esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The electrolyte for dye-sensitized solar cells (DSSCs) is subject of constant innovation, as the problems of leakage and drying greatly reduce the long-term stability of a device. One possible way to solve these problems is the use of gel polymer electrolytes (GPEs) with a gelling structure, which offer different advantages based on the used polymers. Here, potential GPE systems based on dimethyl sulfoxide (DMSO) as solvent for low-cost, non-toxic and environmentally friendly DSSCs were investigated comparatively. In order to observe a potential improvement in long-term stability, the efficiencies of DSSCs with different GPEs, consisting of polyacrylamide (PAN), acrylonitrile-butadiene-styrene (ABS), polyvinyl alcohol (PVA) and poly (vinylidene fluoride) (PVDF) and their blends with poly (ethylene oxide) (PEO), were investigated over a period of 120 days. The results indicate that blending the polymers with PEO achieves better results concerning long-term stability and overall efficiency. Especially the mixtures with PAN and PVDF show only slight signs of deterioration after 120 days of measurement.  
dye-sensitized solar cells (DSSC); gel polymer electrolyte; long-term stability; dimethyl sulfoxide (DMSO); poly (ethylene oxide) (PEO); natural dyes; non-toxic; polymers Article  
Investigation of the Long-Term Stability of Different Polymers and Their Blends with PEO to Produce Gel Polymer Electrolytes
## 2 Bulk Crystal Characteristics
### Spectroscopic Characteristics  
A spectrophotometer, with a spectral sharpness of separation of 0.2 nm, was used to determine the room temperature (RT) absorption spectra. The cutting orientation of the sample was along the \(a\) axis. An Edinburgh Instruments FL920 fluorescence spectrometer was utilized to determine both the room and cryogenic (79 K) fluorescence properties, and the thickness of the sample was 0.5 mm. The absorption spectra and RT fluorescence of Yb\({}^{3+}\):ScBO\({}_{3}\) are presented in Figure 4a. The main absorption peak was at 964.8 nm. Its full-width at half maximal (FWHM) was determined to be 19.86 nm, and the absorption cross-section was determined to be 0.13 \(\times\) 10\({}^{-20}\) cm\({}^{2}\). Diode laser sources are appropriate for the wide absorption bandwidth. At 79 K, the zero-photon line was at 964.8 nm, as exhibited in Figure 4a. Figure 4b exhibits the energy levels of the Yb\({}^{3+}\) ions in the ScBO\({}_{3}\) crystal [43].  
Figure 4: (**a**) Emission cross-section spectra (RT and 79K) and absorption cross-section (RT) spectra; (**b**) diagram of energy level. (Source: [34], © 2015 Optica Publishing Group, Washington, DC, USA).  
The cross-section of the RT emission was determined using the following formula [32; 44]:  
\[\sigma_{em}(\lambda)=\frac{\lambda^{4}I(\lambda)}{8\pi cn^{2}\tau_{rad}\int\ I( \lambda)\mathrm{d}\lambda} \tag{6}\]  
where \(I\) (\(\lambda\)) represents the emission intensity, \(n\) represents the refractive index, and \(\tau_{rad}\) represents the radiative lifetime. As exhibited in Figure 4a, the strongest emission peak (\(\lambda_{peak}\)) was at 1021 nm. Meanwhile, the corresponding emission cross-section (\(\sigma_{em}\)) was determined to be 0.12 \(\times\) 10\({}^{-20}\) cm\({}^{2}\), which was significantly larger than that of Yb\({}^{3+}\):YAG [45] and Yb\({}^{3+}\):YGG [46; 47].  
After testing and fitting, it was determined that the fluorescence lifetime at room temperature was 5.25 ms [48], as shown in Figure 5. Both the exponential and linear fitted decay curves provided the single exponential trend, as shown in Figure 5[49]. Table S1 lists the passive Q-switching performance and corresponding spectral and thermal characteristics of commonly used Yb\({}^{3+}\)-doped crystals, such as Yb\({}^{3+}\):YAG, Yb\({}^{3+}\):YGG, Yb\({}^{3+}\):KGW, Yb\({}^{3+}\):YCOB, and Yb\({}^{3+}\):GdCOB. Among them, the Yb\({}^{3+}\):ScBO\({}_{3}\) crystal possesses the smallest emission cross-section, the longest fluorescence lifetime, and a moderate fluorescence linewidth, indicating its excellent potential for achieving pulsed lasers with moderate, or even high, pulse energy.
## 2 Materials and Methods
\[\hat{t}=\varepsilon\bigg{(}1-\frac{\sigma}{\omega\times\varepsilon_{r}\times \varepsilon_{0}}i\bigg{)} \tag{4}\]  
\[\hat{\gamma}=i\times\omega\sqrt{\mu\times\hat{\varepsilon}} \tag{5}\]  
\[\hat{\eta}=\sqrt{\frac{\mu}{\hat{\varepsilon}}} \tag{6}\]  
\[\hat{T}_{2}=\frac{\hat{\eta}_{2}-\hat{\eta}_{1}}{\hat{\eta}_{1}+\hat{\eta}_{2} }=\frac{\hat{E}_{r}(0)}{\hat{E}_{i}(0)} \tag{7}\]  
\[\hat{T}_{2}=\frac{2\times\hat{\eta}_{2}}{\hat{\eta}_{2}+\hat{\eta}_{1}}=\frac{ \hat{E}_{t}(0)}{\hat{E}_{i}(0)} \tag{8}\]  
Taking the head model, the total reflection coefficient \(\hat{t}_{in}\) seen from the entrance is calculated as  
\[ \begin{array}{c}\hat{I}_{in}=\hat{I}_{air}+\hat{I}_{skin}\times e^{-2(\gamma _{skin}d_{1})}+\hat{I}_{fat}\times e^{-2(\gamma_{skin}d_{1}+\gamma_{fat}d_{2} )}+\hat{I}_{bone}\times\\ e^{-2(\gamma_{skin}d_{1}+\gamma_{fat}d_{2}+\gamma_{bone}d_{3})}+\hat{I}_{ data}\times e^{-2(\gamma_{skin}d_{1}+\gamma_{fat}d_{2}+\gamma_{bone}d_{3}+\gamma_{ fat}d_{4})}+\\ \hat{I}_{CSF}\times e^{-2(\gamma_{skin}d_{1}+\gamma_{fat}d_{2}+\gamma_{bone} d_{3}+\gamma_{tard}d_{4}+\gamma_{CSF}d_{5})}\end{array}  \tag{9}\]  
Then, to find the electric field values reaching the brain, the total transmission coefficient is calculated using Equation (10) [31]. \[\hat{I}_{in}+1=\hat{T}_{in} \tag{10}\]  
The electric field values reaching the brain of the broadband electric field effective values measured in four environments are obtained by  
\[|\overrightarrow{E_{ibrain}}|=|\hat{T}_{in}|\overrightarrow{E_{i}}| \tag{11}\]  
The SAR value occurring in the brain is  
\[SAR_{brain}\left(\frac{W}{kg}\right)=\frac{\sigma_{brain}}{\rho_{brain}} \overrightarrow{E_{ibrain}}|^{2} \tag{12}\]  
The SAR value formed in the head part was calculated using the electric field effective values measured in four environments. \[SAR_{skin}\left(\frac{W}{kg}\right)=\frac{\sigma_{skin}}{\rho_{skin}} \overrightarrow{E_{iskin}}|^{2} \tag{13}\]  
where  
\(\overrightarrow{E_{i}}\): effective value of the measured electric field (V/m)  
\(\rho\): mass density of the tissue (kg/m\({}^{3}\))  
Note: While performing SAR analysis, it was considered that the incoming electric field was perpendicular to the air-skin boundary.
# Enhancing Machine Learning Prediction in Cybersecurity Using Dynamic Feature Selector
###### Abstract  
Machine learning algorithms are becoming very efficient in intrusion detection systems with their real time response and adaptive learning process. A robust machine learning model can be deployed for anomaly detection by using a comprehensive dataset with multiple attack types. Nowadays datasets contain many attributes. Such high dimensionality of datasets poses a significant challenge to information extraction in terms of time and space complexity. Moreover, having so many attributes may be a hindrance towards creation of a decision boundary due to noise in the dataset. Large scale data with redundant or insignificant features increases the computational time and often decreases goodness of fit which is a critical issue in cybersecurity. In this research, we have proposed and implemented an efficient feature selection algorithm to filter insignificant variables. Our proposed Dynamic Feature Selector (DFS) uses statistical analysis and feature importance tests to reduce model complexity and improve prediction accuracy. To evaluate DFS, we conducted experiments on two datasets used for cybersecurity research namely Network Security Laboratory (NSL-KDD) and University of New South Wales (UNSW-NB15). In the meta-learning stage, four algorithms were compared namely Bidirectional Long Short-Term Memory (Bi-LSTM), Gated Recurrent Units, Random Forest and a proposed Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) for accuracy estimation. For NSL-KDD, experiments revealed an increment in accuracy from 99.54% to 99.64% while reducing feature size of one-hot encoded features from 123 to 50. In UNSW-NB15 we observed an increase in accuracy from 90.98% to 92.46% while reducing feature size from 196 to 47. The proposed approach is thus able to achieve higher accuracy while significantly lowering number of features required for processing.  
dynamic feature selection; meta-learner; cybersecurity; random forest; CNN; RNN; GRU; LSTM; Bi-LSTM +
Footnote †: journal: Cybersecurity and Privacy  
0000-0001-5000-0000][PERSON]
## 4 Result Analysis
### Comparison of Annotation Efficiency
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Original** & **Dynamic** & **Dynamic** & & \\  & **Annotation Speed** & **Response Time** & **Response Time** & **Workload** & **Repetitive Labor** \\  & **(Images/Hour)** & **(1500 Images)** & **(15,000 Images)** & & \\ \hline \multirow{2}{*}{LabellImg} & \multirow{2}{*}{50} & \multirow{2}{*}{30 h} & \multirow{2}{*}{300 h} & Re-manually annotate & \multirow{2}{*}{100\%} \\  & & & & both existing and new & & \\  & & & & data & \\  & & & & Re-manually annotate & \multirow{2}{*}{100\%} \\ LabelMe & 40–50 & 35 h & 350 h & & both existing and new & \\  & & & & data & \\ Make Sense & 40–50 & 35 h & 350 h & & both existing and new & \\  & & & & data & \\  & & & & automatically update & \\  & & & & annotations using the & \\ This Method & 50 & \textless{}5 min & \textless{}30 min & \textless{}30 min & \multirow{2}{*}{5\%} \\  & & & & automatically annotate & \\  & & & & new data with the & \\  & & & & augmentation tool & \\  & & & & Automatically update & \\ Supervisely & & & & annotations and & \\  & & & & annotate new data & \\  & & & & May require manual & \\  & & & & inspection and & \\  & & & & adjustment for & \\ Roboflow & & & & classification & \\  & & & & operations; processes & \\  & & & & fewer images at a time & \\  & & & during augmentation & \\  & & & & Deletion operations & \\  & & & may affect existing & \\  & & & annotations; & \\  & & & significant variation in & \\  & & & augmentation & \\  & & & behavior & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of annotation efficiency. accurately by this method greatly surpasses that of other tools, with the advantage becoming increasingly pronounced for larger-scale datasets. These experimental findings comprehensively validate the effectiveness and superiority of the proposed method in constructing dynamic datasets for vertical domain models. In comparison with contemporary automated annotation platforms such as Supervised (Supervised, Mountain View, CA, USA), Roboflow (Roboflow, San Francisco, CA, USA), and Scale AI (Scale AI, San Francisco, CA, USA), this study presents the analysis summarized in Table 8. The proposed method demonstrates superior performance over Roboflow and Scale AI, particularly in scenarios involving category deletion and modification. Compared to Supervised, it offers comparable functionality while providing greater advantages in management workflows and support for localized operations.
# Narrowband Internet of Things (NB-IoT):
## 3 Narrowband-IoT Protocol Stack
### Upper Layers  
Although the focus of this paper is mainly on the features regarding PHY an MAC layers, it is still imperative to address some enhancements, challenges, and potential solutions to the upper layers. Especially the changes that are implemented in Evolved Packet Core (EPC) by adding the Service Capability Exposure Function (SCEF) to manage both IP and non-IP data packets [84].
# Optimal Waveform of the Partial-Respond Signal Based on Minimum Out-of-Band Radiation Criterion
## 4 Conclusions  
A Pan-functional model was established to present optimized efficient spectrum signals based on the minimum out-band energy radiation criterion. The achieved signals provide higher out-band spectral roll-off rate with quasi-constant envelope. This is due to the introduction of the controlled ISI, which are greatly superior to that of the QPSK and MSK signals in frequency spectral. Limiting the minimum Euclidean distance, ISI may slightly affect the adjacent baseband symbol signal reception, and improve the performance of the system. We presented the simulation results of the signal modulation and demodulation schemes with controlled ISI, which have been carried out using the MATLAB software. The results reveal that by taking the restriction of the minimum cross-correlation function into account, for the BER of \(10^{-4}\), the \(E_{b}/N_{0}\) reduces by 0.3 dB. As a future work, we will investigate how to optimize the multi-frequency signals (e.g., OFDM systems) to further improve the achievable spectral efficiency.  
This research has been supported by the National Key Research and Development Program of China-Government Corporation Special Program (2016 YFE0111100), Fundamental Research Funds for the Central Universities (GK2080260160 and GK2080260166).  
[PERSON] did the mathematical modeling and the simulations. She also wrote the draft of the paper. [PERSON] contributed to the revisions and the discussion of the results. [PERSON], [PERSON] and [PERSON] put forward the idea and checked the simulation of this paper.  
The authors declare no conflict of interest.
# A Carbon Benefits-Based Signal Control Method in a Connected Environment
## 5 Evaluation
### Results
#### 5.2.1 Carbon Emission  
Figure 8 depicts the carbon emissions and benefits under different demand levels and CRs using the proposed method. The proposed method demonstrates significant benefits across various demand levels, achieving emission reductions ranging from 5.24% to 17.60%. When the penetration rate of CBCVs is zero, the method degrades into an adaptive approach, utilizing only the signal control optimization from the upper-level model yet still achieving benefits ranging from 5.24% to 6.70%. This indicates that despite the method optimizing signal control to minimize delay, it still delivers emission reduction benefits.  
Upon introducing CBCVs, the emission reduction benefits increase from 6.62% to 17.60%, demonstrating the advantage of the coordinated optimization framework between  
Figure 8: Average carbon emissions (g) and benefits (%) compared with the baseline.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Traffic Demand**} & \multicolumn{8}{c}{**Traffic Demand (V/C)**} \\ \cline{2-9}  & **s-n** & **s-w** & **e-w** & **e-s** & **n-s** & **n-e** & **w-e** & **w-n** \\ \hline I & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 \\ II & 0.5 & 0.5 & 0.8 & 0.5 & 0.5 & 0.5 & 0.2 & 0.5 \\ III & 0.5 & 0.5 & 0.5 & 0.8 & 0.5 & 0.5 & 0.5 & 0.2 \\ IV & 0.5 & 0.5 & 0.8 & 0.8 & 0.5 & 0.5 & 0.2 & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Four unbalanced traffic demand patterns.  
signal control and vehicle trajectories within the method. This also validates the effectiveness of the lower-level model, which optimizes based on stopping frequencies and durations, in significantly reducing intersection emissions.  
Table 4 displays the average carbon emissions for three control methods under the non-uniform traffic demand. The results indicate that the proposed method exhibits significantly greater advantages under non-uniform traffic conditions. Compared to fixed-timing control, it achieves emission reductions ranging from 8.92% to 14.51%, and compared to adaptive control, it achieves reductions from 4.34% to 9.20%. This is attributed to the study's ability to optimize the trajectories of CBCVs, influencing other vehicles to adopt more efficient trajectories and maximizing green phase utilization at each intersection. This mitigates the negative impacts of uneven traffic demands effectively.
## 4 Discussion
### Methodological Differences
#### 4.2.1 CBCT Studies  
CBCT offers a three-dimensional view, allowing precise detection of PRGs across multiple tooth types. Studies like [PERSON] et al. (2022), [PERSON] et al. (2014), and [PERSON] et al. (2018) used CBCT, reporting tooth-based prevalences of 4.5%, 1.4%, and 1.58%, respectively [23; 25; 32]. The relatively lower rates in these studies could be linked to smaller datasets [25; 32] or inclusion criteria limited to incisor teeth only [23].  
[PERSON] & [PERSON]'s (2025) incisor-only prevalence CBCT study (9.4% of patients) and our broader anterior-tooth series (23.6%) agree on two key points in a Turkish population: Type I grooves predominate and maxillary lateral incisors are most frequently affected; the main contrast lies in overall prevalence, which is higher in our larger sample [33].
## 3 The Proposed Range-Division User Relay Selection Scheme
### Allocation of ERSR Radius  
In D2D technology, USU uses global positioning system (GPS) location information and interacts with the information server (i.e., SU) [16]. ETR of a user's device is the upper bound of transmission range. Location information and training signaling of USU can be received by the SU who locates within the ETR.  
For the USU who is near the cell edge, there may be no SU in the ETR. However, for the USU who is near the cell centre, several SUs may be in the vicinity. Based on this situation and the location of USU, ERSR with optional radius should be utilized to adjust the number of URs. For USU in different AAs, radius of the ERSR is different.  
According to the distance between USU and BS, \(\mathcal{A}\) is divided into \(N\) AAs in the order from cell center to edge, \(\mathcal{A}=\left\{A_{1},A_{2},\cdots,A_{n},\cdots,A_{N}\right\}\), \(\bigcup\limits_{n=1}^{N}A_{n}=\mathcal{A}\) and \(\bigcap\limits_{n=1}^{N}A_{n}=\varnothing\). It is obvious that \(DI_{in}^{A_{n}}=DI_{ex}^{A_{n-1}}\), where the inner diameter of \(A_{n}\) is denoted by \(DI_{in}^{A_{n}}\), and the external diameter is denoted by \(DI_{ex}^{A_{n}}\). From the analysis of last Section 3.1, \(DI_{in}^{A_{1}}=2*\gamma_{u}^{1}\), \(DI_{ex}^{A_{N}}=2*R\).  
RDUR is intended to reduce the amount of URs of cell-center USUs through narrowing the receive range of location information and training signaling. Different ERSRs are separately allocated to the USUs within \(N\) AAs. The radius of ERSR allocated to \(A_{n}\) is represented by \(r_{n}\). The radius set for \(\mathcal{A}\) is \(\theta=\left\{r_{n}|n=1,2,\cdots,N\right\}\). Actually, no more than 100 \(m\) distance between users' devices can be efficient in D2D technology [17]. Therefore, \(r_{n}\in\mathcal{E}\), \(\mathcal{E}=\left\{E_{t}|E_{t}\leq 100\text{ m}\right\}\) (In this paper, \(r_{n}\) takes discrete values less than 100 m to reduce system complexity.). \(\mathcal{E}\) is the optional radius set of ERSR. The available \(\theta\) set is \(\Theta\).  
Figure 2: The model of range-division user relay (RDUR).  
For the ERSR set \(\theta\), the relationship between the inner diameter and external diameter of \(A_{n}\) is expressed by  
\[DI^{A_{n}}_{ex}=DI^{A_{n}}_{in}+2*W_{n}, \tag{5}\]  
where \(W_{n}\) is the width of \(A_{n}\) and \(W_{n}=2*r_{n}\).  
In summary, the relationships between inner diameters and external diameters of \(N\) AAs are expressed by  
\[\left\{\begin{array}{l}DI^{A_{1}}_{in}=2*\gamma^{A}_{n},\\ DI^{A_{n}}_{ex}=DI^{A_{n}}_{in}+2W_{n},\\ DI^{A_{n}}_{in}=DC^{A_{n}-1}_{ex},\\ DI^{A_{n}}_{ex}=2*R,\quad when\;DI^{A_{N}}_{ex}\geq 2*R,\\ N{=}\min\left\{n|DI^{A_{1}}_{in}+2\sum\limits_{n}W_{n}>=2R\right\}\end{array}\right. \tag{6}\]  
The UR selection process is shown in Algorithm 1. Its flow chat is shown in Figure 3.
# ODIN IVR-Interactive Solution for Emergency Calls Handling
Second, it is a resource management scheduling system because it optimizes finite resource usage, especially in crowded situations. Additionally, the use of intelligent IVR in emergency systems significantly reduces the impact of non-critical situations, such as a small-impact car accident with no casualties, a fallen tree without any damage after a storm, suspicious sounds, or visual appearances in the night. Therefore,prioritization of calls can be implemented concerning critical deadline satisfaction and the 100% policy. The primary contributions of this paper are as follows:  
* Presenting a viable solution for an interactive chatbot/IVR for emergencies that is ready to be implemented in the National Emergency System 112 of Romania as part of the ODIN112 project. The objective of the ODIN112 research project is to create an integrated computer system for real-time transcription of speech into text for the Romanian language and to recognize the callers' emotional states in emergency 112 calls. This system is based on state-of-the-art deep learning models and aims to improve the management of 112 emergency call recordings and increase the quality, efficiency, and effectiveness of 112 call-management activities. The focus of this paper is on the initial interaction with the user. * Implementing two proofs-of-concept (PoCs) using different technologies, namely web-based and telephony-based IVR solutions, and performing a side-by-side comparison arguing for the best alternative given the specific requirements and specifications. * Performing a detailed evaluation of the telephony IVR solution in terms of efficiency, effectiveness, and satisfaction, while arguing that standardized custom decision trees for interaction represent the most adequate approach for an IVR tackling emerging calls. Even though this article does not present a major scientific breakthrough, we are the first in Romania to design, implement, and evaluate a chatbot/IVR solution tailored to the emergency system in cooperation with the national authorities that manage the National Emergency System 112. The scientific contributions of this paper consist of the design, implementation, and evaluation of a novel component that can easily be integrated into a national emergency system by considering a decision tree for emergencies that classifies user calls into four major categories: (i) medical, (ii) police department, (iii) fire department, and (iv) non-urgent.
# New Hybrid Techniques for Business Recommender Systems
## 1 Introduction
Although collaborative and content-based filtering become applicable through our iterative process, they may not be the best choice because a) collaborative filtering does not lend itself readily to incorporating company demographics (or other forms of general context) and b) because both do not foresee the use of human-provided knowledge about the business domain, which might be helpful. In fact, previous research has argued for the use of case-based reasoning (CBR) in business recommenders [6] because CBR is a proven way of re-using solutions for business problems. Constraint-based recommenders [4] are another family of algorithms that have been put forward as a good way of satisfying business requirements. In our previous work [3], we used a graph, as a simple, flexible and easily extensible means of representing both historic user choices and explicit human knowledge about a business domain together with a random walk approach to generate recommendations. We found that explicit knowledge about associations between solution elements improves  
Figure 1: Iterative process for business consultancy recommenders. the recommender performance. On the other hand, taxonomic knowledge, e.g., about relationships between industries, did not help. While our previous work was able to benefit from a graph's flexibility and ease of incorporating new domain knowledge easily [3; 7], it is not perfectly suited to accommodate and make use of all possibly relevant attributes of a company's context. For instance, it does not easily allow representing and comparing numeric attributes (such as company size) or simple string attributes containing longer passages of text. On the other hand, in another work, we demonstrated that the CBR recommender gave good results where past experience plays a significant role in providing recommendations [8]. Thus, the goal of our extended research was to explore options of combining graph-based random walks with other forms of recommenders, above all CBR-based ones.