esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Result and Discussion
### Effect of Cellulae Addition and Centrifugation  
Enzyme additions were expected to have a stronger effect on samples that contain more particles, as observed. However, methane yield increase from CF to CF-CEL was quite high (40%) considering that centrifugation removed more than 80% of feed VSS (Table 1). The observation that addition of cellulase enzyme had a positive and comparable impact on methane yield both in raw and centrifuged samples (specific methane yield increased by 54% and 40% for RF and CF, respectively (Table 5)) suggests that there are similar fractions of cellulose in large and small particles in such animal manure slurries. The small particles evidently needed to undergo a similar disintegration process as those removed by centrifugation, with maximum rates at approximately the same time both with (Figure 6b) and without (Figure 6a) enzyme addition. Hydrolysis rate constants, K\({}_{\text{h}}\), determined from the slope of the plot \(\ln\frac{\chi_{\text{Na}}-\chi}{\chi_{\text{Na}}}\) against t show how these observations can be included in process modeling. Only the first few days of the plot, where the curve was at its steepest was used to determine K\({}_{\text{h}}\) in accordance  
Figure 6: Measured average biomethane production rate: without cellulase (**a**) for the first 250 h and with cellulase (**b**) for the first 100 h.  
with suggestions by [PERSON] et al. (2014). Enzyme addition led to much higher K\({}_{\rm h}\) values while centrifugation caused marginally larger K\({}_{\rm h}\) (Table 7).
## 3. Maritime Traffic Conditions
Meanwhile, maritime traffic density is a metric that represents the number of navigation units per unit area that exists in an arbitrary period, as opposed to evaluating only the traffic capacity, which is the number of navigation units per arbitrary unit area per hour. Based on the evaluation of maritime traffic density, it is possible to derive a result considering the frequency of the navigating ships (\(p_{1}\)) and the occupied area of navigating ships (\(p_{2}\)) within a gridded sector [32]. Maritime traffic density is also used as an indicator in the design of coastal routes [33]. In addition, there is a potential assessment of the risk model, which is an index that includes the subjective risk of ship operators considering the overall condition of the ship and the environment [34]. It is also used in the development of collision risk notification algorithms for small ships [35]. Various quantitative methods for expressing maritime traffic conditions have different contents, depending on each method, and they must be utilized in compliance with the characteristics of the sea area and the purpose of the review. This study aimed to develop a future maritime traffic condition prediction algorithm for maritime traffic congestion using the quantitative indicators for maritime traffic conditions adopted in the Guidelines on Maritime Traffic Safety Audit in accordance with the Maritime Safety Act in South Korea [36], which is to evaluate the safety of all types of port and water facilities in relation to ships' passages.
# Ceramic Matrix Composites for Aero Engine Applications--A Review
###### Abstract  
Ceramic matrix materials have attracted great attention from researchers and industry due to their material properties. When used in engineering systems, and especially in aero-engine applications, they can result in reduced weight, higher temperature capability, and/or reduced cooling needs, each of which increases efficiency. This is where high-temperature ceramics have made considerable progress, and ceramic matrix composites (CMCs) are in the foreground. CMCs are classified into non-oxide and oxide-based ones. Both families have material types that have a high potential for use in high-temperature propulsion applications. The oxide materials discussed will focus on alumina and aluminosilicate/mullite base material families, whereas for non-oxides, carbon, silicon carbide, titanium carbide, and tungsten carbide CMC material families will be discussed and analyzed. Typical oxide-based ones are composed of an oxide fiber and oxide matrix (Ox-Ox). Some of the most common oxide subcategories are alumina, berylilia, ceria, and zirconia ceramics. On the other hand, the largest number of non-oxides are technical ceramics that are classified as inorganic, non-metallic materials. The most well-known non-oxide subcategories are carbides, borides, nitrides, and silicides. These matrix composites are used, for example, in combustion liners of gas turbine engines and exhaust nozzles. Until now, a thorough study on the available oxide and non-oxide-based CMCs for such applications has not been presented. This paper will focus on assessing a literature survey of the available oxide and non-oxide ceramic matrix composite materials in terms of mechanical and thermal properties, as well as the classification and fabrication methods of those CMCs. The available manufacturing and fabrication processes are reviewed and compared. Finally, the paper presents a research and development roadmap for increasing the maturity of these materials allowing for the wider adoption of aero-engine applications.  
CMC; oxide CMC; non-oxide CMC; material classification; aero-engine applications +
Footnote †: journal: Applied
# Enable Fair Proof-of-Work (PoW) Consensus for Blockchains in IoT by Miner Twins (MinT)
## 4 Miner Twin-Enabled Fair-Mining Mechanism
### Proof-of-Behavior Consensus Algorithm for Fair Mining Enforcement  
The abovementioned SSA-based detection can identify a single misbehaved miner based on its own footprint; however, it cannot handle byzantine scenarios that multiple compromised miners by an adversary collude to violate fair mining policies. By observing a miner's running operations, the calculated cumulative sum (CUSUM)-type \(W\) can indicate a miner's behavior. Inspired by deepfake detection in video surveillance systems [44; 45], our MinT relies on a novel _Proof-of-Behavior_ consensus algorithm that leverages CUSUM-type \(W\) calculated in SSA algorithm to detect multiple dishonest miners in distributed byzantine tolerant scenarios.  
We consider a mining network \(\mathcal{N}\) including \(n_{i}\) miners, where \(i\in\{1,k\}\) and \(k=|\mathcal{N}|\). All dishonest miners are denoted by \(m_{i}\in\mathcal{M}\) and their fraction is \(f=|\mathcal{M}|/|\mathcal{N}|\). We use observed CUSUM-type \(W_{i}\) of miner \(n_{i}\) to demote a behavior vector \(B_{i}=\{b_{1},b_{2},\ldots,b_{d}\}\), where \(b_{k}=w_{k}\in W_{i}\) and \(d\) is the SSA detection time window. Finally, each twin can maintain a global view of collected behavior vectors, which is a matrix \(G=\{B_{1},B_{2},\ldots,B_{k}\}\). The PoB firstly generates a behavior score \(s(i)\) for each miner \(n_{i}\), which is a sum of relative Euclidean distances between other miners' behavior vector. Then, a \(B_{i}\in G\) with minimal behavior score is selected as a benchmark \(B^{*}\).  
The PoB consensus algorithm aims to chooses a behavior vector \(B\), which deviates at least from the distribution of \(G\). However, an adversary can compromise multiple miners that generate large vectors to force \"honest\" miners to choose a byzantine behavior vector as the ground truth one. Thus, our PoB algorithm adopts a _Krum_ aggregation rule to guarantee byzantine tolerance. We assume that honest miners within network \(\mathcal{N}\) store \(G\) including \(n\geq 2f+3\) vectors in which at most \(f\) vectors are generated by byzantine nodes in \(\mathcal{M}\). For \(B_{j}\) belongs to the \(n-f-2\) closest vectors to \(B_{i}\), where \(i\
eq j\), we denote \(i\to j\). Therefore, we could define the consensus score:  
\[s(i)=\sum_{i\to j}||B_{i}-B_{j}||^{2}. \tag{10}\]  
Then, each node can compute behavior scores \(s(1),\ldots,s(k)\) that are associated with miners \(n_{1},\ldots,n_{k}\) separately. By calculating the minimum behavior score  
\[s^{*}=\min_{i\in\{1,\ldots k\}}(s(i)), \tag{11}\]  
all honest miners choose a behavior vector \(B_{i}\) that satisfies \(s(i)=s^{*}\) as the ground truth \(B^{*}\). Given assumption that an adversary controls no more than \(f\) miners, all honest miners can reach an agreement on the unique \(B^{*}\).
# Measuring Collision Risk in Mixed Traffic Flow Under the Car-Following and Lane-Changing Behavior
## 4 Evaluating Mixed Traffic Safety with CAV Degradation and Driving Behaviors
### Simulation
#### 4.4.1 Effects of Penetration Rate of CAVs  
A reduction in the penetration rate of CAV increases the probability of degradation phenomena. Additionally, the CAV penetration rate determines the proportion of the three types of vehicles within mixed traffic flows (see Section 4.1). Therefore, this part will focus on examining the impact of CAV penetration rate on collision risk (Figure 13).  
From Figure 13, it can be seen that in the specific penetration rate range of 0.4-0.6, all four indicators exhibit fluctuations. Among them, \(R_{\text{total}}\) shows relatively sensitive changes. This is mainly because \(R_{\text{total}}\) takes into account both vehicle reaction time and CAV degradation, thereby better reflecting the overall traffic flow safety changes. In contrast, WS\({}^{2}\)DR shows significant fluctuations at a penetration rate of 0.4, while PICUD experiences a large fluctuation at 0.5. These findings indicate that vehicle behavior changes at these penetration rates have a more pronounced impact on traffic safety. On the other hand, the TTC indicator exhibits significant fluctuations later, at a penetration rate of 0.6.  
Figure 12: Impact of CAV degradation in lane-changing. (**a**) Degradation of LCV (LCV transforms into LCV\({}^{\prime}\)); (**b**) degradation of FVT (FVT transforms into FVT); (**c**) degradation of FV (FV transforms into FV\({}^{\prime}\)).  
This suggests that TTC lacks responsiveness and fails to capture risk changes at medium to low penetration rates effectively. Compared to the other three typical SSMs, the absolute value of the linear model's slope for \(R_{\text{total}}\) is the largest, indicating its higher sensitivity to changes in penetration rate. Additionally, its \(R^{2}\) value is the highest, demonstrating that the variation trend of \(R_{\text{total}}\) is well-fitted by the linear model, exhibiting high stability and predictability.  
This phenomenon suggests that at medium penetration rates (0.4-0.6), the increase in degraded CAVs and the complexity of vehicle interactions elevate safety risks in mixed-traffic environments. As the penetration rate continues to increase (above 0.6), a higher proportion of CAVs can better coordinate the flow, thereby reducing overall risk. Therefore, controlling the penetration rate within an appropriate range can balance the advantages of autonomous driving technology with the risks introduced by degradation, ultimately improving traffic safety.
# An Ontology for Spatio-Temporal Media Management and an Interactive Application
## 3 The Software-Defined Media Ontology
### **Media** Class
The **CompositeMedia** subclass represents a composite object of the **Media** class. However, this definition is extended because it is sometimes necessary to specify the start time of the Media object to be included in the description of a composite object. In addition, a **CompositeMedia** object may be created by simply grouping multiple **Media** objects or by grouping the encompassing **Media** objects with their playback start times specified as a pair. An example of the former is a case in which video and audio media are recorded simultaneously to represent a single medium. In this usage, the **hasComponent** property specifies the **Media** object that the **CompositeMedia** object contains. The **hasComponent** property indicates that the linked **Media** object is a component of its own **CompositeMedia** object. The time axes represented by the **Media** objects linked by the **hasComponent** property are all parallel, and each is a **Media** object with the same start time. The **isComponentOf** property describes the inverse relationship to the **hasComponent** property. The latter method, which represents a **CompositeMedia** object by specifying the start time of playback of the encompassing **Media** objects, allows **Media** objects with more complex structures to be expressed. For example, consider multiple audio and video **Media** objects used in producing CDs, DVDs, and Blu-ray discs. The **Media** objects are divided into multiple tracks or chapters and arranged along the time axis. In a typical production flow, media files are created as units of tracks and chapters. These can be represented by **AudioMedia** and **VideoMedia** objects. By arranging each Media object on the time axis of the **CompositeMedia** object, it is possible to describe the **CompositeMedia** object for an entire CD, DVD, or Blu-ray disc. The **MediaEvent** class represents a pair of individual Media objects in a **CompositeMedia** object and its start time. The **CompositeMedia** object is linked to the **MediaEvent** object using the **hasMediaEvent** property. The **isMediaEventOf** property is a property that expresses the reverse relationship. The **eventTime** property of the **MediaEvent** class specifies the start time of the playback section (from **startAt** to **endAt**) of the **Media** object linked by the **MediaEvent** object. The time specified by **eventTime** is expressed in terms of the time axis specified by the **measuredBy** property of the **CompositeMedia** object. If the value of the **eventTime** property of the **MediaEvent** object is zero, it is equivalent to specifying a **Media** object with **hasComponent** as described above. By  
Figure 5: **Media** class structure. combining **CompositeMedia** objects hierarchically, **Media** objects can be represented with arbitrary complexity.
###### Abstract
We propose the Normalized Effective Transfer Entropy (NETE) to remove the noise and errors caused by nonstationarity of the data in the calculation of transfer entropy. Transfer entropy is the most commonly used and important metric for time series causal network inference based on information theory;
3. We use NETE to improve the Multivariate Effective Source Selection algorithm, and then apply it to construct a causal network of telemetry parameters;
4. We propose the DCDSPOT method, which sets multi-tier thresholds for the target parameter to be detected and Weighted Source Parameter (WSP) of the target parameter, and then formulate two criteria to determine the anomaly. Anomaly detection using DCDSPOT has a lower false negative rate and a higher F1-score than other commonly used methods;
5. We apply Probability Weighted Moments (PWM) for parameter estimation of Generalized Pareto Distribution (GPD) instead of Maximum Likelihood Estimation (MLE) to shorten the running time of threshold calculating. The framework of our method is shown in Figure 1.
# Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting
###### Abstract  
Modern energy systems collect high volumes of data that can provide valuable information about energy consumption. Electric companies can now use historical data to make informed decisions on energy production by forecasting the expected demand. Many deep learning models have been proposed to deal with these types of time series forecasting problems. Deep neural networks, such as recurrent or convolutional, can automatically capture complex patterns in time series data and provide accurate predictions. In particular, Temporal Convolutional Networks (TCN) are a specialised architecture that has advantages over recurrent networks for forecasting tasks. TCNs are able to extract long-term patterns using dilated causal convolutions and residual blocks, and can also be more efficient in terms of computation time. In this work, we propose a TCN-based deep learning model to improve the predictive performance in energy demand forecasting. Two energy-related time series with data from Spain have been studied: the national electric demand and the power demand at charging stations for electric vehicles. An extensive experimental study has been conducted, involving more than 1900 models with different architectures and parametrisations. The TCN proposal outperforms the forecasting accuracy of Long Short-Term Memory (LSTM) recurrent networks, which are considered the state-of-the-art in the field.  
deep learning; energy demand; temporal convolutional network; time series forecasting Article
## 4 Performance Degradation of MICP-Treated Soil Under Wet-Dry Cycling
### Mass Loss  
The results of the mass loss tests conducted on the untreated and MICP-treated soil specimens after various wet-dry cycling intervals are presented in Figure 13. The results show that the mass loss rate of the specimens increased with the number of wet-dry cycles, exhibiting a trend of a rapid initial increase followed by a more gradual reduction.  
Figure 12: Damage factor curves of mechanical properties.  
After 15 wet-dry cycles, the total mass loss of the MICP-treated soil specimens was 18.15%. However, the majority of the mass loss, 86% of the total, occurred within the first nine wet-dry cycles, where the mass loss rate reached 15.61%. Furthermore, after 15 drying-wetting cycles, the stabilized expansive soil showed an 15.97% lower mass loss rate than the untreated soil.  
This behavior can be attributed to the mechanism of mass loss during the wet-dry cycling. When the specimens were submerged in water, the water flow caused the erosion and transportation of some soil particles from the internal structure of the specimens. Additionally, the loose surface particles were also carried away by the water solution, leading to the observed mass loss. After the initial rapid mass loss during the first nine wet-dry cycles, the rate of mass loss gradually decreased. This can be explained by the stabilization of the soil structure, where the remaining particles became more tightly bound together due to the MICP treatment, reducing the susceptibility to further mass loss.
## 3 Adaptive Task Traffic-Shaping Algorithm
### Task Request Queueing Theory Model
#### 3.2.2 Adaptive Traffic Shaping Based on Resource Load
This study determines the maximum and minimum token fill rates of the GIS service system as follows:  
\[MaxTokenRate=MaxThread\times\frac{1000}{SingleTaskCostTime} \tag{8}\]  
\[MinTokenRate=\frac{1000}{SingleTaskCostTime} \tag{9}\]  
In the formula, \(MaxTokenRate\) and \(MinTokenRate\) represent the maximum and minimum token fill rates of the GIS service system, respectively. \(MaxThread\) denotes the maximum number of concurrent threads allowed by the GIS service system, and \(SingleTaskCostTime\) represents the average time required to process a single task. In this study, \(MaxThread\) is set to 8, while \(SingleTaskCostTime\) varies depending on the complexity of the task. Specifically, the time cost is defined as 5 ms for simple tasks, 20 ms for general tasks, and 80 ms for complex tasks. Based on the predicted expected token fill rate level, the rate adjustment parameters are determined to calculate the expected token fill rate at time \(t\) using the rate at time \(t\)\(-\)1 (Equation (12)). The resulting value is then used to adjust the token fill rate of the token bucket. Additionally, an exponent a is defined (Equation (10)) to assist in optimizing the token fill rate and to calculate the remaining service capacity of the GIS service system (Equation (11)). \[\delta=\left\{ \begin{array}{cc}\frac{L_{s}}{1+L_{s}},&L_{s}>0\\ 1,&L_{s}\leq 0\end{array} \right. \tag{10}\]  
\[r_{res}=\delta\times\left(1-\frac{Load}{MaxLoad}\right)\times N=\left\{ \begin{array}{cc}\lambda,&L_{s}>0\\ \left(1-\frac{Load}{MaxLoad}\right)\times N,&L_{s}\leq 0\end{array} \right. \tag{11}\]  
\[r=max(min(\alpha\times r_{t-1}+\beta\times r_{res},MaxTokenRate),MinTokenRate) \tag{12}\]  
In the formula, \(r_{res}\) represents the remaining service capacity of the GIS service system; \(r\) denotes the expected token fill rate of the GIS service system at time \(t\); and \(r_{t-1}\) indicates the token fill rate of the GIS service system at time \(t\)\(-\)\(1\).
# Enhancing Telexistence Control Through Assistive Manipulation and Haptic Feedback
## 5 Evaluation  
### Objectives  
This study's main objective was to evaluate the impact of incorporating haptic feedback and assistance modules on users' task performance in terms of accuracy, time to complete a task, and perception of body ownership in the telexistence system.
# Pointer Meter Reading Recognition by Joint Detection and Segmentation
## 4 Experiments
### Evaluation Metric  
Due to our model being composed of three stages, we evaluate the output of each stage to completely quantify the performance of our method. Specifically, for the first stage, the average precision (AP) metric is utilized to measure the location accuracy of the meter dial location network. The AP is measured by computing the area under the precision-recall (P-R) curve, where the definitions of the precision and recall are as follows:  
\[Precision=\frac{TP}{TP+FP}, \tag{4}\]  
Figure 5: Some examples from our pointer meter dataset.  
\[Recall=\frac{TP}{TP+FN}, \tag{5}\]  
where \(TP\), \(FP\), and \(FN\) are, respectively, the number of true positives, false positives, and false negatives. The P-R curve can indicate the relationship between precision and recall for different thresholds. Note that we only consider a bounding box to be correct if its IoU with the ground truth is higher than 0.5. For the second stage, we employ the mean IoU (mIoU) between the segmentation result and its ground truth to assess the performance of the reference point segmentation network, which is formulated as  
\[mIoU=\frac{1}{k}\sum_{i=1}^{k}\frac{p_{ii}}{\sum_{j=1}^{k}p_{ij}+\sum_{j=1}^{k} p_{ji}-p_{ii}}, \tag{6}\]  
where \(p_{ij}\) refers to the \(i\)-th class being segmented as class \(j\), and \(k\) is the total number of all classes. In addition, for the stage of reading dial numbers, the relative error (RE) is used to evaluate the accuracy of dial value estimation, which is defined as  
\[RE=\frac{|v_{e}-v_{a}|}{v_{a}}, \tag{7}\]  
where \(v_{e}\) and \(v_{a}\) denote the estimated value and the corresponding actual value, respectively. In essence, the RE measures the ratio of the absolute error of the pointer reading value to the actual value, and it is a widely used metric for value estimation.