esa-sceva/satcom-chunk-collection huggingface dataset:
# GDPR and Large Language Models: Technical and Legal Obstacles
## 1 Introduction
### Data Protection  
GDPR emphasizes data protection through principles such as data minimization, purpose limitation, and ensuring data integrity and confidentiality [5]. These principles require that personal data is processed only for legitimate purposes, kept accurate and up-to-date, and protected against unauthorized access and breaches [1].  
LLMs are typically trained on extensive datasets that often include personal data scraped from diverse sources such as the internet, books, and articles [22; 37]. During training, these models ingest and transform vast amounts of data, which may inadvertently include sensitive information. Moreover, the ability of LLMs to generate text based on learned patterns can lead to the unintentional disclosure of personal information [37].  
Ensuring data protection in LLMs is challenging for several reasons. First, the requirement for large datasets to achieve high performance may conflict with the GDPR principle of processing only the necessary data. Second, the diverse applications of LLMs make it difficult to restrict data usage to specific, predefined purposes. To mitigate these risks, various privacy-preserving techniques have been developed. A recent narrative review provides a comprehensive overview of methods such as differential privacy, federated learning, homomorphic encryption, and secure multi-party computation, specifically in the context of generative AI and LLMs [53]. These techniques not only mitigate risks like model inversion and membership inference attacks but also align technical safeguards with evolving legal requirements. Finally, protecting the integrity and confidentiality of data within the high-dimensional parameter spaces of LLMs is technically complex [39; 54].  
To mitigate these risks, organizations can anonymize training data to prevent the identification of individuals, incorporate differential privacy techniques during training to limit the influence of any single data point, and employ federated learning to train models without centralizing personal data [54; 55].
## 4 Conclusions  
In this study, by installing calibrated thermometers, flow meters, and power spectrometer for a 30 RT-level water-water geothermal heat pump installed at the site and by including two SCW underground heat exchangers, mechanical piping, circulation pumps, and cross-operation controllers, the following results were obtained:  
1. The average COP values of the balanced well cross-heat exchange system were 3.76 and 3.27 during the cooling and heating operations, respectively. This signifies an improvement of the COP by 23% and 12% during the cooling and heating operations, respectively, compared to that of the existing SCW method of the heat exchange system.
2. The initial underground temperature was maintained constant with a small standard deviation of 0.08-0.12 \({}^{\circ}\)C for 3-5 d of continuous operation when using the balancing well cross-mixed heat exchange system, enabling a relatively stable supply of heat source.
3. A change in operational method from the ordinary SCW-type heat exchange system to the balanced well-intersected heat exchange system improved the COP of the cooling and heating system using geothermal heat and ensured a stable supply of geothermal energy by keeping the initial temperature constant. This could also eliminate the wasting of bleed water.  
Conceptualization, D.K.; methodology, B.Y.; investigation, M.L.; data curation, C.L.; writing--original draft preparation, C.L.; writing--review and editing, C.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (NRF-2018R1D1A1B07049390).  
The authors declare no conflict of interest.
## 3 System Overview
### Problem Description
#### 3.1.2 QoE Fairness  
In previous work, [PERSON]'s fairness coefficient was widely used to describe the fairness state of QoE. Therefore, we use [PERSON]'s fairness coefficient and historical discount QoE to calculate the QoE fairness of multiple video streams over a period of time:  
\[J(QoE_{1}^{\gamma,h},QoE_{2}^{\gamma,h},\dots,QoE_{n}^{\gamma,h})=\frac{( \sum\limits_{i=1}^{n}QoE_{i}^{\gamma,h})^{2}}{n\cdot\frac{n}{\sum\limits_{i= 1}^{n}(QoE_{i}^{\gamma,h})^{2}}} \tag{2}\]\[QoE_{i}^{\gamma h}=\sum_{l=k-h}^{k}\gamma^{l}Q_{i} \tag{3}\]  
where \(QoE_{i}^{\gamma h}\) represents the QoE historical discount score of the \(i\)-\(th\) video stream; \(\gamma\) is the discount coefficient of QoE; \(h\) represents the number of historical blocks to be calculated. When \(h=1\), it means that only the short-term QoE fairness of the current video block is calculated. When \(h=\infty\), it means that the overall QoE fairness of all blocks is calculated.
# AI-Based Classification of Pediatric Breath Sounds: Toward a Tool for Early Respiratory Screening
## 4 Feature Extraction
### Dynamic LPC  
While common audio features capture time and frequency characteristics, they are not ideal for breath sounds. Breath sounds exhibit non-stationary time-varying behavior due to changes in airflow rate and volume throughout the respiratory cycle [48], so conventional speech features cannot be directly applied.  
To better model such signals, represented by \(x(n)\), that have time-varying properties like breath sounds, the adaptive autoregressive (AAR) model [48] can be employed. This model allows the filter coefficients, \(a_{i}\)'s, to vary over time, enhancing the accuracy of the representation.  
A discrete-time AAR process is then expressed as  
\[x(n)=-\sum\limits_{i=1}^{p}a_{i}(n)\:x(n-i)+e(n) \tag{26}\]  
where the time-varying AR coefficients are constructed from  
\[a_{i}(n)=\sum\limits_{k=0}^{q}a_{ik}\:u_{k}(n), \tag{27}\]  
and, in the equation above, \(u_{k}(n),(k=0,1,\cdots q)\) are independent basis functions. The basis function for breath signal modeling is given as \(\{u_{k}(n)=n^{k}\}_{k=0}^{q}\).  
Combining (3) and (26):  
\[x(n)=-\sum\limits_{i=1}^{p}\left(\sum\limits_{k=0}^{q}a_{ik}\:u_{k}(n)\right) x(n-i)+e(n). \tag{28}\]  
Minimimal squared error approach is used to obtain the optimal coefficient:  
and  
\[\mathbf{c}_{i}=\left[\begin{array}{cccc}c_{0i}(0,1)&c_{0i}(0,2)&\cdots&c_{0i}(0, p)\end{array}\right]^{T}, \tag{32}\]  
\[\mathbf{C}_{kl}=\left[\begin{array}{cccc}c_{kl}(1,1)&c_{kl}(1,2)&\cdots&c_{kl} (1,p)\\ c_{kl}(2,1)&c_{kl}(2,2)&\cdots&c_{kl}(2,p)\\ \vdots&\vdots&\ddots&\vdots\\ c_{kl}(p,1)&c_{kl}(p,2)&\cdots&c_{kl}(p,p)\end{array}\right] \tag{33}\]  
Equation (29) becomes  
\[\underbrace{\left[\begin{array}{cccc}\mathbf{C}_{00}&\mathbf{C}_{01}& \cdots&\mathbf{C}_{0q}\\ \mathbf{C}_{10}&\mathbf{C}_{11}&\cdots&\mathbf{C}_{1q}\\ \vdots&\vdots&\ddots&\vdots\\ \mathbf{C}_{q0}&\mathbf{C}_{q1}&\cdots&\mathbf{C}_{qq}\end{array}\right]}_{ \mathbf{\Lambda}}\underbrace{\left[\begin{array}{c}\mathbf{a}_{0}\\ \mathbf{a}_{1}\\ \vdots\\ \mathbf{a}_{q}\end{array}\right]}_{\mathbf{\Lambda}}=-\underbrace{\left[ \begin{array}{c}\mathbf{c}_{0}\\ \mathbf{c}_{1}\\ \vdots\\ \mathbf{c}_{q}\end{array}\right]}_{\mathbf{\gamma}} \tag{34}\]  
The solution for \(\mathbf{a}\) is simplified as  
\[\mathbf{\alpha}=-\mathbf{\Gamma}^{-1}\mathbf{\gamma}. \tag{35}\]  
The vector \(\mathbf{\alpha}\) corresponds to the DLPC coefficients derived from a breath sound segment, effectively reflecting its time-varying acoustic patterns. Given that \(x(n)\) is a non-stationary signal, longer segmentation windows can be used. This enables each segment to be represented by fewer feature coefficients, which in turn lowers the input dimensionality for classifiers and decreases computational demands.
# A Credit-Based Congestion-Aware Incentive Scheme for DTNs
## 4 A Credit-Based Congestion-Aware Incentive Scheme  
### Delay-Based Reward Scheme  
In order to stimulate other nodes to forward messages, the message destination node needs to pay some credits to the forwarding nodes when receiving a message. We propose a delay-based credit reward scheme. In the reward scheme, when a message is successfully delivered, the destination node will pay the forwarding nodes according to the delivery delay, as in Equation (3):  
\[R(Time^{delivery})=\left\{\begin{array}{ll}Credit\left(1-Time^{delivery}/ \mathrm{TTL}\right),&0<Time^{delivery}<\mathrm{TTL}\\ 0,&Time^{delivery}\geq\mathrm{TTL}\end{array}\right., \tag{3}\]where \(R\left(Time^{delivery}\right)\) is the total number of credits that the destination node will pay for forwarding nodes according to delivery delay \(Time^{delivery}\); \(Credit\) is the most credits that the destination node can pay for a message, assuming 1 in this scheme; and TTL is the lifetime of a message.  
It can be seen that the smaller the delay, the more credits the forwarding nodes can get. If the delivery delay is larger than TTL, the credits will be 0.  
Assuming the forwarding path is \(N_{s}\), \(N_{1}\)\(\ldots\)\(N_{k}\), \(N_{D}\), and the delivery delays are \(Time^{delivery}_{N_{s}}\), \(Time^{delivery}_{N_{1}},Time^{delivery}_{N_{2}}\)\(\ldots\)\(Time^{delivery}_{N_{k}}\) respectively, \(Time^{delivery}_{N_{s}}\)\(>Time^{delivery}_{N_{1}}>Time^{delivery}_{N_{2}}>\ldots\)\(Time^{delivery}_{N_{k}}\), then the rewards for each forwarding node are \(R(Time^{delivery}_{N_{s}})-0\), \(R(Time^{delivery}_{N_{1}})-R(Time^{delivery}_{N_{s}})\), \(\ldots\)\(R(Time^{delivery}_{N_{k}})-R(Time^{delivery}_{N_{k-1}})\), respectively. We can see that the credits obtained by each node in CBCAIS only relate to the delivery delay enhancement from the previous node, and the sum is determined by the delivery delay of node \(N_{k}\) as Equation (4):  
\[R(Time^{delivery}_{N_{k}})-0+R(Time^{delivery}_{N_{1}})-R(Time^{delivery}_{N_{k}} )...+R(Time^{delivery}_{N_{k}})-R(Time^{delivery}_{N_{k-1}})=R(Time^{delivery}_{N_{k}}). \tag{4}\]  
**Theorem 1**.: _The CBCAIS scheme is incentive-compatible, i.e., truthful participation is adopted by all the forwarding nodes, despite their selfish nature._  
Proof.: In the delay-based reward scheme, the obtained credits of a node are only determined by the delivery delay enhancement from the previous node, and have no relation with the subsequent node, so a node will forward its messages immediately when encountering another node with smaller delivery delay. On the other hand, if the node does not forward the messages immediately, it will miss some chances to buffer other messages to obtain more credits. Therefore, the CBCAIS scheme is incentive compatible. \(\square\)
## 2 Quantum-Field Theory for Photonic Transport  
We develop in this article a self-consistent quantum field theory for random lasing of ensembles of active Mie resonators. The results are spatial characteristics of the phase transition to random lasing, they classify the threshold to stationary state lasing. The theory includes multiple scattering effects of photons in complex random arrangements which may be classified in incoherent contributions that are expressed in quantum field theory, e.g., as ladder diagrams, the diffuson, and coherent contributions, that are expressed in terms of maximally crossed diagrams, the Cooperon. Methodologically the Bethe-Salpeter equation, see Figure 1c, is a most reasonable self-consistent frame, where the so called memory kernel \(M\) as part of the irreducible vertex \(\gamma\) bares the expansion in orders of maximally crossed diagrams, Figure 1d. All light-matter interactions are represented within the irreducible vertex, e.g., the interaction of the electromagnetic field and its time reversal as well as the light intensity with a complex Mie resonator as a scatterer [40]. Samples of a large number of active Mie resonators  
Figure 1: **(a)** Sketch of a disordered semi-conductor random laser slab, that is open in \(z-\)direction. Photonic transport processes (red) and their time reversal (green) interfere while they feature multiple scattering processes with complex active Mie resonators. The Mie resonance [41] is a so called whispering gallery resonance at the inner surface of the independent Mie scatterer. We use for this work monodisperse Mie spheres. **(b)** 4-level laser rate equation scheme. Straight lines represent the electronic processes, the green line represents the excitation due to the pump field, \(\gamma_{32}\) represents the decay from level \(n_{3}\) to the upper laser level \(n_{2}\), \(\gamma_{nr}\) represents nonradiative decay and as its competitor \(\gamma_{21}\) leads to spontaneous and to stimulated emission processes, which eventually yields inversion and coherent laser radiation \(\hbar\omega\). **(c)** Schematic representation of the _Bethe-Salpeter equation_, compare Equation (6). \(\Phi\) is the intensity field in the sample, \(\gamma_{E\!E}^{\omega}\), is the irreducible vertex. **(d)** The irreducible vertex includes all orders of maximally crossed diagrams (Cooperon) which represent all quantum-coherent interference contributions due to multiple scattering in presence of disorder.  
can be considered, which may act as independent scatterers or as _glasses_, i.e., as an accumulation of agglomerates with an intrinsic correlation. In what follows we derive this theory for the case of monodisperse independent active Mie scatterers, where pronounced dips were found theoretically and experimentally in the results for the transport characteristics, e.g., the scattering mean-free path \(l_{s}\)[34].
# The Influence of Wood Moisture Content on the Processes of Freezing and Heating
## 1 Introduction
Some research [2] discovered that a high freezing rate (\(-10\)\({}^{\circ}\)C\(\cdot\)h\({}^{-1}\)) does not affect wood strength, while slow freezing (by \(-1\)\({}^{\circ}\)C\(\cdot\)h\({}^{-1}\)) significantly reduces all mechanical wood properties, for example, compressive strength (by 20-30%) and the Janka hardness (by 18%). The results of other studies [8,9] showed that cyclic freezing and thawing lead to a reduction in all choice mechanical properties by 15-30% (for example, bending strength, tensile strength, etc.). Other authors [10] discovered that continuous exposure of spruce wood to a temperature of \(-250\)\({}^{\circ}\)C for one week significantly affected only the dimensional stability, but mechanical properties were not affected significantly. Similar work [5] recorded that the long-term exposure (three months) to temperature variations negatively affected the structural integrity of spruce wood. As also shown [2], the temperature variation and the repeated changing phase of water inside wood increase dimensional instability and reduce the mechanical strength. The results of research [11] showed that under temperatures below 0 \({}^{\circ}\)C, moisture content is the most important factor explaining all mechanical properties. Another study [12] found that mechanical properties, cleavage, and shear are the most sensitive to changes in temperature. Frozen wood has a remarkable influence on the processing process. Results [13] showed that in the cutting process, the highest cutting power corresponded to sawing frozen wood, while there was no significant difference between sawing dry and green wood. Next, researchers [14,15] discovered that, especially in environments with sub-zero temperatures, the moisture in frozen logs has a crucial influence on the material's properties. According to [14], the hardness of ice increases exponentially along temperature decreases; the efficiency of cutting processes for cutting frozen wood decreases too. This article deals with the influence of pine wood moisture content on the processes of freezing and subsequent heating of frozen wood in a gaseous environment.
# Evaluation on Radiometric Capability of Chinese Optical Satellite Sensors
## 1 Introduction
Therefore, a comprehensive evaluation of the sensors' radiometric capability is essential before quantitative applications can take place. The objective of this study is to propose a comprehensive procedure for evaluating the radiometric capability of Chinese optical satellite sensors and evaluate two major indicators for radiometric capability: the long-term radiometric stability and the radiometric accuracy. Three Chinese sensors including the Charge-Coupled Device (CCD) camera onboard the Huan Jing 1 satellite (HJ-1), Visible and Infrared Radiometer (VIRR) and Medium-Resolution Spectral Imager (MERSI) onboard the Feng Yun 3 satellite (FY-3) are evaluated in visible and infrared bands based on this procedure. The Badain Jaran Desert test site is selected because of its temporally stable surface condition, which minimizes the impacts of the surface and atmosphere variation. Long time series clear data (out of cloud and haze contamination) are selected, which guarantee a continuous and high frequency monitoring. The radiometric temporal stability is analyzed based on the tendency of long-term top-of-atmosphere (TOA) reflectance variation. The radiometric accuracy is determined by comparing with the TOA reflectance from MODIS after spectrally matching.
# State-of-the-Art Review of Structural Vibration Control: Overview and Research Gaps
## 6 Optimized Control System Designed to Tackle Practical Implementation Challenges
### Control Systems to Handle Time-Delay Issues  
The existence of a time lag between sensing structural responses and applying control forces is a critical issue. Research focused on mitigating time delays in control systems has been reviewed, and it is seen that control algorithms such as the time-delay compensation method based on [PERSON]'s method decentralized output feedback polynomial control (DOFPC), Lyapunov control, the Taylor series expansion of the control force, the decentralized H\({}_{1}\) controller, adaptive control, and the Proportional-Integral-Derivative (PID) controller demonstrate robustness to time delay. [PERSON] [40] conducted a study employing decentralized dynamic output feedback controllers to reduce the H\({}_{\infty}\) norm of the closed-loop system. The formulation of the problem considers the impact of feedback time delay, which is consequently addressed in the design of the controllers. The controller's performance is found to be more efficient when compared to a time-delayed LQG controller. [PERSON] and [PERSON] [193] studied the effect of time delay under random excitation. Comparing three approaches, the study suggests that the Taylor expansion method can serve the dual purpose of assessing both the critical time delay at which control effectiveness diminishes and of evaluating the response with variance. [PERSON] et al. [39] studied the effects of time delay on large-scale semi-active control strategies. This study explored the impact of time delay on the performance of a structure equipped with a large-scale MR damper. The study utilized numerical simulations for earthquakes occurring in both near-field and far-field scenarios. The MR damper operates via four unique semi-active control methods: simple passive control (SPC), decentralized output feedback polynomial control (DOFPC), Lyapunov control, and clipped-optimal control (COC). The findings reveal that all controllers, except for COC, exhibit significant robustness in the face of time delay. In contrast, the clipped-optimal controller requires integration with a compensator to enhance performance when dealing with time delays. An experimental verification of the adaptive neuro-fuzzy inference system (ANFIS) and a novel fuzzy sliding mode controller (FSMC) was demonstrated in the study by [PERSON] et al. [189] to mitigate the effects of time delay and uncertainties.
# Oscillating Nonlinear Acoustic Waves in a Mooney-Rivlin Rod
## 2 Principal Equations
### Equation of State  
Following [30], the first order Mooney-Rivlin hyperelastic potential for an incompressible medium can be written in the form:  
\[W=C_{01}(II_{B}-3)+C_{10}(I_{B}-3), \tag{12}\]  
where \(C_{01},\ C_{10}\) are the material constants. Substituting eigenvalues Equation (11) into Equation (5), yields potential Equation (12) written in terms of stretch \(\lambda=\lambda_{1}\).  
\[W(\lambda)=C_{01}\bigg{(}2\lambda+\frac{1}{\lambda^{2}}-3\bigg{)}+C_{10} \bigg{(}\lambda^{2}+\frac{2}{\lambda}-3\bigg{)}. \tag{13}\]  
A necessary remark concerns the condition of positive definiteness for the Mooney-Rivlin hyperelastic potential Equation (12) or represented in terms of the stretch \(\lambda\) Equation (13). It can be shown [30] that a necessary and sufficient condition for the positive definiteness reads as:  
\[C_{01}\geq 0,\ C_{10}\geq 0, \tag{14}\]but neither can vanish simultaneously. Note also that in view of Equation (10), the considered principal stretch \(\lambda\) is strictly positive. It is assumed that the positive definite condition Equation (14) is satisfied.  
Considering the case of simple tension with \(\sigma=\sigma_{11}\) and \(\sigma_{22}=\sigma_{33}=0\), and taking the derivative of the potential Equation (13) with respect to \(\lambda\), yields the Cauchy stress [15]:  
\[\sigma(\lambda)=\lambda\partial_{\lambda}W(\lambda)=2(C_{01}+C_{10}\lambda) \bigg{(}\lambda-\frac{1}{\lambda^{2}}\bigg{)}. \tag{15}\]  
Now, Equation (15) may be rewritten in terms of strain Equation (10):  
\[\sigma(\varepsilon)=2(C_{01}+C_{10}(1+\varepsilon))\frac{\varepsilon\big{(}3+ 3\varepsilon+\varepsilon^{2}\big{)}}{\left(1+\varepsilon\right)^{2}}. \tag{16}\]  
Equation (16) allows us to derive the tangential elastic modulus \(E(\varepsilon)\):  
\[E(\varepsilon)\equiv\partial_{\varepsilon}\sigma=2(C_{01}+C_{10})\frac{3+3 \varepsilon+3\varepsilon^{2}+\varepsilon^{3}}{\left(1+\varepsilon\right)^{3} }+2C_{10}\varepsilon\frac{6+9\varepsilon+7\varepsilon^{2}+2\varepsilon^{3}} {\left(1+\varepsilon\right)^{3}} \tag{17}\]  
and the long-wave limiting velocity [29; 35; 36], which in the considered case coincides with the speed of sound in the rod [37]:  
\[c(\varepsilon)\equiv\sqrt{\frac{E(\varepsilon)}{\rho}}=\sqrt{\frac{2}{\rho}} \sqrt{\left(C_{01}+C_{10}\right)\frac{3+3\varepsilon+3\varepsilon^{2}+ \varepsilon^{3}}{\left(1+\varepsilon\right)^{3}}+C_{10}\varepsilon\frac{6+9 \varepsilon+7\varepsilon^{2}+2\varepsilon^{3}}{\left(1+\varepsilon\right)^{ 3}}} \tag{18}\]  
Note that in Equation (18), the material density \(\rho\) does not depend upon \(\varepsilon\) due to the assumed incompressibility. Another observation concerns the limiting value of the speed of sound in the rod at \(\varepsilon\to 0\), yielding:  
\[c_{0}\equiv\lim_{\varepsilon\to 0}c(\varepsilon)=\sqrt{\frac{6(C_{01}+C_{10})} {\rho}}. \tag{19}\]
# Monitoring Forest Diversity under Moso Bamboo Invasion: A Random Forest Approach
## 2 Materials and Methods
### Remote Sensing Data
#### 2.2.2 Vegetation Indices  
Vegetation indices play a crucial role in estimating forest parameters through remote sensing [25; 26; 27]. Therefore, in addition to spectral values, we used the raster calculation tool to calculate the following vegetation indices from the raw reflectance values: normalized difference vegetation index (NDVI), normalized difference water index (NDWI), carotenoid reflectance index 1 (CRI1), green difference vegetation index (GDVI), green normalized difference vegetation index (GNDVI), green ratio vegetation index (GRVI), green chlorophyll index (CIgreen), red green ratio index (RGRI), difference vegetation index (DVI), non-linear index (NLI), soil adjusted vegetation index (SAVI), simple ratio index (SRI), and enhanced vegetation index (EVI). The detailed formulas for calculating these vegetation indices are listed in Table 2.