esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this systematic literature review (SLR), a comprehensive survey of existing literature was conducted to gauge the current state of the art of oil-immersed transformers' DGA-based fault classification using machine learning algorithms. In the assessment of published studies, a total of 124 screened papers published between 2014 and 2024 were surveyed using the developed PRISMA framework using a statistics-driven quantitative approach, the defined inclusion and exclusion criteria, and the set of key search words identified.  
In addressing the research questions developed to guide this study, the SLR results show that the majority of the research conducted for transformer fault classification using DGA employs the support vector machine (32%), artificial neural network (17%), and k-Nearest Neighbor (12%) algorithms. However, the introduction of hybrid optimization algorithms by some studies suggests further developments into hybrid algorithms which could be used for binary and multi-classification projects.  
The SLR also revealed that China is at the forefront of this research area with a dominant 53% of the total publications on transformer fault diagnosis and classification based on DGA using machine learning algorithms. Furthermore, it reveals that the majority of the research conducted revolves around fault diagnosis with an emphasis on improving the accuracy of known and widely used techniques such as SVM and ANN. At the same time, limited effort is put into other key algorithm metrics such as precision, Mean Squared Error, and R-Squared. Also, the current works surveyed do not explore regularization techniques for preventing overfitting and underfitting of the proposed models. In addition to these findings, a vast majority of the studies omit the description of the type of data pre-procession method, feature selection, and feature extraction methods used; this presents a gap in determining the most efficient and adaptable method that other researchers can use for their respective projects.  
Additionally, based on the results obtained from this systematic literature review, there is a need for further developments in hybrid machine learning algorithms that could be developed in conjunction with the conventional fault classification methods to find the most optimum approach to classify faults at a high accuracy level.
## 2 Related Work
### Search-Based Algorithm  
The search-based path planning algorithm is a mature path planning algorithm. Based on the established search tree or graph, different search strategies are used to find feasible solutions or even optimal solutions. The basic graph search algorithms for unweighted graphs are Depth First Search (DFS) and Breadth First Search (BFS), both of which are time-for-space methods, with BFS being able to guarantee that the path searched for is optimal. Heuristic search algorithms, such as Greedy Best First Search (GBFS), can improve search efficiency, but cannot ensure that the optimal path is found. Classical algorithms in path planning are Dijkstra, which is an extension of the BFS in a weighted graph, and the A*, which is obtained by adding a heuristic function to the former, taking into account both efficiency and completeness.  
Dijkstra, A*, and their improved algorithms such as block A*, D*, field D*, fringe saving A* (FSA*), and generalized adaptive A* (GAA*) [23; 24] are efficient search-based algorithms. The work proposed in [25] suggested a Lyapunov-based coordination technique that enables low-level trajectory planning with the A* algorithm. The authors of [26] proposed bidirectional A* algorithms for trajectory planning under intermittent measurements. Additionally, they demonstrated the feasibility and consistency of UAV path planning by utilizing convex hull and B-spline curves. In [27], the authors presented the EBS-A* algorithm, which incorporates extended distance from obstacles, simultaneous search in both directions, and smoothing to reduce right-angle turns in path planning. There is also a class of algorithms such as [6; 28; 29; 30; 31; 32], which not only accomplishes a good path planning effect but also optimizes the trajectory. Although this kind of algorithm has analytical  
Figure 1: An overview of sensor-based path planning and obstacle avoidance for SO-DRL. On the right side of the figure are the DRL and the search-based algorithms, both of which manipulate the agent in the environment on the left side. The search-based algorithm performs path planning by the map and corresponding planner and adds experience to the experience pool. The DRL controls the agent only by direct observation and updates the network parameters based on the experience of the search-based algorithm and its own experience.  
completeness, when the map dimension is high or the size is large, it needs to process a large amount of data.
# Photo-Fermentative Bacteria Used for Hydrogen Production
## 2 Photo-Fertanative Bacteria for Hydrogen Production
### Rhodovulum sulfidophilium  
_R. sulfidophilium_ is a marine acid-tolerant photo-fermentative bacterium. They are Gram-negative, rod-shaped bacteria and can accumulate poly(3-hydroxybutyrate) (PHB) to over 50% of cell dry weight when acetate is used as a substrate. Hydrogen production in the presence of PHB by _R. sulfidophilium_ shows that the loss of reducing equivalents to produce hydrogen can be recovered by the degradation of PHB [81].  
In most cases, a neutral initial pH is preferred by the photosynthetic bacteria for optimal hydrogen production, but screening for acid-tolerant strains is important to increase the production of hydrogen in an acidic environment and at higher temperatures. Thus, a Tn7-based transposon was inserted into the genomic DNA of _R. sulfidophilium_ P5 and the hydrogen yield and average hydrogen production rate of 2.16 \(\pm\) 0.10 mol/mol acetate and 10.06 \(\pm\) 0.47 mL/L h were observed, respectively, which were approximately 17.32- and 15.37-fold higher than those of the wild-type strain, respectively [82]. Another strain, _R. sulfidophilium_ TH-102, was studied, and it was analyzed individually and in co-culture with other microbes of dark fermentation; the results showed that the addition of strain TH-102 can stabilize pH, decrease oxygen reduction potential, and prolong hydrogen production. The analyses provided data showing that hydrogen production during dark and photo-fermentation alone was sustained for 72 and 168 h, but when using a co-culture it went from 168 to 216 h. The co-culture with the ratio of dark/photo-fermentation bacteria 1:10 produced the highest hydrogen yield of 1694 \(\pm\) 21 mL/L [83].  
Based on the studies with different photo-fermentative microbes, single-stage and two-stage fermentation have been carried out to produce hydrogen. Single-stage photo-fermentation is more cost-effective than two-stage because it utilizes a wide range of substrates, thus yielding more hydrogen as compared to dark fermentation [84]. Two-stage photo-fermentation involves either coupling dark fermentation with photo-fermentation in sequence or combining bacteria used for dark and photo-fermentation to increase hydrogen yield [85]. By dividing the hydrogen production process into two stages, each stage could be optimized based on specific requirements and could lead to an improved hydrogen production efficiency compared to single-stage photo-fermentation.
# Hypergraph-Clustering Method Based on an Improved Apriori Algorithm
## 3 Method Design of HDHPA
### Hypergraph Partitioning Stage  
After constructing the hypergraph, we use the hypergraph-partitioning algorithm DSP introduced in Section 2.4 to partition the hypergraph. The DSP algorithm adopts the divide-and-conquer approach to perform parameter-free partitioning of the hypergraph from top to bottom, resulting in relatively low time and space complexity. Due to the uniqueness of conditional kernel sub-hypergraphs and their non-intersecting partitions, DSP is unique. A denser sub-hypergraph is more likely to represent a true cluster, while a less dense sub-hypergraph is usually composed of outliers. The result of the DSP algorithm is an ordered list of dense sub-hypergraphs with decreasing density, which reveals all potential clusters and outliers. Therefore, it can achieve good clustering results even if there are a lot of outliers.  
The algorithm first calculates the weight of each hyperedge in the hypergraph, and then divides the hypergraph into a sequence of conditional kernel sub-hypergraphs in decreasing order, and calculates the corresponding density. Then, each conditional kernel sub-hypergraph is divided into disjoint dense sub-hypergraphs by a non-overlapping partitioning algorithm. The dense sub-hypergraphs generated using DSP usually have strong connections between vertices and can be considered as potential clusters to obtain clustering results [32].
# Advancing TinyML in IoT: A Holistic System-Level Perspective for Resource-Constrained AI
## 4 Emerging Solutions and State-of-the-Art Approaches
### Security Mechanisms and Lightweight Encryption  
Securing AI pipelines in resource-limited settings is an integral component of deploying edge intelligence at scale. While the focus is often on performance optimization, insufficient attention to security can expose critical infrastructures to malicious threats [24]. Lightweight cryptography protocols, which are specifically adapted to devices with small footprints, have gained traction [69]. These protocols strike a balance between algorithmic complexity and security guarantees, ensuring that data remain encrypted during transmission and at rest with minimal overhead. As an example, elliptic-curve cryptography (ECC) variants can offer strong encryption with relatively smaller key sizes than RSA-based approaches, thus reducing computational cost [70].  
Beyond encryption, trusted execution environments (TEEs) such as ARM TrustZone or Intel SGX enable the creation of secure enclaves that isolate sensitive computations from the rest of the system [71]. In the context of AI, TEEs can store and execute model parameters  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Device** & **Architecture** & \begin{tabular}{c} **Frequency** \\ **(MHz)** \\ \end{tabular} & **RAM (KB/MB)** & \begin{tabular}{c} **TinyML** \\ **Capability** \\ \end{tabular} & **References** \\ \hline ESP32-S3 & \begin{tabular}{c} Xtensa LX7 \\ (dual-core) \\ \end{tabular} & 240 & 512 KB & \begin{tabular}{c} Medium (basic \\ audio and simple \\ inferences) \\ \end{tabular} & [39] \\ \hline Raspberry Pi Pico & ARM Cortex-M0+ & 133 & 264 KB & \begin{tabular}{c} Low (only very \\ lightweight \\ models) \\ \end{tabular} & [44] \\ \hline Arduino Nano 33 & \begin{tabular}{c} ARM Cortex-M4 \\ \end{tabular} & 64 & 256 KB & \
\begin{tabular}{c} High (sensor \\ integration and \\ embedded ML) \\ \end{tabular} & [37] \\ \hline Jetson Nano & ARM Cortex-A57 & 1420 & 4 GB &
\begin{tabular}{c} Very high (deep \\ learning and \\ computer vision) \\ \end{tabular} & [41] \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of TinyML-compatible hardware platforms.  
securely, preventing adversarial access. Additional layers of defense, like secure bootloaters, can ensure that only verified firmware is executed on the device, thwarting attempts to run malicious code. Implementing such measures requires a comprehensive approach that covers hardware, software, and network layers, all while preserving the fundamental constraints of power, memory, and compute capacity that characterize edge devices [12].
## 2 Related Work  
### System Dynamics Modeling  
The System Dynamics (SD) methodology was developed by [PERSON] [18]. SD was originally introduced to facilitate the understanding of industrial processes. SD is used as a methodological approach to explain the effects of decisions in complex dynamic systems. The SD approach emphasizes time functions [19; 20]. SD Models undergo constant interaction, continuous questioning, testing, and refinement. Based on the feedback concepts of control theory, the SD methodology generates the dynamic behavior of the associated model. The feedback loop structure of systems is represented by causal loop diagrams (CLD) [19; 21]. A feedback loop contains two or more causality-related variables that are self-contained. The variable relationships in the loop can be either positive or negative. A positive relationship means that when one variable increases, the other one increases, too. In a balanced relationship, the change in the variables is inverse. The stock and flow diagram (SFD) is the underlying physical structure of the system. The SFD is normally structured according to the CLD. The stock (level) represents the state of the system and the flow (rate) is changed by decisions based on the state of the system. The stock and flow structure (including feedback) of a system determines the quantitative modes of behavior that the system can adopt. For the development of an SD model, the work in [19] presents a modeling process with the following steps: (i) problem analysis, (ii) system conceptualization, (iii) model formulation, (iv) simulation and verification, and (v) policy analysis and improvement. Figure 1 illustrates the SDSM process.  
In the context of logistics, some studies show the application of SD to LML activities. In [22], the authors analyzed the decisions and interdependencies between customers, retailers, and suppliers using an SD model from an economic perspective. In [23], the authors applied an SD approach to model interdependencies of decisions by various stakeholders and their impact on city logistics. In [24], the authors presented a specific SD application in UL operations. They also used an SD model in [25] to understand customer behavior from an LML perspective. Although modeling efforts are important in urban logistics [12; 26], the simulation seems to be still in the development phase [10]. The most popular simulation approaches remain multi-agent approaches [10; 27]. While SD is still preliminary in its applications for urban freight distribution, it has great potential because it can take into account the complexity of the dynamics and heterogeneity of the systems [23].  
Figure 1: Steps to build an system dynamics simulation model (SDSM) based on the work in [19].
## 3 Enhanced Mathematical Model with Atmospheric and Environmental Effects
### Outage Probability vs. Distance for Different SNR Thresholds  
Figure 11 illustrates the variation in outage probability with respect to distance for three different SNR thresholds: 5 dB, 10 dB, and 15 dB. Outage probability is a critical metric in evaluating link reliability, as it represents the likelihood that the received signal quality will fall below a specified SNR threshold, resulting in a communication failure. In scenarios involving satellite or long-range IoT communications, achieving a sufficiently  
Figure 10: Channel capacity vs. distance for different bandwidths.  
high SNR is often challenging due to distance-induced path loss, making outage probability a significant factor in the design and operation of these systems.  
The plot reveals that as the SNR threshold increases, the outage probability also rises across all distances, but the effect becomes more pronounced at greater distances. For instance, at a distance of 2000 km, the outage probability is approximately 0.005 (0.5%) for an SNR threshold of 5 dB, but this increases to around 0.01 (1%) for a threshold of 10 dB and reaches nearly 0.04 (4%) for a threshold of 15 dB. This trend highlights that higher SNR requirements make the system more susceptible to outages, particularly over extended distances, where signal attenuation is significant.  
The exponential increase in outage probability with distance is especially notable at distances beyond 2500 km. For example, at 3000 km, the outage probability for a 5 dB SNR threshold remains below 0.05 (5%), while for a 10 dB threshold it exceeds 0.05 (5%) and for a 15 dB threshold it approaches 0.25 (25%). This substantial rise in outage probability indicates the critical impact of distance on signal quality, emphasizing the difficulty of maintaining reliable communications in long-range or rural IoT applications where terrestrial infrastructure is sparse or non-existent.  
These results underscore the need for careful consideration of SNR thresholds in system design, particularly in rural or remote IoT deployments where maintaining a robust link over long distances is crucial. Lowering the SNR threshold may improve reliability by reducing outage probability, but this could come at the expense of reduced data rates or compromised performance. Thus, a balance must be struck between SNR requirements and the intended application, especially in scenarios where high reliability is paramount, such as in emergency or environmental monitoring systems.
## 2 Materials and Methods
### RNA Extraction and Quantification  
RNA was extracted from the huMPCs cultured on plates or in EUs, in growth and differentiation conditions. For RNA extraction from huMPCs cultured in the EUs in proliferation conditions, the reservoirs were filled following the order: Reservoir 1, GM (20 mM HEPES); Reservoir 2, GM (20 mM HEPES); Reservoir 3, HAM's F10 (20 mM HEPES); Reservoir 4, RNAlater Solution (Invitrogen, ThermoFisher Scientific, Waltham, MA, USA; #AM7020); Reservoir 5, empty; Culture chamber, GM (20 mM HEPES). For RNA extraction from huMPCs cultured in the EUs in differentiation conditions the reservoirs were filled following the order: Reservoir 1, DM (20 mM HEPES); Reservoir 2, DMEM (20 mM HEPES); Reservoir 3, RNAlater Solution; Reservoir 4, empty; Reservoir 5, empty; Culture chamber, GM (20 mM HEPES). To reduce the detachment of cells from coverslip, HAM's F10 or DMEM were used instead of PBS as the washing media before the addition of RNAlater Solution. The medium of the cultures in the proliferation conditions was changed with the following timing: 1 st change (GM) after 5 days from the start of the experiment, 2 nd change (GM) after other 3 days, 3 rd change (HAM's F-10) after a further 3 days, 4 th change (RNAlater) a5 min after the 3 rd change. For cultures in differentiation conditions, the medium changes occurred as follows: 1 st change (DM) 5 days after the start of the experiment, 2 nd change (DMEM) after an additional 3 days, 3 rd change (RNAlater) 5 min after the 2 nd change. For RNA extraction from huMPCs cultured on plates, the same changes of medium were performed as those used with the EUs (with the addition of 20 mM HEPES).  
To identify the proper RNA isolation kit, cells were seeded on Thermanox(tm) coverslips (4.0 \(\times\) 10\({}^{4}\) cells per coverslip) and grown in DM for 7 days on plates or inside the EUs, and then pre-treated with RNAlater, a nontoxic reagent able to permeate cells and stabilize cellular RNAs. The RNA was extracted with the following kits according to the manufacturer's instructions: (i) mirVana miRNA Isolation kit (Invitrogen by ThermoFisher Scientific, Waltham, MA, USA; #AM1560); (ii) Purelink RNA Mini Kits (Ambion by Life Technologies, Monza, Italy; #12183018A) followed by PureLink miRNA Isolation Kit (Invitrogen by Life Technology, Monza, Italy; #K1570-01), which gave the option of isolating RNA and microRNA separately, (iii) MiNeaasy Micro Kit (Qiagen, Milan, Italy; #217084). The RNA obtained was quantified using a NanoDrop(tm) spectrophotometer and the 260/280 ratios were evaluated.
## 2 Results
### DEGs and DEPs Associated with the Cell Wall Biosynthesis  
When _H. pluvialis_ is cultured in stressed environments, the cell walls of the microalgae are thickened to enhance the capability of resisting adverse factors [45]. Callose, cellulose, and pectins are fundamental structural components of the cell wall and are synthesized with the involvement of many enzymes including callose synthase (CalS), endoglucanase (BcsZ), and cellulose synthase (BcsA) [44]. In our study, a total of 6 unigenes encoding CalS, 1 unigene encoding BcsA, and 15 unigenes encoding BcsZ were identified. The expression levels of genes encoding CalS and BcsZ were decreased significantly on day 2 of the treatment with blue light irradiation compared with white light (Figure 11A). On day 7 of blue light irradiation, the expression levels of _Cals_, _bcsZ_, and _bcsA_ were significantly higher than those under white light. The addition of SA did not significantly affect the expression levels of genes encoding CalS and BcsZ but significantly down-regulated the expression of the genes encoding CalS on day 7 of the treatment. The proteomic data showed that the expression of BcsZ was decreased significantly on day 2 after the treatment with blue light irradiation compared with white light. These results were consistent with those derived from the transcriptomic data. Compared with day 0, the expression of BcsZ was significantly increased on both days 2 and 7 of the treatment with blue light irradiation, indicating that the expression of BcsZ was less affected by blue light than by white light(Figure 11A). These results were consistent with those derived from the proteomic data (Figure 11B).  
Figure 9: Expression profiles of genes—_apx_, _cat_, _ferritin_, and _gr_ (**A**); _sod_, _gpx_, and _grx_ (**B**); and _mdar_, _mgrx_, and _prdx_ (**C**)—involved in the generation of ROS revealed in the pairwise comparisons of _Haematococcus pluvialis_ based on transcriptomic analysis. **’—**indicates significant up-regulation (_p_ < 0.05 and Log2 FC \(\geq\) 1) or significant down-regulation (_p_ < 0.05 and Log2 FC \(\leq\) \(-\)1).
# Magneto-Optical Traps for Cold Atomic Gravimetry: Research Status and Development Trends  
[PERSON]  
The cold atomic gravimeter (CAG) has the advantage of high measurement accuracy and does not need to be calibrated on a regular basis. To achieve cold atom interference, it is first necessary to cool and trap the atoms by magneto-optical trap (MOT). However, there are many types of MOTs, and their trapping and cooling results directly affect the atomic interference, and thus, the measurement accuracy of a CAG. MOTs should be designed or selected correctly for different application needs. This paper reviews the research history of MOTs and analyzes their structure and principles. The current status of applications of different types of MOTs is highlighted. Their advantages and disadvantages are summarized, and perspectives for the development of MOTs for cold atomic gravimetry are presented.  
cold atom gravimeter; magneto-optical trap; Doppler cooling; atom trapping; atom funnel