esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Optimization of DES-TF-SPME Conditions
#### 3.3.3 The Addition of NaCl  
The improvement of extraction efficiency may sometimes result from the addition of salt to aqueous samples, as this lowers the solubility of the analytes. Therefore, to test the influence of the ionic strength on the extraction yield, sodium chloride was added at concentrations from 0 to 0.05 g mL\({}^{-1}\). A gradual increase of the analytical signal was  
Figure 7: Effect of pH of samples (**a**), amount of NaCl (**b**), extraction time (**c**), and desorption time (**d**) on the performance of the DES-TF-SPME method. The measurements were performed using 200 mL samples containing 125 ng mL\({}^{-1}\) of formaldehyde derivative (\(n=4\)). Conditions: (**a**) Extraction time 30 min with the addition of 0.05 mg L\({}^{-1}\) NaCl and desorption with a mixture acetonitrile/water (90/10, \(v/v\)) for 12 min. The pH is a variable value. (**b**) Extraction time 30 min and desorption with a mixture of acetonitrile/water (90/10, \(v/v\)) for 12 min. The pH of the sample is adjusted to 7. The amount of NaCl is a variable value. (**c**) Extraction with the addition of 0.03 mg L\({}^{-1}\) NaCl and pH adjusted to 7, desorption with a mixture of acetonitrile/water (90/10, \(v/v\)) for 12 min. The extraction time is a variable value. (**d**) Extraction time 20 min with the addition of 0.03 mg L\({}^{-1}\) NaCl and pH adjusted to 7, desorption with a mixture of acetonitrile/water (90/10, \(v/v\)). The desorption time is a variable value.  
observed up to 0.03 g mL\({}^{-1}\) NaCl and then a slight decrease at greater concentrations was found. This decrease may result from the increased sample viscosity slowing down the diffusion of 3,5-diacetyl-1,4-dihydrolutione into the sorbent. Therefore, based on the obtained results, 0.03 g mL\({}^{-1}\) NaCl was selected for further testing (Figure 7b).
# Utilizing Duplicate Announcements for BGP Anomaly Detection
## 5 Conclusions  
Two deep learning approaches, namely LSTM and Autoencoder, have undergone experimentation for anomaly detection purposes in BGP networks. To accomplish this purpose, RIPE and Route views datasets, which are publicly available standard benchmark datasets, have been used. The EMA algorithm is applied to time series data to detect anomalies. The process is followed by processing the data in the processing phase. The Oversampling technique T-SMOTE is utilized to handle the imbalanced characteristics present in the dataset. The experimentation is conducted with the created system model, which is tested on two standard datasets, RIPE and Route views. The implementations of the designed models are tested on two different standard benchmark datasets of BGP traffic, namely RIPE and Route Views. The performance of both the deep learning models is calculated in terms of accuracy, F1-score, precision, and recall. The observation of the experimental results clearly states that the proposed Autoencoder model performs better than the LSTM model and gives much uniform performance throughout the datasets. However, there has also been a tremendously improved performance with the experimental results for both classes of datasets when the proposed work is compared to previous models.  
The proposed model effectively detects BGP anomalies but has limitations, including reliance on historical data, computational overhead, and potential false positives in dynamic networks. While T-SMOTE mitigates class imbalance, rare anomalies may still be underrepresented. Future work should focus on real-time integration, hybrid rule-based and deep learning approaches, and adaptive learning to detect zero-day threats. Lightweight models for edge deployment and Graph Neural Networks (GNNs) for AS relationship modeling could further enhance scalability and accuracy in evolving network environments.  
Conceptualization, R.D.V., P.K.K. and V.K.J.; methodology, R.D.V., P.K.K. and M.C.G.; software, R.D.V., P.K.K. and M.C.G.; validation, P.K.K., V.K.J. and V.T.; formal analysis, V.T.; investigation, P.K.K., V.K.J. and V.T.; resources, V.T.; data curation, R.D.V.; writing--original draft preparation, R.D.V., P.K.K. and V.K.J.; writing--review and editing, V.T.; visualization, V.T.;supervision, P.K.K., V.K.J. and M.C.G.; project administration, P.K.K., V.K.J. and M.C.G.; funding acquisition, V.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Wireless Covert Communication with Polarization Dirty Constellation
## 4 Wireless Covert Channels with Polarized Dirty Constellation
### Polar Encoding  
We use the standard notation \(x_{1}^{N}=u_{1}^{N}\cdot G_{N}\) to denote the encoding operation. \(G_{N}\) is the conversion matrix consisting of various permutation operations. [PERSON] provides a recursive formula as  
\[G_{N} =R_{N}(F\otimes I_{N/2})(I_{2}\otimes G_{N/2}) \tag{19}\] \[=R_{N}(F\otimes G_{N/2}).\]  
Let \(I_{k}\) denote the \(k\)-dimensional identity matrix for any positive integer \(k\). We assume \(N=2^{n}\) for positive integer \(n\). It is simple to derive the recursive relationship as  
\[G_{N}=R_{N}[F\otimes(R_{N/2}(F\otimes G_{N/4})). \tag{20}\]  
With the identity \(AC\otimes BD=(A\otimes B)(C\otimes D)\), we can equivalently rewrite Equation (20) as  
\[G_{N} =R_{N}(I_{2}\otimes R_{N/2})(F^{\otimes 2}\otimes G_{N/4}) \tag{21}\] \[=R_{N}(I_{2}\otimes R_{N/2})(I_{4}\otimes R_{N/4})\cdots(I_{N/2} \otimes R_{2})F^{\otimes n},\]  
where \(F^{\otimes n}\) is the n-dimensional Kronecker product of \(F\) matrix with \(F\) equals \(\begin{bmatrix}1&0\\ 1&1\end{bmatrix}\). \(R_{N}\) is converted from the \(N\)-dimensional identity matrix, arranging the column vectors of the identity matrix in the order of odd first and then even. We can express \(R_{N}\) as  
\[R_{N} =[I_{N,odd},I_{N,even}] \tag{22}\] \[=[I_{N,1},I_{N,3},\cdots I_{N,N-1},I_{N,2},I_{N,A},\cdots I_{N,N}].\]  
where \(I_{N,x}\) denotes the \(x\)-th column of matrix \(I_{N}\) for positive integer \(x\in[1,N]\). Let covert message \(u_{1}^{N}\) be any vector over GF (2). We would obtain \(x_{1}^{N}=u_{1}^{N}\cdot G_{N}\) at output. Some preprocessing needs to be performed to combine polar code and dirty constellation.
## 3 The Proposed Approach
### Personnel and Feature Object Detection Based on YOLOv7  
The YOLO series of detection algorithms are well-known for their lightweight and efficient performance, making them highly suitable for edge scene requirements. In each frame, YOLOv7 is used to identify all the drivers and abnormal objects, such as mobile phones and cigarettes. For the detected individuals, the ByteTrack algorithm is employed for multi-target tracking.  
ByteTrack operates based on tracking according to the detection paradigm. It achieves person tracking by associating all the person detection boxes and assigns unique identifiers (IDs) to each individual. Additionally, ByteTrack stores continuous action frames of individuals, providing essential data support for behavior recognition. This combination of YOLOv7 and ByteTrack ensures both accurate detection and robust tracking, laying a solid foundation for subsequent behavior analysis.
# Self-Optimizing Traffic Steering for 5G mmWave Heterogeneous Networks
## 2 Overview of mmWave HetNets
### Characteristics of 5G Networks and Devices  
The mmWave HetNet will focus on enabling extremely high data rates and significant capacity enhancement, as well as creating a multi-function network that can connect new services and empower new user experience. To ensure the above functions, our design should support a wide variety of 5G devices, spectra, and networks, as described in the following.
## 3 Two-Dimensional Frequency Domain  
Focusing algorithms that operate in the frequency domain have a lower computational cost than focusing algorithms that operate in the time domain; according to the literature the Omega-k algorithm has a better focusing quality than the Chirp Scaling algorithm and the range-Doppler algorithm for large synthetic apertures [12], so the Omega-k algorithm will be developed by adding parameters such as Doppler factor [15] for pulsed and continuous-wave SAR radars.
# A Secure IoT-Based Authentication System in Cloud Computing Environment
## 4 Security Analysis
### Resistance to Off-Line Guessing Attack  
Off-line guesswork attacks happen when an attacker obtains all the information stolen from the user, pass through insecure channels, and store in smart CARDS. The attacker can use the information held to guess the user's identity and password.  
We assume that an attacker gets (\(C_{1}\), \(C_{2}\), \(C_{3}\), \(C_{4}\), \(PID_{i}\), \(ID_{cs}\)) that is stored in the user \(U_{i}\)'s smart card and all messages (\(M_{1}\), \(M_{2}\), \(M_{3}\), \(M_{4}\)) that pass by a nonsecure channel in the last session. Then, the attacker wants to guess a pair (\(ID_{i}\), \(PW_{i}\)) from information. He/she can use the equation \(D_{2}=h(r_{u}||PID_{i}||ID_{cs})\oplus ID_{i}\) to confirm her/his guess \(ID_{i}\). According to the above hypothesis, the attacker has \(PID_{i}\) and \(D_{2}\) from \(M_{2}\); \(ID_{cs}\) is from the smart card. Therefore, he/she needs to get \(r_{u}\). Then, \(r_{u}\) can be derived by rearranging \(D_{1}=A_{i}\oplus r_{u}\) to \(r_{u}=A_{i}\oplus D_{1}\). However, the attacker cannot compute \(A_{i}=h(PSID_{i}||ID_{cs}||\mathbf{x})\) without the secret key \(x\) of \(CS\). Therefore, he/she cannot successfully guess \(ID_{i}\). In addition, \(PW_{i}\) only appears on \(C_{2}=h(ID_{i}||x)\oplus h(PW_{i}||n_{i})\), \(C_{3}=n_{i}\oplus h(ID_{i}||PW_{i})\), and \(C_{4}=h(ID_{i}||PW_{i}||n_{i})\). If the attacker wants to guess it, he/she needs to obtain \(ID_{i}\), \(x\) or \(n_{i}\) first. However, the attacker cannot extract those values from intercepted messages. Therefore, he/she cannot successfully guess \(PW_{i}\). The results show that the scheme can resist offline guessing attack.
# An Overview of Intelligent Transportation Systems in Europe
## 5 Key Features of ITSs
### Challenges and Obstacles in the Implementation of ITSs in Europe
#### 5.2.1 Technical Interoperability Issues  
The lack of technical interoperability between different Cellular Vehicle-to-Everything (C-V2X) solutions and existing technologies such as LTE-V2X (Long Term Evolution-based Vehicle-to-Everything) and 5G-NR VANET (Fifth-Generation New Radio Vehicular Ad Hoc Networks) is one of the main issues in ITS deployment. Currently, there is no common standard that facilitates the seamless integration of multiple systems and technologies, making large-scale deployment more difficult [59]. Furthermore, although essential, the principle of technological neutrality is not directly included in the ITS Directive; rather, it is the responsibility of the government to control the radio spectrum. To prevent problems arising from new technologies or inappropriate upgrades, it is essential to guarantee backward compatibility [60].  
One major technical issue with ITS deployment is the incompatibility of various vehicle-to-everything (V2X) protocols, including DSRC (IEEE 802.11p), LTE-V2X and the more recent C-V2X standards. Although these protocols operate on similar spectra (5.9 GHz band), they are not directly compatible at the communication level, which causes problems in fragmentation and integration. In addition, the infrastructure requirements differ: DSRC requires dedicated hardware exclusively for short-range communications, while C-V2X can use cellular networks in addition to roadside units [61].  
Some cities and corridors have begun to deploy dual-mode roadside units that support both DSRC and C-V2X to address these issues. This allows for backward compatibility during the transition phase. In addition, newer 5G-based C-V2X equipment will fall back to LTE-V2X to enable communication with older vehicles. While hybrid approaches offer a temporary solution, continued standardization (e.g., IEEE 802.11 bd, 5G NR V2X) and regulatory harmonization are essential for interoperability and effective ITS deployment in the long term [62].
# Efficacy of TENS in Cervical Pain Syndromes: An Umbrella Review of Systematic Reviews
## 1 Introduction  
Neck pain is defined as \"perceived pain, originating in an area bounded above by the nuchal line, below by an imaginary line passing through the spinous process of T1 and laterally by the sagittal planes tangent to the lateral edges of the neck\" [1, 2, 3]. It affects approximately 30-50% of the general population each year, constituting the 4% of causes of patients referring to Italian Physical Medicine and Rehabilitation (PRM) outpatients [4]. The course is generally benign and acute episodes tend to resolve spontaneously within a few weeks. Relapses are frequent but in 10% of cases the symptoms tend to become chronic [5]. This pathology has a strong impact in the workplace as 11-14% of workers encounter a limitation of work activities [6, 7]. The multifactorial etiology includes non-modifiable factors (age, sex, familiarity, general health conditions, previous traumatic outcomes) and modifiable (postural, behavioral, psychosocial) factors. Often the cause is not identifiable,and we tend to attribute a dysfunctional origin to the onset of pain (\"non-specific\" or \"common\" neck pain), which includes inflammatory, muscular, biomechanical, postural, and neurological components [8; 9]. Joint facets are a potential source of axial and peripheral pain. Facet joint pain has a prevalence of 54% to 67% in patients treated for neck pain. Furthermore, facet spondylosis has been associated with cervical radiculopathy and facet joint osteoarthritis accounts for up to 45% of chronic low back pain cases [10; 11; 12]. The main symptoms are pain, stiffness, reflex contracture of the cervical muscles, limitation of the cervical range of motion (ROM), irradiation to the upper limbs, increased fatigue, dizziness, and headache. Symptoms are often accompanied by visual and auditory disturbances, sleep disturbances, cognitive, and emotional problems (anxiety and depression) [13]. Several treatments have been proposed for cervical pain, including pharmacological therapies (e.g., nonsteroidal anti-inflammatory drugs, paracetamol, steroids, opioids, tramadol, muscle relaxants, psychotropics, anxiolytics, and hypnotics) and other conservative approaches, as the oxygen-ozone therapy [14]. In the rehabilitation process, physiotherapy treatment and instrumental physical therapies are commonly used. Among the latter, the transcutaneous electrical nerve stimulation (TENS) has the greatest evidence of efficacy in the treatment of neck pain (chemical effect, vasodilation, analgesic effect, and thermal effect) and it is the most widely used analgesic chemotherapy (symmetrical or asymmetrical rectangular waves, spike, continuous, or packet-burst). The TENS impulses, due to their short duration, are able to selectively activate the A beta fibers and block nociceptive impulses through the Gate-Control mechanism [15].  
The aim of our article was to investigate the effectiveness of TENS in patients suffering from both acute and chronic cervical pain syndromes.
# A Novel Method for Estimating the Dosage of Cold Atmospheric Plasmas in Plasma Medical Applications
## 3 Experimental Results
### Selection of the Incubation Time  
In the previous studies concerning the cell viability after CAP treatment, the cells were incubated for a certain period of time before the viability assays were conducted. However, different researchers used different incubation durations and seldom discussed the reasons to select such a duration [11; 12; 13]. Therefore, as the first step of this study, we incubated the cells after CAP treatment for different durations, i.e., 0, 6, 12, 18, and 24 h, and compared the measured cell viabilities. It could be seen clearly from Figure 4 that, under a fixed CAP treatment time, the cell viabilities seemed to remain unchanged with the longer incubation time after 12 h. The only exclusion was that, when the CAP treatment time was 3 min, the cell viability after 24 h incubation was higher than that after 18 h incubation. The possible reason might be explained as follows: the low dosage of plasma treatment, e.g., 3 min in this study, could not lead to an unrecoverable lethal effect to the treated cells, and consequently, the cell activity could still increase gradually after a long term incubation. This is, to some extent, consistent with the experimental results presented in Section 4 in which the plasma dosage corresponding to the 3 min CAP treatment time is lower than the value of LD\({}_{50}\). The sub-lethal effects of CAP-treated living biomaterials form a very important and complex research aspect, which is beyond the scope of this paper. Therefore,  
Figure 3: Emission spectrum of the helium CAP jet. The discharge parameters are: \(f\) = 23 kHz, \(V_{\rm d}\) = 3.5 kV, \(Q\) = 8.0 slpm, \(P_{\rm in}\) = 0.8 W.  
we focus on the lethal effects of the CAP-treated HEK293 cells in this paper, and use the cell viabilities corresponding to the 24 h incubation after CAP treatment to calculate the value of LD\({}_{50}\) in the following sub-sections.
## 2 Related Work
### Computer Vision Methods
[24] and other researchers have also explored signal processing techniques like frequency-domain analysis and texture filtering to enhance the robustness of track component detection. Computer vision methods in track defect detection are rapidly evolving: from early single-stage morphological or filtering analyses to comprehensive algorithms based on multi-stage detection, 3D reconstruction, or drone-assisted techniques. These approaches offer high visualization, flexibility in recognizing diverse defects, and ease of integration with deep learning technologies. However, they also face challenges related to large-scale data collection, lighting and noise interference, real-time performance, and hardware costs. Striking a balance between high accuracy, speed, and applicability remains a key ongoing focus in the research of visual detection algorithms in track scenarios.
## 4 Assessment and Analysis of LeGNSS Precise Positioning  
In this section, the satellite visibility of LeGNSS are assessed with different system combinations. We elaborate the LeGNSS PPP performance, including the time span of each satellite, the sky plot, and the convergence time with four stations. Finally, the convergence time and its influence factor of the sampling rate are statistically analyzed for all system combinations on a global scale (they are grid points assumed distributed globally with the space resolution of 5\({}^{\circ}\) by 5\({}^{\circ}\)), followed by the discussion.
## 5 Results and Discussion  
In this study, we used baseline models such as k-Nearest Neighbors (kNN), RF, XGBoost, and a standalone DNN to comprehensively compare our proposed hybrid approach. Each baseline model represents a well-established technique in ML, offering unique strengths and serving as benchmarks for performance evaluation. kNN was selected for its simplicity and non-parametric nature, making it a common reference in classification studies. RF, a robust ensemble method, was included due to its interpretability and ability to handle imbalanced data effectively. XGBoost, a powerful gradient-boosting algorithm, is widely regarded for its accuracy and efficiency in structured data tasks, making it a competitive baseline. Finally, the standalone DNN, representing DL-based approaches, was included to highlight the differences between end-to-end learning and our hybrid feature extraction and classification strategy. Comparing the hybrid model with these diverse baselines enables a clear demonstration of its advantages in accuracy, robustness, and generalization.
## 3 Calculation Results and Influence Factors Analysis
### Lateral Ground Settlement for Simultaneous Exxarated Tunnels with Different Tunnel Spaces  
In this section, the lateral ground subsidence curves of synchronous excavation double-line tunnels under 1.0D, 1.5D, and 2.0D tunnel spacings are analyzed. In Figure 6, the lateral settlement curve of the ground shows a V-shaped symmetrical distribution trend. Under the spatial conditions of 1.0D, 1.5D, and 2.0D tunnels, the ground subsidence of the central lines of the two tunnels reached the maximum values of 4.62 mm, 4.27 mm, and 3.57 mm, respectively. Compared with the 2.0D tunnel spacing, the 1.0D and 1.5D tunnel spacing settlement will increase by 1.05 mm and 0.7 mm, respectively, and the settlement will decrease by 29% and 19%, respectively.  
Figure 5: The maximum ground settlement points of the left line, right line, and axis of the double-line tunnel under different tunnel spacings and excavation spacings/mm.  
This indicates that, as the tunnel's transverse spacing decreases, the maximum settlement of the ground gradually increases, and the width of the settlement trough gradually decreases.
# A Wideband Digital Pre-Distortion Algorithm Based on Edge Signal Correction
## 2 Wideband DPD Model Based on Edge Signal Correction  
In the field of wideband communication systems, the continuous expansion of bandwidth has led to a drastic increase in the computational burden for estimating DPD model parameters. This challenge becomes particularly prominent in ultra-wideband signal processing, demanding higher precision in parameter estimation. The complexity of wideband signals is characterized by irregularities and significant amplitude fluctuations between adjacent frequency components. Accurately characterizing the non-linear characteristics of RF PAs requires the introduction of more parameters and the consideration of higher-order effects. Unfortunately, these effects lead to a sharp rise in complexity. To address these issues, this paper proposes a wideband DPD algorithm based on edge signal correction. By only collecting the signals within the central frequency band and using the filtered frequency domain information for DPD model parameter estimation, the algorithm significantly reduces data processing complexity. Furthermore, by incorporating cross-terms into the model structure and continuously correcting the entire model using edge signals, the fitting accuracy of the model is markedly improved. The core of this method lies in its ability to effectively reduce computational complexity while accurately capturing the non-linear characteristics of RF PAs. Consequently, this algorithm can substantially lower computational demands without compromising accuracy.