esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 General Aspects of Dry Anaerobic Digestion
### Advantages of the Dry Anaerobic Digestion  
Dry AD technologies require from four to ten times [18; 19] less water for dilution than wet AD technologies. Thus, advantages for dry AD include reduced reactor volume, higher volumetric methane yields, lower energy consumption for heating, a positive energy balance, less wastewater, and, consequently, lower logistics costs for fertilizers [8; 9; 13; 20; 21; 22], and only very dry substrates with a TS content of more than 50% require dilution [14]. One of the specific advantages of dry AD is the lack of foam [22]. Using dry ADR also guarantees decontamination of the effluent. Similar to wet AD, about 30% of energy is consumed for bioreactors heating [18].  
Reduced costs for dry AD are a consequence of lower reactor volumes and savings on sorting equipment, as dry AD technologies are more stable and resistant to stones, glass, metals, plastics, and wood [13; 20; 22]. The process of fermentation requires removal of only very coarse particles with a size of more than 5 cm, which reduces the costs of sorting equipment [13].  
The main problem of dry AD systems of municipal solid waste, agricultural waste, and food waste are mixing and transportation, but not biochemical constraints. Equipment for shipping of solids, as a rule, is more expensive than that for liquids. Shipping is carried out using conveyor belts, augers, and powerful pumps, especially designed for high-viscosity flows [22].  
Perfect contact of biomass and a substrate may not be achieved due to a lack of mixing [23]. According to [PERSON] et al., mixing was complicated when the TS content was more than 30% [24]. In addition, during dry anaerobic digestion, percolation was observed, and it was possible to intensify the process by adding straw and wood chips to save water, which could have led to an increase of the specific methane yield by 6% and 11% [25], respectively.  
There are different types of processes in various parts of the reactor due to such mixing. Local inhibition and incomplete fermentation of the substrate can take place [26; 27; 24; 28]. Additionally, because of mixing problems during dry AD, the process has a low rate of reaction, which may be caused by the delay of the dense substrate hydrolysis [2; 27; 24]. [PERSON] et al. [7] pointed out that inhibition of the process occurred at a TS concentration level of more than 40%, due to problems with contact between phases of the substrate and microorganisms. A high TS concentration and dense substrates can lead to obstacles with substrate heating [22].  
Dry ADR requires more inoculum [8]. The dry AD process significantly depends on the activity of the inoculum, which, ideally, should be taken from biogas digesters used for fermentation of such substrates [29].
## 4 Numerical Examples
### Results
#### 4.2.1 Model Analysis
When x = 18, the optimal release time T* is 108.1, the expected minimum total cost is 19,211.82, and the software reliability is 0.8496. When x = 21, the optimal release time T* is 112.7, the expected minimum total cost is 19,261.71 is, and the software reliability is 0.8431. Figure 14 shows graphs of the expected total cost and the software reliability subject to the operating period. Figure 15 shows graphs of the expected total cost based on different operating periods. As shown in Table 9, as the operating period increases, the optimal release time also increases, and the software reliability decreases. Second, we find the impact of the cost coefficients, C1, C2, C3, and C4 on the optimal release time with the expected minimum total cost and the software reliability by changing their values. When C1 = 2.5, the optimal release time T* is 129.1, the expected minimum total cost is 18,907.92, and the software reliability is 0.9074. When C1 = 7.5, the optimal release time T* is 89.2, the expected minimum total cost is 195,395.72, and the software reliability is 0.8135. When C2 = 80, the optimal release time T* is 104.3, the expected minimum total cost is 15,496.43, and the software reliability is 0.8608. When C2 = 120, the optimal release time T* is 100.9, the expected minimum total cost is 22,819.4, and the software reliability is 0.8518. When C3 = 1500, the optimal release time T* is 92.6,  
Figure 14: Expected total cost for the baseline case. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Operating Period** & **EC(T)** & **T*** & **Reliability** \\ \hline x = 9 & 19,024.95 & 88.0 & 0.8729 \\ x = 12 & 19,098.35 & 96.1 & 0.8641 \\ x = 15 (base) & 19,157.92 & 102.6 & 0.8564 \\ x = 18 & 19,211.82 & 108.1 & 0.8496 \\ x = 21 & 19,261.71 & 112.7 & 0.8431 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Optimal release time with expected total cost EC(T) and the software reliability subject to the operating period. the expected minimum total cost is 19,078.56, and the software reliability is 0.8260. When C3 = 2500, the optimal release time T* is 111.2, the expected minimum total cost is 19,233.28, and the software reliability is 0.8767. When C4 = 200, the optimal release time T* is 100.4, the expected minimum total cost if 19,141.58, and the software reliability is 0.8504. When C4 = 400, the optimal release time T* is 104.7, the expected minimum total cost is 19,174.25, and the software reliability is 0.8618. As shown in Table 10, as the values of C1 and C2 increase, the optimal release time decreases, and the software reliability decreases. On the contrary, as the values of C3 and C4 increase, the optimal release time increases, and the software reliability increases.
# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 6 Taxonomy
### Change-Point and Statistical-Test Theory
#### 6.2.3 Multidimensional Class Evaluation  
From Table 19 it is clearly notable that this class predominantly addresses concept drift in general (nine papers), with some explicit references to sudden, incremental, and recurrent concept drift. Only one paper explicitly focuses on data drift, indicating a stronger emphasis on the conceptual aspects of drift rather than purely statistical anomalies. Regarding application contexts, this group presents significant diversity, including process mining, healthcare, energy, IoT, financial systems, environmental sensors, and retail. Among these, process mining and healthcare appear prominently, suggesting that the structured nature and inherent temporal characteristics of these domains align well with statistical and change-point detection methods. Concerning the scope, in this category there is a relatively balanced distribution between global and local explanations. However, the slight predominance of local explanations reflects the capability of statistical methods to highlight precise features or intervals that trigger drift events. In terms of audience, the primary recipients of these methods are data scientists, indicating that the interpretability provided by statistical tests often requires specialized statistical or analytical competencies for effective use. Interestingly, none of the analyzed papers specify explicit quantitative metrics related to interpretability or trustworthiness. This lack of formal metrics implies that these methods primarily offer interpretability through statistical significance and visual indicators rather than standardized quantitative evaluation. Finally, the analysis of learning modes shows a clear preference for online learning approaches, complemented by several hybrid methods, and fewer offline methods. This suggests that the methods within this group are particularly suited for real-time, dynamic data environments, where immediate drift detection is critical.
# Optimised Tuning of a PID-Based Flight Controller for a Medium-Scale Rotorcraft
## 2 System Overview and Mathematical Modelling
### Kinematics  
To transform from the body-fixed reference frame to the Earth-fixed reference frame and vice versa, we define \(R\), the transformation matrix represented in Euler angles, as follows:  
\[\mathbf{R}(\eta)=R_{\psi}(\psi)R_{\theta}(\theta)R_{\phi}(\phi) \tag{1}\]  
\[\mathbf{R}(\eta)=\begin{bmatrix}\cos\psi\cos\theta&\cos\psi\sin\theta\sin\phi- \sin\psi\cos\phi&\cos\psi\sin\theta\cos\phi+\sin\psi\sin\phi\\ \sin\psi\cos\theta&\sin\psi\sin\theta\sin\phi+\cos\psi\cos\phi&\sin\psi\sin \theta\cos\phi-\cos\psi\sin\phi\\ -\sin\theta&\cos\theta\sin\phi&\cos\theta\cos\phi\end{bmatrix} \tag{2}\]  
The position and the velocity in the body-fixed reference frame relate to the inertial reference frame in the following:  
\[\hat{\xi}=\mathbf{R}\mathbf{v}^{B} \tag{3}\]  
This is a special orthogonal group matrix \(SO3\) with interesting properties. For more about the \(SO3\) properties, consult [4]. For orientation, we define \(\mathbf{T}\), the differential transformation matrix, as follows:  
\[\mathbf{T}=\begin{bmatrix}1&\sin\phi\tan\theta&\cos\phi\tan\theta\\ 0&\cos\phi&-\sin\phi\\ 0&\sin\phi/\cos\theta&\cos\phi/\cos\theta\end{bmatrix} \tag{4}\]  
The orientation velocity vector is transformed as follows:  
\[\dot{\eta}=\mathbf{T}\omega^{B} \tag{5}\]  
This transformation matrix exposes one of the drawbacks of using Euler angles: the fact that \(T\) has a singularity at \(\pm 90^{\circ}\). This is not a problem in this investigation as the manoeuvres of interest are not too aggressive. For the design of aggressive and acrobatic rotorcraft, it is better to use Quaternions (see [26; 27] for more information).
## 5 Future Prospects
### The Development of AIoT (Artificial Intelligence of Things)
\begin{table}
\begin{tabular}{p{34.1 pt} p{34.1 pt} p{34.1 pt}} \hline
**No** & **Layer** & **Description \& Features** \\ \hline \hline \multirow{8}{*}{2} & & a. Data Analysis: & \\  & & AI algorithms evaluate data from IoT sensors and devices to find patterns, anomalies, and trends in hydrogen production, storage, and consumption. & \\  & & b. Decision-Making: & \\  & & AI models employ sensor data to make smart decisions in real time, such as altering hydrogen production rates, maximizing energy use, or initiating safety routines. & \\  & & c. Predictive Maintenance: & \\  & & AI anticipates when equipment and infrastructure need maintenance, decreasing downtime and ensuring system reliability. & \\  & & Energy Optimization: AI optimizes energy use and production by matching hydrogen production to demand to reduce costs and environmental impact. & \\  & & d. Data Integration: & \\  & & Collects and transmits data from IoT sensors and devices to the AI layer for analysis and decision-making. & \\  & & Cloud Services: Cloud computing resources are used for data storage, processing, and scalability. & \\  & & e. Secure Communication: & \\  & & Ensures that data is transmitted securely and reliably between IoT devices and the AI layer. & \\  & & Data Management: Data Management is in charge of storing, retrieving, and archiving data for historical analysis and reporting. & \\ \hline \hline \multirow{8}{*}{3} & & This layer is made up of a network of Internet of Things sensors, actuators, and Devices that are deployed across the hydrogen energy system. These gadgets capture data in real-time and interact with physical components. & \\  & & The IoT layer delivers real-world data that flows into the AI layer, allowing for easier monitoring, control, and data-driven decision-making. & \\  & & Features: & \\  & & a. Data Collection: & \\  & & IoT sensors collect data on hydrogen pressure, temperature, and flow rates, energy usage, and other pertinent characteristics. & \\  & & b. Control: & \\  & & In reaction to AI-driven judgments, actuators and devices can remotely control equipment, valves, or processes. & \\  & & c. Environmental Sensors: & \\  & & Monitor environmental factors such as emissions or air quality to ensure regulatory compliance. & \\ \hline \hline \multirow{8}{*}{4} & & This layer represents the physical infrastructure involved in hydrogen creation, storage, and distribution. Electrolyzers, hydrogen storage tanks, pipes, and other components are included. & \\  & & Features: & \\ \cline{1-1}  & Energy Resources and Production & Energy Resources and Production & \\ \cline{1-1}  & Production & This layer represents the physical infrastructure involved in hydrogen creation, storage, and distribution. Electrolyzers, hydrogen storage tanks, pipes, and other components are included. & \\ \cline{1-1}  & & The energy resource and production layer is the hydrogen system’s backbone, and IoT sensors in this layer provide essential data for AI-driven optimization and control. & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._
## 5 Proposed Work
### Adaptive Network Partitioning Strategy  
To effectively cover the widespread geographical regions of the network and optimize energy consumption for big data collection in an LS-WSN, we propose employing  
Figure 6: Optimal point selection and path computation with multiple DCVs.  
Figure 7: Flow chart of the proposed approach.  
multiple DCVs. This provides advantages in terms of scalability, capability, flexibility, interoperability, and robustness. To solve the complex issue of scheduling multiple DCVs in LS-WSN scenarios, we propose an adaptive network partitioning strategy that partitions the entire network into multiple clusters and groups and assigns each group to a DCV. This partitioning scheme prevents DCVs from traveling long distances, thereby confining the scope of movements within the assigned regions. The use of a distributed clustering approach presents several advantages in real-world scenarios, as seen in [60], including energy consumption optimization, improving the route connection (i.e., minimizing delays), and increasing the scalability while reducing traffic. A brief description of the adaptive partitioning scheme is presented below. The network is adaptively partitioned into \(\hat{\mathrm{R}}\) regions, and the BS communicates this result to the DCVs. We adopted the improved _k-means_ algorithm presented in [61] to perform network partitioning, which allows the DCVs to adaptively select a subset of sensor nodes with shorter distances from the centroid of each region. The objective is to minimize the traveling distance of the DCVs and optimize energy consumption. This is computed by taking the mean of (\(x\), \(y\)) coordinates of all the nodes in the region. Nodes are assigned to the closest centroid of their region. Our adaptive network partitioning scheme adopted from [62] is compared to the traditional non-partitioning scheme for both small- and large-scale WSNs using some evaluation metrics such as energy consumption and delays. The results of this evaluation presented in Figures 8 and 9 show that our adaptive network partitioning scheme is superior to the traditional method for LS-WSNs. However, the non-partitioning scheme appears to be effective for small-scale WSNs.  
Figure 8: Comparing adaptive and non-adaptive partition schemes in terms of the number of sensor nodes.  
Figure 9: Comparing adaptive and non-adaptive partition schemes in terms of number of data collector vehicles (DCVs).
# A Weighted PageRank-Based Bug Report Summarization Method Using Bug Report Relationships
## 5 Evaluation Results
### RQ 1. The Effect of Dependency Relationships on Summary Quality
#### 5.1.1 Experiment 1-1. Summary Quality Comparison of Bug Reports without Duplicates Relationships  
If the duplicates relationships do not exist while dependency relationships of blocks and depends-on exist, PRST [6] cannot be applied. To evaluate this condition, we compared the summary quality of the proposed method RBRS with the PRST of the previous study when the duplicates relationships of 36 bug reports were ignored. The results are shown in Table 4.  
The results for Experiment 1-1 in Table 4 shows that when no duplicates relationships exist, the precision, recall, and F1 scores improve by 6.41%, 6.14%, 6.28%, respectively. In our experiments, the number of sentences in extracted summary \(|S|\) is fixed at 25% of the total number of sentences in the bug report. Also, the number of golden summary sentences \(|GSS|\) is about 24% of the total number of sentences in the bug report. As the values of \(|S|\) and \(|GSS|\), which are the denominators of precision and recall, are very similar, precision and recall in our experiments show similar values as well. F1 score, the harmonic mean of precision and recall, show a similar value. As the result, in precision, recall and F1 score, RBRS made the similar percentages of improvement.  
The statistics of 44,600 bug reports described in Table 3 shows that 14,317 bug reports have no duplicates relationships but have dependency relationships, which account for approximately 32.1% of all bug reports. Therefore, we interpret that our method, RBRS, by using the dependency relationships of the bug report, can improve the summary quality by approximately 6.4% for the bug reports that account for 32.1% of the total bug report.  
In addition, most bug reports that have duplicates relationships are often found to be duplicated immediately after the report, and the bug report is closed immediately. As a result, there is often a lack of content for developers to discuss or resolve the bug. In this case, certain sentences or keywords, such as the symptoms or reproducing process created during the bug report, are duplicated. It seems that the summarization result can be biased in such duplicate sentences. Because of the limitations of these duplicates relationships, this bias occurs. Using additional dependency relationships can reduce the bias, resulting in improvement in summary quality.
# Phase Center Corrections for BDS IGSO and MEO Satellites in IGb14 and IGSR3 Frame
## 2 Basic Information
### Satellite and Receiver Antenna Phase Center Corrections  
The metadata related to the BDS-2 and BDS-3 spacecrafts was disclosed by CSNO on 9 December 2019 and 25 December 2019 [19], respectively. The published BDS metadata comprises mass, sizes, and optical properties of the main satellite surfaces, eclipse attitude laws, laser retro-reflector array, and PCO for each satellite. Table 1 lists the released PCO information for the B1 and B3 frequency bands and their ionosphere-free (IF) combinations for BDS IGSO and MEO satellites.  
Since April 2019, the IGS ACs have carried out a third reanalysis of the full history of GPS data collected by the IGS global network since 1994 in a fully consistent way using the latest models and methodology. In this reanalysis process, for some types of antenna, the PCCs for BDS OS signals have been calibrated by Geo++ and archived in the ATX file igsR3_2077.atx (available at [[http://ftp.aiub.unibe.ch/users/villiger/igsR3_2077.atx](http://ftp.aiub.unibe.ch/users/villiger/igsR3_2077.atx)]([http://ftp.aiub.unibe.ch/users/villiger/igsR3_2077.atx](http://ftp.aiub.unibe.ch/users/villiger/igsR3_2077.atx)) (accessed on 30 December 2020)). Table 2 lists all antenna/radome with PCC available for BDS B1I and B3I signals in igsR3_2077.atx. The number of stations equipped with these antennas reached more than 160 by the end of 2019.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**PRN**} & \multirow{2}{*}{**SVN**} & \multicolumn{3}{c}{**B1**} & \multicolumn{3}{c}{**B3**} & \multicolumn{3}{c}{**IF of B1 and B3**} \\ \cline{3-10}  & & **X** & **Y** & **Z** & **X** & **Y** & **Z** & **X** & **Y** & **Z** \\ \hline C06 & C005 & 0.7526 & \(-\)0.0027 & 0.8793 & 0.7473 & \(-\)0.0051 & 1.1980 & 0.76290 & 0.00197 & 0.25985 \\ \hline C07 & C007 & 0.7455 & 0.001 & 0.9824 & 0.7536 & \(-\)0.0051 & 1.1888 & 0.72976 & 0.01286 & 0.58122 \\ \hline C08 & C008 & 0.7494 & \(-\)0.0016 & 1.0 & 0.7493 & \(-\)0.0028 & 1.1895 & 0.7496 & 0.00073 & 0.63167 \\ \hline C09 & C009 & 0.5940 & \(-\)0.0112 & 1.0071 & 0.5960 & \(-\)0.00840 & 1.1805 & 0.59011 & \(-\)0.01664 & 0.67007 \\ \hline C10 & C010 & 0.59870 & \(-\)0.00430 & 0.99540 & 0.60 & \(-\)0.0014 & 1.19480 & 0.59617 & \(-\)0.00994 & 0.60783 \\ \hline C11 & C012 & 0.5766 & \(-\)0.0034 & 1.0742 & 0.57650 & \(-\)0.0051 & 1.1863 & 0.57679 & \(-\)0.00010 & 0.85631 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Officially released phase center offset (PCO) information of B1 and B3, as well as their ionosphere-free combination for BDS IGSO and MEO satellites (unit: m).  
Figure 2: The number of stations with tracking capacity of the BDS-3 representative satellites, i.e., C19, C23, C32, C35, C38, and C59 satellites from DOY 1 to 365, 2019.
###### Abstract
[21] conducted a comprehensive numerical simulation analyzing the long-term variations in temperature and pressure at the Jintan Salt Mine's old gas storage facility. [PERSON] et al. [22; 23] investigated the high-temperature creep characteristics of salt rock under various loading paths and the long-term stability of salt rock gas storage. [PERSON] et al. [24] utilized finite element numerical simulation to couple the surrounding rock temperature field with the stress field, establishing a thermal stress model. [PERSON] et al. [25] derived the functional relationship between gas temperature and pressure within the salt cavity as a function of time when the injection and extraction gas rates are constant based on the variable mass thermodynamics theory. [PERSON] et al. [26] studied the thermodynamic behavior of salt caverns used for gas storage and found that, when the cavern pressure changes more frequently, the water content in the withdrawn gas is smaller. [PERSON] et al. [27] conducted many numerical simulations for a salt cavern in Tuz Golu UGS project at different gas withdrawal and injection rates. [PERSON] et al. [28] derived a viscoelastic-plastic creep constitutive model to investigate the mechanical response characteristics of salt cavern chambers under cyclic storage and release conditions. [PERSON] et al. [29] established a three-dimensional geo-mechanical model of salt cavern gas storage, investigating the response patterns of the surrounding rock under various low-pressure conditions and different gas extraction rates. [PERSON] et al. [30] established a mathematical model for the layered salt rock hydrogen storage caverns and proposed a simulation method suitable for the cyclical charging and discharging of hydrogen in salt caverns. [PERSON] et al. [31] investigated the maximum storage pressure of salt cavern hydrogen storage facilities and proposed the corresponding energy storage capacity, laying the groundwork for the site selection and evaluation of salt cavern hydrogen storage. The aforementioned studies primarily focus on the storage of natural gas in salt caverns, or on other types of repositories like lined or unlined rock cavern or shafts [32]. There is insufficient research on the thermo-mechanical coupling effects in the wells and cavern cavities during hydrogen cyclic injection and withdrawal. This paper establishes an integrated flow and heat transfer model for the cavern and wellbore during hydrogen injection and withdrawal, analyzing the variations in temperature and pressure in both the wellbore and the cavern. The findings provide a theoretical foundation for hydrogen storage projects in salt caverns.