esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Mineral Compositions and Organic Content  
The results show that marine shale exhibits high brittle mineral content (quartz, pyrite, calcite, dolomite) and low clay mineral content (Figure 2, Table 2), which is related to the sedimentary environment of deep-water shelf face with low water energy, and the source of the sediment is mainly sandstone of quartz-rich in Xishui Block [37]. On the contrary, continental shale exhibits high clay content and low brittle mineral minerals (Figure 2), which was deposited in the lacustrine face, and there are intense volcanic activities during the shale depositional period [38], leading to the high content of volcanic rock fragments in the sediment sources. Besides, the continental shale presents obvious micron-scale lithological stratification, whereas the marine shale shows a relatively homogeneous mineral structure (Figure 3).  
Besides, the TOC components of marine and continental shale show obvious differentiation. Due to the intense volcanic, hydrothermal, and seismic activities during the deposition in the Ordos Basin, the lake has formed a eutrophic and high productive water environment, resulting in the higher TOC of continental shale than marine shale.  
Figure 2: Mineral composition and TOC content distribution of marine shale (MS) and continental shale (CS).
###### Abstract  
In unanticipated obstacle scenarios at intersections, the safety and mobility of autonomous vehicles (AVs) are negatively impacted due to the conflict between traffic law compliance and obstacle avoidance. To solve this problem, an obstacle avoidance motion planning algorithm based on artificial potential field (APF) is proposed. An APF-switching logic is utilized to design the motion planning framework. Collision risk and travel delay are quantified as the switching triggers. The intersection traffic laws are digitalized and classified to construct compliance-oriented potential fields. A potential violation cost index (PVCI) is designed according to theories of autonomous driving ethics. The compliance-oriented potential fields are reconfigured according to the PVCI, forming violation cost potential fields. A cost function is designed based on compliance-oriented and violation cost potential fields, integrated with model predictive control (MPC) for trajectory optimization and tracking. The effectiveness of the proposed algorithm is verified through simulation experiments comparing diverse traffic law constraint strategies. The results indicate that the algorithm can help AVs avoid obstacles safely in unanticipated obstacle scenarios at intersections.  
automous vehicles; motion planning; traffic law; violation cost; artificial potential field; model predictive control 2024141626. [[https://doi.org/10.3390/app14041626](https://doi.org/10.3390/app14041626)]([https://doi.org/10.3390/app14041626](https://doi.org/10.3390/app14041626))  
18 January 202411 February 202  
vehicles [5], making it necessary for surrounding vehicles to take illegal actions to avoid obstacles [6]. Rigidly obeying traffic laws will not optimally achieve the ultimate goal of integrating AVs into the road traffic system [7].  
In 2021, German legislation acknowledged that human drivers occasionally need to violate specific traffic laws to meet other driving requirements [8]. The European Union Horizon Commission Expert Group recognized that pursuing greater road safety sometimes necessitates non-compliance behaviors. The group recommended that AVs can decide to violate traffic laws if they can provide explanations, leaving the justice system to determine if the violation was reasonable [9]. In the context of Level 4 and above-level autonomous vehicles, a human driver is not required to take charge when faced with some specific driving scenarios. Addressing unanticipated obstacle scenarios necessitates trajectory planning that incorporates regulatory factors for both extraction and obstacle avoidance. This imperative ensures the continuous regulatory awareness of autonomous vehicles across diverse driving environments, posing a significant challenge in the practical realization of autonomous driving systems. This paper primarily investigates the development of a trajectory planning framework to address the aforementioned functionalities, integrating regulatory considerations into both general and obstacle avoidance trajectory planning.
# Parsing Expression Grammars and Their Induction Algorithm
## 3 Induction Algorithm
### Genetic Programming  
Commonly, genetic programming uses a generational evolutionary algorithm. In generational GP, there exist well-defined and distinct generations. Each generation is represented by a population of individuals. The newer population is created from and then replaces the older population. The execution cycle of the generational GP--which we used in experiments--includes the following steps:  
1. Initialize the population.
2. Evaluate the individual programs in the current population. Assign a numerical fitness to each individual.
3. Until the emerging population is fully populated, repeat the following steps:  
* Select two individuals in the current population using a selection algorithm.
* Perform genetic operations on the selected individuals.
* Insert the result of crossover, i.e., the better one out of two children, into the emerging population.
4. If a termination criterion is fulfilled, go to step 5. Otherwise, replace the current population with the emerged population, saving the best individual, and repeat steps 2-4 (elitism strategy).
5. Present the best individual as the output from the algorithm.  
In order to put the above procedure to work, we have to define the following elements and routines of GP: the primitives (known in GP as the terminal set and the function set), the structure of an individual, the initialization, genetic operators, and the fitness function.  
Individuals are parse trees composed of the PEG's operators \(\sim\), \(\gg\), and \(|\), and terminals are elements of \(\Sigma\cup\{\epsilon\}\), where \(\Sigma\) is a finite alphabet (see an example in Figure 1).  
An initial population is built upon \(S_{+}\) (positive strings, examples) and \(S_{-}\) (negative strings, counterexamples) so that each tree is consistent with a randomly chosen \(k\)-element subset, \(X\), of \(S_{+}\) and a randomly chosen \(k\)-element subset, \(Y\), of \(S_{-}\). An expression forming an individual in an initial population is created by means of a deterministic algorithm given further on. In a crossover procedure, two expressions given as parse trees are involved. A randomly chosen part of the first tree is replaced by another randomly chosen part from the second tree. The same operation is performed on the second tree in the same manner. We also used tournament selection, in which \(r\) (the tournament size) individuals are chosen at random and one of them with the highest fitness is returned. Finally, the fitness function measures an expression's accuracy based on an individual \(e\), and the sample \((S_{+},S_{-})\) with Equation (1):  
\[f(t)=\frac{|\{w\in S_{+}\colon w\in L(G(e))\}|+|\{w\in S_{-}\colon w\
ot\in L( G(e))\}|}{|S_{+}|+|S_{-}|}\,, \tag{1}\]  
Figure 1: Example of a genetic programming individual coded as the expression \(a\gg b\gg(c\gg\cdot d\mid b\mid a)\).  
where \(G(e)\) is a non-circular PEG \(G(e)=(\{A\},\Sigma,\{A\Leftarrow e\},A)\).
# Multi-Label Weighted Contrastive Cross-Modal Hashing
## 2 Related Work
### Supervised Cross-Modal Hashing  
By exploiting the discriminative information of labels, almost all supervised cross-modal methods learn modality-specific hash functions to project multi-modal data (such as image-text pairs) into a common Hamming space. For example, DCMH [43] combines hash code learning and feature learning into a unified deep framework. SSAH [44] is a multi-modal semantic learning network that aims to integrate adversarial learning into cross-modal hashing in a self-supervised manner. CMHH [45] utilizes a pairwise focus loss based on an exponential distribution to penalize similar cross-modal pairs whose Hamming distance is greater than a Hamming radius threshold. Bi_NCMH [29] is a bi-directional relational reasoning-based deep cross-modal hashing method, which generates a similarity matrix from multiple labels so as to fully capture the relationship between instances. Due to the semantic information from annotations, the supervised cross-modal hashing methods are able to embed more discriminative semantics into hash codes to achieve promising retrieval performance.  
Figure 3: The framework of MLWCH. It mainly contains four modules: (i) the compact consistent similarity representation module uses multiple labels to accurately construct the similarity matrix; (ii) the multi-label weighted contrastive learning module realizes a novel contrastive learning strategy in which different positive samples are assigned with different weights based on label sharing; (iii) the hash representation learning module aims to learn high-quality cross-modal hash representations by combining cross-modal semantic similarity learning and weighted contrastive learning; and (iv) the hash function learning module treats the learned hash representation as a supervised signal to preserve semantic consistency. Best view in color.
# Mechanical Properties and Microstructure of Polyvinyl Alcohol (PVA) Modified Cement Mortar
## 3 Results and Analysis
### Capillary Water Absorption  
The water absorption of a material depends not only on its hydrophily but also on its porosity and pore size distribution. Figure 8 shows the capillary water absorption of cement mortars containing 0.2-2.0% PVA. The capillary water absorption rate is plotted on the ordinate, and the square root of time \(t^{0.5}\) is the abscissa. By analyzing the relationship between the capillary water absorption and the square root of the time, the pore characteristics of the mortar can be reflected. According to the test results in the figure, it can be found that the curve shape of water absorption of cement mortars with or without PVA was quite similar, and which can be divided into two stages: A rapid ascending phase (stage I), and a steady phase (stage II). At stage I, water diffuse along the micro-cracks or interconnecting holes and then fill the pores of surrounding mortar. Thus, the more micro-cracks and interconnecting holes, the faster this stage will ascend. Clearly, according to Figure 8, PCM0 had the highest micro-cracks or interconnecting holes, followed by PCM4, and PCM2 had the lowest micro-cracks and interconnecting holes as its ascending rate was the lowest. In stage II, the pores were filled by water to a certain level, the equilibrium between the rates of transporting moisture and filling pores is built. At this stage, the capillary water absorption value is related to the porosity, and a higher water absorption value means a higher porosity. The effect of PVA on the water absorption of mortar may be because (1) PVA reduces the hydrophily, and (2) PVA film fills the pores of the cement matrix. For this reason, the samples containing PVA had a much lower absorption over the control one. Especially for PCM2, the capillary water absorption of cement mortar was the lowest value of 3.21 kg/m\({}^{2}\), which was 60% lower than that of PCM0. However, it should be noted that the addition of PVA will reduce the flow properties of cement mortar, as shown in Figure 4, the higher the PVA content, the lower the slump value was. Particularly for PCM4, the slump value reduced by about 50% as compared to PCM0. The poor flow behavior might cause the uneven dispersion of cement mortar, consequently, the interconnecting holes probably increased with the increase of PVA.  
Figure 7: Effects of PVA content on the volume density of cement mortar.
## Materials and Methods
### Synthesis of Polyester-Furan  
The reaction of the DPA-fur monomer with terephthalaloyl chloride results in a polyester containing pendant furan groups. The ratio of monomer/chain stopper added in this reaction was set at 4:2 to yield an average theoretical chain length of four monomeric repeating units, end-capped by two chain stoppers.  
An amount of 5.00 g (13.8 mmol, 4 eq) DPA-fur, 0.435 g TBAB (8.7%wt of DPA-fur), 0.644 g (6.841 mmol, 2 eq) phenol, 1.411 g (34.2 mmol) NaOH, and 120 mL water were added to a 500 mL round-bottomed flask. Subsequently, the reaction was stirred for 2 h at room temperature. Afterward, 3.125 g (15.39 mmol) terephthalaloyl chloride dissolved in 120 mL chloroform was added to the mixture. The reaction mixture was then stirred overnight at room temperature. Finally, the polymer was precipitated in a large beaker containing 2 L methanol. The precipitation yielded a solid precipitate on the bottom of the beaker, as well as a suspension of small particles in the methanol phase. The methanol phase containing small solid particles was centrifuged in 50 mL cups at 4500 rpm for 15 min. Furthermore, the precipitate obtained in the centrifuge cups was filtered over a Buchner funnel and washed with 50 mL of water. Additionally, the solid precipitate on the bottom of the beaker was also filtered over a Buchner funnel and washed with water. Finally, all the solid products obtained were combined and dried in an oven at 70 \({}^{\circ}\)C for a couple of hours to yield a yellowish-white solid (PE-fur, best yield achieved 74%).  
The \({}^{1}\)H-NMR (300 MHz, DMSO-de) measurements were \(\delta\) = 8.32-8.10 (m, 7H, aromatic not next to ester), 7.55 (s, 1H, fur-p), 7.49 (d, 1H, NH), 7.4-7.2 (m, 8H, aromatic next to ester), 6.37 (s, 1H, fur-m), 6.22 (s, 1H, fur-o), 4.23 (d, 2H, N-CH2), 2.43 (s, 2H,-CH2-), 1.98 (s, 2H,-CH2-), and 1.66 (s, 3H, -CH3).
## 2 Materials and Methods
### Patient Population and Study Design  
The present prospective single-center study obtained the Institutional Review Board approval of the University Politecnica delle Marche Internal Ethic Committee on 9 October 2021. Between September 2021 and May 2022, a total of 40 selected patients were enrolled. To be included, patients had to be previously submitted to a vascular evaluation for suspected chronic limb ischemia performed in the Vascular Surgery or Vascular Medicine Departments of our institution, consisting of clinical history record, physical examination, and measurement of ankle-bracchial index (ABI). The clinical evaluation was also completed by a DUS examination and a laboratory test of renal function. All patients were then submitted to a CTA study of the lower limbs as required by the referring Vascular Surgeon for symptomatic PAD occurring with mild to severe claudication (Rutherford categories 1 to 3), rest pain, or non-healing minor or major ulcers (Rutherford categories 4 to 6). Patients previously submitted to stents intravascular placement, surgical revascularization, or primary amputations were excluded, as well as patients with symptoms consistent with acute limb ischemia [39].  
Documented allergy to iodinated contrast medium, moderate or severe renal impairment (glomerular filtration rate < 45 mL/min), and congestive heart failure were also considered as exclusion criteria.  
After the exclusion of previous allergic reactions to iodinated contrast medium, informed consent to the examination was obtained from all the participants.  
Forty consecutive patients were examined for suspected PAD. Twenty-eight patients were males (median age 70 y, range, 45-85 y) and 12 patients were females (median age 69 y, range, 41-93 y). The median patient age was 69 years, ranging from 41 and 93 years. The calculated median body mass index (BMI) was 25.7 (range, 17-33.5).  
Demographic and clinical characteristics of the study population were summarized in Table 1.  
Figure 1: General diagram of methodology workflow. VNC = virtual non contrast; TNC = true non contrast; DE-CTA = dual-energy computed tomography angiography; MPR = multi-planar reformats; MIP = maximum-intensity projections.
# Classification of Heart Sounds Using Convolutional Neural Network
## 3 Results  
### Performance of the Normal and Abnormal Heart Sound Classification  
We applied stratified five-fold cross-validation to the heart sound dataset described in Section 2.1 in order to train and test the proposed model. The dataset was split into five equal-sized subsets and the proportion of each part was the same as that of the original dataset. Four subsets were used to train and validate the model, while the members of the remaining subset were retained as test samples. This process was repeated five times.  
The sensitivity, specificity, MCC [42] and mean accuracy (Macc) for each fold of the cross-validation were used to evaluate the performance of the proposed model. In clinical applications, sensitivity measures the proportion of sick people who are correctly identified as positive. Specificity measures the percentage of healthy people who are correctly identified as negative. MCC can produce a truthful score for binary classification tasks or multi-class prediction [43]. Macc is not sensitive to the numbers of classes, so it can be used to indicate the performance of the classifier. Because the classification task involved a class imbalance, we applied the arithmetic mean of sensitivity and specificity, that is, the mean accuracy (Macc), to eliminate any reported high virtual accuracy. The classification performances of the proposed method are presented in Table 2. It can be observed that the best outcomes obtained from fourth-fold are 94%.
# Cloud-Edge Collaborative Model Adaptation Based on Deep Q-Network and Transfer Feature Extraction
## 3 Model Framework
### The Algorithm Flowchart and Its Corresponding Pseudocode  
The algorithm flowchart is shown in Figure 3, and the pseudocode of the algorithm is shown in Algorithm 1.  
```
Initialize DQN agent; Load initial detection results from result.xlsx; For each class c in the category set do: Step 1 Construct State Vector for Reinforcement Learning Compute edge-side detection accuracy: acc_edge_c; Compute cloud-side fusion accuracy: acc_cloud_c; Compute accuracy gap: \(\Delta c=\) acc_cloud_c - acc_edge_c; Form state vector \(\text{Sc}=[\)acc_edge_c, acc_cloud_c, \(\Delta c]\); Step 2 Input state into DQN agent and obtain action
```  
**Algorithm 1** The pseudocode of the algorithm. Adaptive Cloud-Edge Collaborative Model Optimization Algorithm  
Figure 3: The algorithm flowchart.  
action_c = DQN.predict(Sc);  
**If** action_c == 1:  
**Load** class-specific training samples (labeled XML);  
**Freeze** all parameters of Faster R-CNN except for classification head of class c;  
**Calculate L\({}_{\mathbf{C}}\)** and **Perform gradient update** only on:  
- head.score.weight[c]  
- head.score.bias[c];  
**Save** the updated parameters as class patch (pth file);  
**Merge** the patch into the base edge model;  
**Step 3** Perform Edge Inference and Update State  
**Perform** inference on multi-view (4-angle) satellite images;  
**Update** result.xlsx with new edge-side predictions;  
**Recalculate** acc_edge_c;  
**Else:**  
No parameter update, retain previous model state;  
**Step 4** Reinforcement Learning Update  
Compute reward r_c = | acc_cloud_c - acc_edge_c!;  
Store experience tuple (Sc, action_c, r_c, Sc + 1) into replay buffer;  
Update DQN by minimizing Bellman loss L_Q using sampled experiences;  
**Step 5** Check Termination Condition  
If | acc_cloud_c - acc_edge_c! | \(<\) threshold:  
Mark class c as \"converged\"; skip further transfer for this class;  
Else:  
Continue iterative transfer learning for class c;  
**End For**  
**Output:** Updated edge model and final result.xlsx with optimized edge-side detection results
# Multi-Robot Formation Control Based on CVT Algorithm and Health Optimization Management
## 4 Experiment and Simulation
### Simulation of Formation Health Optimization Management
#### 4.2.5 Formation Simulation and Results Analysis after Robot Stops Working  
When \(h_{i}(t)\leq 0\) due to other reasons, the robot is considered to be unable to work normally. In order not to affect the overall task of the formation, the insertion construction method was used to regroup multiple robots and re-divide the task area. Taking the square formation as an example, as shown in Figure 15a,b, four robots form a square formation before the number of iterations \(k=120\). After that, when the robot in the lower right corner cannot work normally due to a fault, and \(h_{i}(t)\leq 0\), as shown in Figure 15e, the insertion construction method is used to make the other healthy robots adopt a new formation and reconstruct a new division of the new service area.  
As shown in Figure 15c, when the number of iterations reaches 280, the remaining three robots adopt a new formation in the area and realize a new division of the area. The red plus sign in the figure is the final position of the faulty robot. The position error and cost function diagrams for this process are shown in Figure 15d,f.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(h_{i}(t)\) **When the** & \(h_{i}(t)\) **When the** & \(\begin{array}{c}\text{\bf{Percentage}}\\ \text{\bf{System Is Stable}}\\ \text{\bf{(Normal Situation)}}\end{array}\) & \(\begin{array}{c}\text{\bf{System Is Stable}}\\ \text{\bf{(HOM)}}\end{array}\) & \(\begin{array}{c}\text{\bf{Increase in System}}\\ \text{\bf{Health Value}}\end{array}\) \\ \hline Scenario 1 & 0.26 & 0.48 & 22\% \\ Scenario 2 & 0.04 & 0.38 & 34\% \\ Scenario 3 & 0.15 & 0.40 & 25\% \\ Scenario 4 & 0.02 & 0.30 & 28\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of simulation results in four scenarios before and after adding the HOM algorithm.  
Figure 14: Comparison before and after adding HOM in the fourth scenario: (**a**) \(h_{i}(t)\) of robots under normal conditions; (**b**) \(h_{i}(t)\) of robots under HOM; (**c**) \(e_{i}(t)\) under normal conditions; (**d**) \(e_{i}(t)\) under HOM.  
As shown above, when the insertion construction method is introduced into the CVT it can not only reduce the impact on other robots when there is a faulty robot in the formation but can also compensate for the negative impact of the faulty robot as far as possible, so that the robots in the formation can better respond to the changes in the environment, and the formation control can better adapt to the actual situation, improving the execution and robustness of the robot formation.
###### Abstract  
In recent years, many studies have been conducted on OFDM-based optical wireless communications to develop a 6G communication infrastructure to improve data transmission and reduce the BER. Real-time optimal power management can enhance the data transmission speed and received power in an optical wireless channel under various conditions. This paper discusses implementing a real-time optimal power allocation system using a neural network for OFDM-based optical wireless communications. The system is designed to manage transmitter power, enhancing data transmission rates in optical wireless channels. In system design, data concerning power allocation for various types of OFDM-based optical wireless channels are calculated analytically, including the BER, SNR, fog effects, and fading types in the channel model. Next, a DNN neural model is trained using data generated from the analytical method. The trained model is finally integrated into wireless optical communication transmitter hardware. The experimental results indicate that the embedded power allocation system processes power allocation quickly. The proposed system achieves an average accuracy of 98% in power allocation, surpassing the analytical method. When used in wireless optical communication transmitters, this embedded system enhances speed and accuracy in power management, optimizing the data transmission rate up to 16 Gbps for a 500 m channel.  
Keywords:optical wireless communication; power allocation; embedded system; data rate; bit error rate