esa-sceva/satcom-chunk-collection huggingface dataset:
# Addressing ZSM Security Issues with Blockchain Technology
## 7 Assessment of Blockchain and DAG Platforms to Be Used for the ZSM-Solution Implementation
### Candidate Blockchain and DAG Solutions for Our Approach
#### 7.1.3 R3 Corda  
Another popular blockchain platform is the R3 Corda, which allows the implementation of private permitted networks ready to support various use cases. The consensus process can be implemented using either a crash or byzantine fault-tolerant algorithm. The selection of the desired algorithm depends on the use case scenario as it is stated earlier. Similar to the previous platforms, Corda supports both BFT and Raft mechanisms. Considering our use case presented previously, a notary [48] that uses Raft between nodes that form a network of NPs will present extremely good performance in terms of throughput and latency, at the cost of being more vulnerable to malicious attack by whichever node has been elected as a leader.  
Moreover, the Corda platform supports SCs for the development of several solutions called Cor-Dapps. SCs are defined using a restricted form of Java Virtual Machine (JVM) bytecode, which automatically allows developers to implement the logic of their solution by writing code in a variety of programming languages. Developers are able to use well-developed toolchains and reuse code written in Java or other JVM-compatible languages, which is a fact that widens the application area of this platform. Additionally, Corda supports the development and use of tokens which can be used according to our use case to represent resources, such as CPU, memory and others [48].  
Additionally, the privacy feature is supported in this blockchain as Corda uses several techniques to achieve this functionality. This means that the implementation of private transactions or the execution of private SCs is feasible. At the same time, Corda supports the use of oracles, defined as a network service that is trusted to sign transactions containing statements about the world outside the ledger only if the statements are true. This characteristic allows the secure communication of blockchain with entities outside the network such as the ZSM framework and its services. Additionally, Corda presents high-performance values as it can reach up to 20,000 transactions per second according to their benchmarking results illustrated on the platform's website [43; 48].
## 9 Major Project Issues and Survey Results
### Experience in the Use of iMMS Tools  
One factor that initially created numerous difficulties was the highly innovative approach of the project, as there were no analogous projects that required such an extensive mapping of assets for large real estate portfolios using iMMS devices. Therefore, the surveyors had to build experience in organizing the survey activities and processing the large volume of data collected during the execution of the work itself. In particular, the development of internal procedures to ensure that the work was carried out with quality required time, which led to some delays in the execution of the tasks.
## 4 Results
### Anomaly Detection
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multirow{2}{*}{**Accuracy (\%)**} & \multicolumn{2}{c}{**Precision (\%)**} & \multicolumn{2}{c}{**Recall (\%)**} & \multicolumn{2}{c}{**F1-Score (\%)**} \\  & & **Macro** & **Weighted** & **Macro** & **Weighted** & **Macro** & **Weighted** \\ \hline Test Set 1 & 73.29 & 59.20 & 89.44 & 73.27 & 73.29 & 58.56 & 78.65 \\ Test Set 2 & 74.14 & 56.97 & 91.73 & 72.50 & 74.14 & 55.68 & 80.39 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of defect detection using the anomaly detection engine. Test set 1 refers to test images from batches 1, 2, 3, and 4; test set 2 comprises test set 1 and the entirety of batches 5 and 6. Figure 7: Confusion matrices regarding defects detection using the anomaly detector engine. (**a**) Confusion matrix for test set 1; (**b**) Confusion matrix for test set 2.
## 1 Introduction  
Forage is an important source of nutrients in the diets for ruminants worldwide, providing energy and protein for maintenance, growth, and production [1, 2]. Unreliable weather conditions make it difficult for farmers to produce good quality forage year-round [3] and, as a result, various methods have been used to preserve fresh forages (e.g., hay or silage).  
Ensiling is one of the methods used to preserve fresh forages. Silage has several benefits which include low and stable pH (from the production of acids during the ensiling process) as well as improved dry matter intake (DMI) [4, 5, 6]. To facilitate the fermentation process during ensiling, silage additives such as lactic acid bacteria (LAB) have been added [4, 7]. Inoculation of silage substrate with LAB results in a rapid decline in pH at the beginning of the silage fermentation [8, 9] with a concomitant increase in lactic acid production [9]. Furthermore, there is a reduction in ammonia-N formation in LAB inoculated substrate compared with uninoculated substrates [4]. There are several different strains of LAB available for use during silage making, each with its own unique fermentation characteristics [10; 11]. A number of silage additives are commercially developed with mixed strains of LAB, but they may be lacking in substrate specificity for certain roughages such as rice straw [12].  
In the current study, two substrates with different nutritional characteristics were used to determine the effects of silage inoculants on their nutritive value. Maize (_Zea mays_ L.), grown as a forage feed worldwide [13], is generally preserved as silage because of its low dry matter (DM) content (28-40%), low buffering capacity, and substantial water-soluble carbohydrates which produce organic acids, including lactic acid [11; 14]. Feeding maize silage can increase the forage DM intake [15; 16; 17] and the milk yield as it is rich in metabolizable energy [16; 17]. On the contrary, rice straw (_Oryza sativa_), an agricultural by-product, has relatively low digestibility due to a high level of cell wall components such as cellulose, hemicellulose, and lignin [18; 19]. In Asian countries, ensiling rice straw was also one alternative to re-utilize this byproduct instead of disposing of it by burning and causing air pollution [20; 21]. In the study by [PERSON] et al. [22], feeding rice straw-based total mixed ration to Boer goats can produce favorable carcass characteristics and meat quality.  
The fermentation characteristics of maize and rice straw differ due to their differing nutrient profiles and, as such, the application of appropriate additives for ensiling is essential to get the best out of each forage. The aim of the study was to examine how different LAB strains added during the ensiling process affect the nutritive value and in vitro digestibility of maize and rice straw silages.
# A Review of Research on Diagnosability of Control Systems Based on Structural Analysis
## 2 Diagnosability Architecture for Control Systems Based on Structural Analysis
### A Structural Model for Diagnosability Based on Structural Analysis
#### 2.1.2 Behavioral Model of the System Modules  
Different types of faults are often considered to be different behavioral modes. The behavioral modes of sensors include no-fault, the stuck-at (complete) fault, the fixed (constant) bias fault, the drift bias (time-varying) fault, the gain fault, and the unknown fault [21]. The actuator behavior modes include no-fault, locked, floating, runaway, loss of effectiveness, and the unknown fault [22]. Let \(NF\) stand for no fault, \(U\!F\) for unknown fault, \(C\) for stuck, and B for deviation. \(c_{i}=NF\) represents \(c_{i}\) in a no-fault behavior mode. The set \(\mathbf{B}_{c_{i}}=\{NF,U\!F,\ldots\}\) represents all possible behavioral modes of \(c_{i}\), and \(c_{i}\in\mathbf{B}_{c_{i}}\) means that \(c_{i}\) is precisely one of these behavioral modes.
## 4 Experimental Environment and Results
### Experimental Results
#### 4.2.2 Three-Dimensional Diagram of Nonlinear Ranging Model  
The experiment was conducted in IT-1 building and IT-2 building of Kyungpook National University. Select the area covered by the Wi-Fi signal, select 10 test points in this area, and evenly distribute each test point for 100 times and 10 tests. Every reference point can get a total of 100,000 data values. We obtained the RSSI value and CSI value through MATLAB extraction and analysis. In the actual signal propagation environment, the RSSI value is susceptible to environmental influences. If the measured RSSI value is simply converted into a distance, the ranging error becomes larger. Therefore, when measuring distance, we should pre-process the RSSI value. We use the measured RSSI value multiple times to perform arithmetic average to improve the accuracy of the RSSI value. While CSI is relatively stable, we obtain the effective value of the CSI value by weighting the subcarriers.  
The acquisition process of the ranging model can be divided into three stages; First, the RSSI and CSI of the transmitted signal of the transmitting node are obtained. Next, the receiving node uses the strength value of the received signal and the effective value of CSI to calculate the propagation loss of the ranging model and the parameters related to the indoor environment. Finally, the receiving node uses theoretical or empirical models to convert the signal propagation loss into the distance. The empirical model is a model formed by setting multiple reference points in the actual environment, and then measuring the relationship between signal strength and distance while at the same time combined with a mathematical fitting algorithm. The theoretical model selected in this paper is shown in Equations (3) and (11). Figure 13 shows the 3D graph of RSSI signal attenuation with distance in IT-1 building. Figure 14 shows the 3D graph of CSI effective value attenuation with distance in IT-1 building.  
In practical applications, the complex and changeable indoor environment can cause multipath noise in RSSI and CSI measurements. It is assumed that this noise is additive white noise, which conforms to the Gaussian distribution. We need to choose the most suitable filter to eliminate the influence of noise on RSSI and CSI values, and obtain accurate distance measurement results.  
Figure 12: CSI effective values obtained on antenna 2 in different indoor environments.
## 2 BDS-3 Satellites POD Method
### Processing Strategy  
The satellite orbit determination mainly includes the choice of observation model parameters, the orbit error correction model, and the satellite dynamic model. The important options of the detailed processing strategy for the LEO POD are listed in Table 1.  
Due to the large number of observations to be processed, we selected an arc length of 72 h and a processing interval of 300 s. The elevation angle threshold cut-off elevation is set to \(5^{\circ}\) and \(1^{\circ}\) for ground stations and LEO satellites, respectively [30]. In terms of force model, BDS-3 and LEO satellites suffer from different perturbative forces, since they move at different orbital altitudes, especially in the aspect of non-gravitational forces. For BDS-3 satellites, the solar radiation pressure (SRP) serves as the primary source of non-gravitational forces, and the atmospheric drag is neglected in the BDS-3 POD processing [31]. Different from BDS-3 satellites, the atmosphere drag plays a dominant role in the non-gravitational forces for LEO. We used the DTM94 model for this purpose. The estimated model parameters in a POD fit analysis include the satellite state vector, clock offsets, empirical accelerations, SRP, and atmospheric drag. The estimation process involves a least squares adjustment method, whereby the objective is to minimize the difference between the observed and modeled data by iteratively adjusting the model parameters until a satisfactory fit is achieved [32].  
The whole constellation rotation problem is solved using a ground-based anchoring station, which is located in China and can be linked to all BDS-3 satellites for dual one  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Project** & **Parameters and Models** \\ \hline Elevation Angle Threshold & \(5^{\circ}\) for the ground station and \(1^{\circ}\) for LEO \\ Earth Gravity Field & EIGEN6C (12 \(\times\) 12) for BDS-3 and EIGEN6C \\  & (120 \(\times\) 120) for LEO [33] \\ N-body Perturbation & JPL DE405 [34] \\ SRP & ECOM 5 model for BDS-3 and macro-model for LEO [24] \\ Atmospheric drag & DTM94 [35] for LEO \\ BDS-3 Phase Center Offset (PCO) and & igs14.atx \\ Phase Center Variation (PCV) & igs14.atx \\ Station PCO and PCV & igs14.atx \\ LEO PCO and PCV & None \\ Solid tide and Pole tide & IERS 2010 [36] \\ Relativity & IERS 2010 \\ Ocean tide & FES 2004 [37] \\ Earth rotation parameters & One set per arc \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed processing strategy for the LEO POD.  
way ranging, and its coordinates are fixed to provide the constellation reference. Since the anchor station and the satellite share the same ISL measurement and communication system, the Ka observations of the anchor station and the inter-satellite Ka observations are treated in the same way. The important options of the detailed processing strategy for the ISL POD are listed in Table 2.
## 2 Methods
### Theory of Fuzzy Sets  
The theory of fuzzy sets primarily addresses uncertain or ambiguous problems. Its essence lies in flexibility, which is a fuzzification of the absolute membership relationship found in classical sets [35]. This theory represents the membership degree of elements within a set using characteristic functions. Instead of being represented by a set of two values (0 and 1), the membership degree is now expressed by any value within the interval \([0,1]\), with this value corresponding to the degree of membership [36].  
In the fuzzy set theory, given an argument domain \(U\), \(F(x)\in[0,\,1]\) corresponds to any \(x\in U\). \(F\) is a fuzzy set on \(U\), and \(F(x)\) is the degree of affiliation of \(x\) to \(F\). In this paper, the degree of bottom event failure is described by the trapezoidal fuzzy number \(F(x)=\ (z_{1},z_{2},z_{3},z_{4})\) with the following expression [37]:  
\[F(x)=\left\{\begin{array}{ll}\frac{x-z_{1}}{z_{2}-z_{1}}&z_{1}\leq x<z_{2}\\ 1&z_{2}\leq x<z_{3}\\ \frac{z_{4}-x}{z_{4}-z_{3}}&z_{3}\leq x\leq z_{4}\\ 0&x<z_{1}\text{ or }x>z_{4}\end{array}\right. \tag{1}\]  
The \(F(x)\) affiliation function is shown in Figure 3. Whereas \(b_{0}\) denotes the center of the maximum possibility interval of the fuzzy number, \(b_{1}\), \(b_{2}\), \(b_{3}\), and \(b_{4}\) denote the fuzzy zone boundary.  
Figure 2: Schematic diagram of FTA mapping to FBN.  
In the traditional process of fuzzy number operations, direct calculation using the endpoints of fuzzy intervals is employed, and the max and min principles are utilized for processing fuzzy data. However, this approach does not account for the overlapping effects between fuzzy numbers, potentially leading to accumulation phenomena during fuzzy number operations. In contrast, the weakest \(t\)-norm algorithm can reduce the range of fuzzy intervals in the fuzzy number operations to mitigate the accumulation of fuzziness, while maintaining the primitive form of the fuzzy numbers in the calculations [38].  
Using the weakest \(t\)-norm algorithm, two trapezoidal fuzzy numbers (\(m_{1}\), \(m_{2}\), \(m_{3}\), \(m_{4}\)) and (\(n_{1}\), \(n_{2}\), \(n_{3}\), \(n_{4}\)) are computed as:  
\[\oplus_{Tw}=(m_{2}+n_{2}-max(m_{2}-m_{1},n_{2}-n_{1}),m_{2}+n_{2},m_{3}+n_{3},m _{3}+n_{3}+max(m_{4}-m_{3},n_{4}-n_{3}) \tag{2}\]
## 4 Proposal for Data Transformation to Resolve Incompatibilities between Medical Formats
### Boolean-Non-Boolean Discrepancy and Possible Fixes  
It is possible that the values that need aggregating may come in both Boolean and non-Boolean formats with given data. In that case, putting them together may pose yet another difficult problem due to the system's uncertainty of how should different values should be translated from non-Boolean to Boolean and vice versa. While human medical experts may know the fitting values, they are not able to update the machine's functionality by themselves to get accustomed to the new value.  
A simple fix for this may be normalizing the non-Boolean data. Unlike numeric qualities, the minimum and maximum of Boolean are always \"0\" and \"1\", respectively. As a variant, the normalized data may be assigned \"0\" if it is below average and \"1\" if it is above. This approach may, however, carry its own weakness, due to its assumption that the threshold determining the presence or absence of a certain trait may not always be the average.
## 2 Methodology
### Coding and Software Libraries  
Data pre-processing and post-processing of the pipeline data were carried out using the Python programming language. The classification machine learning algorithms were implemented using scikit-learn, a free machine learning library.
## 5 Discussion  
In this study, we proposed a method for developing control specifications that effectively solve the imbalance problem occurring during the washing machine dehydration process by utilizing BO. Control specification development using BO was carried out in three steps. First, we constructed a UBT dataset, defined input and Output Parameters, and calculated the dehydration performance metric DS. Second, we used a surrogate model based on a GP to predict DS even in unknown regions and generated optimal UBT values through the acquisition function. Finally, we applied the new UBT values to actual washing machines to perform validation tests and confirmed whether performance was improved. In this process, GAUS system automated the processes from experimental planning of the washing machine dehydration process to data measurement, management, and imbalance prevention control specification development using BO, thereby enhancing experimental efficiency and dehydration performance.  
As a result of experiments using the BO method, we were able to optimize the UBT control specifications under load conditions. As the number of test iterations progressed, the dehydration entry time (entry time) and maximum vibration amount (Max Vibration) decreased steadily, and the DS reached 1, confirming that the target performance was satisfied. Moreover, when compared with the manual method of professional engineers under various load conditions, the BO method showed equal or better performance in terms of entry time and vibration amount. Compared to the existing RNN-based method, the BO method could derive control specifications of equal level with significantly fewer test iterations. The RNN model requires more than 5000 tests to build a predictive model, making it less practical, whereas the BO method achieved optimization with up to 500 tests. This implies that the BO method is more suitable for the automation of washing machine control specifications.  
Future research needs to improve the parts that require empirical decisions, such as hyperparameter tuning and the initial value setting of the BO method. Methods for performance improvement according to the type of laundry load are also necessary. Because these factors can significantly impact performance, additional research is required to optimize them. Furthermore, to further enhance the search efficiency of the methodology developed in this study, research is needed to narrow down the search range where the optimal solution is likely to occur by applying appropriate classification methods (e.g., Decision Trees). Such techniques can increase sample efficiency in BO and solve the exploration-exploitation balance problem.  
Moreover, the impact of data quality variability on BO performance has not yet been sufficiently addressed, and we plan to analyze this in greater depth in future research. For example, we plan to design scenarios where data quality is progressively degraded, or specific segments are deliberately excluded, to experimentally assess the robustness of BO in identifying optimal values under such conditions.
# A Watermarking Protocol Based on Blockchain
## 6 Protocol Analysis  
In the conducted analysis, the ideal behavior of the proposed watermarking protocol can be modeled as follows: a content provider \(\mathcal{CP}\)sells the digital content \(X\) to a buyer \(\mathcal{B}\); \(\mathcal{B}\) obtains the protected digital content \(\bar{X}\) from \(\mathcal{CP}\); a blockchain \(\mathcal{BC}\) is a ledger that publishes the tokens that identify each purchase transaction of digital content distributed on the web; a registration authority \(\mathcal{RA}\) generates some specific data that have to be used by \(\mathcal{CP}\) to protect \(X\); a judge \(\mathcal{J}\) decides whether \(\mathcal{B}\) is guilty of releasing pirated copies.  
The ideal behavior is modeled under the following assumptions:  
* \(\mathcal{J}\) and \(\mathcal{RA}\) cannot be corrupted.
* \(\mathcal{CP}\) and \(\mathcal{B}\) can be only corrupted \"statically\", i.e., the set of the corrupt entities is decided at the beginning of the protocol execution and cannot be modified throughout the execution [55].
* \(\mathcal{BC}\) is assumed to be characterized by an \"honest-but-curious\" behavior [55]. As a consequence, \(\mathcal{BC}\) is obliged to follow the rules of the protocol, even though it can try its best to get information from the executed actions. This means that \(\mathcal{BC}\) cannot collude with \(\mathcal{B}\) or \(\mathcal{CP}\), and this is a reasonable assumption, since \(\mathcal{BC}\) is assumed to limit its action to automatically executing a smart contract whose code is approved and accepted in advance and cannot be modified during the life of the blockchain [26; 27; 28; 29; 30].
* Uncorrupt buyers and content providers are assumed to never release pirated copies.  
The assumptions reported above ensure that, if \(\mathcal{CP}\) and \(\mathcal{B}\) are uncorrupt, \(\mathcal{B}\) receives a unique and personalised protected content \(\bar{X}\) during the purchase transaction. Therefore, if a pirated copy of \(\bar{X}\) is found on the web, it can be always traced back to \(\mathcal{B}\) and to the purchase transaction. On the contrary, if \(\mathcal{CP}\) is corrupt, \(\mathcal{B}\) receives a protected content \(\bar{X}\) that cannot be correctly tied to any buyer. As a consequence, nobody can be adjudicated to be a traitor, and the corruption of \(\mathcal{CP}\) ends up being useless and pernicious just for \(\mathcal{CP}\). Likewise, if \(\mathcal{B}\) is corrupt, \(\mathcal{CP}\) can abort the purchase transaction without releasing any content.