esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Regular physical activity plays a critical role in health promotion and athletic performance, necessitating personalized exercise and training prescriptions. While traditional methods rely on expert assessments, artificial intelligence (AI), particularly generative AI models such as ChatGPT and Google Gemini, has emerged as a potential tool for enhancing personalization and scalability in training recommendations. However, the applicability, reliability, and adaptability of AI-generated exercise prescriptions remain underexplored. A comprehensive search was performed using the UnoPerTutto meta-database, identifying 2891 records. After duplicate removal (1619 records) and screening, 61 full-text reports were assessed for eligibility, resulting in the inclusion of 10 studies. The studies varied in methodology, including qualitative assessments, mixed-methods approaches, quasi-experimental designs, and a randomized controlled trial (RCT). AI models such as ChatGPT-4, ChatGPT-3.5, and Google Gemini were evaluated across different contexts, including strength training, rehabilitation, cardiovascular exercise, and general fitness programs. Findings indicate that generative AI-generated training programs generally adhere to established exercise guidelines but often lack specificity, progression, and adaptability to real-time physiological feedback. AI-generated recommendations were found to emphasize safety and broad applicability, making them useful for general fitness guidance but less effective for high-performance training. GPT-4 demonstrated superior performance in generating structured resistance training programs compared to older AI models, yet limitations in individualization and contextual adaptation persisted. A critical appraisal using the METRICS checklist revealed inconsistencies in study quality, particularly regarding prompt specificity, model transparency, and evaluation frameworks. While generative AI holds promise for democratizing access to structured exercise prescriptions, its role remains complementary rather than substitutive to expert guidance. Future research should prioritize real-time adaptability, integration with physiological monitoring, and improved AI-human collaboration to enhance the precision and effectiveness of AI-driven exercise recommendations.  
generative artificial intelligence; large language models; exercise prescription; personalized recommendations; adaptive exercise programs
_Appl. Sci._**2015**, \(5\), 1528-1546; doi:10.3390/app5041528  
_applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
A Local Texture-Based Superpixel Feature Coding for Saliency Detection Combined with Global Saliency  
**Bingfei Nan 1,2, Zhichun Mu 1,*, Long Chen 1 and Jian Cheng 1**  
1 School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China; E-Mails: guogenbbf@163.com (B.N.); chenlongustb@163.com (L.C.); sword_cg@hotmail.com (J.C.)  
2 School of Information Engineering, Jiangxi University of Science and Technology,  
Ganzhou 341000, China  
* Author to whom correspondence should be addressed; E-Mail: mu@ies.ustb.edu.cn; Tel.: +86-10-6233-4995.  
Academic Editor: Antonio Fernandez-Caballero  
_Received: 17 September 2015 / Accepted: 8 November 2015 / Published: 2 December 2015_  
**Abstract:** Because saliency can be used as the prior knowledge of image content, saliency detection has been an active research area in image segmentation, object detection, image semantic understanding and other relevant image-based applications. In the case of saliency detection from cluster scenes, the salient object/region detected needs to not only be distinguished clearly from the background, but, preferably, to also be informative in terms of complete contour and local texture details to facilitate the successive processing. In this paper, a Local Texture-based Region Sparse Histogram (LTRSH) model is proposed for saliency detection from cluster scenes. This model uses a combination of local texture patterns and color distribution as well as contour information to encode the superpixels to characterize the local feature of image for region contrast computing. Combining the region contrast as computed with the global saliency probability, a full-resolution salient map, in which the salient object/region detected adheres more closely to its inherent feature, is obtained on the bases of the corresponding high-level saliency spatial distribution as well as on the pixel-level saliency enhancement. Quantitative comparisons with five state-of-the-art saliency detection methods on benchmark datasets are carried out, and the comparative results show that the method we propose improves the detection performance in terms of corresponding measurements.  
**Keywords:** superpixel feature coding; local texture-based region description; region contrast; salient object/region detection
# _Applied sciences_
## 3 Results and Discussion
### Impact of \(\gamma\)-Irradiation on the Nutrients of Almonds

Table 2: The impact of \(\gamma\)-ir0072 adiation on the almond samples’ fatty acid percentage profiles (\(n=9\)). that, as the dosage of \(\gamma\)-irradiation increased, the MUFA:PUFA ratio increased from 4 to 5.2 (significant at \(p<0.05\)). Therefore, based on the two abovementioned ratios, it is difficult to draw a conclusion regarding whether the nutritional value and the associated health benefits increased or decreased upon irradiation. Our findings align with previous studies that have demonstrated the efficacy of \(\gamma\)-irradiation in reducing aflatoxin levels and microbial contamination in food products [16; 17; 18; 19; 20; 21]. For instance, research has shown that \(\gamma\)-irradiation effectively inactivates aflat in various crops, including peanuts and maize, by generating reactive oxygen species (ROS) that degrade the toxins. The trends observed in our study, such as the significant reduction in aflatoxin levels at higher irradiation doses, corroborate these findings. Additionally, the decrease in vitamin E and linoleic acid content at higher doses highlights the potential impact of irradiation on the nutritional quality of almonds. These results underscore the importance of optimizing irradiation doses to balance aflatoxin reduction with the preservation of nutritional value. The implications of our findings suggest that \(\gamma\)-irradiation is a promising method for enhancing food safety and extending shelf life, particularly for products prone to fungal contamination and aflatoxin production. However, further research is needed to refine the irradiation process and address consumer concerns regarding the safety and acceptance of irradiated foods.
# Impact of Partial Replacement of Cement with a Blend of Marble and Granite Waste Powder on Mortar
## 3 Results and Discussion
### Effects of MGWP on Physical and Mechanical Properties of Mortar
#### 3.2.3 Homogeneity  
The homogeneity of the control mortar and mortar containing MGWP was assessed using the ultrasonic pulse velocity (UPV) test, and the results are presented in Figure 9. It can be observed that the UPV values of all MGWP samples increased with the curing age of the mortar. Moreover, an increase in the percent replacement of MGWP up to 15% led to an increase in UPV compared to the control mix, with the highest increment observed for the mortar containing 10% WGWP. This improvement in UPV could be associated with the high content of calcium and silicate minerals, as the quantity of CaO in MWP and SiO\({}_{2}\) in GWP is considerably high. These minerals lead to the formation of a higher degree of CSH gel, which refines the pores of the mortar, eventually improving the UPV. The quality of the mortar in terms of uniformity ranged from medium to good strength according to IS 13311 (Part 1): 1992 [36].  
The investigation revealed a positive trend between compressive strength and UPV, where an increase in UPV corresponded with improved compressive strength. The higher UPV values observed for the mortar containing MGWP, particularly at 10% replacement, aligned with the trend of increased compressive strength for the same mix, supporting the notion that improved microstructure and mechanical properties are achieved by incorporating MGWP in the mortar.  
Figure 8: Compressive strength of mortar samples with different percentages of MGWP at various curing ages.
## 2 Methodology
### Temporarily Coherent Point Co-registration  
Co-registration of SAR images is the first and vital step in the InSAR processing chain. However, the fact that long cross-sea bridges are usually surrounded by sea area poses a challenge to the conventional co-registration method which employs distributed windows over the whole image to estimate the offsets between corresponding pixels [36; 37]. Since the large portion of pixels is located in the sea, the offsets estimated from these pixels are not stable and vary randomly according to the changing window sizes although their signal-to-noise ratios (SNRs) appear to be higher than the threshold. In order to overcome the distortions on the coherent points caused by conventional co-registration methods, we determine the offset polynomial that will be used for resampling slave images only from the offsets at the identified TCPs [38]. A comparison is made between the offsets estimated from the identified TCPs and evenly-distributed windows over the whole image, where the window size is 256 and the oversampling factor is 2. The result indicates that the global co-registration polynomial can introduce positioning bias to the TCPs, with an extreme value of about 3 pixels in the azimuth direction (see Table 2 and Figure 5).
## 3 ML for SatCom RRM: Architecture and Techniques
### Reinforcement Learning for RRM
All agents are in the same environment and have a common goal; thus, they work in cooperation to maximize the reward where \(\overline{S}_{t}=\{s^{1}_{t},s^{2}_{t},\ldots,s^{B}_{t}\}\) represents the current states of the agents, and \(\overline{A}_{t}=\{a^{1}_{t},a^{2}_{t},\ldots,a^{B}_{t}\}\) represents the actions. In MDP, \(C^{b}_{s_{t}}\) is the capacity offered in the \(b\)th beam in the current state, \(D^{b}_{s_{t}}\) is the traffic demand, \(P^{b}_{s_{t}}\) represents the allocated power, \(BW^{b}_{s_{t}}\) means the allocated bandwidth, and \(\theta^{b}_{s_{t}}\) represents the allocated beamwidth in the \(b\)th beam in the current state. In this sense, \(C^{b}_{s_{t}}\in\{C_{1},C_{2},...,C_{max,b}\}\) is calculated assuming that \(P^{b}_{s_{t}}\in\{P_{1},P_{2},...,P_{max,b}\}\), \(BW^{b}_{s_{t}}\in\{BW_{1},BW_{1},BW_{2},...,BW_{max,b}\}\) and \(\theta^{b}_{s_{t}}\in\{\theta_{1},\theta_{2},...,\theta_{max}\}\). The space of all capacity values that can be assigned to the \(b\)th beam depends on the resources assigned to the \(b\)th beam (Figure 7b). In this sense, by maintaining a given beamwidth, the \(b\)th agent can move north, south, east or west as long as it is within the limits of the resource allocation space. The movement of the agent corresponds to a new power or bandwidth allocation. Each surface represents a beamwidth value. The \(b\)th agent can jump from one surface to another (jump up or jump down) as long as it is within the boundaries of the resource allocation space. This jump corresponds to a new beamwidth allocation. Figure 7: Multi-gent approach (B Agents). (**a**) The RRM problem for multibeam systems is defined as a MDP with a multiagent environment that works cooperatively to achieve maximum reward. (**b**) There are B agents sharing the same space of possible resource allocation, each agent manages the power, beamwidth, and bandwidth in each beam. Information about the convergence time of the algorithm is essential for the RRM system since training is online. Based on this, three different RL algorithms were proposed (Figure 8), i.e., Q-Learning (QL), Deep Q-Learning (DQL) and Double Deep Q-Learning (DDQL). The RRM first observes the current environment, represented by the traffic demand and the offered capacity based on the resource allocation. The agents are trained from the acquired observation data using an RL algorithm to obtain the agents' policies and tp update the resources. This is repeated each time the traffic demand changes. The goal of RL is to extract which actions should be chosen in different states to maximize the reward. In a sense, \(B\) agents are sought to learn a policy, which we can formally view as an application that tells each agent what action to take based on its current state. The agents' policy is divided into two components: on the one hand, how each agent believes that an action refers to a given state and, on the other hand, how the agent uses what it knows to choose one of the possible actions.
# Design and Implementation of DSLMS Algorithm Based Photoelectric Detection of Weak Signals
## 5 Experimental Verification  
### Experimental Environment  
This paper uses the MATLAB 2018b software platform to carry out algorithm verification and simulation analysis. The specific conditions are set as follows: (1) the adaptive filter order is 8; (2) the initial weight coefficient of the filter is 0; (3) according to the assumptions, the parameters of the weak signal, \(s(n)\), are set to be \(kA=0.1,f_{0}+f_{d}=50\) Hz; therefore, the weak signal, \(s(n)\), can be expressed as  
\[s(n)=0.1 cos(2\pi\times 50\times n) \tag{13}\]  
In Equation (13), 0.1 is the signal's amplitude, and 50 is the signal's frequency. To generate the noise, we use a built-in function in MATLAB to generate a Gaussian white noise \(v_{0}(n)\) (\(n\)) with zero mean and a signal-to-noise ratio of 5 dB. The relationship between the signal and the noise is additive; i.e., the noise is added to the original signal. Thus, the final signal received by the system's receiving antenna is  
\[d_{j}=s(n)+v_{0}(n) \tag{14}\]  
where \(d_{j}\) is the signal at the original input of the filter, and \(v_{0}(n)\) corresponds to additive Gaussian white noise with an adjustable signal-to-noise ratio. The original signal \(s(n)\) is shown in Figure 12, the noise superimposed signal \(d_{j}\) is shown in Figure 13, and the noise signal \(v_{0}(n)\) is shown in Figure 14.  
Figure 14: Noise signal, \(v_{0}(n)\).  
Figure 12: Original signal, \(s(n)\).  
Figure 13: Noise-superimposed signal, \(d_{j}\).
## 6 Simulation-Based Implementation, Experimental Results and Discussion
### Delivery Ratio Comparison of E-BIOSARP, BIOSARP and SRTLD  
The average simulated delivery ratio results obtained for E-BIOSARP, BIOSARP and SRTLD at various packet rates for the scenario depicted in Figure 5 are summarized in Table 6. E-BIOSARP provides a higher data delivery ratio compared with the state-of-the-art SRTLD routing protocol but a lower delivery ratio than that of BIOSARP, as shown in Figure 11. The results presented in Table 6 and Figure 11 show that in the absence of malicious nodes, the delivery ratio of E-BIOSARP is superior to that of SRTLD because the processing time required by E-BIOSARP to transfer the packets is less.  
Figure 10: A comparison of the processing time for E-BIOSARP with those for other state-of-the-art security mechanisms: TinyHash, TinySec-AE, Tiny-Sec Auth, EBSS, LBRS-Auth, and SRTLD.  
Figure 9: Packet transfer without encryption.  
The advantage gained by reducing the processing time ultimately helps to decrease the delay in the secure delivery of data packets. Moreover, also in the absence of malicious nodes, the delivery ratio of E-BIOSARP is reduced only negligibly compared with that of BIOSARP as a result of the introduction of the encryption process. Furthermore, the results presented in Table 6 and Figure 11 demonstrate that when malicious traffic is present in the network, E-BIOSARP successfully handles the attacks and largely maintains its delivery ratio, with only a slight decrease due to the time required to process, classify and drop the malicious packets.  
The proposed mechanism reduces the broadcast overhead and the overall processing time by avoiding repetitive rediscoveries, replies, and recalculations; this, in turn, helps to reduce processing delays and the consumption of power from the batteries of the wireless sensor nodes, thereby increasing the WSN lifetime.
## 5 Case Study and Discussion
### Performance Analysis  
For a target hour, the SolPV\({}_{\text{ELA}}\) (ELA) was verified by comparing it using different approaches: LSTM, CNN, and LSTM with an EMA (EL). We computed the performance indices, RMSE, MAE, and R\({}^{2}\), to compare the performance of our proposed algorithm with that of the other models and present the results in Table 2.  
Table 2 demonstrates that the proposed ELA model achieved much better performance. It had the highest correlation (0.96) compared to the other three options in terms of R\({}^{2}\). Furthermore, the approach ranking for RMSE was ELA (83.26), EL (176.43), LSTM (142.41), and CNN (185.07), whereas the approach ranking for MAE was ELA (45.24), LSTM (85.04), EL (110.04), and CNN (105.97). According to these results, the forecasting results of the ELA are almost twice as accurate as the other methods. In addition, the ELA performance differs from the worst case (CNN) by 102 for RMSE and 60 for MAE. Table 2 indicates that the EMA did not reduce the error rate for one-hour-ahead PV forecasting, as revealed by the comparative performances of EL and LSTM.  
Figure 8 illustrates that the ELA model has high accuracy and high stability under different weather conditions in all four seasons. In Case I, winter, the mid-day shape was broken by a passing cloud. The ELA model still improves the predicted output better than the other options. Cases III and IV have normal condition patterns, while two days in Case II, winter, have a lower PV, and the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **R\({}^{2}\)** & **RMSE** & **MAE** \\ \hline ELA & 0.96 & 83.26 & 45.24 \\ EL & 0.80 & 176.43 & 110.04 \\ LSTM & 0.87 & 142.41 & 85.04 \\ CNN & 0.80 & 185.07 & 105.97 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of one-hour forecasting performance indices.  
Figure 7: Comparison performance of Pearson’s correlation coefficient between solar photovoltaic generated power (kWh) and three different parameters, with respect to R\({}^{2}\).  
shape was distorted by the rainy season. However, the ELA model can still track real data under bad weather conditions.
## 2 Material and Methods
### Essential Oil Extraction and Analysis  
The different plant organs at different phases of _P. rivinoides_ (100 g each) were separately subjected to hydrodistillation using a modified Clevenger-type apparatus for two hours. The resulting EO was separated from the aqueous phase, dried with anhydrous sodium sulfate, and stored in dark amber flasks in a freezer at \(-20\)\({}^{\circ}\)C until analysis [30]. The total EO yield was expressed as a percentage value, calculated as weight of EO (g) divided by the weight (g) of fresh plant \(\times\) 100.  
The obtained EOs were diluted in dichloromethane (1 mg/mL) (Tedia, Brazil) and then subjected to Gas Chromatography-Mass Spectrometry (GC-MS) analysis for identification purposes and Gas Chromatography-Flame Ionization Detector (GC-FID) for quantification analysis [31]. The GC-MS conditions used were as follows: analysis was carried out using an HP Agilent GC 6890 gas chromatograph coupled to an Agilent MS 5973N mass spectrometer (Santa Clara, CA, USA), with an ionization energy of 70 eV (positive mode). The EO solution was injected at 1 \(\mu\)L (splitless) and the injector temperature was set at 270 \({}^{\circ}\)C. The sample was run through an HP-5 MS capillary column (30 m \(\times\) 0.25 mm i.d. \(\times\) 0.25 \(\mu\)m film thickness) (Agilent J&W, Santa Clara, CA, USA), with an oven temperature program ranging from 60 \({}^{\circ}\)C to 240 \({}^{\circ}\)C, with an increase of 3 \({}^{\circ}\)C/min (60 min total run), using helium (>99.99%) as the carrier gas, at a constant flow rate of 1 mL/min. The monitoring mass range was \(m/z\) 40-600 atomic mass unit (\(u\)). The GC-FID analysis was carried out using an HP-Agilent 6890 GC-FID gas chromatograph in the same conditions as for GC-MS, except using hydrogen as the carrier gas at a constant flow rate of 1.0 mL/min. The retention times (tR) were measured in minutes without correction [30; 31]. Retention index (RI) and the peak area quantification were obtained based on the GC-FID results. The relative percentage of individual components was calculated based on GC peak areas without FID response factor correction. Linear retention indices (RIs) were calculated for separate compounds relative to \(n\)-alkanes (C\({}_{8}\)-C\({}_{28}\), Sigma-Aldrich, Sao Paulo, SP, Brazil). Constituents were identified by comparison of their calculated RIs with those in the literature [32], and by comparison of the mass spectrum with those recorded by the NIST library (National Institute of Standards and Technology) \"NIST14\" and Wiley (ChemStation data system) \"WILEY7n\" [33]. Additionally, authentic pattern co-injection was performed whenever possible [33].