esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Issues and Challenges in the Defense Against Deliberate Attacks on IECPS  
Due to the limitations of current key technologies, coupled with the complex operational mechanisms of IECPS and the high uncertainty of deliberate attacks, the full-process defense strategy, including preemptive prevention, process response, post-event recovery and summarization, faces challenges such as difficulties in modeling and solving problems, attack detection and early warning, as well as defense assessment and improvement during its implementation.
## 3 Results
### Mass Balance  
Material and substance flow analyses are performed based on mass balances. The composting unit is then built graphically and displayed as Sankey plots. Figure 2 presents the mass flows of wet waste throughout the processes and the loss of material and compounds to the atmosphere (in kg). The water content is significant for the proper accounting of the total mass balance. Since it is added during composting and maturation and accounts for 200 L per Mg of treated materials in the ACT and 90 L per Mg of treated materials during maturation, the quantities are not insignificant (while its use is threefold). It provides temperature control by reducing heat due to its evaporation, it acts as a medium for the dilution and exchange of elements. Finally, it regulates the aerobic conditions in the composting mass. During the aerobic process, the evaporated water and mass loss is calculated to 766.3 kg per Mg \(w/w\), plus 164.5 kg for the maturation state, while an amount of 279.5 kg is rejected. The resulting CLO material is calculated to be 79.73 kg.  
The carbon balance is presented in Figure 3. During the two sub-processes where organic matter degradation occurs, 64.96% of the initial carbon is released into the environment. The primary composting process releases 46.14% of the initial carbon in gaseous form. A significant portion of the initial carbon (31.52%) is diverted to the landfill and contributes to carbon sequestration [62; 63]. Finally, 3.52% is included in the CLO destined for land use. Several studies have investigated the degradation of organic matter and C fate during composting. Production of mature compost requires degradation of 40-83% of the carbon contained in the compost [12]. Most of this carbon is emitted as biogenic CO\({}_{2}\), and a relatively small portion is emitted as CH\({}_{4}\) created in anaerobic pockets in the composting mass.  
The total nitrogen loss during the main composting process (ACT) is 64.99% (Figure 4). A portion (19.68%) of the initial nitrogen is landfilled, and 5.3% is bound to the CLO produced mass. The rest is released in gas form during the maturation phase. The controlled conditions in the ACT provide a stable temperature profile of 45-65 \({}^{\circ}\)C, favoring the thermophiles phase. The above conditions inhibit the nitrification of produced ammonium to NO2 while the dissociation constant (pKa) of NH\({}_{4}\)\({}^{+}\) decreases with increasing temperature, meaning that higher temperatures favor evaporation of NH3. Eventually, ammonia is the most emitted form of N [64; 65]. However, other by-products have not been investigated (i.e., for ammonia the oxidized forms NO and N\({}_{2}\)O are not considered, although aerobic microorganisms may form them). These gases potentially impact the environment. NO may result in ozone depletion in the stratosphere, and N\({}_{2}\)O is an effective greenhouse gas [66].
## 2 Materials and Methods
### Digital Feedback Controller  
The NMPC-GP with the PSO solver is compared with a discrete-time feedback control system developed using the pole placement technique. The crane dynamics are decomposed in a similar manner as in Section 2.1 into the actuated and unactuated parts and identified using an output-error approach to obtain a discrete-time relation between the crane velocity \(v(k)\) and the control input \(u(k)\), approximated by a first-order discrete-time model (21), while the relation between the payload sway angle \(\alpha(k)\) and the velocity \(v(k)\) is given by a second-order discrete-time model (22):  
\[v(k)=\frac{D\big{(}z^{-1}\big{)}}{C\big{(}z^{-1}\big{)}}u(k) \tag{21}\]  
\[\alpha(k)=\frac{B\big{(}z^{-1}\big{)}}{A\big{(}z^{-1}\big{)}}v(k) \tag{22}\]  
where \(z^{-1}\) is the backward shift operator and \(A\big{(}z^{-1}\big{)}\), \(B\big{(}z^{-1}\big{)}\), \(C\big{(}z^{-1}\big{)}\), and \(D\big{(}z^{-1}\big{)}\) are polynomials:  
\(A\big{(}z^{-1}\big{)}=1+a_{1}z^{-1}+a_{2}z^{-2}\),  
\(B\big{(}z^{-1}\big{)}=b_{1}z^{-1}+b_{2}z^{-2}\),  
\(C\big{(}z^{-1}\big{)}=1+c_{1}z^{-1}\),  
\(D\big{(}z^{-1}\big{)}=d_{1}z^{-1}\).  
A digital control law at step \(k\) for parallel feedback loops of the crane position and payload sway angle is proposed as follows:  
\[u(k)=\frac{\big{(}k_{1}+k_{2}z^{-1}\big{)}e(k)+\big{(}k_{3}+k_{4}z^{-1}\big{)} \alpha(k)}{1-k_{5}z^{-1}} \tag{23}\]  
where \(e(k)=x(k)-x_{r}(k)\), \(x(k)\) is approximated by (5); \(x_{r}(k)\) is the reference signal at time \(k\); and \(k_{1}\), \(k_{2}\), \(k_{3}\), \(k_{4}\), and \(k_{5}\) are the controller parameters calculated using the pole placement method. In this case, the 5 th-order closed-loop characteristic polynomial can be presented as  
\[\delta\Big{(}z^{-1}\Big{)}=qSK \tag{24}\]where  
\[q =\big{[}1,z^{-1},\ldots,z^{-5}\big{]},\] \[S =\begin{bmatrix}1&0&0&0&0&0\\ a_{1}+c_{1}&0&0&0&0&-1\\ a_{1}c_{1}-a_{1}+a_{2}-c_{1}&t_{s}d_{1}&0&-b_{1}d_{1}&0&-a_{1}-c_{1}+1\\ -a_{1}c_{1}+a_{2}c_{1}-a_{2}&t_{s}a_{1}d_{1}&t_{s}d_{1}&(b_{1}-b_{2})d_{1}&-b_{1 }d_{1}&-a_{1}c_{1}+a_{1}-a_{2}+c_{1}\\ -a_{2}c_{1}&t_{s}a_{2}d_{1}&t_{s}a_{1}d_{1}&b_{2}d_{1}&(b_{1}-b_{2})d_{1}&a_{1} c_{1}-a_{2}c_{1}+a_{2}\\ 0&0&t_{s}a_{2}d_{1}&0&b_{2}d_{1}&a_{2}c_{1}\end{bmatrix},\] \[K =[1,k_{1},k_{2},\ldots,k_{5}]^{T}.\]  
The desired polynomial \(\delta(z^{-1})\) can be obtained by assuming the desired location of the closed-loop poles \(\lambda_{i}\):  
\[\delta\Big{(}z^{-1}\Big{)}=\prod_{i=1}^{n=5}\Big{(}1-\lambda_{i}z^{-1}\Big{)} \tag{25}\]  
In [56], the desired closed-loop poles were assumed as \(\lambda_{i}=exp(-\omega_{n}t_{s})\), where \(\omega_{n}=\sqrt{g/l}\) is the natural undamped frequency of the payload suspended on a rope of length \(l\), while \(g=9.81\) ms\({}^{-2}\) is the acceleration gravity.
# Real-Time Behaviour of Dredged Slurry Treated by Air-Booster Vacuum Consolidation
## 7 Test Results
### Flux and Pressure of Boosting Air  
The air pressure at the outlet of the boosting pump was set to 20 kPa. However, since pressure is lost due to the airflow and pipe-wall friction, the actual pressure in the booster pipeline (monitored by the air-pressure transducer) will be less than the target value at the outlet. The flux and pressure of the pressurised air, which are recorded by the vortex flow meter and air-pressure transducer, respectively, are shown in Figure 7 for the first 90 s of the boosting duration (i.e., 10 min). It is seen from Figure 6a that the air flux remained low and stable for the first 7 s; meanwhile, an almost linear increase in the air pressure (from 0 to 8 kPa) can be observed in Figure 7b. After, a steep increase in the air flux and a sharp drop in the air pressure can be observed during the time period of 7-10 s. In the following 30 s (i.e., 10-40 s), a steady increase in both the air flux and pressure can be observed, after which they both increased (while fluctuating) and then decreased slightly within the following half minute (i.e., 40-70 s). After 70 s, both the air flux and air pressure became stable. Thus, only the first 90 s of the boosting duration is presented. Figure 7 shows that the stable values of the air flux and air pressure were approximately 0.7 m\({}^{3}\)/h and 11 kPa, respectively.  
Figure 7: Time histories of boosted air: **(a)** air flux; **(b)** air pressure.
## 4 Results and Evaluation
### MOE Performance  
We evaluated the traffic performance of the three scenarios with the following criteria: average delay time, waiting time, travel time, and CO\({}_{2}\) emissions. Results are shown in Figure 13 below.  
The MOE evaluation showed that the traffic management policy was the most important aspect that affected the model performance. The DRL-based scenario had the best results (average delay times were 24.1, 23.5 s, and 22.1 s, corresponding to 3, 2, and 1 vehicles in a platoon). It is 10% and 13% smaller than the MP- and ATSC-based scenarios, as presented in part a) of Figure 13. This result proves the superiority of the trained model compared to the other two models.  
The waiting time of the 3 scenarios is shown in part (b) of Figure 13. The DRL-based scenario had considerably higher performance, with an average waiting time of 10.6 s, because it reached the maximum cumulative reward, as mentioned in Figure 10. Compared with the ATSC- and MP-based scenarios, the average waiting time of vehicles in the DRL-based scenario was improved by 20% and 28%, respectively (in the case of a platoon having 3 vehicles).  
Part (c) and (d) of Figure 13 show that the DRL-based policy was better than other policies in terms of travel time and CO\({}_{2}\) emissions. However, the difference between these policies was not large. The minimum travel time was 95.2 s for the DRL-based scenario,  
Figure 12: Average speed of 3 scenarios (DRL, ATSC, and MP).  
and the longest travel time was 96.7 s for the ATSC-based scenario, with a difference of 4% (in the case of a platoon having 3 vehicles).  
Between these scenarios, the DRL-based scenario had the smallest CO2 emissions (2346 mg/s) for the case with 3 vehicles in one platoon. The MP-based scenarios were slightly better than the ATSC method. The three scenarios improved the traffic efficiency; thus, the CO2 emissions were improved further. However, the degree of improvement in each scenario varied based on the performance of the vehicles.
## 4 Experiments and Results
### Experimental Setup
#### 4.1.2 Data Pre-Processing  
Data preprocessing considers an essential step in machine learning and data mining [50; 51; 52; 53; 54]. The reviews contain incomplete sentences; a large amount of noise; and weak wording, such as words without application, high repetition, imperfect words and incorrect grammar. Unstructured data also have an impact on sentiment classification results. Pre-processing the reviews is needed to maintain a regular structure and reduce such problems. Cleaning data with filters, splitting the data into parts for training and testing, and building data sets with favorite words are a few of the steps employed in our research. Without going into too much depth, we used the following techniques to prepare the data.  
Tokenization  
We divided the text into phrases, words, symbols, or other meaningful elements, thus forming a list of individual words per comment. In each comment, we then used each word as a feature for our training classifier.  
Removing Stop Words  
Comment contains some stop words that have no meaning, such as prepositions, and words that add no emotion value (or, also, able, etc.). The Natural Language Toolkit (NLTK) library provides a stop words dictionary, including words with neutral meaning neutral that are not suitable for sentiment analysis. To remove the stop words from the comment's text, we checked each word in the list against the dictionary and excluded them.  
Capitalization  
Documents and texts containing many sentences and diverse capitalization can be a big problem when classifying big documents. The best approach to deal with inconsistent capitalization is to decapitalize each letter. This technique shows all words in the same feature distance to the text and document, but it poses a significant issue in the interpretation of some words (e.g., \"US\" (United States of America) to \"us\" (pronoun)).
## 2 Network Community Detection
### Popular Algorithms
\begin{table}
\begin{tabular}{l c l l} \hline \hline
**Author** & **Ref.** & **Cat.(No.)** & **Order** \\ \hline [PERSON] & (Graph clustering, 2000 [33]) & GT(1) & \(O(nk^{2})\), \(k<n\) parameter \\ [PERSON] and [PERSON] & (Curvature, 2002 [34]) & GT(2) & \(O(mk^{2})\) \\ [PERSON] and [PERSON] & (Modularity, 2002 [35]) & SDP(1) & \(O(n^{2}m)\) \\ [PERSON] & (Vertex proximity, 2004 [36]) & GT(3) & \(O(n^{3})\) \\ [PERSON] and [PERSON] & (Spinglass, 2004 [25]) & SDP(2) & parameter dependent \\ [PERSON] _et al._ & (Fast greedy, 2004 [23]) & SDP(3) & \(O(nlog_{2}n)\) \\ Newman and Girvan & (Eigenvector, 2004 [8]) & SP(1) & \(O(nm^{2})\) \\ Wu and Huberman & (Linear time, 2004 [37]) & GT(4) & \(O(n+m)\) \\ [PERSON] _et al._ & (Infocentrality, 2004 [38]) & SDP & \(O(m^{3}n)\) \\ [PERSON] _et al._ & ([PERSON] _et al._, 2004 [4]) & SP(2) & \(O(m^{4}/n^{2})\) \\ Donetti and Munoz & (Donetti and Munoz, 2004 [39]) & SDP(4) & \(O(n^{3})\) \\ [PERSON] _et al._ & (Simulated annealing, 2004 [40]) & SDP(5) & parameter dependent \\ [PERSON] _et al._ & ([PERSON] _et al._, 2004 [41]) & SP(3) & \(O(n^{2})\) \\ Latapy and Pons & (Walktrap, 2004 [15]) & SP(4) & \(O(n^{3})\) \\ Duch and Arenas & (Extremal optimization, 2005 [42]) & GT(5) & \(O(n^{2}logn)\) \\ Bagrow and Bollt & (Local method, 2005 [43]) & SDP(6) & \(O(n^{3})\) \\ [PERSON] _et al._ & (overlapping community, 2005 [44]) & GT(6) & \(O(exp(n))\) \\ [PERSON] _et al._ & (label propagation, 2007 [13]) & GT(7) & \(O(n+m)\) \\ Rosvall and Bergstrom & (Infomap, 2008 [45]) & SP(5) & \(O(m)\) \\ Ronhovde and Nussinov & (Multiresolution community, 2009 [46]) & GT(8) & \(O(m\beta logn)\), \(\beta\approx 1.3\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Algorithms for network community detection and their complexities. GT, graph traversal; SDP, semi-definite programming; SP, spectral.
# Parallel Crossed Chaotic Encryption for Hyperspectral Images
## 2 Chaotic Systems and Chaotic Encryption
### Chaotic Systems  
Chaotic systems are dynamical systems that exhibit the following features:  
* **Deterministic**: There is no randomness involved in the system evolution, then if we know the initial condition and parameters, we will be able to predict the system.
* **Sensitive to initial conditions**: A chaotic system is exponentially sensitive to an initial condition, in other words, a small change in the initial condition provokes a big difference in the evolution of the system.
* **Aperiodic**: There is no periodicity in a chaotic system.
* **Bounded**: The state of a chaotic system is bounded, and it maintains chaotic inside this bounded limits.  
The mentioned features make chaotic systems a suitable mathematical tool for encryption. Various chaotic systems have been used for encryption, such as the Lorenz system [17], Ikeda chaotic map [18] and others. Chaotic encryption has been used for electroencephalogram signal encryption [19], video encryption [20; 21; 22]. Chaotic systems, are also useful for designing bio-inspired optimization algorithms [23] or embed secret data with in the image [24].
## 5 Performance Evaluation
### Results and Discussion
In larger networks, traditional methods encounter significant time complexity when searching for optimal VNF placement strategies and performing routing path selection. DDQN-SP exhibits slightly longer running times compared to MRL-SFCP, which is attributed to the use of the shortest path algorithm for routing.
## 5 Inspection and Weighted Compensation of Multi-Sensor Data
### Consistency Inspection of Sensor Data  
For the multi-sensor positioning system composed of ZigBee-GPS/BDS, the \(i\)th positioning data \(x_{i}^{\text{ZGB}}\) can be expressed as  
\[x_{i}^{\text{ZGB}}=x_{i}^{t}+\xi_{i} \tag{31}\]  
where \(x_{i}^{t}\) is the true value, and \(\xi_{i}\) is the measurement noise, \(i=1,2,\ldots,n\).  
The obtained multi-sensor positioning data were arithmetically averaged and inspected for consistency [23]. The specific steps were as follows:  
Step 1 the arithmetic mean of the positioning data, \(\overline{x}\), \(k=1,2,\ldots,T\), was calculated:  
\[\overline{x}_{i}=\left(\sum\limits_{k=1}^{T}x_{i}^{\text{ZGB}}(k)\right)/T \tag{32}\]  
Step 2 according to the arithmetic mean value of the positioning data, the consistency inspection of the multi-sensor positioning data was carried out.  
\[\left|\mathbf{x}^{\text{ZGB}}(k)-\overline{x}_{i}\right|\leq\tau \tag{33}\]  
where \(\mathbf{x}^{\text{ZGB}}(k)=\{x_{1}^{\text{ZGB}}(k),\,x_{2}^{\text{ZGB}}(k), \ldots,x_{n}^{\text{ZGB}}(k)\}\) is the multi-sensor positioning data, and \(\tau\) is the system requirement error.  
Through the set system error requirements, the consistency inspection of the multi-sensor positioning data was carried out. If the difference between the sensor positioning data and the arithmetic mean value was less than the error required by the system, the positioning data was determined to be consistent, that is, credible data, and could be denoised using basic particle filtering. On the contrary, the variance of the sampled data needed weighted correction to meet the sampling requirements of the basic particle filter for data samples.
## 3 Method  
Time series high spatial resolution remote sensing is the study of the technology for constructing high spatial and temporal resolution remote-sensing data and applications of those data. Usually, the spatial resolution and temporal resolution of the time series high spatial resolution remote-sensing data are higher than 30 m and eight days, respectively. In this study, we combined the HJ CCD, GF-1 WFV, Landsat, and MODIS data to establish a time series of high spatial and temporal resolutions for phenology monitoring and crops mapping. Although the GF-1 WFV data covers the Earth surface every four days and HJ CCD data observe the entire Earth at two-day intervals, there are still gaps greater than eight days in the time series data because of cloud contamination. [PERSON] _et al._[41] demonstrated that satellite can monitor the seasonal growth of vegetation successfully if the temporal resolution of satellite data is higher than eight days. To fill the gaps in the time series Normalized Different Vegetation Index (NDVI) data from Landsat, HJ, and GF-1 observations, daily 30 m NDVI data were generated by fusion of MODIS with Landsat, HJ, and GF-1 NDVI data using STDFA.
# Dynamic Behavior of Twin-Spool Rotor-Bearing System with Pedestal Looseness and Rub Impact
## 2 Modeling  
To create a simplified dynamic model of the twin-spool rotor system that fulfills technical requirements, it is essential to use the rotor structure of the real aero-engine as a foundation. The primary components should be retained while making appropriate simplifications to the actual structure.  
In this paper, a simplified dynamics model will be established with PW6000 as an example. The aero-engine twin-spool rotor system comprises HP and LP rotors with the primary mechanical connection between the two pieces being the inte-shaft bearing. Twin-spool rotor systems employ hollow rotor shafts to minimize weight. The LP rotor shaft is positioned inside the HP rotor shaft, creating a connection between the LP compressor and the LP turbine at both ends. The HP rotor shaft is linked to both the HP compressor and HP turbine at both of its extremities. The transmission of vibrations between this system mostly occurs through inter-shaft bearings, where the outer ring is in contact with the HP shaft and the inner ring is in contact with the LP shaft. The HP and LP rotor shafts are equipped with ball bearings that are directly linked to the engine case, together with the inter-shaft bearing, to guarantee the stability of the support structure. The twin-spool rotor system consists of three primary components:  
1. Disks: radial size is much larger than the axial size of the rotating body, such as fans, compressors, and turbines.
2. Rotating shafts: axial size is much larger than the radial size of the rotating body, such as LP shaft and HP drum.  
Bearings: including inter-shaft bearings and ball bearings.  
The coordinate system for the three main components will be established based on the definition of Euler angles. Using this coordinate system, the second type of Lagrange equations and the finite element method will be employed to create the disk, rotating shaft, and bearing elements. By grouping these elements, the complete differential equations of the twin-spool rotor dynamics will be obtained.
## 2 Calculation Methodology of the PVCS Global Cost
### Investment Cost
#### 2.1.1 Energy Chain  
The energy chain corresponds to all the components of the PVCS, allowing the production and distribution of electrical energy. These components are:  
* PV system (PV panels, inverter, connection wiring, installation, and construction costs);
* Stationary storage and lithium-ion technology;
* Charging terminals.  
The investment cost of these different components is estimated following the values recovered from the technical sheets [21] and from the Batiprix costing French database [22] specific to building and public works.  
The investment cost of the PV system \(C_{PV,invest}\) includes the PV purchase cost \(C_{purchase,PV}\), and the cost of workforce \(C_{wf,PV}\). The workforce cost of the component corresponds to the expenses generated by its installation. This cost includes the PV panels, the inverter, and the connection wiring. \(C_{PV,invest}\) is calculated as follows:  
\[C_{PV,invest}=\left(C_{purchase,PV}+C_{wf,PV}\right)\cdot x\cdot y \tag{3}\]  
where \(x\) is the cost coefficient and \(y\) is the sales price.  
The purchase cost of the PV system is calculated as follows:  
\[C_{purchase,PV}=N_{PV}\cdot C_{pv,un} \tag{4}\]  
where \(C_{pv,un}\) is the unit purchase cost for a PV system for one PV panel in \(\mathbf{\xi}\)/unit and \(N_{PV}\) is the number of PV panels of the PVCS.  
The cost of the RC \(C_{intra,rc}\) is calculated as a function of the unit cost of RC \(C_{rc,un}\) in \(\epsilon/\text{m}^{3}\) and the total volume of RC \(V_{tot}\) as follows:  
\[C_{intra,rc}=V_{tot}\cdot C_{rc,un}=(N_{PP}\cdot V_{BA})\cdot C_{rc,un} \tag{12}\]  
where \(V_{BA}\) is the volume of the RC for the parking space in \(\text{m}^{3}\).  
As observed previously, the ISO 15686 standard adds, to these investment costs, maintenance costs.
# QiOi: Performance Isolation for Hyperledger Fabric
## 7 Related Work
### Performance Improvement  
Several studies have proposed to improve the performance of Fabric by optimizing Fabric architecture or enhancing the processing routines (as shown in Table 6). Previous research [1; 16] pointed out the following performance bottlenecks: (1) repeated de-serialization of chaincode information including identity and certificate, (2) sequential validation of transactions during VSCC, and (3) delay of multi-version concurrency control (MVCC), which ensures no read-write conflicts between the transactions. In order to resolve these bottlenecks, they propose to cache chaincode information, to execute VSCC in parallel, and to use bulk read-write during MVCC validation. Their evaluation results [16] showed that the proposed techniques improve the Fabric performance by 2 times and 1.3 times in CouchDB and GoLevelDB, respectively.  
In addition, some studies have suggested enhancing the strategies for endorsement or database access. For example, [PERSON] and [PERSON] [36] improved read transaction processing by separating read and write transactions in the endorsement of peers. They also created a new consensus protocol without using an external project. As a result, they improved the overall latency and throughput by 20%. [PERSON] et al. [37] proposed replacing the database that stores the latest key-value pair in the ledger with an in-memory hash table to enhance the speed of database access. They improved Fabric throughput up to 20,000 tps in Fabric v1.4. [PERSON] et al. [49] also pointed out database access overhead in chaincodes and suggested disabling the compression of GoLevelDB and reducing the size of StateDB and the number of reads and writes in a transaction.  
To sum up, previous studies focused on analyzing performance bottlenecks and optimized Fabric architecture to improve Fabric performance. QiOi differs from these studies in that QiOi addresses the performance interference in Hyperledger Fabric when the Fabric components run on cloud data centers.