esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods  
### Plant Materials  
The study used two color morphs of _R. canina_ flowers, white and dark pink, which are endemic to the mountainous areas of Taleghan, Alborz province, Iran. The flowers were collected from a wild population at 36\({}^{\circ}\)10\({}^{\prime}\)18.8\({}^{\prime\prime}\) N and 50\({}^{\circ}\)46\({}^{\prime}\)12.7\({}^{\prime\prime}\) E and identified by their morphological features of leaves, fruits, and flowers, based on the methods of [93; 94]. A voucher specimen (number 006962) was deposited in the Herbarium Instituti Agronomici Keredjensis (HIAK) by a botanist from the horticulture department of the University of Tehran (Dr. [PERSON]). The flowers were harvested in the evening (6:00 p.m.) before sunset, which previous studies [95; 96] indicated is the optimal time for volatile oil content.  
White and dark pink shrub samples were obtained from the same plant. For each sample, three technical replicates were prepared for molecular analysis. The flowers were picked at two stages of floral development that were important for scent emission: the budding stage (S1) and the open-flower stage (S2) [97]. The development stages were determined by measuring the flower length, weight, and diameter, and by observing the sepal shape and the pollen color and texture. In the budding stage (S1), the sepals were closed but the petal colors were visible. In the open-flower stage (S2), the pollen was light yellow and the petals were fresh and dark. These two stages had higher scent emission than the senescence stage, based on phenotypic characteristics (Figure 7). To prevent RNA degradation, the fresh samples were immediately frozen in liquid nitrogen and stored at \(-\)80 \({}^{\circ}\)C until further analysis. For dried petals powder preparation, the petals were detached and dried in a cool and dark place, then ground into powder [98].  
Figure 7: Different developmental stages of dark pink and white _R. canina_ flowers. Budding stage (S1) (**a**,**c**); open-flower stage (S2) (**b**,**d**).
# On the Fairness of Internet Congestion Control over WiFi with Deep Reinforcement Learning
## 2 Background and Related Works
### Deep Reinforcement Learning in TCP
Whileseveral DRL-based TCP protocols and optimization strategies have been proposed, no prior research has explored the dynamic switching between available CCAs to enhance network performance, improve flow fairness, and prevent starvation, especially in heterogeneous environments, such as in WiFi networks. This paper introduces a DRL-based dynamic switching mechanism designed to tackle these persistent TCP issues. Our results demonstrate significant improvements in key performance metrics such as throughput, RTT, jitter, packet loss, fairness, and the prevention of throughput starvation. Despite advancements in congestion control algorithms, several challenges remain unresolved. A comprehensive understanding of how delay-bounding algorithms adapt to non-congestive network delay variations, the thresholds and characteristics of non-congestive jitter, and the development of ML/DRL frameworks to quantify and mitigate fairness issues is essential. Addressing these gaps will advance the development of congestion control algorithms and contribute to enhanced network performance and fairness [19]. In this study, we focused on TCP unfairness by analyzing the underlying mathematical principles, conducting real-world network tests, and dynamically switching CCAs using DRL techniques. This involved assessing current CCAs' performance under varying network conditions and dynamically selecting the optimal CCA when significant disparities in throughput, latency, loss rate, and sending rate were observed among competing flows. To the best of our knowledge, this is the first study to investigate the dynamic switching of CCAs using DRL to improve flow performance and prevent starvation. Therefore, this research aims to contribute to the field by providing a DRL-based approach that dynamically switches CCAs based on historical network data to better understand and respond to network fluctuations, ultimately improving flow performance and preventing starvation.
## 3 Imperfections of Scalar Approximation
### Phase Sensitivity  
Another notable imperfection of the scalar approximation for DOCs is the analysis of phase sensitivity. According to Equation (5), the phase reconstructed by scalar approximation is independent of the duty cycle \(D\) and sidewall slope \(L\). However, as shown in Figure 5, it is significantly different from the rigorous calculation results for gratings with small periods. The depth of the grating corresponds to a 0.5 wavefront phase change in the simulation. As shown in Figure 5a, the reconstructed phase of the first order is a function of the period and duty cycle. The smaller the grating period, the more sensitive it is to the phase deviation caused by the duty cycle variation. The deviation between the scalar and rigorous phases was set to 1/100\(\lambda\) as a reference, and the range where the deviation meets the requirement decreased rapidly with decreasing periods.  
The reconstructed phase of the gratings with sidewall slope errors was also calculated by rigorous simulation, as shown in Figure 5b. The sensitivity of the phase error to the sidewall slope was still dependent on the grating period. Compared with the duty cycle error, the phase deviation caused by the sidewall slope was not obvious, and the fluctuation was globally controlled within 1/100\(\lambda\) for periods greater than 1 \(\upmu\)m. Therefore, the phase error caused by the sidewall slope can be ignored.
## 3 Results
### Pore Characteristics from FE-SEM  
The pore types of Longmaxi Formation can be divided into organic pores (30%), intra-particle pores (20%), interparticle pores (35%), and microfracture pores (15%) (Figure 4a-c). The organic pores are mainly distributed in the rigid mineral (quartz, raspberry pyrite) framework, narrow micro-cracks, and pores closely combined with clay minerals; Intra-particle pores include the internal pores of clay minerals, the auth  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample ID**} & \multicolumn{5}{c}{**Mineralogical Composition Relative Percent (wt.\%)**} \\ \cline{2-7}  & **Quartz** & **Feldspar** & **Calcite** & **Dolomite** & **Pyrite** & **Clay** \\ \hline MS-1 & 62.5 & 6.0 & 11.0 & 5.9 & 3.5 & 11.1 \\ MS-2 & 73.6 & 3.1 & 2.7 & 7.1 & 1.9 & 11.6 \\ MS-3 & 54.1 & 8.6 & 7.8 & 6.4 & 2.4 & 20.7 \\ MS-4 & 66.9 & 3.5 & 12.7 & 6.7 & 1.8 & 8.4 \\ MS-5 & 45.1 & 3.9 & 5.8 & 11.1 & 18.7 & 15.4 \\ MS-6 & 49.4 & 7.6 & 8.8 & 10.6 & 2.2 & 21.4 \\ CS-1 & 14.6 & 28.4 & 0.1 & 1.7 & 10.1 & 45.1 \\ CS-2 & 19.2 & 10.4 & 5.9 & 7.1 & 14.2 & 43.2 \\ CS-3 & 25.1 & 5.9 & 9.9 & 7.9 & 9.9 & 41.3 \\ CS-4 & 11.4 & 13.4 & 8.2 & 14.7 & 16.4 & 35.9 \\ CS-5 & 20.2 & 8.4 & 8.4 & 5.7 & 25.2 & 32.1 \\ CS-6 & 14.9 & 8.4 & 11.3 & 11.1 & 17.9 & 36.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mineral composition of marine shale (MS) and continental shale (CS).  
Figure 3: The SEM and the mineral identification image by AmicSCAN, (**a**) The SEM of continental shale; (**b**) AmicSCAN image of continental shale; the illite layer presents a significant lamellar distribution; (**c**) the SEM of marine shale; (**d**) AmicSCAN image of marine shale, there is no obvious orientation of illite. Because of the limitation of X-ray, the OM and pores cannot be detected by the AmicSCAN, and the color of OM&pore legend refers to the SEM image (**a,c**).  
and the dissolution pores of carbonate; Interparticle pores are mainly secondary evolution pores surrounding rigid minerals that are not compressed and filled by clay minerals.  
The pore types of the Yanchang Formation are mainly inorganic pores, including interparticle pores (55%), intraparticle pores (20%), fissure pores (20%), and a few organic pores (5%) (Figure 4d-f). Interparticle pores and microfracture pores are mainly distributed around rigid minerals (quartz, pyrite, calcite). Some interparticle pores also include pores of clay that have not been completely compacted under the support of rigid mineral grids; the intraparticle pores are mainly secondary evolution pores developed in carbonate minerals due to dissolution and a small number of authigenic pores of quartz, which is similar to the marine shale; However, due to the low degree of thermal evolution (Ro: 0.79-1.1) [4; 39], only a few of the organic pores can be observed at the edges of the brittle minerals.
###### Abstract  
This paper presents a low-noise reconfigurable sensor readout circuit with a multimodal sensing chain for voltage/current/resistive/capacitive microsensors such that it can interface with a voltage, current, resistive, or capacitive microsensor, and can be reconfigured for a specific sensor application. The multimodal sensor readout circuit consists of a reconfigurable amplifier, programmable gain amplifier (PGA), low-pass filter (LPF), and analog-to-digital converter (ADC). A chopper stabilization technique was implemented in a multi-path operational amplifier to mitigate 1/f noise and offsets. The 1/f noise and offsets were up-converted by a chopper circuit and caused an output ripple. An AC-coupled ripple rejection loop (RRL) was implemented to reduce the output ripple caused by the chopper. When the amplifier was operated in the discrete-time mode, for example, the capacitive-sensing mode, a correlated double sampling (CDS) scheme reduced the low-frequency noise. The readout circuit was designed to use the 0.18-\(\upmu\)m complementary metal-oxide-semiconductor (CMOS) process with an active area of 9.61 mm\({}^{2}\). The total power consumption was 2.552 mW with a 1.8-V supply voltage. The measured input referred noise in the voltage-sensing mode was 5.25 \(\upmu\)V\({}_{\text{rms}}\) from 1 Hz to 200 Hz.  
reconfigurable sensor readout circuit; multimode sensing; microsensors; correlated double sampling (CDS); multi-path operational amplifier; chopper stabilization
# Performance Analysis of Tunnel Boring Machines for
## 5 Discussion  
The present work has been developed with the purpose of identifying correlation models among mechanical and operational parameters of the TBMs, taking into account different classes of tunnel boring machines.  
It should be pointed out that the work represents an improvement over the research currently available in the scientific literature, although some gaps and shortcomings are still present. In general, missing information can be identified in geological and operational issues.  
As for the geological aspects, the necessary information was only partially available (Uniaxial Compressive Strength UCS, Rock Mass Rating RMR, and Geological Strength Index GSI, in fact, were only available in few cases). Furthermore, the lack of information on the geomechanical characterization of rock masses has influenced the efficiency in the evaluation of the models, as the state of fracturing has a great influence on the speed of advancement.  
As regards the analysis of the correlations between the machine parameters and the advancement speeds of the TBMs, various difficulties were encountered: it was not possible to obtain information of this type from the literature, as only the length of the tunnels and the total duration of their construction were available, and this could be a drawback. In fact, by estimating an average advancement value in m/month, it was found that the speeds varied, for the same diameter and type of machine, from less than 200 to about 1000 m/month, and, therefore, it was not possible to provide an accurate evaluation for the individual cases examined.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **S.S. TBM** & **D.S. TBM** \\ \hline \hline Cutterhead power P (kW) & - & 5100 \\ \hline Torque C (kNm) & 21,904 & 32,924 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Minimum values of P and C with which the two types of TBM must work in rock formations with the presence of schists, having chosen a diameter of 11 m.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Open TBM** \\ \hline Cutterhead Power P (kW) & 4140 \\ \hline Torque C (kNm) & 10,476 \\ \hline Thrust S (kN) & 27,556 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Minimum values of P, C, and S with which open TBMs must work in rock formations with the presence of gneiss, having chosen a diameter of 11 m.  
In light of the above considerations, this research can be considered significant for a preliminary assessment of the most suitable machine in a given geological context. Certainly, further implementations of data about geological and operational conditions are needed to better calibrate the models, as well as the addition of further construction data could be an important step to improve the quality of model outcomes.
## 5 Conclusions  
This study analyzed the flavor compounds in milk processed using different heat treatment by E-nose, E-tongue and HS-SPME-GC-MS technology. The results show that the overall flavor performance of milk after heat treatment at 65 \({}^{\circ}\)C for 30 min is similar to that of raw milk, which can maximize the preservation of the original taste of milk. The milk's flavor changed significantly as a result of the heat treatment, with the sweetness being more prominent in the raw milk, salitness in the 65 \({}^{\circ}\)C-treated milk, and bittermess in the 135 \({}^{\circ}\)C-treated milk. HS-SPME-GC-MS identified 43 VOCs in raw, 65 \({}^{\circ}\)C-, and 135 \({}^{\circ}\)C-treated milk--5 aldehydes, 8 alcohols, 4 ketones, 3 esters, 13 acids, 8 hydrocarbons, 1 nitrogen compound, and 1 phenol. Furfural, 2-heptanone, 2-undecanone, 2-furanmethanol, pentanoic acid ethyl ester, 5-octanolide, and 4,7-dimethyl-undecane are unique compounds in 135 \({}^{\circ}\)C-treated milk, reflecting the characteristics of VOCs from milk that has been treated at high temperatures for a long time. We are thinking about how to improve milk nutrition and extend shelf life while ensuring food safety, and we will continue our in-depth research in areas such as processing and packaging materials.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/foods12051071/s1](https://www.mdpi.com/article/10.3390/foods12051071/s1)]([https://www.mdpi.com/article/10.3390/foods12051071/s1](https://www.mdpi.com/article/10.3390/foods12051071/s1)), Table S1. Method for the determination of thermal parameters; Table S2. Thermal parameters for each treatment group. References [42; 43; 44; 45] are cited in the supplementary materials.  
Conceptualization, N.Y. and H.L.; methodology, N.Y. and X.C.; software, N.Y. and Q.Y.; formal analysis, N.Y.; data curation, N.Y. and H.L.; writing--original draft preparation, N.Y.; writing--review and editing, N.Y., H.L. and N.Z.; project administration, H.L. and N.Z.; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by National Key R&D Program of China (22022 YFD1301003, 2022 YFD1301004), The earmarked fund for CARS (CARS-36), The Agricultural Science and Technology Innovation Program (ASTIP-IAS12), the Xinjiang Tianshan Innovation Team (2022D14016) and Xinjiang science and technology projects (2022A02006-3-1).  
Data Availability Statement: Data is contained within the article or Supplementary Materials.  
The authors declare no conflict of interest.
# Outpatient Total Hip Arthroplasty: A Meta-Analysis
###### Abstract  
Introduction:Outpatient total hip arthroplasty (THA) is increasingly popular. This meta-analysis investigated the potential advantages of outpatient regimes for THA. **Methods:** This study followed the PRISMA guidelines. PubMed, Web of Science, Google Scholar, Embase, and Scopus databases were accessed in June 2021. All clinical studies investigating outpatient THA were considered. The outcomes of interest were pain, infection, mortality, revision, dislocation, readmission rates, and deep vein thrombosis (DVT). **Results:** Data from 102,839 patients were included. A total of 52% (153,168 of 102,839 patients) were women. The mean age of patients was 62.6 \(\pm\) 4.6 years, the mean BMI was 29.1 \(\pm\) 1.8 kg/m\({}^{2}\). Good comparability was found in age, BMI, and gender (\(p\) > 0.1). No difference was found in pain (\(p\) = 0.4), infections (\(p\) = 0.9), mortality (\(p\) = 0.9), rate of revision (\(p\) = 0.1), dislocation (\(p\) = 0.9), and readmission (\(p\) = 0.8). The outpatient group demonstrated a greater rate of DVT (OR 3.57; 95% CI 2.47 to 5.18; \(p\) < 0.0001). **Conclusions:** In selected patients, outpatient THA can be performed safely with optimal outcomes comparable with inpatient THA. Clear and comprehensive pre-operative planning should involve a multi-disciplinary group composed of orthopaedic surgeons, anaesthesia and rehabilitation specialists, and physiotherapists. Each centre performing outpatient THA should implement continuous homecoming welfare activity, to supervise physiotherapy and monitor anticoagulant therapy.  
Keywords:total hip arthroplasty; inpatients; outpatients +
Footnote †: journal: Medical  
diseases, a history of transient ischemic attack, chronic obstructive pulmonary disease, untreated obstructive sleep apnoea, obesity, haemoglobin < 130 g/L, diabetes mellitus, end-stage hepatic and/or renal disease, history of delirium or dementia, solid organ transplant, and advanced neoplasm [18; 19].  
Several clinical studies comparing outpatient versus inpatient THA (>2 days) are available [11; 13; 14; 17; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30]. However, most of them have been recently published, and, therefore, have not yet been considered in previous systematic reviews and meta-analyses. This meta-analysis compared outpatient versus inpatient THA (>2 days) in terms of symptoms, rates of infection, mortality, revision, dislocation, readmission, and deep vein thrombosis (DVT). We hypothesised that, for selected patients, outpatient THA provides comparable results to inpatient procedures.
# Spatiotemporal Patterns of Risk Propagation in Complex Financial Networks
## 3 Data and Methods  
### Data  
We collected the daily closing prices of components from the S&P500 and HS300 Indices from January 2016 to December 2020. Stocks with data missing for more than 100 consecutive trading days in the corresponding time period were deleted. Finally, 483 individual stocks were left for the S&P500 market, and 240 were left for the HS300 market. The stock names and stock tick names are summarized in Supplementary Material S2. All of the data were retrieved from the WIND terminal. If the stock price was missing for a particular day, we set the stock price to be the same as the previous day. The previous study showed that these missing data did not result in artifacts [47].  
Here, we use \(P_{i}(t^{\prime})\) to denote the closing price of the i-th stock on day \(t\). To avoid long-term trends, we define the logarithmic price return as  
\[R_{i}(t^{\prime})=\ln[P_{i}(t^{\prime})/P_{i}(t^{\prime}-1)]. \tag{1}\]  
To compare different time series, we introduce the normalized returns:  
\[r(t^{\prime})=\big{[}R(t^{\prime})-\big{\langle}R(t^{\prime})\big{\rangle} \big{]}/\sigma. \tag{2}\]  
where \(\langle\cdots\rangle\) represents calculating the mean value over time \(t^{\prime}\) and \(\sigma\) is the standard deviation of \(R(t^{\prime})\). For simplicity, the volatility is defined as \(|r(t^{\prime})|\).
# Study on Head Stabilization Control Strategy of Non-Wheeled Snake Robot Based on Inertial Sensor
## 2 Previous Related Work  
This section introduces some of our previous work on snake robots. We developed a non-wheeled snake robot with 16 DOF. The robot consists of 16 modular joints, a head module and a tail battery module. The robot is shown in Figure 1. The joint modules of the snake-like robot are orthogonally connected and can perform three-dimensional movements in space. The tail battery module is responsible for powering the snake-like robot. The head module contains a head controller, inertial sensors, a laser rangefinder and other devices. The head controller of the snake-like robot is a computing stick responsible for processing sensor information and overall motion planning of the robot. Relevant work is described in [21].  
The dynamic equation of the snake robot can be represented as follows [21]:  
\[u=M(q)\ddot{q}+V(q,\dot{q})+F\dot{q}+g(q) \tag{1}\]  
In Equation (1), \(M(q)\) represents the mass matrix, \(V(q,\dot{q})\) represents the matrix of centrifugal and Coriolis torques, \(F\) represents the friction matrix and \(g(q)\) represents the gravity matrix.  
The control law for the snake robot can be represented as follows:  
\[y=\ddot{q}_{d}+k_{d}\dot{e}_{q}+k_{p}e_{q} \tag{2}\]  
where \(q_{d}\) represents the desired joint matrix, \(e_{q}\) represents the tracking error, \(k_{d}\) and \(k_{p}\) are positive definite matrices to ensure stability. The specific control block diagram is shown in Figure 2.
# Analyzing TCP Performance in High Bit Error Rate Using Simulation and Modeling
## 3 Results
### FTP Client: Sent Segment ACK Number  
Figures 4 and 5 illustrate the TCP Sent Segment ACK Number for an FTP client. Figure 4 shows the behavior of TCP Sent Segment ACK Number over 2700 sec simulation time. The graphs for \(p_{drop}=0\%\), \(p_{drop}=0.01\%\), \(p_{drop}=0.1\%\), and \(p_{drop}=1\%\) are almost congruent, and only the graph for \(p_{drop}=10\%\) rises at a slower rate and is clearly distinguishable from other graphs. In Figure 5, we have redrawn Figure 4 for the first 80 s of simulation time. A faster rising graph indicates that the FTP client acknowledged TCP segments at a faster rate.
## 3 Case Study: Lishanyuan Landslide  
### Landslide Overview  
The Lishanyuan landslide is located in Xinhua County, Hunan Province, China (see Figure 3). The volume of the entire landslide is approximately 1.08 \(\times\) 10\({}^{5}\) m\({}^{3}\), with a sliding length of 120 m and a width of approximately 300 m. It is a shallow landslide with an average thickness of the sliding surface of approximately 3 m. The main sliding direction of the landslide is 210\({}^{\circ}\). There was a small road passing through the landslide. The landslide started deforming in 1996 due to the erosion at the slope toe and the variation of the water level of the Zhexi Reservoir. It deformed slowly from 1996 to 2012. In 2013, it slid, and the small road moved down. Heavy rainfall during the flood season causes landslides. In April 2018, the slope collapsed again and material with a volume of 600 m\({}^{3}\) rushed into the houses at the hill foot, which caused a direct loss of about RMB 600,000. The on-site investigation showed that the landslide was a small composite gravel landslide.