esa-sceva/satcom-chunk-collection huggingface dataset:
## Transferring Data from Smartwatch to Smartphone through Mechanical Wave Propagation  
**[PERSON] and [PERSON] *  
Device & System Research Center, Samsung Advanced Institute of Technology, 130 Samsung-ro, Yeongtong, Suwon, Gyeonggi-do 443-803, Korea; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +82-31-8061-3980; Fax: +82-31-8061-4556.  
Academic Editor: [PERSON]  
_Received: 17 July 2015 / Accepted: 26 August 2015 / Published: 28 August 2015_  
**Abstract:** Inspired by the mechanisms of bone conduction transmission, we present a novel sensor and actuation system that enables a smartwatch to securely communicate with a peripheral touch device, such as a smartphone. Our system regards hand structures as a mechanical waveguide that transmits particular signals through mechanical waves. As a signal, we used high-frequency vibrations (18.0-20.0 kHz) so that users cannot sense the signals either actually or audibly. To this end, we adopted a commercial surface transducer, which is originally developed as a bone-conduction actuator, for mechanical signal generation. At the receiver side, a piezoelectric was adopted for picking up the transferred mechanical signals. Experimental results have shown that the proposed system can successfully transfer data using mechanical waves. We also validate dual-frequency actuations under which high-frequency signals (18.0-20.0 kHz) are generated along with low-frequency (up to 250 Hz) haptic vibrations. The proposed method has advantages in terms of security in that it does not reveal the signals outside the body, meaning that it is not possible for attackers to eavesdrop on the signals. To further illustrate the possible application spaces, we conclude with explorations of the proposed approach.  
**Keywords:** ultrasonic; acoustic transmission; non-radio communication; intra-body propagation; inter-device communication
## 2 Materials and Methods
### Model Description for GPP Estimation
#### 2.1.1 LUE Models  
The GPP estimates from MOD17 (\(GPP_{MOD17}\)), TL-LUE (\(GPP_{TL-LUE}\)), and MTL-LUE (\(GPP_{MTL-LUE}\)) can be described as:  
\[GPP_{MOD17}=\varepsilon_{ntotal}\times APAR_{total}\times f(VPD_{ daytime})\times g(T_{\min}) \tag{1}\]  
\[GPP_{TL-LUE}=\left(\varepsilon_{msun}\times APAR_{sun-ft}+\varepsilon_{mshd} \times APAR_{shd-ft}\right)\times f(VPD_{ daytime})\times g(T_{\min}) \tag{2}\]  
\[GPP_{MTL-LUE}=\left(\varepsilon_{msun}\times APAR_{sun-mt}+\varepsilon_{mshd} \times APAR_{shd-nt}\right)\times f(VPD_{ daytime})\times g(T_{\min}) \tag{3}\]  
\[g(T_{\min})=\left\{ \begin{array}{cc}0&T_{\min}\leq T_{\min-\min}\\ \frac{T_{\min-T_{\min-\min}}}{T_{\min-\max}-T_{\min-\min}}&T_{\min-\min}<T_{ \min-\max}\\ 1&T_{\min}\geq T_{\min-\max}\end{array} \right. \tag{4}\]  
\[f(VPD_{ daytime})=\left\{ \begin{array}{cc}0&VPD_{ daytime}<VPD_{\max}\\ \frac{VPD_{\max-VPD_{\min}}}{VPD_{\max-VPD_{\min}}}&VPD_{\min}<VPD<VPD_{ \max}\\ 1&VPD_{ daytime}>VPD_{\min}\end{array} \right. \tag{5}\]  
where \(\varepsilon_{ntotal}\), \(\varepsilon_{msun}\), and \(\varepsilon_{mshd}\) are the maximum LUE of the total canopy, sunlit leaves, and shaded leaves, respectively; _f(VPD\({}_{ daytime}\))_ and g(\(T_{min}\)) are the scalars of daytime VPD and the minimum air temperature (\(T_{min}\)); \(T_{min-min}\), \(T_{min-max}\), \(VPD_{max}\), and \(VPD_{min}\) are the model parameters that are specific to vegetation types; \(APAR_{total}\), \(APAR_{sun}\) (or \(APAR_{sun-ft}\), \(APAR_{sun-mt}\)), and \(APAR_{shd}\) (or \(APAR_{shd-ft}\), \(APAR_{shd-mt}\)) are the APAR of the total canopy, sunlit leaves, and shaded leaves, which can be calculated as:  
\[APAR_{total}=PAR_{total}\times(1-e^{-k\times LAI_{total}}) \tag{6}\]  
\[APAR_{sun}=(1-\alpha)\times(PAR_{dir}\times\frac{\cos(\beta)}{\cos(\theta_{ local})}+\frac{PAR_{dif}-PAR_{difu}}{LAI_{total}}+C)\times LAI_{sun} \tag{7}\]  
\[APAR_{shd}=(1-\alpha)\times(\frac{PAR_{dif}-PAR_{difu}}{LAI_{total}}+C)\times LAI _{shd} \tag{8}\]  
\[PAR_{difu}=PAR_{diff}\times\exp\biggl{(}\frac{-0.5\times\Omega\times LAI_{ total}}{0.537+0.025\times LAI_{total}}\biggr{)} \tag{9}\]  
\[C=0.07\times\Omega\times PAR_{dir}\times(1.1-0.1\times LAI_{total})\times\exp (-\cos(\theta_{local})) \tag{10}\]  
where \(\alpha\) is the canopy albedo, \(\beta\) is the mean leaf-sun angle (set as 60\({}^{\circ}\)), \(k\) is the light extinction coefficient, and \(\Omega\) is the clumping index; \(PAR_{total}\), \(PAR_{dir}\), and \(PAR_{dif}\) are a fraction (0.45) of incoming total (\(R_{total}\)), direct (\(R_{difu}\)), and diffuse (\(R_{dif}\)) radiation, respectively; \(PAR_{difu}\) and \(C\) characterize the diffuse photosthetically active radiation (PAR) under the canopy and the contribution of multiple scattering within the canopy, respectively; \(LAI_{total}\), \(LAI_{sun}\), and \(LAI_{shd}\) are the LAI of the total canopy, sunlit leaves, and shaded leaves, respectively.
## 2 Materials and Methods
### Areas of Drought versus Yield Reduction  
The relationship between the extent of drought area and the reduction in crop yield was investigated using the analysis of variance statistical method (ANOVA). In the development period of cereals (DOY 121-201), a comparison of the extent of drought area was made in groups of observations that varied in the level of yield reduction. The observations were divided into four groups of cases (year/voivodeship) where the yields were 5%, 10%, 20%, and 30% lower than the voivodeship multiyear maximum. 'Multi-way ANOVA with repeated measurements' describes the course of moisture conditions represented by the mean percentage of area affected by drought for the four groups of yield reduction level. High yield reductions (i.e., above 30%) are clearly related to a large scale of drought for most of the season. Besides, the differences between the means of the drought area percentage for the group of 30% yield reduction and the other groups were statistically significant in the subsequent periods, starting from day of year (DOY) 145. This was tested by 'contracts analysis', in which the F-test was calculated for the means in the \"30\" group in contrast to the other groups for each DOY time point. The analysis was conducted on a sample of 288 cases and revealed that the percentage of drought area in the voivodeships detected by the DISS index corresponds to the level of yield reduction,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**DISS Range**} & \multicolumn{3}{c}{**Water Deficit Probability**} \\ \cline{2-4}  & **3 Time Steps** & **4 Time Steps** & **5 Time Steps** \\ \hline Below 0.5 & 81\% & 83\% & 88\% \\
0.5–0.8 & 58\% & 61\% & 65\% \\
0.8–1.5 & 36\% & 34\% & 32\% \\
1.5–3.0 & 15\% & 10\% & 8\% \\ Above 3.0 & 3\% & 1\% & 1\% \\ No. obs. & 57,638 & 50,267 & 47,especially for high yield reduction. It concerns the most important parts of the season of cereal growth (June-July).  
The same analysis was made for maize. The period with the greatest impact on the yield reduction of maize is a DOY from 177 to 241 (end of June to end of August). The results of the ANOVA for cereal and maize are presented in Figures 12 and 13. The bars denote the 0.95 confidence interval for the mean value of the drought area. Statistical analysis was conducted for all 16 NUTS 2 regions within the 2001-2018 timeframe. Yield data covering this period were collected from the Central Statistical Office (CSO). The existing increasing linear trends concerning v  
Figure 12: Multi-way analysis of variance (ANOVA) in groups of yield reduction for cereals.  
Figure 13: Multi-way ANOVA in groups of yield reduction for maize.
## Results
### Estimation of the Method Accuracy  
Spike recoveries of all analytes except methyl ethyl ketone, methylene chloride, 3-picoline and \(n\)-hexadecane were 90-105% (Table 5), which is consistent with the results of previous experiments. Lower recoveries of methyl ethyl ketone, methylene chloride, 3-picoline and \(n\)-hexadecane could be explained by high RSDs in their determined concentrations, which were 7.4%, 17%, 43% and 11%, respectively. RSDs of other analytes were 1.8-6.5%.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multicolumn{1}{c}{**Analyte**} & \(\boldsymbol{C_{air}}\) **(ug m\({}^{-3}\))** & \(\boldsymbol{C_{sp}}\) **(ug m\({}^{-3}\))** & \(\boldsymbol{C_{meas}}\) **(ug m\({}^{-3}\))** & **Recovery (\%)** \\ \hline
2,2,4-Trimethylpentane & 22.3 & 100 & 124 & 102 \\ \(n\)-Heptane & 55.0 & 100 & 158 & 103 \\ Methyl ethyl ketone & 88.4 & 10 & 93.6 & 51 \\ Methylene chloride & 20.7 & 10 & 29.6 & 89 \\ Benzene & 80.2 & 100 & 183 & 103 \\ \(n\)-Decane & 17.7 & 100 & 117 & 100 \\
1,1,2,2-Tetrachloroethylene & 0.58 & 10 & 10.6 & 100 \\ Toluene & 25.7 & 100 & 128 & 102 \\
1,2-Dichloroethane & n/d & 10 & 9.3 & 93 \\ \(n\)-Undecane & 50.2 & 100 & 146 & 96 \\ Ethylbenzene & 1.36 & 10 & 11.4 & 101 \\ \(m\)-Xylene & 1.74 & 10 & 11.9 & 102 \\ \(p\)-Xylene & 1.92 & 10 & 12.0 & 100 \\ Proylbenzene & 1.17 & 10 & 11.2 & 100 \\ \(o\)-Xylene & 1.57 & 10 & 11.7 & 101 \\ Chlorobenzene & 0.60 & 10 & 10.6 & 100 \\
1,3,5-Trimethylbenzene & 1.82 & 10 & 11.6 & 97 \\
1,2,4-Trimethylbenzene & 4.33 & 10 & 14.2 & 98 \\
3-Picoline & 8.63 & 10 & 17.2 & 86 \\ Benzaldehyde & 0.83 & 10 & 10.4 & 96 \\ \(n\)-Hexadecane & 132 & 100 & 213 & 81 \\ Naphthalene & 3.80 & 10 & 13.6 & 98 \\ Phenol & 3.52 & 10 & 13.5 & 100 \\ Acenaphthene & 1.61 & 10 & 12.1 & 105 \\ Fluorene & 4.64 & 10 & 14.7 & 101 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Spike recoveries of analytes.  
Figure 2: Effect of storage time on responses of analytes: (**a**)—2,2,4-trimethylpentane, chlorobenzene, 1,2,4-trimethylbenzene, phenol, fluorene, 1,1,2,2-tetrachloroethylene, 1,3,5-trimethylbenzene, naphthalene, acenaphthene, hexadecane; (**b**)—\(n\)-heptane, methylene chloride, 1,2-dichloroethane, 3-picoline, methyl ethyl ketone, \(n\)-decane, \(n\)-undecane, and (**c**)—benzene, toluuene, ethylbenzene, _m,p,o_-xylenes, propylbenzene, benzaldehyde.
## 3 Image Reconstruction  
The image reconstruction process basically consists of two steps: the generation of backscattered signals from the measured and calibrated S21 coefficients, and DAS beamforming.  
The S21 matrix was fed in input to a MATLAB routine that computed the radiofrequency (RF) signals received by each antenna after each transmission event (multi-static configuration with M antennas). To do so, a Gaussian pulse \(s_{TX}\) with a standard deviation \(\sigma=31\) ps, a central frequency \(f_{0}=29\) GHz and a fractional bandwidth (measured at \(-6\) dB) of \(\sim\)60% was considered:  
\[s_{TX}(t)=\cos(2\pi f_{0}(t-4\sigma))\exp\left(-(t-4\sigma)^{2}/\sigma^{2}\right) \tag{2}\]  
Such a signal was multiplied by each term of the scattering matrix S21 in the frequency (\(f\)) domain, to obtain the corresponding received signal \(s_{RX}\) after the application of the inverse Fourier transform, that is:  
\[S_{RX\;ij}(f)=F\{s_{TX}(t)\}\mbox{S21}_{ij}(f) \tag{3}\]  
\[s_{RX\;ij}(t)=F^{-1}\big{\{}S_{RX\;ij}(f)\big{\}} \tag{4}\]  
where \(i\) and \(j\) represent the transmitting and receiving antenna, respectively, and \(F\) denotes the Fourier transform.  
After that, synthetic focusing and DAS beamforming were implemented. The image was discretized in a set of 500 \(\times\) 500 pixels, and for each pixel with coordinates \((x,z)\) the time delay corresponding to the traveling path (\(\tau\)) of the mm-wave from the transmitting  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(\epsilon^{\prime}\) & \(\epsilon^{\prime\prime}\) & \(\alpha\) (dB/cm) \\ \hline _Phantom1_ & 2.53 & 0 & 0 \\ _Phantom2_ & 3.67 & 0.13 & 1.85 \\ _Phantom3_ & 7.62 & 4.33 & 41.25 \\ _Low density ex vivo_ tissues [8], mean value & 6.27 & 3.2 & 33.83 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dielectric Properties of the Produced Phantoms @ 30 GHz.  
antenna in (\(x_{i}\), \(z_{i}\)) to that image point and back to the receiving antenna in (\(x_{j}\), \(z_{j}\)) was computed:  
\[\tau_{ij}(x,z) =\upsilon^{-1}\big{(}\tau_{i}(x,z)+\tau_{j}(x,z)\big{)} \tag{5}\] \[\tau_{i}(x,z) =\sqrt{\big{(}x-x_{i}\big{)}^{2}+\big{(}z-z_{i}\big{)}^{2}}\] \[\tau_{j}(x,z) =\sqrt{\big{(}x-x_{j}\big{)}^{2}+\big{(}z-z_{j}\big{)}^{2}}\]  
where \(v\) is the wave speed in the considered propagation medium. Subsequently, for each pixel, DAS beamforming was applied:  
\[I(x,z)=\sum_{i=1}^{M}\sum_{j=1}^{M}s_{RX\ i,j}\big{(}\tau_{i,j}(x,z)\big{)} \tag{6}\]  
The final image was obtained by applying envelope detection to \(I\), then squaring and finally normalizing the image, which was shown on a linear scale.
# Large Language Model-Powered Automated Assessment: A Systematic Review
## 3 Results
### Challenges in LLMPAA  
#### 3.3.1 Bias and Fairness  
Research has found that the LLMPAA risks reflecting biases and inconsistencies in human assessments. This can lead to ethical issues and fairness concerns in assessment processes. Assessments, particularly those conducted in different linguistic and cultural contexts, may be influenced by this risk. For example, it was observed in a study [36] that, despite LLMs not being influenced by humans' subjective perceptions, they sometimes have stricter grades in certain cases. It has been noted that prior knowledge of human grading increases the risk of bias in LLMs [23]. It has been suggested that LLMs may show variations in assessing student responses based on demographic factors, such as gender or the language spoken at home [2]. Additionally, [PERSON] and [PERSON] found that AI-driven feedback often imposes rigid frameworks that limit creative thinking and cause student frustration, suggesting negative effects on learner motivation [59]. Similarly, algorithmic bias can exacerbate inequity: [PERSON] et al. found that LLM scoring unfairly penalizes students from minority or non-standard linguistic backgrounds [10]. [PERSON] emphasizes that human oversight and clear governance frameworks are essential when integrating AI into assessment [11]. These issues highlight the importance of inclusive design and strong governance in LLMPAA deployments. For these reasons, identifying and minimizing biases is crucial for achieving fair outcomes [13,20]. Moreover, the risk of cultural and linguistic bias in LLMPAA deserves special attention. Although LLMs are designed to be neutral, several studies have shown that these systems may favor dominant linguistic norms embedded in the training data. [PERSON] reports that automated scoring models demonstrated inconsistencies when evaluating responses from multilingual or non-Western students, particularly when the writing style deviated from standard academic English [23]. Likewise, [PERSON] and [PERSON] found that learners whose home language differed from the language of instruction were more likely to receive lower scores, despite similar content quality [2]. These findings suggest that LLMPAA may unintentionally penalize students who use regionally influenced grammar, vocabulary, or culturally grounded rhetorical styles. This has ethical and pedagogical implications, especially for non-native English speakers and students from underrepresented cultural backgrounds. Given that 67% of LLMPAA studies rely on English-language datasets, the models may be inadvertently trained to assess only a narrow range of linguistic expressions. To address this issue, researchers advocate for the inclusion of multilingual and culturally diverse datasets, bias-aware training practices, and regular fairness evaluations. Human raters with cultural sensitivity can also play a vital role in validating model output. Such practices are essential not only for enhancing model performance but also for ensuring equity and inclusivity in educational assessments.
## 3 Results
### Model Training and Test Results
#### 3.2.2 Compare Training Results under Different Learning Rates  
The following figure (Figure 12) shows the loss curves for the ResNet_Attention model trained with different learning rates after being trained on the same dataset. These experiments were conducted to find the optimal learning rate for the model.  
Based on the experiments, it was found that a learning rate of 0.01 resulted in a faster reduction of loss and quicker convergence of the model.
# Negotiation-Free Encryption for Securing Vehicular Unicasting Communication
## Modeling
### System Model  
From Figure 1, we can see that the model in this paper consists of three entities: Certification Authority (CA), Vehicular Cloud (VC), and On-board Unit (OBU).  
1. CA: The CA provides a certificate for each user who has a public key. It is used to identify the true identity of the electronic certificate holder. Additionally, it will prove that the user has a legitimate identity to use the public key. In the data transmission process, CA ensures the authenticity of the vehicle and the cloud, and it also prevents illegal vehicles changing data in this process.
2. VC: The VC is a vehicle cloud agency. Real-time information of each legal vehicle is stored in the Vehicular Cloud, and it will provide a timely response when vehicles make a request for status information. In addition, the VC must hash status information in order to avoid the loss of privacy when a status inquiry is requested. The obtained hash value represents the state of the vehicle at time t.
3. OBU: Each vehicle has an on-board unit (OBU). The OBU is comprised of a hardware security module that is responsible for encrypting the data, storing the status information, positioning and other operations.
# A Robust Wind Turbine Component Health Status Indicator
## 3 Results
### Results Site 1
**Site 1, Healthy case 2 (blades)** presents the analysis performed in the healthy working period of S1_BLD_H1 (investigation of blades case). Figure 9b shows the \(FI\) trend and the SCADA variables under consideration. The \(FI\) ranges between 32% and 50%. In accordance with the FI, the SCADA signals do not show signs of abnormal working. The SCADA signals do not exhibit significant gaps during the analysis period. There are no significant mean changes in the time series of these signals. Although the results between healthy and faulty cases do not seem conclusive, the method does provide sufficient sensitivity to be considered for this type of application. However, there is room for improvement in terms of its sensitivity. It would likely be interesting to investigate the considered cases further by including other variables in the model and examining their long-term evolution. This long-term analysis has already been attempted in the presented faulty case, where blade degradation could only be captured in the final stage of the available period, as the index increased with a steeper slope. Figure 8: Site 1, Time evolution Failure Index (generator). (**a**) Site 1, Faulty case 1 (generator). (**b**) Site 1, Healthy case 1 (generator). **Site 1, Faulty case 3 (gearbox)** presents the analysis performed in the faulty working period of S1_GBX_F1 (investigation of gearbox case). Figure 10a shows the _FI_ trend and the SCADA variables under consideration. The _FI_ ranges between 30 and 90. In this case, a major gearbox failure was reported on 25 May 2012. A notable increase in the _FI_ value is detected between April 1 and April 20, where a value greater than 90 is reached. After that, a slight decrease in the _FI_ value is registered until reaching a value around 60. These values can indicate an early failure detection in the gearbox. **Site 1, Healthy case 3 (gearbox)** presents the analysis performed in the healthy working period of S1_GBX_H1 (investigation of gearbox case). Figure 10b shows the _FI_ trend and the SCADA variables under consideration. The _FI_ ranges between 40% and 50%. In accordance with the FI, the SCADA signals do not show signs of abnormal working. The SCADA signals do not exhibit significant gaps during the analysis period. There are no significant mean changes in the time series of these signals. As a final assessment of the gearbox case at site 1, in comparison with the case of a healthy gearbox, the differences between the analyzed healthy and faulty cases seem to be very sensitive for detecting faults in the gearbox. Therefore, the gearbox is analyzed again at site 2, where more variables are considered to generate the optimal model. Figure 9: Site 1, Time evolution Failure Index (blades). (**a**) Site 1, Faulty case 2 (blades). (**b**) Site 1, Healthy case 2 (blades).
## 4 Conclusions  
In this study, the stress-strain curves and carbonation resistance of recycled aggregate concrete (RAC) after using different recycled coarse aggregate (RCA) treatment techniques were investigated, namely flow-through carbonation, pressurized carbonation, wet carbonation, nano silica (NS) pre-spraying and combined pressurized carbonation with NS pre-spraying. Based on the testing results, the main findings can be summarized below.  
1. The theoretical model for stress-strain curve of natural aggregate concrete was also suitable to RAC after subjecting to the RCA treatment techniques. For all the studied RCA treatment techniques, the peak stress and elastic modulus of RAC were enhanced, but the peak strain did not show significantly changes while the ultimate strain exhibited some reduction.
2. The 7-day carbonation depths of RAC after using flow-through carbonation, pressurized carbonation and combined pressurized carbonation with NS pre-spraying were larger than that of RAC using untreated RCA because of the negative effect the carbonated RCA. However, the 28-day carbonation depth of RAC was reduced after using all the studied RCA treatment techniques. In other words, the carbonation resistance of RAC could be enhanced by using these techniques.
3. Comparing the efficiency of different RCA treatment techniques in enhancement of the peak stress and elastic modulus, the combined pressurized carbonation with NS pre-spraying was the best because both the RA and the new ITZ between RA and the new mortar was enhanced, followed by NS pre-spraying, pressurized carbonation and flow-through carbonation, and the worst was the wet carbonation because only the surface layer of RA was carbonated. The combined pressurized carbonation with NS pre-spraying can significantly improve the performance of RAC, which was better than the other four techniques. Thus, this technique has potential to be used in practical applications.  
Conceptualization, L.L., D.X. and C.S.P.; methodology, L.L., D.X. and C.S.P.; software, L.L.; validation, L.L.; formal analysis, L.L.; investigation, L.L.; resources, L.L. and C.S.P.; data curation, L.L.; writing--original draft preparation, L.L.; writing--review and editing, D.X. and C.S.P.; visualization, L.L.; supervision, D.X. and C.S.P.; project administration, D.X. and C.S.P.; funding acquisition, C.S.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Construction Industry Council (Hong Kong): CICR/03/19.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
Figure 12: Carbonation depth of hardened concrete.  
**Acknowledgments:** The authors wish to thank the Hong Kong Construction Industry Council (CIC) for financial support.  
**Conflicts of Interest:** The authors declare no conflict of interest.