esa-sceva/satcom-chunk-collection huggingface dataset:
# Analytical Pyrolysis and Mass Spectrometry to Characterise Lignin in Archaeological Wood
## 3 Results and Discussion
### Evolved Gas Analysis Mass Spectrometry (EGA-MS)
In order to evaluate the level of lignin oxidation, the ratios between the areas of specific \(m/z\) EITs were calculated. The oxidation level of guaiacyl (G index) and syringyl (S index) lignin was calculated from the Equations (1) and (2). \[\text{S index}=\text{EITs}\ m/z\ 181/(\text{EITs}\ m/z\ 181+\text{EITs}\ m/z\ 167) \tag{1}\]  
\[\text{G index}=\text{EITs}\ m/z\ 151/(\text{EITs}\ m/z\ 151+\text{EITs}\ m/z\ 137) \tag{2}\]  
Lignin depolymerisation level was evaluated on the basis of the yield of dimer formation of dimers during heating. D index was calculated from the Equation (3). \[\text{D index}=\text{EITs}\ m/z\ (272+302+332)/\text{EITs}\ m/z\ (151+181+137+167+272+302+332) \tag{3}\]  
The results of these calculations are shown in Figure 6, where the trends in oxidation and depolymerisation at different depths from the surface of pile 2212 is clearly highlighted. G index and S index increased from the core to the surface of the pile, proving a gradual increase in lignin oxidation. The S index of sample A was slightly higher than the G index, which also confirmed that syringyl-lignin is more prone to oxidation than guaiacyl lignin [25; 89; 90; 91]. On the other hand, the D index increased from the outside to the inside of the pile. Reference wood featured the highest D index, indicating that a significant number of lignin dimers are produced in the pyrolysis conditions adopted. The general decrease in the formation of dimers during the pyrolysis of archaeological wood was therefore related to an increasing depolymerisation of lignin moving to the external parts of the pile.
## 2 Methodology
### Analysis of Actor and Value Network Differences  
To assess how the differences in explored observation spaces translate to differences in the learned actor and value networks, a cross-evaluation methodology was employed.  
For each pair of agents \((i,j)\) within an environment, the observations collected by agent \(i\) during its rollouts were used as input to the actor and value networks of agent \(j\). The following metrics were calculated:  
* **Absolute TD-Error**: The absolute TD-error was computed over agent \(i\)'s trajectory data using agent \(j\)'s value network, measuring how well agent \(j\)'s value function generalizes to agent \(i\)'s experiences. \[\mathrm{TD}_{i\to j}(s_{t},r_{t},s_{t+1})\ =\ \frac{1}{|\mathcal{D}_{i}|}\ \sum_{(s_{t},r_{t},s_{t+1})\in \mathcal{D}_{i}}\Big{|}\,r_{t}+\gamma\,V_{\mathbf{\phi}_{j}}(s_{t+1})\ -\ V_{\mathbf{\phi}_{j}}(s_{t})\Big{|}\.\] (7)
* **Root Mean Squared Error (RMSE) of Value Estimates**: For each state \(s_{t}\) in agent \(i\)'s trajectory, the value estimated by agent \(i\)'s critic, \(V_{\mathbf{\phi}_{j}}(s_{t})\), was compared to the value predicted by agent \(j\)'s critic, \(V_{\mathbf{\phi}_{j}}(s_{t})\). This directly measures how different the output of the two value functions is on states that agent \(i\) visits. \[\mathrm{RMSE}_{i\to j}\ =\ \sqrt{\frac{1}{|\mathcal{D}_{i}|}\ \sum_{s_{t}\in \mathcal{D}_{i}}\big{(}V_{\mathbf{\phi}_{j}}(s_{t})-V_{\mathbf{\phi}_{j}}(s_{t})\big{)} ^{2}}\.\] (8)
* **Kullback-Leibler Divergence (KLD) of Action Logits**: For each state \(s_{t}\) in agent \(i\)'s trajectory, KLD was computed using the action probability distributions (derived from the logits) produced by agent \(i\)'s actor, \(\pi_{\mathbf{\theta}_{i}}(\ \cdot\ |\ s_{t})\), and agent \(j\)'s actor, \(\pi_{\mathbf{\theta}_{j}}(\ \cdot\ |\ s_{t})\). This quantifies the policy divergence by comparing the action probability distributions from agent \(i\)'s actor and agent \(j\)'s actor for states in agent \(i\)'s trajectory. \[\mathrm{KLD}_{i\to j}\ =\ \frac{1}{|\mathcal{D}_{i}|}\ \sum_{s_{t}\in \mathcal{D}_{i}}D_{\mathrm{KL}}\big{(}\pi_{\mathbf{\theta}_{i}}(\ \cdot\ |\ s_{t})\big{\|}\,\pi_{\mathbf{\theta}_{j}}(\ \cdot\ |\ s_{t})\big{)}\.\] (9)  
These metrics were computed for all \(5\times 5\) agent pairings in each of the eight environments, resulting in matrices that reveal the extent of network generalization and similarity.
# A High Dynamic Weak Spread Spectrum Signal Acquisition Strategy Based on Iterative Local Search
## 3 ILS Acquisition Strategy  
In the design of the acquisition algorithm, the acquisition of high dynamic signals involves a two-dimensional search in the time and frequency domains in a short time. This results in the limited sensitivity of the acquisition algorithm to weak signals. The acquisition of low SNR signals requires a long integration time to improve the gain, which results in the poor performance of the acquisition algorithm for dynamic signals. Thus, there is a contradiction between high dynamic signal acquisition and weak signal acquisition.  
In the acquisition strategy of traditional acquisition algorithms, there is no correlation between each acquisition, which results in the contradiction between that the high dynamic signal and weak signal will not be reduce. We propose ILS to improve the traditional algorithm, and multiple acquisition processes of the algorithm are linked through information transfer to improve its adaptability to high dynamic and low SNR environments.
_Algorithms_**2013**, \(6\), 824-856; doi:10.3390/a6040824  
_algorithms_  
ISSN **1999-4893**  
www.mdpi.com/journal/algorithms  
Overlays with Preferences: Distributed, Adaptive Approximation Algorithms for Matching with Preference Lists  
**[PERSON] * and [PERSON]**  
Department of Computer Science and Engineering, Chalmers University of Technology, S-412 96  
Goteborg, Sweden; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Fax: +46-31-7723663.  
_Received: 21 June 2013; in revised form: 7 November 2013 / Accepted: 8 November 2013 / Published: 19 November 2013_  
**Abstract:** A key property of overlay networks is the overlay nodes' ability to establish connections (or be matched) to other nodes by preference, based on some suitability metric related to, e.g., the node's distance, interests, recommendations, transaction history or available resources. When there are no preference cycles among the nodes, a stable matching exists in which nodes have maximized individual satisfaction, due to their choices, however no such guarantees are currently being given in the generic case. In this work, we employ the notion of node satisfaction to suggest a novel modeling for matching problems, suitable for overlay networks. We start by presenting a simple, yet powerful, distributed algorithm that solves the many-to-many matching problem with preferences. It achieves that by using local information and aggregate satisfaction as an optimization metric, while providing a guaranteed convergence and approximation ratio. Subsequently, we show how to extend the algorithm in order to support and adapt to changes in the nodes' connectivity and preferences. In addition, we provide a detailed experimental study that focuses on the levels of achieved satisfaction, as well as convergence and reconvergence speed.  
**Keywords:** matching; overlay networks; distributed; adaptive
## 2 Modeling and Mechanical Analysis of EMS Inertial Anti-Rolling Mass Block
### Mechanical Analysis of Single Mass Block of EMS Inertia Stabilizer
Because the speed \(dx/dt\) of the mass block is small, the Coriolis force \(F_{\rm C}\) is also small, and the force on the mass block in the Y direction can be cancelled by the positioning axis, that is, \(F_{\rm Y}\approx 0\). As shown in Figure 2, the force on the right mass block in the Z coordinate direction is:  
\[F_{\rm Z}=F_{MR}-F_{\rm G}cos\omega t \tag{3}\]  
Figure 2: Mechanical analysis diagram of inertia anti-rolling mass block. where \(F_{MR}\) is the resultant force of upper and lower electromagnetic forces on the right side, as in Equation (4). \[F_{MR}=F_{MUl}-F_{MD}=\frac{N^{2}A\mu_{0}i_{0U}^{2}}{4(g_{0}+x)^{2}}-\frac{N^{2}A \mu_{0}i_{0D}^{2}}{4(g_{0}-x)^{2}} \tag{4}\]  
From above, we can obtain:  
\[F_{Z}=\frac{N^{2}A\mu_{0}i_{0U}^{2}}{4(g_{0}+x)^{2}}-\frac{N^{2}A\mu_{0}i_{0D}^ {2}}{4(g_{0}-x)^{2}}-mgcos\omega t \tag{5}\]  
Set \(\left\{ \begin{array}{l}x_{1}(t)=X_{10}sin\omega t\\ x_{2}(t)=X_{20}sin\omega t\end{array} \right.\), and place it in the line after Equation (1):  
\[\left\{ \begin{array}{l}x_{1}(t)=\frac{F_{Z}(k_{1}-k_{2}-M\omega^{2})+(k_{1} -k_{2})(Fsin\omega t-F_{MR})}{[mM\omega^{2}-(k_{1}-k_{2})(m+M)]\omega^{2}}\\ x_{2}(t)=\frac{F_{Z}(k_{1}-k_{2})+(k_{1}-k_{2}-m\omega^{2})(Fsin\omega t-F_{ MR})}{[mM\omega^{2}-(k_{1}-k_{2})(m+M)]\omega^{2}}\end{array} \right. \tag{6}\]  
\[\left\{ \begin{array}{l}\tilde{x}_{1}(t)=\frac{(k_{1}-k_{2})Fsin\omega t}{(k_ {1}-k_{2})(m+M)-mM\omega^{2}}\\ \tilde{x}_{2}(t)=\frac{(k_{1}-k_{2}-m\omega^{2})Fsin\omega t}{(k_{1}-k_{2})(m +M)-mM\omega^{2}}\end{array} \right. \tag{7}\]  
In order to obtain a large inertial force on the ship base, i.e., \(M\tilde{x}_{2}(t)\), the support stiffness coefficient \(k_{2}\) between the mass block and the lower right electromagnet should be as small as possible. When \(k_{1}\) is brought into Equation (7), it is found that:  
\[\left\{ \begin{array}{l}\dot{x}_{1}(t)=\frac{(\frac{N^{2}A\mu_{0}i_{0U}^{2}} {g_{0}^{2}}\frac{dt}{dx}+\frac{N^{2}A\mu_{0}i_{0U}^{2}}{g_{0}^{2}})Fsin\omega t }{(\frac{N^{2}A\mu_{0}i_{0U}^{2}}{g_{0}^{2}}\frac{dt}{dx}+\frac{N^{2}A\mu_{0} i_{0U}^{2}}{g_{0}^{2}})(m+M)-mM\omega^{2}}\\ \ddot{x}_{2}(t)=\frac{((\frac{N^{2}A\mu_{0}i_{0U}^{2}}{g_{0}^{2}}\frac{dt}{dx}+ \frac{N^{2}A\mu_{0}i_{0U}^{2}}{g_{0}^{2}})-m\omega^{2}]Fsin\omega}{(\frac{N^{ 2}A\mu_{0}i_{0U}^{2}}{g_{0}^{2}}\frac{dt}{dx}+\frac{N^{2}A\mu_{0}i_{0U}^{2}}{ g_{0}^{2}})(m+M)-mM\omega^{2}}\end{array} \right. \tag{8}\]  
In Equation (8), \(\mu_{0}\) is a constant. After the structure of the stabilizer is designed and manufactured, \(N\), \(A\), \(m\) and \(M\) all become constants. Therefore, the inertial acceleration obtained by the ship base is related to the electromagnet current \(i\), the rate of change of electromagnet current \(i\), the air gap \(g_{0}\) between electromagnet and inertial mass block, and the rotational angular speed \(\omega\).
## 2 Overview of the Proposed Method  
Figure 1 illustrates the overview. First, initial nodule candidates are detected from individual slice sections of a thoracic CT scan by our previous detection methods [8, 27, 28], and then square areas of a certain size are settled on the slice sections so as to comprise the nodule candidates. White squares on a slice section in the leftmost box in the figure show examples of such square areas. Additional isometric square areas are settled at the same X-Y positions on the adjacent slice sections. These square areas are formed into volumes of interest (VOIs) in the CT scans.  
Sphere and connected-cylinder models, that represent nodules and blood vessels, respectively, are generated as shown in the topmost box. These object models can be deformed by changing their parameters such as the radius of the sphere model. Template VOIs are produced by projecting the object  
Figure 1: The process of the proposed method.  
models onto them.  
The template VOIs are matched to each VOI in the CT scans, and the posteriori probabilities of hypotheses that the CT VOI arises from the object models are calculated using the Bayes theorem. The most likely object model is searched for by the MAP estimation with an optimization method. The CT VOI is determined to be abnormal if the most likely object model is the nodule model, and vice versa.
# A Novel QoS-Aware ARQ Scheme for Multi-User Transmissions in IEEE802.11 ax WLANs
## 3 Methods
### The Greedy Algorithm
procedureSearchOneStepOptimal(\(\vec{R}^{\prime},BER\)) \(i\Leftarrow 1\);  Initialize the compared group set, \(\mathbf{G}\Leftarrow\emptyset\); whilei\(\leq\) L do \(len_{candidate}\Left len_{MACheader}+len_{payload,i}+\sum_{j=1}^{L}((1+r^{ \prime}_{j})\times(len_{MACheader}+len_{payload,j}))\); if\(len_{candidate}<len_{maxampdu}\) and \(len_{candidate}/rate<T_{sche}\)then \(u\Leftarrow i\);  Insert \(r^{\prime}_{u}\) into \(\mathbf{G}\);  break; else \(i\Leftarrow i+1\); endif endwhile \(i\Leftarrow u+1\); whilei\(\leq\) L do if\(r^{\prime}_{i}\
otin\mathbf{G}\)then \(len_{candidate}\Left len_{MACheader}+len_{payload,i}+\sum_{j=1}^{L}((1+r^{ \prime}_{j})\times(len_{MACheader}+len_{payload,j}))\); if\(len_{candidate}<len_{maxampdu}\) and \(len_{candidate}/rate<T_{sche}\)then \(v\Leftarrow i\);  Insert \(r^{\prime}_{v}\) into \(\mathbf{G}\);  Calculate \(D(u,v)\) using Equation (19); if\(D(u,v)<0\)then \(u\Leftarrow v\); endif
```  
**Algorithm 1** A greedy algorithm searching for the sub-optimal solution of the proposed scheme.
## 3 Results and Discussion
### HCl Elution Experiment  
#### 3.3.1 Effect of HCl Concentration on Lithium Elution Efficiency  
1. A quantity of 0.1 g of the prepared ion sieve was weighed and combined with 100 mL of the lithium extraction mother liquor derived from DCFA within a conical flask. The conical flask was positioned on a rotary stirrer and subjected to stirring for 30 min, during which time the stirring temperature was controlled at 45-50 \({}^{\circ}\)C, and the stirring speed was set at 500 r/min. After the stirring period, the mixture was centrifuged for 2 min, and a sample was extracted from the separated liquid.
2. A 20 mL quantity of KOH solution at pH 8 was added to the separated solid which was then placed on a rotary stirrer and stirred to eliminate the interference of Na\({}^{+}\), Ca\({}^{2+}\) and Al\({}^{3+}\) in solution on the lithium ions. The solid was washed with KOH for 10 min, followed by centrifugation for 2 min. Subsequently, a sample was taken from the separated liquid.
3. The separated solid sample was combined with HCl at concentrations of 0.01 mol/L, 0.05 mol/L, 0.1 mol/L, 0.5 mol/L, 1.0 mol/L, 1.5 mol/L, 2.0 mol/L, 2.5 mol/L and 3.0 mol/L in conical flasks. The mixtures were placed on a rotary stirrer and stirred for 30 min, during which time the temperature was maintained at 45-50 \({}^{\circ}\)C, and the stirring speed was set at 500 r/min. Centrifugation was then performed for 2 min, followed by sample extraction from the separated liquid. Sequential AAS measurements were performed on each sample, and the analysis of elution results is shown in Figure 3.  
As shown in Figure 3, the lithium elution efficiency changed greatly and irregularly when different concentrations of HCl were added, with other conditions remaining the same. When the concentration of HCl was 0.01 mol/L and 0.10 mol/L, the lithium elution efficiency reached 100%; when the concentration of HCl was greater than 0.10 mol/L, which might damage the structure of ion sieve [43] and affect the replacement of Li\({}^{+}\) by H\({}^{+}\), the elution efficiency decreased. Therefore, based on a comprehensive data analysis, an HCl concentration of 0.01 mol/L was optimum.  
Through three parallel experiments, data were recorded, and the relative standard deviation of lithium elution efficiency was calculated. When the concentration of HCl was 2.0 mol/L, the relative standard deviation was minimal at 2.11%. At a HCl concentration of 0.5 mol/L, the relative standard deviation was maximal at 4.80%. Overall, the relative standard deviation range for the experiment results was 2.11% to 4.80%, indicating that the lithium elution rate data had relatively low dispersion and high experimental precision.
## Appendix A  
### Preprocessing  
The fragment-based decomposition preprocessing process follows these steps:  
1. Initial fragmentation: The molecular structure is represented as a graph \(G=(V,E)\), where \(V\) denotes atoms and \(E\) denotes bonds. Bonds that are not part of ring structures but connect ring atoms are identified and removed, breaking the structure into subgraphs \(\mathcal{G}_{1},\mathcal{G}_{2},\ldots,\mathcal{G}_{N}\).
2. Frequency-based filtering: Among the fragmented substructures, those that do not contain ring structures and appear infrequently (below a set frequency threshold, \(f_{c}\)) are selected. These substructures become unique labels in the vocabulary, denoted as \(\mathcal{G}_{1}^{\prime},\mathcal{G}_{2}^{\prime},\ldots,\mathcal{G}_{n}^{\prime}\).
3. Functional group decomposition: Further decomposition focuses on specific functional groups. For instance: - Amide groups: Substructures containing amide groups are identified, and bonds involving the amide C(=O)N are broken to create individual labels. - Carboxyl and ester bonds: Substructures with carboxyl or ester groups are similarly decomposed of separating bonds involving C(=O)O. - Aldehyde and ketone groups: Bonds within aldehyde or ketone groups C(=O) are also separated to generate labeled substructures. - Hydroxyl and ether bonds: Finally, bonds between oxygen and carbon in hydroxyl or ether groups are broken, adding further meaningful labels to the vocabulary.
## 2 Related Works
### Deep Learning-Based Remote Sensing Imagery Methods  
#### 2.2.1 SuSA  
[PERSON] et al. [24] proposed a self-taught semi-supervised autoencoder (SSuA) to carry out effective segmentation on satellite images that lack annotations or have limited labeled data, particularly in the context of multispectral imagery (MSI) and hyperspectral imagery (HSI) datasets.  
The SSuA first needs to pre-process the input remote sensing image. Then, the authors proposed a stacked multi-loss convolutional autoencoder (SMCAE) to extract meaningful features from the input image. Multiple features are combined, and average pooling is applied to obtain a fused feature representation. The fused feature representation is then fed into the semi-supervised multi-layer perceptron (SS-MLP) architecture. The SS-MLP performs upsampling on the feature map and generates the final prediction segmentation result.  
The SSuA approach combines self-learning features and semi-supervised classification to tackle the segmentation task. This approach aims to mitigate the limited availability of labeled data and improve segmentation performance with satellite images.
###### Abstract  
To address the significant degradation of image visibility and contrast in turbid media, this paper proposes an enhanced image dehazing algorithm. Unlike traditional polarimetric dehazing methods that exclusively attribute polarization information to airlight, our approach integrates object radiance polarization and airlight polarization for haze removal. First, sky regions are localized through multi-scale fusion of polarization and intensity segmentation maps. Second, region-specific transmittance estimation is performed by differentiating haze-occluded regions from haze-free regions. Finally, target radiance is solved using boundary constraints derived from non-haze regions. Compared with other dehazing algorithms, the method proposed in this paper demonstrates greater adaptability across diverse scenarios. It achieves higher-quality restoration of targets with results that more closely resemble natural appearances, avoiding noticeable distortion. Not only does it deliver excellent dehazing performance for land fog scenes, but it also effectively handles maritime fog environments.  
polarimetric dehazing; transmittance estimation; multi-scale segmentation-fusion Article
## 2 Multi-Agent Reinforcement Learning Framework and Transfer Learning Approach
### RL Agent Training Process
#### 2.3.3 Transfer Learning from Pre-Trained RL Models  
In addition to online training, the RL framework stored data from interactions within a simulated thermal model environment. After undergoing training on several controller driving cycles data, these pre-trained RL models and agents were then deployed to predict temperatures from new driving cycles data. The approach used statistical methods like mean, standard deviation, correlation, and frequency analysis to match new driving cycles with previously encountered ones. The analysis was performed on the basis of data from the same type of induction motor. When the new cycle data closely resembled the stored cycles data, the system selected an appropriate pre-trained model for temperature prediction, as shown in Figure 6. This saved computational time and resources by avoiding further training. However, if the new driving cycle appeared unique, the RL agent began training with this new controller-driving cycles data. This adaptive approach ensured that the model continuously evolved and learned from new motor usage patterns.  
Experience replay is a critical technique utilized in the training of RL models, particularly in scenarios where acquiring new data is resource-intensive or new data arrives in batches, such as during driving cycles. By storing previously trained models along with their parameters, it can effectively \"replay\" these stored experiences when new data is received. This approach allowed the model to reinforce learning from past data without the need to undergo full retraining with each new batch of data. This method not only conserved computational resources but also enhanced the learning efficiency of the model. Experience replay capitalizes on fewer data samples by frequently revisiting and reprocessing stored data, thus amplifying the training process and minimizing the necessity for constant collection of vast quantities of new data. This strategy was instrumental in refining the model's performance over time, ensuring robustness and adaptability to new situations without the overhead of continuous training from scratch.  
Figure 5: The training process of: (**a**) TD3, (**b**) DDPG, and (**c**) SAC agents.
# IoT's Tiny Steps towards 5G: Telco's Perspective
## 4 IoT in 5G Performance Requirements
### Summary of IoT in 5G Performance Requirements  
Activity-based classes of IoT applications are associated with performance requirements of 5G service classes, i.e., mMTC and uMTC. On the basis of the literature review, each activity-based class (ticketing-, tracking-, monitoring-, and managing/controlling-based class) is associated with mMTC service class, while the monitoring- and managing/controlling-based classes are the only ones linked to the uMTC service class. According to the authors' best knowledge, there is no available literature concerning the uMTC ticketing- and tracking-based classes of IoT applications or such IoT customer service requests.  
Activity-based IoT application classes pose many performance requirements, which have been discussed in terms of data rate, mobility (speed), latency, connection density, reliability, positioning accuracy, coverage, and energy efficiency. These performance requirements have been assigned three-level priorities (high, medium, low) for the purpose of facilitating identification of the enabling technologies used to fulfill them. Based on the literature review, it was found that each activity-based IoT application class poses high requirements in terms of connection density except the uMTC managing/controlling-based IoT application class. Moreover, ticketing- and tracking-based IoT application classes have high requirements in terms of positioning accuracy, coverage, and energy efficiency. Additionally, positioning accuracy is a highly important performance requirement for the uMTC managing/controlling-based IoT application class, whereas coverage is important for both uMTC and mMTC monitoring-based IoT application classes. Finally, latency and reliability represent highly important performance requirements for uMTC monitoring- and managing/controlling-based IoT application classes. A deep and comprehensive understanding of performance requirements of each activity-based IoT application class may facilitate the selection of 5G enabling technologies needed to meet them as described in the following section.