esa-sceva/satcom-chunk-collection huggingface dataset:
# Inter-Blockchain Communication Message Relay Time Measurement and Analysis in Cosmos +
## 2 Related Work
### Cosmos Interoperability  
#### 2.1.1 Cosmos Structure  
Cosmos [1; 10] provides a shared environment that promotes interoperability and scalability by allowing various blockchains to communicate and interact. Cosmos' blockchains are generated independently and concurrently. This new structure allows individual blockchains inside the Cosmos ecosystem to keep their independence while benefiting from horizontal scaling and rapid transaction processing.  
As shown in Figure 1, Cosmos' two essential components are Zones and a Hub, which enable interoperability between heterogeneous blockchains. In this arrangement, Zones represent independent blockchains with their characteristics. In contrast, the Hubs represent central servers that track each Zone's most recent state. This method ensures frictionless token and data transmission between Zones via the Hub while maintaining global consistency of token numbers across the Cosmos network.  
The Inter-Blockchain Communication (IBC) protocol [11; 12] allows Cosmos' different blockchains to interact securely and selectively. IBC allows critical data to flow while maintaining security and privacy. It is compatible with Tendermint-based blockchains such as Cosmos Hub, Osmosis, Evmos, and Juno. Furthermore, blockchains, such as Bitcoin and Ethereum [13] which are not built on Tendermint, can be linked to Cosmos via particular PEG zones. PEG zones enable Cosmos and blockchains from other chains to interact and communicate across chains.
# Partial Order Rank Features in Colour Space
## 2 Background
### Order Relations  
An order relation is an abstraction of the common notion of \"greater than\" used to compare numerical values, in our case pixel values in P (typically the set of 8-bit intensity values). In order to be called a (total) order, a binary relation \(\leq\) needs to satisfy the following four conditions:  
**Definition 1** (Order axioms).: _For all \((x,y,z)\in\mathrm{P}^{3}\),_  
1. \(x\leq x\) _(reflexivity)._
2. _if_ \(x\leq y\) _and_ \(y\leq x\) _then_ \(y=x\) _(antisymmetry)._
3. _if_ \(x\leq y\) _and_ \(y\leq z\) _then_ \(x\leq z\) _(transitivity)._
4. _either_ \(x\leq y\) _or_ \(y\leq x\) _(totality)._  
The last condition guarantees that we know how to compare any pair of pixel values.
# Localization of a BLE Device Based on Single-Device RSSI and DOA Measurements
## 6 System Design and Implementation
### Design and Implementation of iOS Sanner Application  
The scanner device we used is an iPhone 14 (iOS 16), which has an in-built Bluetooth chipset, an IMU sensor, and a GPS receiver. Using Apple's iOS 16 software development kit (SDK), we developed an application with an intuitive user interface that allows users to start/stop the scan, calibrate the scanner, view the results of their scan, and obtain information on the next scan location. The application programming interface (API) provided by IOS SDK has CoreLocation and CoreBluetooth modules that allow for ranging and monitoring of BLE beacons. Monitoring is used in applications that involve proximity sensing from a specific fixed location while ranging scans the advertisement packets of the beacon to estimate the distance. For this research, we used the CoreBluetooth API to scan the advertisement packets at a rate of 50 Hz and read the RSS values from the packet structure. The CoreLocation module of the API provides the current location of the scanner devices, and combining that with the magnetometer readings, we calculated the orientation of the smartphone with respect to true north. Figure 8 is a screen capture of the scanner application, which finds the programmed beacon device, captures and timestamps the RSS values from the BLE beacon, and provides heading information at different measurement locations. The final heading information is the approximate DOA of the beacon device (\(\hat{\theta}\)) calculated using the estimation algorithm discussed in Section 5.4.  
Figure 7: Eddystone packet structure for broadcasting 16-byte beacon ID including 10-byte Namespace ID and 6-byte Instance ID.
## 5 Conclusions  
Performed impedance spectra on lead-acid test cells with stratified electrolyte have shown characteristic changes of the ohmic resistance, the charge-transfer resistance and the double-layer capacitance of negative electrode. The resistances changed initially because of the stratification of electrolyte itself, as the electrolyte conductivity as well as the exchange current of the charge-transfer process depend both on the electrolyte concentration. With cycling the test cell when electrolyte was stratified the generated inhomogeneous sulfation started to increase not only the resistances but also the double-layer-capacitance. These observations could be used for the development of stratification detection algorithms.  
The developed spatially-resolved EEC could not reproduce all observed influences of stratification on the impedance of the lead-acid test cell. The large simulated difference in SoC between top and bottom needs to be verified. Furthermore, the EEC has to contain a model of the sulfate crystal grow, which increases the charge-transfer resistance in charging direction. However, explanations for the decreased ohmic resistance and the increased charge-transfer resistance could be found. Only for the observed increase of the double-layer capacitance further investigations are required to find a reason.  
The following are available online at www.mdpi.com/2076-3417/8/7/1018/s1, Figure S1: Simulated impedance spectra with \(-4.5\) I\({}_{20}\) superimposed DC-current are presented with different features of the model, Figure S2: Simulated impedance spectra with \(-4.5\) I\({}_{20}\) superimposed DC-current at 80% using an 1D and the spatial-resolved model with the same set of parameters, only that the parameters were adapted to spatial-resolved model. Also the measured impedance spectrum for the same DC-current and SoC is shown as well.  
[PERSON], [PERSON], [PERSON] and [PERSON] conceived and designed the experiments. [PERSON] acquired the funding and was the project administrator. [PERSON], [PERSON], [PERSON] and [PERSON] performed the experiments. [PERSON] and [PERSON] analyzed the data. [PERSON] wrote the paper.  
This research was funded by the European EFRE program grant number EU-1-1-081.  
The underlying work was conducted within the project BSMS (EU-1-1-081) funded through the European EFRE program. Funds were received from the same program for covering the costs to publish in open access.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} EIS & Electro-chemical Impedance Spectroscopy \\ SoC & State-of-Charge \\ EEC & Equivalent Electrical Circuit \\ CPE & Constant-Phase Element \\ srEEC & spatially-resolved equivalent electrical circuit \\ OCV & Open Circuit Voltage \\ \end{tabular}
# Physics-Based Noise Modeling and Deep Learning for Denoising Permanently Shadowed Lunar Images
## 2 Datasets  
### LROC NAC Data Information  
The LROC NAC dataset is captured by two high-resolution NACs onboard the LROC, providing a resolution ranging from 0.5 to 2.0 m per pixel. The image size can reach up to 10,000 \(\times\) 52,000 pixels, covering ground areas from approximately 5 \(\times\) 26 square kilometers to 20 \(\times\) 100 square kilometers [35]. From September 2009 to December 2011, LRO operated in a circular polar orbit at an altitude of 50 km, with an image resolution of approximately 0.5 m per pixel. After December 2011, LRO transitioned to an elliptical polar orbit, where the resolution for the northern hemisphere images decreased to 2.0-1.0 m per pixel, while the resolution for the southern hemisphere improved to 1.0-0.4 m per pixel [35]. Additionally, the LROC NAC supports a \"summation\" mode, where pixel merging is used to reduce the image width to 2532 pixels, thereby doubling the pixel scale to enhance the signal-to-noise ratio under low-light conditions [35].
# Impacts of Land-Use Change from Primary Forest to Farmland on the Storage of Soil Organic Carbon
## 4 Discussion
### Effect of Carbon Input and Output on SOC Storage  
SOC storage is the result of a dynamic balance between the amount of plant residues entering the soil and decomposition. Tilling, which involves periodic stirring and mixing of soil, mechanically removes organic carbon from the surface soil, reducing its input. Additionally, farmland soil, which contains a low proportion of insoluble substances in easily degradable crop residues, can enhance SOC mineralization through planting. Under suitable soil moisture conditions, microorganisms in farmland soil will fully contact organic residues in the soil, exposing wrapped carbon to degrading enzymes and accelerating soil mineralization. Incineration of crop residues is also a major cause of reduced SOC storage, particularly in tropical areas. Incineration is a common farmland management measure that emits greenhouse gases into the atmosphere and leaves inert carbon components in the soil.
## 3 Experiments and Discussions
### Displacement Measurement without Wavelength Correction  
The displacement measurement range of the MMI kit was firstly confirmed using the HP5529A interferometer, which has a measurement range of 15 m. In experiments, the 3 DOFLM and HP5529A systems were located on both sides of a linear stage (MSR 100, PARKER, Charlotte, USA), which was moved by a commanded step of 2 to 36 mm long. Figure 9 shows the bi-directional displacement measurement results of the MMI kit and the HP5529A system, determined in duplicate. It can be seen that both outputs were in good agreement. Output sinusoidal signals in the form of a Lissajous circle were found to be very good and stable during motion, as shown in Figure 10. The measuring range of the MMI kit was, thus, confirmed to be at least 36 mm, which is regarded as a long-stroke for a nanopositioning stage.  
The displacement measurement accuracy of the 3 DOFLM system was then calibrated by a commercial nanopositioning and nanomeasuring machine (NMM-1, SIOS Mesftechnik GmbH, Ilmenau, Germany), which has a positioning range of 25 mm in the \(x\)- and \(y\)-directions with sub-nanometer positioning accuracy. The misalignment error of the MMI kit and NMM-1 will induce a cosine error in the displacement measurement, which cannot be ignored in the nanometer range. Therefore, particular effort was put into aligning the laser beam of the MMI kit with the moving axis of the NMM-1. In this alignment experiment, a QPD was adopted to detect the two lateral motions of the moving table of the NMM-1 relative to the laser beam of the MMI kit. An angle mirror mount was applied to adjust the laser beam so that the beam spot was always close to the center of the QPD during the motion of the NMM-1. Figure 11 shows the finally adjusted straightness error of the NMM-1 in the \(x\)- and \(y\)-directions. The maximum straightness error was evaluated to be 1.272 \(\upmu\)m, and it corresponded to a cosine error of 0.04 nm on the displacement measurement in the range of 20 mm. Such a cosine  
Figure 8: Comparison angle results of measured angular error of the nanopositioning stage by the HP5529A and the 3 DOFLM system: (**a**) pitch; (**b**) yaw.  
Figure 7: Calibration angle results of the miniature autocollimator kit: (**a**) pitch; (**b**) yaw.  
The calibration experiments without wavelength correction were then carried out with the nominal wavelength of the laser diode (i.e., 635 nm) given by the manufacturer. The displacement residuals between the 3 DOFLM system and the NMM-1 system were evaluated within \(\pm\)45 nm up to a travel of 18 mm, as shown in Figure 12. The standard deviation of the displacement residuals for five measurements was estimated between 4.49 and 35.98 nm. The residual error between the 3 DOFLM and NMM systems was deemed to be mainly caused by the incorrect laser diode wavelength of the MMI kit. Therefore, it is essential to correct the laser diode wavelength for better measurement accuracy.
## 3 Proposed Method
### Cost-Effective and Privacy-Preserving Transaction Registration  
To prevent operator abuse of power, the proposed method only registers the hash values of transactions on the platform chain \(\mathbb{P}\mathbb{C}_{M}\), while their full content is disclosed later on the dual platform chain \(\widehat{\mathbb{P}\mathbb{C}}_{M}\). This enables a cost-effective and privacy-preserving approach to transaction registration.  
One key feature of the proposed method is its ability to aggregate multiple transaction hashes into a Merkle tree. For example, the data warehouse \(D\) may collect transaction hashes \(tx^{\prime}\) to be disclosed in \(\widehat{\mathbb{P}\mathbb{C}}_{M}\), construct a Merkle tree \(MT\), and request that the root hash is recorded in \(\mathbb{P}\mathbb{C}_{M}\) via a smart contract \(sc_{P}\). This allows a large number of transactions to be registered through a single root hash, optimizing storage and bandwidth. Figure 3 illustrates this scenario; Transaction hashes collected by \(D\) are aggregated into a Merkle tree, with the root hash recorded in \(\mathbb{P}\mathbb{C}_{M}\) and the corresponding transaction contents disclosed in \(\widehat{\mathbb{P}\mathbb{C}}_{M}\) after finalization.  
To ensure the correctness of batch registration, the method requires that transactions grouped under the same Merkle tree be logically and temporally independent. If dependencies exist between transactions, such a grouping may result in erroneous or unpredictable execution outcomes. However, \(D\) has no access to transaction content at the time of Merkle tree construction and thus cannot assess such dependencies. Even if full transaction data were available, detecting interdependencies would require semantic understanding beyond  
Figure 3: Merkle-tree-based batch registration scheme.  
the scope of \(D\)'s role. Therefore, the framework deliberately excludes \(D\) from responsibility for verifying transaction independence.
## 2 Related Work
Authors in a simulation-based study in [13] have come up with a performance guaranteed optimized V2X handover decision algorithm between LTE and IEEE 802.11p. A central controller collects the real-time traffic information about the received signal strength, solves a joint optimization function and informs the vehicles about the appropriate access point (Road Side Unit (RSU) or base stations) to connect with. In the process, the algorithm considers load balancing among all access points, data rate maximization for the whole network, and the vehicle fairness in terms of satisfied vehicles. Another similar data trafficsteering algorithm is presented in [14], where the hybrid V2X technology selection decision is based on the IEEE 802.11p channel congestion and the free LTE Physical Resource Blocks (PRBs) that can be scheduled. In turn, the transmission delay is optimized and more number of vehicles can achieve lower delays. Another related work in [15] considers WiFi and LTE as the possible technologies and their use is instructed by a simple decision tree. WiFi is used if the link quality is good with a possible fallback to LTE otherwise. An electrical autonomous vehicle is used to perform the experimentation in a limited off-road experiment scenario. None of the short-range V2X technologies such as C-V2X PC5 or DSRC/ITS-G5 are considered in this work. The state of the art regarding multi-technology enabled vehicular communication and RAT selection algorithms is summarized in Table 1, however, these contributions only consider a subset of V2X technologies. This constrains the extent of flexibility attained and the RAT selection efficiency. To the best of our knowledge, this is the first study in hybrid V2X domain that considers three vehicular RATs including C-V2X PC5, 802.11p-based DSRC, and 5G long-range. Furthermore, mostly simulation-based studies are conducted in this domain in past. To address this gap, our study proposes and validates experimentally an intelligent decentralized hybrid V2X technology selection algorithm that can dynamically select a vehicular technology based on diverse parameters not limited to the received CAMs but also the current state of the sending V2X node. Moreover, this is the first study in this domain which considers V2X technology selection optimization for joint CAM and DENM transmission scenario.
## 1 Introduction
### Preliminaries
These include (1) the assumption that communication and sensing can efficiently share spectrum, power, and hardware resources without significantly degrading the performance of either function; (2) the existence of advanced signal processing algorithms that can separate and optimize the dual-purpose signals for both communication and sensing; (3) the ISAC system can operate within the confines of existing communication standards and regulatory requirements for both communication and sensing functions. These hypotheses form the foundational assumptions that must hold true, or be engineered to hold, for a successful realization and operation of an ISAC system.
# Review of Flexible Piezoresistive Strain Sensors in Civil Structural Health Monitoring
## 3 Elaboration of Sensor Materials
### Electrodes  
Electrodes serve as the connections between a sensor and a signal receiver instrument. Electrical signals generated from the conductive layer are transmitted to the receiver through electrodes; thus, electrodes should have good conductivity and stability during the overall deformation process of the strain sensor. Stretchable electrodes based on metallic nanoparticles or nanowires, ionic hydrogels and conductive polymers have been widely studied [88; 89; 90].  
For instance, polyacrylamide-sodium alginate hydrogel was used to fabricate a scalable, transparent (90% transmittance) and ultrathin (380 um) electrode that can collect energy from human motion [88]. An aluminum electrode was used to link polymer-based aerogel to the signal receiver [89]. A filament serpentine gold electrode, which has stronger adhesion than ionic hydrogels, was used on epidermal electronic systems [90]. Generally, metallic electrodes are the most commonly used electrode materials in SHM.
## 3 Results  
### Physicochemical Composition and Antioxidant Status  
Table 2 presents the means for the physicochemical parameters of fresh and ripened Roja Mallorquina ewes' cheese and the corresponding original milk. The DM, fat, and protein contents of the milk ranges were 18.3-20.7, 6.58-8.72, and 6.08-7.31 g/100 g milk, respectively; the contents of FC ranges were 46.1-48.9, 22.2-25.0, and 11.9-17.1 g/100 g cheese, respectively; and the RC contents ranges were 62.2-65.5, 27.0-33.0, and 22.8-28.0 g/100 g cheese, respectively. The fat/DM ranges were 48.2-52.5 and from 41.2-52.5 g/100 g DM for FC and RC, respectively. Finally, the ash (g/100 g cheese), NaCl (g/100 g cheese), and pH ranges were 1.57-1.92, 0.88-1.54, and 6.43-6.65 for FC and from 3.04-4.14, 0.84-1.38, and 5.17-5.39 for RC.  
Table 3 shows the fat-soluble vitamins, TPC, and TAC of the dairy products. The retinol and \(\alpha\)-tocopherol contents of milk ranges were 113.4-281.2 and 53.1-156.8 ug/100 g DM, respectively; the contents of FC ranges were 218.9-520.7 and 208.5-549.3 ug/100 g DM, respectively; and the RC contents ranges were 180.0-310.1 and 11.7-49.9 ug/100 g DM, respectively. The TPC content of milk, FC, and RC ranges were 16.8-20.9, 4.0-8.0, and 34.8-69.8 mg GA (gallic acid) equivalents/100 g DM, respectively. The TAC content for milk, FC, and RC ranges were 7.4-50.4, 61.8-139.1, and 56.7-138.9 umol Trolox equivalents/g DM, respectively. All these components were affected by cheese manufacturing. The fat-soluble vitamin contents (expressed as g DM and normalised against fat) were significantly higher in FC (376.4 and 361.7 ug/100 g DM, for retinol and \(\alpha\)-tocopherol, respectively) than in milk (211.4, \(p<\) 0.05; 84.8, \(p<\) 0.01) and RC (233.6, \(p<\) 0.01; 32.6, \(p<\) 0.001). In this respect, an average of 49% of the retinol and 281% of the \(\alpha\)-tocopherol originally present in milk fat were added during the cheese-making process. Furthermore, the retinol content in milk did not differ significantly from the RC (\(p>\) 0.05), but the content of \(\alpha\)-tocopherol in milk was significantly higher than in RC (\(p<\) 0.05; 57% lost during the cheese-making process on average). The TPC content was significantly higher in RC (54.54 mg GA equivalents/100 g DM) than in milk (18.7 mg GA equivalents, \(p<\) 0.01; 193% average increase over milk content during the cheese-making process) or FC (6.16 mg GA equivalents, \(p<\) 0.001). Moreover, the TPC content in milk was significantly higher than in FC (\(p<\) 0.001; 67% on average lost during the cheese-making process). The TAC was significantly higher in cheeses (\(p<\) 0.05) than in milk (22.4, 100.0 and 102.5 umol Trolox equivalents/g DM, for milk, FC, and RC, respectively).
# High-Precision Pointing and Tracking System Design for Near-Space Balloon-Based Optical Observation
## 2 Methods and Analysis
### Secondary Precise Tracking System
#### 2.3.2 Stable Pointing and Tracking Design  
The stable pointing and tracking function of the platform is realized by a combination of servo control, motor drive, motion measurement, angle measurement, and sun position measurement, as shown in Figure 8. The servo control board receives instructions from the upper computer and executes them, returning status information, such as current angle position, receives gyro signals and sun position information, and drives the azimuth and pitch motors using PWM (Pulse Width Modulation) to keep stable and track the sun.  
The motion speed of the balloon-based platform is very low, and the disturbance frequency is low. A MEMS gyroscope was selected as an angular rate sensor with reliable quality and stable accuracy as performance indicators. The rotary transformer in this paper has good stability and adaptability and can achieve a measurement accuracy of 20''.  
In order to achieve various working states of the secondary platform and meet the requirements of the coronagraph observation function, there are 3 control states: the stable control state, the locking control state, and the tracking control state.  
Figure 5: Overall mechanical design of the secondary stable platform.  
Figure 6: Diagram of the azimuth axis system.