esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper conducts a comprehensive experimental comparison of two widely used additive manufacturing (AM) processes, Fused Deposition Modeling (FDM) and Stereolithography (SLA), under standardized conditions using the same test geometries and protocols. FDM parts were printed with both Polylactic Acid (PLA) and Acrylonitrile Butadiene Styrene (ABS) filaments, while SLA used a general-purpose photopolymer resin. Quantitative evaluations included surface roughness, dimensional accuracy, tensile properties, production cost, and energy consumption. Additionally, environmental considerations and process reliability were assessed by examining waste streams, recyclability, and failure rates. The results indicate that SLA achieves superior surface quality (\(R_{a}\approx 2\,\mathrm{\SIUnitSymbolMicro m}\) vs. 12-13 \(\,\mathrm{\SIUnitSymbolMicro m}\)) and dimensional tolerances (\(\pm 0.05\,\mathrm{mm}\) vs. \(\pm 0.15\)-0.20 \(\,\mathrm{mm}\)), along with higher tensile strength (up to 70 \(\,\mathrm{MPa}\)). However, FDM provides notable advantages in cost (approximately 60% lower on a per-part basis), production speed, and energy efficiency. Moreover, from an environmental perspective, FDM is more favorable when using biodegradable PLA or recyclable ABS, whereas SLA resin waste is hazardous. Overall, the study highlights that no single process is universally superior. FDM offers a rapid, cost-effective solution for prototyping, while SLA excels in precision and surface finish. By presenting a detailed, data-driven comparison, this work guides engineers, product designers, and researchers in choosing the most suitable AM technology for their specific needs.  
addictive manufacturing; dimensional accuracy; energy consumption; environmental impact; fused deposition modeling; production cost; reliability; stereolithography; surface finish; tensile strength; usability 2025
# Intelligent Control of Robotic Arm Using Brain Computer Interface and Artificial Intelligence
## 2 Methodology
### Data Pre-Processing  
The second step after fetching the data is to preprocess the raw data acquired from the sensor node that is full of errors and missing values. It is important to refine this data as the machine learning algorithms behave irregularly due to this raw data [10]. The raw data is converted into meaningful data using the pandas preprocessing techniques that consist of removing NaN/Null and missing values. The rows consisting of null values are dropped by the panda's row drop function, whereas the lowest correlated features acquired by the features extraction technique are also dropped by the panda's columns dropping function. The system does not use the filling techniques of pandas for missing values to maintain the data integrity. The meaningful data is converted into a comma-separated file (CSV), and further, these CSV files are fed into artificial intelligence algorithms for processing. We have implemented the restrictions in alpha and beta domains; therefore, the threshold for maximum value acquired by each electrode is set to 32. The proposed work deals with the higher alpha and beta bandwidth of EEG signals. It is generally regarded as a normal rhythm. While a patient is alert, anxious, or has their eyes open, this rhythm is dominant. It shows that we are working in the correct range of EEG waves and choosing the right rhythms. Another type of preprocessing implemented in the study is labeling the activities. We use three classes, as shown in the following Table 3.
# HTTP Adaptive Streaming Framework with Online Reinforcement Learning
## 3 Framework Design  
This section describes the basic assumptions and framework of the proposed scheme. Because video content consists of scenes with exponentially distributed duration, the ABR algorithm of DASH can be modeled as a Markov decision process (MDP) problem, which is mainly used as a basic optimization method. Figure 2 shows how RL is applied to adaptive streaming. After receiving the state, the ABR agent decides the quality of the next requested segment through the neural network. The result after playing the requested segment (quality of the requested segment, playback interruption time, etc.) is reflected in the reward through the defined QoE model. The information related to the network and playback state after video playback is used as the state in making the next ABR decision. To design the framework of the proposed scheme, the state, action, reward, and neural network models should be specified, and the operation process of the framework should be configured in an algorithmic form.
# Multi-Feature Fusion with Convolutional Neural Network for Ship Classification in Optical Images
###### Abstract  
The appearance of ships is easily affected by external factors--illumination, weather conditions, and sea state--that make ship classification a challenging task. To facilitate realization of enhanced ship-classification performance, this study proposes a ship classification method based on multi-feature fusion with a convolutional neural network (CNN). First, an improved CNN characterized by shallow layers and few parameters is proposed to learn high-level features and capture structural information. Second, handcrafted features of the histogram of oriented gradients (HOG) and local binary patterns (LBP) are combined with high-level features extracted by the improved CNN in the last fully connected layer to obtain discriminative feature representation. The handcrafted features supplement the edge information and spatial texture information of the ship images. Then, the Softmax function is used to classify different types of ships in the output layer. Effectiveness of the proposed method is evaluated based on its application to two datasets--one self-built and the other publicly available, called visible and infrared spectrums (VAIS). As observed, the proposed method demonstrated attainment of average classification accuracies equal to 97.50% and 93.60%, respectively, when applied to these datasets. Additionally, results obtained in terms of the FI-score and confusion matrix demonstrate the proposed method to be superior to some state-of-the-art methods.  
ship classification; convolution neural network; multi-feature fusion; HOG; LBP; VAIS +
Footnote †: journal: Applied Physics Letters
## 2 Methodology
### Experimental Setup  
During field experiments, the experimental material mostly consisted of collecting exterior side parameters, the attached sunpace's parameters and internal parameters of the farmhouse [19]. The detailed information of measurement instruments can be seen in Table 2.  
The following parameters were measured in the experiment:  
1. Outdoor side parameters: the portable weather station were used to measure outdoor temperature, humidity and solar radiation strength. The measuring points were located on the room's roof.
2. Attached sunpace parameters: the heat flow meters were used to measure the heat flow of the heat storage partition wall, and the temperature and humidity recorders were used to measure the temperature and humidity of the air layer. Figure 4a shows the configuration of the measuring points.
3. Indoor side parameters: the temperature and humidity recorder was used to measure the indoor temperature and humidity. Figure 4b shows the configuration of the measuring points.  
\begin{table}
\begin{tabular}{c c c} \hline
**Test Instrument** & **Rang** & **Accuracy** \\ \hline HOBO Portable Weather Station & 0–1280 W/m\({}^{2}\) & \(\pm\)10 W/m\({}^{2}\) \\ HOBO Portable Weather Station & \(-\)40\(\rightarrow\)75 \({}^{\circ}\)C & \(\pm\)0.21 \({}^{\circ}\)C \\ HOBO Recorder of Temperature and Humidity & 20–70 \({}^{\circ}\)C & \(\pm\)0.21 \({}^{\circ}\)C \\ TR004 Temperature Recorder & 15–95\% & \(\pm\)3.5\% \\ TNL-3 RLTemperature and Heat Flow Monitoring System & \(-\)500–500 W/m\({}^{2}\) & \(\pm\)5\% \\ \hline \end{tabular}
\end{table}
Table 2: Details of measurement instruments.  
\begin{table}
\begin{tabular}{c c c} \hline
**Building Envelope** & \
\begin{tabular}{c} **Heat Transfer Coefficient after** \\ **Transformation W/(m\({}^{2}\cdot\)K)** \\ \end{tabular} &
\begin{tabular}{c} **Heat Transfer Coefficient before** \\ **Transformation W/(m\({}^{2}\cdot\)K)** \\ \end{tabular} \\ \hline Roof & 0.301 & 1.56 \\ Ground & 0.35 & 0.5 \\ Exterior wall & 0.34 & 1.53 \\ Interior wall & 0.44 & 2.03 \\ Windows & 1.448 & 4.4 \\ \hline \end{tabular}
\end{table}
Table 1: The thermal resistance of the envelope structure before and after the transformation.
# An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision
## 2 Domain Adaptation Techniques
### Overview of Domain Adaptation Techniques
#### 2.1.3 Deep Learning-Based Methods  
Methods for domain adaptation based on deep learning make use of neural networks' ability to learn representations that are independent of the source domain. A well-known method is DANN, which integrates a domain discriminator into the network to learn features that are domain-invariant. By successfully decreasing domain differences, in cross-domain picture classification tasks, DANN has attained cutting-edge performance [88].  
Another popular deep learning-based domain adaptation strategy that was created with image-to-image translation problems in mind is CycleGAN. It is adaptable and relevant to many picture domain adaptation scenarios since it learns a mapping between source and target domains without the necessity for paired data [89].  
Another such model is ADDA (Adversarial Discriminative Domain Adaptation), as shown in Figure 6, which employs an adversarial discriminator for better domain adaptation by aligning feature distributions between the source and destination domains [90]. MADA (Multi-Adversarial Domain Adaptation) employs a multi-adversarial discriminator to enhance domain adaptation across diverse domains, resulting in improved overall performance. It excels particularly in scenarios involving multiple source domains, ensuring the effective alignment of feature distributions and better adaptation outcomes [91].  
MUNIT (Multi-Modal Unsupervised Image-to-Image Translation) strengthens domain adaptation by introducing the concept of multiple methods for unsupervised image-to-image translation. This innovation broadens its applicability, allowing it to deal with a wide range of adaptation tasks with flexibility and efficiency [92; 93].  
A typical deep learning-based domain adaptable model's mechanism is shown in Figure 7.  
General operative is mechanism is based upon transfer learning. With 66 research articles, Europe follows suit, while North America and South America each contributed 104 and 46 publications. With 31 papers, Oceania also shows a notable contribution. The distribution of research throughout these areas shows a broad interest in and international cooperation on enhancing domain adaptation methods [93] and the mechanism of domain adaptation.  
Figure 6: Adversarial Discriminative Domain Adaptation where the unsupervised domain adaptation method combines adversarial learning with discriminative feature learning.
## 1 Introduction
From the perspective of power flow, it is likely that the failure of a power-generation unit in the power grid will lead to a voltage-limit violation of the distribution network, an active-power-limit violation of the line, and load loss [19], which will have a huge impact on the stable operation of offshore oil-gas platform clusters. Hence, there must be stricter requirements for determining the siting and sizing of offshore renewable energy. In view of the shortcomings of the existing research, this study describes a method to improve voltage stability and reduce line losses by siting and sizing WT/PV ES to increase the renewable-energy penetration of offshore oil and gas platforms. The optimization strategy comprehensively considers the static power-flow voltage stability, the principle of cluster partition, the initial investment budget, and other indicators. The strategy can be briefly summarized as follows:  
1. The first stage is capacity optimization for WT and PV. After obtaining the forecasting of wind and solar resources based on the prediction model of the LightGBM network, the WT/PV-connected capacity at each node of the distribution network is optimized, with the goals of power-flow stability and economy. 2. The second stage is cluster partition. Sensitivity analysis is carried out on the distribution network after WT/PV planning. The voltage and line-loss sensitivity of each node is calculated and nodes with similar impacts are grouped into sub-clusters. 3. The third stage is the optimization of ES planning. Taking each sub-cluster as a unit, the optimal location of ES is determined according to the sensitivity-analysis method and, then, the optimal capacity planning of ES is carried out with the goals of minimizing voltage fluctuation, reducing active power loss, and achieving optimal capacity. 4. An algorithm based on a PSO algorithm and a power-flow calculation are used to solve the optimal planning of WT/PV ES following the methods of gas-turbine power constraints and economical constraints for an existing offshore oil-gas platform distribution network. The feasibility of offshore WT/PV ES for an offshore oil-gas platform is assessed. Here, we considered the example of an oil-gas platform in the Bohai Sea of China. With proper manipulation of the WT/PV ES capacity, based on various gas-turbine power outputs, we achieved a 55-66% reduction in line losses and a 30.4-47.5% decrease in voltage fluctuations.
# Design and Evaluation of Dynamic Topology for Mega Constellation Networks
## 3 Problem Analysis
### Combination of Three Fixed Links  
Satellites can establish ISLs with other satellites in the same orbital plane (OP) or different OPs in a mega constellation. In order to take into account the east-west connectivity of the satellites, a uniform connection rule is defined: when \(1\leq i\leq 24\), the odd-numbered satellites in each orbit connect to the satellite with the shortest distance from the adjacent orbit on the left, allowing hop-by-hop transmission along the orbital direction; the even-numbered satellites are only connected to the shortest distance satellite in the right adjacent orbit and complete hop-by-hop transmission along the orbit in the opposite direction. This ensures that two adjacent satellites in the same orbit are a pair, avoiding duplicate connections and that the two closest satellites in adjacent orbits are connected [19]. Since there is only one link between the different satellites, if the connection of the adjacent orbital links between \(OP_{1}\) and \(OP_{23}\) is specified, the satellite connections on \(OP_{24}\) can be predicted directly [20].  
Before the introduction of dynamic links, two combinations are designed for the remaining three fixed links. Figure 2a mainly consists of one adjacent OP ISL (AOPL) and two intra-OP links, while Figure 2b consists of two AOPLs and one intra-OP link.  
Figure 1: Time slice division method based on the number of satellites per orbit.  
* The connection method of intra-OP ISLs plus one AOPL enables the links within each orbit to connect with the preceding and following neighboring satellites at any given time, allowing for hop-by-hop transmission in either direction along the orbit. In the network topology, the AOPLs on the right side provide data transmission from west to east, while the AOPLs on the left provide the opposite direction [21].
* The connection method of one intra-OP ISL plus two AOPLs ensures that the links between the two orbits are always connected to the nearest adjacent satellite nodes at any given time, allowing for hop-by-hop data transmission in the east-west direction. As the adjacent satellite nodes on the same orbit are relatively stationary, permanent links of equal length exist for both forward and backward directions [22].  
The above is a combination of fixed links. Even though the stability of the satellite network topology is relatively maintained to a certain extent, it is difficult to completely break through the limitations of the static topology by relying solely on fixed links. Therefore, this paper introduces a dynamic link in addition to the above description of fixed links, which helps to enhance the flexibility of the mega constellation further.
## 3 Discussion of Experimental Result  
### Array Fabrication  
To validate the simulated performance of the proposed array, a prototype shown in Figure 3 was fabricated and experimentally characterized. Figure 3 shows the photographs of the fabrication process and fabricated structure. The proposed structure was fabricated in a precise manufacturing process along with the accurate installation of the stacked PCB layers. Figure 4 shows the simulated and measured S\({}_{11}\) and the realized gain of the proposed array. The measured S\({}_{11}\) for \(<\)\(-\)10 dB is from 10.55 GHz to 14.25 GHz, which corresponds to 29.8%. The maximum gain measured for the proposed array is 11 dB at 13 GHz.  
Figure 3: Photographs of the proposed fabricated array.
# A Key Management Scheme for Mobile Wireless Sensor Networks
## 4 Evaluation and Comparison
### Attacker Model  
An adversary knows all the cryptographic primitives, the _IDs_ and the public keys of all the nodes. However, he/she does not know the secret keys. With any scheme, after compromising a node, the adversary is able to use that node like it would be authentic. Moreover, the adversary can replay messages and inject new ones.  
Figure 3: Node reintroduction.  
An opponent is able to compromise a node, and achieve the data stored in its memory. By using PCAT, the information that an adversary can obtain from one node about other nodes is limited to public data stored in the authentication table. Therefore, the only advantage that an adversary can achieve by compromising a node corresponds to the possibility to clone that node. However, this issue is common to all the state-of-the-art schemes.  
In order to compare PCAT with the state-of-the-art schemes based on transitory master key, it is required to separately analyze the attacks performed in the initialization and the ones performed in working phases. The following threats are considered:  
* \(x\) nodes are compromised within the working phase, so the adversary achieves all the secret material stored by the \(x\) nodes, apart from the erased data;
* \(x\) nodes are compromised within the initialization phase, so the adversary knows all the secret material stored by \(x\) nodes.
## 1 Introduction
In the Ordos Basin, where coal seams predominantly originate from fluvial-lacustrine deposits, mudstone typically interbeds with coal layers [29]. The naturally low permeability of mudstone makes it an effective aquiclude. Within goat areas, roof strata typically comprise multiple layers including immediate roofs and false roofs in the caving zone, while sandstone generally forms structural layers that function as main roofs and key strata, governing periodic mine pressure behavior [30; 31]. Coal seams primarily serve as mining targets but may also occur as immediate or false roofs that collapse into the goat during the working face advancing. Importantly, roof structure significantly influences the permeability evolution characteristics of compacted broken rock in goat areas [32; 33; 34]. While single-lithology studies indicate that fractured mudstone in shallow coal-bearing strata shows the greatest potential for waterproof recovery [18], actual caving zones in shallow coal seam goals typically contain vertically mixed lithological combinations (Figure 2). This complex arrangement necessitates focused research on the seepage characteristics of compacted broken roof materials with combined lithologies. This study investigates the water-resisting recovery characteristics of compacted lithological combinations in broken roof strata within shallow coal seam goals. Using Shaping Coal Mine as a representative case study of typical shallow coal seam groups, we conducted experiments to analyze the compaction-seepage characteristics (CSCs) of lithologically combined broken rocks under shallow buried stress conditions. The Forchheimer theory was employed to model the seepage behavior of compacted broken rock. To quantify water-resisting capacity recovery, we introduced a void sensitivity index for characterizing permeability evolution during rock compaction. Based on experimental results, we developed a comprehensive permeability analysis model for combined lithology broken rock in shallow goals. Furthermore, this study establishes a compaction-permeability analytical model specifically for caved rock strata in shallow mining conditions.
## The Case of Greece
### Privacy and Data Protection in Greece  
The invibable nature of private and family life is explicitly guaranteed by Article 9 of the Constitution as well as by civil and criminal legislation, which protect these rights against infringements either by state authorities or by other citizens ([PERSON], 1991). Moreover, the protection of privacy is further guaranteed by the Constitution through Article 19 (Confidentiality of letters, free correspondence and communication) and Article 21 (protection of family, marriage, motherhood and childhood, rights of persons with disabilities), while especially the confidentiality of letters and free correspondence and communication are supervised by the independent Communications Privacy Authority.  
Personal data protection, which is inextricably connected to remote sensing technologies39, is established in Article 9A of the Constitution40 and currently regulated by Law 4624/2019, through which national law has been harmonized with Directive (EU) 2016/680. Privacy and personal data are also protected through criminal law, in Chapter 22 of the Penal Code regarding \"infringements of personal confidentiality and communication\" ([PERSON], 2008) and through civil law in Article 57 of the Civil Code regarding the protection of personality ([PERSON], 2007). Personal data protection in Greece is simultaneously directly subject to GDPR regulation.  
Footnote 39: The reason for the creation of a special legal framework for personal data protection lies on the special nature of the information produced by modern technologies, which may relate to certain individuals as well as important aspects of their identity ([PERSON], 2004; [PERSON], 2003; [PERSON], 2011).  
Footnote 40: Article 9A: All persons have the right to be protected from the collection, processing and use, especially by electronic means, of their personal data, as specified by law. The protection of personal data is ensured by an independent authority, which is constituted and operates as specified by law.  
Proper implementation of the personal data protection framework is under the supervision of the independent Data Protection Authority (hereinafter DPA). In the event of conflict between the necessity of safeguarding the environment and the protection of personal data, the necessary balance shall be pursued through the implementation of the principle of proportionality. In this sense, DPA in its Opinion 2/2010 considers that restrictions in personal data protection for the purpose of protecting the environment (as a whole, not only with regard to environmental crime), which is an explicit constitutional provision, are legitimate, as long as requirements set by the principle of proportionality (necessity, appropriateness, stricto sensu proportionality) are met.