esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions/Practical Application  
Researchers and healthcare professionals continually work towards developing effective diagnostic tools and therapeutic interventions for individuals dealing with tremors. The early detection and accurate classification of tremor types are crucial for implementing appropriate management strategies. Advances in technology, including wearable devices and advanced imaging techniques, such as gaming and/or virtual reality, could contribute to better management of tremors and facilitate personalized treatment plans [56]. In conclusion, while tremors are widespread and varied, the recognition of different tremor types, especially hand tremors, underscores the need for precise diagnosis and tailored interventions to mitigate the impact of these involuntary movements on individuals' daily lives.  
Thanks to the findings of the present study, health professionals could provide individualized training protocols to improve postural control and fine motor skills and perform a regular level of physical activity [57]. Moreover, these findings could be used for future comparison studies investigating a clinical population and the effects of different rehabilitative interventions, such as the practice of e-sports and gaming, aiming to improve fine motor skills performances.  
Conceptualization, C.C. and A.F.; data curation, C.C.; formal analysis, O.P.; funding acquisition, C.C.; investigation, O.P., F.D.R. and E.F.; methodology, O.P.; resources, C.C. and A.F.; software, O.P., F.D.R., E.F. and V.G.; supervision, C.C. and A.F.; validation, V.G.; visualization, A.F.; writing--original draft, O.P., F.D.R., E.F., C.C. and A.F.; writing--review and editing, O.P., F.D.R., E.F., V.G., C.C. and A.F. All authors have read and agreed to the published version of the manuscript.  
Project ECS 0000024 \"Ecosistema dell'innovazione--Rome Technopole\" financed by EU in NextGenerationEU plan through MUR Decree n. 1051 23.06.2022 PNRR Missione 4 Componente 2 Investimento 1.5--CUP: H33C22000420001. O.P. and E.F.'s PhD scholarships are founded by The National reform for Recovery and Resilience (PNRR) and Pegaso University, CUP: H36E22000130001.  
The study was approved on 9 June 2021 from the Institutional Review Board of the Department of Human Sciences, Society, and Health of the University of Cassino and Lazio Meridionale (approval Number 11748).  
**Informed Consent Statement:** Informed consent was obtained from all participants involved in the study.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 5 Limitations of Current AGB Maps and Future Improvements
### Forest Biomass Dynamics  
Although many algorithms have been developed to detect trends or monitor forests, few can be directly used to estimate biomass changes or dynamics [128; 129]. Quantifying forest biomass dynamics remains a challenging task. One reason is that current biomass maps have inherently large uncertainties, and the discrepancies in biomass estimates could be even larger than the actual changes in the forest AGB [110]. A further reason is that forest biomass is changing due to intervening disturbance and growth [23; 130; 131; 132]. Much efforts have been made on the detection of disturbances, but little is known about the biomass losses and gains due to these disturbances and the following recovery from disturbances. From a dataset perspective focused on the study of forest biomass dynamics, long-term permanent plot data cannot be applied to large areas due to adequate sampling constraints, while remotely sensed data can detect forest area changes but remain less capable of detecting forest carbon density, especially where the changes is subtle [118; 133; 134]. To infer forest AGB dynamics accurately in the future, we should on one hand continue to improve accuracy of biomass estimates, and on the other hand, broaden our knowledge of biomass losses and gains due to all kinds of disturbance and recovery.
## 7 Applications of the Typical Medium DCA to Systems with Disorder
### Results for Interacting Models
#### 7.4.2 Results from Stat-DMFT  
The role of strong interactions is also of great interest. Unfortunately, the second order perturbation theory-based cluster solver is, naturally, restricted to the weakly interacting regime. Hence, to investigate the interplay of disorder and interactions in the strong coupling regime, we developed a real-space cluster solver based on statistical DMFT coupled with an impurity solver, namely the LMA, that can capture local Kondo physics in a non-perturbative way.  
Since, within stat-DMFT, the hybridization is different for each site, the Kondo scale, \(T_{K}\), acquires a highly non-trivial and skewed distribution, \(P(T_{K})\), as shown in Figure 42. For a fixed \(U=1.6\), the distribution of Kondo scales as a function of \(T_{K}\)[189] is shown for increasing disorder values and a cluster size, \(N_{c}=38\).  
The figure shows that the distribution of \(T_{K}\)s develops a finite intercept at larger disorder values, indicating the formation of local moments. Many studies have shown that a sufficient condition for non-Fermi liquid behavior is a non-zero value of \(P(T_{K}=0)\)[185; 215]. Indeed, the corresponding self-energy shows a crossover from low-frequency Fermi liquid to high-frequency non-Fermi liquid behavior at a crossover scale \(\omega_{c}\). This is shown in Figure 43, where the negative of the imaginary part of the self-energy, \(-\mathrm{Im}\Sigma(\omega)\) is shown on a linear and log-log scale in the left and right panels, respectively. The right panel shows clearly that the frequency dependence is Fermi liquid such as (\(\omega^{2}\)) at low frequencies, and crosses over to \(|\omega|^{\alpha}\), with a disorder-dependent \(\alpha<2\) at higher frequencies. The crossover scale, \(\omega_{c}(W)\) decreases with increasing \(W\), leading us to speculate the existence of a disorder-driven quantum-critical point where \(\omega_{c}(W)=0\). Our results for the crossover scale along with inferences from previous works may be combined to get a schematic phase diagram (shown in Figure 44) of the quantum-critical region of the Anderson-Hubbard model.  
Figure 42: Distribution of Kondo scales vs. \(T_{K}\) for various disorder values in the 3D Anderson-Hubbard model (units \(4t=1\)). For larger \(W\) values, the distribution develops a finite intercept. The inset shows the same data on a log-linear scale. Reprint from [189].  
As the schematic suggests, a quantum-critical point at \(W_{c}\), identified by the vanishing of the crossover scale, separates a Fermi liquid phase from a second phase which we simply call Phase-2. This second phase could not be identified within the TMDCA calculations but can be speculated to be some kind of quantum spin liquid. It was also argued in the work that the quantum criticality cannot be of a local type or a Hertz-Millis-Moriya type, and hence must be of a new type.
# Imperceptible-Visible Watermarking to Information Security Tasks in Color Imaging
### Opportunities to Improve the IVW algorithms  
In the algorithm [23], the performance evaluation is reported in terms of the PSNR in the range of 49 to 69 dB for watermarks with sizes of 320 \(\times\) 320 and 48 \(\times\) 48, respectively. However, its embedding strategy, which is given by Equation (2) or Equation (3), modifies the histogram of the embedding region in the range of [\(m\) - \(T\), \(m\)] or [\(m\), \(m+T\)], as is shown in Figure 4a,b respectively.  
Figure 3: General diagram of the IIVW.  
In the improved IVW algorithm [24], the performance evaluation is reported in terms of the PSNR in the range of 47.97 to 49.5 dB until three binary watermarks are embedded at the same time into a color image. However, in this algorithm, the embedding strategy given by Equation (5) modifies the histogram of the embedding region, as shown in Figure 5.  
As is described in Section 1, the algorithm in [24] improves the algorithm in [23] mainly in the exhibition stage where a QR code as watermark is exhibited in color, limiting its capability to be read. However, both algorithms have a strong similarity in their embedding strategies, which can be analyzed through the histogram of their corresponding watermarked regions. Comparing Figures 4 and 5, both algorithms accumulate the watermark information in the defined upper and lower boundaries with respect to the mean value; in the case of [23], there are \([m-T,m]\) and \([m,m+T]\) to 4a,b respectively, and \([\mu_{Rn}-\Delta JND(p_{i}),\,\mu_{Rn}+\Delta JND(p_{i})]\) to the improved IVW [24]; however, in [23], the watermark is exhibited in color to the HVS, representing an applicability limitation with respect to [24], where the watermark is exhibited in a black and white color that is a better option for QR codes as a watermark. Considering these similarities in the embedding stage, the histogram study of the IVW algorithms reported in [23,24], and observing Figures 4 and 5, in this paper, a new improved version of the IVW algorithms is proposed, where several weaknesses of the algorithm in [23] are improved by the performance of the algorithm in [24]. The embedding strategy is improved, substituting the empirical embedding strength \(T\geq 2\) by the luminance-based just noticeable difference (JNDL), ensuring a high watermark imperceptibility under normal viewing condition. A new exhibition strategy called histogram binarization function is proposed as well, which exhibits the watermark pattern and binarizes the histogram of the watermarked image with respect to the mean value of the embedding region.
## 3 The Development of Detection Technology
### Advances in Volumetric Method  
The volumetric method is widely used in the detection of chloride in most bodies of water, because of its good repeatability and wide detection range. However, the end point of titration is not sensitive enough, so many scholars have explored and improved it. [PERSON] used the volumetric method to compare the end points of titration of different water qualities (pure water, mineral water, and tap water), and took the values measured by ion chromatography as reference. The study found that among the three end points of titration colors (light orange, orange, and orange-red), orange-red had the lowest absolute and relative errors [47]. In the ammonium thiocyanate volumetric method, silver thiocyanate precipitation easily adsorbs excess silver ions in the solution, resulting in an advance of the end point. The results show that adding nitrobenzene, dibutyl phthalate, and other organic matter before titration can reduce the adsorption of ions by precipitation and make the end point more accurate. [PERSON] et al. [48] improved the method by replacing toxic nitrobenzene with soybean oil, and the recovery rate was equal to or better than that of titration with nitrobenzene, and the Cl\({}^{-}\) content in different samples such as salt and brine was tested with good results. [PERSON] et al. [49] used thermostatic semi-automatic devices to measure Cl\({}^{-}\). The standard solution of silver nitrate was used as the titration agent, titration was carried out in a burette equipped with a controllable electronic valve, and self-made software was used to fit the mathematical model to indicate the end point of titration.
## 1 Introduction  
Ensuring the stability of mine workings located at a great depth is one of the most challenging issues facing the mining industry [1]. High stresses within the rock mass have a negative effect on the productivity and safety of mining operations, and in some cases may jeopardize the entire mining project [2, 3]. One of the most serious hazards associated with mining at great depths is a rockburst phenomenon, which may be described as an uncontrolled violent event associated with a sudden release of strain energy, resulting frequently in deformation or even destruction of mine workings [4, 5, 6, 7]. After such an event, the excavation walls usually take the form of a curvilinear cavity, which substitutes the original flat surfaces [8, 9]. As it was pointed out by [PERSON] et al. [10] as well as [PERSON] and [PERSON] [11], the problem of rockburst occurrence is present on a global scale and has been recently noticed in deep mines over the world. However, the knowledge of a rock mass's potential for dynamic and destructive behavior, such as rock and pillar bursts in underground excavations, still has not yet been sufficiently explained, formulated, or consistently applied in practice [12; 13]. Therefore, rockburst hazard has been increasingly common in the mining sector worldwide, often leading to huge social and economic losses [14; 15]. In order to minimize this risk, it is necessary to develop and implement new methods of monitoring and estimating the rockburst risk, especially at great depths, where the stress level is higher and higher. Locating places with an increased probability of instability may be the basis for taking appropriate preventive measures in advance.  
Within this paper, the complex evaluation of rockburst risk in the conditions of Polish underground copper mines has been proposed. Based on the analytical as well as numerical formulations, the characteristics of rockbursts occurring in underground galleries of a selected copper mine have been described and verified. The developed approach is particularly useful for structurally controlled instability events where the rock mass surrounding the opening fails by sliding on the discontinuities and crushing of rock pieces. For estimation of the rockburst potential, a \"rockburst factor of safety\" has been formulated. Generally, it is a ratio of so-called \"critical depth\" to the depth at which the mining operation is currently carried out. The value of the critical depth depends on geological and mining conditions at which a rockburst may develop if the driving forces are equal to the rock mass resistance. For the purposes of local rockburst characteristic determination, a parameter called \"safety index\" has also been introduced. In general, this parameter describes the ratio of resisting forces to driving forces which affects the movement of the wedge. The results of the analysis have been compared with historical cases of rockbursts observed in one of the Polish underground copper mines.
# Hybrid Deep Learning Approach for Automated Sleep Cycle Analysis
## 3 Methodology
### Performance Evaluation  
Once a classification model is trained, evaluating its effectiveness becomes essential to ensure its reliability and generalization. Various metrics can be used to quantify its predictive capability, offering a comprehensive evaluation [71].  
* Confusion Matrix--A matrix of size \(C\times C\), with \(C\) representing the total classes, and each entry \((i,j)\) represents instances of class \(i\) classified as \(j\). This structure enables the computation of key metrics like accuracy, precision, recall, and the F1-score.
* Overall Accuracy--Represents the fraction of correctly classified samples: \[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN}.\] (32) with \(TP\) indicating true positives, \(TN\) true negatives, \(FP\) false positives, and \(FN\) false negatives.
* Precision--Evaluates how reliably positive cases are identified: \[\text{Precision}=\frac{TP}{TP+FP}.\] (33)
* Recall or Sensitivity--Measures how well the model identifies positive cases: \[\text{Recall}=\frac{TP}{TP+FN}.\] (34)
* Specificity--Evaluates the model's ability to accurately identify negative cases: \[\text{Specificity}=\frac{TN}{TN+FP}.\] (35)
* Negative Predictive Value (NPV)--Indicates the proportion of correctly predicted negative cases among all predicted negatives: \[\text{NPV}=\frac{TN}{TN+FN}.\] (36)
* F1-Score--Integrates precision and recall into one measure, being particularly effective for imbalanced datasets: \[F1=2\times\frac{\text{Precision}\times\text{Recall}}{\text{Precision}+\text{ Recall}}.\] (37)
* Jaccard Index or Intersection over Union--Quantifies the overlap between predicted and true positive instances: \[\text{Jaccard}=\frac{TP}{TP+FP+FN}.\] (38)
* Cohen's Kappa Coefficient--Measures the consistency between predicted and true labels, adjusted for agreement by chance: \[\kappa=\frac{P_{o}-P_{e}}{1-P_{e}},\quad P_{o}=\frac{TP+TN}{N},\quad P_{e}= \sum_{i=1}^{k}\frac{T_{i}P_{i}}{N^{2}}.\] (39) Graphical evaluation also complements numerical metrics [70].  
* Receiver Operating Characteristic (ROC) Curve: Displays the true positive rate against the false positive rate for different classification thresholds. A larger area under the curve (AUC) reflects improved classifier performance.
* Precision-Recall Curve: Especially useful for imbalanced datasets, this curve shows the trade-off between precision and recall.  
Using multiple metrics provides a holistic view of model behavior and guides informed adjustments to the hyperparameters. These evaluations can quantify model performance and, furthermore, provide insights into practical strengths and limitations in real-world contexts.
# Dual PID Adaptive Variable Impedance Constant Force Control for Grinding Robot
## 7 Conclusions  
Force control is an important problem in robot contact operation. In order to achieve high efficiency and high-quality robot grinding, a position-based dual PID adaptive variable impedance control method is proposed, PD is used to compensate for the force error, and PID is used to update the impedance parameters to compensate for the environmental disturbance. An overshoot suppression strategy using a nonlinear tracking differentiator is presented to smooth the desired force and reduce the contact force overshoot. The disturbance term of the end force signal is identified using the least square method, and the effect of gravity compensation is verified to reduce the influence of noise on the force control accuracy. The stability, convergence, and effectiveness of the force control algorithm are verified via theoretical analysis, simulations, and experiments. By comparing the results of CIC, AVIC, and DPAVIC with the robot end force control accuracy, the DPAVIC method can be maintained within \(\pm\)0.2 N, and it has high anti-interference ability. The surface roughness of the thin-walled workpiece can be improved to Ra 0.218 \(\upmu\)m.  
In future work, we will consider how to use force sensors to intelligently sense changes in workpiece surface curvature without geometric models and achieve compliant grinding of the workpiece. Conceptualization, [PERSON] and K.G. ; data curation, [PERSON]; formal analysis, [PERSON] and J.S. ; funding acquisition, [PERSON] and J.S. ; investigation, [PERSON] and K.G. ; methodology, [PERSON]; project administration, [PERSON]; resources, [PERSON]; software, [PERSON] and K.G. ; supervision, [PERSON]; validation, [PERSON]; visualization, [PERSON]; writing--original draft, [PERSON]; writing--review and editing, [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Key Research and Development Program of China, grant number 2022 YFB3206701-2; the National Natural Science Foundation of China, grant number 51975335 and grant number 52175419; the Key R&D Program of Shandong Province, China, grant number 2022 CXGC020202; and the Open Fund of Laboratory of Aerospace Servo Actuation and Transmission, grant number LASAT-2022-A01-03. Not applicable. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} ADRC & Active disturbance rejection control \\ AVIC & Adaptive variable impedance control \\ CIC & Conventional impedance controller \\ CNC & Computer numerical control \\ \end{tabular}   
Figure 19: Workpiece surface waviness. (**a**) Effect of CIC on workpiece surface waviness. (**b**) Effect of AVIC on workpiece surface waviness. (**c**) Effect of DPAVIC on workpiece surface waviness.
# A Machine Learning Framework for Student Retention Policy Development: A Case Study
## 4 Experimental Results and Discussion
### Feature Analysis of the Dataset
#### 4.1.9 Feature Importance Analysis Across All Classifiers  
Using the feature importance analysis across all classifiers, we can answer RQ1 by identifying the most frequently selected features. The five most important features across all machine learning (ML) models include the total number of courses passed by the end of the second semester, the student's department, nationality, the number of courses passed by the end of the first semester, and the number of courses failed by the end of the first semester. Among these, the number of courses passed by the end of the second semester consistently emerges as the most critical feature, being ranked as the top feature in four out of the eight ML models and consistently appearing among the top five features across all models evaluated. It is also shown in Figures 2-9 that two or more features related to academic performance are listed among the top five contributors for all ML models except for LR with only one academic performance feature in the top five. This underlines the existence of a strong correlation between academic performance and dropout risk, which aligns with expectations. The inclusion of multiple features related to academic performance among the top features further emphasizes that academic success or failure is an important predictor for dropout prediction.  
The student's department, identified as one of the most important five features in six of the eight ML models, is ranked as the second most frequently utilized feature. This finding supports the observations regarding the perceived difficulty of certain departments and the correspondingly higher dropout rates among students registered in those departments. Nationality is another important feature, appearing among the top five in five of the eight ML models, making it the third most frequently employed feature. The importance of nationality may be attributed to the economic and psychological challenges associated with  
Figure 9: Feature importance graph for the Classification and Regression Trees classifier.  
studying abroad, which could affect a student's decision to continue or withdraw from their studies.  
In contrast, the inspection of Figures 2-9 reveals that the demographic features of birth year and graduation date (month and year) of high school and the enrolment features of registration semester and registration date (month and year) have no impact on classification in three out of the eight ML models. This suggests that these attributes may not contribute to dropout prediction in the context of this study. It can also be seen that the top five contributing features do not include any enrolment attributes other than the department and faculty.