esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Narrow Linewidth External Cavity Semiconductor Lasers
## 4 Confocal F-P Cavity Structure of NLECSLs
The line width of 31 Hz is obtained in the paper. In 2017, [PERSON] et al. [46] focused the light emission of the DFB semiconductor laser chip into a confocal resonant feedback cavity. Therefore, the resonant feedback is re-injected into the DFB diode laser chip. The light emitted from the other side of the DFB laser chip is collimated through an optical isolator and coupled to the single-mode fiber. The Lorentz linewidth of about 630 Hz is obtained by the self-delayed heterodyne device. The corresponding FWHM level technical linewidth is about 30 kHz. In 2018, the ultra-narrow bandwidth dual filter was used as the ECSL of the laser longitudinal mode selection element developed by the Institute of Optoelectronics, Shanxi University. For the angle of the rotating narrow band filter, the laser wavelength coarse tuning range was 14 nm. The linewidth of the narrow-band filter ECSL is measured by the fiber delay beat method. The linewidth is about 187 kHz [47]. In 2018, [PERSON] et al. [48] developed a new on-chip semiconductor laser by introducing the cursor effect and self-injection locking effect between the F-P diode laser on the silicon chip and the external micro resonator. The narrow linewidth of the laser is 8 kHz, and the wide switching range is 17 nm. In 2020, [PERSON] et al. [49] used a narrow-band interference filter for spectral selection, and used a cat-eye reflector for optical feedback to design an ECSL. The ECSL works near 698.45 nm. The tuning range of the current control is more than 40 GHz, and the tuning range of piezoelectric control is 3 GHz. The ECSL line width measured by heterodyne beat frequency is about 180 kHz. In 2021, [PERSON] et al. [50] demonstrated a method of laser frequency stabilization in a wide tuning range by installing piezoelectric ceramic actuators into the Fabry-Perot cavity to stabilize the ultraviolet laser. In order to suppress the piezoelectric drift, the piezoelectric actuator adopts a two-layer symmetrical structure to achieve a tuning range of 14.7 GHz. It can be extended to the wavelength from ultraviolet to infrared. The wavelength of ECSL is 369.5 nm and the linewidth is 20 MHz. In 2021, [PERSON] et al. [51] observed the stable locking region of the injection-locked FP laser by using the delay self-zero difference technique and the RF spectrum of the external cavity FP laser. The center wavelength is 1546.244 nm, and the 3 dB Lorentz linewidth is 100 MHz. Table 5 shows the performance of the confocal F-P cavity structure of NLECSLs. The confocal F-P cavity structure of NLECSLs shows wide band frequency noise suppression characteristics with a narrow linewidth; the confocal cavity length and the cavity mirror's curvature radius must be matched to avoid breaking the mode degeneracy, which means higher requirements for accuracy when using higher finesse cavities.
# Authorship Attribution in Less-Resourced Languages: A Hybrid Transformer Approach for Romanian
## 3 Method
### Prediction Models
#### 3.3.2 Hybrid Transformer  
The hybrid Transformer approach leverages a pre-trained Romanian BERT model, specifically RoBERT [55], to extract contextual representations from input data. These embeddings are then combined with numerical representations from linguistic features, which, after extraction and selection, are first normalized and undergo a linear transformation before being concatenated with the BERT embeddings as illustrated in Figure 3.  
Formally, the problem can be defined as follows: let \(X\) represent the input data comprising textual content and associated numerical features, where \(X=\{x_{1},x_{2},\ldots,x_{n}\}\) and \(n\) denotes the number of samples. Each \(x_{i}\) consists of a text sequence \(x_{\text{test}}\) and a corresponding set of numerical features \(x_{\text{num}}\). The task is to predict the author label \(y\) for each input text. Given a dataset \(D=\{(X_{1},y_{1}),(X_{2},y_{2}),\ldots,(X_{n},y_{n})\}\), where \(X_{i}\) represents the features of the \(i\)-th sample and \(y_{i}\) denotes its corresponding author label, the objective is to learn a mapping function \(f(X)\) that predicts the author label \(y\) accurately for unseen text data.  
In the proposed approach, the textual content undergoes preprocessing to obtain token IDs, input masks, and segment IDs. These inputs are then fed into the encoder part of the Transformer architecture, consisting of multiple Transformer layers. Each Transformer layer comprises a self-attention mechanism followed by a feed-forward neural network layer. This process is repeated for the predetermined number of encoder layers, typically 12 in the BERT model, to produce context-based textual representations. Simultaneously, the numerical features are processed and incorporated into the hybrid feature vector. Finally, the feature vector is passed through a classification layer, which in our case is represented  
Figure 3: Hybrid Transformer model.  
by a feed-forward neural network with softmax activation, to predict the class label of each text sample based on the probability distribution over author labels.
## 4 Experiments
### Ablation Study  
To further show that our method can exploit more informative representations for action recognition contributed by every module of MSKFS, we conduct an ablation study on the NTU RGB+D 60 dataset. Firstly, we directly apply all the frames as input and some commonly used key frame selection methods combined with our backbone model, CTR-GCN, to study the effectiveness of the proposed key frame selection module. These baselines are provided as follows:  
Figure 4: Action prediction performance of different lengths of the sequences.  
All. All skeleton sequences are interpolated to 70 frames into the backbone action recognition model.  
Uniform sampling [33]. This method segments the complete skeleton sequence into 20 equal segments and randomly selects a single frame from each segment to organize a 20-frame sequence as the input of the backbone model for training. Different from [33], we directly sample the middle frame in each segment to avoid introducing any randomness into the final results. In the test time, the target size is set to 20% of the original length.  
Salient posture (ours). This method only selects the salient posture frames, i.e., the frames selected by the first stage of our method, as the input of the backbone model.  
In addition, we validate the effectiveness of the second stage of our method, i.e., the key frame refinement and alignment (KFRA), by providing results of replacing the salient posture frame selection with the uniform-sampling strategy. Table 3 shows the comparison results. With the \"all\" strategy, classification accuracies of 92.3% for Top 1 and 96.3% for Top 5 are achieved, indicating that, without any manipulation, direct interpolation can reduce the frame length to some extent. However, compared to our strategy, i.e., salient posture, the all strategy yields lower accuracy, suggesting that selecting salient pose frames is necessary for action recognition tasks. The \"uniform-sampling strategy\" involves selecting 20 frames at equidistant intervals from the skeleton sequence, achieving classification accuracies of 92.1% and 95.9% on Top 1. Compared to the previous strategy, this strategy shows a decrease in classification accuracy and a noticeable gap compared to the proposed method. This suggests that, for different action sequences, frames selected based on intervals cannot directly be considered key frames, and uniformly selecting salient frames cannot serve as prior knowledge to guide key frame refinement. Our MSKFS method achieves better action recognition accuracy under both settings, proving that the proposed salient pose selection method aids in completing action recognition tasks.
## 5 Conclusions  
The main benefit of the article is the creation of evaluation methodology including specifics in the Czech Republic that are in line with the Evaluation and Assessment plan and FESTA handbook.  
The methodology is divided into three parts: Evaluation preparation, Pilot Site Evaluation, and Evaluation and Assessment. Furthermore, Evaluation and Assessment are divided into three parts, User Acceptance, Impact assessment, and Functional Evaluation, Each part of the methodology is described and explained within the article and shown by an example of one specific use-case.  
User acceptance showed the driver's positive view of the tested PTVS use-case, together with its usefulness, and implementation. The display of the HMI and the distraction of drivers following the displayed warning proved to be a significant factor in the evaluation of C-ITS, as well as a challenge for future studies. Impact assessment indicated a positive impact on the driver's behavior by reducing speed and slowing down more often when using C-ITS. To better understand and validate driver behavior on C-ITS systems, future studies should focus on streamlining the evaluation process for larger numbers of drivers.  
Reference evaluation tests with specific responses to the event were conducted according to the proposed methodology and subsequently evaluated. As the results of the reference tests corresponded to the specified parameters, we consider the methodology to be valid.  
Generally speaking, the evaluation should not be underestimated in terms of newly developed systems. If we focus on the C-ITS area we can see that the conclusions from the example are essential for the further development of C-ITS.  
Conceptualization, M.V.; Testing and methodology, M.V., M.S. and M.M.; data processing, M.M.; project administration and management, Z.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by European Union (CEF), within the C-ROADS project, grant number C-Rods_CZ_2015-CZ-TM-0188-M.  
Not applicable.  
Informed Consent Statement:Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** Two types of data were used in this article: questionnaire survey data and logs recorded during the evaluation. The data from the questionnaire survey are covered by the GDPR Act and therefore it is not possible to publish them. The data that were used to evaluate the impact assessment are available from the authors on official request.  
**Acknowledgments:** The published outputs are supported by voluntaries who joined the testing procedure and shared the personal data for the evaluation. The testing was also performed with the support of project partners (e.g., support of the back-office and control of infrastructure equipment).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results
### Evaluation Criteria  
In this section, we discuss our use of the classic PR curve to evaluate the performance of loop detection. True Positive (TP) represents the number of correctly detected loops; True Negative (TN) is the number of correctly detected non-loops (new locations); False Positive (FP) indicates the number of loops incorrectly identified by the model; and False Negative (FN) is the number of loops missed by the model. Precision and recall are calculated as follows:  
\[Precision=\frac{TP}{TP+FP}Recall=\frac{TP}{TP+FN} \tag{22}\]  
We evaluated the algorithm's performance using the Precision-Recall curve, where a superior loop detection algorithm should aim to maintain high precision while maximizing recall.
# A Research on Manipulator-Path Tracking Based on Deep Reinforcement Learning
## 4 Discussion
Moreover, the SAC algorithm adopts the concept of maximum entropy and considers not only the optimal action, but also other sub-optimal actions to achieve the maximum trade-off between expected return and entropy and to improve its robustness. Therefore, when controlling the manipulator for tracking, the algorithm in this paper is easier to adjust in the face of disturbances. As a result, the present algorithm is more suitable for the path-tracking problem in manipulators than the DQN algorithm and the DDPG algorithm. However, the proposed algorithm still suffers from a deficiency in that the input is the desired path when training on the network. Therefore, it is necessary to train the network first when experiments are conducted and, subsequently, when controlling the robotic arm for path tracking. The proposed algorithm is not yet able to track the input path in real time, and it is more suitable for real work scenarios, in which specific tasks are performed repeatedly.
# Geographical Distribution of Global Radiation and Sunshine Duration over the Island of Cyprus
## 3 Results and Discussion  
### Daily Values  
#### 3.1.1 Monthly Mean Daily Global Irradiation and Mean Daily Sunshine Duration  
Daily global solar radiation was analyzed and compared in this study. Figure 6 shows the temporal evolution of daily global irradiation at the selected stations. The data revealed a common evolution shape with maxima in summer and minima in winter, due to the daily minimum solar zenith angle and daylength (astronomical factors) variation during the year. Large fluctuations occurred in spring and autumn during the transition from cold to warm weather and vice versa. The lowest values were recorded at Prodromos, which is located at an elevation of 1380 m. The highest differences between the stations were observed during the summer months (about 5 MJ/m\({}^{2}\)), while during the winter months they were generally lower than 4 MJ/m\({}^{2}\). Table 3 presents the monthly mean daily values in MJ/m\({}^{2}\) of all the measurement stations. The highest mean daily global solar horizontal irradiation was recorded in June or July and it was almost 29 MJ/m\({}^{2}\) in the coastal areas and about 28 MJ/m\({}^{2}\) in inland plain and semi-mountainous areas. In contrast, it was about 26 MJ/m\({}^{2}\) in the mountainous areas. The geographical distribution of the mean daily global irradiation for the four seasons of the year, as well as for the annual mean daily for the island of Cyprus, is presented in Figure 7. The contour lines on the map were based on the spline method using the ArcGIS software. Spring consisted of the months March, April, and May, while summer comprises June, July, and August. The months in autumn were September, October, and November, while winter consisted of December, January, and February. In winter, the average daily irradiation ranged from 8 MJ/m\({}^{2}\) (in the mountainous sites) to 11 MJ/m\({}^{2}\) (in plain and coastal sites). In summer, it ranged from 24 MJ/m\({}^{2}\) to 28 MJ/m\({}^{2}\), while the annual daily average ranged from 17 to 20 MJ/m\({}^{2}\). Figure 5: Time series plots of the daily irradiation values of global in MJ/m\({}^{2}\) and daily sunshine duration in hours and tenths. (**a**) Pentakomo, (**b**) Athalassa, (**c**) Kambos, (**d**) Agros. The doted red lines represent the end of each year.
## 2 Materials and Methods
### Structural Characterization  
\({}^{1}\)H nuclear magnetic resonance (\({}^{1}\)H\(-\)NMR) spectra were carried out on a Bruker Avance DRX 400 NMR spectrometer (Rheinstetten, Germany) at 25 \({}^{\circ}\)C, in D\({}_{2}\)O for CMP and D\({}_{2}\)O/0.7% NaOH aqueous solution for poloxamer-_graft_-carboxymethyl pullulan (Plx-g-CMP).  
Fourier transform infrared spectroscopy (FT-IR) was performed to confirm the composition of copolymers. The FT-IR spectra of copolymers were recorded in their solid(lyophilized) state pelletized with KBr, on a FT-IR Vertex 70 spectrometer (Bruker, Vienna, Austria) in the range of 4000-400 cm\({}^{-1}\) at a resolution of 4 cm\({}^{-1}\).  
The substitution degree (\(DS\)) with carboxymethyl groups of CMP was determined by conductometric titration of CMP with a 0.1 N HCl aqueous solution, using a conductometer CMD 210 (Radiometer, Copenhagen, Denmark) equipped with a CDC 865 cell. The carboxymethyl groups content (C) of the polymer was calculated with Equation (1):  
\[C(meq./g)\ =\ \frac{V_{HCl}\times c}{m} \tag{1}\]  
where \(V_{HCl}\) represents the volume of HCl aqueous solution determined from the conductometric titration curve, \(c\) is the concentration of HCl solution (mol/L) and \(m\) represents the weight of the dried sample (\(g\)) used in titration. \(DS\) (number of carboxymethyl groups per anhydroglucose unit (UG) in pullulan) was determined with Equation (2):  
\[DS=\frac{162\times C}{162+80\times C} \tag{2}\]  
where \(C\) represents the content of carboxymethyl groups in equivalents/gram of polymer, 80 is the molecular mass of the carboxymethyl radical and 162 represents the molecular mass of anhydro glycosidic unit.  
The number-average molar mass (\(Mn\)) of CMP was measured with a Shimadzu HPLC System with a refractive index detector, a 10 mm \(\times\) 250 mm column packed with 5 \(\upmu\)m mixed bed linear Jordi DVB Glucose gel (Grace Davison Discovery ScienceTM). Polymer solution (0.5 mg/mL, 20 \(\upmu\)L) was injected into column and elution was performed with aqueous 1 N NaOH (0.5 mL/min) at 25 \({}^{\circ}\)C. Dextran standards were used for calibration.
# RACHEM: Reinforced Reliable Computing in Cloud by Ensuring Restricted Access Control
### Key Contributions  
The key contributions of this work are summarized as follows:  
* To propose a secure framework that enables resource computation at the Cloud Service Provider using hard security mechanisms such as access control and cryptography;
* To provide dynamic access control by supporting real-time updates to Access Control Lists and revocation lists and by managing access privileges and membership credentials through identity validation and role hierarchies;
* To develop an efficient revocation and user management scheme that ensures seamless control over user access in dynamic environments;
* To secure role-based access control by binding each role to a secret value, validated via the ACL to prevent misuse or unauthorized privilege escalation;
* To ensure secure distribution and access of decryption keys to authorized users only;
* To prevent unauthorized service access by issuing randomized role secret values and encryption keys, effectively invalidating malicious or revoked users.  
In this study, we propose a secure and dynamic access control framework, tailored for cloud computing environments, leveraging hard security mechanisms, namely access control and cryptography, to ensure secure resource computations at the CSP. The framework introduces a dynamic and role-aware access management model that enables the secure updating of access control policies, efficient revocation of user privileges, and controlled membership management through identity validation and role hierarchies. Access is restricted by managing the ACL and revocation list in real-time, and roles are secured using role secret values, which are validated via the ACL to ensure only legitimate users can obtain the corresponding decryption keys. Unauthorized users are actively invalidated through the assignment of randomized values for both the role secret and the decryption key. These mechanisms collectively contribute to enhanced data protection, fine-grained access control, and trust enforcement within the cloud infrastructure.  
The rest of the paper is organized as follows: Section 2 illustrates the literature survey; Section 3 discusses the proposed access control mechanisms, highlighting various access control models to ensure security in cloud computing systems; experimental results and their comparative analysis are shown in Section 4; formal analysis of the proposed approach is illustrated in Section 5; and, finally, the conclusion of the proposed work is showcased in Section 6. The abbreviations used in this manuscript are listed in Abbreviations section.
## 2 Materials and Methods
### Construction of Model  
The Granger causal statistical analysis methodology was used in model formation. F-statistics and resulting \(p\)-values were generated through the vars package embedded within R software (Version 1.4.1717). The VAR() function created multivariate time-series equations where the endogenous variables in the system are functions of the lagged values of all endogenous variables [26]. Principles of mathematical regression were used within R's functions to generate the multivariate equations. Regression is defined as a system of statistical analysis that estimates the strength of correlation and the directionality of data [26]. The outputted regression equations of the model represent the relationships each variable has with cell proliferation or cell death. A lag order of 1 was determined using the VARselect() function, which outputted the optimal lag based on different information criterion (IC). The Akaike Information Criterion (AIC) tests how well the model fits the data set without overfitting it and therefore was used in determining the IC score.  
Regression analysis alone cannot be used to reveal causality because it only establishes the impact of the predictive variables on the dependent variables tested [26]. In simple regression, the equations merely reflect that \"x\" has a relationship with \"y\". To infer causality, that relationship must reflect that \"x\" causes \"y\". Therefore, after a VAR equation was obtained for each potentially causal variable (intracellular ROS, CA, Ki-67+ count, and G1 activity) vs. cell proliferation, the equation and its associated parameters were inputted into the causality() function. ROS, Ki-67+ count, and G1 activity were subsequently used to assess cell death causality, with CA used as the \"y\" term. The causality function then outputted _p_-values that represented whether or not each relationship displayed statistically significant causality. Tables were generated to organize the _p_-values of each model for later analysis. Models were created from data collected after 3 and 5 min of CHCP treatment.
# Analysis of Permanent Magnet Motors in High Frequency--A Review
## 4 Modeling of Electrical Machines in High-Frequency  
In the previous section, three different analysis tools are described for high-frequency electrical machine analysis. From that analysis, some electrical parameters are obtained (L, R, C), to introduce them in different models. In this section, the main models are classified based on their topology, parameter extraction methods and their main characteristics. Concerning their complexity or size, the models can be classified into two categories. Distributed Parameter Models (DPM) and Lumped Parameter Models (LPM). DPM tend to be more accurate, but they may not be integrated with other system components, as they need intensive computation [39; 51; 52; 56; 58; 61; 62; 86]. By contrast, LPM is more practical, as it can be introduced in a complete electrical drive model, and its parameters can be obtained from simple impedance measurements [13; 17; 20; 21; 22; 23; 24; 26; 27; 31; 32; 33; 37; 54; 55; 73]. Some authors develop high accuracy DPM models and simplify it to LPM with matrix reduction methods [52] or by grouping the RL parameters [58]. A review of the different models is made in Table 5.  
With respect to the simulation domain, some models are working in the frequency domain, for example, to obtain the CM and DM impedance versus frequency. However, to simulate the over-voltages and currents, the time domain is needed. In this domain, the frequency dependency of the parameters is usually considered using lumped parameter circuits, as varying the value for each frequency may not be practical. Two main methods are exposed here. In [58] four parallel RL branches are used to reproduce skin and proximity effects in the resistance and inductance values, where each branch represents a frequency range. The resistance of the first branch, representing the low-frequency range is received directly from FEM values whereas the following branch resistances are calculated by a frequency