esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
* Sialometry: Oral Schirmer test [18; 19], through the impregnation of a strip of millimeter paper measuring 5 \(\times\) 35 mm, through which the salivary flow is displaced when it comes into contact with it (Figure 1). * Visual Analog Scale for Xerostomia (VAS-X): It measures the intensity of xerosoma perceived by the patient [0-10]. * Xerostomia Inventory (XI): It assesses xerostomia with a scale of 11 items. The total score can vary between 11 and 55, representing the severity of the xerostomia. A score of 11 indicates a very slight or non-existent xerostomia, while  
Figure 3: Methodological flow diagram detailing group distribution and the evaluation sequence. Figure 2: TENS measurement device and its proper placement on the study participants. a value of 55 indicates severe xerostonia; it is considered that scores of 14 or higher indicate intense xerostonia [20]. * Oral Health Impact Profile (OHIP-14): It assesses the quality of life related to oral health. The total score varies from 0 to 70, where a higher score indicates a worse quality of life. It was given at the start and the end of the last session [21]. Satisfaction with the treatment: At the end of the treatment, patients evaluated their experience on a scale ranging from 0 to 10, considering aspects such as comfort, perceived discomfort, and general satisfaction. Statistical analysis  
The descriptive and inferential analysis of the data was performed with the free software R (R Core Team, 2024) [22]. An analysis of the general descriptive values of the variables studied was performed. The Shapiro-Wilk test and the Kolmogorov-Smirnov test were used to verify normality and homoscedasticity. Depending on the results of these normality tests, either parametric tests (Student's \(t\)-test or one-way ANOVA) or non-parametric tests (Kruskal-Wallis K) were used. The post hoc test employed was the Bonferroni test, while for the categorical data, Pearson's Chi-square was used to verify their relationship. In all analyses, a level of significance of 0.05 was used.
# A Survey on Hardware Accelerators for Large Language Models
## 3 FPGA-Based Accelerators
### FlexRun  
In 2023, [PERSON] et al. presented an FPGA-based accelerator to speedup the diverse and complex NLP models, called FlexRun [17]. The paper focused on accelerating both recurrent neural network (RNN) models, such as SRNN or long short-term memory (LSTM) models, and attention-based NLP models, such as Transformer and GPT2.  
FlexRun consists of three main schemes, namely, FlexRun:Architecture, FlexRun:Algorithm, and FlexRun:Automation.  
The main advantage of FlexRun is that it exploits the high reconfigurability of FPGAs to dynamically adapt the architecture to the target model and its configuration. The base architecture of FlexRun alleviates the overhead of vector operations by adopting a deeply pipelined architecture. The architecture consists of parameterized pre-defined basic modules that can be configured to fit the input model and its configuration.  
Next, researchers conducted a design space exploration of the algorithms to identify the optimal compute unit design by selecting the best modules for the input models using the FlexRun:Algorithm framework. Finally, using the results from FlexRun:Algorithm, they determined the best architecture for the specific models and implemented this architecture on the FPGAs using a tool called FlexRun:Automation.  
Based on the profiling of NLP and LLM applications, they found that the most computationally intensive tasks were general matrix multiplication and vector processing. Hence, they proposed a modular architecture that consisted of general matrix multiplication units (GEMV units) and vector processing units. GEMV units are composed of multiple SIMD arithmetic units and the vector unit executes vector operations with some additional operators (i.e., reduction, exp, GELU) to support attention-based NLP models.  
For evaluation, they compared FlexRun with Intel's Brainwave-like architecture on a Stratix-10 GX FPGA and a Tesla V100 GPU with tensor cores enabled. Compared to the FPGA baseline, FlexRun achieved an average speedup of 1.59\(\times\) on various configurations of BERT. For GPT-2, FlexRun achieved an average speedup of 1.31\(\times\). Furthermore, compared to the GPU implementation, FlexRun improved performance by 2.79\(\times\) for BERT and 2.59\(\times\) for GPT-2, respectively.
## 2 Materials and Methods
### The Passive Approach  
Manufacturers of the backhaul transmission networks have implemented tools in their systems which monitor and log the signal levels of all links in the network. The tool, known as the network management system (NMS), produces RSL and TSL indicators which are automatically logged by the network operators (i.e., the cellular providers). The passive approach relies on the use of the already existing NMS records as inputs for the CML weather monitoring technology. The major advantage of the passive approach is that it puts neither burdens nor risks on the cellular providers, so it is relatively simple to get them to share this data. However, as the NMS data is kept for network monitoring, and in particular, for monitoring the actual link budget, the RSL (and TSL) signals in the NMS go through a highly nonlinear process. Typically, only the minimum and the maximum RSL (and TSL) values, from the measurements taken over a window of 15 min, are stored. Moreover, these values are quantized at a 0.1-1 dB resolution. Furthermore, as they are mostly used for analysis, the NMS records are rarely available in real time. An example of a typical RSL time series from an NMS record is depicted in Figure 1.  
Figure 1: Typical time series of the minimum/maximum received signal level (RSL), extracted from network management system (NMS) records. The X-axis represents time at 15-min intervals.
## 5 Taxonomy of Technology-Enabled Smart City Applications in 6G Networks
### Internet of Things (IoT)
#### 5.1.4 Internet of Drones (IoD)  
The IoD is a network of coordinated drones with communication capabilities among themselves, pedestrians, and ground infrastructure [90]. The global drone market is expected to reach $43.4 billion by 2027 with a CAGR of 12.56% between 2022 and 2027 [91]. The IoD applications include smart city surveillance, infrastructure monitoring, and maintenance, search, and rescue missions in place of natural/manmade hazards, logistics, traffic control, weather forecasts, disaster management, and events live streaming [92]. These applications require tactile and haptic internet with an ultra-low latency, high data rate, and high reliability requirements.
# Hybrid Dual-Polarization Synthetic Aperture Radar
## Appendix C Terminology
### Stokes Parameters  
The Stokes parameters are an ordered set of four numbers. The names given those numbers vary, depending on the specific discipline or tradition preferred by their user. The three most common forms are (_U, Q, U, V_), (_S\({}_{1}\), S\({}_{2}\), S\({}_{3}\), S\({}_{4}\)_), and (_S\({}_{0}\), S\({}_{1}\), S\({}_{2}\), S\({}_{3}\)_). In every case, in the set of four, the position of a given parameter indicates its meaning. The values of each of the four Stokes parameters does not depend on the polarization basis of their observation. However, the data elements needed to evaluate eachparameter does depend on the polarization basis of the observing receivers. For example, if the receiver channels are linearly polarized, then the second Stokes parameter is the difference between the received powers, whereas, if circularly polarized, that difference appears as the fourth Stokes parameter.  
Note that the terminology \"randomly polarized\" indicates the portion of the field that is not polarized. That is preferred usage, because the word \"depolarized\" is meant in certain sources to indicate that portion of the backscattered field which is polarized but having an orientation that is orthogonal to that which was transmitted [18]. \"Unpolarized\" is an alternative, but still may be subject to ambiguous interpretation.  
The Stokes parameters from an HCP radar can be used to emulate the output \(\sigma^{0}\) image characterizations of a CP radar whose polarization bases are circular on both transmit and receive. In the field of radar astronomy, these terms are known as same-sense circular (\(\sigma^{0}\)\({}_{\text{SC}}\)) and opposite-sense circular (\(\sigma^{0}\)\({}_{\text{OC}}\)), where the terms _opposite_ and _same_ are reflective of the fact that the rotational sense of the transmitted circular polarization does not matter from a science perspective. Contrary to expectations based on experience with linearly polarized radar systems, the opposite-sense backscatter \(\sigma^{0}\)\({}_{\text{OC}}\) corresponds to \"like pol\" such as \(\sigma^{0}\)\({}_{\text{HH}}\). Single-bounce returns (and Bragg backscatter) usually are the strongest because the sense of polarimetric rotation is reversed by a single bounce. Conversely, the same sense backscatter \(\sigma^{0}\)\({}_{\text{SC}}\) corresponds to \"cross pol\" such as \(\sigma^{0}\)\({}_{\text{HV}}\). The OC constituents are almost always stronger than their counterparts, the SC data. The circular-polarization ratio CPR = SC/OC which usually is less than unity, becomes \"interesting\" in areas for which it is greater than unity, as observed with backscatter from planetary water-ice volumetric deposits, for example.
## 6 Experiments and Results
### Performance Evaluation of the DMOGWO Algorithm  
The proposed DMOGWO-based rescheduling algorithm is compared with four other algorithms, namely, NSGA-II [33], MOEA/D [42], SPEA2 [43], and MOGWO [8], with respect to the static and dynamic aspects of the problem, in order to evaluate their overall performance. The reasons for selecting these compared algorithms are as follows: (1) NSGA-II, SPEA2, and MOGWO share a similar structure with the proposed DMOGWO, as they are all non-dominated sorting-based multi-objective optimization algorithms, whereas MOEA/D adopts a decomposition strategy for optimization, providing a different optimization paradigm for comparison. (2) NSGA-II, SPEA2, and MOEA/D have been widely  
Figure 8: Performance evaluation with varying \(N\) values.  
used in the field of production scheduling, and their effectiveness in multi-objective scheduling optimization has been well-validated, making them reliable benchmark algorithms. (3) MOGWO, designed for multi-objective flexible scheduling, is based on GWO, making it closely related to our DMOGWO. As a more recent algorithm, MOGWO allows comparison with both classical and modern swarm intelligence techniques, ensuring a comprehensive performance evaluation.  
To ensure fairness, the traditional multi-objective evolutionary algorithms NSGA-II, MOEA/D, and SPEA2 are configured with the same population size and mutation probability as DMOGWO. For MOGWO, which has been successfully applied to the FJSP, the parameter settings are adopted as described in the original paper. All comparative algorithms utilized the same initialization strategy.
## 2 Materials and Methods
### Adherence Assays  
The materials for adherence assays were prepared as follows. Briefly, the 96-well Polysorp microplate wells (Nunc Immuno plates, Nunc, Denmark) were treated with 250 \(\upmu\)L of PBS or PBS containing the fibrinogen at 200 \(\upmu\)g/mL. After overnight incubation at 4 \({}^{\circ}\)C the wells were washed twice with PBS, blocked with 2% BSA, or incubated with PBS for 30 min at room temperature. Wells were washed twice with PBS and then allowed to dry. The planktonic cells withdrawn at 8- and 96-h (4 mL from each) by centrifugation, as described above, were washed once in ice-cold Tris-HCL (100 mM, pH 6.8) and then divided in two aliquots; the first half was washed with ice-cold TEAB (100 mM, pH 8.5) and the second was washed with Tris-HCL (100 mM, pH 6.8). After centrifugation (each 3 min, 8000\(\times\)\(g\), +4 \({}^{\circ}\)C), the cells were suspended in PBS (2.0 mL). From this suspension 250 \(\upmu\)L was added to the fibrinogen-, BSA- and PBS-treated wells, and plates were incubated at 37 \({}^{\circ}\)C for 2 h (250 rpm). Non-adherent cells were removed, wells were washed twice with PBS, and adherent cells were stained with 200 \(\upmu\)L of the crystal violet solution (0.1%, _wv_) (Sigma-Aldrich) for 30 min at room temperature. Excess staining was washed twice with deionized H\({}_{2}\)O and the stained cells were suspended in 94% ethanol. The adherence was quantified by recording the crystal violet retained by the cells at 595 nm using the Multiskan Sky reader (Thermo Fisher Scientific; Vantaa, Finland). The results were normalized to cell density, which was measured at A\({}_{595}\) from each replica cell sample. The adhesion assay involved four biological replicates, each with four technical replicates. The biological replicate means were compared by the paired _t_-test.
## 2 Materials and Methods
### Materials and Mix Design  
The details of the mix proportioning of the two considered mortars are summarized in Table 1. It can be observed that the water to binder ratio for MNS was \(w/b\) = 0.49, while for MHS it was \(w/b\) = 0.35.  
Two commercial locally available cements, namely CPN40 and CPN50 according to IRAM norms [33; 34] were used for casting the normal (MNS) and the high strength (MHS) mortars, respectively. In particular, CPN50 is a high early-strength Portland cement, analogous to ASTM Type III. Table 2 shows the main properties of the cements used in the experimental campaign. MNS was elaborated by employing only CPN40 cement type, while the MHS binder contained CPN50 and also, blast furnace slag with a specific surface area of 440 m\({}^{2}\)/kg.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **MNS** & **MHS** \\  & **[kg/m\({}^{3}\)]** & **[kg/m\({}^{3}\)]** \\ \hline Water & 287.8 & 301.0 \\ Cement & 589.5 (CPN40) & 602.0 (CPN50) \\ Blast furnace slag & - & 258.0 \\ River sand & 971.0 & 393.1 \\ Crushed sand & 416.1 & 709.8 \\ Super-plasticizer & - & 4.3 \\ \(w/b\) & 0.49 & 0.35 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mix proportioning of the two mortar systems.  
A suitable workability for the mixture MHS, having a \(w/b\) ratio of 0.35, was achieved by the addition of 4.3 kg/m\({}^{3}\) of Sika Viscocrete 3085, a high range water reducing and superplasticizing chemical admixture, made of polycarboxylate polymer.  
A combination of siliceous river and granitic crushed sands was used as fine aggregates, with fineness modulus (FM) of 1.97 and 3.45, respectively. Their water absorption properties and densities are highlighted in Table 3.  
Concrete specimens were cast in steel molds. After 24 h, they were removed from the molds and cured in moisture curing room under 100% relative humidity and 20 \({}^{\circ}\)C during 28 days. Afterwards, specimens were kept under the humidity and temperature conditions of the lab at least seven days till they were submitted to the thermal treatment.
# Digital Twin and Reinforcement Learning-Based Resilient Production Control for Micro Smart Factory
###### Abstract  
To achieve efficient personalized production at an affordable cost, a modular manufacturing system (MMS) can be utilized. MMS enables restructuring of its configuration to accommodate product changes and is thus an efficient solution to reduce the costs involved in personalized production. A micro smart factory (MSF) is an MMS with heterogeneous production processes to enable personalized production. Similar to MMS, MSF also enables the restructuring of production configuration; additionally, it comprises cyber-physical production systems (CPPSs) that help achieve resilience. However, MSFs need to overcome performance hurdles with respect to production control. Therefore, this paper proposes a digital twin (DT) and reinforcement learning (RL)-based production control method. This method replaces the existing dispatching rule in the type and instance phases of the MSF. In this method, the RL policy network is learned and evaluated by coordination between DT and RL. The DT provides virtual event logs that include states, actions, and rewards to support learning. These virtual event logs are returned based on vertical integration with the MSF. As a result, the proposed method provides a resilient solution to the CPPS architectural framework and achieves appropriate actions to the dynamic situation of MSF. Additionally, applying DT with RL helps decide what-next/where-next in the production cycle. Moreover, the proposed concept can be extended to various manufacturing domains because the priority rule concept is frequently applied.  
digital twin; production control; micro smart factory; modular manufacturing system; resilience; reinforcement learning +
Footnote †: journal: Applied Sciences  
1  
Y.H.S.
## 3 Experiment and Analysis
### Experiment Result Analysis
#### 3.2.1 Effect of the Various SNR Conditions
The GSC-SN method would reduce to the conventional GSC method when the estimated noise direction was identical to the desired signal direction. When the estimated noise direction was identical to the actual noise signal direction, the GSC-SN method would achieve the best denoising performance. The trend of the performance of the proposed GSC-SN-MCC method was similar to the GSC-SN method. Better performance of the proposed method could be attained if the estimated noise direction was closer to the actual noise direction. Even when the estimated noise direction was the opposite of the actual noise direction, the performance of the proposed method was still not worse than the conventional GSC method. Table 4 lists the GSC-CC method that could reduce the peak value of the signal distortion, which was independent of the estimated noise direction. The ability to decrease the peak of the enhanced signal distortion by the proposed method was similar to the GSC-CC method. The tables show that the proposed method could work effectively when the estimated noise direction was near the actual noise direction. When the estimated noise direction was far from the actual noise direction, the proposed method could still reduce the peak value of the enhanced signal distortion effectively, and the average of the enhanced signal distortion using the proposed method was similar to the conventional GSC method. Figure 11: Spectrum of signal distortion of the 83 rd frame with different methods when the estimated noise direction was \(60^{\circ}\): (**a**) entire frequency band; (**b**) from 500 HZ to 2000 Hz. Figure 10: Distortion–noise ratio of frames with different methods when the estimated noise direction was \(60^{\circ}\): (**a**) all frames; (**b**) from the 50 th to 100 th frame.
# 3D Face Profilometry Based on Galvanometer Scanner with Infrared Fringe Projection in High Speed
## 3 Experiments
### The Measurement Results Based on MEMS and DLP Projector  
This method and the traditional fringe projection with DLP are compared. In this case, the sample is a face mask shown in Figure 8. Figure 8a shows one of the captured fringe patterns using MEMS. Using the phase unwrapping method of heterodyne and fringe images of Figure 8a, the unwrapped phase is then computed, as shown in Figure 8b. The 3D geometry can be recovered as shown in Figure 8c. The results of the 3D reconstruction mask using the DLP projector is shown in Figure 8d.  
To further visualize the difference between the 3D shape using this proposed MEMS and the DLP method, the same cross-section of two 3D mask geometry shown in Figure 8c,d are plotted in Figure 8e,f (purple and red lines, respectively). The standard deviation and mean error of the cross-section were calculated using Matlab(r) with the version of 2015, which was made by Mathworks, USA. Figure 8e is a comparison of small-scale cross-section data from the mask head, it can be noticed that the MEMS measurement error is larger than DLP. The mean error of small cross-section difference is 0.06 mm and the standard deviation is 0.07 mm. Figure 8f shows that the mean error of larger cross-section difference is 0.15 mm and the standard deviation is 0.19 mm. From this figure, it can be seen that the two methods give very similar results. This demonstrates that the 3D data obtained from  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Position** & **Fitting Diameter** & **Absolute Error** & **Position** & **Fitting Diameter** & **Absolute Error** \\ \hline
1 & 25.352 & \(-\)0.047 & 6 & 25.431 & 0.032 \\
2 & 25.227 & \(-\)0.172 & 7 & 25.245 & \(-\)0.154 \\
3 & 25.456 & 0.057 & 8 & 25.541 & 0.142 \\
4 & 25.562 & 0.163 & 9 & 25.587 & 0.188 \\
5 & 25.426 & 0.027 & 10 & 25.231 & \(-\)0.168 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The quantitative evaluation of standard ball with the diameter of 25.3988 (Unit: mm).  
Figure 7: The measurement results of the standard ball. (**a**) The standard ball; (**b**) Ten measuring positions of the ball; (**c**) 3D reconstruction result of the ball in the first position.  
the proposed MEMS scanning method are reliable and the method can be advantageously applied for face scanning instead of the DLP projection method.
###### Abstract  
In this study, the processes taking place on the surfaces of nanostructured Cu/CuO and Cu/CuO/Pd electrodes at different potential, \(E\), values in the solutions of 0.1 M KOH in H\({}_{2}\)O and D\({}_{2}\)O (heavy water) were probed by surface enhanced Raman spectroscopy (SERS), and the analysis of electrochemical reactions occurring under experimental conditions is presented. The bands of the SERS spectra of the Cu/CuO/Pd electrode observed in the range of \(E\) values from +0.3 V to 0 V (standard hydrogen electrode (SHE)) at 1328-1569 cm\({}^{-1}\) are consistent with the existence of species that are adsorbed or weakly bound to the surface with the energy of interaction close to 15-21 kJ mol\({}^{-1}\). These bands can be attributed to the ad(ab)sorbed (H\({}_{3}\)O\({}^{+}\))\({}_{\text{ad}}\), (H\({}_{2}^{+}\))\({}_{\text{ab}}\), and (H\({}_{2}^{+}\))\({}_{\text{ad}}\) ions as intermediates in reversible hydrogen evolution and oxidation reactions (HER/HOR) taking place on the Cu/CuO/Pd electrode. There was no isotopic effect observed; this is consistent with the dipole nature of the electron-ion pair formation of adsorbed (H\({}_{3}\)O\({}^{+}\))\({}_{\text{ad}}\) and (H\({}_{2}^{+}\))\({}_{\text{ad}}\) or (D\({}_{3}\)O\({}^{+}\))\({}_{\text{ad}}\) and (D\({}_{2}^{+}\))\({}_{\text{ad}}\). In accordance with the literature data, SERS bands at 125-146 cm\({}^{-1}\) and \(\sim\)520-565 cm\({}^{-1}\) were assigned to Cu(I) and Cu(II) oxygen species. These findings corroborate the quantitative stepwise mechanism of water reduction.  
nanotructured CuO; Pd electrode; hydrogen evolution; SERS +
Footnote †: journal: Applied Physics Letters  
**2019**, 9, 5301; doi:10.3390/appr9245301
# ICRP: Internet-Friendly Cryptographic Relay-Detection Protocol
## 6 Applicative Example
### Situation
#### 6.1.2 Data Path  
Using our protocol in this situation means that we have to define where to place the two end points \(\mathcal{S}\) and \(\mathcal{R}\). Concretely, whenever a piece of information is sent from a doctor's device to a hospital database throughout the mobile network, there are three main networks involved in its forwarding:  
1. Radio Access Network (5G-RAN) provides wireless connectivity for devices through 5G antenna (gNB).
2. Core Network (5G-CN) namely enables the authentication of devices and the transfer of user data.
3. Data network (Internet) enables the transfer of user data from the Core Network to its final destination.  
Figure 16 schematizes this process with the 3 involved areas (RAN, CN, and Internet).