esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Economic and Resilience Analysis of Microgrid Systems
#### 3.7.3 Resilience Assessment and Design Strategies  
Resilience has become an important performance indicator for microgrids, particularly in response to risks associated with natural disasters, cyber threats, and supply chain disruptions [30; 36]. Resilient microgrid designs incorporate redundancy, adaptive control mechanisms, and decentralized energy resources to maintain continuous operation during adverse events [32; 39].  
Studies propose resilience metrics based on system recovery time, the ability to supply critical loads during disruptions, and the effectiveness of operational recovery strategies [68; 70]. Simulation tools and scenario-based analyses are frequently used to assess resilience under various failure and recovery scenarios [71; 75]. Design strategies centered on modularity and scalability further enhance resilience by allowing system reconfiguration and phased upgrades without major infrastructure changes [13; 24].
###### Abstract  
Optimizing heat generation capacity is crucial for geothermal system design and evaluation. Computer simulation is a valuable approach for determining the influence of various parameter combinations on a geothermal system's ability to produce heat. However, computer simulation evaluations are often computationally demanding since all potential parameter combinations must be examined, posing significant hurdles for heat generation performance evaluation and optimization. This research proposes an adaptive Kriging-based heat generation performance optimization method. Firstly, a two-horizontal-well geothermal system with rectangular multi-parallel fractures is constructed. The heat production performance optimization problem is then established, and the temperature and enthalpy of the outlet water are calculated using computer simulation and Kriging. A parameterized lower confidence bounding sampling scheme (PLCB) is developed to adaptively update Kriging in order to strike a compromise between optimization accuracy and computation burden. The outcomes of the optimization are compared to those of the Kriging-based optimization approach and other common infill options to demonstrate the efficiency of the proposed method. The outlet temperature curve obtained with PLCB-AKO-1 rose for a longer time and the heat generation power curve reached a stable output without a downward trend. According to the [PERSON] and [PERSON] signed ranks tests, the PLCB-1-AKO technique is statistically superior to alternative strategies.  
heat production performance; Kriging; hot dry rock; geothermal system; numerical simulation +
Footnote †: journal: Applied Sciences  
0024 14 6415 10390 0024 14 6415 10390 0024 1  
Adaptive Kriging-Based Heat Production Performance Optimization for a Two-Horizontal-Well Geothermal System
# Implementing YOLO Convolutional Neural Network for Seed Size Detection
###### Abstract  
The article presents research on the application of image processing techniques and convolutional neural networks (CNN) for the detection and measurement of seed sizes, specifically focusing on coffee and white bean seeds. The primary objective of the study is to evaluate the potential of using CNNs to develop tools that automate seed recognition and measurement in images. A database was created, containing photographs of coffee and white bean seeds with precise annotations of their location and type. Image processing techniques and You Only Look Once v8 (YOLO) models were employed to analyze the seeds' position, size, and type. A detailed comparison of the effectiveness and performance of the applied methods was conducted. The experiments demonstrated that the best-trained CNN model achieved a segmentation accuracy of 90.1% IoU, with an average seed size error of 0.58 mm. The conclusions indicate a significant potential for using image processing techniques and CNN models in automating seed analysis processes, which could lead to increased efficiency and accuracy in these processes.  
Keywords:convolutional neural networks; CNN; seed size detection; seed analysis automation; YOLO; image segmentation; computer vision methods in agriculture; measurement accuracy in machine learning; agricultural process automation +
Footnote †: journal: Applied Sciences
# A Novel Hybrid Path Planning Method Based on Q-Learning and Neural Network for Robot Arm
## 4 Conclusions  
This paper presents a new hybrid approach to path planning based on Q-learning and a neural network for robot arms. To this end, in order to prepare the required information for the hybrid path planning method, the location of the start point, the obstacle, and the target were obtained via image processing using the KNN algorithm. Then, in the first component of hybrid path planning, inspired by the windy grid world problem, the Q-learning algorithm was used to find a sequence of actions such as up, down, left, and right to reach the target cell and avoid collision with the obstacle in a gridded 2D workspace. Using simple action-state spaces led us to model the agent-environment interaction only in programming software instead of linking it with simulator software, which significantly simplified the path planning problem. Then, we use a trained neural network to convert the found actions to the corresponding joints angles, which provides desired movement from a cell to its adjacent cells. Since the weights were trained already before path planning, using them during the angle-finding step is not time-consuming at all. We test this hybrid method both using a simulator and a real robot arm. The results show that this hybrid approach significantly improves the slowness and the complexity of the path planning problem.  
[PERSON] surveyed the backgrounds of this research, raised the idea of hybrid path planning, designed the neural network, performed the simulations, and wrote the manuscript. [PERSON] prepared the experimental setup and implemented the method on the real-world robot. [PERSON] supervised and supported this study. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ICT Creative Consilience program (IITP-2020-2011-1-00783) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation).  
Not applicable.  
Figure 11: Comparison between running time of the hybrid method and other methods under the same conditions.  
Data Availability Statement: The data that support the findings of this study are available from the corresponding author only for reasonable requests.  
The authors highly appreciate the help of members of POSTECH Design Intelligence Laboratory, especially [PERSON] and [PERSON].  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} APF & Artificial Potential Field \\ PRM & Probabilistic Road Maps \\ RRT & Rapidly exploring Random Tree \\ RL & Reinforcement Learning \\ IK & Inverse Kinematic \\ DOF & Degrees Of Freedom \\ DDPG & Deep Deterministic Policy Gradient \\ \end{tabular}
###### Abstract  
This paper presents a study showing the optimization of the mass, direct (self-manufacturing) costs, and energy life-cycle costs of composite floor structures composed of a reinforced concrete slab and steel I-beams. In a multi-parametric study, mixed-integer non-linear programming (MINLP) optimizations are carried out for different design parameters, such as different loads, spans, concrete and steel classes, welded, IPE and HEA steel profiles, and different energy consumption cases. Different objective functions of the composite structure are defined for optimization, such as mass, direct cost, and energy life-cycle cost objective functions. Moreover, three different energy consumption cases are proposed for the energy life-cycle cost objective: an energy efficient case (50 kWh/m\({}^{2}\)), an energy inefficient case (100 kWh/m\({}^{2}\)), and a high energy consumption case (200 kWh/m\({}^{2}\)). In each optimization, the objective function of the structure is subjected to the design, load, resistance, and deflection (in)equality constraints defined in accordance with Eurocode specifications. The optimal results calculated with different criteria are then compared to obtain competitive composite designs. Comparative diagrams have been developed to determine the competitive spans of composite floor structures with three different types of steel I beam: those made of welded sections and those made of IPE or HEA sections, respectively. The paper also answers the question of how different objective functions affect the amount of the calculated costs and masses of the structures. It has been established that the higher (more wasteful) the energy consumption case is, the lower the obtained masses of the composite floor structures are. In cases with higher energy consumption, the energy life-cycle costs are several times higher than the costs determined in direct cost optimization. At the end of the paper, a recommended optimal design for a composite floor system is presented that has been developed on the multi-parametric energy life-cycle cost optimization, where the energy efficient case is considered. An engineer or researcher can use the recommendations presented here to find a suitable optimal composite structure design for a desired span and uniformly imposed load.  
composite floors; mass optimization; direct cost optimization; energy life-cycle cost optimization; mixed-integer non-linear programming; MINLP +
Footnote †: journal: Applied Sciences
## 3 Evaluation Experiments
### Hyperparameters and Training Conditions
#### 3.2.2 Waveform-Based Method  
The input and output of the noise-suppression DNN were waveforms of 2048 samples. The DNN consists of eight convolutional layers with a dilated convolution structure, and the dilated factors are set to \(2^{n},n=0,1,\ldots,7\). The convolution kernel size was \(9\times 1\). The number of kernels in the output layer was 1, and the other layers were 128. The learning rate was set to 0.0001. The number of units of the LSTM layer and fully-connected layer used for the high-pass reconstruction DNN was set to 1024. When training the network, the current time step sample was predicted using the previous 480 samples with backpropagation through time (BPTT) [24]. The learning rate was set to 0.0001, the same as the noise-suppression DNN.
## 2 Materials and Methods
### Chemometric Processing of IR Data  
In view to establishing a spectroscopic scale based on IR signatures of the MIBC according to the response to NAC, we adapted chemometric algorithms previously developed for building an invasiveness scale in lung squamous cell carcinoma [24]. In addition to the signal of tissue, the spectral data present also undesirable contributions, such as paraffin interference since no chemical dewaxing was performed before IR acquisitions. To correct such parasitic interferences and normalize the data, EMSC (Extended Multiplicative Signal Correction) preprocessing was first applied, this pre-processing included in particular a mathematical correction of the spectral interferences originated from paraffin embedding [15; 17; 25]. From this step, the spectral range was reduced to 800-1800 cm\({}^{-1}\) as it corresponds to the informative fingerprint region with limited spectral interferences. The effect of the EMSC pre-processing can be seen in Figure S1 representing the spectral variability of the whole dataset normalized according to the target spectrum (mean spectrum of the dataset). Secondly, data were processed by Kmeans clustering and PLS-DA (Partial Least Square-Discriminant Analysis) to develop an automatic selection of relevant pixels by excluding outliers pixels, i.e., selection of pixels for the subsequent PLS (Partial Least Square) modeling. Finally, a PLS algorithm was implemented to construct a quantitative model permitting to score the spectral data on a numerical scale associated with the response to NAC. This model was developed and optimized by considering image-based cross-validation, in which all pixels/spectra of one image constituted a unique independent sample.
## 4 Results and Discussion
### Comparison between Linear and Non-Linear AES Models in Capturing Writing Quality
HISK + BOSWE and v-SVR [120] explored a method that encompassed both surface-level features and deep-level semantic features. TSLF-ALL [121] proposed a dual-phase learning architecture that integrated the advantages of feature-engineered approaches. Qe-C-LSTM [122] involved a method employing an advanced deep convolutional recurrent neural network for enhanced essay scoring accuracy. The results demonstrated that our proposed approach with Random Forest Regression outperformed the QWK score by 4.8% over the benchmark Qe-C-LSTM. The paired \(t\)-test results indicated that the Random Forest Regression approach significantly improved performance (\(p<0.050\)) when compared with those baselines. The observed improvement in performance can be attributed to a comprehensive and coherent representation of linguistic \begin{table}
\begin{tabular}{l c} \hline \hline
**Approaches** & **QWK Scores** \\ \hline MN [119] & 0.790 \\ HISK + BOSWE and v-SVR [120] & 0.804 \\ TSLF-ALL [121] & 0.801 \\ Qe-C-LSTM [122] & 0.815 \\
**Random Forest Regression** & **0.863** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of QWK values for the holistic rating trait with Random Forest Regression and the baselines. Figure 5: Confusion matrices for the Random Forest Regression model’s performance across various essay scoring dimensions: Ideas, Organization, Style, and Conventions. features that characterize various constructs of writing quality. By encompassing a broad spectrum of linguistic features characterizing different constructs of writing quality, the proposed AES model is better equipped to capture the multifaceted characteristics that constitute effective writing.
# A Local Search-Based Generalized Normal Distribution Algorithm for Permutation Flow Shop Scheduling
## 2 Description of the Permutation Flow Shop Scheduling Problem  
Assuming that n jobs are running sequentially over m machines in the permutation flow that will minimize the makespan, this problem is known as the permutation flow shop scheduling problem (PFSSP). The makespan is measured using time units such as seconds, milliseconds, etc. Therefore, to solve this problem, the best permutation \(c^{*}\) that will minimize the makespan of execution of the last job on the last machine must be accurately extracted. In general, the following points summarize the PFSSP: (1) on each machine, each job \(j_{b}|b=1,2,3,\ldots\ \ldots\ \ldots,n\) could run just once, where \(n\) is the number of jobs; (2) just a job could be executed on a machine \(i_{z}|z=1,2,3,\ldots\ \ldots\,\ m\) at a time with processing time PT, where \(m\) is the number of machines; (3) each job \(j_{b}\) will have a completion time c on a machine \(v_{z}\), and this time is symbolized as \(c(j_{b},\ i_{z})\); (4) each job has a processing time comprised of the set-up time of the machine and the running time; and (5) each job takes a time of 0 when starting. Mathematically, PFSSP could be modeled as follows:  
\[c(j_{1},\ i_{1})=PT_{j_{b},\ i_{1}}\] (1) \[c(j_{b},\ i_{1})=c(j_{b-1},\ i_{1})+PT_{j_{b},\ i_{1}},\ \ \ \ b=2,3,\ 4,\ \ldots\ \ldots,\ n\] (2) \[c(j_{1},\ i_{z})=c(j_{b},\ i_{z-
## 3 Lightweight Cryptography for Smart Objects
### Lightweight Cryptographic Hash Functions
#### 3.3.6 Squash  
SQUASH (SQUare-hASH) [54] is suited to challenge-response MAC applications in constrained devices, such as RFID tags. SQUASH is completely deterministic, so it requires no internal source of randomness. SQUASH offers a 64-bit pre-image resistance. SQUASH is not collision resistant, but this is not an issue since it targets RFID authentication protocols, where collision resistance is not needed. However, if collision resistance is a requirement, for instance in case of digital signatures, SQUASH is unsuitable and other hash functions should be considered.
# A Novel Mobile Communications Authentication Scheme with Roaming Service and User Anonymity
## 5 Security Analysis
### Resist Replay Attack  
The proposed scheme has a timestamp in each transmission process, including the authentication (V1\(\rightarrow\)V5) and roaming (L1\(\rightarrow\)L2) phases. In addition, V1 and L1 are encrypted by using the shared key EC, and therefore, we can imagine that V1 and L1 are secure channels. Therefore, even if an attacker intercepts the message, the message cannot be broken in this secure channel. This allows our proposed scheme to resist replay attacks.
# The Future of Abandoned Shallow Mines as a Function of Precipitation Under Changing Climate
## 1 Introduction  
There are hundreds of thousands of natural or anthropogenic underground cavities and abandoned mines worldwide. A significant portion of these man-made underground cavities are abandoned after the extraction of materials such as stones and minerals. These cavities are subject to natural degradation processes including the effect of climatic conditions, primarily water, and the decreasing of the bearing capacity of the rock and underground structures.  
Recent climate studies looking at different scenarios indicate changes in the intensity and frequency of climatic events: such as heavy rainfall and longer dry seasons. Scientists worldwide have studied the effects of climate change on temperature, weather patterns, rainfalls and more [1,2]. Several studies have focused on the relationship between specific geohazards and climate change, primarily addressing hurricanes, volcanic activity, floods and heat waves. While the relationship between the ground movements (e.g., landslide, clay swelling) and climate change has also been explored, ground movements related to cavities such as underground mines have been less analysed in this context [3]. Precipitation is one of the parameters of climate change. The potential impact of climate change on the stability of underground cavities depends on the influence of water on the long-term hydromechanical behaviour of the rock [4]. The effect of water (the saturation impact) on chalk behaviour, short- and long-term, was fully studied at small scale and large scale.  
The main objective of this study is to learn from past events to better manage future risks. To this end, a database containing over 500 cases of collapse was created, spanningnearly three centuries, to identify the causes of these large-scale collapses. In this article, we present a study focusing on the historical and large-scale collapse of an underground chalk mine (the Beaulieu mine) in 1910, which resulted in seven fatalities. For this case, experts have suggested that an exceptional climatic event occurred before the collapse, but other factors should be investigated to better understand their interaction. The objective is to describe clearly the mechanism behind this massive collapse in the context of climate change. Based on their findings, operational recommendations are made for incorporating climate parameters into the assessment and prediction of collapse risks of abandoned underground mines.
# USD-YOLO: An Enhanced YOLO Algorithm for Small Object Detection in Unmanned Systems Perception
## 2 Related Works
### Object Detection Methods  
Based on the feature extraction methods used, object detection algorithms can be classified into traditional detectors and deep learning-based detectors. Traditional detectorsprimarily rely on handcrafted features, exhibiting weak generalization and poor performance in complex scenes. In contrast, deep learning-based detectors leverage convolutional neural networks to automatically learn features, offering strong feature representation and generalization capabilities, thus achieving superior performance in complex scenarios. Currently, deep learning-based detectors can be further categorized into two-stage detectors and single-stage detectors based on their image-processing approaches [19].  
Two-stage detectors follow a process from coarse detection to refined detection. In 2015, [PERSON] et al. proposed Faster R-CNN [20], which achieves high detection precision but is computationally expensive and slow. Single-stage detectors extract features only once and can search for all objects in a single inference step. [PERSON] et al. [21] proposed the first single-stage detector, YOLO, while [PERSON] et al. [22] proposed the Single Shot MultiBox Detector (SSD). These models significantly improve detection speed but struggle with low precision for small objects. At present, the YOLO series has developed rapidly, with continuous improvements in precision, making it a widely used algorithm for small object detection. Considering the limited computing resources of unmanned systems, we choose YOLOv8s as the baseline model to balance detection precision and speed.  
Recent studies have introduced improved object detection algorithms to address the challenges of small object detection. For instance, [PERSON] et al. [23] proposed FE-YOLO, which integrates deformable convolutions in the neck of YOLO to fuse high- and low-level feature maps, aiming to reduce the semantic gap caused by top-down connections. However, this approach significantly increases the number of parameters. [PERSON] et al. [24] proposed CA-YOLO, embedding a coordinate attention module to suppress redundant background information, though at the cost of additional parameters. [PERSON] et al. [25] introduced TPH-YOLO, incorporating a Transformer prediction head to improve small object detection precision, but this substantially increases parameters and reduces inference speed. [PERSON] et al. [26] applied a Spatial-to-Depth Convolution (SPD-Conv) module in YOLO to enhance depth information in feature maps. However, the rearrangement of feature maps leads to information loss, reducing detection precision for partially occluded small objects. Thus, small object detection algorithms still face challenges in maintaining real-time performance while improving detection precision on low-computation-cost devices. To address this, we designed USD-YOLO, integrating APE, SCConv, and CGAM modules to improve detection precision while maintaining speed without increasing the number of parameters.