esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Approach
### Many-Objective G3P with Seeding  
As the likelihood of having a correct program as a result of the mapping phase is low, we devised a many-objective G3P approach (i.e., MaOG3P) to evolve the grammar-mapped program while maintaining its similarity with the LLM-generated code.  
Figure 3 shows the overview of MaOG3P. We seed the grammar-mapped program into MaOG3P's initial population (generated using the selected grammar associated with the relevant data type for the given problem). The seeding process can initialise the evolution with a starting point that is closer to the solution of the task, potentially enhancing the algorithm's search process. Furthermore, expanding on the input-output error rate fitness evaluation, our approach incorporates an additional layer of sophistication by integrating code similarity measures towards the (unmapped) LLM-generated code as secondary objectives to guide the evolution process.  
We considered four different similarity measures, described in Section 2, for our system. However, our innovation extends beyond mere correctness evaluation. By introducing code similarity measures as secondary objectives, our aim is not solely to identify the correct solution but, more importantly, to strategically steer the search process towards more plausible program candidates.  
While MaOG3P employs various objectives to evolve the population programs during the evolution process, we consider that an individual in the population solved the task only based on the primary objective. In other words, it must accurately pass all test cases for the given task.  
In our study, we modified the tournament selector to accommodate multiple objectives, enabling the algorithm to evolve programs with different criteria. When selecting the parents for the next generation, we perform the same as the standard G3P algorithm for half of the individuals (select parents using input/output error rate). For the other half of the population, we select one with the primary objective (error rate) and the other with a secondary objective (code similarity). Considering we have four different similarity measures as secondary objectives, we iterate with four measures evenly for selecting the parents.  
Determining which individuals can survive in evolution is a critical step for our algorithm. Similarly to the mechanism for selecting parents, we consider all objectives in this step. In this scenario, the primary objective decides which individual can survive for half of the population, while four similarity measures decide the remaining half.
## 2 Fundamentals of SAR for Antenna Sizing and Transmitting Power Consideration
### Incidence Angle  
The selection of incidence angle for a small SAR satellite depends on various factors and specific mission objectives. For the image interpretation of typical SAR applications, a wide range of incidence angles is desirable. Lower incidence angles (near-nadir) provide more vertical information and are suitable for topographic mapping, while higher incidence angles offer enhanced target discrimination and information about surface roughness. Higher incidence angles result in wider swaths and larger coverage areas. This can be beneficial for applications such as wide-area surveillance or mapping. At high incidence angles, shadows and layover effects become more prominent, particularly for tall structures or rugged terrain. This can impact the interpretability and accuracy of the SAR images. Higher incidence angles generally result in stronger received signals, leading to an improved signal-to-noise ratio and better image quality. However, for very high incidence angles, the signal strength may decrease due to reduced backscattering. The choice of incidence angle affects the antenna size and design requirements. Higher incidence angles may require larger antennas to achieve the desired resolution and coverage. Higher incidence angles introduce a higher Doppler frequency shift, which may result in Doppler ambiguities during data processing. Mitigation techniques, such as multiple PRFs or azimuth beam steering, can be employed to address this issue [36; 37; 38]. Incidence angles closer to the near nadir (low incidence) offer better penetration through vegetation, allowing for the detection of ground features beneath the canopy [40]. Considering these factors, a recommended incidence angle interval for a small SAR satellite could be between 15 and 45 degrees [36; 41]. This range provides a good balance between coverage, image interpretability, signal strength, and antenna size. However, the specific mission requirements and objectives should ultimately guide the selection of the optimal incidence angle interval for the SAR satellite.
## 3 Path-Following Control Strategy with Deep Deterministic Policy Gradient
### Deep Deterministic Policy Gradient for Path-Following
#### 3.2.3 Environment  
The environment in which the agent is expected to perform is an a priori known path. The vehicle kinematics should also be considered as part of the training environment, as it is beyond our control. Moreover, it is crucial for the agent to be trained on a wide variety of challenges to enable it to handle generalized situations instead of overfitting to specific paths. Therefore, we propose an algorithm outlined in Algorithm 1 for generating stochastic reference paths.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Value** \\ \hline \(c_{e}\) & 2.0 \\ \(c_{\delta}\) & 0.1 \\ \(c_{\delta}\) & 0.5 \\ \(c_{H}\) & 10.0 \\ \(c_{F}\) & 10.0 \\ \(e_{max}\) & 2.0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Reward function parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Value** \\ \hline \(\mu_{n}\) & 0.0 \\ \(\xi_{n}\) & 0.15708 \\ \(v_{n}\) & 0.15 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ornstein–Uhlenbeck process parameters.  
```
Generated waypoint counter \(n\gets 1\), starting waypoint \(\mathbf{p}_{1}\leftarrow[0,0]\), Number of path waypoints \(N_{w}\in\mathbb{N}\), Range of length between waypoints \([L_{min},L_{max}]\in\mathbb{R}^{+}\) while\(n\leq N_{w}\)do  Sample \(L_{w}\) from \(\mathcal{U}(L_{min},L_{max})\)  Sample \(\theta_{w}\) from \(\mathcal{U}(0,2\pi)\)  New waypoint \(\mathbf{p}_{n+1}\leftarrow\mathbf{p}_{n}+L_{w}[\cos(\theta_{w}),\sin(\theta_{ w})]^{T}\) \(n\gets n+1\) endwhile  Create parameterized path \(\mathbf{p}_{T}(\omega)=[x_{T}(\omega),y_{T}(\omega)]^{T}\) using Cubic Spline Interpolator
```  
**Algorithm 1** Stochastic Path Generator  
In this work, \(N_{w}=\mathcal{U}(2,6)\), \(L_{min}=25\) and \(L_{max}=50\). Some paths randomly generated from this algorithm are shown in Figure 5.
# Spatial and Temporal Dependency of NDVI Satellite Imagery in Predicting Bird Diversity over France
## 3 Results
### Comparison of NDVI Single-Date Models According to the Land Cover Context  
The effect of seasonal changes of NDVI on models performance depends on the bird community but is also modulated by the dominant landscape composition existing in the bird plots. In other words, for the same group of birds, the best NDVI acquisition date may vary according to the dominant land cover. This effect was evaluated for woodland and farmland birds only.  
In forested landscapes, with plots dominated by woody vegetation, late winter (NDVI 6-Mar) was the least accurate time period to explain woodland species richness (\(\Delta D^{2}=5\%\) in comparison to the best date; Table 3). The other periods fitted better with close explanatory power. For farmland species richness, late spring (NDVI 26-Jun) or late summer (NDVI 29-Aug) were the most appropriated.  
In agricultural landscapes, with plots dominated by winter crops or summer crops, end summer (NDVI 14-Sep) and begin of autumn (NDVI 30-Sep) explained better farmland and woodland species richness respectively (Table 3).  
In grassy landscapes, with plots dominated by permanent grasslands, late summer (NDVI 29-Aug) fitted better for woodland birds. For farmland birds, begin of autumn (NDVI 30-Sep) remained the most adapted.  
In a general way, landscape composition in the bird plot had an impact on the best NDVI time period for explaining woodland and farmland birds. However, end summer (NDVI 14-Sep) and begin of autumn (NDVI 30-Sep) appeared to be the most appropriate in majority (Table 3).  
Figure 6: Prediction maps of species richness for farmland (**a**) and woodland (**b**) birds at French national scale. Maps were produced using the best GAM models (i.e., climate with NDVI 26-Jun for SR farmland and climate with NDVI 14-Sep for SR woodland at 250-m spatial resolution) using all the 759 bird plots. Spatial concordance is observed between predictions and maps related to cultivated (**c**) and forested (**d**) areas. These maps (**c**,**d**) were derived from the French land cover map of 2010 (OSO product) [47].  
Predictive accuracies showed close results with explanatory performances (Figure 7). Difference appeared only for farmland birds in agricultural landscapes composed of winter crops but _Rho_ values were generally low for this case. Comparing to the single-date models, the consensus approach enabled to improve the predictions significantly for only two cases (SR woodland in grassy landscapes and SR farmland in agricultural landscapes with summer crops) (Figure 7).
# A Review of Machine Learning and Deep Learning Techniques for Anomaly Detection in IoT Data
## 3 Taxonomy of Anomaly Detection Techniques
### Anomaly Detection Techniques
#### 3.1.1 Machine Learning Techniques
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Techniques** & **Nature of the Data** & **Types of Anomaly** & **Anomaly Detection Types** & **Windowing** & **Dataset** & **Evaluation Criteria** \\ \hline C\_LOF [14] & Data Stream (evolving) & Point anomaly & Unsupervised learning using learning using clustering & Sliding window & synthetic and real-life datasets. & Precision, Recall, and Accuracy \\ \hline AutoCloud [24] & Data Stream (evolving) & Point anomaly & Unsupervised learning using clustering & Sliding window & Artificial and real dataset & N/A \\ \hline TEDA & Data Stream (evolving) & Point anomaly & Unsupervised learning using clustering & Sliding window & Own synthetic data sets & Accuracy, Time complexity \\ \hline Combination of (BDLMs) \& ((\)BBDF\()\) [26] & Data Stream (evolving) & Point anomaly & Unsupervised learning using density & Sliding window & Artificial dataset & Accuracy, the Detection rate \\ \hline HTM [27] & Data Stream & Point anomaly & Unsupervised learning based on HTM & N/A & Dataset of space \
\begin{tabular}{c} \\ imager data \\ stream \\ \end{tabular} & Accuracy \\ \hline Artificial Neural Network [28] & Continuous and image data & Point anomaly & Unsupervised learning on patterns of WSN nodes & Sliding window &
\begin{tabular}{c} The experimental \\ tests that have \\ been conducted \\ and cover more \\ than 27 \\ \end{tabular} & Accuracy \\ \hline MDADM [29] & Continuous data & Point anomaly & Supervised learning & N/A & Own dataset & Accuracy \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of machine learning techniques for data stream anomaly detection.
## 3 Results  
### Chronology of the CQS Loess  
The dating results for the six loess deposit samples obtained from different depths of the CQS section are shown in Table 1. The ages of OSL in the CQS loess profile indicated that the age ranges of the loess deposition section were from 75.9 \(\pm\) 7.5 ka (depth \(\sim\) 230 cm) to 39.7 \(\pm\) 3.4 ka (depth \(\sim\) 50 cm). The generalized age-depth model adopted in the whole loess section is presented in Figure 2. It demonstrated different sedimentation rates during the LG. The sedimentation rate from 75.9 \(\pm\) 7.5 ka to 67.8 \(\pm\) 6.2 ka was calculated to be \(\sim\) 3.70 cm\(\cdot\)ka\({}^{-1}\). It was observed that the sedimentation rate increased significantly to over \(\sim\) 10.26 cm\(\cdot\)ka\({}^{-1}\) between 67.8 \(\pm\) 6.2 ka and 63.9 \(\pm\) 5.8 ka (depth \(\sim\) 200 to \(\sim\)160 cm), and then it decreased from \(\sim\) 5.08 cm\(\cdot\)ka\({}^{-1}\) between 63.9 \(\pm\) 5.8 ka and 58.0 \(\pm\) 5.3 ka to \(\sim\)3.23 cm\(\cdot\)ka\({}^{-1}\) from 48.9 \(\pm\) 4.6 ka to 39.7 \(\pm\) 3.4 ka. No obvious erosion-deposition discontinuity was found in the CQS loess section during the field investigations; therefore, it was inferred that the loess stratigraphy with a thickness of 257 cm constituted aeolian deposition, and accumulation was continuous.  
Figure 2: Age distribution of the CQS loess section.
# Efficient Parallel Algorithms for 3D Laplacian Smoothing on the GPU
## 2 Background
### Iteration Forms  
Two forms of Laplacian smoothing are proposed to select the coordinates of the neighboring nodes during the smoothing process. Form A selects the old coordinates calculated in the previous pass while Form B uses the new coordinates that have been calculated in the current iteration; see the following formulations.  
Form A:  
\[\overline{x_{i}^{q+1}}=\frac{1}{N}\sum_{j=1}^{N}x_{j}^{q}, \tag{1}\]  
where \(N\) is the number of neighbors of node \(i\), and \(\overline{x_{i}^{q+1}}\) is the new coordinate after smoothing in next pass (\(q+1\)).  
Form B:  
\[\overline{x_{i}^{q+1}}=\frac{1}{N}\left(\sum_{j=1}^{N_{q}}x_{j}^{q}+\sum_{k=1} ^{N_{q+1}}x_{k}^{q+1}\right),\ \ \left\{\begin{array}{c}0\leq N_{q}\leq N\\ 0\leq N_{q+1}\leq N\\ N_{q}+N_{q+1}=N\end{array}\right., \tag{2}\]  
where \(N\) is the number of neighbors of node \(i\), and \(\overline{x_{i}^{q+1}}\) is the new position of node \(i\) in the next pass (\(q+1\)). \(N_{q}\) and \(N_{q+1}\) are the numbers of neighbors derived from passes \(q\) and (\(q+1\)), respectively. Form A is clearly a special case of the Form B where \(N_{q+1}=0\).
# Slot Optimization Based on Coupled Airspace Capacity of Multi-Airport System
## 3 Multi-Airport System for Cooperative Optimization of Flight Schedules
### Model Description  
The purpose of airport slot optimization in a multi-airport system is to make full use of public airspace resources and reduce the overall delay rate of flights. However, as a scarce resource, airport slots face a series of problems, such as fairness in the process of adjusting airport slots. Therefore, this study focuses on the actual airport capacity and airport slot management of Chinese civil aviation; conducts overall optimization of airport slots in  
Figure 3: Multi-airport system flight schedule optimization flowchart.  
Figure 2: The 24 h coordinated parameter peak valley diagram.  
a multi-airport system, taking into account the needs of air traffic control, airports, and airlines in the utilization of airspace capacity, ground support resources, and acceptable slot deviation; and establishes a mixed-integer multi-objective optimization model in order to improve the fairness of the deviation of airport slots for all airports and airlines in the airport group.
## 5 Transmission Power Management
### Power Management for MU-Network MIMO  
Unlike SU-network MIMO, each of CEUs in MU-network MIMO shares the entire sub-band \(f_{R2}\) instead, as shown in Figure 1b. This yields serious multi-user interference and thus joint preprocessing among cooperative BSs becomes necessary, as discussed in Section 4. Since the preprocessing matrices are jointly designed for all the CEUs, there is no distinction between intra-cell and inter-cell CEUs (i.e., \(P_{E}=P_{E}^{\prime}\)). Thus, there is no need for \(K_{\rm max}\) for power management in MU-network MIMO. Initially the power \(P_{\rm C}\) for each intra-cell CCU is \(P_{\rm C}=P_{b}\). The total power requirement for cooperative BS \(b\) to serve the \(N_{CEU}\) CEUs is \(T^{(b)}\) as shown in (18). We define the average preprocessing power for a CEU as \(\overline{T}^{(b)}=T^{(b)}/N_{CEU}\). As in SU-network MIMO, the CEU power is \(P_{E}=\alpha P_{\rm C}\). However, the CEU power depends on the average preprocessing power \(\overline{T}^{(b)}\), which is variable because of the time-variant channel. Thus, we further let the CEU power be \(P_{E}=\beta\overline{T}^{(b)}\), where \(\beta\) is determined such that \(P_{E}=\alpha P_{\rm C}\). Therefore, we achieve \(\beta=\alpha P_{\rm C}/\overline{T}^{(b)}\). For each sector antenna, the procedure of power management is as follows. 1. Initialize the power allocation to each of CCUs by \(P_{C}=P_{b}\). 2. Calculate the average preprocessing power \(\overline{T}^{(b)}=T^{(b)}/N_{CEU}\) according to (18). Then set the CEU power \(P_{E}=\beta\overline{T}^{(b)}\), where \(\beta=\alpha P_{\rm C}/\overline{T}^{(b)}\). 3. Next, calculate the total power requirement \(P_{T}=xP_{\rm C}+N_{CEU}P_{E}\). If \(P_{T}>P_{\rm max}\), then reduce the baseline power \(P_{b}\) by a factor of \(\rho_{2}\) such that the value of \(P_{T}\) equals the power constraint \(P_{\rm max}\), where \(\rho_{2}=P_{\rm max}/P_{T}\); elsewise \(\rho_{2}=1\). 4. Finally, we achieve \(P_{\rm C}=\rho_{2}P_{b}\) and \(P_{E}=\rho_{2}\beta\overline{T}^{(b)}\). In summary, the method consists of a CEU power coefficient \(\alpha\) and a power reduction factor \(\rho_{2}\). We adaptively adjust the value of \(\beta\) such that \(P_{E}=\alpha P_{\rm C}\). The power reduction factor \(\rho_{2}\) is used to make sure that the total power requirement does not exceed the power constraint \(P_{\rm max}\).
# _Cutibacterium acnes_ Dysbiosis: Alternative Therapeutics for Clinical Application
## 2 New Therapeutic Strategies
However, this was a study that highlighted the benefit of using a cocktail of phages when dealing with multiple strains of pathogenic _C. acnes_[28]. In another study, [PERSON] and the team isolated a phage from the healthy skin of volunteers that was identified as TCUCAP1. This phage could not only show an efficient lysis effect in in vitro assays with multiresistant clinical isolates of _C. acnes_, but it also demonstrated a reduction in the inflammatory lesions on mice previously injected with a multidrug-resistant clinical _C. acnes_ isolate (PS023) [36]. Also using a mouse model, [PERSON] et al. isolated eight phages in their laboratory and _C. acnes_ strains from AV subjects. The group then administered the phages topically to mice previously infected with a virulent clinical _C. acnes_. The treated mice showed a reduced histological score when compared to the nontreated mice [37]. In a phase 1 cosmetic randomized clinical trial, [PERSON] and the team aimed to identify and characterize phages targeting _C. acnes_ in subjects with mild to moderate acne. They found broad-spectrum phages, including those targeting antibiotic-resistant strains, with safe and specific genomes. A three-phage cocktail, BX001 was employed in a topical bacteriophage. Results showed reduced _C. acnes_ on the face, aligning with predictions from in silico and ex vivo assessments, suggesting the potential for topically administered phages for acne treatment [38]. An important therapeutic aspect is phages' temperate nature, allowing coexistence with their host and enabling gene transfer, including antibiotic resistance genes. The safety of phage therapy relies on understanding these dynamics. A study conducted with antibiotic treated mice was found to increase phage integration into bacterial genomes, enriching the phage metagenome with stress-specific functions, shaping the phage-bacterial network. This enrichment also included functions related to host metabolism, such as a broader carbohydrate pathway in ampicillin-treated mice [39]. Such strategies could provide a protective effect in the gut microflora during antibiotic treatments. Despite all the advantages in this innovative approach, phage therapy also entails some challenges that make its applicability not straight forward. Identifying and isolating appropriate phages can be challenging and time-consuming, since the affinity towards a specific strain is essential to an effective treatment. Another challenge is the regulatory approval. In many countries, phage therapy is considered experimental, failing to check all the necessary bureaucracies for clinical application and, therefore, not reaching global population. One more challenge, that somewhat correlates with the former, is the lack of clinical data. This limitation constricts the flexibility in terms of phage therapy as a personalized treatment, due to its regulatory limitations that, in the end, result in few clinical trials.
## 6 Discussion
### Discussion of the Conceptual Design for Micro-Assembly on RMS  
Three goals for the new conceptual design phase were set. The first goal is to allow the users as much freedom in designing their product ideas as possible, but still be able to automatically prove the feasibility of the modeled ideas. Overall, this goal has been fairly reached. Most but not all characteristics could be modeled by the validators. Especially, forming or reshaping of components is not possible. The used paper surveys are only a proof of concept. So, an automated output of the method is not generated at the moment. However, the validation showed that this is feasible as long as all properties of the data types are given.  
The second goal is that the system should work reasonably well with micro-assembly RMS. The validation came to the conclusion that not all steps of a micro-assembly can be done by only using standard modules of the RMS. The participating machine experts for micro-assembly on RMS estimate that roughly 80% of all operations required for a micro-assembly can be carried out by using standard modules, the remaining 20% need to be done by special modules.  
The last goal was to provide a system that is relatively easy to use. Validation shows that this has not been reached yet. Paper surveys are a very static tool for data acquisition. They are not able to react to a given input and they are also not able to visualize possible inputs to help the user. Software implementations can help reducing these problems by aiding the user with graphical interfaces as well as automatically hiding and showing input forms based on the already given data. Yet, for theautomated output, the system still relies on a full set of properties that may not be certain at the moment the conceptual design phase is done.  
All in all, the conceptual design phase could be validated as a working tool to model product ideas in the field of micro-assembly and proof their feasibility regarding production on RMS. But the described approach still has limitations and does not fulfill all the goals that were set. The method needs to be further refined and enhanced. One main starting point is the clear orientation towards describing the final product model during the conceptual design phase. While this seems to be the right way when creating a method for product development it comes with some downsides. In the conceptual design phase, there exists no construction or detailed design of a product, but instead, mostly an idea what a finished product should do, which operational and physical principles can be used to achieve this and some first thoughts on how this may be implemented. Often neither materials nor geometrical proportions of the used components are certain. This leads to the situation that some of the properties cannot be defined and also should not be defined. But without those properties, the system is not able to propose a processing way. Also, to get a processing order, complex algorithms have to be implemented that need a lot of additional data to function correctly.