esa-sceva/satcom-chunk-collection huggingface dataset:
# Key-Recycling Strategies in Quantum-Key-Distribution Networks
## 3 Problem Statement
First, when a symmetric-encryption algorithm is used to encrypt a service, a hundred-bit key can encrypt a Gbit service, spanning seven orders of magnitude. The amount of data that can be encrypted by the wasted key is defined as follows:  
\[\text{N}_{data}=(V_{\text{QKD}}*P_{PLR}*t)/L_{key}*T*V_{data} \tag{1}\]  
where V\({}_{\text{QKD}}\)*P\({}_{\text{PLR}}\)*t is the amount of key wasted in time t, L\({}_{\text{key}}\) is the key length of the encryption algorithm, T is update period of the key and V\({}_{\text{data}}\) is transmission rate of the service in the network. When V\({}_{\text{QKD}}\) = 10 Mbps, P\({}_{\text{PLR}}\) = 1/10,000, L\({}_{\text{key}}\) =192 bit (192 AES), T = 1s, V\({}_{\text{data}}\) = 1 Gbps, we can  
Figure 4: In the case of multi-hop relay, when a certain relay fails, the problem caused by the traditional QKD scheme. estimate the amount of keys wasted every second is 1 kbit, which can guarantee the secure transmission of 1 G services for about 5 s. Second, in the optical networks, there are special cases such as rerouting caused by network failures and large-scale disasters, which are characterized by a sudden increase in BER and PLR and a large number of nodes. The QKR scheme can be used as a contingency plan in an emergency to alleviate the shortage of end-to-end key generation. Third, the application of the QKR strategy can reduce the requirements of the key-relay process on the network environment and network reliability, thereby reducing the maintenance cost of the operator.
# UPGCN: User Perception-Guided Graph Convolutional Network for Multimodal Recommendation
## 3 Methods
### Model Prediction  
Following the completion of module propagation as outlined in Sections 3.3 and 3.4, we acquire the initial user representation \(\overline{\epsilon}_{u}^{(0)}\) (Equation (12)) and the initial item representation \(\overline{\epsilon}_{i}\) (Equation (18)). Subsequently, we utilize the initial user representation \(\overline{\epsilon}_{u}^{(0)}\) along with the initial embedding of item ID \(e_{i}^{(0)}\) to conduct graph convolution on the interaction graph between users and items, thereby obtaining the final user representation \(y_{u}\) and the transitional item representation \(e_{i}\). Subsequently, we will integrate \(e_{i}\) and \(\tilde{e}_{i}\) (Equation (18)), with the integration process defined as follows:  
\[\overline{\epsilon}_{i}=\frac{\tilde{e}_{i}}{\left\|\overline{\epsilon}_{i} \right\|_{2}}+e_{i} \tag{19}\]  
Finally, we utilize \(\overline{\epsilon}_{i}\) as input to apply GCN on the latent item-item graph \(S\), constructed in Section 3.1.2, employing the GCN operation defined as follows:  
\[\hat{e}_{i}^{(l+1)}=\sum\
olimits_{j\in N_{i}}S_{i}\epsilon_{j}^{(l)} \tag{20}\]  
where \(\mathcal{N}_{i}\) denotes the set of all neighboring nodes for item \(i\), \(\hat{e}_{i}^{(0)}\) is initialized as \(\overline{\epsilon}_{i}\), and the item-item latent graph \(S\) output item representation is denoted as \(\hat{e}_{i}\). The final item representation \(y_{i}\) is obtained by fusing \(\hat{e}_{i}\) with the transitional item representation \(e_{i}\) output from the user-item interaction graph. The integration operation defined as follows:  
\[y_{i}=\hat{e}_{i}+e_{i} \tag{21}\]  
At this point, we have obtained the final user representation \(y_{u}\) and the final item representation \(y_{i}\) for prediction. To generate suggestions for users, we first compute the inner product between \(y_{u}\) and \(y_{i}\) to evaluate the affinity between the user and the candidate items:  
\[s(y_{u},y_{i})=(y_{u})^{T}y_{i} \tag{22}\]  
Subsequently, the candidate items are ordered in descending fashion according to the predicted affinity scores, and the top K items are selected as recommendations for the user.
## 7 Applications
This research group in 2020 proposed a novel method based on fabric phase sorptive extraction (FPSE) followed by gas chromatography-tandem mass spectrometry (GC-MS/MS) for the simultaneous determination of 11 UV filters (ethylhexyl salicate, benzyl salicate, homosalate, benzophenone-3, isoamylmethoxycinnamate, 4-methylbenzylidenecamphor, methyl anthranilate, etcorylene, 2-ethylhexylmethoxycinnamate, 2-ethylhexyl p-dimethylaminobenzoate, and octocylene), in natural and recreational waters. Different types and sizes of sol-gel coated FPSE membranes, sample volumes, extraction times, and types and volumes of desorption solvent were optimized. The optimal conditions involved the use of a (2.0 \(\times\) 2.5) cm\({}^{2}\) FPSE device with PDMS-based coating for the extraction of 20 mL of water for 20 min. Back-extraction was performed by ethyl acetate. Recovery rates under optimum conditions were about 90%. LODs were at the low ng L\({}^{-1}\) in all cases. The proposed validated FPSE-GC-MS/MSmethod was applied to different real samples, including environmental water (lake, river, seawater) and recreational water (swimming pool).
## 3 Methods
### Inversion of Soil Moisture  
In this study, an inversion scheme was developed that made use of the dual polarizations (HH and VV) available from the quad polarization Radarsat-2 data. After minimizing the effect of vegetation on the backscattering coefficient by the modified WCM, a look-up table (LUT) was developed to estimate soil moisture. To build the LUT, the parameters combinations were generated and used in the forward calculation of IEM. This method involved the creation of a table of backscattering coefficients associated with the surface soil moisture and roughness generated by performing multiple runs of the IEM within its validity range. To prevent too-wide parameter spaces and to reduce the size of the parameter spacing, the maximum and minimum values of soil moisture were fixed based on the prior knowledge from the field data collection. The implemented direct search algorithm attempts to minimize the merit function S, as shown in Equation (12), which represents differences between the measured and simulated backscattering coefficients.  
\[S=\sqrt{\frac{1}{2\left[\left(\sigma^{o}_{HH}-\sigma^{o}_{HHSAR}\right)^{2}+ \left(\sigma^{o}_{ry}-\sigma^{o}_{rySAR}\right)^{2}\right]}} \tag{12}\]  
where \(\sigma^{o}_{HHSAR}\) and \(\sigma^{o}_{VVSAR}\) are the backscattering of the Radarsat-2 images at the HH polarization and at the VV polarization, respectively. \(\sigma^{o}_{HH}\) and \(\sigma^{o}_{VV}\) are the simulated backscattering at the HH polarization and at the VV polarization, respectively. The LUT is used to assess the best-fit value for \(\sigma^{o}_{HHSAR}\) and \(\sigma^{o}_{VVSAR}\) with a minimal S; however, because of the measurement's uncertainty and the ill-posed inverse problem, the solution is usually not unique and is instead a range of estimated parameters. The mean value of the estimated parameters is taken as the estimated final result if the results are not unique. To verify the accuracy of this model inversion, a comparison is conducted between the measured and estimated soil moisture values using the correlation coefficient of determination (\(R^{2}\)) and the RMSE.
## 2 Background and Related Work
### Background
#### 2.1.1 Cyberspace Mapping Engine  
At present, there are many cyberspace mapping systems in the world. For example, Shodan [3] in the United States, which mainly scans and identifies network infrastructure equipment such as servers and network cameras, and has a convenient API interface that supports calls from multiple programming languages. The Censys [4] surveying and mapping system developed by researchers at the University of Michigan uses the self-developed Zmap scanning tool to collect detailed information on IP, certificates, and websites, which can help users collect and sort out the organization's attack exposure surface. BinaryEdge [5], a surveying and mapping product from Switzerland, scans the entire network, maps the attack exposure surface of nearly 5 billion devices and 15 million business groups, and is committed to providing real-time threat intelligence information for enterprise organizations, to reduce their risk of being attacked. China also has many network surveying and mapping engines, including Zoomeye [6] from Zhidaochuangyu, FOFA [7] from Huashunxinan, Quake [8], a network-wide space surveying and mapping system independently developed and designed by the 360 Network Security Response Center, and the RaySpace [9] platform from Surbana Security.  
The network mapping engine encompasses the storage of IP network assets across the entire network. In previous applications, the primary focus of the network mapping engine was predominantly centered around network security attack and defense technologies. Through the detection of organizational assets and the simple correlation of vulnerability information, the attack surface of network target assets was sorted out. Nonetheless, the extensive repository of website assets within the network mapping engine holds potential beyond the realm of network attack and defense. It also harbors a significant number of hidden illegal websites. Proficient analysis of these assets can offer valuable technical resources for the governance of illicit industries.  
The network assets stored within the network mapping engine exhibit complexity and diversity. However, supervised learning approaches necessitate labeled samples, which can be costly and time-consuming to obtain. Moreover, limited samples may not effectively represent the entirety of the network assets, further exacerbating the challenge. Under such circumstances, a supervised learning model trained solely on labeled samples may exhibit high accuracy within the training dataset but could be subject to bias. Consequently, when dealing with the analysis of extensive network surveying and mapping asset data, we opt to learn the semantic representation of the data through unsupervised methods. By employing unsupervised clustering techniques, we can effectively discover and identify illegal websites without relying on labeled samples.
# Use of Wireless Sensor Networks for Area-Based Speed Control and Traffic Monitoring
## 5 Radio Coverage Planning  
Radio coverage planning is the process of designing and configuring radio networks, such as cellular networks, radio transmitting systems, or Wi-Fi and LoRaWAN wireless networks, to ensure adequate radio signal range for a specific geographical area. It involves various activities and analyses primarily aimed at optimizing network performance and service quality for end users [62]. The most important aspects of radio coverage planning include the following:  
1. Demand analysis: Defining the requirements related to network capacity and coverage based on predicted traffic and user numbers.
2. Base station location selection: Making decisions regarding the placement of base stations (in the case of cellular networks) or access points (in the case of a Wi-Fi network) to ensure optimal coverage.
3. Radio wave propagation modelling: Applying mathematical and computer models to predict radio wave propagation in different environments, taking into account factors such as land relief, buildings, vegetation, and other obstacles.
4. Network parameter optimization: Adjusting parameters such as transmitting power, frequency, antenna patterns, and others to minimize interference and ensure consistent coverage.
5. Testing and validation: Conducting field tests to verify the propagation model and implementing potential corrections in the coverage plan.
6. Redundancy planning: Ensuring that the network can function effectively even in the event of a failure of one or more network elements.
7. Compliance with regulations: Ensuring that radio network planning and deployment comply with local regulations regarding radio emissions, band sharing, and security.
8. Interference management: Considering existing networks in the area to avoid collisions and interference between systems.  
Radio coverage planning is crucial for ensuring the high quality of telecommunications and wireless services, and its effectiveness directly impacts the end user experience [63; 64]. Therefore, it is an area of intensive research and development, especially in light of the growing demand for data and the emergence of new technologies such as IoT, 5G, or LoRaWAN. It is particularly important in highly urbanized or densely populated environments [65].
## 2 Calibration Status of Landsat 8 Operational Land Imager (OLI)
### Spectral Characterization  
The spectral characterization of the OLI was strictly a pre-launch operation and is described in detail in [PERSON] et al. (2018). Three different sets of measurements, component level, FPM level and system level, contribute to the understanding of the spectral response of the instrument. The integrated instrument level measurements, in principle, best represent the true response, although these are sub-aperture, partial field and with weak signals, so out-of-band and crosstalk are not captured. What can be observed at the integrated instrument level are the small shifts in the spectral response across the focal plane due to the slight non-telecentricity of the telescope (Figure 1a). The shifts cause small differences in the responses across the focal plane depending on the spectral shape of the target (Figure 1b). In the example, the vegetated target has a spectral shape that results in more response variability across the focal plane (although \(<\pm 0.2\%\)) than the bare desert target. The band average integrated instrument spectral response measurements are published at: [[https://landsat.gsfc.nasa.gov/preliminary-spectral-response-of-the-operational-land-imager-in-band-band-average-relative-spectral-response/](https://landsat.gsfc.nasa.gov/preliminary-spectral-response-of-the-operational-land-imager-in-band-band-average-relative-spectral-response/)]([https://landsat.gsfc.nasa.gov/preliminary-spectral-response-of-the-operational-land-imager-in-band-band-average-relative-spectral-response/](https://landsat.gsfc.nasa.gov/preliminary-spectral-response-of-the-operational-land-imager-in-band-band-average-relative-spectral-response/)).  
Spectral consistency in long-term data records is one of the bigger challenges in providing harmonized data. Even though technologies and sensors continue to evolve, perfectly consistent data are not obtainable. Although spectral adjustment factors can be derived to better match two (or more) sensors' data, these factors are target- and atmospheric-dependent, meaning that to accurately calculate them one needs to know the signature of the target and atmosphere in advance, which is clearly not the norm in remote sensing.  
More consistency and characterization in future sensors can help alleviate parts of the problem. Designs with good internal consistency (e.g., telecentric telescopes and inherently more uniform spectral filters) are a good starting point. Instrument-level spectral characterization of the full instrument focal plane will also help in terms of understanding the true spectral response. Selecting common spectral bands is probably the most useful approach for improving interoperability as any adjustments between systems would be small and would generate small additional uncertainties in the time series.
###### Abstract  
Wafers are usually placed in a front opening unified pod (FOUP) while waiting in the line of manufacturing processes; a conventional way for preventing (or removing) contamination is to purge an FOUP with a gas, so as to maintain the cleanliness level. This study investigates the effect of the flow rate of clean dry air (CDA), supplied to the purge system and air curtain, on the moisture prevention performance, while opening the FOUP door in a mini-environment. An initial relative humidity (RH) in the pre-purged FOUP was approximately 0%, whereas an RH in the mini-environment was about 43%. Conventional, diffuser, and panel purge approaches were tested. The results showed that the moisture in the surroundings rapidly entered the FOUP when the door was opened, without using either the purge system or air curtain. When the CDA was then supplied to both the purge system and the air curtain, the RH values decreased significantly, the drop slope of the RH for the diffuser- and panel-purge processes was much larger than that of the conventional-purge process. In addition, the moisture prevention performance when the developed panel-purge system was used was slightly lower than when using the diffuser-purge system. However, the use of the panel-purge method could sufficiently suppress the vibration problem. In the present study, the case with CDA flow rates of 150 L/min to the air curtain and 250 L/min to the panel-purge device had the best moisture prevention performance, because of the smallest values for both the mean and maximum RH values (i.e., 1.3% and 9.5%, respectively) in the FOUP.  
moisture prevention; FOUP; air curtain; panel purge; diffuser
## 4 Conclusions  
This article described a numerical study of nanofluid thin film dusty flow, where SWCNTs flowed past an inclined rotating disk. Equations were analyzed using bvp4c. The main observations can be summarized as follows:  
* The effect of dust momentum on the velocity profile and wall shear stress increased with greater dust particle momentum.
* The thermal dust parameter increased the Nusselt number, which decreased with increases in the ratio of wall temperature to ambient temperature.
* The presence of dust particles increased the temperature and velocity boundary layer.
* The velocity profile and rotational coefficient showed a reciprocal trend.
* With increases in radiation, the thermal and flow field of nanofluid increased compared to those of dusty fluid.  
Conceptualization, M.R. and S.R.; methodology, S.R.; software, S.K.; validation, C.L., formal analysis, Y.N.; investigation, S.R. and D.L.; writing--original draft preparation, S.R.; writing--review and editing, M.R.; supervision, M.R.; project administration, M.R.; funding acquisition, Y.N. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Bio & Medical Technology Development Program of the NRF funded by the Korean government, MSIP(NRF-2015M3A9D7067219) and also supported by the Soonchunhyang University Research Fund.  
This work was supported by the Soonchunhyang University Research Fund.  
The authors declare no conflicts of interest regarding this publication.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\eta\) & **HAM Result** & **Numerical Result** & **Present** \\ \hline
0 & 0.00000 & \(-1.739080\times 10^{-9}\) & \(-1.739080\times 10^{-9}\) \\
0.1 & 0.004739 & 0.004707 & 0.004730 \\
0.2 & 0.018245 & 0.018116 & 0.018234 \\
0.3 & 0.039447 & 0.039159 & 0.039439 \\
0.4 & 0.067284 & 0.066775 & 0.067252 \\
0.5 & 0.100707 & 0.099923 & 0.100699 \\
0.6 & 0.138694 & 0.137585 & 0.138685 \\
0.7 & 0.180252 & 0.178775 & 0.180239 \\
0.8 & 0.224425 & 0.222546 & 0.224386 \\
0.9 & 0.270298 & 0.267994 & 0.270284 \\
1.0 & 0.316999 & 0.314258 & 0.316991 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative values between the numerical and analytical solution (HAM) for \(f(\eta)\) versus varied estimates of \(\eta\) with [PERSON] et al. [39].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\gamma\) & \(\beta_{w}\) & \(I\) & \(\alpha\) & \(Re^{\frac{1}{2}}C_{f}\) = \(2\alpha f^{\prime\prime}\)(0)/(1\(-\phi\))\({}^{2.5}\) \\ \hline
1.2 & 0.5 & 0.1 & 0.1 & 0.1057 \\
1.3 & & & & 0.1014 \\
1.4 & & & & 0.0988 \\  & 0.7 & & & 0.5857 \\  & 0.8 & & & 0.5890 \\  & 0.9 & & & 0.5900 \\  & 0.2 & & & 0.0873 \\  & 0.3 & & & 0.0431 \\  & & 0.2 & & 0.6789 \\  & & 0.3 & & 0.9191 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Numerical values of drag force \(C_{f}Re_{x}{}^{1/2}\) against the distinct estimates of parameters. i.e., \(\delta_{1}=0.5\), \(S_{t}=0.5\), and \(S_{1}=0.5\).
# Isokinetic Strength in Peritoneal Dialysis Patients: A Reliability Study
## 1 Introduction  
Peritoneal dialysis (PD) patients present a condition resulting from multiple physiological and behavioral changes which contribute to muscle wasting [1]. Recent studies showed that PD patients often present decreased levels of physical activity (PA) matching a sedentary lifestyle [2]. These circumstances significantly affect the health condition of those patients and are associated with significant morphortality [3]. Moreover, the nutritional status and sedentary behavior directly affect muscle function, exercise performance, physical function, strength, and health-related quality of life [4; 5; 6]. Although the proportion of patients active during leisure time is low [4], they need adequate levels of strength to accomplish daily life activities such as the need to displace, walking up and down stairs, maintaining a standing position, etc.  
Exercise programs are recommended to increase lean body mass, strength, and physical functioning in frail elderly persons and those with chronic diseases, including PD patients [5]. Precise and sensitive strength tests are required to appropriately extract conclusions in studies focused on the evaluation ofstrength. Among these tests, the hand grip dynamometer and the isokinetic dynamometer are two of the most reliable and widely used devices. In this regard, the handgrip dynamometer has been previously used to evaluate muscle strength of the upper limb in patients undergoing dialysis [4; 7]. Isokinetic dynamometry has been previously used to evaluate the effects of different interventions and therapies on knee flexion and extension strength in dialysis patients [8; 9], whereas, to our knowledge, there is no study evaluating upper limb strength using an isokinetic dynamometer in this population.  
Although there are studies assessing the effects of interventions on the knee strength of patients undergoing dialysis, to our knowledge, no previous study has investigated the reliability of knee isokinetic procedures adapted for use in PD patients with poor muscle strength. In healthy individuals, the use of isokinetic dynamometry to evaluate the knee strength when subjects receive adequate instructions and are familiar with the equipment is sufficiently reliable [10; 11], but the reliability in PD patients still remains unknown, which impairs and limits the appropriate interpretation of results. In this regard, reliability is considered an important prerequisite for the correct interpretation of isokinetic dynamometry data, which allows the clinician to identify whether or not a genuine change has occurred [12]. Therefore, the aim of the current study was to determine the relative and absolute intra-session reliability of peak torque and work measurements for isokinetic concentric knee and elbow extension and flexion in PD patients.