esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliability-Aware Cooperative Node Sleeping and Clustering in Duty-Cycled Sensors Networks
## 6 Performance Evaluation
### Performance Comparison among RCR, REER, BLR and GPSR
For instance, some hops had six cooperative packet forwarders, which included one reference node and five cooperative nodes, while the bottleneck hop only had three cooperative packet forwards, which was because the uneven node distribution in the fixed CN selection region for a given predetermined \(r\_value\) causes an unbalanced number of CNs at every hop. In contrast, with the same average number of CNs, RCR produced the same number of CNs at  
Figure 9: Comparison of ETE reliability and energy consumption with FEC. each hop with the dynamic node distribution. Since the hop distance in RCR is adaptive to the QoS requirement for reliability, there were four CNs at each hop, as shown in Figure 10b. Furthermore, we employed the automatic repeat request (ARQ) mechanism in RCR, while REER did not have that mechanism. With the increase of \(f\), RCR exhibited higher reliability than REER. Figure 9b shows the energy consumption of four schemes, wherein it can be seen that when \(f\) is larger than 0.6, the reliability of GPSR and BLR decreases to zero, so we can skip the statistics of energy consumption. The BLR can partially antagonize link failure, and the sink node receives more data packets with less energy consumption per successful packet delivery. In RCR and REER, the non-cooperative nodes enter sleep status during the data dissemination stage, thus exhibiting a low energy usage. For the same average \(k\), the hop count of RCR is larger than that of REER and RCR has retransmitted. However, the use of ARQ in RCR incurs a higher energy consumption than that in REER. Thus, RCR has a little bit higher energy consumption per successful packet delivery than REER, but it still has a good control for energy consumption. In summary, RCR yields the highest reliability with considerable energy saving in most scenarios with various node distributions. Furthermore, \(k\) can be set to meet a certain specific requirement for reliability, while the adjustment of \(\mu\) can guarantee energy consumption while satisfying QoS demands.
## 3 The Important Role of Optic Flow in Postural Control  
In the last decade, the role of optic flow in the control of posture has received more and more interest from the scientific community and the research on many aspects has been advancing. Many factors have to be considered in the generation of the experimental paradigm, including the dimension of the stimulated visual field, the type of optic flow stimuli used (i.e., moving dots, moving stripes), and the duration of the stimulation.  
This review focuses on an important aspect to take into account: the dimension of the stimulated visual field. The choice of the exact portion of the retina to be stimulated is crucial given that the stimulation of the different central and the peripheral parts of the retina leads to the activation of different geniculo-cortical pathways, which prompts different cortical processing of information, and thus results in a differential activation of motor pathways.  
Until now, several studies have been aimed at uncovering the functional roles of the peripheral and central visual fields in postural control, leading to different conclusions. Many authors have already pointed out that such differences and the controversial results are likely to have arisen from the different experimental protocols and approaches.  
According to the retinal distribution of cone and rod photoreceptors, the definition of central vision ranges between \(2^{\circ}\) and \(4^{\circ}\) of the visual field [36]. In the retina, the density of the cone photoreceptors decreases as the distance from the fovea increases [37; 38]. However, considering that projections from the retina to the cortical area are responsible for processing central vision, the central visual field has been defined as the \(7^{\circ}\) surrounding the fovea, thus including the foveal, parafoveal and perifoveal regions [39].  
Besides the different definitions of the central and peripheral visual fields, the visual stimuli and the methodologies also differed across studies. In some studies, the stimuli were formed by random dot patterns, whereas in other studies, the stimuli were formed by vertical moving bars. In the majority of the studies, the stimuli were projected on a screen placed in front of the participants, whereas in other studies, the stimuli either originated from a side of the visual field or from placing subjects in a room with moving walls. The following chapters specify the protocols and definitions adopted by each study reported in this review, trying to explain how the different protocols conditioned the results.
# A Glider-Assisted Link Disruption Restoration Mechanism in Underwater Acoustic Sensor Networks
## 5 Performance Evaluation
### Evaluation with Different Parameters
The reason is that when performing link restoration, the OVAR, HHVBF and VBF algorithms need to perform more data packet retransmissions to select the more appropriate link. The process of topology reconstruction at this time consumes more energy. The glider-assisted link disruption restoration mechanism just added a node, so the topology is not changed much, so there is no extra energy costs. Therefore, the increase rate of the glider-assisted link disruption restoration mechanism is stable regardless of whether a disrupted link is restored. As shown in Figure 7, the glider-assisted link-disruption restoration mechanism can consume less energy than the OVAR, HHVBF, and VBF algorithms, respectively. For example,  
Figure 6: The connectivity of the network. the communication energy consumption is reduced by 20%, 47% and 58%, respectively, at 1600 s. This proves that the glider-assisted link-disruption restoration mechanism has obvious advantages in reducing communication energy consumption.
# Battery Management System Hardware Concepts: An Overview
## 4 Measurement
### Current Measurement  
The current measurement is of great importance, especially for the determination of the state variables of the battery system. As shown in Figure 1, depending on the battery type, the cell voltage only shows a low dependence on the SOC. A precise measurement of the total current allows for determining the SOC by integrating the current. In the following, established methods of current sensing are presented and compared to each other.  
Current acquisition devices can be divided into two basic sensor technologies: galvanically connected and isolated. The widely used method of shunt resistor current sensing belongs to the galvanically connected technologies. An example for the isolated current acquisition utilizes the electromagnetic properties of the current to acquire the magnetic field strength using hall sensors. Besides the sensor technology, the position in the battery pack has to be considered as well. If the battery system consists of several switchable strings, it can be important to not only implement one main current monitoring device, but one device for each battery string. This is important to trace power misbalancing during operation.
## 5 Case Studies
### Application to Citywide Transfer Analysis

Table 5: The estimated transfer volumes of four stations under six scenarios. The authors also identified some well-designed transfer stations, such as Xiangmi Lake Station and Shangmeilin Station. According to the field survey, some key design elements of good transfer stations are:  
* The metro-to-bus walking distance should be no greater than the acceptable threshold. * Transit agencies should consider opening more transfer bus lines or increase bus frequency at overcrowded metro stations. * Some narrow corridors connecting metro and bus stations should be changed to one-way roads. * Convenient crossing facilities such as pedestrian overpasses and underground subways are crucial for high-quality metro-to-bus interchanges.
## 4 A DFN Approach to Analyze Scale Effects in RQD Measurement
### Conceptual DFN Model
#### 4.1.2 Conceptual Modeling Results  
For each model with pre-defined fracture density, RQD data manipulations were performed separately for three orthogonal wells and four inclined wells. Taking the orthogonal wells as an example, maximum and minimum RQD values were recorded according to different core run lengths and an average of extremum was calculated using all three wells of the same core run length. As illustrated by Figures 8-11, a merging trend of the  
\begin{table}
\begin{tabular}{|l|l l l l l|} \hline \hline  & \multicolumn{2}{c}{**Average RQD of Four**} & \multicolumn{2}{c}{**Maximum RQD of Four**} & \multicolumn{2}{c}{**Minimum RQD of Four**} & \multicolumn{1}{c|}{**RQD of Center Wells**} \\  & \multicolumn{2}{c}{**Surrounding Wells**} & \multicolumn{2}{c}{**Surrounding Wells**} & \multicolumn{1}{c|}{**Surrounding Wells**} & \multicolumn{1}{c|}{**RQD of Center Wells**} \\ \hline \multicolumn{6}{|c|}{P\({}_{10}\)\(\_\)3} \\ \hline X & 94.93\% & 96.06\% & 92.91\% & 93.55\% \\ \hline Y & 95.50\% & 96.29\% & 94.62\% & 94.33\% \\ \hline Z & 92.83\% & 93.49\% & 91.92\% & 94.57\% \\ \hline \multicolumn{6}{|c|}{P\({}_{10}\)\(\_\)5} \\ \hline X & 86.77\% & 89.06\% & 84.94\% & 85.09\% \\ \hline Y & 88.09\% & 91.90\% & 84.36\% & 88.10\% \\ \hline Z & 82.23\% & 84.23\% & 80.14\% & 83.18\% \\ \hline \multicolumn{6}{|c|}{P\({}_{10}\)\(\_\)7} \\ \hline X & 77.36\% & 79.59\% & 75.17\% & 73.70\% \\ \hline Y & 78.00\% & 81.90\% & 75.83\% & 77.80\% \\ \hline Z & 79.73\% & 82.18\% & 77.24\% & 76.30\% \\ \hline \multicolumn{6}{|c|}{P\({}_{10}\)\(\_\)10} \\ \hline X & 63.75\% & 65.91\% & 61.67\% & 65.74\% \\ \hline Y & 69.68\% & 71.69\% & 67.48\% & 65.51\% \\ \hline Z & 59.27\% & 61.43\% & 55.29\% & 56.94\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: RQD of center wells and surrounding wells.  
Figure 7: Center studying well and surrounding wells defined for the representativeness test.  
The results for the inclined boreholes show a very similar pattern. For simplicity, the results are summarized in Figure 16 using 3D charts to show the relationships between the range of measured RQD values (for a selected core run length) and input fracture intensity P\({}_{10}\).  
The following key observations and results can be highlighted:  
* For every fracture intensity, the range of measured RQD values decreased with increasing core run length, and the RQD values converged to a constant value for a 30 m core run length.
* The above result is interesting, considering that the synthetic rock mass model used in the analysis represented a very homogeneous rock mass without pre-defined shear and fault zones. Despite that, for relatively short core run lengths (e.g., 3 m), the rock mass quality ranged from fair to excellent (P10 \(\leq\) 5) and from poor to fair (P10 \(\geq\) 5)
* The relative difference (max RQD minus min RQD) increased with increasing fracture intensity (for a selected core run length).
# Analysis of Development Coordination Levels between Skywalk Systems and Urban Spatial Environments
## 5 Discussion
### Characteristics of Clustering
#### 5.1.1. Type 1 (High Coupling, High Coordination): Attached and Nodal Closed Skywalk Network System  
This type of system is applicable to temperate or subtropical economic centers and is suitable for densely populated, highly educated, and congested cities such as Taipei, China, Hangzhou, Hong Kong, China, Osaka, and others (Table 6). This type of skywalk system is typically longer and wider, connecting more neighborhoods to form a network structure, and constructed in the form of mostly closed and covered windowless corridors, adapting to various climates around the world.  
From another point of view, the construction of such large-scale skywalks comes with high costs and management challenges, leading to increased maintenance expenses for the city. For example, the skywalk system in Guangzhou's Zhujiang New City has encountered problems with damaged facilities thus requiring maintenance; however, despite the investment, the skywalk is not highly utilized, resulting in idle resources and a waste of urban capital.  
1.2. Type 2 (High Coupling, Low Coordination): Traversing and Attached Covered and Windowless Skywalk Nodal System  
This type of system is suitable for cities with cold winters, hot summers, concentrated precipitation, and high white-collar employment, such as Yokohama, Chiba Makuhari, New York, and Cincinnati (Table 7). The skywalks in this type of system are more moderate in length, width, and number of connected neighborhoods, forming a nodal structure. The skywalks mainly traverse and attach to increase the connectivity and flexibility of the city, promoting the functional and spatial integration of the city. They are constructed in the form of enclosed, covered, and windowless corridors.  
However, this type of highly coupled pedestrian system can have a significant negative impact on ground vitality. For example, in Cincinnati, USA, the removal of the skywalk system began around 2000 to address this issue. So far, nearly a dozen skywalks have been removed, accounting for about 30% of the total volume.
# Spatialization and Analysis of China's GDP Based on NPP/VIIRS Data from 2013 to 2023
## 4 Results
### Analysis of GDP Modeling Results
#### 4.1.3 Spatialization Results of GDP  
The GDP from 2013 to 2023 is spatialized using the best fit regression model to obtain the new NTL estimation value of the GDP for each year. The total GDP data are allocated to each NTL data pixel to obtain the spatialized data of GDP. According to the distribution of GDP, ArcGIS is reclassified and the data (unit: million yuan) are divided into five categories--0-5, 5-10, 10-50, 50-100, and greater than 100--for hierarchical mapping. Figure 5 shows the hierarchical mapping results of the GDP spatialization data in 2013, 2015, 2017, 2019, 2021, and 2023.  
\begin{table}
\begin{tabular}{c c c c} \hline \(\mathbf{R^{2}}\) & **MNL** & **TNL** & **CNLI** \\ \hline Linear model & 0.524 & 0.011 & 0.512 \\ Logarithmic model & 0.684 & 0.012 & 0.568 \\ Quadratic function model & 0.795 & 0.015 & 0.715 \\ Power function model & 0.631 & 0.025 & 0.517 \\ Exponential model & 0.421 & 0.027 & 0.408 \\ \hline \end{tabular}
\end{table}
Table 2: \(R^{2}\) values of various function models in 2013.  
\begin{table}
\begin{tabular}{c c c c} \hline \(\mathbf{Year}\) & **Functional Equation** & \(R^{2}\) & **RMSE** \\ \hline
2013 & \(y=-0.115x^{2}+1.957x+2.811\) & 0.795 & 0.91 \\
2014 & \(y=-0.122x^{2}+2.056x+3.158\) & 0.786 & 1.00 \\
2015 & \(y=-0.124x^{2}+2.151x+3.262\) & 0.807 & 1.00 \\
2016 & \(y=-0.135x^{2}+2.331x+3.498\) & 0.842 & 1.00 \\
2017 & \(y=-0.12x^{2}+2.364x+3.209\) & 0.874 & 0.96 \\
2018 & \(y=-0.124x^{2}+2.433x+3.499\) & 0.853 & 1.10 \\
2019 & \(y=-0.094x^{2}+2.113x+4.066\) & 0.722 & 1.69 \\
2020 & \(y=-0.082x^{2}+1.964x+4.172\) & 0.760 & 1.51 \\
2021 & \(y=-0.081x^{2}+2.051x+4.625\) & 0.756 & 1.69 \\
2022 & \(y=-0.081x^{2}+2.068x+5.011\) & 0.737 & 1.82 \\
2023 & \(y=-0.075x^{2}+2.033x+5.259\) & 0.726 & 1.95 \\ \hline \end{tabular}
\end{table}
Table 3: Function equation and error value from 2013 to 2023.  
Figure 4: Best-fitting model for time regression analysis.  
Figure 3: \(R^{2}\) of the regression model of GDP, GDP per capita, and NTL index.
# Fusing Local and Global Information for One-Step Multi-View Subspace Clustering
## 5 Experiments
### Memory Usage Analysis  
We use MATLAB 2018b to run each clustering method independently and recorded the memory usage of each clustering method. Specifically, we recorded the current matlab memory usage once before we ran the program. After the program has finished, we recorded the current matlab memory usage again. Subtracting the first reading from the second reading gives us the memory usage of the method. For example, on the 3 sources data set, as can be seen in Figure 4, the memory usage of our method is small compared with the LMVSC, PMSC, COMVSC and MVSCLG methods. The memory usage of our method is slightly larger than the other remaining methods, but there is no significant difference between our method and the other remaining methods in terms of memory usage by an order of magnitude. Therefore, our method has an appropriate space complexity.
###### Abstract  
Measuring the soil's deformation and strength during loading is essential when evaluating saturated, undisturbed soft clays with the triaxial test apparatus. A photogrammetry method has been developed to measure total and localized volume changes on saturated, undisturbed soft clays during triaxial testing. In this study, an application of the photogrammetry method is presented by performing a series of triaxial tests with different confining pressures on saturated undisturbed soft clays. Three-dimensional numerical simulations of soft clay deformation are conducted using finite element analysis (FEM) software. The research paper showed that the photogrammetry method delivers a more accurate representation of the soil sample's deformation, as conventional tests and simulations tend to overestimate the deviator stress values relative to photogrammetry slightly. The simulation from FEM results show a strong relationship with the localized deformation data obtained through photogrammetry. This photogrammetry method provides valuable theoretical insights and practical uses in engineering applications.  
photogrammetry method; finite element analysis; undisturbed soft clay; triaxial test 2020 IEEEexample: accepted: 22 April 2025
## 4 Discussion
### Alzheimer's Disease and Cardiovascular Risk Factors
#### 4.2.4 Diabetes Mellitus and Alzheimer's Disease
Chronic hyperglycemia in DM leads to the formation of advanced glycation end products (AGEs), which interact with their receptor (RAGE) and trigger a series of pathological events in the brain. This AGE-RAGE interaction induces oxidative stress and activates inflammatory pathways, promoting the accumulation of A\(\beta\) and the \(\tau\) protein, both hallmark features of AD. Additionally, AGEs have been shown to interfere with neuronal function by increasing neuroinflammation and affecting the integrity of the blood-brain barrier, facilitating A\(\beta\) entry into the central nervous system. Together, these processes contribute to the neurodegeneration and cognitive decline observed in patients with both diabetes and AD [78]. As a result of these pathological changes, oxidative damage, chronic inflammation and impaired clearance mechanisms lead to A\(\beta\) aggregation and \(\tau\), ultimately triggering neurodegeneration and cognitive decline. In short, it has been observed that people with high blood glucose levels also have higher levels of A\(\beta\)-peptide, which is related to AD. Also, insulin has a protective role by (i) suppressing A\(\beta\) toxicity,(ii) reducing A\(\beta\) oligomer formation and (iii) regulating extracellular A\(\beta\) degradation through the modulation of insulin-degrading enzyme (IDE) activity. Furthermore, brain insulin regulates the balance between amyloidogenic (APP expression/degradation) and non-amyloidogenic pathways by controlling PI3K/AKT/GSK-3\(\beta\) signalling. However, decreased insulin signalling impairs these protective mechanisms, potentially accelerating AD progression and contributing significantly to cognitive decline and neurodegenerative processes [79]. In short, it has been observed that people with high blood glucose levels also have higher levels of A\(\beta\)-peptide, which is related to AD. This could be because people with diabetes have a lower capacity of body tissues, including the nervous system, to use glucose (sugar) and respond to insulin.
## 4 Discussion
### Satellite Constellation in Simulations  
In the simulation scenario, we chose a representative Walker delta, Globalstar constellation. The constellation has 8 orbits with 6 satellites in each orbit, and the angular distance between the planes is 60\({}^{\circ}\). The orbital period is 113 min, and the orbital inclination is 52\({}^{\circ}\). Globalstar satellites are equipped with four laser terminals with a bandwidth of 10 GHz for ISL communication, and a 1 GHz-bandwidth wireless terminal for communicating with ES, as shown in Table 1. The simulation starts at 8:00:00 UTC on 15 August 2022, and stops at 9:54:00 UTC on 15 August 2022. ES is located in Shanghai, 122.12\({}^{\circ}\) longitude and 31.53\({}^{\circ}\) latitude.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline The number of orbits & 8 \\ The number of satellites per orbit & 6 \\ The orbital period & 113 min \\ The inclination of orbits & 52\({}^{\circ}\) \\ The angle between adjacent orbits & 60\({}^{\circ}\) \\ The bandwidth of ISLs & 10 G \\ The bandwidth of feeder links & 1 G \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the constellation.
## 5 Discussion
### Comparison with the Human-Only Approach  
There are certain differences in the way humans operate in the workflow compared with a completely human-performed SR.  
* Conventional SR methodology might include a _snowballing_ strategy. It is a technique used to identify additional relevant studies that may not have been captured by the initial search strategy [13]. However, this manual process can be time-consuming and labor-intensive, especially for SRs that deal with a large volume of studies [33].  
In the case study, the snowballing process was not used as much. Instead, the researchers collected a large number of articles based on matching keywords and then passed them to the LLM for summarization or extraction. Because LLMs can handle many articles in a short period of time, researchers do not have to worry about the possible chain of connections between older and newer articles.  
An advanced LLM could be deployed to re-extract the subset of studies that passed the previous screening. An advanced LLM is more adept at recognizing the targeted context within the studies, leading to more accurate and relevant data extraction. Consequently, this reduces the variables that researchers need to handle and minimizes the need for extensive data cleaning.
* The hybrid workflow also reduces the chances of bias in selecting or reading articles. If the process is completed manually, researchers could pay less attention to certain articles based on meta-information. In the proposed hybrid workflow, this is not possible as all the articles are handed over to the LLM, which treats all equally.
## 3 ADRC Design and Parameterization
### ESO Design
#### 3.2.2 ESO Gain Adjustment  
In practical motor systems, ESO is capable of simultaneously observing and compensating for internal and external disturbances, avoiding the occurrence of vibration phenomena. Additionally, ESO converts the speed information into new state variables to accurately track the motor speed, thereby ensuring system stability.  
There are several feasible methods for the tuning of parameter \(\beta_{1}\), \(\beta_{2}\), \(\beta_{3}\) in a nonlinear ESO. One of the most classical methods is to replace the three parameters \(\beta_{1}\), \(\beta_{2}\), \(\beta_{3}\) with a single-parameter-observer bandwidth \(\omega_{0}\), and the tuning of the ESO gain can be realized by adjusting \(\omega_{0}\) directly. The specific formula of the system is shown in Equation (33):  
\[\begin{cases}\beta_{1}=6\omega_{0}\\ \beta_{2}=\frac{3\omega_{0}}{50}\\ \beta_{3}=\frac{3\omega_{0}^{2}}{50}\end{cases} \tag{33}\]  
The larger \(\omega_{0}\), the higher the estimation accuracy of the observer, the stronger the immunity to interference, and the faster the transient response. However, too large \(\omega_{0}\) will also bring high-frequency noise or ESO oscillation. The comprehensive performance of this system is taken as \(\omega_{0}\) = 1000, combined with Equation (33), and after parameterization, is taken as \(\beta_{1}\) = 55,000, \(\beta_{2}\) = 36,000, \(\beta_{3}\) = 53,000.