esa-sceva/satcom-chunk-collection huggingface dataset:
# DTI Histogram and Texture Features as Early Predictors of Post-Radiotherapy Cognitive Decline
## 3 Results
### Correlation Between Changes in DTI Features and Cognitive Performance 4 Months Post-Radiation
This model demonstrated the strongest predictive power, explaining approximately 52% of the variance (R\({}^{2}\) = 0.521, adjusted R\({}^{2}\) = 0.425, F(3,15) = 5.428, \(p\) = 0.010). Figure 2B-D illustrate these relationships, showing that greater increases in \(\Delta\)AD\({}_{\text{mean}}\) and \(\Delta\)RD\({}_{\text{kurtosis}}\) were associated with poorer semantic fluency performance. In contrast, changes in \(\Delta\)AD\({}_{\text{skewness}}\) showed a weak positive association. As for MMSE (Table 4), only \(\Delta\)AD\({}_{\text{mean}}\) significantly predicted the performance (\(\beta=-1.536\times 10^{4}\), r = \(-0.402\), \(p\) = 0.040). Figure 2E illustrates this negative association, showing that greater increases in \(\Delta\)AD\({}_{\text{mean}}\) were associated with lower MMSE scores. Neither \(\Delta\)AD\({}_{\text{skewness}}\) (\(p\) = 0.194) nor \(\Delta\)RD\({}_{\text{kurtosis}}\) (\(p\) = 0.438) showed a significant association. The overall model explained approximately 25% of the variance in MMSE scores (R\({}^{2}\) = 0.254, adjusted R\({}^{2}\) = 0.105, F(3,15) = 1.703, \(p\) = 0.209). Regression analysis also revealed a trend toward a negative correlation between \(\Delta\)MD\({}_{\text{kurtosis}}\) and list recognition performance (\(\beta=-0.524\), r = \(-0.451\), \(p\) = 0.053), suggesting that increased heterogeneity in diffusion patterns may be associated with poorer delayed memory. Figure 2: Scatterplots showing associations between change (\(\Delta\)) in diffusion tensor imaging (DTI) features and neurocognitive function test scores. (**A**) Association between \(\Delta\) in the mean of axial diffusivity (\(\Delta\)AD\({}_{\text{mean}}\)) and list learning performance. Association between (**B**) \(\Delta\)AD\({}_{\text{mean}}\), (**C**) \(\Delta\) in skewness of AD (\(\Delta\)AD\({}_{\text{skewness}}\)), and (**D**) \(\Delta\) in kurtosis of radial diffusivity (\(\Delta\)RD\({}_{\text{kurtosis}}\)), and semantic fluency. (**E**) Association between \(\Delta\)AD\({}_{\text{mean}}\) and MMSE score. Solid lines represent linear regression fits, and gray shading indicates 95% confidence intervals. Each dot represents one patient. Statistical parameters, including \(\beta\) coefficient, standardized coefficient (Std. \(\beta\)), correlation coefficient (r), and \(p\)-values, are displayed for each association. *, age-adjusted.
## 2 Literature Review
### Discussion
#### Advantages  
Satellites can survey large expanses of land in a single sweep and provide an expansive perspective of fire behavior. They are also built to last for several decades and serve multiple purposes beyond fire detection. For example, the NASA and NOAA meteorological satellites also track oceanic currents, upper atmospheric climate changes, drought risks and patterns, etc. They are adept at generating a comprehensive view of the biosphere and environmental processes, of which wildfires are only a contributing factor. The extensive data generated are available for public use and have spurred many discoveries and breakthroughs in many diverse disciplines.
## 3 Data and Methods
### Accuracy and Measurement Standards  
In the logging interpretation, the quality can only be determined through the cement core recovery method [20]. This method is not only expensive but will also damage the integrity of proficiency. Therefore, the supervised learning algorithm used in this paper was only through model training, with a high accuracy, reproducing the evaluation results of cementing quality by experts based on CBL-VDL.  
In this paper, the accuracy rate was used as the measurement standard of the model. The accuracy rate is the proportion of the number of correctly classified samples in the total number of samples. For the sample set \(D\), m samples, the calculation formula of the accuracy rate is as follows:  
\[acc(f;D)=\frac{1}{m}{\sum_{i=1}^{m}{\prod(f(x_{i})=y_{i})}} \tag{1}\]  
In this paper, the cross-entropy function was selected as the loss function of the model. Cross entropy can measure the difference between two different probability distributions in the same random variable, which is expressed as the difference between the real probability distribution and the predicted probability distribution. The smaller the value of cross-entropy, the better the prediction effect of the model. It was used with the softmax function in the program to process the output results so that the sum of the predicted values of multiple classifications was 1, and then the loss was calculated through cross-entropy. The calculation formula is as follows:  
\[\text{Loss}=\frac{1}{N}{\sum_{i}{L_{i}}}=\frac{1}{N}{\sum_{i}{-\sum_{c=1}^{M}{ y_{ic}\log(p_{ic})}}} \tag{2}\]  
where \(M\), as a variable, represents the number of classifications; parameter \(y_{ic}\) indicates whether this category belongs to the same category as sample \(i\), and 0 if the same category is 1 but different; parameter \(p_{ic}\) is the prediction probability that sample \(i\) belongs to category \(c\).
# Multi-Granularity Mission Negotiation for a Decentralized Remote Sensing Satellite Cluster
## 3 Experiments and Analyses  
### Experimental Data  
#### 3.1.1 Targets and Mission Status  
We randomly generated a set of targets and missions. The target's initial position was between 2\({}^{\circ}\) N and 2\({}^{\circ}\) S. The target's motion was also random, including course angle \(\psi_{i}\) between \(-180^{\circ}\) and \(180^{\circ}\) and velocity \(v_{i}\) between 0 and 30 knots. The status of the mission defined as the numbers shown in Table 2 was random, between 0 and 5.  
Finally, we got the set of targets and mission information shown in Table 3; the rest of them are listed in Appendix A.
## 2 Related Work and Research Model
### Model Development  
The authors of [15] test the nomological validity of the second-order CFIP construct for the first time by using a model of antecedents-privacy concerns-outcomes (APCO). They use computer anxiety as an antecedent to test the nomological validity of CFIP. Later research proposes a more general APCO model [16]. The authors mention CFIP as one of the possible operationalizations of the privacy concerns concept in the APCO model. In the APCO model, privacy concerns represent only one part in the decision making process known as the privacy calculus [1]. The privacy calculus represents a deliberate trade-off made by individuals weighing up benefits and costs. However, this deliberate process reflected in the APCO model is questioned by prior work on which we built our empirical analysis [2]. This prior work points out the role of biases for privacy-related decision making processes. There is also related research pointing out that individuals follow their \"gut feeling\" when it comes to privacy-related decisions in practice [17]. Following these results, the biases in our model might be always existent for players of Pokemon Go. Figure 1 illustrates a simplified version of the enhanced APCO model proposed by [2]. We simplified and adapted the model to show how the positivity bias influences the respective concepts in the APCO model and to map the high level concepts as risks or benefits to the actually used constructs in our analysis.  
The effect of extraneous factors like the positivity bias depends on the level of cognitive effort that an individual puts into the decision-making process [2]. This process is comparable to the relationship and mechanisms of \"system 1\" and \"system 2\" [18]. The positivity bias induced by childhood brand nostalgia is assumed to have the largest effect on the privacy calculus and the outcome variable (use behavior) in scenarios where Pokemon Go players put in the lowest levels of effort when deciding to play the game. However, the sample consists of active players of the game who regularly use the game, indicating a relatively low cognitive effort that is put in the decision to play the game. Thus, we argue that childhood brand nostalgia can influence the privacy calculus towards using the game more frequently.
# Detection of Volatiles by HS-SPME-GC/MS and Biological Effect Evaluation of Buddha's Hand Fruit
## 2 Materials and Methods
### Determination of Total Polyphenols  
The total polyphenols of EEX and MEX were determined by the Folin-Ciocalteu method [24]. Briefly, a suitable aliquot of each extract was combined with 50 \(\upmu\)L of Folin-Ciocalteau reagent. After 3 min, 100 \(\upmu\)L of a saturated sodium carbonate solution was added and the final volume was made up to 2.5 mL with distilled water. The solutions were incubated in the dark for 1 h at room temperature, then their absorbance was read at 725 nm using a Jenway 6310 spectrophotometer (Keison, Chelmsford, Essex, UK). Tests were performed in triplicate and repeated three times. A calibration curve was prepared with gallic acid as standard at various concentrations (5-100 \(\upmu\)g/mL) and the results were expressed as mg gallic acid equivalents (GAE) per g of fruit part.
## 3 **Emotional and Social Indicator**
### Core Quantifiable Results Analysis  
This study systematically compared the VR-based Co-Relay system with a traditional web platform using [PERSON]'s d effect-size analysis and FDR multiple-test correction. Key findings include: The VR system significantly enhances user engagement, with large effect sizes in single-active duration, cumulative duration, and editing frequency, alongside a marked increase in user-generated content. Social and emotional metrics reveal a substantial expansion in collaborative network nodes and a moderate rise in emotional vocabulary usage, both remaining significant post-FDR correction. Standardized questionnaire results highlight the prominence of presence-related indicators, with environmental presence and narrative immersion achieving extremely large effect sizes. System usability and cognitive load show significant improvements, and team collaboration reaches a large effect level.  
Regarding creativity indicators, while the number of creative solutions exhibits a medium effect size, it does not survive multiple-test correction. Differences in subjective scoring indicators, such as story writing and emotional resonance, are not significant. Although the flow state shows a large effect size, it lacks statistical significance.  
A comprehensive analysis reveals that the VR system significantly enhances user participation depth and sustainability, fosters a sense of presence and immersion, optimizes social collaboration frameworks, and reduces cognitive load. These results offer an empirical foundation for designing VR collaborative narrative systems, particularly in enhancing user engagement, presence, and social interaction. However, in areas like creativity cultivation and emotional resonance, the current system does not yet show a statistically significant advantage over traditional platforms. This issue may require further investigation through in-depth qualitative interviews.
# Cyber4 Drone: A Systematic Review of Cyber Security and Forensics in Next-Generation Drones
## 5 Security and Privacy of UAVs
### Communication Security  
There are two forms of ADS-B: ADS-B in and out. Planes and helicopters are equipped with both types; however, limited UAVs are only equipped with ADS-B. The data transmitted by ADS-B is not secured because it broadcasts information to all adjacent planes. As a result, anyone can listen to the broadcast and even broadcast the data using low-cost technology [132]. Whenever one or more UAVs inside a region receive this broadcast, the initial flight will be disrupted, similar to GPS spoofing. As a result, there's a chance of a crash. Encryption and user identification have been offered as solutions to implement ADS-B security [133; 134; 135].
# Cooperation-Assisted Spectrum Handover Mechanism in Vehicular Ad Hoc Networks
## 4 Performance Evaluation
### Simulation Parameter Settings  
The simulation tool is the NS-2 [30]. Table 1 shows the setting of the simulation parameters. The simulation area is 2000 \(\times\) 2000 m, the signal range of each car is 100 m, and the signal range of the BS is 1000 m, PU and the number of SU cars is 2 to 10, respectively, and the speed of the cars is 5 to 20 m/s. The simulation results are the average value of 30 simulations. The wireless network communication protocol used is IEEE 802.11, and each car is equipped with 2 antennas. The network bandwidth of the BS is 100 Mbps. The movement mode of each vehicle is the random way point. The data packet size is 1500 bytes. The probability of PU cars accessing the network is 0.3 to 0.8. The higher the probability, the lower the time and chance that SU can use. The number of spectrums for partial scanning is \(\alpha\) = 3, and the number of rounds for scanning is \(\beta\) = 2. The scanning spectrum adopts the passive handover mode.
## 6 Conclusions  
In this paper, the finite-element software ABAQUS was used to establish a numerical model of the new SSCP reinforcement for the segmental joints of a shield tunnel, and the virtual tracking element was used to conduct a secondary-stress simulation of the segmental joint. The validity of the numerical model was confirmed by the full-scale test. The main conclusions are as follows:  
1. The use of the virtual-tracking-element technology is feasible for simulating the secondary-stress state of segmental joint reinforcement. The virtual tracking element is similar to a backup of the original element, and it has no influence on the stress or the strain as the structure deforms. By removing and activating the corresponding elements in different steps, the current deformation and secondary-stress characteristics of the segmental joint can be taken into account.
2. The reinforcement capacity of SSCP is not fully utilized when segmental concrete is of the C50 grade. Hence, the utilization of SSCP is greater when reinforcing segmental joint of high-grade concrete. On the other hand, the relatively low level of utilization indicates that SSCP reinforcement has a sufficient safety margin for potential excessive loads.
3. The SSCP can sustain its reinforcement capability under different axial forces. Thus, SSCP is applicable to tunnel reinforcement regardless of the burial depth. In addition, reinforcement in advance is recommended, since the increase in stiffness is more obvious when the tunnel is in the elastic or elastoplastic stage.  
W.D.: conceptualization, methodology, and funding acquisition. C.M.: writing, data analysis. Y.G.: writing and problem modeling. X.L.: methodology and problem modeling. S.L.: review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Shanghai 2020 \"Science and Technology Innovation Action Plan\" Project (no. 20 dz1202600), Yunnan 2022 \"Science and Technology Innovation and Demonstration\" Project (no. 2022-25), NSFC (no. 52090083), and National Key R&D Program of China (no. 2021 YFC3002004).  
Not applicable.  
The authors thank the managers of the Shanghai Huaxia Corrugated Steel Research Institute and Research Institute of Tsingtuo Group for their help with this study.  
The authors declare no conflict of interest.
## 3 Model Predictive Control-Based Longitudinal and Lateral Tracking Control
### Dimension Reduction-Based Errors Calculation  
As shown in Figure 4, three coordinates are introduced for path tracking of the autonomous vehicle, including the earth coordinate system, the vehicle and road moving coordinate systems. The earth coordinate system denotes the vehicle position, vehicle heading angle, the waypoints' position and the tangential angle of the trajectory. The vehicle coordinate system is usually used to denote the vehicle states, e.g., longitudinal and lateral velocities, side slip angle, yaw rate, etc. The road coordinate system is based on the Frenet coordinate system [20] to calculate the lateral and heading angle errors between the vehicle motion and reference trajectories.  
Figure 3: Path generation based on the comprehensive road potential field.  
Figure 2: Geometric description of the reference road.  
The lateral error can be calculated as:  
\[l=sin\;\theta_{r}(x_{r}-x_{e})+cos\;\theta_{r}(y_{r}-y_{e}) \tag{10}\]  
where \(\overset{\rightarrow}{r}(s,\theta_{r})\) denotes the coordinate of target point; \(\overset{\rightarrow}{x}(s,l)\) represent the current position of the ego vehicle; \((x_{e},y_{e})\) and \((x_{r},y_{r})\) are the coordinates in the global Cartesian coordinate system, respectively; \(\theta_{r}\) is the heading angle of the target point in the reference path.  
According to the theory of coordinate transform based on (10), the multi-state errors, i.e., the deviations of tracking speed and trajectory at time of \(t\), can be calculated and regulated as:  
\[\left\{\begin{array}{c}\left[\begin{array}{c}\chi_{Y}^{t}\\ \chi_{v_{x}}^{t}\\ \chi_{\theta}^{t}\end{array}\right]=\mathbf{M}_{\theta_{\text{ref}}^{\text{ref}}} \left[\begin{array}{c}X_{\text{ref}}^{t}-X_{t}\\ v_{\text{ref}}^{t}-v_{x}^{t}\\ \chi_{\theta}^{t}\end{array}\right]\\ \mathbf{M}_{\theta_{\text{ref}}^{t}}=\left[\begin{array}{cccc}sin\;\theta_{\text{ ref}}^{t}&0&cos\;\theta_{\text{ref}}^{t}&0\\ 0&1&0&0\\ 0&0&0&1\end{array}\right]\end{array}\right. \tag{11}\]  
where \(\left(X_{\text{ref}}^{t},v_{\text{ref}}^{t},\gamma_{\text{ref}}^{t},\theta_{ \text{ref}}^{t}\right)^{T}\) are the reference position, target speed and heading angle at time of \(t\); \(\chi_{Y}^{t}\), \(\chi_{v_{x}}\) and \(\chi_{\theta}^{t}\) represent the lateral position, speed and heading angle errors, respectively.
## 2 Materials and Methods
### Analytical Measurements  
Organic carbon (OC) and elemental carbon (EC) were determined from aliquots of the quartz fiber filters (PM\({}_{2.5}\), DB) by thermal-optical analysis, using an OCEC Analyzer (Sunset Laboratory Inc.) and the EUSAAR 2 protocol (EN 16909:2017-06). All other analyses were performed for pooled filter aliquots. The inorganic anions (chloride, nitrate, and sulfate) were extracted using ultra-pure water (Milli-QPlus, Millipore). After sonication and centrifugation, the extracts were analyzed by ion chromatography (Thermo Scientific; AG22A and AS22A, ASRS 300-4 mm, eluent: 4.5 mm Na\({}_{2}\)CO\({}_{3}\)/1.4 mm NaHCO\({}_{3}\)). Cations (sodium, ammonium, calcium, magnesium, and potassium) were extracted with 38 mm methanesulfonic acid, which was also used as eluent. After sonication and centrifugation, the extracts were analyzed by ion chromatography (Thermo Scientific; CG16A and CS16A, CRSR 500-4 mm). Anhydro-sugars (levoglucosan, mannosan, and galactosan) were determined using high-performance anion-exchange chromatography with pulsed amperometric detection (HPAE-PAD) (Dionex ICS 3000; CarboPac MA1, eluent: NaOH gradient (480-650 mm)), following [PERSON] et al. [8]. PAH quantification was performed according to DIN EN 15549:2008. Ultrasonic extraction was performed using dichloromethane and cyclohexane, and samples were analyzed using gas chromatography coupled to a mass spectrometer (GC-MS) (Agilent Technologies; 6890 gas chromatograph, DB-5 MS (0.25 mm \(\times\) 0.25 \(\upmu\)m \(\times\) 30 m), 5973 mass selective detector). Humic-like substances (HULIS) were extracted with ultra-pure water (Milli-QPlus, Millipore) and 0.06 M NaOH. Subsequent to a clean-up step, using an anion-exchange column (ISOLUTE SAX, Biotage), extracts were analyzed using thermal total organic carbon determination as described in [PERSON] et al. [9]. For the quantification of aluminum, filter aliquots were chemically digested using a microwave procedure ([PERSON]; Multiwave 3000) with aqua regia and analyzed using inductively coupled plasma-optical emission spectrometry (ICP-OES) (Thermo Scientific; iCap 6000 Series, radial measure mode) based on [PERSON] et al. [10].
## 1 Introduction
[30] presented a custom-designed wireless gait analysis sensor system for real-time fall detection using an SVM classifier, and six features were extracted for fall classification. Finally, the system achieved an overall specificity of 99.5% and overall sensitivity of 97.0%. This paper proposes a novel method for fall falling posture classification and recognition based on WPT and SVM and has the following contributions:  
1. This paper proposes a fall posture classification and recognition method based on time series analysis and uses SVM to distinguish between various fall postures and daily behaviors, achieving 99% classification accuracy, and good real-time performance;
2. WPT was used to extract multiple features from sample data, and random forest was used to evaluate the importance of the extracted features and obtain effective features through screening. Combining the two can ensure a high accuracy rate of fall gesture recognition, high computational efficiency, and a good application value;
3. The \"Simulated Falls and Daily Living Activities Data Set\" was chosen to verify the effectiveness of the proposed algorithm, which comes from the UCI database, and is a commonly used standard test dataset for machine learning proposed by the University of California Irvine. The experiment and simulation results show that the proposed method has good real-time performance and high accuracy. The remainder of this paper is organized as follows. In Section 2, we describe the methods of preprocessing, feature extraction, and classification. In Section 3, we introduce the raw data and the hardware platform, and two sets of experiments are presented to verify the effectiveness of the proposed method. Then, we discuss the limitation of our method and future work. We conclude this paper in Section 4.
# A Nonlinear Optimization Design Algorithm for Nearly Linear-Phase 2D IIR Digital Filters
## 3 Filter Stability  
One of the major problems underlying the design task is satisfying the stability criterion for the 2D IIR filter transfer function. For the stability constraints of the 2D IIR filter, since the filter transfer function is separable in the denominator, the stability analysis is similar to the 1D case used in [11; 44; 49], where all the poles of \(D_{1}(z_{1},a_{1})\) and \(D_{2}(z_{2},a_{2})\) have to be located inside the unit circle \(\mathbf{U}\) of the z-plane for both \(z_{1}\) and \(z_{2}\).
## 2 A Review of the Steps Necessary for Collecting Science Quality Spectral Data in the Field
Keep the calibration surface clean, but be sure that the procedures for cleaning are well understood and executed properly. 7. As noted above, the sensor optic should generally be positioned at nadir above the target of interest. Use a level to verify the orientation of the instrument. Sometimes, circumstances may warrant off-nadir scanning, but if done in this way, be sure to measure the angle of view and record it. To be absolutely correct, the sensor, when attached to a tripod or any type of vehicle-mounted boom, should be pointed in the principal plane of the sun. To accomplish this,however, the operator will need solar-ephemeris data for the study location, and the orientation of the boom must be changed frequently during the field campaign. A simple, and usually acceptable, approach is to point the boom due-south (appropriate for the Northern hemisphere) at all times during scanning. 8. Other recommendations include replicating each spectral scan numerous times and taking the average. Note that the sensor should be at least 5 m away from nearby objects (e.g., people, trucks, _etc._) during scanning to avoid extraneous reflections emanating from those objects. Check the quality of your data from time to time during the period of data collection. Whenever possible, keep expensive equipment (e.g., controllers, computers, _etc._) out of direct sunlight. Handle all equipment with care. With the above considerations in mind, then, what hardware and software would need to be assembled in an effort to develop a first rate system for the field collection and subsequent analysis of spectral data?