esa-sceva/satcom-chunk-collection huggingface dataset:
# Carbon Footprint Assessment on the Viability of Utilizing Brewer's Spent Grain to Produce Biochar
## 1 Introduction
### Post-Production Utilization of Biochar, Syngas, and Bio-Oil  
A study by [PERSON] et al. [9] compiled data from 27 peer-reviewed papers that utilized LCA methodology to evaluate the environmental impact of pyrolysis-based biochar systems that specifically used biochar for soil amendment. The selected studies were published between 2011 and 2019 from several global regions. The functional units employed in these studies varied considerably depending on factors such as the use of wet or dry biomass, time, or land. The system boundaries also varied due to the inclusion of different feedstocks, particularly when the biomass was derived from waste streams. Most of the studies adopted a system expansion approach to consider the downstream utilization of syngas and bio-oil. The application rate of biochar to the soil exhibited significant variation across the studies (from 1 t/ha to 30 t/ha). Biochar stability also differed; most authors used conservative values (80% or lower) for the concentration of stable biochar.  
Most of the studies considered the impact of biochar on soil, acknowledging its potential to increase crop yield; the availability of nutrients; the sorption of pollutants; its positive impact on the microbial population in soils; and its ability to reduce methane and nitrous oxide emissions [9]. However, the overall impact of biochar on soil varied amongst studies, depending on the specific benefits considered. Despite differences, the results of all 27 studies consistently demonstrated the trend that biochar-soil amendment systems benefit climate change mitigation [9]. The primary benefits are carbon sequestration driven by carbon storage from the biochar in soil, crop yield increase, which reduces the use of fertilizer, and a reduction in methane or nitrous oxide emissions from biochar [9].  
[PERSON] et al. [9] reported that GHG emissions from the pyrolysis process were consistently offset by the carbon capture benefit of biochar and the energy production offsets achieved through the utilization of the pyrolysis co-products, syngas, and bio-oil.  
Bio-oil can be utilized in replacement of heating oil or refined into gasoline, ethanol, and/or other chemical compounds [20]. Syngas is primarily used for generating power and heat through stand-alone combined heat and power (CHP) plants or through co-firing the gas in large-scale power plants [21]. A system expansion approach that considers the utilization of syngas and bio-oil from pyrolysis can achieve fuel savings and emissions reductions by utilizing these co-products for heat and electricity within the processing system.
# A Model and Quantitative Framework for Evaluating Iterative Steganography
## 5 Conclusions  
This study presents a detailed characterization and modeling framework for a class of iterative steganographic techniques. It introduces a novel method for quantifying and evaluating the performance of these techniques using the Incremental Information Function (IIF). Through a case study employing a selected video steganography method, the research illustrates the application of the IIF to analyzing the properties of iterative steganographic techniques, including the measurement of robustness and capacity by determining characteristic IIF values. This approach offers a fresh perspective on the information dynamics within iterative steganographic systems, with the potential to advance both the implementation of steganography and the effectiveness of steganalysis.  
The study achieves its primary objective by developing a formal method for quantifying and analyzing information increments in iterative steganographic systems. It introduces an effective framework for assessing the efficiency and robustness of these techniques, supported by practical examples that illustrate its application to elucidate the properties of the studied steganographic methods. This advancement lays the foundation for further research and development in the field of steganography, offering a systematic and robust analytical tool to evaluate and enhance the performance of iterative techniques.
## 2 Data and Methods
### Kolmogorov-Smirnov (K-S) Test  
The Kolmogorov-Smirnov (K-S) test, a significance testing method, leverages sample data to determine if the population from which the sample was taken differs significantly from a theoretical distribution. This technique utilizes F(x) to denote the theoretical cumulative probability of each sample observation against the theoretical distribution, and S(x) for the actual cumulative probabilities. The test's statistic, D, is derived as the maximum absolute difference between F(x) and S(x): D = max\(|F(x)-S(x)|\).  
Under the null hypothesis, D follows the Kolmogorov distribution. The test calculates a corresponding \(p\)-value; if \(p\) falls below the significance level \(\alpha\), the null hypothesis is rejected, suggesting a significant deviation between the sample's population and the theoretical distribution. Conversely, a higher \(p\)-value indicates no significant difference. In this study, the K-S test was applied to compare the inversion results from an optimized method against those from a traditional approach, assessing significant enhancements in the optimized method's outcomes.  
In this study, an optimized method is used to quality control the raw radial wind data from scanning Doppler Lidar and optimize the inversion results under different complexities of wind field environments (inside and upwind of a wind farm). The proposed method improves the quality of vertical wind profiles from near the ground level to the heights impacted by wind turbine blades. Such kind of information can be used for wind power prediction as well as studies of the climatic and environmental effects of wind farm operations. Due to the lack of in situ boundary layer wind profile data, the derived wind speed and wind direction are validated using observations from 10 m and 70 m wind tower data and DBS reconstructed data.
## 2 The Application of Metamaterials
### Wind Turbines  
Metamaterials, with their remarkable capabilities, have found applications beyond solar energy, extending into the realm of wind turbines, as well. Traditional wind turbine blades are typically designed to prioritize aerodynamics, which can inadvertently limit their efficiency in capturing the full potential of the wind's energy. However, metamaterials offer a transformative solution by altering the flow of wind around the blades, enabling them to more effectively harness and capture energy. This breakthrough has sparked growing interest in wind farms, with researchers actively exploring new avenues to enhance and to optimize various associated technologies.  
[PERSON] et al. [35] have made notable strides in the field of proposing groundbreaking metamaterial absorbers (MAs) specifically engineered to strongly absorb microwave frequencies at 2.46 GHz. By effectively engineering strongly absorbed incident microwaves and converting their energy into heat, this MA holds immense potential for microwave deicing applications in wind turbine blades. Through comprehensive multi-physical simulations, [PERSON] demonstrates the feasibility and effectiveness of the MA in generating heat, distributing thermal energy, and facilitating controlled temperature rises during microwave irradiation.  
Moreover, the application of metamaterials extends beyond energy optimization in wind turbines to address the issue of noise disruption. [PERSON] and her team at MTT (Metamaterial Technologies, Inc.) have pioneered the development of metamaterial interfaces, which are specifically designed to mitigate the adverse effects of noise pollution associated with wind turbines. By implementing large-scale unit cells of acoustic metamaterials, they have successfully reduced the noise emissions during wind turbine installation. This innovative solution not only contributes to minimizing noise disturbances, but it also holds promise in safeguarding marine biodiversity with regards to underwater noise pollution.  
This convergence of metamaterials and wind turbine technology represents a compelling frontier, showcasing how these remarkable materials can revolutionize sustainable energy production. From improving energy capture efficiency to mitigating noise disruptions and promoting ecological harmony, metamaterials continue to unveil transformative potential in enhancing the sustainability and effectiveness of wind energy solutions.
## 4 Design and Implementation  
This section highlights the design and implementation of OOK, BFSOOK, and proposed HFSPDM. Discussion on each modulation is further divided into encoding and decoding steps. Since, all the demodulation techniques need to undergo common preprocessing, a standard preprocessing function is implemented as shown in Algorithm 1.  
Figure 5: Decoding Steps.  
```
1:Initialize a Buffer(size)
2:functionPreprocessing(image)
3: Convert image to grayscale
4: Enhance the contrast
5: Binarize the image
6: Dilute the image
7: Find dimensions of the brightest and largest blob
8: Mask the background
9: Crop the original image
10: Resize the cropped image based on found ROI
11: Grayscale, Enhance Contrast and Binarize the Cropped Image
12:endfunction
```  
**Algorithm 1** Common Image Preprocessing.
# Analyzing the Economic Performance of a TLS-Based Structural Safety Diagnosis Process
## 3 Results  
### Overview of Case Projects  
In this study, a total of three projects involving the introduction of TLS-based 3D scanning reverse modeling for structural safety diagnosis were selected. Each project has varying service periods and investigation scales, as summarized in Table 2. All three cases were conducted by the same structural safety diagnosis firm (Company A), which enabled a direct comparison of two diagnostic processes--the traditional manpower-based method and the TLS-based 3D scanning reverse modeling method--for the same buildings and structures.  
The labor cost for each engineer was calculated based on the Korean engineering labor cost standard for the construction sector in 2022, the year when the case projects were executed. Engineers were categorized into professional engineers, senior engineers, and junior engineers. Specifically, labor costs for each case project were determined based on the Korean engineering labor cost standard for the construction sector in 2022.  
For Project A, the traditional approach required one professional engineer, two senior engineers, and one junior engineer, resulting in a daily labor cost of KRW 1,172,789. In contrast, the TLS-based 3D scanning reverse modeling approach reduced the number of senior engineers by one, lowering the daily labor costs to KRW 890,244. In Project B, the conventional method required one professional engineer, eight senior engineers, and one junior engineer, totaling a daily labor cost of KRW 2,868,059. With the TLS-based 3D scanning reverse modeling approach, the number of senior engineers was reduced to four, resulting in lower daily labor costs of KRW 1,737,879. Lastly, Project C initially employed one professional engineer, eight senior engineers, and one junior engineer, with a daily labor cost of KRW 2,868,059. However, after implementing the TLS-based 3D scanning reverse modeling method, the number of senior engineers decreased to three, significantly reducing daily labor costs to KRW 1,455,334.  
The company's initial investment cost for implementing TLS-based 3D scanning reverse modeling (e.g., equipment purchase and software licenses) was a total of KRW 120 million. This cost was reflected in the economic evaluations across the three selected projects.
# Addressing ZSM Security Issues with Blockchain Technology
## 3 The ZSM Framework Overview: Architecture and Open Security Issues
### Open Security Issues
Although this solution is tested for the security assurance of network equipment following the 3 GPP's Security Assurance Methodology (SECAM), other technologies could be studied to protect management functions. Additionally, the multitenancy issue is answered using policies applied to each tenant that uses the ZSM framework. The policy mechanism aims to provide a sufficient security layer for the users of the ZSM framework to avoid the exploitation of multi-tenancy which may lead to the loss of sensitive data of E2E services and the loss of the frameworks' reputation. However, this solution is based on security requirements defined by authors and cannot by itself be considered as a high-security level solution. Moreover, the access control of management services (MnS) is another major issue, as the exhaustive usage of management resources by a malicious entity may cause the mis-operation of these critical services. Robust access control mechanism, including identification processes, authentication, authorization and audit of MnS usage, should be applied to prevent MnSs and other management resources of the ZSM framework from being misused by MnS consumers, according to the authors. Considering that ZSM is implemented in a multi-domain environment, the standardization team proposes techniques to enhance the security of MnSs by introducing authentication and authorization mechanisms, which check the trust relationship among entities in ZSM. AI and ML are the main technologies used in the ZSM framework and their reliability and robustness should not be left open to dispute. The decisions of AI/ML affect the ZSM network operation as they perform closed-loop operations for the efficient deployment of \begin{table}
\begin{tabular}{c||c c c c c} \hline
**Security issues** & Trust relationship between multiple management domains & Security risks due to the vulnerability of management functions & Access control for management services in a multi domain scenario & Security risk of AI/ML model and protection of AI/ML models in ZSM & Complexity level \\ \hline
**Current ZSM** & & & Authentication and authorization & Risk assessment based on the technologies to increase ZSM security \\
**Approach—** & & & & & \\
**Solutions** & & & & & \\  & & & & & \\  & & & & & \\ \hline
**Blockchain-based** & & & Use of SCs to eliminate the vulnerabilities and apply which are & Use SCs for & Store the AI/ML decisions in the ledger and guarantee dataset & Low—Use blockchain \\
**ZSM approach—** & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \end{tabular}
\end{table} 
Table 1: Solutions to security issues: Current ZSM vs. Blockchain-based ZSM. E2E cross-domain services. A comprehensive risk assessment for the AI/ML vulnerability issue based on the Adversarial ML A Threat Matrix is presented in [21] and possible countermeasures are proposed at a high level only.
## 5 Simulation Results and Discussion
### Security Analysis  
The sharing data in our scheme is encrypted with the BH-WABE technique, which is secure against a chosen plaintext attack if the decisional bilinear Diffie-Hellman (DBDH) assumption holds. We analyze the security properties of our scheme as follows:  
**Data confidentiality:** The data is encrypted using access policy, and the confidentiality of data can be guaranteed against users who do not hold a set of attributes that satisfy the access policy. In the encryption phase, though the cloud performs encryption computation for the data owner, it still cannot access the data without the attribute key. During the decryption phase, since the set of attributes cannot satisfy the access policy in the ciphertext, the cloud server cannot recover the value \(A=e(h,h)^{\gamma t}\) to further get the desired value of the global key (GK). Therefore, only the users with valid attributes that satisfy the access policy can decrypt the ciphertext. The data are encrypted with a random symmetric key CK, and then CK is protected by BH-WABE. Since the symmetric encryption and BH-WABE scheme are secure, the confidentiality of outsourced data can be guaranteed against unauthorized users whose identities are not in the set of receivers' identities defined by the data owner.  
**Fine-Grained Access control:** The fine-grained access control allows flexibility in specifying differential access policies of individual data. To enforce this kind of access control, we utilize BH-WABE to escort the symmetric data encryption key. In the data encryption phase of our scheme, the data owner is able to enforce an expressive and flexible access policy and encrypt the symmetric key that is used to encrypt the data. Specifically, the access policy of encrypted data defined in the access tree supports complex operations, including both AND and OR gate, which is able to represent any desired access conditions.
## 2 Theoretical Background
### Reliability Analysis  
Given that the failure probability is established as a constraint in RBDO, conducting a reliability analysis (RA) is essential. In this study, reliability was defined as the probability that the quality of the bearing hub formation met the desired design performance under various scenarios resulting from uncertainties in material properties and the preform design. Reliability analysis was used to evaluate the probability that the system's performance failed to satisfy its constraints, i.e., the probability of failure. The reliability \(R\) may be expressed using Equation (2).  
\[R=P[g(x_{i}<0)]=\int_{g(x_{i})<0}f(x_{i})\;dx \tag{2}\]  
\[\mu(\hat{y_{i}})=\frac{1\sum_{i=1}^{n}\hat{y_{i}}}{N} \tag{3}\]  
\[\sigma^{2}(\hat{y_{i}})=\frac{1\sum_{i=1}^{n}[\mu(\hat{y_{i}})-\hat{y_{i}}]^{2} }{N} \tag{4}\]  
In this context, \(P\) represents the probability of a specific event occurring, \(g(x)\) is a function that evaluates whether the system's performance meets the defined constraints, and \(f(x_{i})\) denotes the probability density function (PDF) of the design variables. When \(g(x)<0\), the system performance satisfies the constraints, while \(g(x)\geq 0\) indicates that the system performance fails to meet the constraints. The objective of reliability analysis was to determine the probability of system failure, which was defined as the case in which \(g(x)\geq 0\). Equation (3) provides the mean value of the observed formation outcomes, where \(\mu\) signifies the mean, \(\hat{y_{i}}\) is the experimentally or analytically derived value, and \(N\) indicates the total number of data points used in the experiment or analysis. Equation (4) defines the standard deviation of the observed formation outcomes, serving as a criterion to assess the variability of these outcomes, with \(\sigma^{2}\) symbolizing the variance.  
The integration of the reliability function is generally computed analytically using Monte Carlo simulations. However, this method requires extensive computation time due to the repetitive sampling necessary for accurate predictions. Therefore, this study employed the enhanced dimension reduction method (eDR), which is known for its superior accuracy and efficiency compared to traditional reliability calculation methods.  
Figure 3: Function of probability density.
# A Coordinated Supply Contract for a Two-Echelon Supply Chain Considering Learning Effects
## 2 Literature Review
### The Development of the Learning Effect  
The learning effect or LC effect is an economic concept that signifies an increase in a worker's efficiency as the task progresses, indicating a faster pace of task completion. In the context of manufacturing, the learning effect refers to the phenomenon where unit production time decreases as production volume increases. Study on the learning effect dates back to [PERSON] [9], who investigated the LC effect in the aircraft industry. Within the realm of learning curves, various models have been developed. Among them, the log-linear model expressed below stands out as a widely used and straightforward type of LC model [9; 27]:  
\[c_{x}=c_{1}x^{-b} \tag{1}\]  
where \(x\) is the total number of units made, \(c_{x}\) is the cumulative average per-unit production cost for the first \(x\) units, and \(b\) is the exponent measuring the strength of learning. It is usually expressed as \(b=-log_{2}r\), where \(r\) is the learning rate. For example, if the production cost for the first product is 100, i.e., \(c_{1}=100\), with the learning rate \(r=0.8\), then we have \(c_{2}=80\), \(c_{4}=64\), \(c_{8}=51.2\), etc. [PERSON] [9] found that the learning rate is about 80% in aircraft manufacturing, meaning that the per-unit production cost decreases by 20% for every doubling of total units made. The simplicity of the log-linear model caused many researchers to apply it when examining the learning effect in OM problems [10; 14; 33]. However, the log-linear model has the limitation that the cumulative average per-unit production cost, \(c_{x}\), converges to zero as \(x\) approaches infinity. To address this problem, plateaued log-linear models are introduced by incorporating an additive constant \(c\) that accounts for the steady-state worker's performance. This constant reflects the performance level attained when learning is completed or when workers' improvement is constrained by machinery limitations [10; 34; 35]. Some researchers combine the log-linear model with the exponential upturn function [36; 37], i.e., \(c_{x}=c_{1}x^{-b}e^{-vx}\), where \(v\) is a positive parameter leading to an upturn for the large values of \(x\). The exponential model may enhance predictions in extended production runs to offer improved predictions for long-duration production processes. Various models suitable for a range of tasks and manufacturing environments have been studied in addition to the learning-effect models mentioned above. For comprehensive learning-curve models, refer to [28; 38; 39; 40; 41].
# Reliability-Aware Cooperative Node Sleeping and Clustering in Duty-Cycled Sensors Networks
## 6 Performance Evaluation
### The Impact of \(f\) and \(K\)  
In the simulations, the hop counts were set to 14 and \(\mu\) was equal to zero. \(k\) is varied from one to 10, and \(f\) was varied from 0.15 to 0.75. Figure 6(a) shows the obtained results for the impact of \(K\) and \(f\) on reliability. The OPNET simulation results match with the theoretical results obtained by MATLAB, which are shown in Figure 4(a).  
According to the results presented in Figure 6(b), wherein the impact of \(k\) and \(f\) on ETE delay is shown, the larger \(f\) is, the longer the ETE delay is. However, when the value of \(f\) is determined, the ETE delay converges to the stable value with the increase of \(k\). Thus, we need to make a compromise between \(k\) and \(f\).  
Figure 6: Probe (PROB) packet format and sending mechanism.
## 1 Introduction
Figure 1b shows the schematic of the proposed framework to quantify the spatial uncertainty in AM parts and incorporate spatial uncertainty in a computer simulation model to better predict the mechanical performance of the AM parts. A strut is initially additively manufactured using a mask projection stereolithography technique [15]. The sidewall profile caused by the layer-by-layer manufacturing process is extracted from a microscope image, based on which a probabilistic surface model is generated. The details of the three steps involved in generating the probabilistic model are given in the following sections.