esa-sceva/satcom-chunk-collection huggingface dataset:
# Rank-Based Ant System with Originality Reinforcement and Pheromone Smoothing
## 5 Conclusions and Future Work  
This work proposes an extension of the Rank-based Ant System that incorporates originality reinforcement and pheromone smoothing strategies in order to avoid early stagnation and improve the quality of the solutions. The performance of the approach is analysed and compared with AS, AS\({}_{Rank}\), MMAS, and ACS, over TSP and SOP benchmarks showing very promising results. Contrary to the occasional reinitialization of pheromone trails employed by MMAS [14; 17], the proposed algorithm relies strongly on search diversification through frequent smoothing of the pheromone trails. Therefore, the promising results obtained by the proposed approach are not due only to the pheromone smoothing mechanism, but also to the use of the proposed criterion for triggering the pheromone smoothing. Additionally, the comparative results show that the originality reinforcement strategy helps to improve the quality of the Rank-based Ant System when it is combined with the pheromone smoothing strategy. Together, they enable the proposed approach to maintain the rapid convergence to high-quality solutions during the first iterations of AS\({}_{Rank}\), in addition to reaching higher-quality solutions in the long run. These features can be advantageous in those optimization problems where the available time for computation is limited or not known beforehand. In this regard, we consider as an interesting future research line the adaptation of the proposed approach to dynamic optimization problems (where the search domain changes over time). For example, the rapid convergence of the proposed ACO algorithm could be advantageous in Dynamic TSP [14; 39], where new cities can be added or removed during run time and the new shortest tour after each transition should be found as quickly as possible. We also consider the analysis of the proposed method with other combinatorial optimization problems such as the Vehicle Routing Problem and different modifications of the original TSP [14]. Other interesting potential extension of this work is its hybridization with local search strategies and/or other metaheuristics for better performance. As previously indicated in Section 4.2, the present method does not include any local search, since our primary goal is to enhance the pure state-of-the-art ACO algorithms without the addition of other heuristics. However, it has been remarked by several authors that adding sophisticated local search
# A Cross-Platform Personalized Recommender System for Connecting E-Commerce and Social Network
## 4 Evaluation and Analysis
### Analysis of the Collaborative Filtering Algorithm Based on the User Profiles (CFUP)
#### 4.5.2 Comparison Results with Collaborative Filtering Algorithm  
This experiment attempts to show whether the collaborative filtering algorithm based on user profiles (CFUP) has better performance in terms of prediction precision. In order to answer this question, we make a comparison with the tradition collaborative filtering algorithm (CF), and the result is shown in Figure 12. The abscissa is the number of neighbors.  
Figure 11: The impact of parameter \(\mu\) on MAE.  
Figure 12: The comparison results of CF and CFUP.  
It can be observed from Figure 12 that MAE decreases as the number of neighbor users increases in the different algorithms. It changes sharply at the beginning, and then more slowly before finally seeming become a stable number. However, the general recommendation result of CFUP is better than that of the traditional CF.
## 1 Introduction
FLS, its laws are designed based on some knowledge about the control system, with adaptive laws can overcome the system parameters variation, as well as adjust the controller gains. Some studies such as [PERSON] et al. in [37], [PERSON] et al. in [38], [PERSON] et al. in [39], and [PERSON] et al. in [40] employed FLS with online-tuning for adjusting the controller gains to adopt with the manipulator behavior. Their results have shown an improvement in performance when applying adaptive laws. However, in those reports, the payload is supposed to be well-known or the uncertainties are supposed to be bound. Unfortunately, in practice, these uncertainties/payloads maybe randomly changed and cannot be exactly measured. According to the above reports, this paper employed the FLS for adjusting the control feedback gains to improve the manipulator performance with the unknown variant payload. To overcome this issue and achieve higher performance, we suggest using disturbance observer (DO) [41; 42; 43] to detect the change of the external payload mass. Based on the above reasons, we consider a 3-DOF hydraulic manipulator with unknown payload to verify the proposed algorithm. Compared with the existing work, the main contributions of this study are highlighted as follows:  
* First, the dynamics of the 3-DOF hydraulic manipulator including actuator is derived. * Second, the backstepping sliding mode control is proposed for the tracking performance and the disturbance observer (DO) is utilized to observe the variation of the external payload torque. * Third, the two FLS is used to adopt the system performance. The first FLS is applied for tuning switching gain based on its tracking error and error changing rate. The other FLS is used to boost the controller gains with the input is the estimated load torque. * Finally, some simulated comparisons are given between the proposed controller scheme and the conventional controller to demonstrate the effectiveness of the proposed algorithm. This paper is organized as follows: Section 2 presents the manipulator dynamics including EHAs. The motion of the manipulator in Cartesian space, as well as joint space and actuator-space, is discussedin this section. The proposed algorithm for the system is designed with the proof of the stability and robustness by using Lyapunov theory depicted in Section 3. Section 4 provides some simulation results and experimental results are discussed in Section 5. Conclusion and future work are presented in Section 6.
## 3 Results and Discussion
### Crop Spectrotemporal Signature Library (CSSL)  
The wealth of information derived from a large satellite image collection can be efficiently utilized only if the data are stored in a platform with a clear and organized structure, where users can query and access the data needed. This objective is served by the Crop Spectrotemporal Signature Library (CSSL), which is a database containing the spectral and textural information described in the previous section, accompanied by metadata and in-situ observations whenever available. In the framework of the project, 4.5 TB of VHSR satellite data have been acquired and archived; while this is, by and large, proprietary data, the information extracted out of it such as the field spectra is not. Hence the CSSL is acting as an open-access information platform, where a user can query, explore, and acquire a time-series of crop-specific VHSR spectra.  
With the CSSL, we aim to realize a publicly available information resource that supports others in image-based research on smallholder agriculture. Eventually, all data and all code around it will be available to research teams with such stated interests, and our team will support its use and further expansion. Our ambition is to make it a valuable public global good that serves to address, study, and understand the intrinsic heterogeneity of smallholder farming.
# Research Progress of Polysaccharide-Gold Nanocomplexes in Drug Delivery
## 6 Drug Release from Polysaccharide-AuNPs
In the presence of an inflammatory microenvironment or upon encountering lysosomes, the HA undergoes degradation, leading to the release of TPCA-1. Thus, TPCA-1 exerts an anti-inflammatory effect and offers potential therapeutic benefits for the future treatment of rheumatoid arthritis (RA), but its anti-inflammatory activity and in vivo anti-RA mechanisms still require in-depth studies [162]. Besides the common pH-responsive drug release mechanism, new nano-drug delivery systems have been developed to achieve cascade-stimulated drug release. One of the commonly used cascade response stimulations is the pH/NIR co-stimulation. [PERSON] et al. proposed a novel approach utilizing aldehyde/catechol functionalized hyaluronic acid (DAHA) and (HECS)-modified AuNRs platforms for combined chemo-photothermal therapy in breast cancer. Adjusting the polysaccharide-AuNPs onto the nanoplatforms could co-deliver AuNRs and DOX to the cancer cells. Moreover, introducing AuNRs enabled the nanoplatforms to possess pH/NIR dual-responsive drug release properties. This means the drug release can be triggered by either the pH level or the near-infrared light [163]. [PERSON] et al. developed a novel gold coupling (AuNPs-NHN\(\rightleftharpoons\)Dox-mPEG) by modifying the positions of PEG and the drug on the gold nanosurface. This modification involved shielding DOX through polyethylene glycolization, which enhanced drug solubility, stability, and dispersion. The modified system also enabled responsive drug release in two steps upon exposure to an acidic environment in the lysosome. The sustained delivery of the modified system into the nucleus was intended to improve antitumor efficacy in vivo [164]. [PERSON] et al. developed nanoplatforms highly responsive to pH, GSH, and hyaluronidase, targeting HER2 overexpression. These nanoplatforms possess dual-targeted receptors for HER2 and CD44, exhibiting desirable size and excellent dispersion. Moreover, when exposed to near-infrared radiation, MCF-7 cells efficiently generate reactive oxygen species and heat. The innovative combination therapy of PDT/PTT demonstrated a remarkable therapeutic effect on HER2-positive breast cancer. However, there is still a great challenge in reducing the non-specific nanoparticle accumulation in reticuloendothelial system organs, especially in the liver [165].
# GDPR and Large Language Models: Technical and Legal Obstacles
## 2 Detailed Analysis of GDPR Articles Challenging for LLMs
### Article 25--Data Protection by Design and by Default  
The complexity of LLM architectures poses significant challenges when attempting to integrate data protection measures from the outset. Continuous data ingestion and frequent updates further complicate maintaining consistent protection standards. Moreover, enhancing data protection may sometimes come at the cost of model performance, creating a difficult balance between privacy and functionality [22; 39; 54].  
To overcome these issues, organizations are adopting various privacy-preserving techniques, such as differential privacy, federated learning, and homomorphic encryption, to secure personal data during both training and inference [54; 55]. Designing models with modular components that can incorporate data protection measures independently, as well as integrating automated compliance tools into the development lifecycle, are additional strategies that can help ensure adherence to GDPR's requirements. Best practices include conducting regular privacy assessments and embedding privacy by design principles throughout the development process [17].
## 1 Introduction
The frequent use reflects the high interest in this fusion method, but it also indicates that there might be limitations which have so far hindered broader applications. This study identifies possible limitations of the ESTARFM algorithm and aims to improve these for a better applicability on large spatial scales (e.g., more than one Landsat tile). For this purpose, the algorithm was implemented in a framework facilitating the generation of time series and automatically accounting for regional differences in the heterogeneity of the study area. A further aim was to enhance the application of ESTARFM in cloud-prone areas by improving the availability of useable input Landsat data. Lastly, the benefits of the developed ESTARFM framework are demonstrated by applying it to the generation of a 30 m NDVI time series for a large, cloud-prone, and heterogeneous region of West Africa.
# A Method Improves Speech Recognition with Contrastive Learning in Low-Resource Languages
## 1 Introduction
wav2 vec-S [31] proposes use of the semi-supervised pre-training method of wav2 vec 2.0 to build a better low-resource speech recognition pre-training model. Many relevant contrastive learning models have been proposed in low-resource speech recognition, but the selection of negative samples for speech remains unmentioned. Changes in speech are continuous and minor within a small range. Thus, the approach of defining negative samples as samples from different parts of the same speech tends to ignore the fact that they may also have the same pronunciation as the positive samples. This subtle change is still very important for low-resource speech recognition. Therefore, we propose the false negatives impact elimination (FNIE) method to discover potential false negative samples in speech contrastive learning and optimize the corresponding loss function to eliminate the impact of false negative samples, which improves the quality of the negative sample set and thus allows the model to learn better speech representations. We evaluate the results of our method for English, Mandarin, Uyghur, and Uzbek in the low-resource case and compare the effect of the number of negative samples on the model. The experiments show that our method learns better speech representations and achieves higher accuracy in low-resource speech recognition compared to the original model for the same number of elements in the final negative sample set. The FNIE method can also further improve the accuracy of low-resource speech recognition when increasing the number of negative samples deteriorates the learning effect. The main contributions of this paper are as follows:  
1. We propose the false negatives impact elimination (FNIE) method and optimize the corresponding loss function to improve the quality of the negative sample set of speech, allowing the model to learn better speech representations and achieve better results in low-resource speech recognition;
2. We found that simply increasing the number of negative samples does not improve the model's ability to learn speech representations;By training our model on a variety of low-resource languages, we have validated its feasibility and effectiveness.
# Driverless Bus Path Tracking Based on Fuzzy Pure Pursuit Control with a Front Axle Reference
## 2 Controller Design  
As shown in Figure 1, the path-tracking controller is divided into lateral control and longitudinal control. Lateral control calculates the steering angle to track the desired path, and longitudinal control cause deceleration to follow the desired velocity. In this section, the model of the driverless bus is proposed in Section 2.1. Then the controller is introduced with respect to lateral control and longitudinal control in Sections 2.2 and 2.3.
# Enhancing Flow Field Performance of a Small Circulating Water Channel Based on Porous Grid Plate
## 4 Discussions of the Fluid Field Characteristics
### The Influences of the Pumping Flow Rate \(Q\)  
For the circulating water channel, the flow velocity of the test section depends on the power of the water pump, which is generally described as the pumping flow rate \(Q\). In order to meet the requirements of obtaining different flow velocities in the test section, the power of the pump should be adjustable. Figures 19 and 20 give the results of the flow characteristics when the pumping flow rate of the pump is different. Figure 19 shows the velocity and turbulence distribution characteristics along the axial and lateral directions of the test section. Figure 20 indicates the velocity and turbulence intensity contours of several cross-sections and gives the maximum velocity and turbulence intensity values. It can be seen that increasing the pumping flow rate can improve the flow velocity of the test section, while it will result in the increasing of the turbulence intensity. The turbulence intensity near the outlet of the test section is lower than that near the inlet. Besides, it can be seen from Figure 19 that, with the increase of the pumping flow rate, the turbulence intensity uniformity along the lateral direction gradually decreases, while the velocity uniformity at the corresponding area hardly changes. Therefore, when analyzing the hydrodynamic characteristics of the tested device along the axial direction, the device should be arranged near the outlet area of the test section; when analyzing the hydrodynamic characteristics of the tested device along the lateral direction, the pumping flow should be appropriately reduced to improve the uniformity of the turbulence intensity.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Spacing** & **30 mm** & **40 mm** & **50 mm** & **60 mm** & **70 mm** \\ \hline Velocity inhomogeneity \(\beta\) & 1.2456 & 1.1327 & 1.1240 & 1.1684 & 1.1915 \\ Average turbulence intensity & 0.0603 & 0.0531 & 0.0491 & 0.0510 & 0.0658 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The results of velocity inhomogeneity and average turbulence intensity when the screens are arranged at different spacings.  
Figure 18: The distribution of the turbulence intensity along the axial direction and the lateral direction when the screens are arranged at different spacings: (**a**) along the axial direction, (**b**) along the lateral direction.  
Figure 19: The velocity and turbulence intensity distribution characteristics at different pumping flow rates. _Q_: pumping flow rate.
# Split-And-Delay Unit for FEL Interferometry in the XUV Spectral Range
## 3 White Light Interferometer for Time Delay Diagnostics
### Experimental Design of the WLI  
The diagnostics setup for a precise determination of the delay introduced by the SDU is based on a [PERSON] white light interferometer. All of its components except the white light source and the imaging system are placed in a vacuum chamber in close proximity to the SDU (see Figure 2). The interferometer employs a white light diode with a mean wavelength \(\lambda_{D}=593\) nm as the illumination source. The diode is mounted on top of the vacuum chamber and illuminates the interferometer through a viewport. The beam from the diode is split equally into two arms by a 20 mm broad-band beam splitting cube. The SDU is placed at the end of the horizontal arm. The vertical arm terminates with a super-polished reference Si mirror mounted on a translational piezo stage. After reflection, the two beams recombine in the same beam splitter and pass through a vacuum window to a camera placed outside the chamber. The camera can image a 10 \(\times\) 10 mm\({}^{2}\) area of the SDU surface with a lateral resolution of 8 \(\upmu\)m at a full frame rate up to 60 Hz.
## 2 Background and Theory
### Feature Extraction Techniques
#### 2.2.7 Correlation Dimension  
The correlation dimension fractal dimension (CDFD) [50] is a widely used measure of fractal complexity. The CDFD serves as an effective feature extraction technique that quantifies the degree of self-similarity and chaotic behavior in EEG signals. It estimates the dimensionality of the signal's attractor in the phase space, offering insights into the underlying neural dynamics during MI tasks. The CDFD is computed using the correlation sum \(C(r)\), which measures the probability that two points in the reconstructed phase space are within a given distance r, as in Equation (22):  
\[C(r)=\underset{N\rightarrow\infty}{\lim}\frac{1}{N}\overset{N}{\underset{i=1 }{\sum}}\overset{N}{\underset{j=1}{\sum}}\overset{N}{\underset{j=1}{\sum}} \Theta\big{(}r-\big{|}\big{|}X_{i}-X_{j}\big{|}\big{|}\big{)} \tag{22}\]  
In this context, \(X_{i}\) and \(X_{j}\) are points in the embedded phase space, \(\Theta\) is the Heaviside function (which counts pairs within the radius r), and N is the number of points in the trajectory. The correlation dimension \(D_{2}\) is estimated as the slope of the log-log plot of \(C(r)\) vs. \(r\), as in Equation (23):  
\[D_{2}=\underset{r\to 0}{\lim}\frac{\Delta\text{log}\,C(r)}{\Delta\text{ log}\,r} \tag{23}\]  
In practical computation, a range of radius values \(r\) is chosen, and the relationship is approximated using a least-squares fit in the log-log domain. The CDFD provides a measure of EEG signal complexity, with higher values indicating greater fractal dimensionality and more complex neural dynamics.
## 5 Results Evaluation and Discussion  
To appraise the performance of proposed CSFSA, the CSFSA is emulated in Contiki NG operating system and Cooja simulator. Contiki NG is an open source operating system  
Figure 8: Border router connectivity with external network.  
Figure 9: Operation of proposed CSFSA.  
which is designed especially for memory constrained systems. Cooja is an open source simulator which is used for the development of Contiki NG, as it provides simulation environment by allowing developers to simulate and test code/system before running it on the actual hardware.
## 3 Experiments on the Bearing Characteristics of Bag Grouting Piles  
### Test Overview  
As the effectiveness of the vibrating-string-type steel concrete strain gauge is related to the strength of the solidified and hardened cement mortar grouted by the bag, three bag grouting piles were randomly selected at the site for investigation. After 28 days of construction, it was completed, and core sampling tests were conducted by drilling holes and core samples were taken from the upper, middle, and lower sections within the range of the tested pile length for unconfined compressive strength testing. A summary of the unconfined compressive strength of the three bag grouting piles is presented in Table 2. An unconfined compressive strength diagram of the field core sample is shown in Figure 6. The test results show that the unconfined compressive strength of the core samples, aged beyond 28 days, exceeded 5 MPa, which is in accordance with the relevant specification and design requirements [14].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Pile Number** & **Settling Volume/mm** & **Allowable Load Capacity/kPa** \\ \hline
1\# & 13.29 & 208 \\
2\# & 17.19 & 190 \\
3\# & 12.64 & 203 \\
4\# & 14.11 & 195 \\
5\# & 9.64 & 192 \\
6\# & 11.99 & 172 \\
7\# & 11.41 & 184 \\
8\# & 13.11 & 178 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary table of settlement and allowable bearing capacity of composite foundation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Test Number** & \begin{tabular}{c} **Pile** \\ **Diameter/m** \\ \end{tabular} & \begin{tabular}{c} **Age** \\ **/d** \\ \end{tabular} & \begin{tabular}{c} **Coring Section** \\ **/kN** \\ \end{tabular} & \
\begin{tabular}{c} **Damage Load** \\ **Strength** \\ **/MPa** \\ \end{tabular} &
\begin{tabular}{c} **Compressive Strength** \\ **Average Value/MPa** \\ \end{tabular} \\ \hline
1 & 0.4 & \(\geq\)28 &
\begin{tabular}{c} top \\ middle \\ bottom \\ top \\ \end{tabular} & 89.5 & 11.67 & 11.03 \\
2 & 0.4 & \(\geq\)28 &
\begin{tabular}{c} top \\ top \\ \end{tabular} & 83.46 & 10.86 & \\
3 & 0.4 & \(\geq\)28 &
\begin{tabular}{c} middle \\ bottom \\ \end{tabular} & 82.6 & 10.68 & 11.08 \\
3 & 0.4 & \(\geq\)28 &
\begin{tabular}{c} bottom \\ top \\ \end{tabular} & 90.12 & 11.69 & \\
3 & 0.4 & \(\geq\)28 &
\begin{tabular}{c} top \\ middle \\ bottom \\ \end{tabular} & 89.5 & 11.75 & 11.50 \\
3 & 0.4 & \(\geq\)28 &
\begin{tabular}{c} bottom \\ top \\ \end{tabular} & 77.76 & 10.23 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Unconfined compressive strength test results of core samples after 28 days of pile formation.  
Subsequently, a single-pile composite foundation load test was conducted on a selected bag grouting pile with a pile length of 12 m and a pile diameter of 0.4 m. The test aimed to measure the pile side friction resistance and pile end resistance during graded loading, using vibrating-string-type steel concrete strain gauges and vibrating-string-type earth pressure gauges, as depicted in Figures 7 and 8.