esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Corrosion Studies
#### 3.4.3 FESEM and EDX Studies  
Field emission scanning electron microscope (FESEM) and energy-dispersive X-ray (EDX) analysis techniques were used to determine the elements in _P. ginseng_ extract. Figure 4 depicts the FESEM images of corroded copper specimens without and with optimized inhibitor (1000 ppm of methanolic fruit extract of ginseng). From the images, one can easily observe the corroded surface of the copper specimen when exposed to an acidic medium from the damage, pits and scratches in the absence of inhibitor (Figure 4a). However, the surface image of the specimen that experienced the same acidic environment in the presence of the inhibitor shows a smooth and clear surface without discoloration  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \multirow{3}{*}{**Conc.**} & \multicolumn{10}{c}{**Temperatures (\(\pm\)1 K)**} \\ \cline{2-13}  & \multicolumn{3}{c}{**303**} & \multicolumn{3}{c}{**313**} & \multicolumn{3}{c}{**323**} & \multicolumn{3}{c}{**333**} & \multicolumn{3}{c}{**343**} \\ \cline{2-13}  & **\(\mathbf{\upsigma}\)** & **IE  
(Figure 4b). It proves that there is no initiation of corrosion taking place on the specimen exposed to an acid environment with inhibitor. Figure 5 depicts the EDX spectra which present the elements that exist in copper dipped in 1 M HCl and copper dipped in 1 M HCl with 1000 ppm of inhibitor prepared from _P. ginseng_ plant extract. Table 5 presents the elements present on the surface of the copper specimen that underwent acid corrosion (1 M HCl) without and with inhibitor (1000 ppm of ginseng plant extract).  
Figure 4: Field emission scanning electron microscope (FESEM) micrographs of copper dipped in **(a)** 1 M HCl and **(b)** 1 M HCl with 1000 ppm of inhibitor.  
Figure 5: Energy-dispersive X-ray spectroscopy (EDX) spectra of copper dipped in **(a)** 1 M HCl and **(b)** 1 M HCl with 1000 ppm of inhibitor.
# Dropped Object Impact Analysis Considering Frequency and Consequence for LNG-FPSO Topside Module
## 6 Conclusions  
Offshore structure accidents occur frequently, and falling objects are the most common marine accident types. The collision of a falling object in the operation of the topside crane with the deck structure or deck equipment can cause structural damage or loss of production. Recently, QRA has been widely used as a tool for decision-making in the offshore industry.  
This study focused on dropped object risk assessment based on frequency and consequence analyses in the design of a modern offshore platform. A modified QRA procedure was developed for assessing production module protection against accidental external loads. Based on the proposed QRA procedure, frequency and consequence analyses were performed to solve the dropped object problem. An exceedance curve was plotted, and a high-risk management item was derived through this process. In particular, the difference between the potential energy and impact energy according to drop orientation was verified via simulation and experiment. The maximum vertical displacements in the case of a drop height of 3 m are \(-79.6\) mm (experimental value) and \(-75.5\) mm (analytical value). This is an error of approximately 5.2%. In the case of a drop height of 5 m, the maximum vertical displacement value during the experiment is \(-98.4\) mm and the FEA value is \(-95.2\) mm, with an error rate of approximately 3.3%. Based on the simulation results of a suction drum bundle, a large difference in impact energy according to the orientation of the dropped object was quantitatively confirmed. When the object dropped in a specific orientation, the impact energy was confirmed to be up to 4.7 times greater than the assumed potential energy.  
In conclusion, a dropped object risk assessment should be performed according to the proposed QRA procedure. Subsequently, drop impact analyses and simulations should be performed to calculate the maximum impact energy and the results incorporated into a robust structural design for modern offshore platforms. The proposed procedure  
Figure 26: Simulation results (case 3, 90\({}^{\circ}\)).  
and simulation method provide a step-by-step guide to assess the damage capacity of a production area as well as damage frequency and consequences.  
Conceptualization, K.L. and H.R.; methodology, K.L.; software, K.L. and H.R.; validation, K.L. and H.R.; formal analysis, K.L. and H.R.; investigation, H.R.; resources, K.L.; data curation, H.R.; writing--original draft preparation, H.R.; writing--review and editing, K.L.; visualization, K.L. and H.R.; supervision, K.L.; project administration, K.L.; funding acquisition, H.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Preliminary Insights into 3D Cheiloscopy for Forensic Applications: A Pilot Study
###### Abstract  
Background: Cheiloscopy, a forensic technique based on the uniqueness of labial traces, has traditionally relied on analog methods. While simple, these methods present significant limitations in terms of precision and reproducibility. The introduction of 3D technology, specifically high-resolution optical scanners, represents a pivotal advancement in overcoming these challenges. Objective: This pilot study aimed to explore the feasibility and potential advantages of 3D cheiloscopy in forensic science by analyzing its precision and repeatability compared to traditional analog methods. Methods: Two participants were selected as a pilot sample, and their lip impressions were captured using both analog techniques and a high-resolution intraoral scanner (Medit i700). A total of 40 samples were collected, equally distributed among operators with varying levels of experience. Surface deviation analysis was performed to compare the methods. Results: The 3D models demonstrated greater uniformity and resolution compared to the analog impressions, which were more prone to errors caused by material quality or operator pressure. Surface deviation analysis showed an average similarity of 70%, with a standard deviation of 0.229 mm. The digital methods also significantly reduced operator-dependent variability. Conclusions: This pilot study highlights the potential of 3D cheiloscopy to improve precision and standardization in forensic applications. While the small sample size limits the generalizability of the findings, the results provide a foundation for further research with larger and more diverse datasets. Future studies should explore the capability of 3D cheiloscopy to accurately match individuals and further validate its applicability in forensic and clinical contexts.  
3D cheiloscopy; forensic analysis; digital forensic techniques +
Footnote †: journal: Applied Sciences  
_Article_  
Preliminary Insights into 3D Cheiloscopy for Forensic Applications: A Pilot Study  
A Pilot Study. _Appl. Sci._**2025**, _15_, _1726_https////creativecommons.org/licenses/by/4.0/.
## 2 Denoising Autoencoder Based Noise Reduction
### Denoising Autoencoder  
An autoencoder is a kind of feed-forward neural network with a thin middle layer (bottleneck). It attempts to reproduce the input at the output [22], as shown in Figure 2. It usually used for novelty detection and deep neural architectures for robust feature extraction and classification. The use of DAE and its deep version DDAE to perform NR was initially proposed by [PERSON] et al. [17].  
The NR method based on a DDAE covers two phases: namely, training (offline) phase and the testing (online) phase [15, 17, 22, 23]. In the offline step, a pair of noisy and clean speech signals are prepared to represent the input and output of the DDAE model, respectively. Through this processing,  
Figure 2: **Denoising autoencoder** (DAE) with one hidden layer \(h_{y}\). \(Y_{i}\) and \(\hat{X}\) are the noisy-enhanced speech, respectively.  
the DDAE model can learn the statistical differences between clean and noisy speech. Let \(Y\in\mathbb{R}^{n}\) be the input for the DDAE model with the \(i\) the hidden layers:  
\[\begin{split} h^{1}\Big{(}Y_{n}^{E}\Big{)}=\sigma\Big{(}W^{1}Y_{n}^ {E}+b^{1}\Big{)}\\ \vdots\\ h^{i}\Big{(}Y_{n}^{E}\Big{)}=\sigma\Big{(}W^{i-1}h^{i-1}(Y_{n}^ {E})+b^{i-1}\Big{)}\\ \hat{X}_{n}^{E}=h^{i}\Big{(}Y_{n}^{E}\Big{)}+b^{i}\end{split} \tag{3}\]  
where \(\Big{\{}W^{1},\dots,W^{i}\Big{\}}\in n\ast m\) refers to the connection weight matrix, \(\Big{\{}b^{1},\dots,b^{i}\Big{\}}\in\mathbb{R}^{n}\) are the bias vectors for the \(h\) hidden layers. \(n\) denotes the frame index for the short-time-Fourier transform (STFT) of the \(m\) (training) vector. \(\hat{X}_{n}^{E}\) is the vector containing the logarithmic amplitudes of enhanced speech corresponding to the noisy counterpart \(Y_{n}^{E}\). The nonlinear function \(\sigma(.)\) of the hidden neuron is the logistic function defined as:  
\[\sigma(t)=\frac{1}{(1+exp^{-kt})} \tag{4}\]  
where the logistic function and the identity function [23] are used in the hidden layer and the output layer separately. \(k\) is the steepness, and \(t\) is the output of each unit in the neural network. If \(t\) is a huge positive number, then the output is approximately 1, and if \(t\) is a very negative number, then the output is approximately 0. In the testing (online) phase, the parameters of the DDAE are obtained from the training phase to transfer \(Y_{n}\) to clean speech signal \(\hat{X}_{n}\). Additionally, the DDAE in this phase does not learn about the noise signal as the types of noise in both phases are different [13,17].
## 5 Simulation Results
### First Experiment  
In the first experiment, the position is evaluated in the absence of multipath and by ignoring the propagation loss. The idea is to test only the capability of a system based on DVB-T, and GNSS is only used in the initialization phase. The simulation length is set to 20 s. The four DVB-T emitters are placed in this way: the two emitters (E1 (\(-\)1,000,0) and E2 (8,000,0)) placed on the x-axis have quite different distances with respect to the origin of the reference system, while the other two (E3 (0,4,000) and E4 (0,\(-\)4,000)) on the y-axis are symmetric with respect to the origin.  
Figure 11 shows the acquisition results. Four correlation peaks are visible. They correspond to four different propagation times with respect to the four DVB- T emitters at the initial location, from where the user enters the obstructed area. Since the propagation loss has not been considered, the four correlation peaks have the same amplitude.  
Figure 11: Acquisition results (correlation).  
Figure 7 presents the estimated trajectory. In order to make the figure more visible, the trajectory plots are done every 0.4 s, which means that one estimated point between two presented points is dropped. In the figure, two different estimated trajectories are visible. The green circles always stay close to the true trajectory, while the red diamonds deviate from the true location after the user reaches the origin of the reference system. This is because when the receiver reaches the origin, it receives four signals, and two of them have the same propagation time (E3 and E4 are symmetric with respect to the origin). Then, two different situations may exist in the absence of additional information:  
1. The receiver still makes the correct signal association after this point, and the receiver will estimate the trajectory correctly. This is similar to the case of signals from different emitters with significantly different propagation times. In this case, the simulation results show good performance, even in highly dynamic scenarios (e.g., 100 m/s), as shown by the green circles. In our simulation, the receiver achieves a performance with a Root Mean Square (RMS) = 6.8 m, Mean error = 6.0747 m and standard deviation \(\sigma_{p}=3.0559\) m.
2. The receiver makes a wrong signal association after reaching the origin of the reference frame, which means the signal emitted by E3 is associated with E4, and the signal of E4 is considered as coming from E3. In this case, the estimated trajectory deviates from the true one, as shown by the red diamonds.  
Since we cannot know the true trajectory in the real cases, some additional information (e.g., the cell ID information, map information for map matching, signals from other system) is needed to determine which trajectory is correct in the case that two or more signals have a similar propagation time. Two methods have been introduced in Section 4.
# Deep Reinforcement Learning for the Agile Earth Observation Satellite Scheduling Problem
## 3 Methodology
### Training Method  
The parameters of the GDNN must be obtained by learning from large batches of training data. In the proposed method, we apply the proximal policy optimization (PPO) proposed by [33] to train the GDNN.  
The training framework of PPO follows actor-critic [34], which includes an actor network with the parameters \(\Theta_{Q}\) and a critic network with the parameters \(\Theta_{V}\). The pseudo-code is shown in Algorithm 2.  
```
0: Initialize the training parameter cropping factor \(\epsilon\), the mean square error factor \(c_{1}\), the entropy factor \(c_{2}\), the batch size \(K\), the parameter update step size \(Tp\), the parameter update optimization times \(k\), the number of training episodes \(N\)
0: The GDNN optimal network parameters \(\Theta\)
1:repeat
2: Generate the instance \(Emp=\big{\{}\mathbf{E},\mathbf{v},S^{\mathrm{sat}}\big{\}}\).
3:while\(s_{t}\) is not done do
4: Choose \(a_{t}\) by sampling according to \(p_{\Theta_{Q}}(a_{t}|s_{t})\).
5: Execute \(a_{t}\), gather \(r_{t}\) and \(p_{\Theta_{Q}}(a_{t}|s_{t})\), update \(s_{t+1}=(\mathbf{E}_{t+1},\mathbf{v}_{t+1})\).
6: Store \(\big{\{}s_{t},a_{t},r_{t},p_{\Theta_{Q}}(a_{t}|s_{t})\big{\}}\) in sampling pool.
7:\(tp=tp+1\)
8:if\(tp=Tp\)then
9: Update \(r\)
10:repeat
11:repeat
12:\(u_{t}(\Theta)=\frac{p_{\Theta_{V}}(a_{t}|s_{t})}{p_{\Theta_{Q}}(a_{t}|s_{t})}\)
13:\(\hat{A}_{t}=\hat{r}_{t}-V_{\Theta}(a_{t}|s_{t})\)
14:\(L_{t}(\Theta)=\mathbb{E}_{t}\big{[}L_{t}^{CLIP}(\Theta)+c_{1}L_{t}^{VF}( \Theta)+c_{2}S[p_{\Theta}](s_{t})\big{]}\)
15: Update \(\Theta\) using SGD.
16:until All \(\lfloor Tp/K\rfloor\) batches trained
17:until Update \(\Theta\) with \(k\) epochs
18:\(\Theta_{V}=\Theta_{Q}\)
19: Clear Sampling pool.
20:\(tp=1\)
21:endif
22:endwhile
23:until All \(N\) instances end.
24:return\(\Theta\)
```  
**Algorithm 2** GDNN-PPO algorithm  
At each step \(t_{p}\), we sample the action \(a_{t}\) based on the probability of the actor output \(p_{\Theta_{Q}}(a_{t}|s_{t})\) and save the sample \(\Big{\{}s_{t},a_{t},r_{t},p_{\Theta_{Q}}(a_{t}|s_{t})\Big{\}}\) in the sampling pool. The parameters are updated when the number of samples \(t_{p}\) reaches the parameter update step size \(Tp\). We first update the reward \(r_{t}\) according to Equation (29) to represent an assessment of the expected reward of action \(a_{t}\). The critic evaluates the value \(V_{\Theta}(a_{t}|s_{t})\) of the actor and probability \(p_{\Theta_{Q}}(a_{t}|s_{t})\). Then, the loss is calculated in Lines 12-14, and the actor parameters \(\Theta_{Q}\) are updated in Line 15 according to stochastic gradient descent (SGD) [35]. The parameters \(\Theta_{V}\) of the critic are updated by copying the updated parameters of the actor. Finally, the optimal model parameters are generated through iteration.  
\[\hat{r}_{t}=\sum_{k=i}^{n}r_{k}=\sum_{k=i}^{n}\gamma*pri_{j_{k}} \tag{29}\]
## 4 Genetic Algorithm Configuration
### Why We Chose a Genetic Algorithm  
The design space of our dual-inclination Walker-\(\Delta\) constellation is mixed-integer, highly non-linear, and populated with numerous local optima. Classical gradient-based or branch-and-bound methods either fail to converge or scale poorly once the number of orbital planes exceeds \(\sim\)5. Among stochastic heuristics, the GA remains the most mature and well-documented technique for satellite constellation problems [35; 36; 37]. Key advantages include the following:  
* **Native integer encoding**: Design variables \(\{i,p,n,F\}\) are stored directly as integers, avoiding real-to-binary conversions required by some PSO or SA implementations.
* **Transparent constraint handling**: Hard thresholds on the revisit time and throughput are incorporated via adaptive penalties, which is more intuitive than the cooling schedule of SA or the inertia/acceleration tuning of PSO.
* **Diversity preservation**: Elitism, combined with tournament selection, maintains global search pressure, whereas PSO often loses diversity after 20-30 iterations [36].
## 5 Group Velocity and Group Velocity Dispersion (GVD)  
The knowledge of group velocity and dispersion property of MNFs are very useful for designing fiber-based quantum sensing and quantum light generation. They have dependences of the dimension of a diameter and the wavelength of an optical wave. Group velocity of the MNF can be given as [1; 3]  
\[v_{g}=\frac{c\beta}{n_{1}^{2}k_{0}[1-2\Delta(1-\eta)]}. \tag{17}\]  
Figure 11: Effective diameters of the fundamental modes as a function of diameter (a) at \(\lambda=780\) nm and (b) at \(1.5\) μm.  
Figure 10: Fraction of power of the fundamental modes inside the core (a) at \(\lambda=780\) nm and (b) at \(\lambda=1.5\) μm. Grey solid lines present the diameters for confining 10% and 90% energy inside the core.  
Diameter-dependent group velocities of the fundamental modes are shown in Figure 12a. As the diameter decreases, group velocity, \(v_{g}\) approaches the speed of light in vacuum because most of the light energy propagates in air. When the diameter becomes large enough, \(v_{g}\) approaches \(c/n_{1}\) which is the group velocity of a plane wave in the optical fiber. For a large value of the wavelength compared to the diameter, \(v_{g}\) approaches the speed of light, \(c\), as shown in Figure 12b. Based on the group velocity, we can obtain diameter- and wavelength-dependent group velocity dispersion (GVD) which is written by [3; 54]  
\[GVD=\frac{d(v_{g}^{-1})}{d\lambda}. \tag{18}\]  
The optical dispersions of MNFs are affected by both the material dispersion and the waveguide dispersion. In particular, the waveguide dispersion is strongly modified by the waveguide dimension. Therefore, when we determine the optimum diameter for generating entangled photon pairs, it is useful to estimate GVD parameter of MNFs according to the changes of the diameter and the wavelength as depicted in Figure 13. As the MNF diameter and the wavelength increase, zero GVDs are up-shifted.
## Materials and Methods
### Extracellular Vesicles Separation and Characterization  
HVEC cells were grown in complete Roswell Park Memorial Institute (RPMI) 1640 medium, supplemented with 10% fetal bovine serum, 1% Penicillin/Streptomycin and 1% Glutamine, at 37 \({}^{\circ}\)C, 5% CO\({}_{2}\), until 80% confluence was reached [19; 20]. Complete medium was removed and cells were rinsed thrice with sterile phosphate buffer saline (PBS). Ten mL of serum free RPMI 1640, supplemented with 1% penicillin/streptomycin and 1% glutamine, was added to each plate. A serial centrifugation protocol previously described [21] was used to isolate EVs produced by 2 \(\times\) 10\({}^{7}\) cells from serum free medium, after 24 h incubation. The quantity of isolated EVs obtained by serum-free culture medium is greatly increased while retaining EV biophysical and size properties [22]. Before further analysis, EV pellets were resuspended in 50 \(\upmu\)L PBS 10 mM supplemented with 1:1000 protease inhibitor cocktail. EV preparation purity was graded by a colorimetric nanoplasmonic (CONAN) assay [9], and related UV-vis spectroscopy measurements performed by a EnSight multimode plate reader (Perkin Elmer, Waltham, Massachusetts, United States. EV titration was performed as described in [23]; the calibration line was built with POPC (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine, Avanti Polar Lipids, Alabaster, AL, USA) liposomes of about 60 nm size.  
EV biochemical characterization was performed by western blot [24]. For the analysis sodium dodecyl sulfate (SDS) sample buffer was added to isolated EVs and samples were boiled 5 min at 95 \({}^{\circ}\)C. Samples were electrophoresed and analyzed with rabbit anti-GM130 (Rockville, MD, USA), mouse anti-Alix (Santa Cruz Biotechnology, Dallas, TX, USA), mouse anti-Hsp 70 (Enzo Life Science, Lausen, Switzerland), mouse anti-CD63 (Merck-Millipore, Darmstadt, Germany), mouse anti-annexin-V (Santa Cruz Biotechnology). HVEC lysate was used as a control (15 \(\upmu\)g of proteins).  
For atomic force microscope (AFM) imaging, EV samples were diluted 1:10 in Milli-Q water. Measures of 7-10 \(\upmu\)L were spotted on mica substrates--freshly cleaved with the help of adhesive tape to produce clean, atomically flat surfaces (Grade V-1, thickness 0.15 mm, size 10 \(\times\) 10 mm--and let dry at room temperature [25]. Samples were then analyzed with a NaioAFM microscope (Nanosurf, Liestal, Switzerland), equipped with MultiGD-G probes (BudgetSensors, Sofia, Bulgaria) and run in dynamic mode. Scanning parameters were tuned according to instrument and probes' manufacturers. Images were processed using WSxM 5.0, a freeware scanning probe microscopy software [26].  
For MC experiments, pellets were resuspended in 300 \(\upmu\)L of PBS. The scheme of the experimental process is sketched in Figure 1.
# Analysis of the Scalability of UWB Indoor Localization Solutions for High User Densities
## 4 Localization Schemes and MAC Considerations
### Two-Way Ranging
#### Advantages  
* Location could be computed by the mobile nodes.
* Anchors do not have to be synchronized.
## 2 Related Works
### Clamping Force Adjustment  
Deformation force is measured by evaluating variations in the clamping force. An improper clamping force can cause severe clamping deformation and affect the machining accuracy. Hence, it is important to control the clamping force to ensure the machining quality. [PERSON] et al. [18] proposed a method to optimize both the acting position and amount of clamping force, aiming at reducing the machining deformation and making the deformation distribution more uniform. [PERSON] et al. [19] optimized the clamping force and fixture layout for assemblies of thin-walled parts to reduce the gap elimination rate and the total clamping force. Using the genetic algorithm and the finite element method, [PERSON] et al. [20] optimized the fixture layout and clamping force to improve the clamp positioning accuracy. [PERSON] et al. [21] proposed a finite element method-based approach to determine the clamping layout and clamping force to minimize the displacement of selected edges and minimize the equivalent force of the workpiece. [PERSON] et al. [22] proposed a method to control the machining deformation of an aero-engine blade by adaptively adjusting the fixture.  
The aforementioned studies improve the manufacturing accuracy by optimizing the clamping force. However, they only consider the effect of clamping deformation; more specifically, they mainly consider the impact of structural stiffness when deciding the clamping force, while for a better monitoring of deformation force, the impact of residual stress should also be considered.