esa-sceva/satcom-chunk-collection huggingface dataset:
# A Carrier Estimation Method Based on MLE and KF for Weak GNSS Signals
###### Abstract  
Maximum likelihood estimation (MLE) has been researched for some acquisition and tracking applications of global navigation satellite system (GNSS) receivers and shows high performance. However, all current methods are derived and operated based on the sampling data, which results in a large computation burden. This paper proposes a low-complexity MLE carrier tracking loop for weak GNSS signals which processes the coherent integration results instead of the sampling data. First, the cost function of the MLE of signal parameters such as signal amplitude, carrier phase, and Doppler frequency are used to derive a MLE discriminator function. The optimal value of the cost function is searched by an efficient Levenberg-Marquardt (LM) method iteratively. Its performance including Cramer-Rao bound (CRB), dynamic characteristics and computation burden are analyzed by numerical techniques. Second, an adaptive Kalman filter is designed for the MLE discriminator to obtain smooth estimates of carrier phase and frequency. The performance of the proposed loop, in terms of sensitivity, accuracy and bit error rate, is compared with conventional methods by Monte Carlo (MC) simulations both in pedestrian-level and vehicle-level dynamic circumstances. Finally, an optimal loop which combines the proposed method and conventional method is designed to achieve the optimal performance both in weak and strong signal circumstances.  
GNSS receiver; High sensitivity; MLE; KF +
Footnote †: journal: Journal of Sensors
## 4 Performance Evaluation and Discussion  
To compare the predictive performance of the proposed RF-IMF model with ARMA and random forest models, the predicted values of the testing time period are compared. Table 2 compares the actual rainfall value with the predicted values of all three models from 2001 to 2020.  
Both the ARMA and RF models failed in tracking the rapid variations in annual rainfall; however, the proposed RF-IMF model succeeded in predicting the extreme annual rainfall. This is clear from the predicted rainfall values for years 2018 to 2020. The year 2018 reported the highest rainfall ever in the history of Kerala, resulting in the largest flood the state has ever witnessed. The RF-IMF model predicted a value of 337 cm, which is  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Year/Model** & **Actual Rainfall (cm)** & **ARMA (0,0,2) (cm)** & **RF (cm)** & **RF–IMF (cm)** \\ \hline  
\hline \end{tabular}
\end{table}
Table 2: Actual rainfall and predicted rainfall values.  
Figure 8: A bar plot of the predicted and observed annual rainfall for RF–IMF model.  
much closer to the actual value of 352 cm. The comparison of these models based on the performance metrics such as MAE, MSE, RMSE, MAPE and R\({}^{2}\) are presented in Table 3.  
MAE, MSE, RMSE and MAPE were the error type measures of the deviation of predicted results from actual values. Lower values of these indicate better prediction, while a higher value of R\({}^{2}\) is desirable. The higher the R\({}^{2}\) value, the better the model is at predicting rainfall. The values in Table 3 indicate that the proposed RF-IMF model gives the highest accuracy as it has the lowest values for all the error metrics and highest value of R\({}^{2}\). The R\({}^{2}\) score of RF-IMF is 0.76, which is much higher than the other two models, indicating its power to follow the trend in annual rainfall pattern. Even though the proposed RF-IMF model outperforms the other two models, the extreme amount of rainfall seen in the recent years poses a challenge to our prediction model. Incorporating the various climatic indices such as ENSO (El Nino Southern Oscillation), IOD (Indian Ocean Dipole) and SST (sea surface temperature) will definitely improve the prediction accuracy.
###### Abstract  
In the lumber and wood processing industry, most visual quality inspections are still done by trained human operators. Visual inspection is a tedious and repetitive task that involves a high likelihood of human error. Currently, new automated solutions with high-resolution cameras and visual inspection algorithms are being tested, but they are not always fast and accurate enough for real-time industrial applications. This paper proposes an automatic visual inspection system for the location and classification of defects on the wood surface. We adopted a faster region-based convolutional neural network (faster R-CNN) for the identification of defects on wood veneer surfaces. Faster R-CNN has been successfully used in medical image processing and object tracking before, but it has not yet been applied for wood panel surface quality assurance. To improve the results, we used pre-trained AlexNet, VGG16, BNInception, and ResNet152 neural network models for transfer learning. The results of the experiments using a synthetically augmented dataset are presented. The best average accuracy of 80.6% was obtained using the pretrained ResNet152 neural network model. By combining all the defect classes, a 96.1% accuracy of finding wood panel surface defects was achieved.  
defect detection; quality control; wood veneer; data augmentation; transfer learning; faster R-CNN; deep learning Article
# Research Progress on Modulation Format Recognition Technology for Visible Light Communication
## 4 Deep Learning-Based Modulation Recognition Methods
### Summary of Section 4

Table 4: Summary and comparison of deep learning-based modulation recognition methods. Figure 9: Timeline and keywords of recent research on deep learning-based modulation format recognition in visible light communication.
## 2 Quality Control Algorithms for GPS Observations  
GPSQC consists of five data quality/availability parts: geometric distribution (_sky plot_, _satellite elevation_, _azimuth_), dilution of precision (_DOP_), multi-path (_MP1_, _MP2_) of L1 and L2, ionospheric delays (_I1_, _I2_) of L1 and L2, and cycle slip effects (_cyc_) of phase observations. The fundamental algorithms adopted in GPSQC follow the same algorithms used in the TEQC program. GPSQC uses the cycle slip algorithm developed by [PERSON] [8] and the multi-path estimation equations in [5].
## 2 Nanodiamonds Synthesis Techniques
### Nanodiamond Purification Methods  
Work-up and post-processing procedures aimed at purifying reaction products are often a critical step in the synthesis of NDs. Several methodologies have been developed to cope with the two most common and abundant contaminants: other forms of carbon (mostly graphitic) and metals and their oxides.  
For the carbon contaminants, the common principle underlying purification is selective oxidation, exploiting the higher reactivity of sp\({}^{2}\) carbon with respect to the sp\({}^{3}\) carbon of the diamond phase. Examples are the above mentioned oxidation in air reported by [PERSON] [31], which was further investigated in a subsequent paper [46] where selectivity towards sp\({}^{2}\) carbon was achieved by keeping the temperature in the range of 400-430 \({}^{\circ}\)C. This procedure provides a simple and effective solution for the case of PLA NDs where, even though metallic impurities are avoided, the sp\({}^{2}\) carbon fraction can be so high as to actually be the main reaction product. A similar method, but employing boric anhydride to enhance selectivity, was also reported [47]. Another route that has been explored is oxidation with ozone-enriched air [48; 49; 50], though these procedures require significantly more complex equipment.  
Figure 5: Characterization of the NDs from graphite ablation in water. (**a**) Typical Scanning Electron Microscopy (SEM) image, after removal of the non-diamond phase of carbon, showing clustered nanoparticles of about 100 nm size. (**b**) Energy-dispersive X-ray Spectroscopy (EDXS) spectrum of the nanoparticles. The carbon peak is detected at 0.27 keV. Moreover, the peaks of silicon at 1.74 keV and of oxygen at 0.52 keV are observed. They come from the substrate on which the particles are deposited and from the oxidized surface layers of the ablated powders, respectively. (**c**) Raman spectrum (obtained under 532 nm excitation wavelength) of purified particles. The main peak at 1580 cm\({}^{-1}\) is the graphite G peak, while the peak at 1335 cm\({}^{-1}\) (shown in detail in the inset) is attributed to compressively-strained NDs. Reproduced with permission from [40], [PERSON], 2018.  
Liquid-phase oxidation reactions are by far the most commonly employed procedures. These generally require strong acids, which provide the additional advantage of also removing some metal-based impurities. Examples include HClO\({}_{4}\), concentrated HNO\({}_{3}\), H\({}_{2}\)SO\({}_{4}\) alone and in combination with HNO\({}_{3}\), HCl in combination with HNO\({}_{3}\), and HF [29; 51; 52; 53]. These treatments require temperatures from 80 \({}^{\circ}\)C to more than 200 \({}^{\circ}\)C, thus, specialized equipment is needed that adds to the complexity and cost. Yet, this is necessary, especially in the case of detonation NDs, where metal-based impurities are present in significant quantities, coming from the equipment involved in these processes, such as steel detonation chambers. For a comprehensive review, though it is focused on detonation NDs, the reader should consider [PERSON] [54].
# SPD-Safe: Secure Administration of Railway Intelligent Transportation Systems
## 2 Materials and Methods--Related Work
### Network Layer Protection  
Several schemes are suggested in the literature for protecting communication in WSNs, attempting to address pertinent security concerns (e.g., [56; 57; 58; 59]).  
The Reputation-based Framework for Sensor Networks (RFSN) [56] authenticates underlying nodes with the Timed Efficient Stream Less Tolerant Authentication protocol ( TESLA) [60], implementing a beta Bayesian formulation for fading and evaluating the reputation of the routing operation and the legitimacy of the reported sensed variables. Ariadne [57] also utilizes a TESLA variant for authentication, collecting feedback regarding the successful delivery of packets to choose optimum communication paths and avoid malicious behavior. The Cooperative Secure routing protocol based on ARAN (CSRAN) [58] integrates digital certificates and asymmetric cryptography for authentication. As in the case of RFSN, it uses a Bayesian distribution for fading and, when a node detects malicious activity, it automatically re-routes communication from that point on. The Secure Resilient Reputation-based Routing (SR3) [59] adopts lightweight cryptography (LWC) and symmetric modules for security and authentication. Fading is accomplished by a First In, First Out (FIFO) finite list. The system combines reputation with a reinforced random walk algorithm, producing enhanced load-balancing at the cost of a high intermediate forwarding node count.  
Despite the plethora of proposed solutions, and while most can tackle basic security attacks and malfunctioning cases, there are still various open avenues for attackers, including flooding attacks in congested periods, topology-related attacks, and jamming [61; 62].
## 5 Results and Discussion
As an assumption for the simulation, straight-line driving was taken, with the driver triggering the aid system after introducing correction to the steering system. In automated mode, triggering should occur when the frame turn was below 0.5\({}^{\circ}\) and driver activity (steering wheel rate) was low. The aim of the aid system action was to prevent articulated frame twist, if it occurred, but the flow in the aid system was limited to 10% of the main steering system. The results of the simulation are shown in Figure 13. After 0.3 s, the frame twist was reduced to the demanded level and the yaw angle of the driver and vehicle was below 0.1\({}^{\circ}\). The angular deviation did not increase and the lateral displacement increased very slowly, reaching the permissible 0.1 m (driver perception limit) in about 10 s. During this period, no driver action was needed. The vehicle movement was stable to aid the driver system's operation and driver effort was significantly lowered--the interval between driver action increased by four to five times, meaning that lateral displacement was a major factor of driver reaction. Thus, the system's behavior was similar to cars' steering systems, and it allowed reaching higher speeds of vehicle motion with a lower level of driver effort and concentration. Figure 11: Example of simulation for anticipative driver sitting before front axle of vehicle—(**a**) hydraulic actuator displacement, **(b**) lateral displacement of front—1 and rear—2 axle, **(c)** yaw angular driver deviation, **(d)** hydraulic actuator displacement, **(e)** lateral displacement of front—1 and rear—2 axle, and **(f)** yaw angular driver deviation. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Vehicle Velocity (km/h)** & **Derivative Time (s)** & **Actuator Displacement (mm)** & **Oscillation Period (s)** \\ \hline
30 & 0.33 & 1.5 & 4.40 \\
33 & 0.36 & 1.65 & 4.40 \\
36 & 0.38 & 1.4 & 4.22 \\
39 & 0.41 & 1.1 & 4.26 \\
42 & 0.44 & 1.1 & 4.33 \\
45 & 0.47 & 1.1 & 4.30 \\
48 & 0.50 & 1.0 & 5.12 \\
51 & 0.51 & 1.0 & 4.55 \\
54 & 0.52 & 0.8 & 6.40 \\
57 & 0.53 & 0.8 & 5.43 \\
60 & 054 & 0.7 & 6.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Simulation results of high-speed driving with an inert driver model and a driver at the front of the vehicle. Figure 12: Example of simulation for inert driver siting before front axle of vehicle—(**a**) hydraulic actuator displacement, (**b**) lateral displacement of front—1 axle and rear—2 axles, (**c**) yaw angular driver deviation, (**d**) hydraulic actuator displacement, (**e**) lateral displacement of front—1 axle and rear—2 axles, and (**f**) yaw angular driver deviation.
###### Abstract
Among different semiconductor-based heterogeneous photocatalysts, TiO\({}_{2}\) seems to be one of the best and most effective materials in photocatalytic pollutant degradation. It is considered as environmentally friendly, non-toxic, and relatively cheap, and is available in sufficient quantities compared to other semiconductor materials [14]. TiO\({}_{2}\) can exist in three different polymorph phases [15], which are, in order of abundance, rutile, anatase, and brookite [16]. Anatase is considered as the most photocatalysts active polymorph, while rutile is the most common, employed as a white pigment, an opacifier, and a UV radiation absorber [16]. A pure anatase phase is mainly synthesized via hydrothermal methods, which involve a high pressure and reaction temperature, or using sol-gel methods under ambient pressure. In the hydrothermal process, the morphological shape of TiO\({}_{2}\) can be controlled [17], but the time and energy consumption levels are relatively high [18] and the upscaling of the process is often difficult. With the sol-gel method, mainly amorphous or low-crystallinity TiO\({}_{2}\) will be obtained, and for the formation of a highly crystalline TiO\({}_{2}\) phase, an additional annealing step is often necessary [19; 20]. Morphological shape control with methods involving an annealing step is more challenging [21] because the high temperature necessary to form TiO\({}_{2}\) with high crystallinity leads not only to an increase in the primary crystallite size but also often to an aggregation or agglomeration of the primary formed TiO\({}_{2}\) particles or to sintering processes. In this work, a simple method was used to synthesize anatase TiO\({}_{2}\) in a reproducible manner [22]. The obtained material was characterized via various methods and used for the photocatalytic degradation of diclofenac sodium (DCF) under simulated solar irradiation. The aim of this work was to study the effects of two important synthesis parameters, namely the temperature of the acid-assisted sol-gel synthesis process and the calcination temperature, on the photocatalytic performance of the anatase phase in the degradation of DCF. Finally, a response surface methodology (RSM) was used to predict the synthesis and calcination temperatures that would result in the anatase phase exhibiting the fastest photocatalytic degradation and mineralization of DCF.
## 1 Introduction  
For several decades, text summarisation has attracted a lot of attention in the natural language processing (NLP) community. Text summarisation focuses on condensing large documents into shorter versions that retain the key points, either through extracting important sentences or generating concise summaries. Large-scale text summarisation is an essential skill for content management, information retrieval, and decision-making in a variety of fields. Also, NLP techniques, including text summarisation, have been extensively used in analysing online customer reviews to extract valuable insights and improve decision-making processes [1]. Text summarisation techniques have undergone significant evolution over time, driven by advancements in computational power, availability of data, and NLP [2] methodologies. The primary approaches include extractive, abstractive, hybrid, query-based, multi-document, and domain-specific summarisation.  
The early 1990s approaches to text summarisation were extractive, using statistical techniques [3] requiring redundancy removal techniques in the post-processing phase to improve summary quality. This involved selecting key sentences or phrases directly from the original text, initially relying on statistical methods such as frequency-based scoring and TF-IDF (Term frequency-inverse document frequency) [4]. Without abstraction, they select statements without rephrasing, resulting in redundant or disconnected summaries with poor contextual flow. Semantic comprehension, domain-specific reasoning, and implicit meanings are difficult for these approaches to handle in complicated or lengthy texts [5]. Over time, graph-based methods like TextRank [6] and machine learning models emerged [7], culminating in transformer-based models such as BERTSUM [8] in recent years.  
Abstractive summarisation [9], on the other hand, generates new sentences by rephrasing content, requiring a deeper understanding of the text. Early rule-based systems evolved into sequence-to-sequence models with attention mechanisms [10; 11], and with the introduction of transformer architectures in 2018 they have emerged as the most significant and recent advancement in text summarisation. Transformers have revolutionised the field, e.g., BERT (Bidirectional Encoder Representations from Transformers). [8] in 2020 and GPT (Generative Pre-trained Transformer) [12] from 2018 to 2023 and PEGASUS (Pre-training with extracted gap sentences for abstractive summarisation) [13] in 2020, offering greater coherence and faithfulness. Text summarisation is a rapidly evolving topic with a growing body of research; therefore, it is imperative to perform a thorough bibliometric study to identify the trends, patterns, and key players influencing this field.
# IRS, LIS, and Radio Stripes-Aided Wireless Communications: A Tutorial
## 2 Several Promising Techniques: IRS, LIS, and Radio Stripes
### Categorizing Recent Studies on Large Intelligent Surfaces
#### 2.2.4 Capacity/Data Rate Analyses of LIS-Aided Communications  
Massive MIMO systems have developed into cutting-edge LIS technology, providing for huge gains in capacity enhancements. The large antenna arrays offer significant advantages [31]. To keep the methodology and deployment simple, it is best to design and set up the LIS with panels that are either enabled or disabled and are linked to terminals based on how well they propagate [6; 32]. It is noticeable that complex optimization difficulties might arise as a result of the corresponding spatial allocation of resources to maximize terminals as well as overall bit rates [16].  
The distribution of power received from users is not uniform over the surface; the same user's signal is received with varying signal intensities from different regions of the LIS. We can take advantage of this by employing localized digital signal processing, resulting in the more effective use of computational resources and connectivity bandwidth without severely compromising system performance [33].  
When the number of terminals increases, the LIS performs very well and is very good at reducing interference. This makes it a strong candidate for data transmission in communication systems that go beyond mMIMO and increase capacity [31].  
Other LIS research has focused on the LIS principal concept [34; 35], system performance [25], communication modeling and fundamentals of the LIS [31; 36], aspects of deployment by recognizing and addressing implementation issues, as well as effective LIS implementation [8; 33].  
Table 2 lists the main contributions of recent research on the LIS.
# Polysaccharide Hydrogels Based on Cellulose and Chitosan for Drug Sustained-Release Applications
## 3 Results and Discussion
### In Vitro Release of Hydrogel Loaded with IL-2  
#### 3.5.1 Analysis of Drug Loading Rate and Encapsulation Efficiency of Hydrogels  
In the study by [PERSON] et al. [34; 35; 36], the relationship between IL-2 concentration and absorbance was established using the principle of ultraviolet spectrophotometry to create a standard curve. The maximum absorption wavelength (\(\lambda_{\text{max}}\)) of IL-2 in phosphate-buffered solution within the range of 190-300 nm was determined to be 278.0 nm (Figure 6a). Based on this \(\lambda_{\text{max}}\), the standard curve equation y = 0.0123x + 0.044 was derived, with a high linear correlation coefficient (R\({}^{2}\) = 0.9911) (Figure 6c). The corresponding IL-2 concentration was calculated from the absorbance values. For the CAA-IL-2 drug-containing solution, the maximum absorption wavelength (\(\lambda_{\text{max}}\)) was found to be 252.0 nm (Figure 6b). Under this condition, the standard curve equation y = 0.0291x + 0.0211 was obtained, with a high linear correlation coefficient (R\({}^{2}\) = 0.9965) (Figure 6d).  
IL-2, as a cytokine abundant in amino and carboxyl groups, can form stable interactions with chitosan through hydrogen bonds, resulting in a homogeneous mixed system during the dissolution process. During the preparation of the CAA-CS drug-loaded composite gel, the acetoacetyl groups of CAA react with the amino groups of chitosan via a double-tube injection approach, forming stable enamine bonds (-C=N-), thereby establishing a three-dimensional cross-linked network structure. In this process, a portion of IL-2 forms complexes with chitosan and is effectively entrapped in the interlayer of the CAA-CS gel; another part is incorporated into the internal network of the gel, and a small quantity of IL-2 adheres to the surface of the gel.  
The released IL-2 concentration was then calculated based on this standard curve. When the ratio of IL-2 to chitosan increased from 1.5 wt% to 10 wt%, the encapsulation efficiency of IL-2 initially increased and then decreased. At a ratio of 12.5 wt%, the maximum encapsulation efficiency reached 83.3% (Figure 6f). The drug loading rate of IL-2 increased as the proportion of IL-2 to chitosan gradually increased from 1.5 wt% to 12.5 wt%, reaching a maximum of 13.95% (Figure 6e). This indicates that the three-dimensional network structure of the CAA-CS hydrogel can provide a larger loading area for the drug, which is beneficial for drug encapsulation and loading.