esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The First Modal Inertial/Invariant Single Load Vectors Pushover Analysis Methods
### The NI Method  
The N1 pushover analysis method offers a practical alternative to the N2 method, assessing seismic response using nonlinear static analysis without a defined equivalent SDOF system.  
The title 'N1' indicates its characteristics: '\(N\)' refers to the method being nonlinear and '1' denotes that it employs a single multi-degree-of-freedom (MDOF) structural model. The N1 method aims to evaluate displacement demand by adjusting the results obtained from response spectrum analysis (RSA) to reflect nonlinear structural behavior. This method borrows from FEMA 368 and FEMA 369 [51] methodologies, enhancing them by allowing for stiffness reduction and thereby lengthening the time. This is a departure from the traditional N2 method, which typically acquires this by the bilinearization of the capacity curve. The N1 method has been shown to match the accuracy of the N2 method, but only when a lateral load distribution proportional to the first mode shape is included. The following relationship illustrates the comparison between the N1 and N2 methods:  
\[D_{el}=\Phi_{n}\Gamma_{1}S_{de}(T_{1}), \tag{16}\]  
In the equation, \(\Delta_{el}\) represents a displacement or deformation parameter. \(\Phi_{n}\) is the \(nth\) mode shape of the structure, indicating how it deforms in its \(nth\) mode of vibration. \(\Gamma_{1}\) is the modal participation factor for the first mode, quantifying the contribution of this mode to the overall response. \(S_{de}(T_{1})\) denotes the spectral displacement for the first mode, which depends on the fundamental period \(T_{1}\) of the structure. This equation is used in seismic analysis to estimate displacements under certain loading conditions.  
The N1 method for pushover analysis is used in irregular buildings with vertical irregularity, primarily for comparison or seismic demands. Researchers have refined it into the \"N1 corrected method,\" aligning more closely with the N2 method.  
Figure 5: Graphical representation of the energy-based pushover analysis method [28].
# Assessing the Techno-Economic Feasibility of Bamboo Residue-Derived Hard Carbon
## 2 Materials and Methods
### Discounted Cash Flow Analysis
#### 2.4.2 Operating Expenditure Assumptions  
The OPEX for the hard carbon production facility was estimated based on a combination of literature values, industrial engineering assumptions, and region-specific market prices. The main cost categories include feedstock procurement and transport, chemical inputs, utilities, labor, maintenance, and regulatory overheads, as shown in Table 1.  
Feedstock and chemical costs were calculated using the BR and sulfuric acid consumption intensities presented in Table 1. The baseline BR price was set at 350 CNY per tonne, while sulfuric acid was assumed at 750 CNY per ton [29]. The BR transportation cost was based on estimated province-level distances and a unit freight cost of 0.3 CNY per tonne-kilometer, as discussed in Section 2.2.  
Utility costs were derived from the process-specific energy demand (6.0 kWh/kg of hard carbon) and water consumption. Electricity prices for each province were sourced from [[https://www.nengy.net/](https://www.nengy.net/)]([https://www.nengy.net/](https://www.nengy.net/)) [30], and the industrial electricity rate was selected based on the \"single-rate\" tariff category for medium- to high-voltage users (35 kV or 110 kV), which is commonly applied to large-scale manufacturing facilities in China. A state-wide water price (4.83 CNY/ton) is applied. A conservative thermal efficiency of 70% was assumed to account for losses in all stages.  
Labor costs were estimated using the DOE Labor Estimator Mini Tool based on the book Chemical Process Engineering: Design And Economics [31; 32], with the inclusion of a 0.95 overhead multiplier to account for supervision and fringe benefits. To reflect a conservative estimation strategy and ensure robustness under potential labor-intensive operations, the workforce size was doubled relative to the baseline staffing requirement suggested by the tool.  
Maintenance and repair costs were assumed to be 6% of the total fixed capital investment (FCI) annually. An additional 15% of this maintenance cost was allocated for spare parts, consumables, and routine maintenance supplies.  
Depreciation was calculated using the Modified Accelerated Cost Recovery System (MACRS) 7-year schedule. Local taxes and insurance were included as 3% of FCI annually, covering property tax, operational insurance, and regulatory compliance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Cost/CNY** & **Capacity** \\ \hline Feedstock and Product Treatment & 2,160,000 & 2000 tons per day \\ Pyrolysis & 223,920,000 & 2000 tons per day \\ Graphitization & 384,000,000 & 2000 tons per day \\ Cooling Water and Other Utilities & 33,840,000 & 2000 tons per day \\ Water Management & 51,840,000 & 2000 tons per day \\ \hline \hline \end{tabular}
\end{table}
Table 2: Equipment cost assumptions and throughput capacity (2021 baseline).
###### Abstract  
**Background.** Cardiac contractility modulation (CCM) is a novel device-based therapy for patients with heart failure with reduced and mild reduced ejection fraction (HFFEF/HFmEF). CCM increases cardiac performance and produces reverse left ventricular remodeling, with improved symptoms, functional capacity, quality of life, and reduced HF-related hospitalizations. However, to date, little evidence is available on the effects of CCM on right ventricle (RV) function. Therefore, we analyzed the effects of CCM on RV systolic function and RV-pulmonary artery (PA) coupling. **Methods.** Twenty-one (65 \(\pm\) 12.5 years) patients with NYHA class III, ejection fraction \(<\) 40% and QRS \(<\) 120 ms were assessed at baseline. During follow up, two patients had died, and so nineteen patients were evaluated six months after CCM therapy. Using echocardiography, tricuspid annular systolic excursion (TAPSE), myocardial systolic excursion velocity (RVs), and RV free-wall strain was measured. PA systolic pressure (PASP) was estimated from tricuspid regurgitation, adding the right atrial pressure estimation. The RV-PA coupling was calculated as TAPSE/PASP ratio. **Results.** After six months, patients who underwent CCM therapy showed a reduction in RV diameters and improved RV systolic function, as evidenced by the increase in both TAPSE (16.6 \(\pm\) 4.2 mm vs. \(18.5\pm 3.6\) mm; \(p<\) 0.05), RVs (10.1 \(\pm\) 1.8 cm/s vs. 11.3 \(\pm\) 11.4 cm/s; \(p<\) 0.05), and RV strain (\(-13.7\pm 1.8\)% vs. \(-15.6\pm 2.3\)%; \(p<\) 0.05). CCM also determined a reduction in PASP (34.2 \(\pm\) 9.8 mmHg vs. \(28\pm 6.2\) mmHg: \(p<\) 0.05) and an increase in the TAPSE/PASP ratio (0.52 \(\pm\) 0.14 mm/mmHg vs. 0.66 \(\pm\) 0.23 mm/mmHg; \(p<\) 0.05). **Conclusions.** At six months, CCM increases RV reverse remodeling and performance, reducing RV size and improving RV systolic function, PASP, and RV-PA coupling.  
cardiac contractility modulation; right ventricular function; right ventricle-pulmonary artery coupling
## 4 Overview of Research Projects on AI/ML for Communications and Networking
### U.K.-EPSRC and U.S.-NSF
In April 2021, NSF announced a new partnership with private industries on intelligent and resilient next-generation systems with an anticipated funding of approximately USD 40 million, which seeks to accelerate the adoption of intelligent decision making in future networking systems as a means to enhance their resilience under extreme operating conditions [134]. At least two of the primary considerations of this call are relevant to the use of AI/ML techniques in next-generation networks [135]. Firstly, AI can be used to predict disruptions in the availability of resources and prescribe solutions to sustain an acceptable quality of the offered service, e.g., in V2X scenarios, historical data and radio environmental maps can be used to predict the degradation in signal reception quality while travelling through some problematic areas. Then, AI algorithms may trigger beforehand, when the channel quality is good, the downloading of more data packets for graceful service performance degradation. Secondly, investigating the required resilience in computational capabilities for sustaining a good-enough performance in distributed learning schemes under faults and failures is another important topic to pursue. For example, let us consider federated learning, where some local learners fail to send their model updates to the server, i.e., a UE runs out of battery. Developing an AI-based component that can predict these failures and advise appropriate countermeasures to mitigate the performance loss is a rather unexplored topic.
## 3 Results
### Impact of Meteorological Factors on GPP  
#### 3.3.1 Heterogeneous Adaptation Mechanisms of GPP to Multi-Dimensional Meteorological Stressors  
Partial correlation analysis revealed complex response relationships between meteorological factors and grassland productivity (Figure 4). The VPD-GPP correlation exhibited distinct topographic differentiation: 1.42% of areas showed significant positive correlations in the central Altai Mountains and parts of Tianshan Mountains, while 2.78% demonstrated significant negative correlations in the northwestern Junggar Basin and oasis margins of Tarim Basin (Figure 4a,e).  
TEM and PRE predominantly exerted positive influences on GPP, with no significant negative correlation areas detected (negative correlations \(\leq\) 1.09%). Notably, 78.58% (TEM) and 87.61% (PRE) of regions did not show significantly positive correlations (Figure 4b,d,f,h). For SSR, positive effects slightly dominated (85.59% were not significantly positive correlated), though 10.39% exhibited non-significant negative correlations. Significant negative correlation zones (0.13%) were concentrated in eastern Tianshan Mountains and southern Tarim Basin (Figure 4c,g).  
Figure 3: Spatial distribution of linear regression trends of meteorological factors in Xinjiang from 2001 to 2020. (**a**) Vapor pressure deficit (VPD). (**b**) Temperature (TEM). (**c**) Downward surface shortwave radiation (SSR). (**d**) Precipitation (PRE). Proportion of significance levels of each meteorological factor trend: (**e**) VPD, (**f**) TEM, (**g**) SSR, (**h**) PRE.
## 5 Results
### Experimental Results
The transmission qualities of all three equalization schemes are demonstrated in Figures 13 and 14. The wider eye-opening of the PPE schemes compared to the PE-only verifies that the transmission quality is boosted using a few-tap pre-equalizer. When the transmission qualities of the ZF-PPE and the joint-PPE are measured, better eye-opening is exhibited by the joint-PPE scheme. Figure 11: Experimental setup of \((2\times 2)\) optical MIMO system for performance evaluation of the proposed joint-transceiver scheme. Figure 14: Eye diagrams of the joint-PPE, ZF-PPE and MMSE PE-only using PAM-4 transmission. Figure 12: Overall BER performance comparison between the PE-MMSE, ZF-PPE and Joint-PPE with PAM-2 and PAM-4 formats, where four-tap pre-equalizers are utilized in both PPE schemes. Figure 13: Eye diagrams of the joint-PPE, ZF-PPE and MMSE PE-only using PAM-2 transmission. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Scenario** & **Modulation Format** & **Equalization** & \(L_{\mathbf{p}}\) & \(L_{\mathbf{f}}\) & \(P_{\mathbf{r}}\) (in dBm) & \(P_{\mathbf{r}}\) Gain \\  & & & & & **at BER of \(10^{-4}\)** & **(in dB)** \\ \hline First & PAM-2 & PE-only & - & 30 & \(-7.58\) & - \\ scenario & & ZF-PPE & 4 & 26 & \(-8.42\) & 0.84 \\  & & Joint-PPE & 4 & 26 & \(-10.16\) & 2.58 \\ Second & PAM-4 & PE-only & - & 30 & \(-2.13\) & - \\ scenario & & ZF-PPE & 4 & 26 & \(-3.08\) & 0.95 \\  & & Joint-PPE & 4 & 26 & \(-4.81\) & 2.68 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Measured average optical received power \(P_{\mathbf{r}}\) gain of the \((2\times 2)\) optical MIMO system at \(10^{-4}\) BER using a \(1.4\,\mathrm{km}\) long MMF channel at \(f_{\mathrm{T}}=5\,\mathrm{GHz}\).
###### Abstract  
This study proposes a smart fault-tolerant control system based on the theory of Lorenz chaotic system and extension theory for locating faults and executing tolerant control in a three-level T-type inverter. First, the system constantly monitors the fault states of the 12 power transistor switches of the three-level T-type inverter; if a power transistor fails, the corresponding output phase voltage waveform is converted by a Lorenz chaotic system. Chaos eye coordinates are then extracted from a scatter diagram of chaotic dynamic states and considered as fault characteristics. The system then executes fault diagnosis based on extension theory. The fault characteristic value is used as the input signal for correlation analysis; thus, the faulty power transistor can be located and the fault diagnosis can be achieved for the inverter. The fault-tolerant control system can maintain the three-phase balanced output of the three-level T-type inverter, thereby improving the reliability of the motor drive system. The feasibility of the proposed smart fault-tolerant control system was assessed by conducting simulations in this study, and the results verified its feasibility. Accordingly, after the occurrence of the fault in power switches, the balanced three-phase output line voltage remained unchanged, and the quality of the output voltage was not reduced by using the integration of the proposed fault diagnosis system and fault-tolerant control system for a three-level T-type Inverter.  
chaos theory; extension theory; T-type converter; fault diagnosis; fault-tolerant control; conversion through Lorenz chaotic system; chaos eye +
Footnote †: journal: Applied Sciences
###### Abstract  
In recent years, semantic segmentation of high-resolution remote sensing images has been gradually applied to many important scenes. However, with the rapid development of remote sensing data acquisition technology, the existing image data processing methods are facing major challenges. Especially in the accuracy of extraction and the integrity of the edges of objects, there are often problems such as small objects being assimilated by large objects. In order to solve the above problems, based on the excellent performance of Transformer, convolution and its variants, and feature pyramids in the field of deep learning image segmentation, we designed two encoders with excellent performance to extract global high-order interactive features and low-order local feature information. These encoders are then used as the backbone to construct a global and local feature fusion network with a dual encoder (GLFFNet) to effectively complete the segmentation of remote sensing images. Furthermore, a new auxiliary training module is proposed that uses the semantic attention layer to process the extracted feature maps separately, adjust the losses, and more specifically optimize each encoder of the backbone, thus optimizing the training process of the entire network. A large number of experiments show that our model achieves 87.96% mIoU on the Potsdam dataset and 80.42% mIoU on the GID dataset, and it has superior performance compared with some state-of-the-art methods on semantic segmentation tasks in the field of remote sensing.  
remote sensing image; gated convolution; transformer; atrous convolution +
Footnote †: journal: Applied sciences
# Dual-Neighborhood Tabu Search for Computing Stable Extensions in Abstract Argumentation Frameworks
## 4 Algorithm Details
### Definition of Neighborhood Moves  
\(L\) can be represented as a set pair \((IN,OUT)\). Here, \(IN=\texttt{in}(L)\) is the set of arguments labeled as in, and \(OUT=\texttt{out}(L)\) is the set of arguments labeled as out. The neighborhood moves of the DNTS algorithm consist of two types: one is to move an argument from \(IN\) to \(OUT\), and the other is to move an argument from \(OUT\) to \(IN\).  
According to Definition 8 for legal in and out labels, we only adjust for mislabeled arguments. Figure 5 provides a specific example of this process. In this example, the function \(flip(v)\) is defined to flip the label of the current argument \(v\). If \(L(v)=\texttt{in}\), it flips to out; conversely, if \(L(v)=\texttt{out}\), it flips to in.  
In Figure 5, the initial state \(IN=\{a,b\}\), \(OUT=\{c,d,e\}\). According to Definition 8 for legal in and out labels, we identify and flip the labels of mislabeled arguments. First, by performing neighborhood move (1), we move argument \(a\) from \(IN\) to \(OUT\), flipping its label from in to out. Next, neighborhood move (2) moves argument \(e\) from \(OUT\) to \(IN\)achieving the flip of argument \(e\)'s label from out to in. In the following sub-section, we detail how to evaluate moves.
## 4 Simulation
### Impact of Moving Speed  
In DTMSNs, the moving speed of sensor nodes has a considerable impact on the performance of data gathering. The following experiments show the network performance of the four protocols under different node moving speeds. As shown in Figure 6, with the increment of node moving speed (from 1 m/s to 5 m/s), the performance of MEDR and direct transmission becomes better since higher moving speeds can shorten the time interval of meeting with the sink node, which means more delivery chances, so the performance is improved. However, once the moving speed exceeds 5 m/s, increasing the moving speed will decrease the performance of MEDR, FAD and direct transmission. This phenomenon can be explained by the following two aspects: on the one hand, due to the communication range of each node being fixed to 5 m in this paper, very fast moving speeds may make the connection time between any two nodes too short to complete the delivery of messages when they meet each other; on the other hand, the period of sending a hello message is fixed to 0.5 s for each node. If a sensor moves too fast (e.g., 8 m/s), the sensor node has already moved 4 m during the time interval of sending two hello messages, which is prone to result in the updating of neighbor list hardly keeping up with the changes of the network topology. In Figure 6, we also find that the performance of the epidemic protocol descends with the increase of node moving speed. The reason is that nodes meet more frequently at higher moving speeds, which results in tremendous amounts of message copies being generated and forwarded among meeting nodes, as well as a mass of wireless collisions, correspondingly.
# On the Application of Machine Learning to the Design of UAV-Based 5G Radio Access Networks
## 4 ML-Based Designs
### Radio Resource Allocation  
Radio resource allocation (RRA) in U-RANs includes functionalities such as power control, spectrum management, beamforming design, backhaul management, cache management, and computational resource management. Such tasks can be jointly managed by resorting to ML tools, as depicted in Figure 5. Different variables of user patterns and behavior, such as spatial positions and communication requirements (e.g., their need of the data traffic rate, their delay tolerance, and reliability), can be considered as main user patterns that can be injected into the ML box. User pattern data can be gathered in an offline manner or can be predicted at a specific time during the day. RL techniques can be used to single out the best A2G connections that minimize interference,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**RL Elements** & **UAV Speed** & **UAV Location** \\ \hline Action & Changing UAV speed & Changing location \\ State & Current UAV speed & Current UAV location \\ Reward & Increase total throughput & Increase coverage \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic reinforcement learning elements in U-RANs.  
whereas SL approaches are more suitable for dynamic bandwidth assignments. The number of far-flung connections should be accurately controlled to mitigate interference. The optimum number of connections can be also achieved in a learning process using RL. State is the number of links at a certain time, action consists of adding or removing the certain number of UAV-based radio links. When there is no dataset available, RL can also be used to generate input data for the SL approach. Hidden information in user traffic pattern and priority of some users over the others can be extracted from data via ML techniques in order to build an area traffic map. UAV features, such as flight time and speed, and characteristics of the RAN interface, can be used in combination with a path loss map and specific information of weather conditions [11; 34; 40; 55; 65].
# Use of Lignin, Waste Tire Rubber, and Waste Glass for Soil Stabilization
## 2 Materials and Methods
### Lignosulfonate, Glass Powder, and Rubber Tire Waste Samples  
The sodium lignocellulosicate used in the experiments was supplied by Aker Chemical Co, Ltd. (Istanbul, Turkey). Sodium lignocellulosic is a brownish powder that is easily soluble in water and has good stability. It has good dispersibility, cohesiveness, and chelation. The chemical properties of sodium lignocellulosic are known--Ph value of 4-5, moisture of 3-6%, ash content of 17-19%, density of 0.5 g/cm\({}^{3}\), and water-insoluble matter ratio max of 0.2%. It can be used as a water reducer in engineering applications, as shown in Figure 3a.  
The glass powder used in the experimental study came from Karikcam Recycling Plant Co, Ltd. (Eskisehir, Turkey). The company collected waste glass from the city and then crushed, pulverized, and distributed it in the laboratory. The additive considered in the study was waste glass powder with a maximum size of 2 mm. The glass powder is processed at a local recycling plant, where glass bottles and other recyclable glass items are crushed to achieve a particle size distribution of ~2 mm. The specific gravity of the glass powder is 2.66. The oxide contents of the glass powder are also known: SiO\({}_{2}\) at 67.5%, CaO at 14.15%, Na\({}_{2}\)O at 12.51%, and Al\({}_{2}\)O\({}_{3}\) at 2.18%. As can be seen in Figure 3b, the glass powder consists mainly of sand-like particles, although a limited number of silt-like particles is also present.  
The rubber tire samples were collected by Mirkacev Environmental Technologies Co (Mersin, Turkey). The rubber was obtained from rubber tire waste containing 5 mm thick steel wires. The rubber crumb sample was sieved and collected with a sieve size of 425 \(\upmu\)m. This size was chosen to prevent the size of the rubber particles in the rubber-clay matrix from being random. This allows the rubber crumbs to blend well into the mixture. The crumb rubber has a spherical shape and a rough surface, as can be seen in Figure 3c. In addition, rubber has properties such as a low specific unit weight, good durability, and high shear and tensile strength, which makes it a suitable material for ground improvement.  
It is expected that the selected waste materials will have various positive effects on the weak soil due to their structural properties. It is expected that rubber tires will improve the flexibility of the soil, glass powder will increase the mechanical strength, and lignin will contribute positively to the bearing capacity of the soil due to its binding properties.  
Figure 3: Waste used for soil improvement: (**a**) lignocellulosicate, (**b**) glass powder, and (**c**) rubber tires.