esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Analysis of the Influence of the Control Parameter and Improvement Methods
### Improvement Methods  
As discussed in Section 5.1, choosing a lower PLL bandwidth and current control bandwidth let Re\(\left\{\text{Y}_{\text{inv}}(\text{j}\omega)\right\}\) expand its positive area to increase the stability margin of the inverter. In the case that the resonant frequency of the grid impedance is unknown, the method of reducing the control bandwidth can maximize the robustness of the inverter.  
Another way to improve the stability is to install the filter on the output side of the inverter to eliminate the influence of the grid impedance resonance on the inverter. If the resonant frequency of the grid impedance is known, a low-pass filter with a cutoff frequency below the resonant frequency or a notch filter can be connected on the output side of the inverter.  
To demonstrate the effect of these two improved methods, a time domain simulation with the same process and parameters of Figure 12 is carried out. The resonant frequency of the grid impedance is set to 1730 Hz, the same as in Figure 12c,d. The time domain simulation is started without the capacitance and after 0.3 s, the capacitance is connected, forming an LCL circuit. The simulation results are shown in Figure 15.  
Since the resonant frequency of the grid impedance is 1730 Hz, a low-pass filter with a cutoff frequency of 850 Hz is connected to the output side of the inverter in the simulation. As shown in Figure 15a,b, the inverter-grid system is stable after 0.3 s. Comparing Figures 13c,d and 15a,b, the stability of the inverter-grid system with two different control methods can be seen to have significantly improved.  
Figure 15: The simulation waveform result of (**a**) the dq-based control method with a low pass filter; (**b**) the PR-based control method with a low pass filter; (**c**) the dq-based control method with a reduced control bandwidth; (**d**) the PR-based control method with a reduced control bandwidth.  
The simulation results for reducing the control bandwidth are shown in Figure 15c,d. The PLL bandwidth and current control bandwidth are simultaneously reduced to 20% of the original value (Table 1). As shown in Figure 13c,d, the inverter-grid system is stable after 0.3 s. Comparing Figures 12c,d and 15c,d, the stability of the inverter-grid system with the two different control methods have been significantly improved.
## 2 Methodological Option for Including Additional Carbon Sinks into Ecological Footprint Methodology
### The Litter, Dead and Soil Pools  
The litter pool is currently disregarded within the carbon sequestration estimate used within ecological footprinting [1]. However, the accumulation of litter on the forest floor (assuming the area is not significantly disturbed) can increase the overall forest carbon pool. Litter accumulation and subsequent decomposition is an important mechanism for the return of nutrients such as P and N to the forest nutrient cycles. The rate and extent of litter decomposition is determined by a number of factors. However, litter decomposition is most closely determined by the activity of microbial communities and soil fauna. Microbial activity is influenced by prevailing climatic conditions and the bio-physical nature of the litter [6]. Other factors, such as clear cutting, have been suggested as increasing litter decomposition, as the forest floor is generally warmer and wetter following clear filling. However, other authors suggest that the effect of clear filling in practice depends on climatic factors, as wind exposure may dry litter and counteract the effect of increased sunlight and moisture [7]. Also overlooked is the function of the dead wood pool in relation to carbon sequestration. As with litter, the sequestration rate of the (unused) dead pool is determined by the net gain (accumulation minus decomposition) of carbon. The capacity of forest soils to sequester carbon is governed by the residency and accumulation of a resistant slowly decomposable C pool. Climatic factors such as soil temperature and rainfall can determine microbial activity, which will ultimately determine loss of soil organic carbon (SOC) through respiration [8]. Because of disturbance, new forestry plantations will likely be net sources of carbon emissions for a number of years until in-growth and detritus begin to return carbon to the soil [9]. Once a forest area has been stabilized, practices such as ensuring a continuous canopy cover and attempting to replicate the natural patterns of disturbance will assist in maintaining both a high wood yield and an increased SOC pool [10]. Although being less than the carbon stored in trees, the additional forest sinks are significant when viewed collectively. Hence, this method may have significant implications for footprinting in general.
# Performance Comparison of ANFIS Models by Input Space Partitioning Methods
## 3 Fuzzy Rule Generation Method According to Input Space Partitioning Method
### Scatter Partitioning Method
#### 3.2.1 Subtractive Clustering (SC)  
Subtractive clustering (SC) [47; 48] is a method of dividing an input space into \(n\) subdivided specific areas by analyzing \(n\)-dimensional input data to create clusters. The SC method divides the input space into appropriate clusters even if the user does not specify the number of clusters. The user sets the radius of the cluster, which is the range of influence from the center of the cluster when the input space is considered as a unit hypercube, and has a value between 0 and 1. If the radius of a cluster is set too small, the size of the cluster becomes small, so that the number of clusters increases, thus increasing the number of fuzzy rules. By contrast, if the radius of the cluster is set to be large, the size of the cluster becomes large, so that the number of clusters decreases and the number of fuzzy rules also becomes small. Figure 4 shows the SC method in which clusters are generated according to the cluster radius. The red dots represent the data, and the black dots represents the center of the cluster. Next, the procedure of the SC method will be described.  
* **Step 1:** The density of each data item in the input space is calculated using the density function of Equation (8). \[P_{i}=\sum_{j=1}^{m}\exp\left(-\frac{\|x_{i}-x_{j}\|}{\left(\frac{r_{a}}{2} \right)^{2}}\right)\] (8) Here, \(r_{a}\) is a positive constant and represents the radius of the cluster that the user can set.
* **Step 2:** Finds \(x_{c1}\), which are the data having the highest density value from \(P_{i}\), and this value becomes the center value of the first cluster.
* **Step 3:** The center of the cluster found in Step 2 is removed by Equation (9). \[P_{i}=P_{i}-P_{c1}\times\exp\left(-\frac{\|x_{i}-x_{j}\|}{\left(\frac{r_{a}}{2 }\right)^{2}}\right)\] (9) Here, \(r_{b}\) is a constant having a positive value and represents the radius of the elimination function. \(P_{c1}\) represents the density measurement value of \(x_{c1}\) calculated in Step 2.
* **Step 4:** Repeat Step 2 to Step 3 until the highest density measurement value is smaller than the set value.
# Comparative Assessment of Shear Demand for RC Beam-Column Joints under Earthquake Loading
## 3 Numerical Study  
### Scope  
A numerical study is presented addressing the estimated shear demand for beam-column joints of an RC 3D frame structure using different methods for the seismic structural analysis, such as modal-response-spectrum-analysis (MRSA), non-linear-static-analysis (NLSA) and non-linear-time-history (NLTH). The selected benchmark structure is the SPEAR frame which was tested in 2004 pseudo-dynamically (PsD) at the European Joint Research Centre at Ispra, Italy. Details of the structure as well as of the structural analysis methods are given in the following.
## 4 Evaluation Metrics  
### Throughput (TP)  
TP is a key performance metric in networking that measures the rate at which data packets are successfully transferred between the source and destination. Instantaneous throughput is defined as the sum of all the packets received during a specific time interval. The average throughput, on the other hand, considers the data packets received within a unit of time at the receiver node [54]. The network's throughput can be calculated using the following equation:  
\[TP=\frac{\sum S_{RP}}{\sum\Delta t}, \tag{3}\]  
where \(S_{RP}\) represents the sizes of received packets, and \(\Delta t\) is the time duration between the stop time \(F_{t}\) and the start time \(E_{t}\).
# Rapid Target Extraction in LiDAR Sensing and Its Application in Rocket Launch Phase Measurement
## 2 Principle and Methods
### Principle of Rapid Target Point Cloud Extraction Method
#### 2.1.1 Cylindrical Projection
The relationship is given by:  
\[UK=V \tag{8}\]  
where:  
\[U= \begin{bmatrix}x_{s1}&y_{s1}&1&0&0&0&-x_{s1}x_{I1}&-y_{s1}x_{I1}\\ 0&0&0&x_{s1}&y_{s1}&1&-x_{s1}y_{I1}&-x_{s1}y_{I1}\\ x_{s2}&y_{s2}&1&0&0&0&-x_{s2}x_{I2}&-y_{s2}x_{I2}\\ 0&0&0&x_{s2}&y_{s2}&1&-x_{s2}y_{I2}&-x_{s2}y_{I2}\\ x_{s3}&y_{s3}&1&0&0&0&-x_{s3}x_{I3}&-y_{s3}x_{I3}\\ 0&0&0&x_{s3}&y_{s3}&1&-x_{s3}y_{I3}&-x_{s3}y_{I3}\\ x_{s4}&y_{s4}&1&0&0&0&-x_{s4}x_{I4}&-y_{s4}x_{I4}\\ 0&0&0&x_{s4}&y_{s4}&1&-x_{s4}y_{I4}&-x_{s4}y_{I4}\\ \end{bmatrix}  \tag{9}\]  
\[V= \begin{bmatrix}x_{I1}\\ y_{I1}\\ x_{I2}\\ y_{I2}\\ x_{I3}\\ y_{I3}\\ x_{I4}\\ y_{I4}\end{bmatrix}  \tag{10}\]  
The mapping matrix can be obtained by solving using the least squares method:  
\[K=\Big{(}U^{T}U\Big{)}^{-1}U^{T}V \tag{11}\]
## 3 Verification of Numerical Simulation Method  
### Four-Point Bending Simulation of the Ice Model Based on FEM-SPH Adaptive Algorithm  
The damage to ice under vertical load is mainly in the form of bending. Prior to the numerical computation of a water-exiting vehicle breaking ice, it should be verified whether the sea ice model based on the FEM-SPH adaptive algorithm can accurately simulate the process of damage to ice.  
A four-point bending numerical computation model was established as shown in Figure 5. The mesh size of the ice model was set to 20 mm \(\times\) 20 mm \(\times\) 20 mm, 15 mm \(\times\) 15 mm \(\times\) 15 mm, and 10 mm \(\times\) 10 mm \(\times\) 10 mm so as to obtain three differ  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Properties** & **Value** \\ \hline Diameter of vehicle/(mm) & 10 \\ \hline Length of vehicle/(mm) & 100 \\ \hline Length and width of ice/(mm) & 200 \\ \hline Thickness of ice/(mm) & 10 \\ \hline Length and width of water domain/(mm) & 200 \\ \hline Height of water domain/(mm) & 120 \\ \hline Length and width of air domain/(mm) & 200 \\ \hline Height of air domain/(mm) & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the numerical model.  
ent sets of meshes, which were denominated as schemes 1, 2, and 3, respectively. The three sets of meshes were used in the numerical computation.  
Figure 6 shows the ice load curves over time for different schemes; it can be seen that the computation results of schemes 1 and 2 are much different, but the computation results of scheme 2 are close to those of scheme 3. The average load error for scheme 1 and scheme 2 is 13.85%, and the average load error for scheme 2 and scheme 3 is only 2.18%. In other words, the computation result converges when the mesh size of the ice model is 15 mm \(\times\) 15 mm \(\times\) 15 mm. In this paper, the simulation result of scheme 2 is compared with the disclosed four-point bending experimental results [45]. The curve for the relationship between ice load and time in the four-point bending simulation and experiment is presented in Figure 7. Evidently, the numerical simulation result is basically consistent with the experimental result. Hence, the proposed ice model based on the FEM-SPH adaptive algorithm can greatly simulate the characteristics of bending damage to ice.  
Figure 5: The model of four-point bending numerical simulation.  
Figure 6: Convergence test of the four-point bending model.
## 3 Experimental Results and Discussion  
### Out-of-Plane Test  
#### 3.1.1 Quasi-Static Compression  
Two sizes of specimens were studied. The loading speed was 2 mm/min; thus, the strain rate for the \(20\times 20\times 10\) specimen was \(3.37\times 10^{-3}\pm 8.0408\times 10^{-6}\) s\({}^{-1}\), and the strain rate for the \(8\times 8\times 4\) specimen was \(8.31\times 10^{-3}\pm 5.4029\times 10^{-5}\) s\({}^{-1}\). Firstly, as shown in Figure 4a, the fracture stress of the bigger specimen was higher, while the fracture strain values of the two sizes of specimens were similar, i.e., \(2.8861\times 10^{-1}\pm 2.895\times 10^{-2}\) and \(2.9064\times 10^{-1}\pm 1.366\times 10^{-2}\), respectively. Secondly, as obviously shown in Figure 4b, there were two phases during the loading. The first phase possibly represents the compact procedure, where the pores among the specimen were compressed outward. The second phase possibly represents the bearing procedure, where the fibers began holding the load. The bigger specimen could hold the loading more stably, and it seems that the bigger specimen became stable more quickly during the first phase. As shown in Figure 4a, the compressive strength of the \(20\times 20\times 10\) specimen was \(634.8947\pm 66.3778\) MPa, which is similar to the compressive strength of \(\sim\)650 MPa in Reference [4] using the same size of specimen.  
As shown in Figure 5, the specimens had a shear failure feature. As shown in Figures 5a and 6a, many fibers were squeezed out along the four edges of the specimen and, thus, a little square was presented at the center of the surface. Therefore, an inner shear generated in some places, which resulted in the fibers being cut off as shown in Figures 5b and 6b. Another feature is that, when failure occurred, there was always a loud popping sound like an explosion, and the pieces of fibers splashed all over the place.
# Enhancing Street-Crossing Safety for Visually Impaired Pedestrians with Haptic and Visual Feedback
## 2 Related Works
### Challenges in Mobility for Visually Impaired Individuals
#### 2.1.2 Assistive Technology Solutions for Safe Crossing  
To address these specific challenges, researchers have proposed various assistive systems to help visually impaired individuals cross streets safely. Among smartphone-based solutions, [PERSON] et al. developed a pedestrian traffic signal detection system capable of effectively recognizing various shapes and types of traffic signals [36], thereby providing crossing assistance to visually impaired users. Similarly, [PERSON] et al.'s system not only provides voice prompts for traffic signal and vehicle information but can also automatically adjust signal timing based on user needs, significantly aiding visually impaired individuals in crossing roads safely [37]. In the domain of wearable devices, [PERSON] et al. designed a head-mounted device that delivers real-time audio feedback to convey information about crosswalk locations and traffic signal states. Experimental results have demonstrated the system's effectiveness in providing safe crossing guidance for visually impaired individuals [4]. [PERSON] et al. introduced a wearable system capable of detecting the status of nearby traffic signals in real time and guiding users safely through intersections via voice alerts [38]. Additionally, [PERSON] et al. developed an intelligent guide-dog harness system that identifies surrounding moving obstacles and traffic signal states, improving the safety of visually impaired pedestrians in complex traffic environments through voice reminders [39].  
Subsequent research highlights the benefits of tactile notification methods, as audio feedback alone can be compromised by ambient noise and the cognitive demands of processing multiple auditory cues simultaneously [22; 23]. Tactile cues, on the other hand, operate independently of hearing and demonstrate greater accuracy in complex traffic scenarios [25]. Comparative studies indicate that tactile feedback frequently surpasses auditory signals in terms of user preference and task efficiency [26; 40]. Many visually impaired pedestrians also express a preference for tactile guidance, as it allows them to simultaneously monitor environmental audio cues [30]. Additionally, recent research has compared tactile and visual feedback for individuals with residual light perception, finding that visual cues provide superior performance in such cases [27].
## 3 Challenges and the State of the Art Progresses
### Architectural Choices behind Performance and Security Concer
#### 3.3.3 On-Chain vs. Off-Chain Data Storage  
Intuitively, data stored on chain are more secure, trustable, tamper-evident, and single point of failure tolerant. However, data on-chain are available to everyone in the chain and with little privacy guaranteed. The amount of data stored on chain is limited, either because the limitation of the protocol or because of the huge cost of storing data on-chain. The storing cost is very high because every node in the blockchain has to keep a complete copy of the data. This is why even storing kilobytes can cost a fortune. Further, it is hard to query for data in blockchain because blockchain is not a SQL server and querying on-chain data is very expensive.  
On the contrary, off-chain data provide privacy and have negligible storing cost, but they do not take advantage of the security, reliability and trustability features of blockchain.  
There are many options for storing data on the blockchain. Instead of storing the complete raw data on-chain, we can choose to store only the hash or a subset of data on-chain. By storing only the hash or a subset of data, the storing cost is magically reduced. The raw data can be stored in a relational database or in a file system with the corresponding hash value used as the index.
## 5 Concrete Carbonation Mechanism Analysis  
### Carbonation Mechanism Analysis of Ordinary Concretes  
For the convenience of analysis of the carbonation mechanism of cement paste after CO2 absorption, the carbonation mechanism of ordinary concrete is analysed first in this section. The internal mechanism before concrete carbonation is shown in Figure 13a. After cement particles react with water, C-S-H gel is produced and solid-phase crystals Ca(OH)2 are produced through hydration [34; 35]. The internal mechanism after concrete carbonation is shown in Figure 13b. CO2 in air reacts with Ca(OH)2 in the cement gel after it penetrates the pores and capillaries of the concrete, producing CaCO3 which may precipitate due to its low solubility. To assure constant concentrations of Ca2+ and OH+ in the pore solution, Ca(OH)2 in the original solid phase is dissolved to maintain a balanced Ca2+ content.  
Furthermore, the product layer may restrict the reaction speed between CO2 and alkaline components in concrete particles since CaCO3 precipitates on capillary pore walls, but it may not influence the ultimate carbonation process of concrete. The continuous diffusion of CO2 will enable it to continually react with Ca(OH)2, thus decreasing the Ca(OH)2 content and decreasing the alkalinity of the concrete. As such, the carbonation resistance of concrete is weakened.  
Figure 12: SEM image of carbonized cement by ultrasonic vibration agitation: (**a**) 0%; (**b**) 0.88%; (**c**) 1.76%; (**d**) 2.20%.  
Figure 13: Carbonation mechanism of ordinary concrete: (**a**) Non carbonized; (**b**) After carbonization.
## 5 Conclusions  
The trials conducted in our study revealed that fecal ash could serve as a practical solution for the sorption of urea. Maximum urea adsorption was recorded at 92.8% under an initial concentration of 13.5 g.L\({}^{-1}\) with an adsorbent loading of 1.0 g at a low temperature of 30 \({}^{\circ}\)C. The results were also validated by both ANN models, i.e., TRAINLM and TRAINBR yielding identical optimized parameters for the urea adsorption. The Temkin model depicted the best results in terms of coefficient of correlation, indicating a tendency of the adsorption phenomenon to follow homogenous or heterogeneous processes. The results from our study further illustrate the effectiveness of ANN models for reliably analyzing the efficacy of fecal ash as an adsorbent for urea recovery from human urine. Bayesian regularization algorithm exhibited the highest accuracy of prediction for revealing potentially complex relationships and to determine the feasibility of using fecal ash as a prospective adsorbent for the recovery of plant essential nutrients (such as urea from wastewater) on a larger scale. Although fecal ash's adsorption efficiency falls short of conventional carbon-based adsorbents (e.g., activated charcoal), it compensates for this shortcoming by being a practical, sustainable solution. Most of these carbon-based adsorbents accumulate over soil or have to be replenished after the process of adsorption.  
On the contrary, fecal ash can be used as an alternative for fertilizers after the adsorption process. This enables urea adsorbed from the wastewater stream to be directly supplied back into agricultural soil and leads to a simultaneous valorization of human waste. Lastly, it also provides an adequate answer for wastewater treatment, thereby providing a self-sustainable solution to sanitation by treating human waste with human waste.  
Design of experiments, M.G. and J.D.; experimentation and data collection, A.T., A.S. and R.M.; analysis of the experiments, M.G.; ann modeling, V.C.; manuscript draft, A.T., A.S. and R.M.; editing and reviewing, A.S., M.G., J.D. and V.C. All authors have read and agreed to the published version of the manuscript.  
The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.  
Not applicable.  
All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.
## 2 Mechanisms Specific to QoS and Multicast in Ad Hoc Networks
### Mechanisms for Resource Estimation
#### 2.5.2 Mechanisms for Estimation of Bandwidth in Contention-based MAC Ad Hoc Networks  
With contention-based MAC, the actual resource can either be direct or indirect of the type \"is there enough available resources?\". The direct estimation is a hard problem when the wireless channel uses a multiple access scheme. Transmissions from a node may block or interfere with transmissions from other nodes. Correct estimation therefore depends on full topology information and detailed knowledge of interference and transmission range for each node. Indirect estimation is based on end-to-end probing. Here the state of the network is not needed. Instead, the available resources are estimated based on whether the requesting node could be added with acceptable QoS for the information flow.
## 2 Obstacle Grid Map and True Shortest Path
Since SGPs are the shortest paths limited to eight directions of movement, TSPs are the shortest paths allowed to move in any direction. The routes and grids traversed by SGPs and TSPs are largely the same when navigating obstacles. We also observed that SGPs navigate obstacles by making multiple diagonal movements, which, after translation, intersect with the obstacle at a series of vertices. The lines connecting these vertices form the TSP, as shown in Figure 4. Therefore, we propose the following conjecture: TSPs consists of a subset of obstacle vertices that intersect the equivalent path (described in Section 3.2) of SGPs with obstacles. Therefore, this paper proposes an SGP vertex extraction and filtering algorithm (SG-PVEFA) based on this hypothesis. This algorithm extracts the obstacle vertices that the SGP passes through by constructing equivalent paths and models them into a chain structure. Figure 3: SGP and TSP (Where B is the starting point and E is the end point). Then, it is filtered by an improved dynamic programming algorithm. It has been verified that the path searched by SGPVEFA is consistent with the TSP. The overall flow chart of SGPVEFA is shown in Figure 5.