esa-sceva/satcom-chunk-collection huggingface dataset:
# Engineering a Combinatorial Laplacian Solver: Lessons Learned +
## 4 Evaluation
### Results
#### 4.2.6 Cache Behavior  
The nearly-linear running time of the Laplacian solver was proven in the RAM machine model. To get good practical performance on modern out-of-order superscalar computers, one also has to take their complex execution behavior into account, e.g., the cache hierarchy.  
One particular problem indicated by our experiments is that the number of cache misses increases in the LogFlow data structure when a bad spanning tree is used. Note that querying and updating the flow with this data structure corresponds to a dot product and an addition, respectively, of a dense vector and a sparse vector. The sparse vectors are stored as sequences of pairs of indices (into the dense vector) and values. Thus, the cache behavior depends on the distribution of the indices, which is determined by the subtree decomposition of the spanning tree and the order of the subtrees.  
We managed to consistently improve the time by about 6% by doing the decomposition in breadth-first search order, so that the indices are grouped together at the front of the vector. In contrast, the actual decomposition only depends on the spanning tree. Furthermore, we could save an additional 10% of time by using 256-bit AVX instructions to do four double precision operations at the same time in LogFlow, but this vectorized implementation still uses (vectorized) indirect accesses.  
In our experiments, we get about 5% cache misses by using the minimum weight ST on the 2D grid, compared with 1% when using CG. In contrast, the special ST yields competitive cache behavior. Not surprisingly, since the Barabasi-Albert graph has a much more complex structure, its cache misses using the sparse matrix representation increase to 5%. In contrast, the cache misses improve for larger graphs with LogFlow since the diameter of the spanning tree is smaller than on grids and the decomposition, thus grouping most indices at the start of the vector.  
From the benchmarks, we can infer that the micro-performance in terms of cache misses suffers from indirect accesses just as in the case of the usual sparse matrix representations. Furthermore, the micro-performance crucially depends on the quality of the spanning tree. For good spanning trees or more complex graphs, the micro-performance of the Laplacian solver is competitive with CG.  
Figure 7. The Laplacian solver with the special ST as a smoother on a \(32\times 32\) grid. For each number of iterations of the solver, we plot the current error and the absolute values of its transformation into the frequency domain. Note that (a) and (k) have a different scale.
## 5 Limitations and Future Research Directions
### Operating Limitations of Renewable Energy Resources for VSM Implementation  
In deriving the power-frequency dynamics in Section 3.1, the types of renewable energy resources are not specified, but uniformly considered as \"negative load\". This is reasonable since only the frequency support of ESS-based VSM is concerned, especially in the small-scale power system. If frequency control is implemented on renewable energy resources, a similar analysis of the power-frequency dynamics can be conducted, by  
Figure 20: The control performance when FAESS and SAESS adopt the proposed VSM control. (**a**) Without SoC energy control. The waveforms show that the SoC of FAESS deviates from the desired value 0.5 (**b**) With SoC energy control applied to FAESS. The waveforms show that the SoC of FAESS is controlled near 0.5.  
assuming a common system frequency and following the same procedure of multiple-machine analysis in the conventional power system [36; 37]. This is feasible, for both grid-forming (VSM) controlled and grid-following grid-supporting renewable energy resources.  
However, the constraints of renewable energy resources must be considered in practical VSM implementation [38]. For example, normally photovoltaic (PV) generation can only be shed in the over-frequency case to provide frequency support. Therefore, it is difficult to implement VSM on PV generators in case of low sunlight or at night. By contrast, wind turbine generator (WTG) can also contribute in the under-frequency case by releasing the kinetic energy in the turbine and generator rotor. Nevertheless, auxiliary control must be added so that the reduced rotor speed due to inertial and damping support remain within the allowed range [39; 40; 41]. Similarly, how to maintain VSM operation when wind turbine cannot rotate at very low wind speed is challenging. Though the capacitor on the DC bus of Type IV WTG can provide the energy needed for WTG at standstill, the power and energy capacity of the capacitor must be carefully selected. The procedure of analysis and design proposed in this work provide a guideline for future research.
# SVPWM Method for Multilevel Indirect Matrix Converter with Eliminate Common Mode Voltage
## 2 Multilevel Indirect Matrix Converter and the Proposed SVPWM Method
### The SVPWM Method for Elimination the CMV
#### 2.2.2 Inverter Stage Control  
Once the switching states of the rectifier stage have been decided, the SVPWM is applied to control the three-level T-Type NPC inverter. With the three-phase load, the system variables (voltages or currents) in \(abc\) coordinates can be transformed to \(\alpha\beta\) coordinates by using the transformation defined in (5). \[\left[ \begin{array}{c}V_{\alpha}\\ V_{\beta}\end{array} \right]=\frac{2}{3}\left[ \begin{array}{ccc}1&-\frac{1}{2}& -\frac{1}{2}\\ 0&\frac{\sqrt{3}}{2}&-\frac{\sqrt{3}}{2}\end{array} \right]\left[ \begin{array}[] {c}V_{A}\\ V_{B}\\ V_{C}\end{array} \right] \tag{6}\]  
where \(V_{A}\), \(V_{B}\), \(V_{C}\) are three-phase output voltage and \(V_{\alpha}\), \(V_{\beta}\) are corresponding voltage sequence in \(\alpha\beta\) plane given by Clark transform. From (5), the size of each vector can be calculated and given in Table 4. The space vector diagram of the three-level T-type NPC inverter includes of 27 vectors which can be split into three categories: Zero, large, medium and small vectors. There are 6 large vectors, 6 medium vectors, 12 small vectors and 3 additional zero vectors as shown in Figure 5. On the plane vectors as shown in Figure 5, each vector indicates the connection of the output phase to the DC-link bus. For example, vector \(V_{13\,(1\text{-}1\text{-}1)}\) represents the connection of the output phase \"\(A\)\" to the positive pole \"\(P\)\", output phase \"\(B\)\" and \"\(C\)\" to the negative pole \"\(N\)\" of DC-link bus. There are three kinds of modulation strategies for three-level NPC inverter, i.e., carrier-based PWM, SVPWM and Selected Harmonics Elimination. Among three methods, the SVPWM has been a very popular PWM technique. In the space vector approach, over one PWM cycle, a specific number of voltage vectors are selected and used in a specific time length and sequence. The SVPWM with zero CMV was firstly presented in [21], but the neutral-point voltage imbalance stills exist due to the neutral-point current. In this paper, the novel SVPWM method is presented to obtain the zero CMV and to cancel the neutral-point current.
# Considering the Optimization Design of Urban Bus Network Scheduling
## 2 Synchronized Optimization Model for Network and Scheduling
### Model Construction  
Consideration of optimizing the design of city bus networks with scheduling aims to minimize passenger time cost and bus operation cost. Constraints include the route length, headway, non-repeating route stations, and non-linear route coefficients. A synchronous optimization model is developed for both the network and scheduling.
## 2 Queue Scheduling and Optimization Mechanism of Large-Scale LEO Satellite Communication Networks
### On-Board Switch Architecture for LEO Satellites  
In large-scale LEO satellite communication networks, the onboard switches consist of a receiving buffer module, a packet header processing module, a switching matrix, and multiple sending buffer modules for inter-satellite links. Within the switch, each sending  
Figure 4: (**a**) A scenario with two time-sensitive flows. (**b**) Transmission packet loss caused by a short queue. (**c**) Transmission time out caused by a long queue.  
buffer module is responsible for the traffic scheduling and forwarding of an inter-satellite link, divided into eight queues (Q0-Q7) according to IEEE 802.1 Qch [16]. We assume that the CQF mechanism is applicable to satellites [17]. For time-sensitive traffic, its packets are stored in Q6 and Q7. For non-time-sensitive traffic, its packets are stored in Q0 to Q5. The structure of onboard switches in large-scale LEO satellite communication networks is illustrated in Figure 5[17]. As shown in Figure 5, the PSO-QSO mechanism proposed in this paper is applied to optimize the settings of the numbers and lengths of the queues in \(T_{X}\) buffers.
## 4 Dataset
### Statistical Parameters  
For modelling, first, data were randomly divided into training and testing groups, with 70% of data selected as the training set and 30% as the testing set. Tables 1 and 2 show the statistical parameters of the input and output parameters for the training and testing sets, respectively. Figure 2 also presents the frequency and cumulative frequency histograms of all input and output data.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Statistical Index** & **SC (\%)** & \(\sigma_{n}\) (kg/cm\({}^{2}\))** & **SL (\%)** & **Dr (\%)** & **CSR** & \(\Delta\)H (mm)** \\ \hline Min. & 0 & 1 & 30 & 60 & 0.289 & 0.11 \\ Max. & 100 & 5 & 100 & 85 & 0.779 & 2.92 \\ Mean & 51.98 & 3.03 & 64.72 & 71.42 & 0.49 & 1.08 \\ Median & 50 & 3 & 62.5 & 70 & 0.5 & 0.94 \\ Standard deviation & 41.66 & 1.39 & 27.12 & 10.46 & 0.11 & 0.73 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical features of training data.  
Figure 2: The frequency and cumulative frequency histograms, along with the statistical features, of the input and output data. (**a**) SC, (**b**) \(\sigma_{n}\), (**c**) SL, (**d**) Dr, (**e**) \(\Delta\)H, (**f**) CSR.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Statistical Index** & **SC (\%)** & \(\sigma_{n}\) **(kg/cm\({}^{2}\))** & **SL (\%)** & **Dr (\%)** & **CSR** & **\(\Delta\)H (mm)** \\ \hline Min. & 0 & 1 & 30 & 60 & 0.297 & 0.12 \\ Max. & 100 & 5 & 100 & 85 & 0.769 & 2.85 \\ Mean & 45.37 & 2.92 & 61.48 & 71.22 & 0.51 & 1.18 \\ Median & 50 & 3 & 62.5 & 70 & 0.5 & 1.03 \\ Standard deviation & 39.17 & 1.47 & 24.66 & 9.98 & 0.1 & 0.77 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical features of testing data.  
As can be seen, the SC varied in the range from 0 to 100%, \(\sigma_{n}\) varied in the range from 1 to 5 kg/cm\({}^{2}\), SL varied in the range from 30 to 100%, and Dr varied in the range from 60 to 85%. Additionally, the measured collapse settlement varied in the range from 0.11 to 2.92 mm and the CSR varied in the range from 0.289 to 0.779. The wide range of input and outputs parameters confirms the high variety in the samples.
# Timed-SAS: Modeling and Analyzing the Time Behaviors of Self-Adaptive Software under Uncertainty
## 3 Approach Overview  
The time behaviors of the SAS systems are difficult to describe and analyze quantitatively, as they run in the uncertainty environment. To this end, this paper created an approach called Timed-SAS following three steps, defining the time constraints and time evaluation metrics, constructing the NPTA based formal modeling templates, and creating a set of SMC-based quantitative analysis templates, as shown in Figure 2.  
Concretely, this approach is composed of three steps:  
1. Definitions on the time constraints and time evaluation metrics. In the Timed-SAS approach, the time constraints are defined based on the MAPE-K automatic computing architecture, and they are divided into two categories, i.e., the deterministic time constraints and the uncertainty delay time constraints. The former consists of the _Monitoring-Period_ in the Monitor process and the _Triggering-Delay Time_ in the Analyze process; while the latter refers to the uncertainty delay time in the self-adaptation loops caused by message processing, network congestion, algorithm running, resource scheduling and etc. The time evaluation metrics include the _Self-adaptive Steady Time_ and the _Self-adaptive Adjusting Time_, which can be used to evaluate the performance and the response-time of the self-adaptation strategies;
2. Construction of the formal modeling templates. The formal modeling templates are created to explicitly describe the self-adaptation processes, the time constraints, and  
Figure 2: An overview of the Timed-SAS approach.  
the uncertainty environment. The templates for the self-adaptation processes are constructed based on the MAPE-K automatic computing architecture. The time constraint templates include the deterministic time-modeling templates, i.e., _Monitoring-Period_ and _Triggering-Delay Time_, and the uncertainty delay time modeling templates, e.g., _Uniform-Distribution Delay_ time and _Normal-Distribution Delay_ time. In addition, the uncertainty environment modeling template was created to describe the stochastic system behaviors, and the dynamic system loads;
3. Creation of the quantitative analysis templates. This set of templates provides a quantitative description and analysis of the desired self-adaptation properties and time properties, as well as a simulation on the time evaluation metrics. It can also be used to analyze and optimize the time constraints and the self-adaptation strategies of the SAS systems.  
In conclusion, the Timed-SAS approach provides systematic definitions on the time constraints for the self-adaptation processes, and on the performance evaluation metrics for the self-adaptation loops. Furthermore, it presents a set of formal modeling templates and quantitative analysis templates for the above time constraints and evaluation metrics. The above templates can consolidate design knowledge for reuse, can alleviate modeling difficulty, and improve modeling and analysis efficiency of the SAS systems.
# Challenges, Threats, Security Issues and New Trends of Underwater Wireless Sensor Networks
## 3 Threats and Challenges in Underwater Wireless Sensor Networks (UWSNs)
### Hello Flooding Attack  
In some routing protocols of UWSNs, nodes broadcast hello messages to inform the presence to one-hop neighbors. An adversary may launch attacks by recording the hello packets and sending them from a super node with higher transmit power and larger communication range. These replayed hello packets can reach nodes which are out of communication range of the source node. Any node  
Figure 9: Two methods of wormhole attack; (**a**) using radio channel; (**b**) using wired channel.  
that uses the source node as the next hop in a route but that is out of that node's communication range will be unable to forward packets.  
Pairwise authentication and geographic routing protocols are feasible countermeasures for this attack. In pairwise authentication schemes, nodes can verify bidirectional links before constructing routes. Geographic routing protocols [54] that let nodes discount hello messages from nodes without communication range can also prevent UWSN from this attack. Geographic protocols require each node to know its location and be able to communicate that location to other nodes. However, precise positioning is also a challenging research topic in UWSNs.
## 3 Problem Formulation and Model Design
### Perform ELIA and Bitrate Selection Simultaneously  
As shown in Figure 4, in the middle of each time period, each agent acquires the users receiving data within its own coverage area and obtains its state \(S_{i}\). The input states \(S_{i}\) of each agent are combined as the current set of states \(\mathcal{S}\), which is fed into each agent's actor network to obtain the predicted action \(A_{i}\). To solve the action conflict problem between different agents and optimize the user's QoE, the action that brings the best QoE to the user will be executed, after calculating the QoE under different actions. In this way, the set of actions \(A\) and the set of rewards \(R\) for all agents are obtained, and the next state \(\mathcal{S}^{\prime}\) is influenced by the current action and the next time period of user information. Additionally, when an epoch ends, \(done=Ture\) is set and the rest of the time is set to \(False\). The 5-tuple \([\mathcal{S},A,R,\mathcal{S}^{\prime},done]\) is stored in the experience pool for training. When there are enough training data, training starts. The training process follows the principle of centralized training and step-by-step execution; i.e., the actor predicts the data individually for each agent with its own observed data, while the critic trains by taking the state \(\mathcal{S}\) and action set \(A\) of all agents as input to estimate the value. After training, we just need to test each agent's actor with the test set. The specific process is shown in Algorithm 2.  
```
Input: Initialized model parameters \(\Theta\), edge server set \(E\), the maximum value \(M\) of the episode Output: Actor network model for edge servers
1 Initialize the experience pool ReplayBuffer.
2 forepisode = 1 to \(M\)do
3 fort=1 to \(T\)do
4 Execute Algorithm 1 to obtain the current state \(\mathcal{S}\);
5 Each agent obtains the action \(A_{i}=\mu(s_{i}^{j},\theta^{\mu})+\mathcal{N}_{t}\)
6 Each edge server \(e_{i}\) executes the action \(A_{i}\) according to the current strategy to obtain a new state \(\mathcal{S}^{\prime}\), and calculates the reward \(R_{i}\) according to (8);
7 if\(\mathcal{S}^{\prime}=\mathcal{S}\)then
8 done = True
9
10 else
11\(done\) = False
12
13 end if
14 Store \([\mathcal{S},A,R,\mathcal{S}^{\prime},done]\) into ReplayBuffer.
15\(\mathcal{S}\leftarrow\mathcal{S}^{\prime}\);
16 for\(e_{i}\in E\)do in parallel
17 Sample data sample \(j\) from ReplayBuffer;
18 Calculate the estimated value \(y_{i}^{j}=R_{i}+\gamma Q_{i}^{\psi}(\mathcal{S},A)\) according to (11);
19 Based on this estimate, the parameters of the critic network are updated by minimizing the loss value;
20 Update the parameters of the actor network \(\Theta_{i}\) according to the loss value and (10).
21
22 end if
23
24 end for
```  
**Algorithm 2**AMARL.
## 3 Results and Discussion
### Characterization of TK221: Insights from SEM-EDX, Microtrac, ATR-FTIR, and XPS
Further, the ATR-FTIR and XPS peaks indicate the presence of TODGA and CMPO in TK221 coated \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Spectral Peak**} & \multirow{2}{*}{**Chemical State**} & \multicolumn{2}{c}{**Binding Energy (eV)**} \\ \cline{3-4}  & & **This Study** & **Reference** \\ \hline O(1s) & P=O/C=O & 532.02 & [43] \\ O(1s) & C-O & 534.22 & [43] \\ O(1s) & Chemically bonded water & 535.75 & [41; 44; 45] \\ C(1s) & C-O/C-C & 285.96 & [20; 40; 42] \\ C(1s) & Amide & 286.87 & [20; 40; 42] \\ C(1s) & C=O & 288.67 & [20; 42] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: XPS data and chemical states of fresh TK221 resin. Figure 2: (**a**) XPS wide scan of fresh TK221, (**b**) XPS narrow scan spectrum of fresh TK221 at C(1s), and (**c**) XPS narrow scan spectrum of fresh TK221 at O(1s). on PS-DVB. The presence of these functional groups is significant, as they are responsible for the extraction properties of the resin. The identification of these functional groups using ATR-FTIR and XPS analysis supports the use of TK221 in metal ion extraction and other related applications. However, further testing and analysis will be necessary to fully understand the performance of TK221 in various extraction processes.
# Tunable RF Filters Based on Liquid Crystal for Space Applications
## 3 Qualification Tests and Results
### Test Results
#### 3.2.1 RF Performance Measurements over Test Phases  
All RF performance measurements for the frequency setting of 19,780 MHz are shown in Figure 9. The measurements are plotted versus specification (straight lines in orange color).  
Note that between the test steps, not only were the loads, as listed in Table 2, applied, but also in each test step, the filter was set to the other targeted frequencies via commanding the control electronics, respectively. The frequency setting of 19,780 MHz is shown as an example.  
Measurements over all test phases coincide well and remain within specification. Especially, also the values at temperature extremes (TV2 at +65 \({}^{\circ}\)C and TV3 at -10 \({}^{\circ}\)C) show very little drift over frequency. There is some deviation in the measurement after the mechanical shock test (PST), where the response curve is homogeneously shifted upwards in frequency by about 20 MHz. This shift is no longer present in subsequent test phases. The measurement at the final functional test (FFT) shows a slight increase in bandwidth, with the right side of the response curve remaining constant and the left side being shifted downwards by about 15 MHz.  
Figure 9: RF performance measurements over test phases (see Table 2 for meaning of the labels).
# Optimised Tuning of a PID-Based Flight Controller for a Medium-Scale Rotorcraft
## 4 Simulation Results and Discussion
### Controller Implementation Results
#### 4.2.3 Robustness  
Finally, the rotorcraft was perturbed with a constant gust wind to verify the robustness of the tuned PID controllers. The wind was introduced at time \(t=7\) s, to see if the rotorcraft would be able to return to equilibrium. Figures 14 and 15 show the position and orientation of the rotorcraft when it is subjected to a 10 m/s gust wind from the starboard side. All hover PID controllers are able to recover from the gust with 5 s as shown in Figure 14. It is noted, however, that the steady-state error in height is increased. The velocity graph, in Figure 15, shows that the total lateral rotorcraft speed does change at 7 s and recovers asymptotically.  
For gust perturbation in forward speed, the rotorcraft, even though it still tracks the forward speed correctly, drifts and does not recover the desired position, as shown in Figure 16. The ACO-PID seems to be more effective in its response, while the GA-PID responds the worst to gust. The hypothesis is that, due to its strict adherence to the objective function during tuning, it struggles with any effect outside the norm.  
[PERSON] (2018) has covered, extensively, the shortcomings and perils of adaptive control on aircraft systems and their lack of amicable proofs, leading to the large number of documented experimental aircraft incidents. However, the PID gains derived in this paper are offline in nature and the computational intelligence optimisation algorithms only assist in the selection of these gains. The algorithms themselves do not form part of the final aircraft flight control system.  
This also clarifies what might appear as a contradictory conclusion from the presented numerical simulations: that ACO-PID performs better for hover while the GA-PID performs better for forward flight. This does not mean that the rotorcraft flight control system will fly ACO and then change to GA, but that the results of the optimisations (i.e., the gains) are programmed to the flight controller. These gains are subjected to similar scrutiny for any PID-based flight controller gains determined through other documented and popular methods that are, albeit, not optimal.  
Figure 13: The nonlinear rotorcraft velocity time history 10 m/s.  
Figure 14: The position history of the rotorcraft subjected to starboard gust disturbance at hover.  
Figure 15: The speed of the rotorcraft in response to starboard gust disturbance at hover.