esa-sceva/satcom-chunk-collection huggingface dataset:
# FUXYA2020: A Low-Cost Homemade Portable EDXRF Spectrometer for Cultural Heritage Applications
## 3 FUXYA2020 Performance on Real Cases in Cultural Heritage Field
### Metals  
The FUXYA's capability to get reliable quantitative evaluation on metal alloys was tested measuring the binary and ternary gold alloys (1 mm thickness) reported in Table 5 with the obtained results. We coupled the X-ray tube with a Cu filter (25.4 \(\upmu\)m of thickness) so that incident radiation could be considered quasi-monochromatic at the Rh K\(\alpha\) line at 20.2 keV (see Section 2.1). Spectra deconvolution and elemental determination were made by AXIL/QXAS, a free quantitative analysis package developed by IAEA [58]. Calculations were made considering monochromatic excitation, single element reference standards (i.e., a pure Cu certified metal plate, a pure Ag certified metal plate, a pure Ag certified metal plate, a pure Au certified metal plate), individual instrumental constants and applying normalization for the obtained concentrations. The acquisition parameters were 40 kV and 60 \(\upmu\)A for 600 s. Characteristic emissions considered were K\(\alpha\) for Cu, and L\(\alpha\) for Ag and Au. The same was done for 100 s acquisitions, still obtaining the right concentrations, even if with higher errors, especially on Ag concentration (data not shown).
## 3 Results
### Consumer Acceptance of Measured Samples  
To verify the models, the consumer acceptance of the control sample (K) was compared with the optimized sample with 2.22% DF (O1) and 6.80% DF (O2) (Figure 4).  
The addition of DF at O1 and O2 levels did not significantly change the evaluated sensory quality attributes of the bread. Increasing the DF content of the bread had a positive effect on its softness and moisture, regardless of the amount of its addition. On the other hand, porosity and color were rated lower in samples with fiber addition compared to the control group. This is related to the fact that adding DF to bread resulted in a darker color of bread. A similar effect was observed in the study by [PERSON] and [PERSON] [45] and [PERSON] et al. [46]. This is because most fiber preparations are intensely colored. The higher the DF addition was, the lower the porosity and color of the bread was assessed. The O1 and O2 samples had similar overall acceptability as the control sample. However, adding fiber will positively affect the evaluation of this attribute. The O1 sample was rated even  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**DF (\%)**} & **Pore Size** & **Porosity** & **Pore Density** & & **Firmness** & **Springiness** & **Chewiness** & **Cohesiveness** & **Enthalpy** \\  & **(mm\({}^{2}\))** & **(\%)** & **(per cm\({}^{2}\))** & & **(N)** & **(\(-\))** & **(N)** & **(\(-\))** & **(J/g)** \\ \hline \multirow{2}{*}{2.22} & \multirow{2}{*}{1.04} & \multirow{2}{*}{25.32} & \multirow{2}{*}{4.28} & C * & 4.81 & 0.59 & 1.93 & 0.59 & 295.05 \\  & & & & M ** & 4.71 & 0.52 & 1.9 & 0.54 & 287.15 \\ \multirow{2}{*}{6.80} & \multirow{2}{*}{1.12} & \multirow{2}{*}{27.40} & \multirow{2}{*}{4.17} & C & 4.72 & 0.57 & 1.82 & 0.57 & 288.43 \\  & & & & M & 4.41 & 0.52 & 1.74 & 0.69 & 278.14 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The verification of the model with the measured values of texture parameters and enthalpy.  
Figure 4: Scores of consumer acceptance analysis of bread: K—control; O1—optimized sample with 2.22% coat DF; and O2—optimized sample with 6.80% coat DF.  
higher than the control sample. Similar results for evaluating the sensory characteristics of bread were obtained by [PERSON] and [PERSON] [47], who showed that the bread with substitution up to 20% of oat flour was similar to the control bread.
# IoT Group Membership Management Using Decentralized Identifiers and Verifiable Credentials
## 3 Design
### Owner Onboarding and Endpoint Management  
Endpoint owners generate one or more _did:self_ DIDs referred to as \(Owner_{DID}\). Moreover, for each of their IoT endpoints, owners generate a public-private key pair and a DID document that includes the public key in the _authentication_ verification method; keys and documents are then installed in the IoT endpoint. Therefore, for each \(Owner_{DID}\), there are as many DID documents as the IoT endpoints of the owner. IoT endpoints will use private keys for signing CoAP responses and the installed DID documents for authenticating themselves. Device owners update periodically the keys and the corresponding DID documents of their IoT endpoints. Our system follows the convention that the new keys will use the same key identifier as the one used by the keys being replaced.  
A group administrator can authorize an endpoint owner to participate in a group simply by issuing a _membership_ VC. In our system, VCs are encoded as CBOR objects and are signed by the group administrator using CBOR Object Signing and Encryption. A CBOR-encoded VC in our system includes (among others) the following claims:  
* jti: An issuer-specific VC _identifier_.
* iss: The \(Group_{DID}\).
* sub: The \(Owner_{DID}\).
* iat: A timestamp indicating the VC's issuance time.
* exp: A timestamp indicating the VC's expiration time.
* vc: The actual VC (see the following).  
The vc property of a _membership_ VC includes a claim, named _group_, that contains the FQDN of the group the endpoint owner is allowed to participate. An example of membership VC is presented in the following Listing 1 (in this example, we are using JSON representation for clarity reasons).  
```
1{
2\"jti\": \"member1\",
3\"iss\": \"did:self...\",
4\"sub\": \"did:self...\",
5\"iat\": 1650558962,
6\"exp\": 1681663521,
7\"vc\":{
8\"type\":[\"membership\"],
9\"credentialSubject\":{
10\"group\": \"building1.floor1\",
11}
12}
13}
```  
Owners will receive as many VCs as the number of groups in which they can participate. Owners, finally, install the VC(s) to the corresponding IoT endpoints. Figure 1 provides an example of the onboarding process. In this example, an owner has configured two IoT endpoints with the appropriate DID documents. Then, the owner receives two VCs one for the group \"/building1/energy\" and another for the group \"/building1/roof\". Finally, the owner installs to each endpoint the appropriate VCs. As it can be observed, the roof panel is configured with two VCs which means it can participate to both groups.
## 3 Results  
A total of 2382 contractions were scored. For each detection method the mean and standard deviation were determined regarding the contraction and the delineation accuracy. The results are presented in Table 3.  
The squared RMS got the best score in all three evaluated parameters. The Teager and the Hilbert envelope methods showed the worst performance in all parameters, particularly the false negative rate. Despite the wavelet energy estimator having lower contraction accuracy compared to the RMS, its standard deviation was roughly half that of the RMS. These two operators had similar performances for delineation accuracy.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Detection Method** & **Contraction Accuracy (\%)** & **Delineation Accuracy (\%)** & **False Negative Rate (\%)** \\ \hline Wavelet Energy & 92.28 \(\pm\) 6.66 & 79.19 \(\pm\) 13.60 & 1.93 \\ Teager Energy & 65.57 \(\pm\) 25.05 & 71.12 \(\pm\) 10.61 & 4.74 \\ RMS & 93.64 \(\pm\) 12.08 & 83.99 \(\pm\) 12.67 & 1.51 \\ Squared RMS & 97.15 \(\pm\) 4.66 & 89.43 \(\pm\) 8.10 & 0.63 \\ Hilbert Envelope & 73.00 \(\pm\) 19.85 & 71.99 \(\pm\) 13.59 & 4.41 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Contraction and delineation accuracy for each energy-based method.  
Figure 6: A contraction with 100% delineation rate as shown by arrows 1 and 2.  
Figure 7: The double arrow identifies a contraction that was not detected by the energy method.  
One should also consider the very low false negative rate presented by the squared RMS estimator followed by the RMS estimator. In all, the false negative rates regarding all estimations presented a remarkably low count.  
It could be anticipated that, providing that most of the contraction energy is within the obtained delineation limits, its signal spectral parameters should be well represented--namely, the peaks' locations, a feature widely used in EHG processing. In this regard, Figure 8 shows the wavelet spectrum for a selected contraction with 30% boundary variation between the five different detection methods. As far as spectral analysis is concerned, all the peaks' location estimations overlapped for this case. It is beyond the scope of this report to produce a statistical analysis for this matter, despite its relevance, for example, for classification algorithms based on the spectral peaks' locations. This will be addressed in future work.  
The Hilbert envelope method was demonstrated to produce longer contraction estimations, thus being an interesting tool for EHG events such as the LDBF. This is clear in the last plot of Figure 4.
## 2 Methodology
### Data Acquisition  
The EMG and ECG data used in this study were acquired from 59 healthy, untrained young volunteers (i.e., 23 years old on average). All the subjects were considered free from any disease and have been briefed about the procedures involved, and informed consents have been obtained. An EMG channel was simultaneously recorded with ECG using the Porti system from the Twente Medical Systems International (TMSi) equipment. The Porti is a 32-channel ambulatory and stationary system for physiological research. An EMG channel was obtained from the vastus lateralis muscle (VL), placed according to the recommendations of Surface Electromyography for the Non-Invasive Assessment of Muscles (SENIAM) [53]. In summary, 16 European expertise groups are working together with the  
Figure 3: Gender identification process based on electromyography-heart rate variability (EMG- HRV) feature fusion. The selected features of EMG and HRV are combined using Combinatorial Fusion Approach (CFA) to obtain the gender classification result.  
SENIAM project for the development and applications of surface EMG (sEMG). The recommendation of the sEMG electrode placement is partly based on simulation outputs. The sampling frequency of raw EMG and ECG biosignals used in this study was about 2048 Hz. Higher sampling frequency was chosen to prevent a signal loss and to avoid too much noise during the stepping exercise. Plus, it is also to avoid jitter in the estimation of the R-wave fiducial point for ECG signal. Also, the stepper machine was used to carry out the stepping activity. In order to get the same stepping rate among the subjects, a metronome (i.e., 60 beats per minute) was used simultaneously with the stepping activity. Generally, a metronome is a device used by musicians that indicate the time at a selected rate by giving a regular tick.
# Representing and Validating Cultural Heritage Knowledge Graphs in CIDOC-CRM Ontology
## 3 Aligning COURAGE Entities to the CIDOC CRM Ontology
### Generation of CIDOC CRM Facts  
Based on the previously described mapping tables, a console application was built to generate an RDF file containing the selected COURAGE facts translated to CIDOC CRM.  
Regarding fact generation, the first decision was to use the original COURAGE entities instead of creating their pairs for CIDOC-CRM representation. In this way, an entity has properties from both COURAGE and CIDOC-CRM ontologies and can be used in both 'ontological worlds' for inferencing or querying. This solution also makes validation and error detection easier.  
Figure 11: Creator role representation in CIDOC-CRM.  
Figure 10: Birth event diagram in CIDOC-CRM ontology.  
As seen previously in the mapping tables and diagrams, a fact in COURAGE often requires the creation of several CRM facts, including new entities. These entities may be connected either with the described entity (e.g., collection names) or with a general 'entity pool' such as first names of persons (as appellation instances). In the first case, the entity id is based on the id of the described entity, with some discriminators appended to it. When the property may have several objects for the same subject, a number is also added to the URI. For example, if we have multiple names of an organization in multiple languages, we would use URIs like couragen:n490770-1-Title, couragen:n490770-2-Title for the new E35_Title instances, or just n49246-Birth for the new E67 Birth instance. In the second case, the new entities are identified by their type and content, for example, courage: TimeSpan-1956 denoting the year 1956.  
First, we experimented with CONSTRUCT SPARQL queries to generate CIDOC CRM facts, which would have been a more interoperable solution. However, the creation of several new URIs within a CONSTRUCT query made this approach too difficultly, and a more manageable programmatic approach (using C#) was selected.
## 5 Experiments and Discussion
### Numerical Simulation Analysis of the Platoon  
A feasible calculation method to find the dynamic gap in the platoon lane-changing process is provided in the lane-changing process of Section 4.1. In this case, general analysis that covers all situations is considered, that is, \(FV,~{}PV,~{}TFV\), and \(TPV\) all exist. The dynamic lane-changing gap of the platoon consisted of three parts:  
1. The safe distance between the front vehicle in the target lane and the \(LV\) in the platoon, which was calculated using the SPF.
2. The optimal distance between the middle vehicles in the platoon, which was calculated using the IPF.
3. The safe distance between the following vehicle and the trail vehicle in the platoon, which was also calculated using SPF.  
For this section, we only considered the situation of stable driving of the platoon; therefore, the velocity and acceleration of vehicles in the platoon were consistent, while those of the front vehicle and the following vehicle were set to be consistent. Three combination strategies about velocity and acceleration of non-platoon vehicles were set, which were \(v_{f}=20\) m/s, \(a_{f}=0\) m/s\({}^{2}\); \(v_{f}=20\) m/s, \(a_{f}=2\) m/s\({}^{2}\); and \(v_{f}=30\) m/s, \(a_{f}=0\) m/s\({}^{2}\). Each strategy had two conditions where the number of vehicles of the platoon was set to three and six, respectively. The curves of the dynamic gap in the platoon lane-changing process under these six cases are shown in Figure 10.  
As shown in Figure 10, in the same case, the dynamic gap in the platoon lane-changing process was larger with the higher velocity and acceleration of the platoon. However, the acceleration of the front and following vehicle in the target lane had a limited effect on the dynamic gap. This was mainly because the dynamic gap consisted of three parts. The velocity and acceleration of the non-platoon vehicles had no influence on the distances between the middle vehicles in the second part. However, the velocity and acceleration of the platoon had a conspicuous impact on the distances of all three parts. In addition, as the number of vehicles increased, the dynamic gap increased. When there were enough vehicles in the platoon, the distance of the second part had a greater influence on the dynamic gap than that of the first and third parts.  
Figure 10: The curves of the dynamic gap in the platoon lane changing process under the six cases: (**a**) \(v_{f}=20\) m/s, \(a_{f}=0\) m/s\({}^{2}\), \(n=3\); (**b**) \(v_{f}=20\) m/s, \(a_{f}=2\) m/s\({}^{2}\), \(n=3\); (**c**) \(v_{f}=30\) m/s, \(a_{f}=0\) m/s\({}^{2}\), \(n=3\); (**d**) \(v_{f}=20\) m/s, \(a_{f}=0\) m/s\({}^{2}\), \(n=3\); (**e**) \(v_{f}=20\) m/s, \(a_{f}=2\) m/s\({}^{2}\), \(n=6\); (**f**) \(v_{f}=30\) m/s, \(a_{f}=0\) m/s\({}^{2}\), \(n=6\).
## 4 Lower-Level Dynamic Response Priority-Based Response Instruction Allocation in VPP
### VPP Response Process Based on DERs Dynamic Response Priority Sequence  
The formulation of the dynamic response priority of DERs and the decision-making process of the VPP control center are:  
In the first step, the ITU of DER needs to send its normalized time margin and state margin in addition to its adjustment capability to the VPP control center. For TCLs, \(h\)\({}_{\text{la,TCL}}\) is issued in advance by the VPP control center.  
In the second step, the VPP control center assigns weight coefficients according to the improved time margin and state margin of DERs. The priorities of all DER devices are arranged in ascending order to form the dynamic response priority sequence of DERs.  
In the third step, after receiving the control instruction from the VPPs cloud control platform, the VPP control center determines the DERs that need to participate in the response according to the principle of \"minimum over-cutting\", and then sends the instruction to the corresponding DER equipment.  
Figure 8: Improved time margin curves with different remaining time amplification factor.  
It is worth noting that the control instruction sent to the DER device contains a logical value characterizing the response to be engaged and a regulation time duration flag. The former tells the ITU that it needs to participate in the response, and the latter helps the ITU adjust DER's operating state (i.e., ON or OFF state for ON/OFF-type device, power setpoint for converter-type device).
## 3 Verification by Numerical Simulation  
### Model Building  
Numerical simulations were conducted to validate the applicability of the method and discuss sensor optimization for the deflection calculation. Both single-span and three-span H-section steel beams were designed for finite-element analysis. The total length for the single-span and continuous beams was 8 m and 12 m, respectively. The supports of the single-span beams were divided into two types: simply supported and clamped-clamped. The material properties of these beams were as follows: elastic modulus \(E=2.0\,\times\,10^{11}\) N/m\({}^{2}\); linear mass density \(\rho=7.8\,\times\,10^{3}\) kg/m\({}^{3}\). More details about the models are illustrated in Figure 3.  
In the finite element analysis, the average strain measured by \(m\)th LGSS with the gauge length \(L_{m}\) can be extracted by:  
\[\overline{\varepsilon}_{m}=\frac{h}{L_{m}}\big{(}\theta_{i}-\theta_{j}\big{)} \tag{9}\]  
in which \(h\), \(\theta_{i}\) and \(\theta_{j}\) are neutral axis height, rotation angle of node \(i\) and \(j\), respectively (Figure 4).  
Figure 3: Different forms of simulation beams.
## 2 Materials and Methods
### Engineered Features
#### 2.1.2 3D-Local Octal Pattern (LOP)  
The limitations identified previously have led us to develop a new 3D-LOP descriptor. This innovative descriptor focuses on both texture direction and magnitude. It is designed to capture highly discriminative features from nodule images by analyzing eight directional components derived from spatial derivatives in the \(x\), \(y\), and \(z\) axes. Additionally, the descriptor evaluates the texture's magnitude by examining these spatial derivatives, thereby enhancing the detection of detailed edge features within the specified nodule region. This approach aims to improve the accuracy and reliability of nodule characterization in medical imaging studies.  
Let the region of interest for a nodule be represented by \(N(x,y,z)\). The introduced descriptor determines the first-order derivatives of the grayscale values for adjacent voxels along the \(x\), \(y\), and \(z\) axes, expressed as \(N^{\prime}_{r}(g_{c})|_{r=x,y,z}\). Simultaneously, it calculates the first-order derivatives of the grayscale value for the central voxel in the directions of \(x\), \(y\), and \(z\) which are defined as follows:\[N^{\prime}_{x}(g_{c}) =N(g_{x})-N(g_{c}) \tag{3}\] \[N^{\prime}_{y}(g_{c}) =N(g_{y})-N(g_{c})\] (4) \[N^{\prime}_{z}(g_{c}) =N(g_{z})-N(g_{c}) \tag{5}\]  
where \(g_{x}\), \(g_{y}\), and \(g_{z}\) represent the grayscale intensities of voxels adjacent to the central voxel \(g_{c}\) in the horizontal, vertical, and axial directions, respectively.
## 3 Analysis of Calculation Results  
### Comparison of Pump Performance  
Figure 6 shows the external characteristic parameters of the full tubular pump with different \(R_{\text{b}}\). \(H\) decreases with the increase of \(R_{\text{b}}\) under all flow rates, which shows that the internal backflow and the work capacity of the impeller are intensified and decreased, respectively, due to the increase of \(R_{\text{b}}\). Compared to \(H\) under \(R_{\text{b}}\) = 0 mm (without motor rotor), \(H\) under \(R_{\text{b}}\) = 2 mm decreased by 10.9%, 7.2%, and 13% at 0.8\(Q_{\text{des}}\), 1.0\(Q_{\text{des}}\), and 1.2\(Q_{\text{des}}\), respectively. As shown in Figure 6b, because the motor rotor requires additional mechanical energy for rotation, \(P_{\text{s}}\) under \(R_{\text{b}}\) = 0 mm is obviously lower than \(P_{\text{s}}\) under \(R_{\text{b}}\) = 0.5/1/2 mm. In the interval of \(R_{\text{b}}\) = 0.5-2 mm, \(P_{\text{s}}\) decreases with the increase of \(R_{\text{b}}\) under all flow rates. Figure 6c shows that \(\eta\) under \(R_{\text{b}}\) = 0 mm is significantly higher than \(\eta\) under \(R_{\text{b}}\) = 0.5/1/2 mm. Compared to \(\eta\) under \(R_{\text{b}}\) = 0 mm, \(\eta\) under \(R_{\text{b}}\) = 2 mm decreased by 8.4%, 9.5%, and 18.8% at 0.8\(Q_{\text{des}}\), 1.0\(Q_{\text{des}}\), and 1.2\(Q_{\text{des}}\), respectively. In the interval of \(R_{\text{b}}\) = 0.5-2 mm, because both \(H\) and \(P_{\text{s}}\) decrease with increased \(R_{\text{b}}\), \(\eta\) remain stable with the increase of \(R_{\text{b}}\) under 0.8\(Q_{\text{des}}\) and 1.0\(Q_{\text{des}}\). At 1.2\(Q_{\text{des}}\), however, the head decreases greatly with the increase of \(R_{\text{b}}\), and hence \(\eta\) gradually decrease with the increase of \(R_{\text{b}}\).
# Particle Swarm Optimization-Based Unconstrained Polygonal Fitting of 2D Shapes
## 3 Unconstrained Polygonal Fitting
### Equal Area Principle  
The equal area principle (constraint) has been used in [13] restricting the area of \(P\) to be equal with the area of the given shape. This is a reasonable constraint, in the sense that we except in many cases that the optimal solutions of the _UPF_ problem have almost the same area with the given shape \(S\). So, by adding this extra constraint, we reduce the search space of the problem without mainly affecting the performance of the method. Additionally, some applications may also include this requirement. Let \(A(P)\) denote the areas of polygonal curve \(P\), \(A(P)=\sum_{p\in I}I_{P}(p)\). According to the Equal Area constraint, it holds that the area of polygonal curve \(P\) is equal with the area of shape \(S\) meaning that \(A(P)=A(S)\). Therefore, the optimal solution of Unconstrained Polygonal Fitting problem under equal area principle is the closed polygonal curve \(P\) that maximizes the \(IoU(P)\) under equal area principle.  
\[P^{*}=argmax_{P}IoU(P),\ \ A(P)=A(S) \tag{4}\]  
Hereafter, we describe a simple procedure that applies the equal area principle on a polygonal curve \(P\), yielding a new polygonal curve \(\tilde{P}\) so that \(A(\tilde{P})=A(S)\). Let \(C\) be the centroid of \(S\) (the centroid of \(P\) can be also used; the centroid of \(S\) is more preferable for stability reasons in an iterative process) and \(\bar{P}_{i}\) be the \(i\) vertex of polygonal curve \(\bar{P}\), \(i\in\{1,...,N\}\).  
\[C=\frac{1}{A(S)}\sum_{p\in S}p \tag{5}\]  
\[\bar{P}_{i}=C+\sqrt{\frac{A(S)}{A(P)}}\cdot(P_{i}-C) \tag{6}\]