esa-sceva/satcom-chunk-collection huggingface dataset:
However, for the measurement of the foot, a major limitation of the existing 3D scanning methods is that it only works during non-weight-bearing conditions because the sole of the foot cannot be measured when it is in contact with the floor under weight-bearing conditions. Standing on a glass platform may allow the measurement of sole morphology using 3D scanning sensors under the glass [31; 32; 34]. However, slight movement of the foot under weight-bearing conditions or image distortion owing to the refraction of the light through the glass during scanning may affect the accuracy of the measurements. Special calibration of measurements for these conditions will be needed to improve the accuracy. Another limitation is that these methods may not be applicable to measurements of alignment-corrected feet as the handsof the therapist may interfere with the measurement. Recently, a novel Single-Image-Based Pin-Array Impression Reconstruction Method (SIBPAIR) was developed for sole morphology measurements based on a commercially available foot assessment system (FAST, Enford International Co., Taiwan) (Figure 1). It provides a tool that overcomes the limitations of the existing methods in measuring the three-dimensional surface of the sole, enabling the measurement of the associated morphological parameters under partial or full weight-bearing conditions. However, the reliability of the novel approach has not yet been established. The aim of the current study was to assess the intra-rater, inter-rater, and inter-session reliability of the overall protocol using the SIBPAIR method for measuring foot morphological parameters related to the classification of the foot types based on the medial longitudinal arch morphology, namely FL, FW, AI, CSI, SAI, AH, NAH, and HVA, in terms of intraclass correlation coefficients (ICC). The reliability of the SIBPAIR method itself was also assessed in terms of the reproducibility of the measured morphological parameters for a set of given foot sole impressions. It was also hypothesized that there would be no significant differences between raters or between sessions for any of the measured morphological parameters.
## 4 Evaluation Results and Discussion
### Performance across Session Durations  
To study the impact of session durations on the performances of the models, the obtained results over all the databases with respect to different session durations are plotted in Figure 4. Note that the first six databases in the horizontal axis (i.e., _W-SQoE-I_, _W-SQoE-II_, _W-SQoE-III_, _W-SQoE-IV_H264_, _W-SQoE-IV_pH264_, and _W-SQoE-IV_uH264_) contain only short sessions with the lengths of from 8 to 28 s. For the next five databases (i.e., _TR04f_, _TR04p_, _VL04_, _VLDB_, and _TRDB_), they consist of medium sessions of about 1-minute in length. With the last five databases (i.e., _TR06f_, _TR06p_, _VL13_, _TRCQ_, and _VLCQ_), long sessions (i.e., \(\geq\)3 min) are included. To facilitate the performance comparison between the models, Figure 5 shows the average and standard deviation (stdev) of the performances of each model over all the databases.  
Figure 3: Average performance and mean difference of the models using the _DBS1_, _DBS2_, and _DBS3_ sets. (**a**) average performance; (**b**) mean difference.  
From these figures, we divide the models into four groups. The first group consists of _[PERSON]'s_ (both _ori_ and _mod_), _[PERSON]'s_, and _[PERSON]'s_ models that have rather low average performances (i.e., PCC \(\leq\) 0.58, SROCC \(\leq\) 0.56, and RMSE \(\geq\) 0.71). For the models in the second and third groups, their average performances are acceptable (i.e., 0.62 \(\leq\) PCC \(\leq\) 0.81, 0.65 \(\leq\) SROCC \(\leq\) 0.82, and 0.45 \(\geq\) RMSE \(\geq\) 0.65). In the second group, the included models are _[PERSON]'s_, _ATLAS_, _P.1203_, _CQM_, and _biQPS_ that have drastically variable performances (i.e., stdevPCC \(\geq\) 0.11, stdevSROCC \(\geq\) 0.09, and stdevRMSE \(\geq\) 0.08). The third group is composed of _[PERSON]'s_ and _[PERSON]'s_ models with both the _ori_ and _mod_ cases. In general, their performances are stable with stdevPCC \(\leq\) 0.09, stdevSROCC \(\leq\) 0.11, and stdevRMSE \(\leq\) 0.12. For the fourth group, it contains three models of _LSTM_, _SQI_, and _KSOI_ that have quite high average performances (i.e., PCC \(\geq\) 0.81, SROCC \(\geq\) 0.84, and RMSE \(\leq\) 0.46). In the following, the models in each group will be discussed in detail.  
Figure 4: Performance of all the models with respect to different session durations. (**a**) PCC; (**b**) SROCC; (**c**) RMSE.  
Figure 5: Average and standard deviation performance of the models over all the databases. (**a**) average performance; (**b**) standard deviation performance.
## 3 Results and Discussions
### Analysis of Volatile Components in V. kulingiana Leaves  
Using HS-SPME-GC-MS technology, in total,165 VOCs were characterized in _V. kulingiana_ leaves, representing 79.83% of the total volatile content. These compounds mainly include terpenoids, alcohols, carboxylic acids, heterocyclic compounds, aromatic hydrocarbons, and ketones (Table S1). The main compounds with relative contents over 1% are shown in Table 3. Among them, the terpenoids of \(\beta\)-ionone (8.24%), 3-(4-methyl-3-pentenyl)furan (2.37%), and linalool (1.68%) are known for their floral and citrus-like aromas, which may play a crucial role in the fragrance of _V. kulingiana_[41; 42; 43]. Additionally, there is a significant presence of heterocyclic compounds, such as dihydroactinidiolide and 2-methyl-3-furanthiol, with a relative content of 1.55% and 1.35%, which could contribute a meaty or fishy taste to _V. kulingiana_[44; 45]. Furthermore, ketone compounds like 2,6,6-trimethylbicyclo[3.2.0]hept-2-en-7-one can potentially enhance the sweet aroma of _V. kulingiana_[46]. Based on these findings, we speculate that the rich terpenoid and alcohol compounds in _V. kulingiana_ leaves might be the primary sources of their distinctive fragrance. The presence of these volatile components reveals the material basis for the unique aroma of this wild vegetable, further validating that the related traditional knowledge has a certain scientific basis.
## 2 Materials and Methods
### Examination of Rheological Properties  
The rheological behavior of HHP-treated blood plasma suspensions was investigated using an MCR 92 rheometer (Anton Paar Ltd., Les Ulis, France) in rotational mode. The properties of the samples under varying shear stress were determined using a concentric cylinder (CC27-SN16145) measuring system with the following parameters: cup diameter 28.920 mm, bob diameter 26.651 mm, bob length 40.003 mm, active length 120.2 mm and positioning length 72.5 mm. Anton Paar RheoCompass software (version 1.21.852) was used to control the instrument and thus record the curves. Measurements were performed in triplicate for each sample at 20 \({}^{\circ}\)C. The shear stress analysis was performed at decreasing shear rates between 1000 and 1 s\({}^{-1}\), where 31 data points were recorded. The Herschel-Bulkley model (Equation (1)) was used to describe the rheological properties of the samples [23,24]. All R\({}^{2}\) values of the fitted model were higher than 0.95.  
\[\tau=\tau_{0}+\mathrm{K}\gamma^{\mathrm{n}} \tag{1}\]  
where \(\tau\)--shear stress (Pa); \(\tau_{0}\)--yield stress (Pa); \(\gamma\)--shear rate (s\({}^{-1}\)); K--consistency coefficient (Pa s\({}^{\mathrm{n}}\)); n--flow behavior index (dimensionless).
# Privacy-Preserving Federated Singular Value Decomposition
## 5 Differentially Private Federated SVD Solution  
**Privacy Analysis of FedPower. ** Algorithm 1 injects noise both at the local (Step 6) and the global (Step 8) levels. Consequently, the claimed privacy protection of Algorithm 1 is \((2\varepsilon,2\delta)\)-DP, which originates from \((\varepsilon,\delta)\)-LDP and \((\varepsilon,\delta)\)-CDP [18]. Firstly, as we highlighted in Table 1, LDP and CDP provide different privacy protections; hence, merely combining them is inappropriate, so the claim must be more precise. Instead, Algorithm 1 seems to provide \((\varepsilon,\delta)\)-DP for the clients from the server and stronger protection (due to the additional central noise) from other clients and external attackers. Yet, this is still not entirely sound, as not all computations were included in the sensitivity calculation; hence, the noise scaling is incorrect. Indeed, the authors only considered the sensitivity of the multiplication with \(\mathbb{Z}\) in Step 3 when determining the variance of the Gaussian noise in Step 6; however, the noise is only added after the multiplication with \(\mathbb{D}\) in Step 5. Thus, the sensitivity of the orthogonalization is discarded. **Privacy-Enhanced FedPower. ** We improve on FedPower [18] from two aspects: (1) we incorporate clipping in the protocol to bound the sensitivity of the local operations performed by the clients and (2) we use SA with DP to obtain a strong privacy guarantee. For this reason, similar to FedPower [18], we assume that for all \(i\) the elements of \(\mathbb{M}_{i}^{\prime}=\frac{1}{s_{i}}\mathbb{M}_{i}^{\mathsf{T}}\mathbb{M}_{ i}\) are bounded with \(\hat{m}\). In Algorithm 1, the computations the nodes undertake (besides noise injection at Step 6) are in Steps 3, 5 and 12, where the last two could be either discarded for the sensitivity computation or completely removed, as explained below. * Step 12: Orthogonalization is intricate, so its sensitivity is not necessarily traceable. To tackle this, we propose applying the noise before, in which case it would not affect the privacy guarantee, as it would count as post-processing. * Step 5: We remove this client-side operation from our privacy-enhanced solution, as it is not essential; only the convergence speed would be affected slightly. The FedPower protocol with enhanced privacy is present in Algorithm 3, where besides the orthogonalization, clipping is also performed with \(\hat{z}\). The only client operation which must be considered for the sensitivity computation (i.e., before noise injection) is Step 3. We calculate its sensitivity in Theorem 1. **Theorem 1**.
## 2 Intelligence Artificial (AI)-Based Resource Allocation Algorithms in Dynamic Traffic Scenario
### State-of-the-Art
Conversely we propose a solution in which: (i) both NFVI and transport infrastructures are modeled; (ii) the prediction is performed by using an LSTM recurrent neural network with an asymmetric loss function where the cost is dependent on the lost traffic amount. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Work** & **Prediction** & **Prediction Methodology** & **Minimized Loss Function** \\ \hline [PERSON] et al. [12] & Resource & Linear & Regression, Support Vector Machine & Symmetric \\ [PERSON] et al. [15] & Traffic & LSTM & Symmetric \\ [PERSON] et al. [17] & Resource & Regression & Linear, & Symmetric \\  & & K-Nearest & Neighbor & \\  & & Regression & & \\ [PERSON] et al. [13] & Traffic & Convolutional & Neural & Asymmetric \\  & & Networks & & \\
**Our Proposal** & Traffic & LSTM & Symmetric \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between our proposal and the main related works. A preliminary result on the advantages of the traffic prediction with an asymmetric loss function has been investigated in [20] when the prediction is based on Seasonal Auto Regressive Integrated Moving Average (ARIMA) processes. In this manuscript we extend the proposed solution to the case of LSTM-based predictions. The following contributions are added in this manuscript:  
* An innovative prediction algorithm based on an LSTM recurrent neural network with an asymmetric cusp loss function is proposed;
* The performance of electrical networks is investigated; conversely, resource allocation for Optical NFV networks is investigated in [20];
* Extensive numerical results are reported in which the operational costs of an NFV network with resource allocation based on symmetric and asymmetric LSTM are evaluated respectively;
* New results are presented with respect to [20] in which the resource allocation is not only performed after on the prediction step but the new approach allows for a multi-step prediction and resource allocation.
# Key Technologies for THz Wireless Link by Silicon CMOS Integrated Circuits
## 4 Conclusions  
Since atmospheric attenuation is relatively small in the 300 GHz band, it is the last remaining frequency band where a wide frequency band near 70 GHz can be used. It was shown that a 300 GHz band transmitter and receiver can be realized with a silicon CMOS integrated circuit with lower frequency performance than of a compound semiconductor. When a commercial CMOS baseband signal processor for optical communication is combined with a terahertz front end, terahertz communication can be put to practical use at an early stage. However, the performance of a terahertz transceiver using a CMOS integrated circuit is still insufficient. It is necessary to further improve the performance by combining a power amplifier and a low-noise amplifier with a compound semiconductor with a CMOS integrated circuit. A traveling-wave tube is also conceivable for applications requiring watt-class power [33]. Heterogeneous integration of CMOS integrated circuits with compound semiconductors and traveling wave tubes may be the key to solving future challenges.  
High-power signals have been generated in the UHF band since the first magnetron was invented in 1921, and research on microwaves such as radar became popular. Fifty years after the invention of the magnetron, in 1970, single mode fiber was invented, and optical communication was subsequently actively studied. As a result, large-volume long-distance transmission became possible, and society changed drastically. There are signs that terahertz communication will open up a new world in a further 50 years hence, in 2020. Wireless communications in the 300 GHz band that can realize the same data rates as wired communications have the potential to affect space plans after 2020. The ability of terahertz communication to connect places that cannot be connected by wired communication, and enabling the same volume as optical communication, will significantly change the society of the future.  
**Funding:** The research results on the 300 GHz band CMOS transceiver was partially supported by the Ministry of Internal Affairs and Communications.  
Figure 18: Estimates of the performance required when 300 GHz band communication is realized in space. If the transmission power of terahertz communication increases, a broadband communication link toward space becomes possible.  
**Acknowledgments:** Applications of terahertz communication include contents obtained through discussions with Dr. [PERSON] of the National Institute of Information and Communications Technology (NICT). We thank the members of NICT, Panasonic and Hiroshima University participating in this project.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Hydrogeochemical Effects of Hydrogen Storage on Reservoir Rock and Cap Rock
The used modeling program, PHREEQC, has no capabilities to simulate gas transport between the different cells (multiphase flow). To overcome this limitation, a separate transport model provides an initial amount of H\({}_{\text{2(g)}}\) in the cap rock for the kinetic calculation to simulate the influence of H\({}_{\text{2(g)}}\) on the cap rock properties in the case of a gas loss from the reservoir rock by, for example, new fractures or natural faults. The initial amount corresponds to the maximum available amount of H\({}_{\text{2(g)}}\) that could be available per cell in the cap rock and represents a worst-case scenario. Modeling results indicate an increase in pH from 6.4 to 7.8 in the cap rock brine after 30 years of storage. The intense contact of hydrogen with the cap rock results in a total loss in porosity, which increases the sealing capacity of the cap rock. This decrease in porosity is caused mainly by albitization, whereas the dissolution of halite, quartz, illite, and anhydrite counteracts a stronger porosity decrease. Precipitation of dolomite is negligible. The potential secondary phase pyrite is precipitated whereas dawsonite, machiawite, and sulfur are not formed.
## 3 System Model
### Privacy Requirements  
In the context of the presented network and trading model, it is desired that the following two requirements regarding a participant's privacy should be accomplished:  
Figure 1: System model.  
* _Participant's anonymity and untraceability:_ Protect the identities of participants and make them untraceable in the SMP. Their identities should be protected from identity-related attacks, such as address reuse, Sybil and blockchain analysis attacks during trading in the SMP.
* _Transactional data privacy:_ Ensure protection against transactional data privacy attacks, such as inferential attacks and data leakage.
## 4 Proposal for Data Transformation to Resolve Incompatibilities between Medical Formats  
With different types of heterogeneity, it becomes increasingly difficult to manage patient data in health systems, especially in large amounts. As a result, different solutions are still being proposed for various applications, and ways to automate data transformation and normalization for usage in machine learning systems are still sought after. This section covers performance comparison of machine learning methods, as well as possible suggestions on such data transformation in the context of diagnosis with the application of NLP, as well as possible problems in data transformation requiring further research.  
Many new algorithms have recently been developed, but have not seen a practical application yet. This experiment aims to put novel modifications of machine learning methods to use by evaluating them against a more \"classic\" approach, to see how different algorithms act in the case of data with missing values, as well as to help to test the NLP-based data transformation part. Particularly interesting in the context of heterogeneous data, however, are the novel modifications to fuzzy logic systems, with their fuzzy logic being interesting to observe within the context of incomplete or ambiguous samples. At the same time, Support Vector Machines have other draws when it comes to dealing with a large amount of data, making their performance in the context of an expanding dataset interesting.  
For these reasons, the three algorithms chosen to be tested in this study are TWNFI, TR-SVM and Naive Bayes classifier, as a component to evaluate cardiovascular disease risk based on transformed data that were taken from various sources and automatically transformed [50; 59; 60].  
TWNFI is also interesting to study in this context due to the individual quirks each algorithm possesses. TWNFI tailors its current model to the input, and it would take fewer steps to load during the feature changes too. To go with the experiment, a more conventional machine learning method--the Naive Bayes classifier--has also been chosen along with these.  
The primary reason to evaluate cardiovascular disease risk is to identify those individuals who may be at an increased risk of developing this condition. Early identification of individuals at risk can lead to earlier interventions which can reduce the risk of developing cardiovascular disease, and can also help to identify those individuals who may need to make lifestyle changes in order to reduce their risk. Additionally, assessing cardiovascular disease risk can help to guide treatment decisions for those already diagnosed.
# Thermal Time Constant of PV Roof Tiles Working under Different Conditions
## 4 RC Circuit Model  
The material parameters of roof tile layers as well as atmospheric conditions (e.g., wind direction and speed, irradiance, ambient temperature) greatly affect the temperature of PV cells constituting the photovoltaic roof tile. A working PV roof tile also generates heat as a result of its internal processes. A resistance-capacity (RC) model of a photovoltaic roof tile helps to identify temperature changes of the PV cells, which are the PV roof tile components, with a sudden change in their working conditions (particularly atmospheric conditions and irradiance). The temperature of PV cells in photovoltaic roof tiles changes exponentially with a discontinuous (sudden) change in irradiance, while the RC time constant of a PV roof tile is defined as the time necessary to reach 63% of the total temperature change value. The proposed RC model of the studied PV roof tile helps to identify its \(\tau_{RC}\) under variable working conditions. Thermal mechanisms of PV roof tiles were expressed by correlating their electrical equivalents (resistance and capacity) with thermal resistance and thermal capacity, used for defining heat transmittance in their layers. Figure 4 presents a substitute wiring diagram for the RC model of a studied single PVL68 photovoltaic roof tile (Figure 4a) and for the entire roof structure with built-in PV roof tiles (Figure 4b). Figure 4a,b covers the PV cell as a whole (i.e., assembly of the three layers of cells listed in Figure 3). \(I_{PV}\), \(U_{PV}\) (Figure 4a,b) is a photovoltaic current and voltage of solar cells, respectively. \(P_{PV}\) is a electric power generated by a solar roof tile, calculated as multiplication of the \(I_{PV}\) and \(U_{PV}\).  
\(R_{th\_front}\) and \(R_{th\_back}\) stand for the resistance of heat transfer on the outer \(R_{se}\) and inner \(R_{si}\) surface of the item respectively. The resistance values, depending on the direction of the heat stream flow, were specified in [36] PN-EN ISO 6946:2017 and are presented in Table 4.  
The time constant of the reference PV roof tile or the entire roof construction can be identified according to Equation (6), based on identified thermal resistance and heat capacity values (Tables 3 and 4) and wiring diagrams presented in Figure 4:  
\[\tau_{RC}=\Big{(}R_{TH}+R_{th\_front}+R_{th\_back}\Big{)}C_{TH}. \tag{6}\]The identified time constant values for the PVL68 photovoltaic roof tile and the entire roof amounted to \(\tau_{RC\_pv}\) = 18.80 min and \(\tau_{RC\_rof}\) = 99.43 min, respectively. In the analysis of the entire roof construction with PV roof tiles, the RC time constant value increased due to the high heat capacity of wood boards laid under the photovoltaic roof tiles.
## 3 Proposed Solution
### Semantic Interoperability
#### 3.1.9 Semantic Interoperability Establishment  
Semantic interoperability is not only concerned with the syntactical packaging of data but also the concurrent transmission of the semantics of those data (meaning). This procedure is performed by assessing metadata (data about the data) and merging the apiece data component into a shared and controlled vocabulary. Semantic interoperability makes a system understandable to an unknown system by taking the benefit of structural data exchange and vocabulary, including data codification. Therefore, the data interpretation becomes more accessible through information technology to the receiving end. The highest level of interoperability among the systems is represented by semantic interoperability. Semantic interoperability deals with the messaging format as well as concerns with the message content. Figure 2 shows the resultant ontology graph view, for social media activity by single user ID, defined by OWL language.