esa-sceva/satcom-chunk-collection huggingface dataset:
# Controlling Industrial Robotic Arms Using Gyroscopic and Gesture Inputs from a Smartwatch
## 2 The State of the Art
Although not reliant on smartwatches, this exemplifies how forthcoming human-robot interaction systems may incorporate semantic comprehension derived from voice and motion data collected via wearable devices. [PERSON] et al. [19] examined hand and arm gesture modalities in human-robot interaction, encompassing EMG, IMU, and vision-based methodologies. Their findings indicate a trend towards multimodal fusion and context-aware systems that amalgamate wristwatch data with environmental sensing. [PERSON] and [PERSON] [20], from the Scientific Bulletin of UPB, developed IRIS, a wearable device for monitoring motion disorders. While centered on healthcare, their techniques for movement measurement and noise filtration can enhance wristwatch sensor processing. [PERSON] et al. [21] performed an extensive survey of collaborative robots, emphasizing that they require intuitive interfaces and safe interactions in industrial settings. Their analysis highlights the importance of wristwatch systems in future HRC designs. Recent advancements in control theory have led to the widespread adoption of methods such as Active Disturbance Rejection Control (ADRC), filtered disturbance rejection control, and multilayer neurocontrol schemes, particularly in handling uncertain, high-order nonlinear systems. For example, [PERSON] and [PERSON] [22] applied an ADRC-based voltage controller to stabilize DC microgrids using an extended state observer, achieving high robustness in fluctuating conditions. Similarly, [PERSON] [23] provided a frequency-domain analysis and practical guidelines for ADRC implementation in industrial settings. While these methods are highly effective, they often require complex modeling, parameter tuning, and expert intervention. In contrast, the smartwatch-based gesture control system proposed in this study emphasizes accessibility, rapid deployment, and user-friendliness. Rather than competing with advanced control frameworks, this approach complements them by enabling intuitive human-robot interaction, especially in collaborative or assistive scenarios. These articles offer an extensive overview of how wearable human-machine interfaces, particularly smartwatch-based systems, are transforming human-robot interaction into collaborative industrial settings. Each study enhances the detailed understanding of how gesture-based, intuitive, and wearable control methods may improve safety, efficiency, and accessibility in robotic systems.
# GDPR and Large Language Models: Technical and Legal Obstacles
## 2 Detailed Analysis of GDPR Articles Challenging for LLMs
### Article 30--Records of Processing Activities  
Documenting the vast and complex data processing activities of LLMs is another considerable challenge. The scale of these operations, combined with the integration of data from numerous dynamic sources, makes it difficult to accurately track data provenance and all processing steps [22; 25]. Furthermore, the frequent updates and retraining cycles inherent to LLMs necessitate continuous record-keeping, which can be highly resource-intensive [55].  
Potential solutions include leveraging automated documentation tools that generate and update processing records based on data ingestion and training activities. Establishing comprehensive data logging mechanisms is essential to capture detailed information about data sources, processing purposes, and the transformations applied. In addition, conducting regular audits can ensure that records remain accurate, current, and compliant with GDPR requirements [1].  
**Scalability and Feasibility Considerations.** Machine unlearning aims to remove data related to individual users from trained models without full retraining [74]. However, scaling unlearning techniques to large language models (LLMs) with billions of parameters is notoriously challenging. Current unlearning methods typically require retraining or extensive fine-tuning, both computationally expensive and often impractical for models at this scale [49; 74]. Additionally, federated learning, although advantageous for data privacy, introduces significant computational and communication overhead as model sizes increase, exacerbating bandwidth limitations and runtime inefficiencies [75]. Empirical studies demonstrate that the communication demands of federated learning with large-scale models frequently exceed available resources, creating significant barriers to practical deployment [75]. Moreover, maintaining detailed, auditable records for large AI models and datasets poses additional infrastructure and compliance challenges. As data volumes and model complexities grow, organizations struggle to manage and store comprehensive metadata required by regulations, often leading to inconsistent documentation and compliance issues [76; 77]. These scalability, infrastructure, and compliance hurdles collectively represent critical feasibility gaps between theoretical frameworks for responsible AI and their practical implementation at scale.
## 4 Opportunities and Open Research Areas in VANET Multimedia
Given the limited availability of bandwidth, conveying essential information in a compact yet meaningful manner becomes crucial. Instead of transmitting full-motion videos or high-resolution images, audio-visual transmission can effectively communicate important details using concise visual cues and accompanying audio prompts. This approach optimizes bandwidth utilization, reduces network burden, and ensures efficient communication within bandwidth-constrained vehicular environments. Mitigating driver distraction is another significant benefit of audio-visual transmission. While images and videos can provide detailed information, they also have the potential to distract drivers and divert their attention from the road. In contrast, audio-visual transmission delivers pertinent information in a non-intrusive manner. By presenting tangible information through audio prompts and minimalistic visual cues, it minimizes driver distraction while still providing valuable context and awareness. Drivers have varying visual acuity and may differ in their familiarity with complex visual displays. Audiovisual transmission could offer a more inclusive way to communicate with drivers. By using both auditory and visual elements, it ensures that all drivers, regardless of their abilities or language skills, can receive important information. The audio-visual transmission offers significant opportunities in DSRC, as it provides a richer information  
Figure 4: Opportunities and Open Research Areas in VANET Multimedia. Figure 5: Infrastructure to vehicle (V2I). experience, economizes bandwidth, mitigates driver distraction, and enhances accessibility and universality. These advantages make audio-visual transmission a promising research area in vehicular communication systems, aiming to improve communication efficiency, driver safety, and overall driving experience.
## 2 Materials and Methods
### Statistical Analysis
#### 2.5.2 Data Analysis  
All data are presented as means \(\pm\) SD and as percentages (%) for differences observed at baseline and at the end of the study. The normal distribution of continuous variables was tested via Kolmogorov-Smirnov test. Changes in subjects' characteristics at baseline were estimated with one-way ANOVA. In this study, one-way ANOVA was used to compare the baseline characteristics of the four study groups to ensure homogeneity. Repeated measures ANOVA was used to define significant differences in all variables tested for each study group, at baseline and at the end of the study. Repeated measures ANOVA was used to estimate significant differences amongst the four study groups, at baseline and at the end of the study. This statistical approach allows for the comparison of means between groups and within groups over time, taking into account the repeated measurements. This method is particularly useful in longitudinal studies where the same participants are measured repeatedly over time, such as in this study where measurements were taken at baseline and at the end of the study. Repeated measures ANOVA allows for the comparison of means between groups and within groups over time, taking into account the repeated measurements. This approach is appropriate for this study design because it allows for the analysis of changes in variables within each group from baseline to endpoint, while also accounting for the correlation between the repeated measurements. Statistical analysis was performed with the IBM-SPSS Statistics (version 21.0 IBM Corp, Armonk, NY, USA). Correlations were performed by using the x2 test. The level of statistical significance was at \(p\) < 0.05.
## 3 Results and Discussion
### MIMO Performance Analysis
#### 3.4.3 Mean Effective Gain  
For MIMO systems, independent antenna gain cannot be considered as the final performance metric. In addition to the typical standalone antenna gain, the mean effective gain was introduced; it is indicative of the gain performance of the antenna in a MIMO configuration. The typical formulae reported in the literature for the computation or determination of the \(MEG\) is reported below [27].  
\[MEG_{i}=0.5u_{\text{irad}}=0.5(1-\sum_{i=1}^{K}\abs{S_{ij}}) \tag{6}\]  
The typical range for the \(MEG\) is from \(-12\) to \(-3\). The \(MEG\)s based on simulation and hardware measurements are reported in Figures 20 and 21, for mid-band and high-band, respectively. Here, it can be noted the values for the \(MEG\) for all of the antennas in both of the MIMO configurations are within the allowed range. So, the antennas are not only performing well in the standalone, but also in the MIMO environment.
###### Abstract  
To test the nonlinearity and non-stationarity of measured dynamic signals from a bridge structure with high-level noise and dense modal characteristics, a method that combines the adaptive signal decomposition with the recurrence analysis is proposed to solve the difficulty of testing nonlinearity and non-stationarity of bridge structure signals. A novel white noise assistance and cluster analysis are introduced to the ensemble empirical mode decomposition to alleviate mode-mixing issues and generate single-mode intrinsic mode functions. Combining the hypothesis-testing scheme of nonstationary and nonlinear synchronization and surrogate techniques, a data-driven recurrence quantification analysis method is proposed and a novel recurrence quantification measure pairs are set up. To demonstrate the efficacy of the proposed methodology, complex signals, which are collected from a carefully instrumented model of a cable-stayed bridge, are utilized as the basis for comparing with traditional nonlinear and non-stationary test methods. Results show that the proposed multiscale recurrence method is feasible and effective for applications to a nonlinear and non-stationary test for real complex civil structures.  
multiscale recurrence analysis; ensemble empirical mode decomposition; bridges; complex signals +
Footnote †: journal: Applied Sciences
# Honey Therapy in Diabetic Foot Ulcers: A Promising Strategy for Effective Wound Healing
## 4 Honey's Antimicrobial Properties
### Non-Peroxide Antimicrobial Activity
#### 4.2.5 Osmotic Effect  
Honey is mostly composed of sugar and water, with sugars comprising 95-99% of honey's dry matter [107], classifying honey as a super-saturated solution of sugars [131]. In fact, the high sugar concentration found in undiluted honey was able to exert osmotic pressure on bacterial cells, killing them through osmosis [74, 131]. Additionally, the interaction between sugars and water molecules decreases the free available water and impairs microorganism growth [131, 132]. Despite these benefits, the current evidence shows that the osmotic effect contributes to honey's antimicrobial properties but that it is not the major factor. For instance, artificial honey showed effectiveness only against _P. aeruginosa_ but not against _S. aureus_ or _Streptococcus agalaciae_[39]. Honey's high osmolarity also led to an outflow of lymph, providing nutrition for the regeneration of tissue [88].
# Gamma-ray Bursts: 50 Years and Counting!
## 7 The High-Energy (AGILE and Fermi) Era (2007-Ongoing)  
Until the early 2000s, most GRB observations had been carried out exclusively in the hard-X-ray, dim-gamma-ray regimes in order to exploit the technological advances then available to optimize the capabilities (localization, spectral and temporal ) of the detectors. In fact, until that time, only one mission had an instrument on board that was sensitive beyond a few MeV: CGRO. As seen in Section 3, CGRO had four instruments on board that covered an unprecedented energy range of 6 orders of magnitude, from 20 keV to 30 GeV. The Energetic Gamma Ray Experiment Telescope (EGRET) [106] was an instrument covering the higher-energy bands, sensitive between 30 MeV and 30 GeV. To achieve such high-energy detections, EGRET used the pair-conversion technique. The detector consistedof three components: a tracker, a calorimeter, and an anticoincidence detector. The tracker, a multilayer thin-plate spark chamber, was used to convert gamma-ray photons into electron-positron pairs, whose trajectories, reconstructed through the multiple interactions and the crossing of the different layers of the detector, were used to estimate the direction of arrival of the photon, reaching high angular resolutions. The NaI(Tl) calorimeter was used to collect the electron-positron shower and measure its energy. Finally, in order to reject unwanted signals from charged particles (cosmic rays), the telescope was covered by a plastic anticoincidence scintillation dome with a high-enough efficiency to reject charged particles but not to veto gamma rays. During its active life, EGRET allowed the observation of high-energy emission from some GRBs [107; 108; 109], not all of which could be explained by a simple extension of the models of the time [110], thus showing the need for optimal timing and fast broadband detectors to extend the studies of such phenomena to higher energies. In 2007 and 2008, two new missions for the observation of the gamma-ray sky were launched that substantially changed our way of understanding GRBs, starting a new high-energy era of observation characterized by the possibility of observing energies that, up to that moment, had been inaccessible (\(>\)_GeV_).
## 3 Results and Discussion
### Model Prediction  
Eight groups of test data in Table 3 were used for prediction. The comparison of the true value, the unimproved BP neural network predicted value and the improved BP neural network predicted value is shown in Figure 16.  
Figure 15: Training results: (**a**) VB; (**b**) \(F_{d}R\); (**c**) \(F_{d}C\).  
The error between the predicted value and the true value of the neural network model optimized by the improved Grey Wolf algorithm was much smaller than that between the predicted value and the true value of the ordinary BP neural network. The maximum prediction error of VB optimized by the improved Grey Wolf algorithm was 22.54 \(\upmu\)m, which was 71.2% smaller than that of the BP neural network at 78.33 \(\upmu\)m. The root mean square (RMS) error of VB optimized by the improved Grey Wolf algorithm was 13.21 \(\upmu\)m, which was 63.82% less than the average value of the BP neural network, and was less than 10% of the average true value. The prediction error of \(F_{d}R\) was not more than 3%, and the prediction error of \(F_{d}C\) was not more than 1%. Therefore, the neural network model optimized by the improved Grey Wolf algorithm can better predict the drilling quality of GFRP, and had higher accuracy and optimization efficiency than the ordinary BP neural network.  
The scatter plot shown in Figure 17 shows the linear correlation between the predicted values of the neural network and the experimental results. The coefficient of determination (R\({}^{2}\)) indicated the degree of fit between the experimental results and the predicted values. the R\({}^{2}\) of the training set for VB, \(F_{d}R\) and \(F_{d}C\) were 0.9651, 0.9793 and 0.9228, respectively, and that of the test set were 0.9898, 0.9172 and 0.9911. It indicated that the predicted values of the neural network in the training and test sets fitted well with the experimental values.  
Figure 16: Prediction results: (a)VB; (b) \(F_{d}R\); (c) \(F_{d}C\); (d) error of VB; (e) error of \(F_{d}R\); (f) error of \(F_{d}C\).
# Non-Destructive Testing Applications for Steel Bridges
## 3 NDT Methods with Potential Application to Steel Bridges
### Test Methods Applicable to Structural Steel Cables, Tendons, and Ropes
#### 3.3.1 Magnetic Flux Leakage Testing (MFL)  
In this method, to detect defects including corrosion, breaks, loss of cross-section, and pitting of steel components, the element is magnetized through the use of a strong external magnet [60]. The magnet source can be either a permanent or electrically activated magnet. In this method, the magnetic field is present between the probes of the magnet, so that a defect in the steel element leads to the leakage of the magnetic field in the material from its flux path [61]. The magnetic leakage is detected by magnetic sensors (e.g., a Hall sensor) placed between the magnet poles (Figure 23) [28]. The first reported use of this method for stay cables was the work by [PERSON] in 2001 [62]. Figure 24 shows its application for I-310 Hale Boggs Bridge in Louisiana [10]. This technique has also been modified and implemented to inspect the stay cables of a bridge in Thailand (RAMA IX Bridge) [62].  
Figure 23: The methodology of MFL non-destructive testing method [28].  
Figure 24: MFL application for a cable steel bridge [10].  
MFL can also be implemented to detect flaws in bridge hanger cables and cables of cable cars with smaller diameters than stay cables. The equipment implemented for the MFL test for the stay cables includes a modular magnetizing and sensing unit mounted on the cable, and travels along the cable to detect flaws. The time for application of this testing method is 10-20 min per cable. However, the time typically is extended to 1 day per cable due to cable surface preparation and logistical matters. There are some limitations to this method, including safety considerations, because of the intense electromagnetic field, the need for heavy preparation of surface cable, and the heavy and expensive equipment [44].
###### Abstract  
This study aims to evaluate and classify the ripening stages of yellow-fleshed kiwifruit by integrating spectral and physicochemical data collected from the pre-harvest phase through 60 days of storage. A portable near-infrared (NIR) spectrometer (900-1700 nm) was used to develop predictive models for soluble solids content (SSC) and firmness (FF), testing multiple preprocessing methods within a Partial Least Squares Regression (PLSR) framework. SNV preprocessing achieved the best predictions for FF (R\({}^{2}\)P = 0.74, RMSEP = 12.342 \(\pm\) 0.274 N), while the Raw-PLS model showed optimal performance for SSC (R\({}^{2}\)P = 0.93, RMSEP = 1.142 \(\pm\) 0.022\({}^{\circ}\)Brix). SSC was more robustly predicted than FF, as reflected by RPD values of 2.6 and 1.7, respectively. For ripening stage classification, an Artificial Neural Network (ANN) outperformed other models, correctly classifying 97.8% of samples (R\({}^{2}\) = 0.95, RMSE = 0.08, MAE = 0.03). These results demonstrate the potential of combining NIR spectroscopy with AI techniques for non-destructive quality assessment and accurate ripeness discrimination. The integration of regression and classification models further supports the development of intelligent decision-support systems to optimize harvest timing and postharvest handling.  
portable near-infrared spectroscopy; harvest quality prediction; fruit ripening classification; machine learning model +
Footnote †: journal: Applied sciences  
Article
# Verification of Control System Runtime Using an Executable Semantic Model +
###### Abstract  
The paper outlines a methodology for validating the accuracy of a control system's runtime implementation. The runtime takes the form of a virtual machine executing portable code compliant with IEC 61131-3 standards. A formal model, comprising denotational semantics equations, has been devised to specify machine instruction decoding and operations, including arithmetic functions across various data types, arrays, and subprogram calls. The model also encompasses exception-handling mechanisms for runtime errors, such as division by zero and invalid array index access. This denotational model is translated into executable form using the functional \(\mathbb{F}^{4}\) language. Verification involves comparing the actual implementation of the virtual machine against this executable model. Any disparities between the model and implementation indicate deviations from the specification. Implemented within the CPDev engineering environment, this approach ensures consistent and predictable control program execution across different target platforms.  
denotational semantics; model checking; IEC 61131-3; control software; \(\mathbb{F}^{4}\); runtime
# An Unsupervised Deep Learning System for Acoustic Scene Analysis
## 2 The Proposed Method
### Binaural Representation  
A stereo audio recording \(\mathbf{s}\) consists of a left-channel recording \(\mathbf{s}_{l}\) and a right-channel recording \(\mathbf{s}_{r}\). It is believed that the two channels of a stereo audio recording have complementary information.  
Traditional approaches transform the stereo audio into monaural audio by simply averaging the signals of the two channels; i.e., \(\mathbf{s}_{a}=(\mathbf{s}_{l}+\mathbf{s}_{r})/2\). This preprocessing decreases the signal-to-noise ratio, particularly in a situation in which the sound source or the microphone array is moving. To utilize the complementary information of the two channels of stereo audio, a binaural representation has been used in supervised acoustic scene classification [16]. Here, we apply it into unsupervised acoustic scene clustering.  
The binaural representation contains four channels, which are the left channel \(\mathbf{s}_{l}\), right channel \(\mathbf{s}_{r}\), average channel \(\mathbf{s}_{a}\), and side channel \(\mathbf{s}_{s}\) respectively, where \(\mathbf{s}_{s}=\mathbf{s}_{l}-\mathbf{s}_{r}\) records the arrival time difference between the sound waves recorded by the two microphones. We denote the binaural representation as \(\mathbf{s}_{b}=\{\mathbf{s}_{l},\mathbf{s}_{r},\mathbf{s}_{a},\mathbf{s}_{s}\}\). Then, MFCCs are extracted from the four channels respectively, and we denote them as \(\mathbf{\Omega}\).