esa-sceva/satcom-chunk-collection huggingface dataset:
# Semi-Supervised Method for Underwater Object Detection Algorithm Based on Improved YOLOv8
## 4 Results
### Implementation Details  
1. Experimental Environment The hardware configuration for this experiment includes an Intel(R) Xeon(R) Platinum 8457C processor and a single L20 GPU. The operating system is Linux, and PyTorch 2.0.1 is used as the deep learning framework, with CUDA version 11.8.
2. Experimental Parameter Settings The model's input size is 640 x 640, and the batch size is set to 64. The optimizer is SGD, with an initial learning rate of 0.01, a momentum of 0.937, and a weight decay of 0.0005. The hyper-parameters for semi-supervised learning framework are set as follows: weight of the semi-supervised loss weight, 0.5; pseudo-label's confidence threshold, 0.5.
# A Systematic Review of Cyber Range Taxonomies: Trends, Gaps, and a Proposed Taxonomy
## 5 Discussion
### Addressing the Research Questions  
**RQ1: What existing taxonomies and systematic literature reviews on cyber ranges were conducted between 2014 and 2024?**Our review identified 18 primary taxonomies plus 206 supporting studies, confirming wide agreement on seven foundational dimensions while exposing conceptual drift in advanced areas such as AI and federation. The literature review identified several foundational taxonomies, most notably from [PERSON] et al. [11] and [PERSON] et al. [12], which form the backbone of scholarly discourse in this area. More recent systematic reviews broadened the scope to include scenario design [12; 13], federation [14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25], and advanced orchestration tools [63; 64; 103]. These collectively show how the domain evolved from basic, VM-centric testbeds to sophisticated environments incorporating diverse dimensions (e.g., _Monitoring_, _Management_, _Teaming_).  
**RQ2: Where do existing cyber range taxonomies converge and diverge?** Convergence is strongest around _Scenario_, _Environment_, and _Learning_. Divergences emerge around how to treat advanced topics (AI-driven agents, container-based architectures) and whether to include socio-technical or economic factors. For instance, [PERSON] et al. [11] treated technology and environment under a single umbrella, whereas [PERSON] et al. [12] introduced separate dimensions for _Econometrics_ and _Recovery_. These divergences highlight the need for a flexible yet comprehensive taxonomy that captures both well-established and emerging elements of cyber range design.  
**RQ3: What influence have recent papers had on current cyber range taxonomies, and is there a need for an updated taxonomy?** The recent literature underscores the fast-paced evolution of cyber range technologies, particularly around federated architectures, containerization, and AI. The acceleration of these technologies, as well as federated range topologies, justifies the need for an updated taxonomy. Our findings confirm that a stand-alone _Technology_ dimension is critical, warranting an updated taxonomy that recognizes the complexity of modern infrastructures.
###### Abstract  
There are many different fields in which wireless sensor networks (WSNs) can be used such as environmental monitoring, healthcare, military, and security. Due to the vulnerability of WSNs, reliability is a critical concern. Evaluation of a WSN's reliability is essential during the design process and when evaluating WSNs' performance. Current research uses the reliability block diagram (RBD) technique, based on component functioning or failure state, to evaluate reliability. In this study, a new methodology-based RBD, to calculate the energy reliability of various proposed chain models in WSNs, is presented. A new method called D-Chain is proposed, to form the chain starting from the nearest node to the base station (BS) and to choose the chain head based on the minimum distance \(D\), and Q-Chain is proposed, to form the chain starting from the farthest node from the BS and select the head based on the maximum weight, \(Q\). Each chain has three different arrangements: single chain/single-hop, multi-chain/single-hop, and multi-chain/multi-hop. Moreover, we applied dynamic leader nodes to all of the models mentioned. The simulation results indicate that the multi Q-Chain/single-hop has the best performance, while the single D-Chain has the least reliability in all situations. In the grid scenario, multi Q-Chain/single-hop achieved better average reliability, 11.12 times greater than multi D-Chain/single-hop. On the other hand, multi Q-Chain/single-hop achieved 6.38 times better average reliability than multi D-Chain/single-hop, in a random scenario.  
chain head; leader node; reliability block diagram; wireless sensor networks; single chain; multi-chain single-hop; multi-chain multi-hop
## 5 Experiments
### Schedule Optimization
#### 5.3.3 Results and Analysis  
Table 4 shows the experimental results for spaced repetition optimization, where the columns represent different methods and the rows represent different simulation environments.  
In general, the results indicate that the proposed framework (referred to as \"Ours\") outperforms all baselines in terms of ARP across the three memory models, suggesting that it effectively optimizes the scheduling of review intervals to enhance long-term memory retention. To investigate the significance of our proposed method, we perform a Friedman test [35] w.r.t. different simulation environments over baseline methods. The \(p\)-value is 0.0094, which suggests that we reject the H0 assumption (the algorithms are equal) at the 0.99% confidence level, i.e., our method significantly outperforms the baselines. For the EFC environment, MEMORIZE and HLR cannot be applied in this environment because they require the half-life parameter, which is not contained in the environment. ANKI shows a worse result than RANDOM. Our method achieves a moderate improvement compared to RANDOM and ANKI. For the DHP environment, ANKI shows moderate performance and is slightly better than RANDOM. MEMORIZE shows better performance than the other baseline methods. However, HLR's performance is worse than that of RANDOM. For the THLR environment, ANKI and HLR show better results than RANDOM. However, the performance of MEMORIZE is even worse than that of RANDOM. Across all environments, ANKI shows a moderate performance. It performs slightly better than RANDOM in DHP and THLR but does not adapt as effectively to the EFC environment. MEMORIZE illustrates a significant improvement compared with RANDOM in DHP but fails in THLR. HLR shows a worse performance than that of RANDOM in DHP but gets a better result in THLR. The experimental results illustrate that none of the baselines show consistent performance across all environments. In contrast, our proposed  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{2}{c}{**EFC**} & \multicolumn{2}{c}{**DHP**} & \multicolumn{2}{c}{**THLR**} \\  & **SUM** & **MEAN** & **SUM** & **MEAN** & **SUM** & **MEAN** \\ \hline RANDOM & 52,439.40 & 0.867 & 28,803.55 & 0.475 & 13,482.70 & 0.178 \\ ANKI & 13,264.31 & 0.637 & 13,702.83 & 0.584 & 12,425.18 & 0.185 \\ MEMORIZE & - & - & 35,350.10 & 0.817 & 12,694.96 & 0.169 \\ HLR & - & - & 10,516.85 & 0.468 & 25,169.26 & 0.355 \\ \hline Ours & 88,548.67 & **0.920** & 162,482.57 & **0.942** & 18,572.39 & **0.372** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental results for spaced repetition optimization. The bold item indicates the best performance.  
framework beats all competing methods over all environments. The experimental results show our framework's superior performance for spaced repetition optimization utilizing Deep Q-Networks augmented with a Long Short-term Memory mechanism for learning optimal review intervals.
# Time-Series Prediction of Failures in an Industrial Assembly Line Using Artificial Learning
## 4 Results and Discussion
### Discussion
Although the current study specifically utilises configuration data from the aerospace sector--chosen for their high-precision, low-volume characteristics and critical safety demands--the underlying machine-learning architectures (such as LSTM, GRU, MLP, CNN-RNN, ANFIS, and NAR) are inherently domain-agnostic and can be retrained or fine-tuned for other industrial settings. What distinguishes this work is not the limitation to a particular industry, but rather the demonstration that the time-series prediction of assembly failures using artificial learning is feasible and beneficial even in the most sensitive and stringent production contexts. This implies that in less safety-critical or more high-volume environments, the implementation might be more straightforward and computationally less demanding. While this study provides critical insights into AL model performance for failure prediction, several limitations warrant consideration. First, the exclusive focus on a single variable (\"configuration of failure\") restricts the model's ability to account for multivariate interactions, such as environmental conditions or machine-specific parameters, which are often critical in industrial failure dynamics. Also, some architectures like transformers or attention-based networks may offer superior temporal modelling. It is one of the rising questions whether a dataset originated from a single aerospace assembly line can be generalised to other manufacturing sectors or failure types. Additionally, computational resource constraints (8 GB RAM, CPU-only processing) precluded hyper-parameter optimisation for computationally intensive models like LSTM and CNN-RNN, potentially underrepresenting their capabilities. Nevertheless, the study's rigorous within-context analysis establishes a foundational framework for AL-driven predictive maintenance, with future research directions emphasising multivariable integration, cross-industry validation, and advanced computational resources.
## 2 Materials and Methods
### Testing and Characterization
#### 2.3.2 Microstructural Characterization  
The samples were ground and polished to remove any deformations or scratches on their surfaces, and etched to reveal grain boundaries. The etchant used for these materials was a solution containing 20 mL of acetic acid, 1 mL of HNO\({}_{3}\), 60 mL of ethylene glycol, and 20 mL of water. An optical microscope Leica DM2500 M was used to observe the grain characteristics. After the images were obtained, MATLAB software was used to calculate the average grain size of the materials. Further, the presence and distribution of the intermetallic phases and nanoparticles were studied using a JEOL JSM-6010 PLUS/LV Scanning Electron Microscope (SEM), Peabody, MA, USA. Elemental analysis was performed using energy dispersion spectroscopy (EDS).
# The EGNOS Augmentation in Maritime Navigation +
## 3 Experimental Setup
### Kinematic Test  
On 29 September 2021, a kinematic data collection was carried out onboard a small boat, during an oceanographic campaign in the Gulf of Naples as shown by the trajectory in the Figure 5. One hour of 1 Hz measurements was collected using a smartphone and a GNSS receiver located approximately at the center of mass of the boat. In detail, two sensors were used in the kinematic test to collect the raw data:  
* Xiaomi Mi 8, an Android smartphone equipped with a Broadcom BCM47755 chipset that is a dual-frequency GNSS chip able to track navigation messages, PR observables, accumulated delta ranges for GPS, Glonass, BeiDou and Galileo. The application used to store the raw observables in a RINEX (Receiver Independent Exchange Format) file is Rinex ON application;
* u-blox ZED-FoP, a multi-band GNSS receiver able to the concurrent reception of GPS, GLONASS, Galileo and BeiDou. The device was connected to a multi-band GNSS patch antenna capable of receiving L1 and L2 frequencies [19] and the software dedicated to collection of the raw data.  
Figure 4: Position Dilution of Precision (PDOP), Vertical DOP (VDOP) and Horizontal DOP (HDOP)values as function of the time.  
Especially for the kinematic data collection mode, the satellite visibility and geometry must be investigated, since the boat is moving and, consequently, the characteristics of the operational context change epoch by epoch. For this analysis, data collected by the u-blox were used. The number of GPS satellites is plotted as function of time in Figure 6, while the PDOP, VDOP and HDOP values during the kinematic test are shown in Figure 7.  
Figure 5: Trajectory travelled by the boat in the Gulf on Naples on 29 September 2021.  
Figure 6: Number of visible GPS satellites during the kinematic test.
## 3 Results and Discussion
The effect of H\({}_{2}\)O on sensing CO\({}_{2}\) can be explained by the electrolysis of water on electrode 4, which occurs along with the electrolysis of carbon dioxide (reaction 1) as follows:  
Figure 3:3  
The Gibbs energy for carbon dioxide splitting is close to that of water splitting in the considered temperature range (\(\Delta\)G is \(\sim\)180 kJ/mol and \(\sim\)200 kJ/mol for CO\({}_{2}\) and H\({}_{2}\)O splitting, respectively, at 600 \({}^{\circ}\)C [23]); therefore, the decomposition of H\({}_{2}\)O and CO\({}_{2}\) should occur under similar values of the applied voltage. The presence of water vapor in the gas provides a larger number of oxide ions to be pumped out into the inner chamber of the CO\({}_{2}\) cell across the electrolyte; consequently, the limiting current increases as the H\({}_{2}\)O concentration rises. Thus, to avoid the cross-sensitivity effect toward water and ensure the proper detection of CO and CO\({}_{2}\) concentrations by the designed sensor, the analyzed gas is to be preliminarily dried. A reliable sensor has to satisfy such requirements as good reproducibility of the sensor signal and a sufficiently short transient time (the time required for 90% change of the sensor signal after a change in gas composition). The dynamic response of the sensor limiting currents to stepwise changes in the concentrations of CO and CO\({}_{2}\) in the gaseous mixtures N\({}_{2}\) + CO and N\({}_{2}\) + CO\({}_{2}\) at 600 \({}^{\circ}\)C is presented in Figure 3:4 It can be seen that the sensor signal is well reproduced, with a scatter below 1%. The longest transient time for the CO and CO\({}_{2}\) sensing reaches \(\sim\)6 min, which is in accordance with the requirements of practical applications. Figure 3:5 Dynamic response of the sensor signal to changes in concentrations of CO and CO\({}_{2}\) at 600 \({}^{\circ}\)C. Error bars are smaller than the symbol size. Long-term stability testing of the sensor is currently being conducted; the sensor readings currently remain stable for nearly 2000 h, with a scatter not exceeding 1%. This demonstrates good long-term stability and reliability of the sensor. It is worth noting that the calibration of each sensor of the proposed design is required as well as of all amperometric-type sensors because deviations in geometric parameters of a diffusion barrier may lead to dispersion of the sensor signal. It should be noted that calibration of the amperometric-type sensors is simple due to the linear relationship between the limiting current and the concentration of a target gaseous species (Equation (4)).
## 2 Materials and Methods
### Data Analysis Methods
#### 2.5.2 Data Preprocessing  
To improve model performance, Monte Carlo cross validation (MCCV) was performed to eliminate outliers in the sample dataset of this study [22]. The raw spectral noise was filtered using Savitzky-Golay smoothing (SG) method [23; 24]. The first order polynomial was used to fit the spectral data within the filtered window of 3 to decay the effect of random noise present in the spectral data on the effective information of the sample.
# A Novel Cascaded Modular Photovoltaic Energy Storage System for Partial Shading Conditions
## 2 Topology, Operation Principle and Optimization Strategy of Proposed PV/ES System
### Energy Optimization Strategy
#### 2.3.3 Control Scheme of Grid-Connected DC/AC Converted  
Figure 6 shows the control scheme of the grid-connected DC/AC converter. From the figure, a virtual synchronous generator (VSG) controller in [37] is introduced to control the active and reactive power. The reference value of the active power for the VSG controller is generated at the beginning of the optimization cycle, and remains constant throughout the cycle, realizing the constant grid-connected active power of PV/ES system in a certain time period. To ensure that all of the ES modules have a sufficient charging and discharging margin in each cycle, the energy optimization strategy generates a reference value of the active power according to the currents and voltages of all PV modules and the voltages and SOC of all ES modules.
# Influence of Droughs on Mid-Tropospheric CO\({}_{2}\)
## 4 Discussion  
In this paper, we explore the influences of droughts on CO\({}_{2}\) and discuss possible sources (e.g., circulation, NEE, biomass burning, and fossil fuel emissions) for the CO\({}_{2}\) anomalies in the atmosphere. It should be mentioned that the NEE from CASA model also has some limitations. For example, the  
Figure 4: **(a)** The mean value of Net Ecosystem CO\({}_{2}\) Exchange (NEE), from the CASA model, in dry years (JJAS of 2003, 2007, and 2010); **(b)** the mean value of NEE in wet years (JJAS of 2006, 2008, 2011, and 2012); **(c)** NEE differences between the dry and wet years; and **(d)** NEE differences within 5% significance level and 1% significance level are highlighted in light green and dark green. Units for NEE are g C m\({}^{-2}\) mon\({}^{-1}\) in **(a–c)**.  
CASA model used in this paper does not include CO\({}_{2}\) and nitrogen fertilization, which might influence the NEE results. [PERSON] et al. [46] utilized TCCON column CO\({}_{2}\) and aircraft profile CO\({}_{2}\) to estimate NEE and found that the CASA model underestimates the NEE by 25%. In another study, [PERSON] et al. [47] found that NEE from the CASA model is underestimated in the mid-latitudes. One way to improve the model is to use more observations. We can use an inverse model (such as GEOS-Chem adjoint model) to identify possible weakness in the CO\({}_{2}\) surface fluxes. Observation data, such as AIRS CO\({}_{2}\), can be used to better understand/constrain the net ecosystem exchange from the CASA model by the inverse modeling in the future.  
In addition to the factors discussed above, other factors can also affect the CO\({}_{2}\) fluxes during droughts. In both urban and farm areas, there is more irrigation to help crops and landscapes to survive during droughts, which will lead to a small difference of CO\({}_{2}\) fluxes from biosphere and soil during dry and wet months. During dry months, surface temperature is higher than during wet months. The high-temperature anomalies result in warm soil, which leads to increased CO\({}_{2}\) emissions due to decomposition in the soil [48]. Moisture can contribute even more to the positive CO\({}_{2}\) anomaly in the atmosphere. Meanwhile, low soil moisture during droughts will limit plant growth, which can also influence the CO\({}_{2}\) flux from the biosphere.  
In this paper, we focus on the influence of droughts on CO\({}_{2}\) over the southwestern USA, since drought has been a severe problem over this region in recent years. Precipitation has different behaviors over different regions; for example, precipitation increases in some areas while decreases in other areas [49; 50; 51]. We will explore the impacts of droughts on CO\({}_{2}\) in other regions in the future.
# An Effective Off-Grid DOA Estimation Algorithm Using Difference Coarrays with Limited Snapshots
## 4 Performance Analysis
### Computational Complexity Analysis  
A comprehensive computational complexity analysis, including the search-based, EPUMA, SS-MUSIC, and the proposed methods, is summarized in Table 1. A dense angular sampling number \(D\) is utilized for the search-based method to improve the estimation performance. EPUMA and SS-MUSIC demand substantial snapshots \(T\) for reliable covariance matrix computation. Our method requires only limited snapshots and few iterations \(H\), resulting in substantially reduced complexity compared to existing techniques.
# Security Architecture for Swarms of Autonomous Vehicles in Smart Farming
## 3 Results
### Security Architecture for Swarms of Autonomous Vehicles
#### 3.2.3 Protection of Data Integrity
A JSON Schema [37] is a grammar language to validate the syntax of a JSON document through a set of metadata about the meaning and valid values of the object's properties. Regarding data validation, JSON schemas should be defined to establish the structure and content of all JSON objects to be exchanged. * Communication channel C: VPNs implement hashing mechanisms to ensure that data have not been intercepted and altered when travelling from one VPN endpoint to another. In the case under study, the proposed Hamachi VPN checks the integrity of all data exchanged by using HMAC-SHA-1. * Communication channel D: as in the case of the communication channel A, the integrity of this data link can be guaranteed by TLS. * Communication channel E: as expressed in the previous sections, the authors of this study consider that the components of the middleware can trust one another as they can be part of the same monitored Cloud. * Data Repository: a good practice to ensure that information can be restored in case its integrity is compromised is the implementation of regular data backups. Moreover, databases could include data integrity checks based on fixed schemas or predefined sets of rules. For example, columns in a relational database could be forced to be declared upon a defined domain (e.g., semantically established in an ontology or taxonomy) to abstract the heterogeneity and to ensure that all information is stored according to a common information model that guarantees interoperability or is validated by regular expressions specifying the search patterns. In the case under study, it is recommended that all data entered by operators through the Mission Console is checked by regular expressions.
# A Two-Stage Multi-Objective Optimization Algorithm for Solving Large-Scale Optimization Problems
###### Abstract
This paper proposes a two-stage large-scale optimization algorithm based on clustering for large-scale multi-objective optimization problems. The main contributions are summarized as follows. 1. The decision variable clustering strategy effectively categorizes all variables into two distinct groups: those related to diversity and those related to convergence, thereby facilitating the evolutionary process. This approach employs the angle between the convergence direction and the sampled solution as a distinguishing feature, subsequently clustering these features using the k-means method. Variables associated with smaller angles contribute more significantly to convergence, whereas those with larger angles play a more substantial role in enhancing diversity. 2. By employing the non-dominated dynamic weight aggregation method, Pareto optimal solutions are identified in multi-objective optimization problems (MOPs) with varying Pareto front shapes. Subsequently, the optimization problem involving reference lines is addressed by leveraging these solutions within the Pareto optimal subspace, thereby resolving the population diversity issue. 3. To assess the efficacy of the proposed algorithm, comprehensive empirical evaluations were performed on a range of benchmark problems. These evaluations involved comparisons with several advanced MOEAs designed for addressing both multi-objective optimization problems and large-scale optimization challenges. The experimental results conclusively demonstrate that the algorithm introduced in this study is capable of effectively solving large-scale optimization problems characterized by numerous decision variables, achieving commendable performance outcomes. In the latter half of the article, we first reviewed the relevant concepts in Section 2. The Section 3 introduces some details of the main process of the proposed algorithm. The Section 4 is related experiments and results. The Section 5 is the conclusion and outlook for future work.