esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
Citric acid monohydrate (Food Grade) was purchased from a local market (Volos, Greece). KBr (>99.5%) was purchased from Chem-Lab (Zedlegem, Belgium). D2O (99.9%) was purchased from Deutero GmbH (Kastleanu, Germany). Trolox (6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid) was purchased from Sigma (St. Louis, MO, USA). The KBr was dried at 140 \({}^{\circ}\)C for 2 h prior to use, while all other chemicals were used as received.  
Thermogravimetric analysis (TGA) measurements were carried out using a Shimadzu TGA-50 thermogravimetric analyzer (Shimadzu, Tokyo, Japan). Differential Scanning Calorimetry (DSC) measurements were performed using a Shimadzu DSC-50 calorimeter (Shimadzu, Tokyo, Japan). Fourier Transform Infrared (FTIR) measurements were performed using a Thermo FTIR spectrometer (Thermo Fisher Scientific, Waltham, MA, USA). Antioxidant activity was evaluated spectrophotometrically. A Christ freeze dryer (model Gamma 1-20, Martin Christ Gefriertrocknungsamagen, Osterode am Harz, Germany) was used for the preparation of the freeze-dried sample.  
Four samples were examined, namely, (a) raw citric acid monohydrate, (b) citric acid monohydrate heated at 100 \({}^{\circ}\)C, (c) citric acid monohydrate freeze-dried and (d) citric acid recrystallized from a D2O solution. The recrystallized sample was prepared by solution casting. The solution was left at room temperature for 3 days for complete solvent evaporation. For the freeze-drying process, initially the sample was frozen at \(-20\)\({}^{\circ}\)C under atmospheric pressure. Then, vacuum was applied, and simultaneously the plate in which the sample was placed was heated in order to provide the latent heat of sublimation. During freeze drying, the temperature of the drier was stabilized at \(-12\)\({}^{\circ}\)C.  
The raw citric acid sample was examined with DSC, TGA and FTIR. All other samples were examined with FTIR. Aqueous solutions of the raw and the heated samples were examined for their antioxidant activity. The Ferric Reducing/Antioxidant Power (FRAP) assay was performed using the modified method by [PERSON] et al. [19]. In brief, 100 \(\mu\)L of the raw and heated samples (0.1 g/mL) was mixed with 2.9 mL of freshly prepared FRAP reagent. Samples were mixed and incubated at 37 \({}^{\circ}\)C for 10 min, and the absorbance of each sample was measured at 593 nm. Trolox was used as a standard for the calibration curve. The results were expressed in \(\mu\)M of Trolox equivalents. The DSC and TGA measurements were performed with a heating rate of 10 K/min from room temperature up to 165 \({}^{\circ}\)C under nitrogen atmosphere (20 mL/min flow). The FTIR measurements were carried out with a resolution of 2 cm\({}^{-1}\) by performing 64 scans in absorbance mode. Baseline correction and normalization at a scale of 0-1 was applied in all the FTIR spectra. In order to facilitate the comparison of the spectra of the samples, the approach of spectra subtraction was adopted.
# Visualisation of Information Using Patient Journey Maps for a Mobile Health Application
## 5 Prototype Development and User Evaluation
### Pilot Study
#### 5.1.4 Results  
According to the survey results, Type A scored the highest in the Ease of Use, Satisfaction, Usefulness, and Aesthetics categories, whereas Type B scored the highest in the Information Architecture and Easy Understanding of Information categories (Figure 4). The average score for all questions was the highest for Type A. This result was also confirmed by the in-depth interview responses, which revealed that the horizontal and vertical layouts were more comfortable to use and easier to navigate. However, these results were not statistically significant because there were only a small number of participants, and the scores for each prototype were slightly different.  
\begin{table}
\begin{tabular}{c c} \hline \# & **Questions** \\ \hline
1 & Which of the medicines you take to treat high blood pressure is listed first? \\
2 & How high did your fever rise when you got the flu? \\
3 & As a result of the disc test, how many times did bones grow in the cervical spine? \\
4 & When you saw the doctor for the fracture, which hospital did you see? \\
5 & When did you start rehabilitation treatment for the disc herniation? \\ \hline \end{tabular}
\end{table}
Table 3: Examples of preset questions for extracting information from the prototype.  
The advantage of the vertical and horizontal types is that they can provide more information in a large space. The results suggest that the process of using the vertical and horizontal layouts to navigate information on mobile interfaces is similar, which may have contributed to the familiarity of the participants with Type A and B and their favourable ratings. Prior to the experiment, the excessive amount of information and inconvenience of long scrolling, owing to the expanded information area, appeared as the major shortcomings of the prototypes. However, easy information selection is more important than a large information display area. Furthermore, the majority of participants did not experience scrolling-related issues, indicating that expanding the information area is more effective than providing information in a fixed area.  
Based on the results of the pilot study, we decided to upgrade the vertical and horizontal visualisations that were evaluated during the pilot prototype study. Moreover, we investigated the placement of information in the final prototype design so that it could be easily navigated by considering scrolling and the amount of information, rather than attempting to reduce the information (owing to concerns regarding the amount of information).
## 3 Proposed Improved Hybrid SSA with an EDO (ISSAEDO) Algorithm
### SDM Results  
This subsection provides an explanation and analysis of the findings acquired from the presented ISSAEDO algorithm, as well as a comparison to the SDM algorithm. Table 4 displays the values of the derived parameters and their corresponding RMSE results. Table 4 shows the values of the five unknown parameters: \(I_{ph}\), \(I_{sd}\), \(R_{s}\), \(R_{sl}\) and \(\gamma\). Based on the RMSE values reported in Table 4, the presented ISSAEDO acquired the best RMSE value (9.86021877E-04). MADE produced the second-best value for RMSE (9.8602187789E04).  
\begin{table}
\begin{tabular}{l l l} \hline \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**SDM/DDM**} \\ \cline{2-3}  & **LB** & **UB** \\ \hline \(I_{ph}(A)\) & 0 & 1 \\ \hline \(I_{sd}\), \(I_{sd1}\), \(I_{sd2}\) (\(\mu A\)) & 0 & 1 \\ \hline \(R_{s}(\Omega)\) & 0 & 0.5 \\ \hline \(R_{sh}(\Omega)\) & 0 & 100 \\ \hline \(\gamma_{r}\), \(\gamma_{1}\), \(\gamma_{2}\) & 1 & 2 \\ \hline \end{tabular}
\end{table}
Table 2: Search boundary of parameters for different PV models.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **SDM** & **DDM** \\ \hline \(G_{max}\) & 12,000 & 25,000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Stopping condition (\(G_{max}\)) of different PV models.  
The statistical outcomes (the minimum, maximum, mean, and standard deviation) derived from the presented ISSAEDO and competitive algorithms are shown in Table 5. In comparison to state-of-the-art approaches, the provided ISSAEDO algorithm achieves the best minimum RMSE values for the SDM. The MADE method is the only one that earns the best maximum RMSE and lowest STD values. The provided ISSAEDO algorithm is the only one that achieves the best mean RMSE values. This demonstrates that, when compared to previous algorithms, the given ISSAEDO is capable of achieving consistent and dependable solutions.  
In addition, the parameters obtained from the given ISSAEDO were employed to calculate the measured voltages, simulated current, and Individual Absolute Error (IAE) for the SDM. These values were subsequently used to generate characteristic curves. The comparison between the measured model data and simulation data obtained from the reported ISSAEDO for the SDM is depicted in Figure 4. The voltages that were measured and the IAE curves are compared in Figure 5. The simulation data exhibit a high level of compatibility with the model data presented in this figure, suggesting that the estimated values of the unknown parameters obtained through the employed ISSAEDO approach are characterized by a remarkable level of precision.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Algorithm** & \(\mathbf{I_{ph}}\) **(A)** & \(\mathbf{I_{sd}}\) **(\(\mathbf{\mu}\)A)** & \(\mathbf{R_{s}}\) **(\(\mathbf{\Omega}\))** & \(\mathbf{R_{sh}}\) **(\(\mathbf{\Omega}\))** & \(\mathbf{\gamma}\) & **RMSE** \\ \hline ISSAEDO & 0.7607755300 & 0.3230207850 & 0.0363770930 & 53.7185252000 & 1.
# A GNSS/5G Integrated Three-Dimensional Positioning Scheme Based on D2D Communication
## 2 Methods
### Positioning Model in 5g
#### 2.2.1 Distance Based Positioning  
Without loss of generality, the BSs and the MUs are all seen as the nodes which can function as reference nodes; \(\left(x_{i},y_{i},z_{i}\right)\) and \(\left(x_{j},y_{j},z_{j}\right)\) are the positions of node \(i\) and \(j\), respectively. For a requesting node \(i\) and a reference node \(j\), the distance between them can be represented as  
\[d_{ij}=\sqrt{\left(x_{i}-x_{j}\right)^{2}+\left(y_{i}-y_{j}\right)^{2}+\left( z_{i}-z_{j}\right)^{2}} \tag{1}\]  
Intuitively, Figure 2a illustrates the two-dimensional positioning procedure based on trilateration when there are three reference nodes. The red star represents the true position of a requesting node. When the TOA or the RSS measurements between the requesting node and the reference nodes are known, the distances between them can be obtained. Regardless of the error in distance measurements, the three blue curves shown in Figure 2a intersect in a red star. By analogy, four reference nodes are needed for a requesting node to decide its three-dimensional position and for four spheres to meet at one point.
# Shape Optimization of Single-Curvature Arch Dam Based on Sequential Kriging-Genetic Algorithm
## Methodology  
### Geometric Model of a Single-Curvature Arch Dam  
The shape optimization of single-curvature arch dams involves determining the geometric shape and size of arch dams. Therefore, first, the geometric model of an arch dam should be established, and the geometric structure of the arch dam can be determined on the basis of the arch crown cantilever and horizontal arch ring.
## 2 Materials and Methods
### Sentinel-1 SAR Interferometry
#### 2.3.1 Basic Concept
Hence, radar signals received by the sensor from damaged structures such as buildings and road pavements can lead to coherence loss. In principle, under ideal condition co-event image pair coherence (\(\gamma_{\alpha}\)) would be lower than that of the pre-event image pair coherence (\(\gamma_{pre}\)). The coherence difference (\(\gamma_{d\overline{\mu}}\)) between those two pairs can be used to delineate damaged areas after the disastrous event. In this study, the coherence difference is calculated as follows [17; 18]:  
\[\gamma_{diff}=\gamma_{pre}-\gamma_{\alpha} \tag{5}\]
## 2 Materials and Methods
### A Proposal for Risk Assessment of Low-Frequency Noise
The factor has three degrees--high, medium, and exceptionally low probabilities. A point rating is assigned to each degree, which is the sum of the limit values at the individual considered frequencies (16-100 Hz). The limit values were determined according to the ISO 226 hearing threshold curve [30]. The standard does not indicate a value at 16 Hz; therefore, this limit value has been set by the LFN limit values given by the EU states. The values for the frequencies, together with the point rating and determination of the Pa factor degree itself, are described in Table 2 and Equation (3). The factor of the probability of exposure to LFN with non-auditory effects (_Pna_) determines the probability at which the worker is exposed to LFN with non-auditory effects during work. The noise levels at the frequencies of 16 Hz-100 Hz are assessed (as well as with the _Pa_ factor). Since the legislation only gives measurements of the aid of the A-weighting filter, the values measured by the Z-weighting filter represent the extra auditory effects in particular; there are no limits for this type of frequency weighing. The factor has three degrees, and the individual degrees are the sum of the values of the acoustic pressure level values at the individual frequencies given in Table 3 and Equation (4). The LFN limit values are determined according to the measurements made at the W1-W4 workplaces. \[R=f\left(Se,\;Cl\right) \tag{1}\]  
\[Cl=Ex+Pa+Pna+M \tag{2}\]  
\[Pa=\sum_{f}PRa_{f} \tag{3}\]  
Here, _PRa_ is the point rating (Table 2) and \(f\) is the frequency. \[Pna=\sum_{f}PRna_{f} \tag{4}\]  
Here, _PRnu_ is the point rating (Table 3) and \(f\) is the frequency. A description of the extent of the resulting acoustic risks is presented, along with the suggested CMARA method, in Figure 1. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Frequency**} & \multicolumn{5}{c}{**Factor _Pa_ Degree**} \\ \cline{2-7}  & \multirow{2}{*}{**Limit**} & **Point** & \multirow{2}{*}{**Limit**} & **Point** & \multirow{2}{*}{**Limit**} & **Point** \\
**(f) [Hz]** & & & & & & **Rating** \\
**Value [dB]** & & & & & **Value [dB]** & **Value [dB]** & **Rating** \\ \hline
16 & over 74 & 2 & 74–59.2 & 1 & under 59.2 & 0 \\
25 & over 68.7 & 2 & 68.7–55 & 1 & under 55 & 0 \\
40 & over 51.1 & 2 & 51.1–40.9 & 1 & under 40.9 & 0 \\
63 & over 37.5 & 2 & 37.5–30 & 1 & under 30 & 0 \\
80 & over 31.5 & 2 & 31.5–25.2 & 1 & under 25.2 & 0 \\
100 & over 26.5 & 2 & 26.5–21.2 & 1 & under 21.2 & 0 \\  & & & & & 8–12–\(\rightarrow\)high probability (5)–\(H\) & \\  & & & & & 4–7\(\rightarrow\)medium probability (3)–\(M\) & \\  & & & & & less than 3–\(\rightarrow\)exceptionally low probability (1)—\(L\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Determination of the _Pa_ factor. Source: own research.
# A Multi-Machine and Multi-Modal Drift Detection (M2D2) Framework for Semiconductor Manufacturing
## 5 Illustrative Example
### Phase 5: Active Learning and Maintenance Recovery  
Any industrial detection system must evolve with real-world feedback. Here, M2D2 leverages active learning to continually refine its thresholds and classification boundaries. If an operator deems a flagged sample to be a false alarm, the system updates its internal parameters to lessen the rate of future false positives under similar conditions. Conversely, when a newly encountered fault is corroborated, the relevant samples join a \"labeled anomaly\" set, thereby training the model to become more sensitive to this novel deviation. Crucially, M2D2 also accommodates maintenance recovery. Whenever operators complete part swaps or recalibrations, they log messages such as \"_maintenance done_\" or \"_part replaced_\", prompting the system to realign its baseline or threshold distributions. For instance, if Machine 3 consistently shows high reconstruction errors due to a faulty vacuum pump, finalizing \"part replaced\" allows M2D2 to treat the machine as being returned to normal operation, as shown in Figure 2, thus preventing residual pre-maintenance anomaly data skewing in post-maintenance detection. This loop ensures that once physical issues are rectified, false positives do not linger purely because of the historical fault state. For Machine 4, active learning likewise helps the model adapt to recurring recipe changes. Once any maintenance or recalibration is completed post-switch, M2D2 resets its expectations, preventing repeated false alarms around known transition points.
# Sensing Performance of a Vibrotactile Glove for Deaf-Blind People
## 2 Methods
### Apparatus
The explained rotation frequency is modulated by means of an on-off keying signal in order to achieve two keying frequencies, i.e., 1 Hz or 10 Hz, as shown in Figure 3. Thus, two possible stimuli are implemented: 170 Hz modulated with 1 Hz or 170 Hz modulated with 10 Hz. Two performance parameters should be taken into account: the time between the onset and the end of a burst, \(t_{1}\), and the time between the onsets of two bursts, \(t_{2}\) (Figure 4). As the burst duty cycle is 50%, then \(t_{2}\) is two times \(t_{1}\). The 1 Hz and 10 Hz frequencies, in combination with the 50% duty cycle, meet the \(t_{1}\) and \(t_{2}\) timing constraints to achieve an optimum performance [20]. At the other end of the communication channel, the afferent interface is based on a general purpose mobile phone or tablet. Contemporary mobile phones are like small computers, which allows for a broad set of applications. Therefore, an application that provides the user with a set of pieces of information, i.e., concepts or ideas, was developed, as shown in Figure 5. This set contains the pieces of information that somebody might want to convey to a deaf-blind subject. The unimpaired user can select these messages or ideas from an icon menu, by typing them or by voice recognition. Thus, the previous afferent interface based on a personal computer [36] is substituted by a more portable one. The channel between the vibrotactile HMI and the mobile phone is a Bluetooth link. Thus, only one mobile phone at a time can be linked to the efferent HMI that delivers the vibrotactile information to the deaf-blind subject. Figure 4: Performance parameters \(t_{1}\) and \(t_{2}\), related to the keying frequency and the duty cycle; \(t_{1}\) is the time between the onset and the end of a burst, and \(t_{2}\) is the time between the onsets of two bursts. Figure 5: Set of concepts to be selected on the mobile phone/tablet screen, e.g., OK, not OK, emergency, etc.
# Multi-Temporal InSAR Structural Damage Assessment: The London Crossrail Case Study
## 3 Methodology  
### MT-InSAR  
Synthetic aperture radar multi temporal interferometry (MT-InSAR) is a well established technique for ground deformation monitoring characterized by millimetric accuracy [1, 18]. MT-InSAR combines sets of SAR images acquired in interferometric mode in order to obtain time-series of deformation for points on the ground maintaining stable scattering properties in time [1, 38]. MT-InSAR measurements provide coverage of ground deformation at high resolution and spatial scales not reasonably accessible with in situ measurements such as leveling and GPS.  
A variety of software tools are available for implementing multi-image InSAR techniques. In this work we make use of SARPROZ [39] and adopt a non- linear single master multi-temporal time series analysis approach [38]. For further details on the technique we refer the reader to [1, 38, 39].
## 2 Antenna Array Structure
### Fabrication Process  
The fabrication of the antenna is made by standard photo-lithographic method based on a chemical etching process as illustrated in Figure 2. This involves the removal of the unwanted conductive regions of the metallic layer. The photo-lithographic process uses light to transfer the antenna pattern from a photomask to a photosensitive chemical photoresist on the substrate. The thickness of the photoresist layer is typically 1 micron. A series of chemical treatments is performed to etch the exposure pattern on the metal. This procedure is comparable to a high precision version of the method used to make printed circuit boards. Precision of this technique is typically down to less than 2 microns and the repeatable accuracy is \(\pm\)10 microns, which means that the fabrication method will have negligible effect on the performance of the antenna whose dimensions are specified in Table 1. The vias are created using the standard through-hole technology. This involves drilling a hole through the dielectric substrate which then needs to be cleaned of debris from the drilling operation before a conductive wire is inserted through it. The wires are cut to size and both ends of the wire are soldered to the microstrip surface to create an electrical short circuit. Alternately, through-hole plating process can be used to metallize the via-holes. Through-hole plating involves chemically coating the holes with a thin layer of copper through a process called electroless copper deposition. This gives the copper plating a base to build up from. The full amount of copper is then electroplated in the via holes by connecting the board to an electrical charge so that the boards act as cathodes for the electroplating process.  
In the next section, the composite right/left-handed (CRLH) structure is applied in the antenna structure to incorporate metamaterial characteristics to extend the arrays impedance bandwidth.
## 3 Results
### Screening Results of Fungi and Insects Inducing Agarwood Formation in A. sinensis
\begin{table}
\begin{tabular}{c c c c} \hline
**Fungi** & **Figure 1** & **Genus Name** & **Species Name** \\
**Numbering** & **Numbering** & **Genus Name** & **Species Name** \\ \hline CX-1 & A & _Fusarium_ & _Fusarium proliferation_ \\ CX-2 & B & _Penicillium_ & _Penicillium_ \\ CX-3 & C & _Penicillium_ & _Penicillium chrysogenum_ \\ CX-5 & D & _Fusarium_ & _Fusarium proliferation_ \\ ZCX-1 & E & _Phaeacremonium_ & _Phaeacremonium alvesi_ \\ ZCX-4 & F & _Trichoderma_ & _Trichoderma atroviride_ \\ ZCX-5 & G & _Fusarium_ & _Fusarium solani_ \\ ZCX-7 & H & _Fusarium_ & _Fusarium proliferation_ \\ ZCX-8 & I & _Neodeightonia_ & _Neodeightonia subglobosa_ \\ ZCX-13 & J & _Acrodontium_ & _Acrodontium saloneum_ \\ ZCX-15 & K & _Penicillium_ & _Penicillium_ \\ ZCX-17 & L & _Pestalotiopsis kenyama_ & _Pestalotiopsis kenyama_ \\ YB-1 & M & _Fusarium_ & _Fusarium equiseti_ \\ YB3-1 & N & _Fusarium_ & _-_ \\ HLH & O & _Leptographium_ & _Leptographium koreanum_ \\ YML & P & _Fusarium_ & _Fusarium proliferation_ \\ \hline \end{tabular}
\end{table} 
Table 3: Fungal identification results. Research on insect-induced agarwood formation remains limited, with only 14 studies retrieved (primarily from [PERSON] and [PERSON]). Based on a literature analysis of insect-mediated mechanisms, candidate species from _Cerambycidae_, _Formicidae_, _Cossidae_, _Bethylidae_, and _Scolytidae_ were evaluated. Survival assays in trunk microhabitats (Table 4) revealed significant interspecific differences; _Odontottermes formosanus_ exhibited obligate social dependence with rapid mortality (<24 h) upon isolation; _Scleroderma guani_ survived up to 48 h without gallery formation; _Tomicus yumanensis_ maintained viability for 96 h in gallery-free conditions; and _Nadezhdiella cantori_ demonstrated exceptional xylem colonization capacity, sustaining 14-day survival while constructing continuous larval galleries. These results establish _N. cantori_ as the sole species capable of autonomous _A. sinensis_ infestation under experimental conditions, leading to its prioritization for fungal-insect co-induction trials.