esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In recent years, the concept of autonomous navigation systems has gained substantial significance, with the potential to change the traditional concept of autonomous navigation. The presented numerical simulation investigates the feasibility of a ship's autonomous navigation system through a laser communication infrastructure handled by a two-degrees-of-freedom (DoFs) acquisition, tracking and pointing (ATP) system able to enable ship-satellite data transmission. The methodology introduced presents the geometrical and kinematic delineation of the model, coupled with the implemented control system, aimed at assessing the pointing accuracy. The minimum requested pointing accuracy is 100 \(\upmu\)rad and the analysis highlights the need of using methodologies to reduce the pointing error. Two approaches are investigated to examine a possible improvement of the system, and results show that the pointing phase is influenced less by ship motions and more by errors that occur during the satellite's positioning and the ship motion acquisition process. A trade-off in choosing parameters to improve the system's accuracy leads to a satellite's first targeting time of 0.25 s alongside the probability of hitting the target once every 0.0013 s. The reliability of the system is evaluated through a brief sizing of the optical electromechanical component of the system using the trade-off parameters chosen to improve the pointing phase accuracy.  
maritime navigation system; remote control; satellite navigation 2023
###### Abstract  
In this paper, a fluorescence resonance energy transfer (FRET)-based sensor for ultra-sensitive detection of H\({}_{2}\)O\({}_{2}\) was developed by utilizing the unique enzymatic properties of peroxiredoxin (Prx) to H\({}_{2}\)O\({}_{2}\). Cyan and yellow fluorescent protein (CFP and YFP) were fused to Prx and mutant thioredoxin (mTrx), respectively. In the presence of H\({}_{2}\)O\({}_{2}\), Prx was oxidized into covalent homodimer through disulfide bonds, which were further reduced by mTrx to form a stable mixed disulfide bond intermediate between CFP-Prx and mTrx-YFP, inducing FRET. A linear quantification range of 10-320 nM was obtained according to the applied protein concentrations and the detection limit (LOD) was determined to be as low as 4 nM. By the assistance of glucose oxidase to transform glucose into H\({}_{2}\)O\({}_{2}\), the CFP-Prx/mTrx-YFP system (CPmTY) was further exploited for the detection of glucose in real sample with good performance, suggesting this CPmTY protein sensor is highly practical.  
Keywords:hydrogen peroxide; biosensors; peroxiredoxin; thioredoxin +
Footnote †: journal: Applied Physics Letters
## 3 Results  
HCA Discrimination of Waters Based on Conventionally Used Physico-Chemical Parameters (Ions Type and Concentration, pH and \({}^{17}\)O-NMR)  
The nine different water types are listed in Table 1, with their general information including the concentration of ions (Na\({}^{+}\), K\({}^{+}\), Mg\({}^{2+}\), Ca\({}^{2+}\)), pH and \({}^{17}\)O-NMR values. The results of HCA are represented by dendrograms in Figure 1, where the dendrogram in the upper panel (Figure 1a) shows natural groupings of water samples described only using information on their ion content, whereas, in the lower panel (Figure 1b), in addition to the ion content, the pH and \({}^{17}\)O-NMR values were included as descriptors. When the analysis was performed using only concentrations of ions (Na\({}^{+}\), K\({}^{+}\), Mg\({}^{2+}\), Ca\({}^{2+}\)), the HCA algorithm found six clusters of similar water samples for the similarity level of 0.8 (Figure 1a). In this case, pure (Q) and processed pure water (PQ) show a high degree of similarity (blue cluster), and so do Changbai (Cha) waters, which are processed in different years--in 2016 (Cha2016) and 2014 (Cha2014) (orange cluster). Moreover, if waters were assessed based only on their ion content, waters that are sourced from different geographical locations--Changbai (Cha) and Ganten (Gan)--could not be well separated (magenta cluster). If additional parameters, such as the pH and \({}^{17}\)O-NMR, are included in the analysis, for the level of similarity of 0.8, the waters are classified into eight groups (Figure 1b), with waters Cha2016 and Cha2014 in one cluster, meaning that physico-chemical parameters are not enough to successfully discriminate between waters processed in different years, i.e., conventional parameters are not able to provide information on the age of the water.
## 3 Results and Discussion
### Phase I: Greenhouse Column Study
#### 3.2.3 Plant Growth in the Greenhouse Column Study  
The total plant biomass of camelina increased with increasing compost rates with significant yield increases in columns with compost rates of 10% and 20%. The dry weight of roots increased with the increase in compost rate (except that of 10%) but the increase was not statistically significant across different compost rates (Table 2). The dry weight of stems increased with increasing compost rates, and the increase was significant at compost rates of 10% and 20%. For leaves, the dry weight of increased with increasing compost rates and the increase was significant at the maximum compost rate of 20%. The dry weight of pods and seeds did not show any specific trend with increasing compost rates, but the maximum and significant increase was observed in camelina plants at the compost rate of 10%.  
The total plant biomass of field pennycress increased with increasing compost rates, with significant yield increases in columns with compost rates of 10% and 20%. In roots and leaves, the dry weight increased significantly with increase in compost rate in columns with compost rates of 10% and 20% (Table 2). The dry weight of the stems, pods, and seeds also increased with increasing compost rates, but the increase was significant only in columns with the maximum compost rate of 20%.  
Figure 2: Top panel: Total Cu (mg/L) in leachates of camelina (**A**) and field pennycress (**B**) and soil as affected by different rates of compost during the study period. Bottom panel: Total Cu (mg/kg) in soil in camelina (**C**) and field pennycress (**D**) during the study period. Data are reported as mean \(\pm\) S.D (\(n=3\)). Means are compared by Tukey Kramer-HSD test (\(\alpha=0.05\)). The treatment levels that are not connected by the same letter are significantly different from each other.
## 3 Results and Discussion
### Characteristics of the Soil and Paper-Ash  
The mineralogical composition of the soil used is presented in the Supplementary Material (Figure S1). The results of the XRD analyses revealed that the soil is composed of quartz, dolomite, feldspar, calcite, illite/muscovite, clinochore, and hornblende. The paper-ash is a fine-grade, highly alkaline (pH-12) hydraulically active material, with a specific surface area (BET) of 8.1 m\({}^{2}\)/g, containing approximately 40% Ca, 6% Si, 5% Al, and 2% Mg. Ba, Fe, S, K, Zn, and Ni are present in lower concentrations and some other elements in trace amounts. Phase analyses revealed the presence of calcite, lime, portlandite, quartz, talc, gehlenite, anhydrite, and dolomite, together with an amorphous phase. SEM/EDS analysis results correlated with the XRD analysis (Figure 2A). The amorphous phase consists primarily of Si, Al, and Ca in different proportions (Figure 2B). Based on the leaching test, applying the standardized leaching procedure [(37; 38)], it was found that the above-mentioned elements are present in paper-ash as low water-soluble species. Only the concentration of Ba exceeded the limiting value for inert materials, set by the current Slovenian landfilling legislation [(37; 38)].  
Figure 2: SEM micrographs of the paper ash; (**A**) showing the presence of different constituent phases; (**B**) the results of the semi-quantitative chemical analysis (EDS) of paper ash amorphous phase.  
To separately follow the carbonation process for the paper-ash itself, a paste was prepared by mixing the paper-ash with an excess amount of demineralized water. The XRD analyses were performed at seven different time intervals to define the evolution of the calcite and portlandite mineral phases. The results in Figure S2 showed an increase in the relative quantity of calcite and a decrease in the relative quantity of portlandite.  
Investigation of the Immobilization Mechanisms, Stability of the Mineral Phases of Cd, Pb, and Zn after the Remediation, and the Environmental Impacts of Soil Remediation  
To study the mechanisms of immobilization and the stability of the mineral phases of Cd, Pb, and Zn after remediation, and to evaluate the environmental impacts of soil remediation, partitioning of Cd, Pb, and Zn were performed in uncontaminated, contaminated, and remediated soils over a period of one year in different time intervals by applying a sequential extraction procedure and the data were compared with complementary results from the XRD analyses.
# A Zero-Reference Low-Light Image-Enhancement Approach Based on Noise Estimation
## 3 Results
### Application of Low-Light Images in Mines and Indoor Squares  
To verify the practicality of our method, the proposed method was used for mine images and indoor square low-light images for enhancement, and the original low-light image and the enhanced image are shown in Figure 6.  
In Figure 6, the image enhanced by our method reveals a clearer distribution of debris, coal rods, and gangue in the coal flow in comparison with the original low-light image.
## 2 Description of the LMS-Based Algorithms
### Filtered-X LMS Algorithm with Normalized Step-Size Applied to a Feed-Forward Configuration  
In many applications, the power of the noise source is not stationary. Assuming a fixed step-size parameter in the FXLMS algorithm, the coefficients updating becomes slower as the power of the signal decreases, whilst instability can occur when the power increases. Many proposals can be found in the literature [18; 22] to automate the process of choice of an adequate step-size parameter. For example, in [19], the step-size is normalized with respect to the power of the reference signal. However, this approach does not make it possible to take into account the effect of the secondary path filter applied to the reference signal. Other authors propose determining the step-size parameter as a function of the energy at the error microphone [20], even though in this case, the noise not coherent with the primary source could be included, altering the step-size evaluation. One of the best choices is computing a step-size parameter based on Griffiths' cross-correlation between the filtered reference signal and the error signal [21]. This approach makes it possible to vary the step-size parameter on the basis of the gradient of the updating coefficient, resulting in a robust algorithm, though the computational complexity for a multichannel system significantly increases. In this article, a normalization of the step-size with respect to the power of the filtered reference signal, as proposed in [29], is examined. In this case, Equation (3) must be substituted with Equation (7), in which the step-size parameters become functions of variable \(n\):  
\[w_{j,k}(n+1)=w_{j,k}(n)-\mu_{j}(n)\sum_{i=1}^{2}e_{i}(n)x_{ji}^{\prime}(n-k) \quad k=0,1,\ldots,K-1. \tag{7}\]  
The step-size parameters are evaluated at each time instant \(n\) as defined in Equation (8):  
\[\mu_{j}(n+1)=\frac{\mu_{in}}{(M+K)\max_{i=1,2}\{P_{ji}^{\prime}(n+1)\}}, \tag{8}\]  
in which \(\mu_{in}\) is an initial value and the power of the filtered reference signal \(P_{ji}^{\prime}\) is updated with the following law:  
\[P_{ji}^{\prime}(n+1)=\beta P_{ji}^{\prime}(n)+(1-\beta)\sum_{k=0}^{K-1}x_{ji}^ {\prime 2}(n-k), \tag{9}\]  
where \(\beta\) is a \"forgetting\" factor (\(0\leq\beta<1\)), which determines the speed of updating of the step size. Low values of \(\beta\) are chosen when the reference signal is highly variable, while high values are preferred for stationary contests. The choice of the maximum power of the filtered reference signal \(P_{ji}^{\prime}(n)\) makes it possible to select the minimum step size, thus limiting the possibilities of instability, despite a slower convergence rate. For the application, the step-size parameters are constrained between two arbitrary limits: a maximum value \(\mu_{max}\), to avoid instability due to the large value of \(\mu_{j}\), and a minimum value \(\mu_{min}\), to avoid slow convergence and coefficients updating based on the quantization error associated with fixed-point math (\(\mu_{min}\leq\mu_{j}(n)\leq\mu_{max}\)).  
Figure 3: Schematic representation of the offline estimation procedure.
# Comparison of Distributed Tamper-Proof Storage Methods for Public Key Infrastructures
## 4 Alternative Implementations and Concept Analysis
### Sia Skynet  
In its abstract form, Sia offers the same functionalities as plain IPFS storage systems when using its _Skynet_ API. For public key storage, Skynet offers a method to upload arbitrary data using a file and a corresponding filename. Again, following set conventions, the filename will be using the naming scheme \(<ID_{S}.json>\). After uploading a file to the Skynet network, the user receives a so-called _Skylink_, which can be compared to the concept of a CID that is used in IPFS. Essentially, a Skylink allows direct access to the given file.  
To support the query of an uploaded public key, an alternative approach needs to be proposed, since the previously described concepts that apply for private and public IPFS solutions do not apply to Sia Skynet. Other than Web3.Storage, for example, Skynet does not offer a direct API endpoint to query all uploaded files. Therefore, we propose an alternative approach using the available _SkyDB_[33] that is offered by the Sia Blockchain. Other than the Skynet system, the SkyDB is directly stored on the Sia Blockchain and thus only allows for small datasets to be stored. By adding a mapping file, called _devices.json_ for this case, a mapping can be created for each new \(ID_{S}\)-\(PK_{S}\) pair using the Filename and the returned Skylink ID. On creation, the _devices.json_ file will be stored on-chain and can be queried, edited, and updated at any time. After a successful query of the mapping file, the desired \(ID_{S}\)-\(PK_{S}\) mapping for the given Sensor Node can be extracted.  
To support the special storing of the TPM's public key, this approach also follows the same principle as described in the previous section for IPFS.
## 5 Discussion  
The DC, DDC, and CDC cluster-based associative classification algorithms are similar to our developed methods. All algorithms utilize the \"APRIORI\" algorithm to generate the strong class association rules and hierarchical clustering algorithm to cluster the CARs. After clustering the CARs, the representative CAR is selected for each cluster to produce the final classifier. The DC method was built based on a \"direct\" (depending only on rule items) distance measure (for clustering purposes), and the method for extracting a representative CAR was based on a cluster center. The DDC method was formed based on a \"direct\" (depending only on rule items) distance measure, and the method for extracting a representative CAR was based on database coverage. The CDC method was formed based on the \"combined\" (depending only on rule items and database coverage) distance measure, and the method for extracting a representative CAR was based on database coverage. The key differences between the newly developed methods and the DC, DDC, and CDC methods are that the proposed methods applied a \"combined\" distance metric (with different parameters), and a representative CAR was extracted based on the cluster center.  
The newly developed models were comparable to all aforementioned associative classification models, both in accuracy and size. The most important advantage of our proposed methods was to generate a smaller classifier on bigger datasets compared to associative and traditional (a previous study proved that traditional rule-based algorithms are sensitive to dataset size) rule-learners.  
We also tried to measure the accuracy and other evaluation measures (Precision, Recall, and F-measure) of the complete rule set without clustering and representative rule extraction, but the accuracies were never better than the best accuracy in the result table.  
Our proposed algorithm has some limitations:  
* To obtain enough class association rules for each class value, we need to apply appropriate minimum support and minimum confidence thresholds. That is, we should take into consideration the class distribution of each dataset;
* Associative classifiers may get lower results in terms of classification accuracy than other traditional classification methods on imbalanced datasets, because imbalanced class distribution may affect the rule-generation part of associative classification algorithms;
* The newly developed distance metric is dependable to \(\alpha\) parameter: if we lower the \(\alpha\) parameter, we get a higher number of clusters (higher number of rules) with better accuracy, and if we increase the \(\alpha\) parameter, we obtain a smaller classifier with slightly lower accuracy, which is an expected behavior.
# NFT Technology for Enhanced Global Digital Registers: A Novel Approach to Tokenization
## 9 Practical Applications and Use Cases
### Tokenization of Cultural Heritage and Other Valuable Digital Assets
#### 9.1.4 Digitization of Museums and Art Collections  
Museums, galleries, and private collectors can leverage NFT-based tokenization to catalog and manage their art collections. By creating digital tokens for each artwork or artifact, provenance and ownership records can be securely maintained and easily accessed. Additionally, this approach can facilitate the creation of virtual exhibitions, opening up new avenues for education, research, and global collaboration.
# Applying Machine Learning in Cloud Service Price Prediction: The Case of Amazon IaaS
## 6 Discussion
### Implications for Cloud-Usage Strategies and Planning  
The primary objective of forecasting cloud pricing does not solely revolve around achieving cost reduction, but rather focuses on empowering users to make better-informed decisions. The determination of cloud service pricing is a complex matter that is impacted by a range of factors, such as the types of instances available, the geographical regions in which the service is offered, and the various payment options that customers can choose from. Nevertheless, the task of price prediction constitutes only a fraction of the overall equation. This tool serves as a valuable resource for users to enhance their cloud utilization strategies and planning.  
This assumes that a user makes a decision to reserve instances in a particular region and selects an upfront payment option, taking into consideration our projected prices. Subsequently, the user discovers the presence of idle reserved instances in their possession, prompting their desire to engage in the process of selling said instances. In this scenario, our predictive model can assist the user in determining the most advantageous moment to sell these instances by forecasting future prices. By strategically selling assets during periods of predicted price increases, users have the potential to recover a greater portion of their initial investment.  
Moreover, the inclusion of regional price predictions in our model would enable users to make informed decisions regarding both the timing and location of their sales. The variation in demand levels and pricing fluctuations across different regions underscores the importance of accurately predicting prices on a regional basis. Such predictive capabilities would yield significant benefits.
## 3 Results and Discussions
### Model Performance Comparison
Figure 12a,b illustrate the performances of the five models in the voltage and SOC prediction tasks, respectively. From Figure 12a, it can be observed that the MHA-BiLSTM model consistently provides predictions closest to the actual output voltage of the equivalent circuit model throughout the entire period, with a deviation ranging from 0.015 V to 0.025 V, demonstrating the highest prediction accuracy. In comparison, the LSTM and GRU models exhibit slightly lower accuracy, with slightly larger deviations, but still within acceptable ranges. The A-BiLSTM and A-LSTM models show significantly larger deviations, approximately 0.05 V higher, which is likely due to overfitting. Figure 12: Comparison of simulated and predicted results of voltage and SOC, with prediction errors for five models over a selected five-minute interval in the simulated battery data: (**a**) Voltage predictions, (**b**) SOC predictions, (**c**) Voltage prediction errors, and (**d**) SOC prediction errors. For SOC prediction, a similar trend in the voltage prediction is observed, as shown in Figure 12b. The MHA-BiLSTM model again demonstrates the highest prediction accuracy, with deviations ranging from 0.02 to 0.04. LSTM and GRU models achieve reasonable accuracy, falling into the second tier, while A-BiLSTM and A-LSTM exhibit larger deviations, exceeding 0.1. Figure 12d provides a clearer view of the SOC prediction error trends. During the first two minutes, all models maintain acceptable prediction deviations. However, as time progresses, the prediction errors for all models increase significantly, particularly during the final two minutes, where the error accumulation effect becomes more pronounced. The MSE and MAE results for different models are summarized in Table 3, respectively. It can be observed that in both the voltage and SOC predictions, the MHA-BiLSTM model had the lowest MSE values, at 0.000902 and 0.002253, respectively, indicating the highest prediction accuracy. In contrast, other models, such as A-BiLSTM, A-LSTM, and GRU, exhibited higher MSE values, with A-BiLSTM having the highest error in SOC prediction at 0.020177. For the voltage prediction, the GRU model had a relatively low MSE of 0.010469, but its error in SOC prediction remained significantly higher than that of the MHA-BiLSTM, at 0.007566. Overall, the MHA-BiLSTM model consistently demonstrated a superior performance across both tasks.