ Switching frequency (\(f_{sw}\)) & 12 kHz \\ Filter resistance (\(R_{f}\)) & 10 m\(\Omega\) \\ Filter inductance (\(L_{f}\)) & 1.8 mH \\ Filter capacitance (\(L_{f}\)) & 470 μF \\ \hline \hline \end{tabular}
\end{table}
Table 2: System specifications.  
Figure 11: First case study results: (**a**), total active power of load; (**b**), produced total active power share of sources; (**c**), total reactive power of load; (**d**), produced reactive power share of source.
# Seismic Performance of Ductile Column with Rectangular Hollow Cross-Section in RC Building
## 4 Discussion
### Finite Element Analysis
#### 4.5.1 Analysis Outline  
The nonlinear FEM analysis was carried out by FINAL [26], which is commonly used for RC structure analysis in Japan. Figure 14 shows the geometry of the FEM model and meshing. The concrete was modeled as hexahedral elements, and truss elements were assigned for the reinforcing bars. The bond-slip behavior between the concrete and reinforcement was also considered. Since the hexahedral elements were assigned for the concrete, the tapered shape of the hollow part was reproduced by a fine stair-stepped shape, as shown in Figure 14. Half of the specimen was modeled by utilizing the symmetric condition, and the displacement in the Y direction was constrained. The axial load was provided as distributed axial forces in the Y direction on the top of the upper stub, and then the lateral displacement was applied to the top of the upper stub, as was done in the experiment.  
Stress-strain hysteretic models of the concrete and steel, as well as the bond-slip model between them, are shown in Figures 15 and 16, respectively. For the concrete, the hysteresis curve described by [PERSON] et al. [27] was employed, and the modified [PERSON] model [28; 29] was used in the stress-increasing region up to the peak strength. Regarding the characteristics of the softening region after the peak, the Nakamura-Higai model [30] was assigned for the core concrete, while the constitutive model with sharp stress degradation after the peak was employed for the cover concrete [26]. [PERSON]'s four-parameter model [31], adopting the coefficients of [PERSON] et al. [29], was used for the failure condition under the triaxial stress. The tension behavior of the concrete was assumed to be linear until the crack development, and after cracking, the tensile softening property was modeled according to [PERSON] et al. [32]. Concerning the constitutive law of reinforcing  
Figure 13: Neutral axis positions at ultimate flexural state by cross-sectional analysis: (a) CM2; (b) CM12 and CM13.  
bars, the modified Menegotto-Pinto model [33] was used for the hysteresis curve. The envelope was set to be bilinear and the stiffness after the yielding was assumed to be \(1/1000E_{s}\), in which \(E_{s}\) represents [PERSON]'s modulus. The slip-bond relationship between the concrete and longitudinal reinforcement was modeled according to [PERSON] et al. [34] and the AIJ guidelines [35].
## 1 Introduction
[26] proposed a hybrid proximal policy optimization (HPPO) algorithm, which can efficiently solve the hybrid action space based on the proximal policy optimization (PPO) algorithm. We summarize the difference between our work and the existing related references in Table 1.  
Based on the above discussion, this paper studies the multi-UAV-assisted maritime IoT systems, in which the missions of the UAVs consist of collecting data from buoys and offloading the data to the OBS. The UAVs could adaptively change the mission mode according to the network resources and mission requirements. Our goal was to minimize the total mission completion time by jointly optimizing the UAV trajectories, mission mode selection, the transmit power of the buoys, and the buoy-UAV and UAV-OBS association relationships with the constraints of energy consumption and a no-fly zone. The main contributions of our paper are summarized as follows: \begin{table}
\begin{tabular}{c} discrete \\ and \\ continuous \\ \end{tabular} & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between our work and the related references. * We propose an adaptive data transmission mission mode framework for the multi-UAV-assisted maritime IoT system. Specifically, UAVs are allowed to change the mission mode in each time slot according to the channel quality and data volume, which improves the flexibility of offshore data collection and offloading. * A multi-UAV trajectory optimization algorithm based on multi-agent HPPO (MAHPO) is proposed to overcome the problem of a hybrid discrete and continuous action space. The total mission completion time is effectively shortened through the flexible scheduling of the UAV trajectories, mission mode, and transmit power of buoys. * In order to reduce the exploration difficulty and action space dimension of the MAHPO algorithm, we propose an algorithm based on the stable marriage problem (SMP) to determine the buoy-UAV and UAV-OBS association relationships. The simulation results show that the proposed algorithm can effectively reduce the total mission completion time with low computational complexity. The remainder of the paper is organized as follows. The multi-UAV data collection and offloading model and the problem formulation are given in Section 2. Then, the problem analysis and the proposed algorithm are given in Section 3. The simulation results are given in Section 4. Finally, we give a conclusion in Section 5.
## 3 Methodology
### Model Accuracy Evaluation and Feature Importance Analysis  
3.4.1. Model Accuracy Evaluation  
The receiver operating characteristic (ROC) curve is widely used to evaluate the results of geological hazard susceptibility assessment [45]. The ROC curve plots the relationship between the false positive rate (FPR) on the x-axis and the true positive rate (TPR) on the y-axis. The area under the curve (AUC) is calculated to measure the accuracy of the model's predictions. The AUC value ranges from 0 to 1, with values approaching 1 indicating higher accuracy in model predictions, while values farther from 1 indicate lower accuracy.
## 4 Discussion
Underlying many human diseases, endoplasmic reticulum stress is a typical molecular pathophysiological process, for the development of which a disturbance of protein folding is of great importance, which could be the reason for the accumulation of chemically aggressive proteins or inactive proteins in the lumen of ER [44]. To avoid these effects, eukaryotes have an unfolded protein response (UPR) mechanism. This mechanism is a signaling cascade with a common trigger mechanism mediated by transmembrane proteins--PERK (PKR-like ER kinase, also known as PEK, EIF2 AK3), IRE1\(\alpha\) (\(\alpha\)-isoform of inositol-dependent enzyme type 1) and ATF-6 (activating factor transcription 6). The regulatory domain of these proteins is embedded in the lumen of the endoplasmic reticulum. Normally, the domain is associated with the BiP chaperone (immunoglobulin-binding protein, also known as GRP78-glucose-regulating protein and HspA5) [45]. It has been shown that selenium nanoparticles are able to inhibit a variety of signaling proteins participating in endoplasmic reticulum stress under the action of H\({}_{2}\)O\({}_{2}\). These proteins include protein kinase R-like endoplasmic reticulum kinase (PERK), eukaryotic initiation factor 2 (elF2\(\alpha\)), CHOP and p-PERK activating transcription factor 4 (ATF4). In addition, SeNPs are able to regulate the process of mitophagy under endoplasmic reticulum stress through inhibition of the AMPK/mTOR/PINK1 signaling cascade [46]. In cortical astrocytes, spherical SeNPs also cause adaptive ER stress, which is not accompanied by the apoptosis activation, but, on the contrary, leads to the activation of genes encoding protective proteins [29]. Similarly, a slight depletion of the Ca\({}^{2+}\) depots of the endoplasmic reticulum observed under the action of SeNrs on L-929 fibroblast cells with an increase in gene expressions of ER-stress proteins, does not cause pronounced cell death. The ER-stress registered in fibroblasts may have an adaptive value and protect cells in the event of pathological effects. Whereas in A-172 glioblastoma cells, we observe more pronounced ER-stress at the level of Ca\({}^{2+}\) signaling and gene expression, which causes their apoptosis under the action of SeNrs and even necrotic processes under the action of high doses of SeNPs for 48 h. Depletion of Ca\({}^{2+}\) depots of the endoplasmic reticulum can lead to the Ca\({}^{2+}\) overload of mitochondria and activation of BCL-2-dependent apoptosis [47; 48]. Another pathway of activation of apoptosis upon depletion of the ER is an [Ca\({}^{2+}\)]i increase, its accumulation in mitochondria, depolarization of mitochondria and release of cytochrome c [49]. In addition, overload of mitochondria with Ca\({}^{2+}\) ions during ER-stress leads to the hyperproduction of ROS through NADPH oxidase 4 and disturbance of mitochondria [50]. In our experiments on a human glioblastoma cell line, after exposure to spherical SeNPs or selenium nanorods, the increase in expression of both ER-stress genes engaged in the apoptosis and genes encoding proteins adjusting the mitochondrial pathway of cell death increased.
# The Effect of Green Areas on Urban Microclimate: A University Campus Model Case
## 3 Results and Discussion
### Regression Model and Applicability
Figure 6, Contour plots of T for Coniferous Plants, illustrating interaction effects among A, M, WS, and AT. The most prominent interactions appear in the AT\(\times\)M and M\(\times\)AT plots, where red and yellow zones indicate stronger cooling effects. The AT\(\times\)WS plot also shows notable gradients, indicating how AT modulates the influence of WS. These plots provide visual evidence for the nonlinear and interactive behavior of environmental variables, underscoring the importance of multivariate considerations in green infrastructure planning. According to Table 3, the RSM model is statistically significant (F = 69.34, \(p\) < 0.001). Linear terms explain a significant portion of the model's variance (9.96%), with AT being the strongest variable (7.98%), followed by M (1.93%). Quadratic terms account for 1.96% of the total variance, while two-factor interactions contribute 4.82%. The M\(\times\)AT interaction (F = 41.35, 3.88%) has been identified as the most significant interaction. The model exhibits a low error rate (8.74%, Adj SS = 592.22) and is considered reliable. \[ \begin{split}\text{T = 86.6 + 0.0162 A \text{ = } 0.887\text{ M }- 43.9\text{ WS }- 1.281\text{ AT }- 0.000070\text{ A\times A }+ 0.00101\text{ M\times M }+ 22.36\text{ WS}\times\text{WS }- 0.00620}\\ \text{AT}\times\text{AT + 0.000046 A\times M }+0.0062\text{ A\times WS }+0.000152\text{ A\times AT }- 0.032\text{ M\times WS }+0.03822\text{ M\times AT }- 0.612\text{ WS}\times\text{AT }\end{split}  \tag{4}\]  
Table 3, ANOVA results for shrubs. The model explains 91.26% of the total variance in T. Among the linear factors, AT is the most dominant (70.34%), followed by M (5.50%) and WS (7.75%). However, A has a negligible effect (0.02%, \(p\) = 0.559). Significant interactions include M\(\times\)AT (F = 41.35) and WS\(\times\)AT (F = 8.26), confirming the importance of combined climatic effects. The model's reliability is supported by a low error contribution (8.74%) and statistically significant interaction terms. According to Figures 7 and 8, the model conforms to a normal distribution, and the residuals are randomly distributed. The Pareto chart identifies AT, M\(\times\)AT, and M as the most influential factors. Contour plots indicate that the optimal response variable is  
Figure 6: Contour plots of T, illustrating the interaction effects of variables on the response, with specific hold values for parameters A, M, WS, and AT for Coniferous Plants. achieved in the red and yellow regions, with AT and M factors being the key determinants. In conclusion, AT is the most significant independent variable, and its interaction with M enhances the predictive power of the model.
# Measurement of Head-Related Transfer Functions: A Review
## 3 Overview of HRTF Measurement Setups
### Multi-Microphone Setups  
Figure 10 shows several HRTF measurement setups based on the \"reciprocity method\", where multiple microphones are placed in a sphere layout (see setups R\({}_{\text{A}}\), R\({}_{\text{B}}\) and R\({}_{\text{D}}\)) or on a circular arc (see the setup R\({}_{\text{C}}\)). [PERSON] et al. [57] first introduced the \"reciprocity method\" to simultaneously measure HRTFs from different directions by using a microphone array as shown in part I of R\({}_{\text{A}}\). One node of the microphone array can be seen in part IV of R\({}_{\text{A}}\). In HRTF measurements, the microphone array surrounds the subject and a pair of miniature loudspeakers is placed in subject's ears (see parts II and III of R\({}_{\text{A}}\)). Excitation signals are reproduced via the pair of miniature speakers (left and right ear loudspeakers are excited in sequence), and the HRTFs from all microphone directions can be measured simultaneously. Setups R\({}_{\text{B}}\) and R\({}_{\text{D}}\) are similar to the setup R\({}_{\text{A}}\), only the microphone types, the number and distribution of microphones are different. The setup R\({}_{\text{C}}\) is designed to measure near-field HRTFs on the horizontal plane, where multiple microphones are placed on a circular arc with a radius of 0.2 m around a dummy head (see part I of R\({}_{\text{C}}\)). The microphones and miniature loudspeakers used can be seen in parts II and III of R\({}_{\text{C}}\). Based on the \"reciprocity method\", HRTFs from multiple directions are able to be measured within a few seconds. Additionally, the inter-equipment reflections are lower than in multi-loudspeaker setups because of the small-size microphones. However, due to the poor performance of the miniature loudspeakers at low frequencies and the limited playback level of excitation signals, most acoustic laboratories prefer to use \"direct measurement method\" to measure HRTFs.  
Figure 10: Examples of system setups based on the reciprocity theory [57; 143; 144; 145] (all pictures are taken from available publications, some of them are partly cropped to fit into the overall picture).