esa-sceva/satcom-chunk-collection huggingface dataset:
# Sliding Suffix Tree
## 3 Sliding Suffix Tree
### Queries
Then, \(B\) consists of repetitive patterns \(P\), where \(P=W[x:n-|B|]\)._  
Proof: Observe two occurrences of \(B\) in Figure 3a. Let \(P=W[x:n-|B|]\). Due to overlapping, string \(W[x:x+|P|-1]\) equals string \(W[n-|B|+1:n-|B|+|P|]=B[1:|P|]\). Consequently, due to new overlapping, \(W[x+|P|:x+2|P|-1]\) now equals \(W[n-|B|+|P|:n-|B|+2|P|-1]=B[|P|+1:2|P|]\). Repeating this observation, we construct \(B=P^{k}P^{\prime}\) where \(k=\lfloor\frac{|B|}{|P|}\rfloor\) and the last repetition \(P^{\prime}\) is a prefix of \(P\), and may be empty. Let \(Y=y_{1},y_{2},\ldots\) represent positions stored in such leaves of \(\mathcal{T}_{Q}\) that \(n-2|B|+1\leq y_{i}<n-|B|\) for each \(y_{i}\in Y\). \(y_{1}\) and \(y_{2}\) in Figure 3c represent two such positions. In Step 1, we computed positions of \(Q\) inside the first occurrence of \(P\) in \(B\), as seen in Figure 3c by the third and the fourth \(Q\). To compute subsequent positions of \(Q\) in the repetitions of \(P\) in \(B\), for each \(y_{i}\in Y\) we add positions \(y_{i}+|P|,y_{i}+2|P|\ldots\) up to \(n-|Q|+1\) to the resulting set. This approach will add all remaining occurrences of \(Q\) in \(B\) and complete the query. The fifth occurrence of \(Q\) in Figure 3c is in the repetition of \(P\) in \(B\). Figure 3: (**a**) Occurrences of \(B\) in the window, if \(x>n-2|B|\). (**b**) Repeated pattern \(P\). (**c**) Potential occurrences of \(Q\). \(y_{1}\) and \(y_{2}\) are positions stored in the finalized suffix tree. The second two positions of \(Q\) are obtained in Case 3, Step 1. The last position of \(Q\) is obtained in Case 3, Step 2. ```
Input: Query \(Q\), Unfinalized suffix tree \(\mathcal{T}\), Active node \(\beta\), Buffer \(B\), Current position in a stream \(n\) Output: Positions of \(Q\) in the window
1\(S\leftarrow\) empty list
2\(x\gets leaf(\beta)\)
3\(\mathcal{T}_{Q}\leftarrow\mathit{navigate}(\mathcal{T},Q)\)
4 for each leaf \(v\) in \(\mathcal{T}_{Q}\)do
5\(y\leftarrow\mathit{position}(v)\)
6 add \(y\) to \(S\)
7 if\(x\leq y<x+|B|\)then
8\(|P|\gets n-|B|-x\)
9\(y^{\prime}\gets y+|P|\)
10 while\(y^{\prime}\leq n-|Q|\)do
11 add \(y^{\prime}\) to \(S\)
12\(y^{\prime}\gets y^{\prime}+|P|\)
13
14 return\(S\)
```  
**Algorithm 1**Query procedure in the sliding suffix tree. The time complexity of the query first requires \(O(m)\) time to navigate \(Q\) in \(\mathcal{T}\), and then time to solve the three cases above. The first two cases are solved in \(O(1)\) time. Step 1 of the third case is solved in \(\Theta(|\mathcal{T}_{Q}|)=O(occ)\) time, since we add at most one position per leaf of \(\mathcal{T}_{Q}\) to the resulting set. Step 2 of the third case may add multiple positions per leaf of \(\mathcal{T}_{Q}\), and is bounded by \(O(occ)\) time in the worst case. **Theorem 1**. : _We can find all occurrences of query \(Q\) in a sliding suffix tree in time \(\Theta(m+occ)\) for constant alphabet size._
## 10 Open Challenges and Future Directions
### Collaborative Decision Making  
The perception of the environment, the states, the allocation and coordination of tasks, as well as the consensus are important factors that influence the behavior of UAVs in a swarm. Each autonomous UAV contributes to the mission progress by performing actions in a collaborative manner. In this context, the level of human control is reduced to a few start-up operations or maintenance operations.  
Several challenges remain to be addressed, including consensus building, task allocation, collective failure management, and perception.  
* **Definition of a consensus** on which the UAVs base themselves to carry out individual actions that contribute to a convergence towards a common goal.
* **Task allocation**, which consists in dynamically assigning tasks according to the state and capacity of the UAVs. To allow a high level of parallelism, tasks can be distributed fairly if the UAVs are homogeneous. Otherwise, it will be done according to the workload and computing resources of each UAV. It should also be noted that the assignment of tasks is based on a parallelism paradigm (synchronous parallelism, asynchronous parallelism, or bulk synchronous parallelism) that ensures that the tasks are executed concurrently.
* **Collective failure detection** in the swarm makes it possible to detect the UAVs that have abnormal behavior, or that carry out actions that do not contribute to the realization of the mission. For example, due to a hardware malfunction (e.g., low battery or defective rotor), a UAV may not respond appropriately to some tasks assigned to it. The collective management of the failures in the swarm must make it possible to detect the UAVs whose behavior does not contribute to the progress of the mission. It is also necessary to consider an activity continuity plan that allows the other UAVs to continue the mission and to adapt to any type of change.
* **Perception of the environment** represents the information collected by the UAV. Each UAV uses onboard sensors to build a local representation of the environment. The combined set of perceptions allows the swarm to build a global representation and make more predictive decisions. For example, to calculate the most optimal path that reaches a target, we can choose the closest drone. See Section 4.2.2 for more details. These challenges open future directions as follows:
* **The deployment of a swarm using a multi-agent-based model** for better coordinating the actions and interactions of the drones to enhance the individual and/or collective decision making.
* **The implementation of a reinforcement learning algorithm** on each drone to control its behavior based on the reward of good actions.
# BER Aided Energy and Spectral Efficiency Estimation in a Heterogeneous Network
## 2 Analysis  
Complex relationship between SE and EE of multiuser radio networks is determined by compromising the involved throughput, overall system energy, frequency resources distribution, traffic flow patterns, acceptable erroneous protocol data unit rates, and achieved vs. target QoS level.  
Generally, the SE of wireless communication networks is the ratio of the data rate \(R\) [b/s] to the bandwidth \(B\) [Hz] that is needed to achieve \(R\)[13].  
Moreover, the radio channel \(EE\) [b/J] is the ratio of bit energy \(E_{b}\) to the noise spectral density \(N_{0}\), i.e., EE expresses the count of information bits per energy unit.  
So, the Shannon formula for radio channel capacity \(C\) [b/s], originally depending on channel bandwidth \(B\) and mean power \(P_{s}\), can be expressed by \(SE\) and \(EE\) as follows [13]:  
\[C=B\cdot log_{2}\bigg{(}1+\frac{P_{s}}{P_{N}}\bigg{)}=B\cdot log_{2}\bigg{(}1+ \frac{E_{b}}{N_{0}}\cdot\frac{R}{B}\bigg{)}=B\cdot log_{2}(1+SE\cdot EE) \tag{1}\]  
Specifically, for the case of the Additive White Gaussian Noise (AWGN) channel, having given \(P_{s}\) and \(B\), where we consider \(EE\) as the ratio \(C/B\), (1) implies that:  
\[SE=\log_{2}(1+SE\cdot EE) \tag{2}\]  
Thus, we can explicitly express \(EE\) as a function of \(SE\):  
\[EE=\frac{2^{SE}-1}{SE} \tag{3}\]  
In the simplest case of a single-BS and a single-UE wireless network, Equation (3) enables the analysis of the \(SE\) vs. \(EE\) relationship in linear and non-linear power and energy regions, thus aiming to enable considerably enlargements of throughput and data rate [13].  
From these considerations, it is obvious that increasing the data rate requires a significantly larger power of the received signal. This implies BS-to-UE distances of the order of several times 10 m, but still in the linear region that tolerates considerably larger values (at the price of smaller \(SE\), due to \(EE\) reduction by propagation impairments).  
In the non-linear region of (3), however, a considerably larger \(EE\) can be achieved, as stronger received signals enable lower cell dimensions down to 10 m, with the variety of cell classes comprising micro-, nano-, pico-, and femtocells. These enable statistical distribution of \(EE\) that is close to the uniform one, with significantly larger \(SE\), and, thus,the throughput and rationally distributed coverage still have good enough \(EE\) (even in such areas crowded with active users) and considerably lower electromagnetic radiation [13].  
So far, the HetNet's overall efficiency was analyzed by considering both SE and EE and determining the SINR for each UE within the \(k\)-tier of HetNet having \(N_{T}\) tiers overall.  
Each tier (e.g., \(k\)-th) is modeled by a homogeneous PPP \(\Phi_{k}\), with the transmit power of \(P_{k}\), BSs density of \(\lambda_{k}\), and the SINR threshold of \(\tau_{k}\) (often referenced as \"bias\") at UE, respectively.
## 5 Magnetic Field Generation Techniques  
A controllable external magnetic field is required to control magnetically actuated microrobots. Usage of a magnetic field to control microrobots has key advantages such as long-range, fast response, precise actuation, high dexterity, high actuating force or torque, minimal interactions with tissues, high penetration depth, and 2D and 3D actuation capability [129]. The requirement of a complex setup to generate a magnetic field is a disadvantage of magnetically actuated microrobots [130]. Since microrobots are most commonly used in biomedical applications, the static magnetic field strength for clinical procedures should be less than 2 T, according to the United States Food and Drug Administration (FDA) Agency [16].  
Magnetic torque-based and magnetic gradient-based are the two actuation methods that are used to actuate magnetic microrobots [131]. Among them, torque-based actuation is the most efficient actuation method [132]. Torque-based actuation requires a uniform magnetic field that can be generated using a Helmholtz coil system [133]. Due to the nature of the coil arrangement, this system has limited space. However, a weak rotating magnetic field can be generated over a relatively long distance, such as inside the living organisms [44]. This generates a lateral screwing motion, which deviates the original moving direction of the microrobot due to the applied torque on the microrobot [28]. Gradient-based actuation can be performed using a convergent magnetic field. Two independent coils [91], four independent coils [48], eight independent coils [88], a permanent magnet attached to a robotic manipulator [134], and a magnet array [135] can be used to generate a convergent magnetic field. These types of actuation methods cannot generate a large magnetic gradient over a long distance [28].
## 4 Results and Discussion
The \(L_{T-C}\) at TCI is a time-varying function of velocity. As shown in Figure 6 is the tool-chip interface contact length in TCI in the model, the increase in cutting speed is accompanied by a decrease in cutting force, and the \(L_{T-C}\) at TCI will also decrease. When the cutting force tends to stabilize, the \(L_{T-C}\) between the tool and the chip decreases to a minimum of 0.372 mm. The TPF behavior occurs at the \(L_{T-C}\) of the TCI. As the \(L_{T-C}\) of the TCI decreases, the space where the friction occurs will be compressed. At this time, the friction force of cutting SiCp/Al by EVC is the smallest, and the cutting force and the interface stress are the smallest. Figure 5: Changing trend of cutting force. (**a**) Cutting force prediction in EVC cutting; (**b**) Cutting force in conventional cutting SiCp/Al. The stress distribution at TCI is an important parameter reflecting the cutting state. In EVC machining, the stress distribution at TCI is a time-varying function of the distance (x) from the cutting point to the tool tip. In this part, the MATLAB is used to simulate the stress distribution at TCI with four different cutting speeds (V = 600 m/min, V = 450 m/min, V = 300 m/min and V = 150 m/min). The results are shown in Figure 7. The average stress showed a downward trend and finally reached the lowest steady state, which also verified the previous prediction of the cutting force model. As shown in Figure 7, the faster the cutting speed, the more obvious the decrease in the distributed stress value. Figure 6: Tool-chip interface contact length in TCI in the model. Figure 7: Distributed stress at the distance from cutting point to tool tip with different speed in the model. (**a**) V = 150 m/min; (**b**) V = 300 m/min; (**c**) V = 450 m/min; (**d**) V = 600 m/min.
## 2 Materials and Methods
### Determination of the Beam Quality Specifiers  
The IAEA TRS-398 and DIN 6800-2 protocols use the tissue-phantom ratio, \(TPR_{20,10}\). It is defined as the percentage of absorbed dose measured on the beam axis at depths of 20 and 10 cm in a water phantom at a constant source-detector distance (SDD) of 1 m and a field size of 10 \(\times\) 10 cm [1,2,4]. \(TPR_{20,10}\) describes the approximate exponential decrease of a photon beam at a depth of the maximum dose, excluding electron contamination [13]. \(TPR_{20,10}\) values were determined from direct measurements as well as estimated from the _PDD_ using Equation (8) [2,4]:  
\[TPR_{20,10}=1.2661.PDD_{20,10}-0.0595 \tag{8}\]  
Here, \(PDD_{20,10}\) is the percentage depth dose at 10 to 20 cm in water for a standard field size of 10 \(\times\) 10 cm at an SSD of 100 cm. AAPM TG-51 uses \(\%dd\) (10)\(x\) as a beam quality index, defined as the _PDD_ at 10 cm depth in water, due to photon-only exclusion of electron contamination [1]. At beam energies below 10 MV, \(\%dd\) (10)\(x\) = \(\%dd\) (10). For beam energies \(\geq\)10 MV, \(\%dd\) (10)Pb was measured with a 1 mm lead foil placed to interrupt the beam at 30 cm from the phantom and determined using Equation (9):  
\[\%dd\ (10)_{x}=\left[0.8116+0.00264\%dd(10)_{\text{Pb}}\right]\%dd\ (10)_{\text{Pb}} \tag{9}\]  
The _PDD_ was measured using an FC65-G chamber mounted on a [PERSON] computerized water phantom. Photon beam quality conversion coefficient (\(K_{Q}\)) values for the ionization chambers of interest in radiotherapy are available in a lookup table as a function of \(\%dd\) (10)\(x\) in AAPM TG-51 and as a function of \(TPR_{20,10}\) in IAEA TRS-398. On the contrary, the beam quality conversion factors for the plane-parallel chambers were determined using calibration coefficients for the user beam (\(Q\)) and the reference beam (\(Q_{0}\)) qualities according to the method described by [PERSON] [14].  
\[k_{Q,Q_{0}}=\frac{N_{D,\omega}^{Q}}{N_{D,\omega}^{Q_{0}}} \tag{10}\]
# A Review of Research on Diagnosability of Control Systems Based on Structural Analysis
## 3 Diagnosability Evaluation Based on Structural Analysis
### Quantitative Evaluation  
The quantitative evaluation of diagnosability focuses on quantifying the ease of diagnosing faults. The current method of quantitative diagnosability evaluation based on structural analysis is a measure of the similarity of the residuals associated with the fault.
# Cooperative Buffering Schemes for Time-Shifted Live Streaming of Distributed Appliances
###### Abstract  
Distributed appliances connected to the Internet have provided various multimedia services. In particular, networked Personal Video Recorders (PVRs) can store broadcast TV programs in their storage devices or receive them from central servers, enabling people to watch the programs they want at any desired time. However, the conventional CDNs capable of supporting a large number of concurrent users have limitations in scalability because more servers are required in proportion to the increased users. To address this problem, we have developed a time-shifted live streaming system over P2P networks so that PVRs can share TV programs with each other. We propose cooperative buffering schemes to provide the streaming services for time-shifted periods even when the number of PVRs playing back at the periods is not sufficient; we do so by utilizing the idle resources of the PVRs playing at the live broadcast time. To determine which chunks to be buffered, they consider the degree of deficiency and proximity and the ratio of playback requests to chunk copies. Through extensive simulations, we show that our proposed buffering schemes can significantly extend the time-shifting hours and compare the performance of two buffering schemes in terms of playback continuity and startup delay.  
time-shifting; cooperative buffering; distributed appliance; P2P streaming 2021 11 11527. [[https://doi.org/10.3309/app112311527](https://doi.org/10.3309/app112311527)]([https://doi.org/10.3309/app112311527](https://doi.org/10.3309/app112311527))
## 4 Experimental Study in Long Delay Regime
### Locking Condition  
The measured laser dynamics and locking map are plotted in Figure 12. The current of DFB#2 is fixed at 25 mA while the current of DFB#1 is adjusted to tune the frequency detuning between the two lasers at free running state. There are two differences between locking conditions in short and long delay regimes. First, the locking condition is insensitive to coupling phase. Coupling phase in the fiber link varies slowly over time since the refractive index and length changes with ambient temperature fluctuation. However, mutual injection locking remains, and a continuous locking region is mapped. Secondly, mutual injection locking only occurs at weak coupling in the long delay regime. In the high attenuation region, the locking range broadens as the coupling strength increases. For coupling strength higher than -37 dB, the locked region breaks into two narrow branches and then disappears. The process to establish mutual injection locking state is described in Appendix A in detail.  
Figure 11: Experimental setup for mutually coupled lasers in the long delay regime.  
Figure 12: (a) Optical spectra and intensity noise spectra of mutually injection locked state (MIL), F: four wave mixing (F), periodic oscillation with relaxation frequency (R) and chaos (C); (b) locking map of mutually coupled DFB lasers with long delay.
# Research on Slope Stability Prediction Based on MC-BKA-MLP Mixed Model
## 4 (M-C)-BKA-MLP Prediction Model
### Comparison and Verification of Different Methods  
To further validate the accuracy of the BKA-MLP model's prediction results, nine case samples were selected from the dataset. These samples encompassed three soil types--clay, sandy soil, and conglomerate--and three slope categories--gentle, moderate, and steep. The detailed classification and parameters are presented in Table 5. The limit equilibrium method and Geostudio numerical simulation were employed to calculate the safety factor for each case slope, with the results compared against those predicted by the BKA-MLP model.  
Figure 7: Comparison of the results of the top four models.  
Figure 8 illustrates the comparative analysis of safety factor predictions across multiple cases using different methods. The relationship between the vertical safety factor and the horizontal case number clearly demonstrates the superiority of the BKA-MLP model. As observed from the curve trends, the correlation between the predicted and actual values of the BKA-MLP model is significantly higher than that of traditional methods, such as the limit equilibrium method and numerical simulation, particularly in high-risk cases with low safety factors (in areas where case numbers are larger). The BKA-MLP model maintains stable prediction accuracy, whereas the limit equilibrium method and conventional simulations exhibit noticeable deviations. This indicates that the BKA-MLP model can optimize MLP hyperparameters globally through the BKA algorithm, effectively addressing the limitations of traditional methods, which rely heavily on empirical parameters and lack generalization under complex conditions. Additionally, the BKA-MLP curve exhibits minimal fluctuation, suggesting robust prediction results, which are critical for engineering safety assessments. In summary, the BKA-MLP model demonstrates significant advantages in terms of prediction accuracy, stability, and adaptability to complex conditions, providing a more reliable and intelligent solution for slope geotechnical engineering stability analysis.
# Black Carbon in the Air of the Baikal Region, (Russia): Sources and Spatiotemporal Variations
## 2 Methods
### Equipment and Expedition Route for Analyzing Black Carbon in the Air above Lake Baikal
During the first two days of the expedition, measurements were carried out in the southern basin, along the coastline of its east and west coasts, at a distance of 0.5 to 3.0 km from the shore, and in the central part. The west and east coasts in this area include large settlements, such as Listvyanka, Kultuk, Slyudyanka, Baikals, and Babushkin, and there are major highways and railways connecting the European part of Russia with Siberia and the Far East. The RV moved from the southern basin to the central basin towards the estuary of the Selenga River, the lake's largest tributary. Thereafter, after crossing the section through the lake from the east coast to the west coast, the route passed along the west coast from the central basin to the northern basin, towardsits northernmost extremity in the towns of Nizhneangarsk and Severobaikalsk. Then, the RV moved from the west coast to the east coast towards Ayaya Bay, which hosts the national park and is one of the background sites of the lake. After passing the northern part of the lake, the RV moved along the east coast towards the central basin, entering the largest bays (Chivyrkuy and Barguzin), and finally returned to the southern basin (Figure 1). Figure 1: Routes of comprehensive scientific expeditions in the water area of Lake Baikal, 2019 and 2023 (**a**); RV “Akademik V.A. Koptyug” and location of measuring equipment on the upper deck, 2019 (**b**). An MDA-02 aethalometer was used to study eBC in the air above Lake Baikal in 2019 and 2023. Sampling was carried out for 24 h in an automatic mode for 10 min: once every half hour in 2019 and once every 15 min in 2023. The values averaged over the sampling period were entered into the computer. Taking into account the weather conditions, measurements influenced by the RV (ship exhaust pipe) were excluded from the general dataset. The 2019 measurements were primarily conducted under anticyclonic weather conditions. Data from space observation tools (MODIS, NOAA, and CALIPSO), the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) and Navy Aerosol Analysis and Prediction System (NAAPS) models, meteorological parameters of the Ust-Barguzin station, and the Arctic and Antarctic Research Institute (AARI) charts of synoptic situation above the Northern Hemisphere ([[http://www.aari.ru](http://www.aari.ru)]([http://www.aari.ru](http://www.aari.ru)), accessed on 6 June 2024) were taken into account during the route expeditions at Lake Baikal for an objective and complete analysis of the transport of air pollutants to Lake Baikal.