esa-sceva/satcom-chunk-collection huggingface dataset:
# A Watermarking Protocol Based on Blockchain
## 5 Watermarking Protocol  
The proposed watermarking protocol is an enhancement of the buyer friendly and mediated protocols presented in [22; 23; 24]. It has been designed and developed according to what is reported in Section 3. Therefore, it exploits the blockchain technology to avoid the participation of a TTP in the core of the protection phase so as to simplify and secure the basic interaction scheme characterizing the protocols described in [22; 23; 24]. The result is an innovative watermarking protocol in which the blockchain is employed to lock in a public ledger the main tokens characterizing purchase transactions. In fact, such tokens are collected and controlled by executing a specific smart contract: if they turn out to be correct, the ongoing purchase transaction is automatically validated and completed without the direct intervention of a TTP. Even though the proposed protocol can run without a centralized control, it still needs a TTP acting as a trusted web distributor of security tokens, such as one-time public and private key pairs and encrypted \"nonces\" [51], needed to complete the purchase transactions of protected digital content according to the original buyer friendly and mediated approach [22]. Moreover, the proposed protocol needs a further TTP, called \"judge\". It does not participate in the phase of the protocol that applies the protection to the digital content distributed on the Internet. It only participates in the subsequent \"identification and arbitration phase\" needed to determine the identity of an illegal distributor of a copy of a protected digital content [22; 23; 24]. In fact, the TTP and the judge could even coincide, but conventional certification authorities do not usually implement the service performed by the judge [17; 22]. More precisely, the proposed watermarking protocol is characterized by a protection scheme in which: (1) the seller or content provider \(\mathcal{CP}\) releases content in an encrypted and watermarked form; (2) the buyer \(\mathcal{B}\) can obtain the protected content by simply decrypting it; (3) the purchase transaction of a protected digital content occurring between the buyer and the content provider is validated by automatically executing a smart contract within a blockchain \(\mathcal{BC}\), which takes charge of controlling all the tokens generated by the transaction; (4) buyer and content provider take part in transactions that employ security tokens guaranteed by a \"registration authority\" \(\mathcal{RA}\)[22; 23; 24]; (5) a judge \(\mathcal{J}\) guarantees the dispute resolution protocol and determines if a buyer is guilty of having released pirated copies [22; 23; 24]. The protocol consists of two subprotocols: the _protection protocol_ and the _identification and arbitration protocol_. The meanings of the symbols used to describe the protocol are reported in Table 1.
## 2 LEO Satellite Network Model  
### Satellite Network Scenario  
The LEO satellite network scenario is shown in Figure 1. A set of satellite nodes in the network is represented as \(\text{Sat}=\{\text{S}_{1},\text{ S}_{2},\ldots,\text{S}_{N}\}\). The network mainly comprises periodic, high bandwidth, and computing-storage services, which primarily consume satellite bandwidth, computing, and storage resources. The network contains \(k\) time slots, and there exists a set of time slots \(\text{T}_{K}=\{\text{T}_{1},\text{ T}_{2},\ldots,\text{T}_{k}\}\), where the time interval between each time slot is \(\Delta\text{T}\), and the network topology remains fixed within each time slot. The corresponding adjacency matrices \(\text{Map}=\{\text{M}_{1},\text{ M}_{2},...\text{M}_{k}\}\) are generated for each time slot based on the static topology, and each adjacency matrix encompasses information on inter-satellite link visibility, link distance, and the availability of satellite-to-ground links. Dynamic topology scenarios can therefore be converted into static slices, and services can be deployed within time slots based on the topology at the current moment. A task tuple \(\{\text{Z},\text{ P}_{Z},\text{ T}_{Z}\}\) is used to represent each service flow, where \(Z\) is the set of service identifiers, \(\text{P}_{Z}\) is the set of service transmission paths, and \(\text{T}_{Z}\) is the time when the service starts transmitting until it is received at the gateway station.  
The satellite transmits service data via the inter-satellite wireless communication link, with the topology remaining unchanged throughout the transmission process. The source and destination nodes of any service in the network are denoted as \(\text{S}_{a}\) and \(\text{S}_{b}\). There are N\({}_{z}\) satellites on the service transmission path \(\text{P}_{z}\), and the propagation delay at the nodes is defined as follows:  
\[\text{t}_{\text{pro}}^{i}=\frac{\text{L}_{ij}}{\text{\theta}_{\text{pro}}}, \forall i,j\in\text{P}_{z},\forall z\in\text{Z}, \tag{1}\]  
where \(\text{L}_{ij}\) is the inter-satellite distance from node \(i\) to node \(j\), and \(\text{\theta}_{\text{pro}}\) is the propagation speed, and the value is equal to the speed of light. The time consumed by node \(i\) when sending service packets to the next node \(j\) is called the transmission delay:  
\[\text{t}_{\text{trans}}^{i}=\frac{\text{E}_{z}^{i}}{\text{\varphi}_{i}}, \forall z\in\text{Z}, \tag{2}\]where \(\mathrm{E}_{z}^{i}\) represents the data size sent by each satellite, and \(\varphi_{i}\) is the transmission rate of the switch on each satellite.
# Addressing ZSM Security Issues with Blockchain Technology
## 2 Related Work
### Examining Blockchain as a Mean for the Security Enhancement in NGNs
Concluding, to the best of our knowledge none of the existing works clearly propose an architecture to answer how cross-domain resource management could be implemented in a secure manner using blockchain technology in ZSM-aligned networks. The current research aims to present a blockchain-enabled ZSM architecture for the secure E2E service deployment where blockchain and ZSM are combined. Combining several studies together to address all the issues would not necessarily result in solving all of them as unintended interactions/interplays may be revealed. Additionally, adopting the definition of complexity of [32], the combination of technologies and the introduction of large numbers of components would result in a significant complexity increase, while relying on a single technology (blockchain technology) introduces less complexity.
# Study on the Anti-Interference Performance of Substrate-Free PEDOT:PSS ECG Electrodes
## 3 Results and Discussion
### Effect of Dynamic Factors on Impedance and ECG  
#### 3.2.1 Effect of Friction between the Fabric and the Electrode  
The friction between the fabric and the electrode dynamically influences the partial gap \(d\) between the electrode and the skin and mildly stimulates the skin's nerves. This friction simulates a real-life scenario, occurring at 2 Hz with an approximate force of 0.05 N in a back-and-forth motion. According to an equivalent circuit model analysis, the friction will mainly affect the equivalent capacitance \(C_{es}\) and will mildly affect the resistance \(R_{s}\) of the skin, causing the skin-electrode impedance to dynamically change with the friction. In addition, based on the marked electrode positions, it was observed that the experimental friction between the fabric and the electrode did not result in electrode displacement.  
Figure 8 compares the impedance, ECG waveforms, and spectra of the electrodes under friction and non-friction conditions. Figure 8a-c show the impedance measurements at 1 Hz, 10 Hz, and 30 Hz. The data demonstrate that the effect of friction on impedance increased as the frequency decreased. At low frequencies, the skin-electrode impedance of the electrode subjected to friction exhibited a significant increase compared to the electrode without friction, indicating a more pronounced effect of friction on low-frequency impedance, consistent with the theoretical analysis. Figure 8d,e present the ECG spectra and waveforms, respectively. It could be observed that when the PEDOT:PSS electrode was subjected to friction from insulating fabric, the ECG interference was primarily concentrated in the low-frequency components, reflected on the waveform as baseline interference. In the spectrum of Figure 8d, it could be seen that the signal amplitude for the electrode that suffered friction was higher than that without friction, especially in the low-frequency band. Figure 8e also clearly demonstrates that the ECG signal with friction exhibited more baseline interference.  
A quantitative analysis revealed that under friction conditions, the SNR was \(2.4128\pm 7.0784\) dB, while without friction, it was \(9.2164\pm 0.6696\) dB, indicating that friction degraded the ECG signal quality. Consequently, during daily wearable ECG monitoring, the friction between clothing and the electrode primarily affected the low-frequency skin-electrode impedance, leading to ECG baseline interference.  
Figure 7: Results of different electrode shapes with the same area (**a**) Bode plot of skin–electrode impedance; (**b**) Spectrogram of ECG; (**c**) ECG waveforms of different shapes.
# Physical Simulation Experiment for Visualizing Pulverized Coal Transport in Propped Fractures
## 2 Materials and Methods
### Experimental Material  
1. Pulverized coal and proppant  
Pulverized coal particles ranging from 80 to 100 mesh (0.15 to 0.18 mm) and 100 to 150 mesh (0.106 to 0.15 mm) were employed to analyze the impact of different pulverized coal particle sizes on coal transport. Natural quartz sand particles with a size of 16 to 20 mesh (0.85 to 1.18 mm) were chosen as the proppant. The mass fraction of SiO\({}_{2}\) in the quartz sand exceeded 99.97%, with a hardness of 7, exhibiting a brittle property without joints, and a density of 2.65 g/cm\({}^{3}\). Prior to each test, the quartz sand was thoroughly rinsed in high-purity distilled water until it was free of suspended impurities, and then the sand was spread over an area measuring 20 cm \(\times\) 2.63 cm.
2. Carrier fluid  
Four types of transport fluids (Table 1) were prepared to analyze the effects of fracturing fluids with different properties on pulverized coal transport. Among them, the active water fracturing fluid had a KCl concentration of 1.5%. The agglomerated fracturing fluid (1.5% KCl + 0.05% KO) caused the pulverized coal to settle and accumulate at the solution's bottom, while the dispersed fracturing fluid (1.5% KCl + 0.06% SA) allowed the pulverized coal to disperse and remain suspended in the solution. The KO in the agglomerated fracturing fluid was a compound surfactant consisting of anionic surfactant fast T (sodium dioctyl sulfosuccinate) and nonionic surfactant OP-10 (octylphenol polyoxyethylene ether-10) in a ratio of 9:1. The SA in the dispersed fracturing fluid was a compound surfactant composed of the anionic surfactant sodium dodecyl sulfate and the nonionic surfactant dodecyl alcohol ethoxylated ether in a ratio of 1:2.  
Figure 1: Schematic of the experimental setup for the physical simulation of pulverized coal transportation.
## 2 Immunomodulatory Function of Extracellular Hsp60, Hsp70, and Hsp90
### Amyotrophic Lateral Sclerosis  
ALS is a chronic inflammatory demyelinating disease that affects motor neurons and is characterized by atrophy and paralysis of muscles, with progressive aggravation over the years [78]. This disease occurs sporadically, but a small percentage is familial with mutations in specific genes, such as the gene encoding the free-radical-scavenging enzyme superoxide dismutase-1 (SOD1) [79]. An important aspect of SOD1-associated ALS is the deposition of SOD1 in large insoluble aggregates in motor neurons. The SOD1 mutated protein mediates the induction of the disease through the dysregulation of the heat shock response (HSR)-apoptosis axis [80]. The development of ALS is linked to the formation of intracellular aggregates of misfolded proteins [78]. Few data are available regarding the involvement of molecular chaperones in ALS onset. Motor neurons of ALS patients have an intrinsic deficit in the ability to activate the HSR and, consequently, do not readily regulate Hsp expression, as shown, for example, for Hsp70 [81]. It has been observed that the Hsp70/Hsp40 pair is complexed with the mutant form of the SOD1 protein in cultured neuronal cells [82]. However, data indicate that the increase of Hsp70 level alone is not sufficient to ameliorate mutant SOD1-protein-mediated toxicity in mouse models [83]. Histamine is neuroprotective through the HSR in motor neurons and microglia cell cultures, and in vivo in spinal cord and cortex from symptomatic SOD1-G93A mice [84]. These results emphasize the relevance of histidine-induced Hsp70 stimulation for preserving motor function [84]. Further, the intraperitoneal administration of human recombinant exogenous Hsp70 increased lifespan, delayed the onset of symptoms, preserved locomotor function, and prolonged motoneuron survival in a mouse model of ALS [85]. Extracellular Hsp70 stimulates the survival of neurons following injury [86] and overexpressed Hsp70 induces the survival of astrocytes [87]. Under stress, astrocytes increase the release of exosomes enriched in Hsp70, with positive implications on the survival of nearby neurons [88]. Interestingly, exosomes derived from cancer cells express Hsp70 on their surface, which allows their interaction with target cells carrying surface Hsp receptors [15]. Hsp70 (DnaJC5/Hsc70 complex) is also believed to be involved in the extracellular release of proteins associated with neurodegenerative disease as part of its chaperoning functions [89]. Exogenous Hsp70 protects from oxidative damage death in motor neurons through binding and sequestration of toxic proteins [90].
## 2 Materials and Methods
### Statistical Analysis
#### 2.5.2 The 400 m Sprint Analysis  
For the full 400 m sprint, GNSS-derived step counts were verified against the bicycle-mounted camera, which tracked the total steps taken throughout the sprint. Additionally, the GNSS-derived total sprint time was calculated by determining when the cumulative horizontal distance reached 400 m. This value was compared to the finish-line time recorded by the stationary 2D camera to assess accuracy. Percentage match was used as the evaluation metric, providing a quantitative measure of agreement between the GNSS-derived and reference sprint times.  
To further analyze the _SF_, we compared the GNSS-derived _SF_ estimates with those obtained from IMU data across the entire 400-m distance. Data were separated for Subject A and Subject B to account for individual differences in running characteristics. We also divided the _SF_ data by left and right legs to detect potential asymmetries in the _SF_, using IMU-derived IC events as the reference standard. Furthermore, we investigated the effects of track geometry by analyzing distinct straight (125-195 m and 325-400 m) and curved (5-115 m and 205-315 m) sections, while transitional zones were excluded to maintain clean comparisons. Statistical metrics, including the _RMSE_, _bias_ with 95% CI, SD, \(R\), and ICC, were computed. Agreement between methods was visually assessed using Bland-Altman plots and scatter plots.  
To illustrate dynamic changes in the running parameters throughout the sprint, we visualized the GNSS-derived _SF_, _SL_, and _running velocity_ for each step across the entire 400-m distance. These step-by-step visualizations demonstrate how the system can capture every stride variation and highlight performance-relevant factors, such as the peak velocity, changes in _SL_, and gradual decline in running velocity as fatigue sets in. Furthermore, by marking periods of reduced GNSS positional accuracy (i.e., 'Float' solutions), we enhanced transparency regarding data quality. This approach underscores the method's potential utility for detailed training analyses, enabling coaches and athletes to pinpoint stride-by-stride mechanics which were previously averaged over larger intervals.
# Progress in Transparent Nano-Ceramics and Their Potential Applications
## 6 Discussion
### Relationship between Nano Powders and Performance  
According to the types of materials, transparent nano-ceramic materials are mainly divided into metal oxides and non-metal oxides [2]. Transparent nano-ceramics based on metal oxide materials mainly include those based on alumina (Al\({}_{2}\)O\({}_{3}\)), magnesia (MgO), zirconia (ZrO\({}_{2}\)), yttria (Y\({}_{2}\)O\({}_{3}\)), lutetia (Lu\({}_{2}\)O\({}_{3}\)), and other oxides. Transparent nano-ceramics based on non-metal oxide materials mainly include those based on AlON and AlN, silalon and silicon nitride (Si\({}_{3}\)N\({}_{4}\)), and fluoride. To meet the needs of different scenarios, the required nano-transparent ceramic functional materials can be prepared by selecting different nano powder. For example, in order to solve lighting applications, alumina (Al\({}_{2}\)O\({}_{3}\))-based nano-powders can be selected for sintering. To meet the requirements of the gain medium of high-powered lasers, doped YAG transparent nano-ceramics are generally selected. There  
Figure 18: Average diameter of nano powders in the reviewed transparent nano-ceramics [12; 14; 15; 17; 43; 52; 81; 83; 86; 88].  
fore, by selecting different types of nano powders for preparation, we can obtain different types of transparent nano-ceramics. For example, some need to have high-temperature resistance, and some need to have a magneto-optical effect. Judging from the existing research on transparent nano-ceramics, those based on alumina (Al\({}_{2}\)O\({}_{3}\)) are popular research topics and have also been used in commercial applications that are closely related to the chemical and physical properties of Al. In addition, the doping of rare earth elements can significantly improve the performance of transparent nano-ceramics. The most typical example is the doping of rare earth elements in YAG transparent nano-ceramics, which can significantly improve their performance. Additionally, the Verdet constant of TAG transparent nano-ceramics can be changed by adding rare earth elements.
# A QUBO Model for the Traveling Salesman Problem with Time Windows
## 1 Introduction
It is the norm, rather than the exception that the locations must be visited within a specific time window. The optimal or near-optimal solution to such problems is not only important in terms of distance and time minimization but also in terms of environmental issues, such as fuel consumption minimization. Although TSPTW has been investigated in its classical form, no work is known to us that studies this problem within the QUBO framework, using the D-Wave platform, or even quantum annealing in general. **Contribution**. In this paper, we give the first, to the best of our knowledge, QUBO formulation for the TSP with Time Windows. The existence of an Ising or QUBO formulation for a problem is the essential precondition for its solution on the current generation of D-Wave computers. For the vanilla TSP, there exists such a formulation, as presented in an elegant and comprehensive manner in [33], which has enabled the actual solution of TSP instances on the D-Wave platform (see [36; 37; 38]). In contrast, prior to this work, the TSPTW had not been cast in the QUBO framework. This can be attributed to the extra difficulty of expressing the time window constraints of TSPTW. We hope and expect that the formulation presented here will lead to the experimental execution of small-scale TSPTW instances. This paper is organized as follows. The most relevant to our study work is presented in Section 2. Section 3 is devoted to the standard definitions and notation of the conventional Traveling Salesman Problem with Time Windows. Section 4, the most extensive section of this article, contains the main contribution of our paper. It includes an in-depth presentation of all the required rigorous mathematical definitions and the proposed modeling that allows us to map the TSPTW into the QUBO framework. Finally, conclusions and ideas for future work are given in Section 5.
## 3. Results and Discussion
### Volume-Free Optimal Equal Bale Dimensions Design
#### 3.3.2 Example 3  
The same mechanical parameters of the film and wrapping parameters constraints as in the previous examples are assumed. Film widths \(b_{f}=0.75,\ 0.9\) m and \(4\leq p_{l}\leq 16\) are considered. For \(b_{f}=0.75\) m, similarly as in Example 1, the set of integer solutions \(p_{b}\) of inequalities expressed by Equation (39) is empty for all \(p_{l}\) considered, while for the wider film for \(p_{l}=14\) solution to Problem 3 exists. When the lower constraint expressed by Equation (22) is changed into \(\delta_{min}=0.1\) m, then for some \(p_{l}\) Problem 3 has a solution. These solutions are listed in Table 4. For every \(p_{l}\), nonempty \(\mathcal{P}_{3}\) is a singleton (a unit set), thus step 5 of the selection of best film layers composition \(p_{l}=p_{b}+p_{u}\) can be omitted here; however film usage \(FC\) is added in the last column.
# A landslide Warning Method Based on K-Means-ResNet Fast Classification Model
## 2 Methodology
### The Proposed Model  
In order to improve the accuracy of landslide warning and reduce the false and missing alarms caused by the wrong thresholds, we propose a landslide warning model based on the K-means-ResNet algorithm.  
The proposed model has four parts: constructing landslide displacement subsequence, extracting features and K-means clustering, obtaining input features by gray correlation analysis [31] and ResNet classification (Figure 3).  
(1) Construct landslide displacement subseries  
Landslide displacement is a non-smooth time series that varies with time. The displacement time series data are used to generate more equal-length time series for data analysis by the sliding window.  
(2) Extract features and perform K-means clustering  
Traditional clustering methods cannot be applied to time series data directly, so we extract features of the time series subsequences for analysis.  
Deformation velocity can directly reflect the landslide status. The extracted velocity has a different order of magnitude, so it is normalized, that is, mapped to an interval of \([0,1]\), to ensure the accuracy and validity of the clustering results. The K-means algorithm is used to cluster the velocity and acceleration. Based on the clustering results, a dataset with labels is constructed to identify the categories prone to landslides, and take them as the basis for landslide warning.  
(3) Obtain input features by grey correlation analysis  
Figure 2: Residual structures of the ResNet. The left panel is for the networks with fewer layers, such as ResNet-18 and ResNet-34. The right one is for the networks with more layers, such as ResNet-101 and ResNet-152.  
The deformation of landslide is caused by a combination of factors. Therefore, a dataset consisting of landslide triggering factors and monitored displacement is constructed to select the deformation characteristics factors by gray correlation analysis.  
(4) ResNet classification  
The classification dataset is constructed based on the feature factors using clustering results as labels, and it is divided into a training set and a test set. The ResNet classification model is applied to the datasets, and the results are compared with those of the MLP model, the LeNet model and the traditional threshold warning method.  
Figure 3: Flowchart of the proposed method.
# Crop Yield Prediction Based on Agrometeorological Indexes and Remote Sensing Data
## 5 Discussion
Meanwhile, the applicability of this research method in other study areas could be tested with highly efficiency because the model constructed in this study has explorability, simplicity, and convenience. Therefore, the wide use of the cloud computing platforms for data calculation and processing is a new trend which will assist greatly in yield estimation with big data processing.