esa-sceva/satcom-chunk-collection huggingface dataset:
# Sillcom: A Communication-Efficient Privacy-Preserving Scheme for Indoor Localization
## 2 Related Works
While Delta sharing optimizes the communication overhead for distance computation in the online phase and garbled circuits reduce the number of online communication rounds for top-\(k\) computation, the use of correlated OT for generating multiplication triples introduces additional offline communication. Moreover, the communication bandwidth consumed by evaluating garbled circuits in the online phase further limits the query performance and scalability of the scheme. [PERSON] et al. [20] employed inner product encryption (IPE) for their design in a cloud outsourcing computing scenario. However, their solution still required several hundred milliseconds to perform a secure localization query, even when the number of anchors was small (three or eight). [PERSON] et al. [17] also designed a sublinear query scheme by introducing an HNSW-based retrieval structure. Although their retrieval algorithm hides the real Euclidean distances by computing them through IPE, it still leaks the user's query access pattern, which is a common privacy leakage issue in sublinear \(k\)NN query schemes. Therefore, we used FAPRIL [14], which guarantees both security and concrete performance, as our comparative benchmark. In addition to the cryptographic-based schemes introduced above, there are also designs based on anonymization techniques [29; 30], differential privacy [31; 32], and federated learning [33; 34]. These approaches adopt different technical methodologies and are considered parallel to cryptography-based designs. Specifically, anonymization and differential privacy typically rely on data perturbation or obfuscation mechanisms, while federated learning distributes the training process without directly sharing raw data. In contrast, cryptography-based schemes aim to achieve strong, provable security guarantees through secure computation protocols. Since these approaches are built upon fundamentally different threat models, system architectures, and privacy definitions, this paper does not analyze or compare them in detail. We recommend [35] for further insights.
# A Review of the Role of Critical Parameters in the Design and Operation of Biogas Production Plants
## 3 Process Parameters Involved in a Biogas Production Plant  
As mentioned earlier, the characteristics of various process parameters impact heavily on the quantity and quality of biogas production. Variables that are broadly relevant to AD include feedstock type, reactor type, pH, temperature, retention time, organic loading, pressure, and inhibitory components. Properties such as inoculum type, codigestion, and pretreatment type are directly linked to feedstock, while temperature, mixing, retention time, organic loading, volatile fatty acids, pH, and pressure are greatly influenced by the type of reactor used and the biochemical interactions involved. The effect of all these parameters on various aspects of anaerobic digestion in light of the present state-of-the-art is widely covered in the following subsections.
## 3 Discussion
### Esters  
Fruit esters are generated by the action of enzymes and/or fermentative metabolism processes, which can be enhanced by various factors, such as maturation (intrinsic factors), microbial growth (biotic factors), as well as climate and seasonal temperature (extrinsic factors) [23]. The aroma quality assessment of 39 melon cultivars conducted by [PERSON] et al. [10] showed that ethyl acetate was present in the highest amount among esters, followed by 2,3-butanediol diacetate. This perfectly agrees with our experimental results, where 2,3-butanediol diacetate was even detected as two stereoisomers. Additionally, [PERSON] et al. identified ethyl acetate as the main ester in several varieties of melon, both climacter and non-climacter ones [32]. This volatile compound contributes to the complexity of the aroma and gives positive notes but, if present at too high a level, it becomes an off-flavor, conferring the typical \"solvent\" or \"ethereal\" odor to the over-ripened fruit. The biosynthetic pathway of a huge number of plant VOCs can be traced back to primary metabolism. For example, some aliphatic esters can be produced by the oxidation of free fatty acids--such as linoleic and linoleic acids--generating short-chain compounds, or starting from some amino acids such as valine and aminobutyric acid [28; 32]. It is known that biochemical reactions are regulated by various factors such as physical conditions, kinetic factors, and enzymatic activity. As regards ester formation, it is generally believed that they can also be enzymatically formed by the combination of an alcohol with an acyl group. In particular, ethyl esters and acetate esters can be formed following two different biosynthetic pathways (Figure 7), but they have pyruvate acid as the same precursor [25].  
Pyruvic acid is the key product of glycolysis, a biological process that occurs in the cytoplasm of the cell and that is present in all living organisms [50; 51]. This process consists of a chain of reactions in which several enzymes are involved, and wherein glucose is converted to two molecules of pyruvate acid. In plants, glucose is constantly available in  
Figure 7: Scheme of the biosynthetic pathway which leads to the formation of ethyl and acetate esters, starting from pyruvate acid [25].  
large quantities, as it is the final product of photosynthesis and represents the most suitable form of carbohydrate storage.
## 3 Results and Discussion
### Adsorption Isotherms of MIPs
The binding data were fitted with the Langmuir model and the Freundlich model [54; 55]. The Langmuir model assumed that the adsorption took place on a homogeneous surface with monolayer coverage. The binding sites were also assumed to be energetically equivalent, and there were no interactions between molecules adsorbed on adjacent sites. The equation of the Langmuir model is described as follows:  
\[Q_{\mathrm{e}}=\frac{Q_{\mathrm{m}}K_{\mathrm{L}}C_{\mathrm{e}}}{1+K_{\mathrm{ L}}C_{\mathrm{e}}} \tag{7}\]  
where \(Q_{\mathrm{e}}\) (mg g\({}^{-1}\)) is the amount of gossypol adsorbed on the adsorbent at equilibrium, \(C_{\mathrm{e}}\) (mg L\({}^{-1}\)) is the free gossypol concentration in the solution at equilibrium (mg L\({}^{-1}\)), \(Q_{\mathrm{m}}\) (mg g\({}^{-1}\)) is the maximum adsorption capacity of the adsorbent, And \(K_{\mathrm{L}}\) (L mg\({}^{-1}\)) is the Langmuir constant, which is related to the affinity of the binding sites. The Freundlich model was an exponential equation that described reversible adsorption and was suitable for multilayer adsorption of a heterogeneous system and not restricted to the formation of the monolayer. It took the form as Equation (8):  
\[Q_{\mathrm{e}}=K_{\mathrm{F}}C_{\mathrm{e}}^{1/\mathrm{n}} \tag{8}\]  
where \(K_{\mathrm{F}}\) (mg g\({}^{-1}\)) and n are both the Freundlich constants, which represent the adsorption capacity and adsorption favorability of the system, respectively. If \(n>1\), it suggests favorable adsorption and increased adsorption capacity. The binding data of adsorption for gossypol were fitted with the Langmuir and the Freundlich models by nonlinear regression. The nonlinear regression plots of the two models are shown in Figure 4B, and the parameters calculated from isotherm models are listed in Table 5. The applicability of the isotherm models to the adsorption behaviors was evaluated based on the correlation coefficient (R\({}^{2}\)). Results showed that the Freundlich model better fit the experimental data of gossypol on MIP2 than the Langmuir model. It implied that the binding site configurations in MIP1 and MIP2 were heterogeneous in respect to the affinity for gossypol. However, the Langmuir model was found to better fit the experimental binding data of gossypol on MIP3. Results demonstrated that high-affinity binding sites and low-affinity binding sites coexisted in MIP1 and MIP2, and for MIP3, binding occurred on a homogeneous surface by monomolecular layer sorption. Additionally, the Langmuir model yielded a better fit for NIP1 and NIP2, indicating the adsorptions of gossypol onto the NIP2 could be monolayer adsorption. Noticeably, the adsorption of NIP3 could be better described by the Freundlich model. The Scatchard equation was adopted to evaluate the binding ability of different sites in MIPs and NIPs. The Scatchard equation is expressed as follows:  
\[\frac{Q_{\mathrm{e}}}{C_{\mathrm{e}}}=\frac{Q_{\mathrm{m}}-Q_{\mathrm{e}}}{k_{ \mathrm{D}}} \tag{9}\]
# Enhancing Route Guidance with Integrated V2X Communication and Transportation Systems: A Review
## 4 V2X Communication Architecture
### Application of Communication Protocols in ITS
It is found that LiDAR combined with V2X can pave the way for fully autonomous driving. Therefore, by connecting with V2X, LiDAR can bring fundamental changes to autonomous driving.
In particular, because of the unique chemical cross-linked structure and large specific surface area, nanogels have lower dissociation/degradation rates and better water retention capacity, which results in higher colloidal stability than in polymeric micelles. To the best of our knowledge, there are no reports on nanogel-embedded contact lenses for extended drug delivery. Zwitterionic polymers interact with water molecules via both electrostatic forces and hydrogen bonding to form a hydration layer [30]. Accordingly, zwitterionic nanogels possess high water-binding capability and anti-protein-fouling performance [31]. Thus far, researchers have introduced zwitterionic polymers into the hydrogel matrix to suppress the protein/bacteria adsorption of contact lenses. Likewise, [PERSON] et al. [32] developed contact lenses with a surface modified with copolymers poly(3-trimethoxysilyl-propyl-methacrylate)-block-poly(2-methacryloyloxyethyl phosphorylcholine) (PMPS-\(b\)-PMPC), which showed significant surface- wettability improvement, protein adsorption resistance, and superior antibacterial adhesion properties. Additionally, some zwitterionic compounds, such as sulfobetaine methacrylate (SBMA), carboxymethyl betaine (CMB), etc., were used as comonomers and incorporated into the contact lens hydrogel matrix to improve anti-b biofouling properties [33; 34; 35]. Herein, we prepared PSBMA nanogels by a convenient reflux-precipitation polymerization method [36], and the anti-inflammatory drug levofloxacin (LEV) was then incorporated into nanogels via hydrophobic and intermolecular interactions. Nanogel-embedded contact lenses were then prepared by cast molding method with the drug-loaded nanogels dispersed in pre-monomer solutions comprising HEMA and N-vinyl-2-pyrrolidone (NVP) for extended ophthalmic drug delivery (Scheme 1). The drug-release behaviors and other properties relevant to wear comfort, including transparency, hydrophilicity, swelling ability, mechanical properties and cytotoxicity were further characterized.
## 3 Results
### SMCI Model Adaptations
#### 3.2.4 Classification  
The nine-category classification using the adaptive SMCI model was trained and tested using the DEAP data before using the BDAT dataset for testing. The DEAP model was compared to the SMCI model on the same DEAP training data, with a maximum difference of 2% accuracy between the two models. The pleasure and dominance results of the SMCI models were similar in accuracy to the original DEAP models (Table 5).
# On the Energy Performance of Iridium Satellite IoT Technology
## 2 Related Work
### Iridium Satellite Technology  
While a full Iridium system description or specification is not publicly available, several research works have overviewed the Iridium technology [8; 9], evaluated its performance [9; 10], or used it for various kinds of applications [11; 12; 13; 14; 15; 16].  
[PERSON] et al. presented the main concepts, architecture, and services of the Iridium system [8]. [PERSON] et al. describe the system fundamentals in more detail, and provide a network performance analysis in terms of communication latency [9]. [PERSON] et al. performed an experimental latency characterization of Iridium [10].  
Iridium has been used as the communication means in many scenarios for both voice and data communications [9; 12]. Among the latter, Iridium has enabled many IoT use cases in a diversity of remote scenarios including polar, marine, desert, forest, and UAV-based, among others [11; 12; 13; 14; 15]. Iridium has also been exploited for positioning applications [16].
## 4 Discussion
### Limitations  
There are several limitations in this study. First, owing to the relatively low incidence of CHD, we used the small volume of CHD data from limited institutions. Our training data consisted of only normal cases; however, further CHD data collection is needed as test data for the validity and reliability evaluation of detecting cardiac structural abnormalities, by cooperating with other hospitals throughout Japan or globally. Second, our fetal ultrasound videos were obtained using the same type of ultrasonography machine. In terms of the robustness, we have to verify whether SONO works in a different equipment and setting. Third, SONO consisted of mainly apical view data and could not handle any kind of fetal presentations. Inputting further non-apical view datasets to the CNN might resolve this limitation. Finally, it was still hard for SONO to capture the isomerism, complete transposition of large vessels, and the subtle changes of the cardiac substructures, such as a ventricular hypertrophy, ventricular septal defect, and valve abnormalities. Therefore, we have to consider add-on technologies including image segmentation, for further accurate detection of these findings.
## 3 System Development
### Telepresence Robot Hardware
#### 3.2.5 Lidar Sensor-RPLiDAR-A1M8-360  
Lidar, which stands for Light Detection and Ranging, is a cutting-edge remote sensing technology that uses laser light to map the surface of objects and environments. The basic principle of Lidar is to emit a laser beam toward the target object and measure the time it takes for the laser to return to the system after reflecting off the target. This allows the system to calculate the precise distance to the object and create a detailed 3D map of the environment. Lidar is also increasingly being integrated into telepresence robots, where it plays a crucial role in improving navigation and control. Telepresence robots can be controlled remotely, allowing users to operate a physical robot in a remote environment. Lidar technology provides telepresence robots with the necessary information to navigate their environment and interact with their surroundings, making telepresence robots more effective and efficient in various applications.
## 5 Conclusions  
We combined FWI theory with PINNs to simulate the forward propagation of seismic waves, establishing a foundation for PIRNN-based FWI. Building upon this, we introduced scale decomposition and developed the dual-PIRNN FWI network architecture, significantly reducing GPU resource usage in PIRNN-based full-waveform inversion. Data conversion between different scales was implemented for shot gathers, facilitating inversion on downscaled models. This conversion accelerates model convergence while maintaining an acceptable level of inversion resolution. Our simulations on the Marmousi model demonstrate that, within a specific range of parameter settings, the proposed dual-PIRNN FWI network architecture effectively reduces GPU resource consumption while preserving inversion quality. Furthermore, it exhibits robustness and reduces dependence on the initial model.  
However, our method still faces several limitations that require further investigation. Although our approach effectively reduces computational costs, it may result in the loss of high-frequency information in complex geological structures. Additionally, insufficient source-receiver coverage can lead to stripe artifacts in the inverted regions, and significant deviations in the initial model may cause the inversion to converge to local minima. To address these challenges, our future work will focus on developing FWI methods that are less sensitive to initial models while incorporating physics-based regularization terms to constrain the inversion process. Furthermore, increasing the source-receiver coverage can enhance convergence performance. These developments will improve the practical applicability of our method in real-world seismic exploration.  
Conceptualization, C.L. and J.L. ([PERSON]); methodology, J.L. ([PERSON]); software, J.L. ([PERSON]); validation, J.L. ([PERSON]) and H.C.; formal analysis, L.Q.; investigation, L.Q.; resources, J.G.; data curation, J.L. ([PERSON]) and J.G.; writing--original draft preparation, [PERSON] ([PERSON]); writing--review and editing, [PERSON] ([PERSON]); visualization, C.L. and J.L. ([PERSON]); supervision, C.L.; project administration, C.L. and J.L. ([PERSON]); funding acquisition, C.L. and H.C. All authors have read and agreed to the published version of the manuscript.  
The research was supported by the National Natural Science Foundation of China (Grant No. 42474168).  
\begin{table}
\begin{tabular}{l c c c} \hline
**Components** & **Original Scale** & **DownTime** & **DownTimeSpace** \\ \hline Intermediate activations & 32.1 Gb & 16 Gb & 2.3 Gb \\ \hline Model parameters & & & \\ Optimizers Status & 1.8 Gb & 1.6 Gb & 1.6 Gb \\ Other usage & & & \\ \hline \end{tabular}
\end{table}
Table 6: Parameters for different scale Marmousi models.  
Figure 19: Inversion velocity profiles with different scales. (**a**) Velocity profiles at x = 300 m. (**b**) Velocity profiles at x = 700 m.
# Precise Photon Correlation Measurement of a Chaotic Laser
## 4 Experimental Results
Based on the repeated measurements we obtained the range of coherent time variation. Figure 3 is the three corresponding time series of the chaotic laser. The bandwidth of the chaotic laser was in the order of GHz and we obtained the coherence time of chaotic laser through 3 dB linewidth spectrum. Considering that the ninth order correction of the second order photon correlation \(g^{(2)}(\tau)\) was close enough to the theoretical limit, we experimentally took the ninth order correction within 10 ns and theoretically employed the same order fitting. The experimental photon correlations \(g^{(2)}(\tau)\) were fitted by ideal expressions, as shown in Figure 4. For photon-bunching chaotic light, the \(g^{(2)}(\tau)\) can be written as \(g^{(2)}(\tau)=1+bexp(-2\tau/\tau_{c})\) (\(b\): bunching amplitude, \(\tau_{c}\): coherence time) [23]. Figure 4 shows the experimental and theoretical fitting results for weak chaos (\(b=0.479\), \(\tau_{c}=0.768\) ns), chaos (\(b=0.524\), \(\tau_{c}=0.651\) ns), and strong chaos (\(b=0.626\), \(\tau_{c}=0.535\) ns). Figure 4: The ninth order correction of second order photon correlation and the theoretical fitting. The bias current J = 1.5 Jth and feedback strength (**a**) \(\eta\) = 2.66%, (**b**) 8.87%, (**c**) 30.31% are the same as those used in Figure 2.  
Figure 3: Measured three corresponding time series of the chaotic laser. The bias current J = 1.5 Jth and feedback strength (**a**) \(\eta\) = 2.66%, (**b**) 8.87%, (**c**) 30.31% are the same as those used in Figure 2.  
Figure 2: (**a**) Measured frequency spectrum and (**b**) optical spectrum of the chaotic laser, when J = 1.5 Jth and \(\eta\) = 2.66% (weak chaos), 8.87% (chaos), 30.31% (strong chaos).
# Cross-Layer Energy Optimization for IoT Environments: Technical Advances and Opportunities
## 4 Energy Efficient Techniques in the IoT
### Energy Efficient Techniques at Local Processing and Storage Layer
#### 4.2.1 Energy Harvesting Techniques  
All You can Send for Energy Harvesting Networks (ALLYS) [36] has been suggested to adjust the transmission probability of nodes based on the residual energy of individual nodes and the network capacity in distributed energy harvesting WSNs-enabled IoT. In ALLYS, a frame of fixed size with adjustable slot transmission probability is used to control the channel access by sink nodes while broadcasting both frame size and transmission probability to reduce the collisions. The probability of harvesting \(k\) energy units during \(T\) time slots of a frame can be formulated as:  
\[p\Big{[}E^{h}=k\Big{]}=\left(\begin{array}{c}T\\ k\end{array}\right)\Big{(}p^{eh,\leavevmode\
obreak\ \leavevmode\
obreak\ suc}\Big{)}^{k} \Big{(}1-p^{eh,\leavevmode\
obreak\ \leavevmode\
obreak\ suc}\Big{)}^{T-k} \tag{6}\]  
where, \(E^{h}\) is the amount of harvested energy of a node during a frame, then average harvested energy is given as:  
\[E\Big{[}E^{h}\Big{]}=\sum_{k=0}^{T}kp\Big{[}E^{h}=k\Big{]}=Tp^{eh,suc} \tag{7}\]  
Efficient Solar Power Management System for Self-Powered IoT Node (ESPMS) [37] is an architecture which achieves higher end-to-end energy efficiency while avoiding linear regulator and presenting a power convertor based on on-chip switched capacitor in IoT system. In the proposed architecture (Figure 11), there are two stages: DC-DC\({}_{1}\) and DC-DC\({}_{2}\) for DC-DC converters between harvesters and loads.  
Energy harvested roadside IEEE 802.15.4 wireless sensor networks for IoT applications [38] has been suggested while considering different traffic conditions based on the vehicles' movement in WSNs-enabled IoT applications using energy harvesting techniques. To this perspective, an adaptive energy efficient technique (ABSD) has been introduced that adapts MAC parameters related to IEEE 802.15.4 sensor nodes to provide queue occupancy level and to offer traffic levels. EH-ABSD (enhanced form of ABSD) is also introduced by integrating a new MAC parameter (energy backoff) and energy harvesting technique, which improves battery lifetime (energy efficiency), network throughput and QoS values. Energy consumption by sensor node is formulated as:  
\[E=\Bigg{\{}\Big{[}(1+N)I^{tr}+I^{rc}-(2+N)I^{lstn}\Big{]}\frac{X^{packet}}{R}+ \Big{(}2^{BO}-2^{SO}\Big{)}I^{slap}+2^{SO}I^{lstn}\Bigg{\}}U+E^{cir} \tag{8}\]  
where \(E^{cir}\) is energy consumed to operate electrical circuit in the sensor node, \(I^{tr}\), \(I^{rc}\), \(I^{lstn}\) and \(I^{slap}\) are electrical current used in transmission, receiving, listening and sleeping mode, respectively. Figure 12 illustrates EHWSN architecture for moving vehicles.  
Figure 11: Typical power management architecture in IoT.