esa-sceva/satcom-chunk-collection huggingface dataset:
# QoS-Guaranteed Radio Resource Management in LTE-A Co-Channel Networks with Dual Connectivity
## 4 Evaluation
### Simulation Results
#### 4.4.3 Evaluation for the Resource Allocation of PF Scheme  
Scenario 3 was designed to show the effectiveness of the MTS scheme as compared to the PF scheduling. In addition, it also tries to demonstrate that the PF scheduling is not suitable in dual connectivity (DC) mode. Figure 11 compares the QoS satisfaction of the MTS scheme in DC mode as well as that of the PF scheme in both DC and signal connectivity (SC) modes. Clearly, the MTS scheme  
Figure 10: Satisfaction ratio of the different scheduling schemes in scenario 2.  
Figure 9: Satisfaction ratio of the different cell selection schemes.  
outperformed the PF scheduling, in particular, it outperformed the PF scheme by at least 34.55% in the QoS satisfaction of the whole system. Since the PF scheduling is based on the fairness principle among UEs, it suffered the disadvantage that some UEs with poor CQI may have participated in competition of the radio resources. As a consequence, it was interesting to observe that the simulation results showed that the system satisfaction of the PF scheme was better in the SC mode than that in the DC mode.  
While the MTS scheme achieved better QoS satisfaction, we may wonder its fairness. The fairness index of a scheme is defined as follows.  
\[\text{Fairness index}=\frac{\left(\sum_{i=1}^{n}x_{i}\right)^{2}}{n\times \sum_{i=1}^{n}x_{i}^{2}} \tag{22}\]  
where \(x_{i}\) is the throughput of the \(i\)th UE and \(n\) is the number of UEs in the system.  
Table 7 shows the fairness index of the MTS in DC mode as well as the PF scheduling in both DC and SC modes. We observed that PF in SC mode had the best fairness index, while the MTS in DC mode had the lowest fairness index. However, the difference was not very significant. In other words, the MTS scheme could achieve much higher QoS satisfaction by sacrificing a little fairness as a tradeoff.
## 3 Method of Prediction and Optimization
### Long Short-Term Memory (LSTM)
#### 3.1.1 LSTM Introduction  
In this paper, we employ LSTM to understand complex fluctuation of curves and generate predicted curves about incoming several hours. Due to the shortcoming of RNN, such as vanishing and exploding gradient problem, LSTM was developed to analyze and predict more accurately and efficiently [26]. We briefly introduce the structure of LSTM in this section. LSTM has a memory called \"cell\", which is used to store the state vector summarizing the sequence of the past input data. The state of the cell is updated by input data, output data and previous state of cell. Assume \(c_{t}\) denote the state of the memory cell at time \(t\). Then, \(c_{t}\) in a LSTM model can be updated by the following equations recursively.  
\[i_{t}=\sigma\Big{(}W_{x,i}x_{t}+W_{h,t}h_{t-1}+b_{t}\Big{)}, \tag{22}\]  
\[f_{t}=\sigma\Big{(}W_{x,f}x_{t}+W_{h,t}h_{t-1}+b_{f}\Big{)}, \tag{23}\]  
\[o_{t}=\sigma\Big{(}W_{x,\rho}x_{t}+W_{h,o}h_{t-1}+b_{o}\Big{)}, \tag{24}\]  
\[g_{t}=\tanh\Big{(}W_{x,c}x_{t}+W_{h,c}h_{t-1}+b_{c}\Big{)}, \tag{25}\]  
\[c_{t}=f_{t}\otimes c_{t-1}+i_{t}\otimes g_{t}, \tag{26}\]  
\[h_{t}=o_{t}\otimes\tanh(c_{t}), \tag{27}\]  
where \(\sigma(x)=\frac{1}{1+e^{-x}}\) is sigmoid function; \(x\otimes y\) denote element-wise product; \(W_{x,i}\), \(W_{h,i}\), \(W_{x,f}\), \(W_{h,f}\), \(W_{x,o}\), \(W_{h,o}\), \(W_{x,c}\), \(W_{h,c}\) are weight matrix for linear transformation; \(b_{t}\), \(b_{f}\), \(b_{o}\), \(b_{c}\) are bias vectors; three gating vectors \(i_{t}\), \(f_{t}\), \(o_{t}\) denote input, forget, and output respectively; \(g_{t}\) is the state update vector and \(h_{t}\) is the output hidden state vector.  
Figure 2 depicts the basic structure of LSTM. The zero configuration of \(f_{t}\) can let the network forget the information \(c_{t-1}\) stored in the memory cell. The input gate \(i_{t}\) and the output gate \(o_{t}\) can control the information flow from the input to the output. Note that the state of gate is learned from training data. In order to acquire the information corresponding to the given target, we add an additional output network to the hidden state \(h_{t}\)[27].
## 4 Discussion
### Limitation and Future Directions  
However, in a study with preliminary data, some limitations exist, but are in fact potential sources of improvement. As a result, a larger sample size, extending to several teams with different training strategies over multiple seasons, would allow more general conclusions to be drawn about injury prediction. In addition, the GPS data and questionnaires collection and imputations methods can also be improved. With regard to the completed questionnaires, the influence of greater diligence in the use of these questionnaires by players would be fundamental to observe. As for GPS data, they are present in an average form compared to their initial acquisition frequency of 10 Hz. In the race for performance, it would be interesting to observe the consequences of using all the raw values acquired at this frequency. Also, due to the differences between players, individualization could be considered in regards to the variables relating to external load (data extracted from GPS), by computing speed and acceleration thresholds specific to each player beyond which injuries is likely to occur. By doing so, the predictive potential of GPS variables could be greatly increased, and could have an influence on the training strategies implemented by coaches. Since not-injured players are much easier to find in datasets and injury is not a controllable factor, data augmentation could be used in order to simulate more injury examples from the real ones. Those artificial examples would probably improve the predictive performances of classifiers.
## 6 Therapeutic Potential of Cannabinoids in Gut Diseases
### Therapeutic Potential of Phytocannabinoids
#### 6.2.4 Phytocannabinoid Adverse Effects  
The most common adverse effects associated with cannabis use are vomiting and abdominal pain. A subset of users may also develop cannabinoid hyperemess syndrome (CHS), which is described in individuals chronically engaging in heavy cannabis use. Those affected by CHS experience abdominal pain and persistent vomiting, which is often improved by compulsive hot showers. Symptoms of CHS cease upon termination of cannabis use [122; 123; 124; 125]. Another serious condition associated with cannabis use is adult intussusception (AI). Intussusception refers to invagination of one bowel segment onto another, potentially leading to obstruction or bowel perforation if not treated. This condition mostly occurs in children, and in adults is usually secondary to another disease, such as neoplasia or post-operative adhesions or strictures. No underlying functional causes were found in individuals suffering from cannabis-associated AI. Patients complained of severe abdominal pain, vomiting, diarrhea, and abdominal cramps [124].  
Symptoms are dependent on mode and frequency of use, with inhalation of cannabis being a risk factor for developing chronic bronchitis and exacerbating pre-existing respiratory conditions. Oral administration of THC is not burdened with the above adverse effects, but is characterized by much lower systemic bioavailability, as well as slow and highly variable absorption [126]. Frequent cannabis use is associated with greater risk of developing cannabis use disorder (CUD), as well as possible negative effects on cognition [125].  
Adverse effects associated with CBD administered orally were less severe, the most common being gastrointestinal symptoms such as diarrhea, vomiting, nausea, abdominal pain, and constipation. The most common non-GI related adverse effects were somnolence, fatigue, and elevation of serum ALT/AST activity [127].  
It is important to bear in mind that studies investigating adverse effects of cannabinoids are few in number, and often have few participants. Some adverse effects such as CHS and cannabis-associated AI are described only in case studies, and the physiological mechanisms by which they occur are far from obvious. This strongly shows the need for more studies investigating the mechanistic basis of cannabinoid effects on the GIT, as well as interactions between cannabinoids and other substances affecting the GIT. The lack of data on adverse effects for endocannabinoids is probably due to their rapid breakdown by FAAH and MAGL in vivo.
## Materials and Methods  
### Sampling and Chemical Analysis  
The study area is a typical suburban residential area located in the Republic of Korea. The village (35.457\({}^{\circ}\) N, 127.464\({}^{\circ}\) E) has a population of about 50 people, and there is an asphalt mix plant (35.457\({}^{\circ}\) N, 127.469\({}^{\circ}\) E, elevation 223 m, area of 35,000 m\({}^{2}\)) located about 300 m away from the residential area (Figure 1). Two integrated samplers, a low volume sampler with a flow rate of 16.7 L/min (URG-2000-30 EHS/filter pack; URG-2000-30 FG, Chapel Hill, NC, USA) and a high volume sampler (1013 L/min: Thermo Fisher Scientific, Waltham, MA, USA), were deployed for sample collection during the measurement period [29; 30]. The nearby asphalt mix plant was established in 1995, and after the first lung cancer patient in the area was diagnosed in 1999, the National Center for Disease Control (NCDC) announced that 14 of the 50 residents of the village had been diagnosed with cancer [31; 32]. Therefore, the incidence rate of cancer patients in this village is substantially higher than the national average. To identify pollutant transport into the village, a sampling site located in the center of the village, which is east of the asphalt mix plant, was selected. In total, 61 PAH samples (24 h integrated samples, every six days) were collected from December 2014 through to December 2015.  
To compare the PAH concentrations in the suburban residential area with those of other locations in the Republic of Korea, samples were simultaneously collected at two control sites (the Korea Institute of Science and Technology (KIST) and Baengnyeong Island (BI)) from April through to July, 2016. KIST is located in Seoul, Republic of Korea, 7 km from the city center (37.603\({}^{\circ}\) N, 127.047\({}^{\circ}\) E); it is 400 m from the highway and is influenced by traffic but is not influenced by any major industrial sources. The samples were collected from the rooftop of a two-story building. In total, 21 24 h integrated PM\({}_{2.5}\) samples were collected using a high-volume sampler. At the same times, samples were collected from a measurement station on BI in the Yellow Sea (37.967\({}^{\circ}\) N, 124.630\({}^{\circ}\) E). The northernmost and westernmost part of South Korea, BI is located 740 km east of Beijing and 211 km west of Seoul. The measurement station was established by the National Institute of Environmental Research (NIER) as a core background site of the National Monitoring Network to track dust and pollution plumes transported from China. In previous studies conducted at this site, increased concentrations of sulfate and organic aerosols were found and attributed to the influence of pollution plumes transported from eastern China and the Korean Peninsula [3; 7]. All samples were shipped and stored frozen until analysis.  
Figure 1: Characteristics of wind direction and wind velocity at the locations and sampling sites used in the study.
## 1 Introduction  
Daegunjeoong-tang (DGJT; Daikenchuto in Japanese; Da-Jian-Zhong-Tang in Chinese) is a herbal medicine prescription first recorded in Gumgoeyorak ((), Jin Gui Yao Lue in Chinese), a famous traditional oriental medicine book written by [PERSON] in the Han Dynasty, and consists of four herbal medicines, _Zingiber officinal_ Rosc., _Panax ginseng_ C.A.Mey., _Oryza sativa_ L., and _Zanthoxylum schinfolium_ [PERSON]. [PERSON]. [1]. According to Gumgoeyorak, this prescription was used for patients with medical conditions and symptoms such as abdominal cramps, abdominal pain, fatigue, poor appetite, chills, and pale complexion [1]. _Z. officinal_ promotes antispasmodic action and secretion of digestive juices, and _P. ginseng_ has antibacterial, anticancer, and antiulcer effects [2]. _O. sativa_ replenishes sugar to smooth the energy metabolism of cells, and _Z. schinfolium_ promotes antihelminit action, digestion, and absorption, and has antifungal, antibacterial, and antipain activities [2]. Each herbal medicine contains a range of compound types including phenolic compounds, gingerols, and shogaols in _Z. officinal_[3]; triterpenoid saponins and ginsensoides in _P. ginseng_[4]; polysaccharides and maltose in _O. sativa_[5]; and quaternelli oils oleic acid, palmitic acid, and estragole, flavonoid glycosides hyperoside and quercitrin, and the alkaloids schinfolium in _Z. schinfolium_[6, 7, 8].  
DGJT is one of the most prescribed Kamp medicines in Japan, and various biological activities have been reported; these include anticancer action [9, 10], improved intestinal blood flow [11; 12], and beneficial effects against diseases of the digestive system such as gastric ulcer, enteritis, bowel motility, and ileus [13; 14; 15; 16]. Based on these biological activities, several clinical studies have also been conducted that examined diseases related to postoperative analytic ileus [17; 18], postoperative constipation [19; 20], and postoperative intestinal obstruction [21; 22]. Various nonclinical/clinical efficacy studies have been reported, but the current analysis methods for quality assessment of DGJT have not been validated. Therefore, it is necessary to establish an analysis method for the quality control of DGJT.  
In this study, a simultaneous reverse-phase high-performance liquid chromatography (RP-HPLC) and liquid chromatography tandem mass spectrometry (LC-MS/MS) multiple reaction monitoring (MRM) methods were developed for the quality control of DGJT using four marker components: hyperoside (from _Z. schinifolium_), quercetin (from _Z. schinifolium_), ginseng Reg1 (from _P. ginseng_), and 6-gingerol (from _Z. officinal_).
# Effect of Aging on Class G High Sulfate-Resistant Oil Well Cement Under High Relative Air Humidity
## 2 Materials and Methods
### Aging of Cement  
Class G oil well cement was placed on stainless steel trays with a thickness of 1-2 cm in a closed aging chamber maintained at 25 \({}^{\circ}\)C \(\pm\) 2 \({}^{\circ}\)C, and the relative humidity was controlled by saturated KCl solutions to 90% RH \(\pm\) 3% RH. The samples were aged for periods of 0 d (labeled A0), 7 d (A7), 14 d (A14), and 28 d (A28). The aged cement was collected upon completion of the predetermined aging times. The change of relative air humidity in the aging environment is shown in Figure 1. The uncertainty is computed based on the following formulae:  
\[S_{d}=\sqrt{\frac{1}{n-1}{\sum\limits_{i=1}^{n}}(x_{i}-\overline{x})^{2}} \tag{1}\]  
\[\mu_{A}=\frac{S_{d}}{\sqrt{n}} \tag{2}\]  
\[\mu_{B}=\frac{\Delta_{ins}}{divisor} \tag{3}\]  
\[\mu_{C}=\sqrt{\mu_{A}^{2}+\mu_{B}^{2}} \tag{4}\]  
In Equation (1), \(S_{d}\)-the standard deviation; n--the number of measurements; \(x_{i}\)--the i-th measured value; \(\overline{x}\)--the average of the measured values.  
In Equation (2), \(\mu_{A}\)--the Type A uncertainty resulting from repeated measurements; \(S_{d}\)--the standard deviation; \(n\)--the number of measurements.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**SiO\({}_{2}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **Fe\({}_{2}\)O\({}_{3}\)** & **TiO\({}_{2}\)** & **CaO** & **MgO** & **SO\({}_{3}\)** & **K\({}_{2}\)O** & **Na\({}_{2}\)O** & **LOI** \\ \hline
21.47 & 4.11 & 5.48 & 0.35 & 62.75 & 1.34 & 2.24 & 0.48 & 0.05 & 1.07 \\ \hline \end{tabular} Note: LOI is the loss on ignition at 1000 \({}^{\circ}\)C.  
\end{table}
Table 1: Chemical composition of class G oil well cement (wt.%).  
Figure 1: The curves of relative air humidity during cement aging.  
In Equation (3), \(\mu_{B}\)--the Type B uncertainty based on the instrument's precision; \(\Delta_{ins}\)--the instrument's uncertainty, which is set to 3%RH in this paper; and the divisor is taken as \(\surd 3\).  
In Equation (4), \(\mu_{C}\)--the combined uncertainty; \(\mu_{A}\)--the Type A uncertainty; and \(\mu_{B}\)--the Type B uncertainty.  
The calculated relative air humidity uncertainty based on the measured data is 1.7% RH.
## 2 Routing Protocol for LLNs
### Downward Traffic  
Besides the predominant Multi-Point to Point (MP2P) traffic flows, RPL also supports Point to Multi-Point (P2 MP) traffic, i.e., flowing down the DODAG. For this purpose, Destination Advertisement Objects (DAOs) (Figure 4) are propagated up the DODAG. They contain (amongst others) the RPLInstanceID, copied from DIO messages.  
RPL specifies two Modes Of Operation (MOPs) for P2 MP traffic, one of which may be enabled at a time in an RPL Instance: (1) storing mode (stateful); and (2) non-storing mode (source-routed). To facilitate these MOPs, a DAO contains groupings of RPL Target- and Transit Information options [4] (Sections 6.7.7 and 6.7.8). Each contiguous set of Transit Information options pertains to the preceding (contiguous) set of RPL Target options. That is, RPL Target options represent the reachability of IPv6 addresses/prefixes/multicast groups, while Transit Information options specify attributes of the paths to the targets (i.e., reachable destinations) that immediately precede them. For the sake of simplicity, assume that the DAO parent set contains only a node's preferred parent, DAOs are always unicast, and there is only one root disseminating a single subnet-wide prefix.
## 1 Introduction  
Genomic analysis performed by the Cancer Genome Atlas (TCGA) Research Network revealed numerous clear cell renal cell carcinoma (ccRCC) gene mutations including titin (TTN), polybromo 1 (PBRMI), SET domain containing 2 (SETD2), BRCA1-associated protein 1 (ubiquitin carboxy-terminal hydrolase) (BAP1), and lysine (K)-specific demethylase 5C (KDM5C) [1]. A new research field called radiogenomics has been developed. This field is based on the association between imaging phenotypes, macroscopic expression of processing that takes place at the molecular level and is detectable as imaging features, and genomic of diseases (gene mutations, gene expression patterns and other genome-related feature) [2,3].  
The von Hippel-Lindau (VHL) tumor suppressor gene represents the most mutated gene in clear cell renal cell carcinoma (ccRCC) [4]. It regulates the hypoxia inducible factor (HIF) protein, which modulates the transcription of several genes, including vascular endothelial growth factor (VEGF). These genes control angiogenesis, proliferation, tumor cell migration and permeability. The mutated VHL gene can lead to an accumulation of HIF with consequent upregulation of the neoangiogenic cascade [5; 6]. Thanks to the advancement of molecular sequencing technology, further key genetic mutations have been identified in addition to the already known inactivation of the VHL gene. Radiogenomics allows for the understanding of and the analysis of the biology and intrinsic heterogeneity of the cancers [7].  
Currently, the role of visceral adipose tissue (VAT) in the pathogenesis of ccRCC is well known. A large percent of VAT, through an altered secretory activity of adipokines, mediating factors and molecules, may influence ccRCC induction and progression [8; 9]. In fact, a larger quantity of abdominal adipose tissue, mostly VAT, was detected in ccRCC patients, demonstrating an association between VAT and ccRCC [10; 11].  
Adipose tissue compartments and muscle mass can be distinguished by computed tomography (CT), a non-invasive imaging technique for the qualitative and quantitative analysis of tissues [12; 13; 14]. The semiautomatic quantification of the abdominal adipose tissue compartments and abdominal muscle mass areas performed on a single slice allows for the obtaining of body composition data [10; 11; 13; 14].  
To date, adipose tissue and skeletal muscle mass in ccRCC patients with TTN gene mutation has not been evaluated. In the present study, using CT, we evaluate body composition in ccRCC with VHL and TTN gene mutation.
## 1 Introduction and Related Research  
Indoor positioning has garnered significant attention in recent years due to its critical role in applications such as autonomous navigation, robotics, and smart environments. Unlike outdoor positioning systems, such as Global Positioning System (GPS), which suffer from signal attenuation and multipath effects indoors, indoor positioning requires alternative solutions. Existing indoor positioning technologies can be broadly categorized into magnetic, inertial, acoustic, optical, radio frequency (RF), and vision-based approaches, as shown in Table 1.  
Magnetic-based positioning determines location by analyzing perturbations in the Earth's magnetic field or magnetic materials. This technique exhibits high robustness for indoor fingerprinting. However, sensor-dependent variability can lead to inconsistent results [1; 2; 3]. Inertial methods utilize accelerometers and gyroscopes to infer movement and orientation without relying on external infrastructure. This feature makes them ideal for GPS-denied environments. These systems often suffer from cumulative errors and sensor drift over time [4]. Acoustic methods leverage characteristics such as time delay and signal attenuation to estimate distance. These systems are suitable for low-light or visually complex environments. However, signal attenuation over long distances or obstacles can present challenges [5]. Optical positioning technologies employ infrared or visible light signals to estimate target locations. While capable of achieving high precision, they are sensitive to lighting variations, occlusion, and reflective surfaces [6]. RF-based systems included Wi-Fi, Bluetooth low energy (BLE), radio frequency identification (RFID), and ultra-wideband (UWB). They are well-suited to complex indoor scenarios due to their ability to penetrate walls and obstacles. Vision-based positioning uses fixed or mobile cameras to extract spatial features from nature environment. Although they can provide high-resolution spatial information, these systems tend to be computationally intensive and often fail under poor lighting conditions [7]. In addition to localization, vision-based obstacle recognition plays a pivotal role in applications such as autonomous driving, drone navigation [8], and railway safety systems [9]. Current monocular vision-based obstacle detection techniques are generally divided into two categories: feature-based and motion-based methods. Feature-based approaches extract visual characteristics such as color, shape, texture, or edges to detect and classify obstacles. Machine learning algorithms, including neural networks and support vector machines (SVMs), have further improved obstacle classification performance by
# The Contribution of Near-Surface Geophysics for the Site Characterization of Seismological Stations
## 3 Application of the Geophysical Surveys and Processing Results
### Seismological Station at Villa, Attica (VILL)
1D S-wave velocity model, derived from the MASW technique, revealed one seismic layer with velocity \(V_{S1}\) = 1160 m/s, which is considered to be the velocity of the seismic bedrock, while the S-wave velocity for the first 30.0 m of the subsurface was calculated at 1217 m/s. Finally, the results of the ERT technique, also presented in Figure 6, revealed a resistant layer (>7000 Ohm.m), dominating across the section at depths greater than 0.5 m.