esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliability of a Global Gait Symmetry Index Based on Linear Joint Displacements
###### Abstract
In fact, a previous small study [22] has shown that by excluding the least reliable joint angle components, as usually performed for other indices [23; 24], the index reliability was slightly lower, contradicting the expectations. A radical alternative, that may solve this problem, would be to not include angles at all. Considering the relationship between a segment angular and linear positions, joint angles could be replaced by the linear displacements of joints. Based on the 3D linear displacement of several joints, [PERSON] [25] was able to identify and model walking patterns that are distinct between males and females. Similarly, gait symmetry could be reported from the bilateral differences in the linear position of joints (in relation to a central position), instead of the bilateral differences in joint angles. An advantage of calculating symmetry this way is that the segments' end positions (the joints) are not affected by the longitudinal orientation of the segment coordinate system, which is the most affected by marker placement inconsistency [20]. Based on this premise, a new global gait symmetry index was developed, as presented hereafter. This new index, the Linear Global Gait Asymmetry (LGGA), is calculated using the same equation as the GGA and replacing the joint angles by the linear positions of the segment's distal extremities in relation to the pelvis. Thus, the LGGA could be a useful tool to complement the clinical gait analysis, by informing, quantitatively, the clinician of the overall level of gait symmetry of a patient and how it is progressing throughout the rehabilitation process. Thus, the aim of this study was to develop and assess the inter-session intra-rater reliability and measurement error of the LGGA. The authors hypothesised that the LGGA will be more reliable than global symmetry indices comprising joint angles.
# Enhancing Impact Localization from Fluid-Pipe Coupled Vibration under Noisy Environment
## 4 Conclusions  
We propose a wavelet-based impact source-locating approach for use in water mains by measuring water-pipe coupled waves with vibration accelerometers for structural health monitoring. As for a real-time monitoring scheme, wavelet analysis itself is not solely adequate due to the computational load. The algorithm we proposed is, therefore, a complementary monitoring algorithm, which provides more information than conventional rapid screening parameters, such as peak detection or RMS value over threshold.  
The coupled wave approach is a good candidate to provide even longer-range monitoring than that provided by the conventional bending wave approach due to its non-dispersive propagation at low frequencies. In addition, this approach is also useful for detecting relatively low-magnitude impact sources, such as third-party damage and minor pressure transients with the vibration sensors. However, the vibration signal is always corrupted by various kinds of noise from the surroundings. The proposed approach, therefore, makes use of the wavelet de-noising performance and additional signal processing steps to improve noise reduction and signal detectability. In particular, the proposed procedure demonstrated that the burst noise that often contaminates signals with similar features in CWT was successfully removed.  
The example experiments have shown that wavelet-based impact-source localization is able to estimate locations more precisely with lower error than the conventional cross-correlation algorithm. To improve this approach further, future research should be devoted to identifying detectability with respect to the signal-to-noise ratio and propagating distance.  
Conceptualization, C.-S.P. and D.-J.Y.; methodology, C.-S.P.; experimental design: S.-H.L.; writing--original draft preparation, C.-S.P.; writing--review and editing, S.-H.L. and D.-J.Y.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by Korea Ministry of Environment supported through Advanced Water Management Research Program (grant number 127587) and also partly supported through Project for Developing Innovative Drinking Water and Wastewater Technologies (grant number 2020002700016) by Korea Environment Industry & Technology Institute.  
Not applicable.  
This work is supported by Korea Research Institute of Standards and Science under the project \"Infrastructure safety measurement (KRISS-20011051)\".  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Implementing Digital Signal Processing Techniques for CSI Data Analysis
#### 2.5.4 Butterworth Low Pass Filtering  
After the initial filtering steps, the processed CSI data begin to reveal respiratory wave patterns. However, these signals are often accompanied by higher frequency noise, which is not expected in a static sleeping environment where the only significant movement is the breathing of a person. To isolate the breathing signal, we apply Butterworth low pass filtering, effectively eliminating any components with frequencies exceeding a predefined threshold. By setting this threshold just above the typical range of human RRs, we can isolate the breathing waveform. Figure 9 demonstrates this filtering, comparing the original input, laden with high-frequency noise, to the refined output that prominently features only the breathing wave. The parameters for this filtering process, specifically the order of the Butterworth filter and the cutoff frequency, are denoted as \(\delta\) and \(\epsilon\), respectively, in our study.  
The transfer function \(H(s)\) of an \(\delta\)-th order Butterworth low-pass filter is given by:  
\[H(s)=\frac{1}{\sqrt{1+\left(\frac{s}{\omega_{c}}\right)^{2\delta}}} \tag{12}\]  
where \(s\) is the complex frequency variable, and \(\omega_{c}\) is the normalized cutoff frequency.  
In the discrete domain, using the Nyquist frequency \(f_{s}/2\) where \(f_{s}\) is the sampling frequency, the normalized cutoff frequency \(\omega_{c}\) is given by:  
Figure 8: Example of applying CSI linear interpolation to standardize the sequence dimensions.
## 4 Simulation Results and Discussion
### Simulation Results in the Case without Constructive Constraint  
In this part, we consider the BSN deployment case without constructive constraints, where BSNs move inside an area of 100 \(\times\) 100 square meters following the mobility model of WPAM [23,28]. The handover performance is investigated under different hysteresis margins. For the proposed handover method, hysteresis margin refers to the distance between the APs boundary and the position where the outage requirement is met. By changing the hysteresis margin, the outage requirement is also changed. The results are shown in Figures 7 and 8.  
margin increases. For the proposed scheme, the handover rate also decreases and remains similar to that of the hysteresis-based scheme when the hysteresis margin is relatively small. This is because a BSN is more likely to avoid unnecessary handover when the hysteresis margin increases. When the hysteresis margin increases, the handover rate of the proposed scheme remains stable since most of the unnecessary handovers have been alleviated.  
Figure 8 shows the comparison of the number of outages for the three schemes. As can be seen, the number of outage of the basic scheme is lowest at the expense of much higher handover rate as shown in Figure 7. The number of outages of the proposed scheme significantly outperforms that of hysteresis-based handover (about 22% on average), especially when the hysteresis increases. This is because as long as the BSN user is predicted to enter another AP with high confidence probability, the handover is performed immediately to reduce outage.
# Structural Optimization of Slotting Nozzle Used to Improve Coal-Seam Permeability
## 3 Materials and Methods
### Experimental Design and Procedures  
For the same working conditions, the outlet water jet slotting depth of a slotting nozzle is a key factor to evaluate its real working performance. In this study, the slotting capacities of the original and optimized slotting nozzles were compared. The experimental specimens were poured with concrete. The size of the test box was 1 m\({}^{3}\) and water was used as the jet. The experimental procedures were as follows:  
1. The high-pressure pipeline connection was completed and the slotting device was pushed to the appropriate position in the test box;
2. The test box was closed, the pipeline connection checked, and the pump switched on;
3. When the pump was stable, the pressure rose rapidly to 28 MPa and measurement of elapsed time was started;  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Slotting** \\ **Nozzle** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Experiment** \\ **Number** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Q (L/min)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Pressure** \\ **Difference** \\ \end{tabular} } & \multicolumn{2}{c}{\begin{tabular}{c} **Flow Coefficient** \\ **Calculated** \\ **Value** \\ \end{tabular} } & \multicolumn{2}{c}{\begin{tabular}{c} **Average** \\ **Value** \\ \end{tabular} } \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Original \\ slotting nozzle \\ \end{tabular} } & 1 & 88.2 & 28.0 & 0.8857 & \\  & 2 & 89.1 & 28.0 & 0.8948 & 0.8897 \\  & 3 & 88.5 & 28.0 & 0.8884 & \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Optimized \\ slotting nozzle \\ \end{tabular} } & 1 & 87.9 & 28.0 & 0.8824 & \\  & 2 & 89.7 & 28.0 & 0.9005 & 0.8958 \\  & 3 & 90.1 & 28.0 & 0.9045 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Flow-coefficient testing conditions and results.  
Figure 7: Specimens for uniaxial compressive strength testing.  
4. Every 60 s, the pump was rapidly switched off, the water jet slotting depth was measured, and the characteristics of the slots were photographed and recorded; and
5. Steps (1)-(4) were repeated until the specimen was penetrated or the slotting depth no longer increased with slotting time.
# Robot Scheduling for Assistance and Guidance in Hospitals
## 2 Related Work  
In the medical industry, the use of robots is increasing year by year [5]. Mobile robots [6] specialize in assisting nurses in transporting medical supplies and moving patients to other areas. Chat-type robots help patients measure body temperature and detect whether they wear masks.  
At the University of California, San Francisco Medical Center [7], they introduced 25 intelligent transport robots named TUGs, as shown in Figure 2a. Assist medical personnel in transporting medicines, goods, laboratory specimens, and food, automatically navigate through the hospital floor plan that is stored in the robot, avoid obstacles at any time, and use wireless network communication to control the opening and closing of elevator doors.  
In India, a Mitra robot [8] is responsible for allowing patients that are diagnosed with new coronary pneumonia that are in isolation to keep in touch with their relatives and friends, as shown in Figure 2b. The face recognition technology that is built into the robot's eyes can memorize human faces. The robot can actively greet the patient when it sees them. It allows the patient to make video calls with relatives and friends through the touch screen that is built into the robot's chest. In addition, the Mitra robot can assist nurses in disinfection work, answer patient questions, allow doctors and patients to conduct remote consultations, and remind patients to take medication.
# An Improved Fuzzy Vector Signature with Reusability
## 3 Definitions
### Security Models
#### 3.2.2 SIG-Privacy  
SIG-privacy means that an adversary cannot ascertain any information on fuzzy input data \(w^{\prime}\) from a signature \(\sigma_{w^{\prime}}\). The adversary cannot distinguish between a valid signature \(\sigma_{w^{\prime}}\) and a signature corresponding to a uniformly random fuzzy data \(u\) without a corresponding verification key. We say that an FVS scheme is SIG-private if the advantage that any PPT adversary \(\mathcal{A}\) wins against the challenger \(\mathcal{C}\) in the following game for \(j=1,\ldots,q\) is negligible in \(\lambda\):  
\(\bullet\)**Setup**: \(\mathcal{A}\) selects target correlated random variables \(W=(W^{*},W_{1},\ldots,W_{q})\in\mathcal{W}\) and gives it to \(\mathcal{C}\). \(\mathcal{C}\) chooses \((w^{*},w_{1},\ldots,w_{q})\in(W^{*},W_{1},\ldots,W_{q})\). Then, \(\mathcal{C}\) runs \(\mathbf{Setup}(1^{\lambda},w_{j},n,d,\ell,t)\) for \(j\in[1,q]\) and \(\mathbf{Setup}\)\((1^{\lambda},w^{*},n,d,\ell,t)\). \(\mathcal{C}\) sends \(\{\mathsf{VK}_{w_{j}},\mathsf{SP}_{j}\}_{j=1}^{q}\) and \(\mathsf{SP}^{*}\) to \(\mathcal{A}\). \(\bullet\)**Query**: \(\mathcal{A}\) issues a random variable \(W^{\prime}\) correlated with \((W^{*},W_{1},\ldots,W_{q})\), a message \(m_{k}\), and an index \(j\in[1,q]\) of the signing parameter \(\mathsf{SP}_{j}\). \(\mathcal{C}\) chooses \(w^{\prime}\gets W^{\prime}\) correlated with \((w^{*},w_{1},\ldots,w_{q})\), runs \(\mathbf{Sign}\)\((\mathsf{SP}_{j},w^{\prime},m_{k})\), and gives the resulting signature to \(\mathcal{A}\). \(\bullet\)**Challenge**: \(\mathcal{A}\) issues a message \(m^{*}\). \(\mathcal{C}\) obtains \(\sigma^{*}\leftarrow\mathbf{Sign}(\mathsf{SP}^{*},w^{*},m^{*})\) and selects a bit \(b\in\{0,1\}\) at random. If \(b=0\), \(\mathcal{C}\) sends \(\sigma^{*}\) to \(\mathcal{A}\). Otherwise, \(\mathcal{C}\) selects a random input \(u\) from a uniformly random distribution \(U\), obtains \(\sigma^{*}\leftarrow\mathbf{Sign}(\mathsf{SP}^{*},u,m^{*})\), and gives \(\sigma^{*}\) to \(\mathcal{A}\). \(\bullet\)**Guess**: \(\mathcal{A}\) outputs its guess \(b^{\prime}\in\{0,1\}\). If \(b^{\prime}=b\), \(\mathcal{A}\) wins the game.  
**Definition 2** (SIG-privacy).: _An FVS scheme \(\Pi_{f}\) is SIG-private if, for any PPT adversary \(\mathcal{A}\) against SIG-privacy, there exists a negligible function \(\
u(\lambda)\) such that_  
\[\mathsf{Adv}_{\Pi_{f},\mathcal{A}}^{\text{SIG}}(\lambda)\triangleq\Big{|} \Pr\big{[}b=b^{\prime}\big{]}-\frac{1}{2}\Big{|}\leq\
u(\lambda).\]
## 2 Materials and Methods
### Differential Scanning Calorimetry (DSC)  
SLN and NLC samples were analyzed by DSC using a Mettler TA STAR\({}^{\mathrm{e}}\) instrument (Mettler Toledo, Greifensee, Switzerland) equipped with a DSC 822\({}^{\mathrm{e}}\) cell and a Mettler STAR\({}^{\mathrm{e}}\) V8.10 software. Indium and palmitic acid (purity \(\geq\) 99.95% and \(\geq\) 99.5%, respectively; Fluka, Buchs, Switzerland) were used to calibrate the instrument. For each sample, 100 \(\upmu\)L was sealed into a calorimetric pan, and DSC analyses were carried out by heating the sample from 5 to 65 \({}^{\circ}\)C (rate: 2 \({}^{\circ}\)C/min) and then cooled from 65 to 5 \({}^{\circ}\)C (rate: 4 \({}^{\circ}\)C/min) for at least three times. A pan filled with 100 \(\upmu\)L of the aqueous phase used for lipid nanocar preparation was used as the reference. To calculate the sample crystallinity index (CI), the melting enthalpy of the lipid in the nanocarriers was expressed as a percentage of the melting enthalpy of the bulk lipid cetyl palmitate, which was considered totally crystalline as reported in previous works [22; 23]. ViTE CI was taken equal to zero as this compound was liquid at room temperature and did not show any melting peak. Each experiment was run in triplicate.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Code**} & \multicolumn{5}{c}{**Ingredients (\% w/w) *} \\ \cline{2-6}  & **Oleth-20** & **GO** & **CP** & **VitE** & **IDE** \\ \hline SLN & 9.00 & 5.00 & 7.00 & - & - \\ SLN IDE1 & 9.00 & 5.00 & 7.00 & - & 1.00 \\ SLN IDE1.5 & 9.00 & 5.00 & 7.00 & - & 1.50 \\ NLC & 9.00 & 5.00 & 6.00 & 1.00 & - \\ NLC IDE1 & 9.00 & 5.00 & 6.00 & 1.00 & 1.00 \\ NLC IDE1.5 & 9.00 & 5.00 & 6.00 & 1.00 & 1.50 \\ \hline \hline \end{tabular}
* Water phase q.s. 100.00%  
\end{table}
Table 1: Composition of the oil phase of lipid nanoparticles. GO = glyceryl oleate; CP = cetyl palmitate; VitEt = tocopheryl acetate, IDE = idebenone, SLN = solid lipid nanoparticles, NLC = nanostructured lipid carriers.
## 5 Discussion  
In the testing and validation, the POC implementation of the DCC-based calibration data exchange and management platform proved to work as intended, providing an efficient way to exchange calibration data digitally, as all the features fulfilled the requirements. As the implementation was only developed as a POC, some areas for additional refinements were also found, which was as expected.  
Due to the work being the first collaboration initiative for POCs on the utilization of DCCs for the participating organizations, the scope of the work was kept relatively narrow in terms of the whole calibration management process. Consequently, the results of the POC do not fully cover all parts of the processes that are necessary in the industrial calibration management. Still, the work provided valuable knowledge and a basis for further collaborative development in digitalization.  
The chosen approach for implementing the platform was developed based on a relatively simple infrastructure utilizing already widely used services, such as the Azure AD. In addition, the signing and validation service was integrated into the platform. Thus, the number of new and separate systems required for enabling the secure use of DCCs was minimized. This approach suits well the requirements of the industry, especially when smaller organizations are considered, as simplicity and ease of deployment were given relatively high priority. For example, in the testing and deployment of the platform, the cloud-based implementation of the signing service was found to be the preferred option compared to the server being deployed at the corresponding organization. In the tested cases, the data exchange was limited to only DCCs, but the same approach will be applicable also for other similar data formats or documents, e.g., calibration orders or invoices.  
However, there are limitations for implementing the infrastructure in such a way for the NMIs and other organizations essential for maintaining national and regional infrastructures, as, e.g., dependencies on services provided by foreign companies may be considered as a threat to the security of the infrastructure. For these situations, approaches based on the proprietary infrastructures, such as the approach used on the Metrology Cloud [13], could be the better alternative, despite the potential complexity or significantly higher operation costs. Naturally, different types of solutions will be ideal for different organizations; thus, the availability of both simple and robust implementations would be beneficial provided that the compatibility and interoperability of the different systems can be ensured.
## 3 Results and Discussion  
### The Physical Properties of the Prepared Ionic Liquids  
The successful synthesis of three ionic liquids was thus established by the similarity between the estimated values and the obtained \({}^{1}\)H NMR, EA, and LC/MS data. As shown in Table 1, the viscosity of the ILs depends mainly on the intermolecular forces, so the viscosity increases with the increase in the carbon number of the cationic alkyl chain due to van der Waals forces. The reason for the lowest room-temperature viscosity of NA is its asymmetric structure, small molecular weight, and short side chain, which reduces the ion-ion forces and increases the ionic mobility. The experimental results showed that the viscosity of the ILs decreases with the increase in temperature due to the increase in the kinetic energy of the IL molecules. The viscosity of the ILs flow activation energy (E\({}_{\rm flow}\)) was calculated using the Arrhenius equation. As shown in Table 1, NA has the lowest E\({}_{\rm flow}\) (8.1 kcal/mol). The E\({}_{\rm flow}\) of all QA RTILs are lower than that of glycerol (12.3 kcal/mol) and imidazolium-based ILs (e.g., E\({}_{\rm flow}\) of AlMeImCl is 14.4 kcal/mol), owing to the electrostatic forces found in ionic liquids [16]. This shows the convenience of operation using QA RTILs. Furthermore, the thermal decomposition temperature of the prepared QA RTILs was determined by TGA. The results indicated that the cation structure and alkyl chain length do not have a significant effect on the thermal stability of QA RTILs. The thermal stability of ionic liquids mainly depends on the substituents' electrical negativity and anionic nucleophilicity [17]. The thermal stability of QA RTILs can be improved by using fluoride-based anions, such as bis(trifluoromethylsulfonyl) amide and hexafluorophosphate (PF\({}_{6}\)), but the cellulose dissolution ability of QA RTILs with fluoride-based anions is not good. For the purpose of comparison, we also prepared tetrahexylammonium hexafluorophosphate ILs (N\({}_{6666}\)PF\({}_{6}\)). The thermal stability of N\({}_{6666}\)PF\({}_{6}\) (T\({}_{\rm d}\) = 339 \({}^{\circ}\)C) is much better than that of TA (N\({}_{6666}\)OAc), but its melting temperature (134 \({}^{\circ}\)C) is also much higher than that of TA, thus resulting in its limited application. In our work, cellulose can be easily dissolved in these QA RTILs at mild temperatures; thus, high thermal stability is not very necessary for cellulose dissolution using these QA RTILs.
## 3 Information Centric Networking (ICN)
### Routing and Forwarding  
Routing is one of the most critical parts of the ICN/NDN network since it is significantly different from the current Internet architecture. As opposed to routing by the address of a host, ICN routes by the names of the requested data. Two packet types are used in NDN to identify content: interest packets and content packets. Each router maintains three data structures: content store (CS), pending interest table (PIT), and forwarding interest base (FIB). CS stores or caches data, PIT records interest entries and FIB is used to map the outgoing interface for each Interest request. Currently, two approaches are used for routing: name-based and name resolution-based. In name-based routing, the Interest (request for specific content) is routed from the consumer to the producer of the content or cache node using the name of the requested data to resolve the next hop in the network. Name resolution-based routing uses a name-resolution service employing two steps: the consumer node sends messages to the name-resolution server (NRS) with the name of the desired content, and the NRS server resolves the name with a single, or set of, addresses of content producers or caches that have the requested content. NRS provides the name resolution function for translating an object name into some other information, such as a locator, another name, metadata, etc., that is used for forwarding the object requested. More specifically, it is a service that is provided by the ICN infrastructure to help a consumer to reach a specific piece of information. The consumer provides a persistent name and the NRS returns a name or locator that can reach a current instance of the requested object [51].