esa-sceva/satcom-chunk-collection huggingface dataset:
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 2 Core Principles of SLAM: Front-End and Back-End
### SLAM Front-End
#### 2.1.1 Feature-Based SLAM  
Underwater VSLAM primarily relies on feature-based methods. These methods focus on identifying and tracking distinctive points or regions of interest (keypoints) within  
Figure 3: Overview of visual odometry and VSLAM systems. VSLAM is an extension of visual odometry, incorporating loop closure detection and global optimization to refine the overall trajectory and minimize accumulated drift. (Adapted from Gadipudi et al. [7]).  
images. Standard feature detectors include scale invariant feature transform (SIFT) [8], speeded up robust features (SURF) [9], and oriented FAST and rotated BRIEF (ORB) [10]. These detectors are robust to variations in illumination, rotation, and scale, making them suitable for underwater environments where lighting conditions can be challenging. The impact of feature point geometrical composition on localization accuracy has been investigated [11]. It showed that selecting features with higher depth variation improves localization accuracy by providing better cues for camera motion estimation.  
One prominent example of a feature-based SLAM system is ORB-SLAM3 [12]. The architecture and main modules of ORB-SLAM3 are shown in Figure 4. This system offers several key advantages:  
* **Visual-inertial Fusion**: ORB-SLAM3 tightly integrates visual data with inertial measurements (e.g., accelerometers and gyroscopes) to achieve robust and accurate pose estimation, particularly beneficial underwater where visual data may be limited.
* **Multi-map SLAM:** The system can manage multiple maps during long periods of limited visibility. When the robot revisits a previously explored area, these maps can be merged, improving overall map accuracy.
* **Global Re-use of Information:** Unlike traditional VO systems that only use recent information, ORB-SLAM3 leverages data from all previously observed keyframes, improving overall system accuracy.
# Fault Detection and VSC-HVDC Network Dynamics Analysis for the Faults in Its Host AC Networks
## 4 Performance Analysis
### Robustness against Noise  
The performance of the proposed method was tested with the noise condition. The target signal was contaminated by white Gaussian noise. The test was performed for three different signal-to-noise (SNR) values: 40 dB, 50 dB, and 60 dB. The phase A current obtained during the three-phase fault created at t = 2.5 s and at location F\({}_{1}\) was considered at t = 2.5 s. The fault resistance was 20 \(\Omega\). The results are shown in Figure 12a,b. Figure 12a shows the signal condition for various SNRs.  
The DTE surpassed the threshold only for the fault conditions and remained below it during no-fault situations. A fault was detected within 2 ms, which means the impact of noise on the detection time was negligible. With the results, it was found that the proposed method worked satisfactorily with the presence of noise in the signal.  
To compare the method with the existing and conventional solutions, different methods like TME, V&I THD, ROCOC and ROCOV, ROCOF, the FFT, ST, STFT, WT, VMD-HT, and HHT were considered, and the analysis obtained after the comparison is depicted in Table 6. From the comparative analysis, it can be observed that the presence of noise and different sampling frequency on the response of the proposed method is negligible. The detection time offered by the proposed solution was also much lower (i.e., less than 5 ms), which is commendable.  
Figure 11: Results for SLS. (**a**) Current waveform for phase A, (**b**) DTE for phase A during SLS.
## 3 Materials and Method
### Proposed Method
#### 3.3.1 Policy-Learning Module
A fully connected layer maps the hidden states to a probability distribution over the pricing action space, denoted as \(\pi_{\theta}(a|s)\), with the softmax function ensuring proper probability constraints. Specifically, given the state representation \(h_{t}\) at time \(t\), the policy function is defined as \(\pi_{\theta}(a_{t}|s_{t})=\text{softmax}(Wh_{t}+b)\), where \(W\) and \(b\) are trainable parameters. The resulting probability distribution is used to sample a pricing action \(a_{t}\), which is then passed into the environment for interaction. To enhance exploration capabilities and generalizability, the policy-output stage incorporates an \(\varepsilon\)-greedy strategy and an intrinsic curiosity module (ICM). In the \(\varepsilon\)-greedy strategy, the action with the highest probability \(a^{*}=\arg\max_{a}\pi_{\theta}(a|s)\) is selected with probability \(1-\varepsilon\), while a random action is selected with probability \(\varepsilon\), with \(\varepsilon=0.1\) in the experiments. The ICM further defines an intrinsic reward \(r^{\text{int}}t=||\hat{\phi}(s_{t+1}^{*})-\phi(s_{t+1})||^{2}\) based on the prediction error between state-action pairs, thereby encouraging exploration in underrepresented regions of the state space and alleviating policy degradation caused by sparsity in economic behavior data. This design of the policy-learning module offers several mathematical advantages. First, the transformer-based state modeling process is equivalent to constructing a class of learnable dynamic feature kernel functions \(K(s_{i},s_{j})\) within the state space, where the self-attention mechanism adaptively adjusts the weight distribution among features, significantly outperforming fixed-structure convolutional or recurrent units. Second, the multi-head mechanism enables the parallel modeling of local and global dependencies across multiple subspaces, enhancing the policy's sensitivity to complex market dynamics such as promotional events and seasonal fluctuations. Moreover, the probabilistic output of the policy, combined with the \(\varepsilon\)-greedy exploration mechanism, provides interpretable and elastic action selection, theoretically mitigating the risk of converging to suboptimal local solutions. Figure 2: Architecture of the policy-learning module. The design includes feature embedding, a transformer encoder with multi-head attention, a strategy head for action distribution, and dual exploration and intrinsic curiosity.
# Nomological Deductive Reasoning for Trustworthy, Human-Readable, and Actionable AI Outputs
## 3 Current XAI State of the Art
### Explanator Formats
#### 3.1.4 Numerical/Graphical Explanations  
As indicated by their name, numerical or graphical explanations provide a quantitative understanding of the model's predictions or decisions. These explanations often rely on numbers, charts, or statistical representations to help users understand the model's behavior. The key methods in this category include:  
**Partial Dependence Plots (PDPs)**[77; 78]: PDPs provide a graphical representation of the relationship between a feature and the predicted outcome while holding other features constant. This method helps to visualize the effect of a feature on the model's prediction.  
**Feature Importance Scores**[79]: Feature importance methods rank the features based on their contribution to the prediction. For example, Permutation Feature Importance measures the change in the model's performance when a feature's values are shuffled, providing insights into how much that feature impacts the model.  
**SHAP Values**[80]: SHAP values provide a numerical explanation by attributing each feature's contribution to the final prediction. These values can be visualized through **force plots**, offering a combination of both numerical and graphical insights into the model's decision-making process.  
**ICE** (Individual Conditional Expectation) Plots [81]: These plots illustrate how the model's prediction changes when a particular feature varies while others remain fixed, offering a detailed visualization of the impact of specific features.  
**Concept Activation Vectors** (TCAVs) [82]: Testing with Concept Activation Vectors is an explanation method used to interpret neural networks where the language of explanation is expanded from that of input features to include human-friendly concepts for greater intelligibility.  
XAI methods that provide numerical or graphical explanations are abundant, including, but not limited to, Distill-and-Compare [83], Feature Contribution [84; 85], Gradient Feature Auditing (GFA) [86], Shapley-Lorenz-Zonoid Decomposition [25], Probes [87], and Contextual Importance and Utility [88]. Illustrations of numerical and graphical explanations can be found in [81].
# A CSI Fingerprint Method for Indoor Pseudolite Positioning Based on RT-ANN
## 2 Indoor Pseudolite Fingerprint Positioning Method
### Positioning Principle
where \(W_{ki}\) is the weight of the \(i\)th point in the k nearest reference points, \(L_{ki}\) is the distance from the to-be-located point to the \(i\)th point in the k nearest reference points, \(L_{imin}\) is the minimum distance from the to-be-located point to the \(i\)th point in the \(k\) nearest neighbor reference points. \(a\) is the maximum weight value, which is usually 1; \(b\) is the half-peak width, which can be adjusted according to the value of \(k\). The coordinates of each adjacent reference point are combined with the corresponding weight, and the coordinates of the point to be located can be obtained by the WKNN algorithm:  
\[(x,y)=\frac{\sum_{i=1}^{k}W_{ki}\times(x_{i},y_{i})}{\sum_{i=1}^{k}W_{ki}} \tag{4}\]
###### Abstract  
This study was conducted to evaluate the feasibility of using electrosorption to remove cesium (Cs+) ions from aqueous solutions using the membrane capacitive deionization (MCDI) process. The electrochemical properties were analyzed using cyclic voltammetry (CV) and impedance spectroscopy (EIS). The activated carbon electrode coated by a polymer layer showed higher specific adsorption capacity (SAC) and removal efficiency of Cs+ than the AC electrode. The effects of potential, flow rate, initial Cs+ concentration, and pH values were investigated to optimize the electrosorption performance. The electrosorption capacity increased with an increase in the applied potential and the concentration of Cs+ in the influent water. The pH value is an important parameter on electrosorption performance. The removal of Cs+ ions was affected by the pH of the influent water because H+ ions acted as competing ions during the electrosorption process. Cs+ was preferentially adsorbed to the electrode in the early stages of adsorption but was later replaced by H+. A higher presence of H+ ions could reduce the adsorption capacity of Cs+ ions. The ion-exchange layer coated AC electrode was shown to be favorable for the removal of Cs+, despite the limited electrosorption ability in a highly acidic solution.  
capaitive deionization; electrosorption; Cs+ ion removal; ion-exchange layer Article
## 3 Imaging with Compact EUV and SXR Microscopes Based on a Laser Plasma Sources
### EUV Full-Field Imaging  
In this experiment CT26 fibroblast cells, Chrysodilimus cells and diatoms were imagined using the EUV microscope and visible light microscope. The EUV images were acquired with 200 EUV pulses--22 s exposure time and detector temperature of \(-20\)\({}^{\circ}\)C. A sample of CT 26 cells fixed with 30% hexamethidisilazane (HDMS) in absolute EtOH on top of 30 nm Si\({}_{3}\)N\({}_{4}\) membrane was imaged with the visible light microscope (VIS) as depicted in Figure 4a and at 13.84 nm wavelength with the EUV microscope--Figure 4b. It can be seen that the EUV image shows a very high contrast and resolution enhancement and permits to investigate features of the order of 100 nm or smaller in size.  
Chrysodilimus cells imaged using the EUV microscopes can be seen in Figure 4c, while the diatoms deposited on top of 30 nm thick Si3N\({}_{4}\) membrane are depicted in Figure 4d, with a small inset showing diatom structures as small as a few hundred of nanometers. The EUV images exhibit superior spatial resolution and much higher optical magnification (410\(\times\)) comparing to visible light microscopy--Figure 4a.  
Except for the CCD background removal, adjustment of their brightness and increased contrast in the inset of Figure 4d to show the smallest observable features, the EUV images were not altered in any other way.
# Mobile Autonomous Sensing Unit (MASU): A Framework That Supports Distributed Pervasive Data Sensing
## 3 The MASU Framework
### Internal Mechanisms
#### 3.4.3 Fault Tolerance Mechanisms  
Due to the dynamism and uncertainty of some network infrastructures, the MASU defines various fault tolerance mechanisms to deal with changes and failures in active roles that are taking part in a collaborative sensing activity. Such mechanisms can be classified into two categories: tolerant to role failure and tolerant to resource limitations. In order to detect a role failure, the framework monitors the state of each one of the roles that were activated to perform the activity. As explained previously, the manager role monitors the state and behaviour of the services offered by all the other roles. Similarly, both manager and monitor perform mutual service monitoring on each other.  
Concerning the mechanisms of tolerance to resource limitation (e.g., battery, memory or CPU) the monitor and manager keep track of any event in the collaborative activity: new units join, some disappear, others have poor network connectivity or run out battery, CPU or storage capacity, etc. If any of these changes occur, the Cost Function must be executed to determine the viability and usefulness of the collaborative sensing activity. If the output of this function is positive, the MASU framework will restart the Role Selection and Activation mechanisms for the Sensing Tier, reassigning roles as appropriated according to the results of the ROA algorithm.  
In the case that many new units join the collaborative sensing activity, it is possible that the cost required to share the sensed data is too high, and therefore the output of the Cost Function is negative. This situation would imply that the collaborative sensing activity is not beneficial for the overall group of participating units so that it has to end. However, this situation could be solved by creating two parallel sensing activities.
# Mechanical Properties of Concrete with Bamboo Chips
## 1 Introduction  
Natural aggregates used in construction are gradually being depleted due to industrialization and urbanization. Therefore, the development of sustainable construction materials (e.g., bamboo) and recyclable construction resources is necessary. Bamboo is inexpensive, grows rapidly, and is widely distributed around the world. In addition, over the last few decades, this material has beenincreasingly used in developing countries, owing to its flexibility and resistance to seismic loading. However, the compressive resistance of bamboo is relatively low, hence, the use of this material for structural members is limited to light loads. Recently, several studies have considered the use of bamboo-reinforced concrete members as an alternative to the use of steel members [1; 2; 3; 4; 5; 6]. [PERSON] investigated bamboo as a potential replacement for steel rebar and determined the flexural strength of bamboo-reinforced concreteand focused on bamboo as an engineering material, where durability is essential, and treated the bamboo with oil for waterproofing [7; 8]. Coated bamboo splints exhibited good durability and strength in bamboo bonded with mortar. In addition, concrete bricks reinforced with bamboo fibers were arranged vertically and horizontally and subjected to repeated loads.  
Bars of bamboo culm, splint, or fiber have mainly been used for structural members and reinforcement of concrete. Published studies on the use of bamboo chips for mixing concrete are rare [9; 10]. Therefore, in the present study, coarse aggregates in concrete specimens were replaced with 10%, 20%, 30%, and 100% (wetted bamboo only) bamboo chips and cured for 7 and 28 days. The chips were treated dried, coated, and wetted in order to determine the effect of water absorption on strength development. The compressive and tensile strength of the bamboo were evaluated from the viewpoint of potential bamboo usage in coarse aggregates, where the bamboo inclusion ratio and treatment type are considered.
## 5 Discussion
Another Japanese study reported a GPP case estimate of 7.46 patients per million, with 2.87 patients per million presenting with acute GPP [22]. A review of a national insurance claims database in the Republic of Korea conducted between 2011 and 2015 estimated a prevalence of 1.2 cases per 10,000 persons. More interestingly, in more than 200,000 patients with psoriasis, 2.0-2.7% were classified as having GPP [23]. Since the first description of GPP by [PERSON] in 1910, several descriptions and diagnostic criteria have been developed, up to the ERASPEN criteria. In contrast to plaque psoriasis, in pustular psoriasis, the interleukin (IL)-36 pathway appears to play a pivotal role in the pathogenesis [2]. The recognition of loss-of-function mutations in the IL-36 receptor antagonist gene (IL36 RN) and the overexpression of IL-36 cytokines in skin lesions supports the central role of IL-36. Recently, other mutations (i.e., variants of _CARD14_, encoding a keratinocyte adaptor protein, and _AP1S3_, which encodes a subunit of the adaptor protein 1 complex) have been identified in patients with GPP [1; 2; 5]. Transcriptome analysis of GPP lesions has shown an increase in innate immune inflammation response over TH1/TH17-related transcripts including IL-18, IL-36\(\alpha\), IL-36\(\gamma\), and enriched neutrophil and monocyte transcripts such as CXCL, CXCL, and CXCLs [24]. The emerging development of specific inhibitors of IL-36R signaling (i.e., spesolimab and imisolimab) was followed by very encouraging results in terms of safety and efficacy in clinical trials [25]. Recent PPP trials are also focusing on the blockage of IL-36. Despite these advantages, there are still many important knowledge gaps, and further epidemiological studies and national registries are needed to characterize pustular psoriasis. The systematic genetic characterization of the patients may identify additional genetic variants that could provide effective therapeutic targets [26]. We acknowledge some limitations of the study. First, a potential selection bias in the source of the patients may have caused the underreporting of mild forms of plaque psoriasis because they were not referred to the hospital. We also did not test for the presence of genetic mutations (i.e., IL36 RN, CARD14, and AP1S). Finally, we did not extrapolate our results to reflect the national population-level estimates. Nonetheless, this study has some strengths including the fact that all patients were visited by us, by the rheumatologist, and when needed, we also performed a biopsy for diagnostic confirmation over a period of seventeen years.
# Identifying Influential Nodes of Complex Networks Based on Trust-Value
## 2 Methods
### Trust-PageRank Algorithm  
Information is transmitted to adjacent nodes according to the trust-value. Nodes with higher trust-value receive more information. By introducing trust-value into PageRank to identify the influential nodes, the trust-PageRank (TPR) algorithm was constructed.  
**Definition 4**.: _In the process of the iterative voting of trust-PageRank, the influence of node \(i\) at time \(t\) is defined as_  
\[TPR_{i}^{t}=\frac{1-\alpha}{n}+\alpha\sum_{j\in N_{i}}T(i,j)TPR_{j}^{t-1}, \tag{7}\]  
_where \(T(i,j)\) denotes the trust-value of node \(j\) to node \(i\) obtained by Equation (6), \(n\) is the count of nodes in the network, \(N_{i}\) represents the set of neighbors of node \(i\), and \(\alpha\) is the jump probability. In the experiments, we take \(\alpha=0.85\)._  
The algorithm initializes the input network data, iteratively obtains the nodes' similarity set, and then calculates the similarity and degree ratios according to the similarity and initialization sets. Then, the trust-value between the nodes is obtained, and is combined with the PageRank to count the TPR indicator value of each node. The specific steps of the algorithm are shown in Algorithm 1.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \(v\) & **1** & **2** & **3** & **4** & **5** & \(S_{v}\) \\ \hline
1 & - & 0.6 & 0.52 & - & 0.47 & 1.59 \\
2 & 0.6 & - & - & - & 0.51 & 1.11 \\
3 & 0.52 & - & - & 0.6 & 0.47 & 1.59 \\
4 & - & - & 0.6 & - & 0.51 & 1.11 \\
5 & 0.47 & 0.51 & 0.47 & 0.51 & - & 1.96 \\ \hline \end{tabular}
\end{table}
Table 3: The similarities of nodes. The short dash indicates that there is no edge between the two nodes.  
Figure 3: Example of the calculation of trust-value.  
```
0:\(G=(V,E)\);
0:rankList;
1:for\(v_{s}\in V\)do
2: Initialize the network and count the set of adjacent nodes of \(v_{s}\);
3:endfor
4:for connected node \(v_{u},v_{v}\in V\)do
5:if\(v_{u}=v_{v}\)then
6:\(s(v_{u},v_{v})=1\);
7:else
8:\(s(v_{u},v_{v})=0.1\);
9:endif
10: simRationMap\(\leftarrow\)\(s(v_{u},v_{v})\); //put the initial similarity values of nodes into the simRatioMap
11:endfor
12:for\(v_{i}\in V\)do
13:for\(v_{j}\in N_{i}\)do
14: Calculate \(s(v_{i},v_{j})\) according to Equation (4);
15: simRationMap\(\leftarrow\)\(s(v_{i},v_{j})\);
16:endfor
17:endfor
18:while ite<Iteration do
19:for\(v_{k}\in V\)do
20:for\(v_{m}\in N_{k}\)do
21: Calculate the sum of the degree of adjacent nodes of \(v_{m}\) and the similarity between \(v_{m}\) and its adjacent nodes;
22: Calculate the similarity ratio \(R_{s(m,k)}\) by Equation (2);
23: Calculate the degree ratio \(R_{d(m,k)}\) according to Equation (5);
24: Calculate the trust-value \(T_{mk}\) of the node \(v_{k}\) to \(v_{m}\) using \(R_{s(m,k)}\) and \(R_{d(m,k)}\) using Equation (6);
25: Calculate the \(TPR_{k}\) value of node \(v_{k}\) according to Equation (7);
26:rankList \(\leftarrow\)\(TPR_{k}\); // Putting the value into the rankList.
27:endfor
28:endfor
29: ite++;
30:endwhile
31:rankList.sort(); // return the sorted rankList.
```  
**Algorithm 1** Trust-PageRank Algorithm