esa-sceva/satcom-chunk-collection huggingface dataset:
# Network Defense Strategy Selection with Reinforcement Learning and Pareto Optimization
## 2 Game Theory on Pareto Optima and Q-learning
### Simple Single Objective Q-Learning
The Q-value for an action gives a specific state is defined as:  
\[Q^{\pi}(x,a)=R_{x}(a)+\ \gamma\sum_{y}P_{x,y}(\pi(x))V^{\pi^{*}}(y) \tag{6}\]  
By iteratively updating the Q-value for each state-action pair, we can slowly determine the best action for each state, giving us:  
\[V^{*}(x)=\ \max_{a}Q^{*}(x,a) \tag{7}\]  
Figure 1: Example of Q-learning in a Markov Process. As previously described, updating the Q-values requires a simple iterative function. Assume that all state-action pairs have a pre-defined initial Q-value \(Q(x_{0},\ a_{0})\). The specific steps for an agent using Q-learning are visible in Figure 2 and defined as:  
* Determine the current state \(x_{t}\);
* Choose an action \(a_{t}\), either by exploring (with a probability of \(\varepsilon\)) or exploiting (with a probability of \(1-\varepsilon\)) the current Q-value;
* Receive a reward \(r_{t}\) for having performed the action;
* Observe the resulting state \(x_{t+1}\);
* Adjust the Q-value using a learning rate \(\alpha\), according to:  
\[Q(x_{t},a_{t})\leftarrow\ Q(x_{t},a_{t})+\ \alpha\cdot\Big{(}r_{t}+\ \gamma\cdot \max_{a}Q(x_{t+1},a)-Q(x_{t},a_{t})\Big{)} \tag{8}\]  
Choosing an action by exploration just means randomly selecting one of the possible values. Choosing an action by exploitation means selecting the action with the highest Q-value [54]. The Q-learning process can, of course, be expanded to multiple dimensions [55]. The primary change is that a state-action pair results in a reward vector instead of a scalar reward. The second change is that in the maximization function for \(Q(x_{t+1},\ a)\) becomes a multi-objective optimization problem. A solution may be optimal in one dimension, but not in another. We can use the previously described Pareto optimization approach to determine the optimal solution for this problem.
## 3 Method
```
//inputs: //*'eval_sdb'isthesigneddistanceboundrepresetingashape //*'IN'isthesgridresolution functionapply_grid_hopping(eval_sdb,N) { for(inti=0;i<N;++i) for(intj=0;j<N;++j) { intk=0; while(true) { //settheoriginoftheray floatx=-1.0/2.0+1.0/(2.0*N)+i/N; floaty=-1.0/2.0+1.0/(2.0*N)+j/N; floatz=-1.0/2.0+1.0/(2.0*N)+k/N; //useraymarchingtoteterminewmuchtomovalongtheray floatt=trace_ray( [x,y,z], [0.0,0.0,1.0], //originoftheray eval_sdb, //signeddistancebound
1.05*(1.0/2.0-z), //maxdistancetotravel Math.sqrt(6.0)/(2.0*N)//distancetotsurfacerequire ); //setthenewvalueofzanditsassociatedcell,(i,j,k) z=z+t; k=Math.floor(N*(z+1.0/2.0-1.0/(2.0*N))); //areweoutsidethepolygonizationvolume? if(k>N-1||z>1.05/2.0) break; //polygonize cell(i,j,k)...//<-polygonizaitoncodegoeshere,e.g.,Marchingcubes //movefurtheralongthezdirection ++k; } }
```  
**Algorithm 1** Pseudocode for the gridhopping method  
If the ray intersects the surface and we denote the closest intersection to \(\mathbf{r}_{0}\) with \(\mathbf{r}^{*}\), then the above iteration converges to \(\mathbf{r}^{*}\). This is because of the following:  
1. \(|f_{S}(\mathbf{r}_{n})|\geq 0\);
2. On the ray between \(\mathbf{r}_{0}\) and \(\mathbf{r}^{*}\), \(f_{S}(\mathbf{r})=0\) only for \(\mathbf{r}=\mathbf{r}^{*}\);
3. The iteration will never \"overshoot\" \(\mathbf{r}^{*}\) because \(f_{S}\) is a signed distance bound. See [1] for additional analysis.
## 2 Materials and Methods
### Materials  
#### 2.3.1 Stage 1--LF-DSS Treatment and M-BGS Production  
_Chlorella_ sp. biomass (UTEX 636) was used for the experiment. The microalgae were cultivated in and pre-adapted to the medium on leachate from anaerobic waste digesters. Initial microalgal biomass concentration in the photo-bioreactor was 500 mgTS/dm\({}^{3}\). The experiment proper was commenced after one full hydraulic residence period in the H-PBR.  
Liquid fraction of digested sewage sludge (LF-DSS)--sourced from the MWWTP in Olsztyn and collected in a retention tank--was used as the medium for treatment and for growing microalgae into microalgal-bacterial granular consortia, then into M-BGS. The digested sewage sludge was sourced from a digester running under the following operating parameters: organic load rate (OLR)--approx. 2.0 kg DOM/m\({}^{3}\cdot\)d, hydraulic retention time (HRT)--20 days, process temperature--35 \({}^{\circ}\)C. The liquor from DSS dewatering by chamber filter presses was stored in a 1000 m\({}^{3}\) underground retention tank, then fed into the H-PBR. The H-PBR, with an active volume of 1.0 m\({}^{3}\), was supplied with 100.0 dm\({}^{3}\) LF-DSS/d. Hydraulic retention time (HRT) was 10 days. At the start of the experiment, the H-PBR was fed with 50% treated effluent and 50% LF-DSS. The digesters and the LF-DSS storage tank are shown in Figure 3.  
Carbon dioxide and oxygen were supplied to the H-PBR microbial community in the form of diffused ambient air and CO\({}_{2}\)-rich air from the gas in the LF-DSS tank. The air was bubbled in through the bottom of the H-PBR at 50 m\({}^{3}\)/h. The profiles of the LF-DSS and the air from the gas phase of the LF-DSS storage tank are given in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **LF-DSS** & \\ \hline
**Indicator** & **Unit** & **Value** \\ \hline COD & mgO\({}_{2}\)/dm\({}^{3}\) & 719.3 \(\pm\) 57 \\ \hline TOC & mgC/dm\({}^{3}\) & 524 \(\pm\) 62 \\ \hline TP & mgP/dm\({}^{3}\) & 26.8 \(\pm\) 1.8 \\ \hline P-PO\({}_{4}\) & mg P-PO\({}_{4}\)/dm\({}^{3}\) & 21.1 \(\pm\) 2.4 \\ \hline TN & mgN/dm\({}^{3}\) & 52.9 \(\pm\) 4.7 \\ \hline N-NH\({}_{4}\) & mg N-NH\({}_{4}\)/dm\({}^{3}\) & 46.3 \(\pm\) 3.9 \\ \hline pH & - & 7.24 \(\pm\) 0.13 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of the LF-DSS and air fed into the PBR.  
Figure 3: LF-DSS storage tank (**a**), enclosed digester and chamber filter press building, with the H-PBR in the foreground (**b**).
# Performance Evaluation of the New Compound-Carrier-Modulated Signal for Future Navigation Signals
## 2 NSCC and Its Properties  
### Mathematical Model  
The multicarrier-based NSCC signal mainly realizes the function of pseudo-range measurement with carrier phase and spread spectrum code phase, as well as transmitting the low rate integration navigation and communication messages. For the \(i\)-th transmitter, the time-domain mathematical expression is given by:  
\[S^{i}\left(t\right)=\sum_{m=1}^{M}A_{m}^{i}C_{m}^{i}\left(t\right)D_{m}^{i} \left(t\right)\cos\left(2\pi\left(f_{0}+\Delta f_{m}\right)t+\varphi_{m}\right) \tag{1}\]  
where, \(M\) is the number of subcarriers, the index \(m\in\left[1.\mathrm{M}\right]\) refers to the m-th subcarrier, \(A_{m}\) is the amplitude of the m-th subcarrier, \(C_{m}\) is the pseudo-random noise (PRN) spreading code sequence for the m-th subcarrier, \(D_{m}\) is the navigation data for the m-th subcarrier, if any,\(f_{0}\) is the initial frequency, \(\Delta f_{m}\) is the frequency interval between the m-th subcarrier and the initial frequency; \(\phi_{m}\) is the initial phase of the m-th subcarrier. Furthermore, the transmitters are distinguished by the PRNs sets. As for sub-carriers of the NSCC signal, the PRNs and frequency orthogonality can be utilized to suppress the inter-channel interference (ICI).  
As mentioned above, the NSCC signal has a large set of constituent parameters, such as the number of subcarriers, the subcarrier frequency interval, the power distribution and the subcarrier PRNs. These structural parameters provide large degrees of freedom and flexibility for signal design. The parameter configuration is generally adjusted according to constraints and requirements. Therefore, in order to optimize the GNSS receiver performance, a judicious choice of these parameters is necessary.  
Furthermore, according to the independence of its sub-carriers, the NSCC signal can split its PSD flexibly in order to avoid deliberate interferences and coexist with the legacy signals sharing the same frequency bandwidth. Thus, the split spectrum characteristic is its distinct superiority feature.  
For the sake of simplicity, we set up two basic NSCC configurations for the reference baseline compared with the legacy navigation signals. The so-called baseline is based on a monomodal and a bimodal structure which are extracted from the features of the legacy navigation signals. The monomodal structure concentrates the sub-carriers near the bandwidth center, and the bimodal structure distributes the sub-carriers symmetrically near the bandwidth edges.
# The Novel Role of Solvents in Non-Surgical Endodontic Retreatment
## 7 Effects on Dentin Structure  
During NSER, solvents are inevitably in contact with dentin for some time. For a long time, investigations have highlighted a decrease in enamel and dentin hardness, due to the significant softening effects of chloroform, xylene, and halothane, with a time-dependent effect [51]; however, others do not confirm these findings [52; 53]. Recent protocols suggest longer periods of dentin exposure to solvents after removing the bulk of the obturations. Some apprehension has, thus, arisen as to whether solvents can alter the dentin surface's chemical composition, with potential changes in its microhardness, and consequences on the bond strength of the sealers [53; 54]. A recent systematic review [55], including push-out assessments, has stressed that the heterogeneity of the studies prevented a reliable conclusion from being reached. However, chloroform and xylene seemed to raise further concerns.  
Despite reducing dentin's hardness, the novel solvent proposals of MEK and ethyl acetate are reported as being preferred over chloroform, which caused the most significant decrease [56]. A different experimental design associating MEK with the specific co-solvents TCE and OOil significantly increased dentin hardness after NaOCI and EDTA treatment [57]. Regarding direct dentin exposure, the MEK/TCE group showed no significant differences from the control (saline). MEK/OOil produced a significant hardness increase, independently of being used directly or after the NaOCI/EDTA standard final irrigating protocol.  
The effect of solvent agitation on dentinal structure, per se, has been scarcely studied, and with ambiguous results. UA was reported to elicit a decline in dentin hardness when using MEK, ethyl acetate, and chloroform [56]. On the other hand, a study with the solvent mixtures MEK/TCE and MEK/OOil found no evidence of UA causing an additional decrease in dentin's hardness [57]. Findings from endodontic irrigating solutions such as NaOCI, chlorhexidine, or EDTA also tend to diverge. Investigations on EDTA's effect on dentin microhardness found that diode laser agitation caused higher hardness reductionthan EDTA alone. However, there were no significant differences with UA or photon-induced photoacoustic streaming [58,59]. The different methodologies and chemistries regarding the compounds might explain the contradictory outcomes.
# Hybrid Wavelet-CNN Fault Diagnosis Method for Ships' Power Systems
## 3 System Description
### Description of Simulation Model  
The simulation model was based on a 3-phase squirrel-cage 4 kW IM and a fault block for simulating the faulty operation. The completed model is depicted in Figure 1, and information about the basic blocks of our model, which are the IM and the block of fault, is presented in Table 1. Our aim was to simulate stator winding faults in the IM that were caused by short circuits. The 3-phase block of faults provided by Simulink is ideal for causing not only phase to phase faults, but also phase to ground faults. Six different kinds of short circuits were simulated: Phase A--ground (G), Phases B-G, Phases C-G, Phases A-B, Phases A-C, and Phases C-B. Lastly, fault resistance (R) is an important parameter of the simulation model since it controls the fault intensity. When the value of the fault resistance decreases to near zero, the insulation faults increase to a full interturn short-circuit value, according to [5]. On the other hand, increased resistance causes indistinguishable faults.
# WSN Nodes Placement Optimization Based on a Weighted Centroid Artificial Fish Swarm Algorithm
## 3 Weighted Centroid Fish Swarm Algorithm
### Procedure of the Weighted Centroid Fish Swarm Algorithm
#### 3.1.3 Fish Swarm Initialization through WCA  
The core of the centroid algorithm (CA) is that unknown nodes estimate its location according to the geometric center combined with all its communications anchor nodes [11]. The principle of centroid algorithm is shown in Figure 2.  
In Figure 2, solid dots A\((x_{1},y_{1})\), B\((x_{2},y_{2})\) and C\((x_{3},y_{3})\) are the anchor nodes, and hollow dot D\((X,Y)\)is the unknown nodes to be located. a, b, c are the distances between D and A, B, and C respectively.  
Select three nodes close to D to form trigon. Determine the trigon centroid. This is the location to be determined, which is expressed as:  
\[(\overline{X},\overline{Y})=(\frac{1}{n}\sum_{i=1}^{n}\overline{x}_{i},\frac{ 1}{n}\sum_{i=1}^{n}\overline{y}_{i}) \tag{5}\]  
where \(n\) is the number of anchor nodes.  
The location \((\overline{X},\overline{Y})\) of the nodes to be located is estimated based on the distance centroid method. Let \(T\) the uniform distributed random number between 0 and 1. Generate the initial fish swarm:  
\[\left\{\begin{array}{l}x=\overline{X}+(-1)^{[T\times 10]}\times T\times\delta_ {x}\\ y=\overline{Y}+(-1)^{[T\times 10]}\times T\times\delta_{y}\end{array}\right. \tag{6}\]  
where \(\delta_{x}=|\overline{X}|/2\), \(\delta_{y}=|\overline{Y}|/2\).  
The weighed centroid algorithm (WCA) rationally distributes the weights to make the location more precise. The less distance between the unknown node and anchor node, the greater the impact of the anchor node on the unknown node [14]. WCA can be expressed as:  
Figure 2: Principle of the centroid algorithm.  
\[P_{i}(x,y)=\sum^{N}{(w_{ij}\times B_{j}(x,y))}/{\sum^{N}w_{ij}} \tag{7}\]  
where \(w_{ij}\) is the weight of anchor node \(j\) to unknown node \(i\), and \(w_{ij}=1/{(d_{ij})}^{n}\); \(P_{i}(x,y)\) is the estimated location of unknown node \(i\); \(B_{j}(x,y)\) is the location of the anchor node \(j\); \(d_{ij}\) is the distance between the anchor node \(j\) and the unknown node \(i\); \(n\) is weight coefficient ranging from 1 to 4; \(N\) is the number of the anchor nodes.  
The average error location can be expressed as:  
\[ERROR=\frac{1}{N\times R}{\sum^{N}_{i=1}{\sqrt{{\left({x_{i}-\overline{x}_{i} }\right)^{2}+\left({y_{i}-\overline{y}_{i}}\right)^{2}}}}} \tag{8}\]  
where \((x_{i},y_{i})\) is the actual location of the unknown node; \((\overline{x}_{i},\overline{y}_{i})\) is the estimate location of the unknown node; \(R\) is the radio range of WSNs.  
Figure 3 shows 50 WSNs nodes uniformly distributed with \(100\times 100\) m area. Suppose \(R\) is 50 m. From Figure 4, the error of CA is larger than that of WCA. The location determined by WCA is closer to actual location and more accurate.  
Figure 3: Node distribution of WSNs.
# In-Motion, Non-Contact Detection of Ties and Ballasts on Railroad Tracks
## 3 Methodology
### Sensors  
The sensors selected for the study are shown in Figure 2. They include an inductive proximity sensor for ferrous materials, distance LiDAR sensors, temperature and humidity sensors, and accelerometers. The inductive proximity sensor is on the far-left side of the cart, right on top of the tie plates. Two distance LiDAR sensors point downward on the right and left sides of the cart. Humidity, temperature, and acceleration are ancillary sensors for possible application of the track cart in other railroad-related system developments, such as evaluating the migration of flange grease on rails and assessing track stability through Doppler LiDAR sensors. Because these studies are outside this paper's scope, these sensors will not be discussed here.  
The sensor selection was based on an early evaluation of various sensors, in which some were rejected and others selected because of their promise of yielding satisfactory results. The sensor selection was based on availability, cost-effectiveness, ease of integration, and effectiveness in identifying ties or ballasts. The sensors that were not selected were infrared temperature sensors (or cameras) and time-of-flight LiDAR sensors. They were found to be too slow for in-motion measurements, even at low speeds. Others, such as time-of-flight acoustic sensors, radars, and surface reflectivity/intensity sensors, were proven to provide insufficient functional resolution to capture tie and ballast patterns definitively.  
Figure 2: Sensor installation onboard the track cart used for in-motion differentiation of ties and ballasts.  
The sensors selected for further integration into the system and track evaluation were an inductive proximity sensor for ferrous materials and distance LiDAR sensors, as will be discussed further next.
## 2 STAR Algorithms to Convert Carrier Phase to Excess Phase and Bending Angle Profiles  
This section summarizes the initial COSMIC-2 data preparation and processing procedures to covert the COSMIC-2 carrier phase to the excess phase. Section 2.1 summarizes the flow chart for converting COSMIC-2 L0 data to L2 data products. Section 2.2 describes the initial preparation of COSMIC-2 L0 and L1a data for further processing. Section 2.3 describes the LEO receiver CLOCK bias estimation with POD. Section 2.4 presents COSMIC-2 excess phase calculation approaches, including carrier phase model description, receiver and transmitter clock bias correction, geometric range corrections, and phase error correction method. Section 2.5 describes how we use ROPP to convert COSMIC-2 excess phases to bending angle and refractivity profiles. Section 2.6 describes how we used the ERA5 dataset for validation/comparison.
## 3 Methodology
### Formulas for Key Metrics  
1. User Attention (N) The number of followers serves as an indicator of a user's level of engagement with a specific cultural dimension. This is determined by aggregating the weighted support counts of all valid comments across various value dimensions, where the weight of each comment is calculated as the number of likes it has received plus one. The \"+1\" accounts for the comment's author, while the number of likes represents the supporters of that comment. This metric reflects the total scale of user support for the viewpoints within that dimension, and it forms the fundamental basis for measuring user participation. \[N=\sum_{i=1}^{n}(L_{i}+1)\] (1)
2. Attention Ratio (A) The Attention Ratio measures the proportion of user focus on a specific cultural dimension relative to the overall user attention. It is calculated by determining the ratio of the attention for each dimension to the total attention across all dimensions. \[A=\frac{N_{i}}{N_{\text{total}}}=\frac{\sum_{i=1}^{n}(L_{i}+1)}{\sum_{\text{ total}}(L_{i}+1)}\] (2)
3. Sentiment Score (E) The sentiment score focuses on the positive and negative tendencies of comments. The weighted sum of the sentiment differences is calculated, with the attention metric serving as a weight factor: \[E=\sum_{i=1}^{n}\bigl{(}(P_{i}^{+}-P_{i}^{-})\cdot(L_{i}+1)\bigr{)}\] (3)
4. Satisfaction (S) Satisfaction is calculated based on the sentiment score, normalized by the total amount of user attention to the dimension. It serves to measure the combined intensity of emotions and user engagement within that dimension. Satisfaction is computed as the ratio of the sentiment score to the amount of user attention. \[S=\frac{E}{N}=\frac{\sum_{i=1}^{n}\bigl{(}(P_{i}^{+}-P_{i}^{-})\cdot(L_{i}+1) \bigr{)}}{\sum_{i=1}^{n}(L_{i}+1)}\] (4)  
This metric reflects the quality of users' focus and the emotional bias in the given dimension. It provides a unified quantitative framework for evaluating cultural value dimensions. Through rigorous data processing and calculation methods, it quantifies users' emotional tendencies toward the cultural value of Qianmen Street.
## 3 Methodology
### Feature Extraction from Both Types of ROIs
For VBM-based ROI, the selection was done manually based on cluster size, ROI_CL, for which we selected the cluster with the highest number of voxels, e.g., AD vs. CN voxels result from the ADNI dataset \(t\)-test yielded 30 clusters, of which we selected 14 clusters with significant voxel numbers, while rest small-sized clusters were neglected (please refer to the Supplementary Data file ADNI_AD_CN_p0.001_FWE_BrainLabels). Similarly, the remaining four clusters were selected from the AD vs. CN WM \(t\)-test and CN vs. MCI \(t\)-test (please refer to the Supplementary Data file ADNI_CN_MCI_p0.001_FWE_BrainLabels). For ROI_AAL the selected clusters were matched with the AAL template, to obtain significant anatomic regions, then those standard ROIs were used to create the mask for further voxel detection from each ROI, for feature detection. On initial matching 23 ROI from AD vs. CN (16 GM and seven WM), nine ROIs from AD vs. MCI, and 11 ROIs from CN vs. MCI were detected. We selected the one with the highest number of voxels from each cluster and the overlapped regions. In total there were 18 ROIs from the cluster and 14 ROIs from the AAL atlas, as shown in Figures 6 and 7, which were tested separately to obtain the best classification result.
# Assessing the Impact of an EV Battery Swapping Station on the Reliability of Distribution Systems
## 5 Evaluation of Distribution Network Reliability  
An assessment of BSS for the improvement of distribution network reliability is the last but also important part of our work, which will show a positive role in reliability enhancement. The whole procedure consists of three parts, namely initialization of the base data, optimal power flow, and reliability criterion calculation. The process is as follows:  
1. Using LHS to form the behavior patterns of EV users and distribution components failure states. The behavior patterns of EV users include the time series of the arriving EV number and SOC of each replaced empty battery, while the distribution components failure states consist of a time series of the failure states of the external grid, transformers and load buses.
2. According to the procedure described in Section 3.2, calculate the available generation capacity \(P_{t}^{AGC}\) and real-time load of BSS \(P_{t}^{BSSL}\). Therefore, the total available generation capacity provided by the whole system \(P_{t}^{fg}\) can be described by the summation of power from grid \(P_{t}^{S}\) and \(P_{t}^{AGC}\); the total load of distribution \(P_{t}^{td}\) can be calculated by adding the load of every load bus and \(P_{t}^{BSSL}\).
3. Start a new simulation year. For each time \(t\), if \(P_{t}^{td}\) is greater than \(P_{t}^{fg}\), it means that the distribution system suffers an outage. Perform optimal power flow with the goal of minimum load shedding and determine the load shedding amount in each load point.
4. Calculate the reliability criterion of the distribution system, including the system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), system expected energy not supplied (SEENS), load average interruption frequency index (LAIFI), load average interruption duration index (LAIDI), and load average expected energy not supplied index (LEENS).
5. Judge whether the procedure converges. Choose SEENS as the basis for convergence judgment. If \(\sigma(\text{SEENS})/\left[\sqrt{N^{y}}\times E(SEENS)\right]\leq 0.05\), the procedure converges, where \(\sigma(\cdot)\) and \(E(\cdot)\) denote the standard deviation and expectation of the sample and \(N^{y}\) denotes the total simulation years [18]. Otherwise, repeat (1)-(4) until it converges.
6. Take the average value of reliability indexes of each simulation year as the final index values.  
The flow chart of the assessment procedure of the reliability of a distribution network can be seen in Figure 6.