esa-sceva/satcom-chunk-collection huggingface dataset:
# An Attention-Based Method for Remaining Useful Life Prediction of Rotating Machinery
## 2 Attention-Based RUL Prediction of Rotating Machinery
### Attention-Based GRU for RUL Prediction
#### 2.2.1 Working Principle of the Attention Mechanism in RNN  
When introduced to an RNN, the attention mechanism calculates the similarity weight between the input vectors and network hidden states. Figure 4 shows how the attention mechanism works in an RNN; the state is assumed to be comprised of a series of two tuples \((Key,Value)\).  
Figure 4: Principle of the attention mechanism.  
When the current input sequence is \(Query\), the \(i\)-th state is expressed as \(H_{i}=(Key_{i}\), \(Value_{i})\). There are \(L\) hidden states calculated by the RNN; by calculating the similarity or relevance between the input, \(Query\), and hidden state, \(Key\), we can obtain the weight coefficient of \(Query\) corresponding to \(Key\), namely the relevance weight of the hidden state to \(Query\). Then, the weighted sum between the relevance weight and \(Value\) is calculated to obtain the similarity value between \(Query\) and the \(L\) hidden states. Operations of the similarity value and \(Query\) are performed to adjust the model outputs. The dot product is used as the similarity calculation function, and the expected attention value in the attention mechanism is obtained by Equation (4), where \(Similarity(\cdot)\) is the similarity calculation function:  
\[Attention(Query)=\sum_{i=1}^{L}Similarity(\;Query\;,Key_{i})*Value_{i} \tag{4}\]
## 2 Materials and Methods
### Statistical Analysis  
The land cover changes were investigated over the YRB from 2001 to 2018 using transfer matrix analysis for the MODIS and CCI land cover maps. A fraction-based transfer matrix approach, in particular, was applied to the CCI data with the PFTs [48; 49]. The approach assumed that there was no transition between the same PFT, and the transferred amount among different PFTs depended on the missing amount of each PFT for two consecutive years. More details of the method are described in [PERSON] et al. [48].  
We cross-compared the GPP estimations over the YRB, respectively, using static land cover schemes across the entire study period (termed staMOD and staCCI for the MODIS and CCI data, respectively) and dynamic land cover schemes (termed dynMOD and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Land Cover Type** & **Linear Regression** & **R\({}^{2}\)** & **RMSE** & **Bias** \\ \hline DBF & GPP = 64.07-NIRv \(-\) 2.20 & 0.65 & 2.14 & 0.14 \\ EBF & GPP = 44.50-NIRv \(+\) 2.60 & 0.45 & 2.02 & 0.56 \\ ENF & GPP = 64.51-NIRv \(-\) 1.41 & 0.65 & 1.96 & 0.10 \\ MF & GPP = 59.49-NIRv \(-\) 2.93 & 0.70 & 1.86 & 0.04 \\ SHR & GPP = 36.18-NIRv \(-\) 0.87 & 0.70 & 0.72 & \(-\)0.03 \\ GRA & GPP = 68.13-NIRv \(-\) 1.62 & 0.74 & 1.94 & 0.05 \\ CRO-C3 & GPP = 55.38-NIRv \(-\) 1.97 & 0.65 & 2.14 & 0.14 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Coefficients and overall performances of the linear models between NIRv and GPP proposed by [PERSON] et al. [22]. R\({}^{2}\), root mean square error (RMSE), and bias values are presented for each vegetation type.  
dynCCI for the MODIS and CCI data, respectively). For the static schemes, the coefficients of the NIRv-GPP model were treated as constant values across the entire study period for each pixel, which were assigned by the land cover map in 2001. For the dynamic schemes, the coefficients of the model were treated as annual varying variables parameterized from the annual land cover type for each pixel. The cross-model comparison was conducted at the pixel and sub-basin levels. All of the models using the static/dynamic LC schemes were driven by the same NIRv data. Hence, the GPP difference between the two static schemes could be attributed to the difference between the multisource products, and that between the static and dynamic schemes resulted from the land cover change.  
The Theil-Sen estimator was adopted to detect the inter-annual changes in land cover fraction, NIRv, and modeled GPP by pixel across the vegetated area of interest. The Theil-Sen estimator is a nonparametric method with a better robustness to outliers [50]. Its significance was evaluated by the Mann-Kendall trend test. The Kruskal-Wallis test was used to study the reliability of a result by comparing the result with other products. The test is a nonparametric method to investigate whether two or more groups of continuous or discrete variables come from the same probability distribution [51]. The Bonferroni-Dunn test was used to calculate the Dunn's \(p\)-value after performing the Kruskal-Wallis test analysis [52; 53].
# Low-Light Photodetectors for Fluorescence Microscopy
## 4 Emerging Point Detectors
### Sspd  
In recent decades, the superconducting nanowire single photon detector (SSPD) has become an increasingly popular device, with applications ranging from sensing to quantum communications for single photon detection with high efficiency, precise timing, and low noise [24; 40]. The crucial high-speed feature of the SSPD is represented by the TTS (\(<\)50 ps) the fluctuation between the true arrival time of a photon and the electrically registered arrival time recorded by the system. [PERSON] et al. showed that the use of low-latency materials lowered the TTS of the SSPD, and demonstrated that the temporal resolution can be 2.6 \(\pm\) 0.2 ps for visible wavelengths and 4.3 \(\pm\) 0.2 ps at 1550 nm using a specialized niobium nitride SSPD [41].  
Figure 18 shows a schematic of the SSPD. The SSPD consists of superconducting nanowire with a thickness of a few nanometers that senses photons. Single photon absorption by the SSPD suppresses superconductivity, which in turn generates a voltage spike that can be used to detect the photon.  
The SSPD is free of afterpulses because it returns to the superconducting state without generating afterpulses after photon detection. The afterpulsing-free characteristic of the SSPD is a clear advantage for time-resolved fluorescence microscopy. The SSPD has been used for fluorescence microscopy and applied to FCS [43; 44]. Although the SSPD has these excellent features, its operation is inconvenient due to its small active area and low operating temperature. The very small active area (\(\sim\)10 \(\upmu\)m) makes the optical alignment difficult and the extraordinary low operating temperature (\(\leq\)4 K) requires the liquid helium cooling system.  
Detailed comparisons have been made between SSPDs, SPADs, and other photon-counting technologies in [27; 40; 44].  
The characteristics of SiPMs and SSPDs are summarized in Table 4.  
Figure 18: Schematic of the operating principle of the superconducting nanowire single photon detector (SSPD): The SSPD consists of a superconducting nanowire with a thickness of a few nanometers that senses photons. A bias electrical current flows through the nanowire during the operation: (**a**) the entire area is in the superconducting state prior to photon absorption; (**b**) single photon absorption takes place in the superconducting nanowire; (**c**) superconductivity is locally suppressed by energy excitation via the photon absorption; (**d**) the bias current makes the suppressed area resistive and the resistive area expands across the nanowire, which in turn generates a voltage spike; (**e**) as current is diverted, the resistive area relaxes to the superconducting state. Adapted with permission from [42]. Copyright 2020, American Chemical Society.
# Assessing Interactive Web-Based Systems Using Behavioral Measurement Techniques
## 2 Literature Review
### Behavioral Measurements Techniques
#### 2.3.1 Heuristic Evaluation  
Heuristic evaluation is a method for evaluating usability based on expert insights built mainly on predefined principles. As an engineering method for evaluating usability, heuristic testing is performed as part of an iterative design process for evaluating and determining interface design usability issues. It is carried out by a group of evaluators who evaluate the interface based on standard usability principles called heuristics [52].  
In heuristics' evaluation, the UI is assessed based on the opinions of several evaluators who observe an interface to decide on the upwards and downwards direction [53].  
Heuristic evaluation has some advantages as well as disadvantages. With the help of heuristic evaluation, researchers find more problems than with other usability evaluation methods [54]. For instance, it is claimed that heuristic evaluation addressed 72% of problems, whereas user testing found only 10% and 18% of the problems that were mutual to both [15]. It is a relatively cheap and fast method of evaluation [55].  
On the other hand, there are several concerns that must be considered when using heuristic evaluation. One such concern pertains to the limited scope of the predefined heuristics or guidelines, which may result in the identification of only a subset of usability issues. Another concern that demands attention is the subjectivity of evaluators, as different evaluators may identify distinct issues or rate the severity of issues differently. Moreover, the absence of user feedback may impede the ability of heuristic evaluation to capture all usability issues. The efficacy of the evaluation is also contingent upon the expertise of the evaluators, which could pose a challenge if inexperienced evaluators are enlisted. Furthermore, heuristic evaluation may be a time-consuming and costly endeavor, particularly if multiple evaluators are involved. Lastly, heuristic evaluation may not be suitable for all types of user interfaces, such as those that necessitate specialized domain knowledge or entail complex interactions.  
Despite these concerns, heuristic evaluation can still be a valuable method for evaluating usability, particularly when employed in conjunction with other methods, such as user testing. Therefore, it is imperative to take these concerns into account when utilizing heuristic evaluation and to supplement it with other methods to obtain a more comprehensive evaluation of usability.
# Topology of the World Tourism Web
## 3 Results and Discussion
### Does the Tourism Network Behave as a Social or Technological Network?
#### 3.2.6 Homophily  
If we again observe the communities (in the weighted graph; Table 2, Figure 6), the underlying geographical proximity may be an indication of homophily, in a manner. Namely, such behavior may not be related as much to the likeness but can be attributed to the lower costs of short-distance travels (which are also less time-consuming). For instance, not many people regularly travel to different continents on their tourist travels.  
Previous research also revealed an expressed homophily between the countries in the same continent, meaning that the tourism flow is larger within the region than between regions. The authors explain that these results may occur due to physical proximity and lower travel expenses but also due to the similarity in the culture and diplomatic relationship between the countries [7]. Besides geographical proximity, [PERSON] et al. [8] emphasize the common culture and language within the clusters. In comparison to [PERSON] et al. [8],this research reveals a fewer number of communities. For example, most of the European countries form a single community and moreover include Russia and some African countries. The African countries that belong to this community are also former colonies, indicating the possibility of the lingering effects of those relationships. Moreover, this community overcomes language barriers and cultural differences. Similarly, both North and South American countries are strongly interconnected within one community, which also favors geographical proximity over cultural differences as an explanation. Still, the sixth community (Table 2) contains countries that are spread over three continents, so in this case, cultural similarities may be a better explanation. This may mean that globalization trends, cultural cohesion, and the increase in tourism do not enforce changes in the tourism network evenly. So, we suggest that two variations of homophily occur in the network: the first one is guided by the opportunity (geographical proximity, low cost, less time consumption), and the second one is guided by similarities (socioeconomic factors, common culture, language). The revealed variations of homophily are in line with [15], who suggest an ecological approach to tourism due to the expected change in tourist behavior, with the exception that tourists were governed by proximity in their destination choices long before the current pandemic situation.
###### Abstract  
This study aimed to determine whether proximal tibiofibrular joint (PTF) morphology was related to knee alignment in osteoarthritis (OA) patients. Methods: 67 OA patients were enrolled in this study. The morphology of the PTF including articular shape type, articular surface area, joint inclination, relative articular height, and joint declination were measured from 3D models. The knee alignment of each subject was characterized as varus, normal, or valgus according to the femorotibial angle (FTA). The FTA was measured on weight-bearing X-rays. Multinomial logistic regression analysis was used to evaluate the association between PTF morphology and knee alignment. Results: there were significant differences between varus, valgus, and normal FTA groups in terms of shape type (\(p\) = 0.021), inclination of the PTF (\(p\) = 0.025), relative articular height (\(p\) = 0.019), and PTF) declination angle (\(p\) = 0.011). A higher relative articular height (OR: 0.608, 95% CI: 0.205-0.998, and \(p\) = 0.017) and lower declination angle (OR: 0.632, 95% CI: 0.601-0.887, and \(p\) = 0.019) were found to be associated with an increased likelihood of having a valgus FTA rather than a varus FTA. Conclusion: our results indicate that PTF) morphology is associated with knee alignment.  
knee osteoarthritis; proximal tibiofibrular joint; bone model; femorotibial angle; knee alignment; biomechanics  
31 January 2021  
Accepted: 2 March 2021  
Published: 4 March 2021  
Published: **Note**: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
## 4 Mathematical Model and Distinct Features of Active Dynamics
### Distinct Features of Active Dynamics in Fluid-Filled State  
As discussed in Section 2.2, the natural frequency \(f_{\rm n3}\) of an actuator mover in a fluid-filled state is typically above 200 Hz. The bands for the distinct feature research of active dynamics are mainly involved in the mid- and high-frequency bands, that is, \(f>>f_{\rm n2}\). Subsequently, the nonlinear model in Equation (16) can be simplified using the method described in Section 3.1. The inertial track was turned off, and \(y_{2}=0\). The FFRs of the reaction force \(f_{1}\) and transmitted force \(f_{5}\) to the actuator current \(i\) can be expressed by Equation (18).  
\[\begin{array}{l}-\frac{F_{1}}{T}=\frac{A_{1}K_{\rm M}}{A_{3}k_{3}}k_{\rm M} \cdot\frac{1}{1-\lambda^{2}+2\beta_{3}\lambda}=\frac{Gk_{\rm M}}{1-\lambda^{2 }+2\beta_{3}\lambda}\\ -\frac{F_{2}}{T}=k_{\rm M}\cdot\frac{G-\lambda^{2}}{1-\lambda^{2}+2\beta_{3} \lambda}\,\ \lambda=\lambda_{3}=\frac{\omega i}{\omega_{3}}=\frac{f}{f_{\rm n3}} \end{array} \tag{18}\]  
In the mid-frequency band, \(\lambda\) can be zero because \(f<<f_{\rm n3}\). Distinct features can be obtained and expressed by Equation (19). It should be noted that the active FRF of \(f_{1}\) and \(f_{5}\) in the mid-frequency band are equal to a constant \(F_{i,\rm\omega,A}\), which is referred to as the **4 th horizontal segment**. Therefore, the coupling parameter \(A_{3}K_{3}\) can be identified basedon the known parameter \(k_{\rm M}\) and parameters \(A_{1}\) and \(K_{\rm u1}\) identified by the distinct features of passive dynamics.  
\[-\left.\frac{F_{1}}{I}\right|_{\lambda\to 0}=-\left.\frac{F_{5}}{I}\right|_{ \lambda\to 0}=\frac{A_{1}K_{\rm u1}}{A_{3}K_{3}}k_{\rm M}=F_{i,\infty,4} \tag{19}\]  
In the high-frequency band, \(\lambda\) can be \(\infty\) because \(f>>f_{\rm n3}\). Similarly, distinct features can be obtained and expressed by Equation (20). It can be seen that the chassis-side active dynamics are the voice coil constant \(k_{\rm M}\), which is referred to as the **5 th horizontal segment**\(F_{i,\infty,5}\). Moreover, the active force reappears on the chassis side at a ratio of 1:1 and is not transmitted to the engine side.  
\[-\left.\frac{F_{1}}{I}\right|_{\lambda\to\infty}=0\,\ -\left.\frac{F_{5}}{I} \right|_{\lambda\to\infty}=k_{\rm M}=F_{i,\infty,5} \tag{20}\]  
At the peak of \(\left.\left|-F_{1}/I\right.\right|\), the distinct features, including the **peak value**\(A_{\rm p,1}\), **peak frequency**\(f_{\rm p,1}\), and peak frequency ratio \(\lambda_{\rm p,1}\), can be expressed as Equation (21). Therefore, parameter \(\xi_{3}\) can be identified by the known 4 th horizontal segment \(F_{i,\infty,4}\). Subsequently, \(f_{\rm n3}\) can be identified by the distinct features of \(f_{\rm p,1}\).  
\[A_{\rm p,1}=\left|-\frac{F_{1}}{I}\right|_{\rm p}=F_{i,\infty,4}\cdot\frac{1}{ 2\xi_{3}\sqrt{1-\xi_{3}^{2}}}\,\ \lambda_{\rm p,1}=\frac{f_{\rm p,1}}{f_{\rm n3}}=\sqrt{1-2\xi_{3}^{2}} \tag{21}\]
## 4 Conclusions  
This study investigated the removal efficiency of different algae, namely, _H. fusiformis_, _G. gracilaria_, and _C. fragile_, in removing heavy metals (Cd\({}^{2+}\), Cu\({}^{2+}\), Ni\({}^{2+}\), and Pb\({}^{2+}\)) from contaminated water. _H. fusiformis_ was selected for further studies to improve their biosorption capacity using acid and base treatments. The enhancement of the biosorption capacity of _H. fusiformis_ by modification with a base was due to the carboxylate and ester functional groups on the base-treated algae. The equilibrium data fitted well in the Freundlich model (except for Pb\({}^{2+}\)). The reaction rates obtained from the kinetic models for 1 M NaOH-treated _H. fusiformis_ followed the decreasing order of Cd\({}^{2+}\)\(>\) Ni\({}^{2+}\)\(>\) Cu\({}^{2+}\)\(>\) Pb\({}^{2+}\). The maximum biosorption of metals was in the order Pb\({}^{2+}\)\(>>\) Cu\({}^{2+}\)\(\approx\) Ni\({}^{2+}\)\(>\) Cd\({}^{2+}\), which was consistent with their electronegativity values rather than the hydrolysis constant and ionic radius. The increase in solution pH, especially in the range of pH 2-4, enhanced the metal biosorption by 1 M NaOH-treated _H. fusiformis_ because of feasible precipitation and less protonation of the adsorbent surface under the high concentration of OH\({}^{-}\) ions. Algal biomass can be easily obtained in large quantities, and these features will enable this biomass to be a biosorbent material for heavy metal removal in the future.  
B.N.P.: writing--original draft preparation, methodology, formal analysis; J.-K.K.: validation, data curation, visualization; writing--review and editing, C.-G.L.: writing--review and editing; S.-J.P.; conceptualization, writing--review and editing, supervision. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Korea Environment Industry & Technology Institute (KEITI) through the project to develop eco-friendly new materials and processing technology derived from wildlife, funded by the Korea Ministry of Environment (MOE) (2021003240003).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 4 Proposed System
### Calculation of the Lateral Force
#### 4.4.1 Using a Load Cell  
The pinned-end rigid link (Figure 5) was instrumented with a 250 kN load cell that was placed just before the swivel attached to the head of the column and connected to the data acquisition system. The load cell measures the lateral force acting on the specimen due to the inertial force from the IMS and the P-\(\Delta\) force created from the overturning moment of the IMS. However, it does not include the inertial mass of the pinned-end rigid link between the load cell and specimen, the axial load system, or the contribution of the specimen to the inertial force. Therefore, the lateral force acting on the column is defined as:  
\[F_{col}(t)=F_{LC}(t)+\ddot{x}_{abs}(t)\left(\psi_{1}m_{2}+m_{3}+\frac{m_{4}}{2}\right) \tag{7}\]  
where \(F_{col}\) is the force measured by the load cell, \(\ddot{x}_{abs}\) is the absolute acceleration measured for the specimen and \(\psi_{1}\) is the percentage of mass of the link system between the load cell and specimen. \(F_{col}\) and \(\ddot{x}_{abs}\) are positive in the direction towards the specimen.
# Request Expectation Index Based Cache Replacement Algorithm for Streaming Content Delivery over ICN
## 4 Simulations and Results
### Scenario 1: Hierarchical Topology with One Producer
For instance, both the TLP-TTH and RXI-based algorithm adopt two-levels of the request popularity for cache replacement, and both algorithms significantly improve the network performance compared with LRU and LFU, which only take the simple chunk-by-chunk request probability into account, as confirmed in all four figures of Figure 5. In addition, unlike TLP-TTH, the RXI-based algorithm includes the chunk-level internal request probability and introduces a unified estimation criteria of possible future request probability for all cached chunks, which may belong to different files and be located in different positions in the file. Therefore, by further considering the internal relationships among chunks within a file, the estimation of future request probability is more accurate, so that the network performance is further improved by the RXI-based algorithm, as shown in Figure 6. Nevertheless, accurate estimation of request probability requires more calculation resources. Due to the simple principle of LRU and LFU, the eviction time complexity can achieve \(O(1)\). By contrast, the eviction time complexity of TLP-TTH and RXI-based algorithm is higher, but still able to attain linear time complexity \(O(n)\). In addition, we are curious about how the RXI-based algorithm performs in different domains of a network. Therefore, we recalculate the cache hit ratio of scenario 1 (cache size = 800 chunks) in different network regions. One interesting observation is that, compared with the other algorithms, the RXI-based algorithm increases the cache hit ratio at the edge of the network and at the intermediate routers allocated at the core network, as shown in Figure 7. The improvement in the intermediate network is mostly due to the accurate and independent estimation of the future request probability of the cached chunks at each router, and the eviction of the chunk that has the lowest expectation of been requested later. Figure 6: Simulation results of hierarchical topology. (**a**) average cache hit ratio; (**b**) average hop count; (**c**) network traffic; (**d**) total retrieval time. LFU: Least Frequently Used; TLP-TTH: Two-level popularity oriented time-to-hold policy.
## 2 Data Transmission in a Wireless Seizure Detection System
### Transmission of Compressed EEG
Data compression is performed for every epoch of \(N=512\) samples (equivalent to 4 s at a sampling rate of 128 Hz) of EEG signals. At each sampling period, the intermediate values of the compressive measurements are updated with the current EEG signals and buffered in the embedded memory on the sensor node. At every \(N\) sampling period, the compressive measurements computed over the last \(N\) periods of all channels are concatenated and transmitted to the data server. Upon the receipt of the compressed signals, the data server performs data reconstruction and seizure detection.