esa-sceva/satcom-chunk-collection huggingface dataset:
# Sensor Observation Service API for Providing Gridded Climate Data to Agricultural Applications
## 3 Methodology
### Simple Mesh ID Conversion  
As for each grid, AmGSD internally uses a mesh ID system that follows the Japanese public third order mesh ID [7]. This Japanese ID system requires rather complicated calculation to encode latitude and longitude or to decode the mesh ID into latitude and longitude. To simplify encoding and decoding on latitude and longitude into the mesh ID, we implement a simple mesh ID equation.  
Equation:  
\[simple\,\,mesh\,\,ID=X\times 10,000+Y \tag{1}\]  
where:  
simple mesh ID is formated in XXXYYYY  
\[X=\frac{(latitude-lat0)\times 3600}{\Delta lat} \tag{2}\]\[Y=\frac{(longitude-lon0)\times 3600}{\Delta lon} \tag{3}\]  
latitude is the latitude of the target grid in degrees longitude is the longitude of the target grid in degrees lat0 = 24; lowest latitude of AmGSD coverage in degrees _lon0_ = 122; lowest longitude of AmGSD coverage in degrees _Alat_ = 30; grid spacing of AmGSD in latitude in arcseconds _Alon_ = 45; grid spacing of AmGSD in longitude in arcseconds  
Clients can filter data within the grid of interest by specifying the simple mesh ID to retrieve data instead of using a bounding box. We use the Uniform Resource Name (URN) concept to refer to AmGSD and each grid in SOS. Concatenation of the procedure name and the simple mesh ID indicates the specific grid for querying data, as described in Table 1. For instance, a user wants to retrieve climatological data of farms that are located on or around latitude 42.8 and longitude 143.2. By referring to the area in the mesh, the location in the latitude and longitude is converted to the simple mesh ID. In this case, the simple mesh ID is 22561696. To filter data within this mesh, the URN is urn:NARO1 km:Agro:22561696. Additionally, this URN is a filter parameter for offering the tag in the getObservation request, which is described in Section 3.5.3. If clients want to use a bounding box for filtering grids, clients should use a bounding box that has two identical points, which is equivalent to a single coordinate.
## 2 Public Safety Network
### Drones for Public Safety
Recently, a lot of studies have investigated various design challenges such as 3D deployment, energy efficiency, and time flight constraint. The optimal 3D deployment of drones for extending the coverage area and enhancing the QoS is discussed in [56]. The idea is supported by a study [57] that discusses the 3D deployment of a multiple drone base station to maximize the coverage area and maintain the link quality between drones and the ground station by using the practical swarm optimization technique. Furthermore, drones' 3D deployment represents a key technology that can assess drones to deliver network services for public safety and disaster management. Therefore, the authors of [58] proposed a practical swarm optimization technique to enable the drones' 3D deployment to provide a large coverage area, maintain connectivity, and satisfy users' QoS requirements and drone capacity. Also, 3D deployment for swarm drones was introduced for maximizing the available lifetime of drones and the total throughput of all users [59].
## 3 Results and Discussion
### Comparative Analysis  
The correlation coefficients between the micro-crack increment and four types of statistics (the range, average, average deviation, and standard deviation) on the MC strain rates have been calculated and plotted in Figure 11. All of the correlation coefficients are positive, which illustrates that the crack increment is positively correlated with the four types of statistics both in case 1 and case 2. Three types of statistics in case 2 are lower than those in case 1, meaning that the existence of a flaw would degrade the correlation between the strain rate field and damage evolution and reduce the dispersion of the strain rate field.  
In Figure 11, these two curves have a similar tendency: the minimum of each case is the correlation coefficient between the strain rate average and crack increment; the maximum of each case is the correlation coefficient between the strain rate average deviation and crack increment. This means that the crack growth is highly correlated with the average deviation. Therefore, the average deviation of the MC strain rate field is a better choice for making discriminating judgments and evaluations of specimen damage evolution.  
The volume strain rates of MCs and their statistical data could illustrate the damage evolution during the loading process from varying points and degrees. For integrating preceding analysis, the applications of each parameter have been collected and listed in Table 2. In this table, the plus sign (+) means a parameter could facilitate the corresponding function, and the minus sign (-) has the opposite meaning.  
Summarizing the above work, Table 2 shows the following results: the MC strain rate contour maps can tell the damage position; the average deviation can judge the crack increment; the average fluctuation and other statistics can indicate the damage stage of samples. Therefore, comprehensive analysis of the MC strain rates and the corresponding statistics can be used for the collapse prediction of rock-like material.  
Figure 11: Correlation coefficient between micro-crack increment and statistical data of MC strain rate.
# Application of the Segmented Correlation Technology in Seismic Communication with Morse Code
## 5 Discussion  
In this study, we have presented the SCTMC to extract the target signal (SOS) and built a seismic communication system. By comparing the results of footstep signals, vibroseis signals and hammer signals, the signal transmitter suitable for complex environments is selected. According to Morse code, a vibroseis source is employed to transmit a 200 Hz SOS signal, which is received by geophones and recorded by seismometers at 60 m. However, the energy of the target signal is so weak that the band-pass filter cannot extract the target signal. Hence, the SCTMC is put forward to handle the seismic data, which extracts the target signal and suppresses other signals. Although its amplitude is only 0.2% of the amplitude about the original signal (in Figure 6a), the extracted signal can be converted into the binary code 1010101001011011010010101110 by setting a reasonable threshold. In this study, the communication distance can also be extended by improving the filtering algorithm or increasing the number of vibroseis and geophones. This kind of information recognition is at the expense of information transmission, which is relatively low at present. However, with the development of seismic communication technology, the relay distance will be extended or the communication speed will be improved.
# Brain and CSF Alzheimer's Biomarkers Are Associated with SERPINE1 Gene Expression
## 1 Introduction  
_SERPINE1_ (plasminogen activator inhibitor, PAI) encodes a member of the serine proteinase inhibitor superfamily. It is one of the most important inhibitors of fibrinolytic activity thus leading to pathological fibrin deposition and tissue damage [1]. This role is attributed to the fact that SERPINE1 is the principal inhibitor of tissue plasminogen activator (tPA, encoded by the gene _PLAT_) and urokinase-type plasminogen activator (uPA, encoded by the gene _PLAU_).  
Plasminogen is expressed primarily by the liver and is therefore present in the plasma, but mRNA levels have been detected in various tissues including brain, adrenal, kidney, testis, heart, lung, uterus, spleen thymus and gut [2]. The plasminogen activator system is pivotal in various physiological and pathological processes, encompassing coagulation, fibrinolysis, inflammation, wound healing and malignancy [1].  
SERPINE1 is also known to inhibit cell migration by competing for vitronectin binding to integrins [3]. Recent findings reveal vitronectin plays multiple roles within the nervous system, where it contributes to activities such as neural differentiation and neurogenesis, while also having a role in regulating axon size and providing support and guidance for neurite extension [4]. By interacting with integrin receptors in vascular endothelial cells, vitronectin can reduce the permeability of the blood-brain barrier [4].  
Amyloid beta (A\(\beta\)), one of the hallmarks of Alzheimer's disease (AD), is known to increase tPA and uPA [5]. In turn, tPA and uPA cleave plasminogen (encoded by the gene _PLG_), resulting in the activation of the serine protease plasmin [5]. Plasmin was shown to degrade A\(\beta\) fibrils, therefore attenuating A\(\beta\) neurotoxicity [5]. In AD, brain tissues contain reduced levels of plasmin when compared to controls [6].  
SERPINE1 was shown to accumulate in blood samples from mild cognitively impaired (MCI) and AD patients [7]. Given the established correlation between plasma SERPINE1 and cognitive impairment, SERPINE1 is proposed as a potential biomarker for early AD detection and diagnosis [7]. Consistent with this finding, _SERPINE1_ hippocampal RNA prevalence is significantly increased in AD patients relative to controls [8].  
In the present study, we examined SERPINE1 concentrations in control and AD brain tissue and contrasted _SERPINE1_ mRNA levels as a function of neurofibrillary tangles density, senile plaque density and Braak stages. To validate SERPINE1 as a potential presymptomatic AD biomarker, we analyzed data from the PREVENT-AD cohort, which consists of asymptomatic individuals with a family history of late-onset AD. CSF SERPINE1 protein levels were measured and contrasted with classical AD pathological biomarkers such as tTau, pTau181 tau and A\(\beta\)42. We replicated our biochemical findings in living ADNI subjects spanning various disease stages.
## 2 Standard Synopsis
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Standard**} & \multicolumn{2}{c}{**OV1**} & \multicolumn{2}{c}{**OV2**} & \multicolumn{2}{c}{**UV1**} & \multicolumn{2}{c}{**UV2**} \\ \cline{2-9}  & **p.u.** & **Clearing Time [s]** & **p.u.** & **Clearing Time (s)** & **p.u.** & **Clearing Time (s)** & **p.u.** & **Clearing Time (s)** \\ \hline IEEE Std. 1547-2003 & 60.5 & 0.16 & - & - & 59.3 & 0.16 & - & - \\ IEEE Std. 1547-2018 & 61.2 & 300 & 62.0 & 0.16 & 58.5 & 300 & 56.5 & 0.16 \\ KS C 8564-2015 & 60.5 & 0.16 & - & - & 59.3 & 0.16 & - & - \\ KS C 8564-2021 & 61.5 & 0.16 & - & - & 57.5 & 300 & 57.0 & 0.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: DG response to abnormal frequency by standards. varied up to 393.8% compared to the rated power, the detection technique by effective power fluctuation is not widely used as a type of active method. \[\left[\left(\frac{V}{V_{max}}\right)^{2}-1\right] <\frac{\Delta P}{P_{inv}}<\left[\left(\frac{V}{V_{min}}\right)^{2}-1\right] \tag{1}\] \[Q_{f}\left[1-\left(\frac{f}{f_{min}}\right)^{2}\right] <\frac{\Delta Q}{P_{inv}}<Q_{f}\left[1-\left(\frac{f}{f_{max}} \right)^{2}\right] \tag{2}\]  
Active techniques based on reactive power fluctuation techniques are widely used. In IEEE Std. 1547, they increase from a maximum of 2.4% to 12.8%, and in KS C 8564, they increase from a maximum of 2.4% to 10.8% to enable the detection of islanding operations based on OF/UF. As can be seen from this point, it can be confirmed that the fluctuation range of reactive power for islanding operation detection based on frequency relay is about 5 times larger. With this amount of fluctuation, the size fluctuation of reactive power may cause the deterioration of power quality. Therefore, in this paper, based on a small level of reactive power fluctuation, the authors propose an improved technique that can detect islanding operation without reaching the threshold value of the frequency relay.
# Offloading of Atomic Tasks in Satellite Networks:
## 3 Task Offloading Model
### Atomic Task Cache Model  
We mainly cache atomic tasks received by satellites. The atomic task cache stores the calculation results of high-frequency atomic tasks on high-orbit satellites. The low-medium orbit satellite issues an atomic calculation task offload request. If the atomic calculation task on the low-medium orbit satellite has been cached to the high-orbit satellite, the low-medium orbit satellite does not need to offload the atomic calculation task to the high-orbit satellite repeatedly. We only need to transmit the cached result to the low-medium orbit satellite.  
In Definition 1, We define computing task cache decision variables to determine whether to cache an atomic task to a high-orbit satellite.  
Given a satellite network \(G=(V,E,T)\), the atomic task request queue \(Q\) received by the satellite \(s,s\in V\), and \(q_{i}\) indicates a atomic task requests. Then at a certain moment \(t\), \(\rho^{s}_{q_{i}}(t)\) denotes that whether the calculation result of \(q_{i}\) has been cached, it has a value range in \(\{0,1\}\).  
The Definition 1 is about the cached decision variable range, where \(\rho^{s}_{q_{i}}(t)=1\) means that the calculation result of atomic task \(q_{i}\) has been cached, on the contrary, \(\rho^{s}_{q_{i}}(t)=0\) means that the atomic task \(q_{i}\) is not cached and needs to be calculated locally. Therefore, the atomic calculation task cache variable on the satellite \(s\) can be expressed as \(\rho^{s}_{Q}(t)=\{\rho^{s}_{q_{1}},\rho^{s}_{q_{2}},\cdots,\rho^{s}_{q}\}\).  
The use of inter-satellite links can achieve near-real-time data transmission between high-orbit satellites and low-medium orbit satellites. Therefore, we do not consider the time consumption of data transmission between high-orbit satellites and low-medium orbit satellites. In addition, we do not consider packet loss in the task queue, because our goal is to achieve minimal time cost consumption while completing all tasks.
## 3 Method
### Basic Theory and Techniques for GRU
#### 3.2.1 GRU Algorithm Flow  
The GRU unit structure is shown in Figure 6. The basic flow of the GRU algorithm is as follows:  
\[\text{Update gate}:z_{t}=\sigma(w_{z}\cdot[h_{t-1},x_{t}]) \tag{8}\]  
\[\text{Reset gate}:r_{t}=\sigma(w_{r}\cdot[h_{t-1},x_{t}]) \tag{9}\]  
The reset gate is used to decide how to merge the hidden state of the previous moment with the input of the current moment, and the update gate is used to decide whether to pass the hidden state of the previous moment to the current moment.  
Calculation of candidate hidden state of GRU:  
\[\widetilde{h_{t}}=tanh\left(w_{h}\cdot\left[r_{t}\bigodot h_{t-1},x_{t}\right]\right) \tag{10}\]  
where \(\odot\) means multiplication by element.  
GRU hidden status update:  
\[h_{t}=(1-z_{t})\bigodot h_{t-1}+z_{t}\bigodot\widetilde{h_{t}} \tag{11}\]  
The final hidden state, \(h_{t}\), will be the output at the current moment and passed to the GRU as input at the next moment.  
In the above formula, \(x_{t}\) represents the input at the current moment, \(h_{t-1}\) represents the hidden state at the previous moment, \(z_{t}\) is the update gate, \(r_{t}\) is the reset gate, \(\widetilde{h_{t}}\) is the candidate hidden state, and \(h_{t}\) is the hidden state at the current moment. By iterating over the above process, the GRU can gradually update the hidden state based on the input sequence and output the corresponding hidden state at each moment. This allows the GRU to effectively capture long-term dependencies in sequence data, suitable for tasks such as natural language processing, time series prediction, and more.
# Application of the KINEROS 2 Model to Natural Basin for Estimation of Erosion
## 3 Erosion Estimation Models Used
### Usle
#### 3.1.1 Determination of Rainfall Erosivity Factor (\(R\))  
Rainfall-runoff erosivity factor is defined as the capacity of rain to produce erosion. It is also defined as the rainfall energy factor, and it is the product of total rainfall energy (E) and the maximum 30-min rainfall intensity (\(I_{30}\)) [1; 13].  
\[R=\sum\
olimits_{i=1}^{n}R_{i}=\sum\
olimits_{i=1}^{n}E_{c}I_{30} \tag{2}\]  
where \(R_{i}\) is the rainfall energy factor of each rainfall, \(i\) is the number of rainfall events during the study year, \(E_{c}\) is the storm kinematic energy (in _MJ/ha/year_), and \(I_{30}\) is the maximum intensity (in _nm/h_) during a 30-min rainfall interval [13; 19].  
Figure 2: Study area description: (**a**) Study area and sample location, (**b**) surface soil composition map from Sigma ([[http://iea.ad/sigma](http://iea.ad/sigma)]([http://iea.ad/sigma](http://iea.ad/sigma)), accessed on 17 December 2013) [3].  
First it is necessary to determine kinematic energy for every storm event and then obtain the R factor for this event. Total \(R\) factor will be the sum of R factors of each event [3,13,20].
## 1 Introduction
The commonly reported reasons for the clinical failures of endocrowns are tooth fracture and debonding of endocrowns [19]. Hence, researchers suggest occlusal thickness for the endocrowns to be at 1.5-2 mm. However limited scientific evidence is available to support this recommendation [5]. Clinicians routinely encounter the ETT with occlusal deficiency ranging from 3 to 7 mm. The reports from earlier researches regarding the effect of increased occlusal thickness on the endocrown fracture resistance are not conclusive. [PERSON] et al. [20] report the enhanced fractured resistance with increased occlusal thickness, while [PERSON] et al. [21] recommended 3 mm for reducing catastrophic failures. The preparation height of over 4 mm would lead to shorter pulp chamber depth for retention and hamper the stress distribution. [PERSON] et al. [22] recommended the retentive length including a post at least equaled to crown height. Thus, the extension into the root canal to enhance the retentive area equals to coronal height could be a significant model to be evaluated. The significant difference in biomechanical behavior between all-ceramic materials is also expected to influence the clinical performance of endocrowns. Oclusal forces on the molar region are predominantly is axial loading; whereas the premolar region chewing function is comprising of axial loading accompanied by lateral forces. Consequently, the durability of restorative material depends on the elasticity of modulus, along with resilience under shear stresses. Generally, the long-term success of endocrowns is dependent on the selection of appropriate ceramic material for each clinical situation. With the availability of a large number of ceramic materials, the dentist should select a suitable restorative material. Though the few researchers have explored the fracture resistance of endocrowns in different occlusal thickness, there is no literature exists on the fracture resistance of various all-ceramic crowns in different thicknessesalong with radicular extension at premolar region. Thus, this in-vitro study aimed to evaluate the effect of different occlusal preparation depths, radicular extension and all-ceramic materials on the fracture resistance of premolar endocrowns. The null hypothesis of the study was (1) the occlusal preparation depth and radicular extension would not affect the fracture strength of endocrowns. (2) Different all-ceramic materials do not affect the fracture resistance of endocrowns. (3) The different preparation depth or materials would not affect the failure type.
# Static Evaluation of a Midimew Connected Torus Network for Next Generation Supercomputers
## 5 Cost Effectiveness Analysis of MTNs
### Cost Performance Trade-Off Factor  
The CPTF enabled the assessment of the network by providing a crucial static parameter. The CPTF summarized four of the most important parameters: diameter, node degree, wire complexity, and total number of nodes. This provided a rapid comparison regarding costs, as opposed to comparing each characteristic individually. The diameter indicated the upper latency and bandwidth. The node degree was related to the router costs and scalability. The wire complexity was directly proportional to the wire costs through the layout links. The number of nodes was the actual processing elements that were required for the execution of the programs. The \(CPTF\) was calculated using Equation (3).  
\[CPTF=\frac{N_{d}\times G_{p}}{D},\;G_{p}=\frac{W_{L}}{total\;\#\;nodes} \tag{3}\]  
The CPTF of the MTN was higher than for the TESH, the MMN, the torus, and the mesh. It was slightly lower than the TTN, and notably different from the TFBN. Therefore, the MTN provided a good trade-off between performance and costs.
# A Deep Learning Approach with Feature Derivation and Selection for Overdue Repayment Forecasting
## 2 Related Work
### Deep Learning in Online Lending  
Deep learning has the characteristics of end-to-end learning, thus eliminating the complicated manual design process. On one hand, the feature representation ability of neural networks is extremely powerful, and so the deep leaning approaches mentioned in Section 2.1 can achieve better results than general machine learning methods and probabilistic models. On the other hand, the parameters of neurons in the network can hardly represent meaningful mathematical information of the input features, and so neural networks are poorly interpretable.  
Recently, some works have introduced deep learning into the online lending domain. The authors in [40] transfer the learning algorithms of LSTM, the attention mechanism and word2 vec [41], which are effective in the NLP domain, into online lending and propose a credit scoring method. The online operation record data of borrowers in online lending are regarded as a sentence with multiple words. Word2 vec is applied to produce latent representation embedding for the behavior record. Instead, we divide the original behavior data into static attributes and dynamic sequences. Our proposed feature derivation and selection method is applied on the static features afterwards.  
The authors in [42] develop deep learning models to predict the trading volume of the online market based on the trend of change in investor sentiment. TextCNN [43] is introduced to classify the sentiment of investor comments, and LSTM is utilized to analyze the trend of the trading volume. The prediction of the daily trading volume can be regarded as a time-series problem, while in our research, overdue repayment forecasting is an event-series problem.