esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
This way, it is possible to exploit today's multi/many-core machines transparently. This paper extends the preliminary paper [17] presented at the Simultech 2023 conference, where the basic idea of PB-KM was introduced. Differences from the conference paper are indicated in the following. * A more complete description of the evolutionary approach, which is the basis of the proposed clustering algorithms, is provided. * PB-KM now includes a mutation operation in the second step of recombination. * An original development of PB-RS is presented which, with respect to standard Random Swap [8; 9], is more apt to move directly to a good clustering solution. * More details about the Java implementations are furnished. * All previous execution experiments were reworked, and new challenging case studies were added to the experimental framework, exploiting synthetic (benchmark) and real-world datasets. The paper first describes the evolutionary approach of PB-KM and PB-RS, then reports the experimental results of applying them to several datasets. The simulation results confirm that the new algorithms can ensure accurate clustering and good execution times. The paper is structured as follows. Section 2 reviews the related work about the K-Means and methods for seeding. It also overviews the fundamental aspects of Random Swap, the evolutionary clustering of GA-K-means and Recombinator-K-Means, which inspired the algorithms proposed here. The section also describes some external measures suited to assess the clustering quality. The operations of the PB-KM and PB-RS algorithms are presented in Section 3. Some implementation issues in Java are discussed in Section 4. Section 5 reports the chosen experimental setup made up of synthetic datasets and real-world ones, and the experimental results tied to the practical applications of the developedtools. The execution performance of the new algorithms is also demonstrated. Finally, conclusions are drawn together with an indication of ongoing and future work.
## 4 Discussion
### Implications for Low-Resource Languages  
Our work has significant implications for low-resource languages, which often lack sufficient parallel data for traditional XLS methods. By demonstrating that even a small number of semantically relevant examples can substantially improve performance, we provide a practical pathway for extending XLS capabilities to more languages.  
The MuRXLS approach is particularly valuable for the X\(\rightarrow\)English direction, which is often the primary need for information access from low-resource languages. For example, our results with Burmese\(\rightarrow\)English show improvements of up to 4.20 ROUGE-L points using semantically selected examples, making information in Burmese significantly more accessible to English readers.  
These language-specific patterns indicate that while MuRXLS provides consistent benefits across diverse languages, the magnitude and nature of improvements are influenced by fundamental linguistic properties. Understanding these patterns can inform future development of language-adaptive retrieval strategies and help practitioners set appropriate expectations for different language pairs.
# TPSQLi: Test Prioritization for SQL Injection Vulnerability Detection in Web Applications
## 4 Methodology and Implementation
### Test Prioritization Algorithm
#### 4.4.2 Mathematical Formulation of Test Prioritization Algorithm  
In this subsection, we introduce the algorithm for test prioritization of various SQL injection vulnerability detection techniques. We define two sets: one representing all current detection techniques and the other representing the set of test targets. As technology evolves, new techniques may emerge. We assume there are currently \(n\) detection techniques and \(m\) test targets for this discussion.  
For each test target, we conduct \(n\) attacks and record the duration of these attacks, resulting in \(n\times m\) different results, which are then used to calculate the weight scores. We establish three rules to construct this test prioritization model:  
* Rule 1: If a technique fails to exploit a vulnerability successfully, it receives zero points in the weight score calculation.
* Rule 2: The total score for the same test target is \(n\), with faster exploits earning higher scores.
* Rule 3: The score calculation considers the difference in exploit time using the reciprocal ratio of these times.  
Moreover, we use the following mathematical formulas to express the model:  
* Techniques: \(n\).
* Successful exploits: \(a\).
* Failed exploits: \(n-a\).
* Success exploit time: \(S_{1},S_{2},\ldots,S_{a}\).  
* Fail exploit time: \(F_{1},F_{2},\;\ldots,\;F_{a}\).
* Weights (Both \(W_{F_{i}},\;W_{S_{i}},\;W_{i}\) set to zero in the initial process): \[W_{F_{i}}=0,\;\forall i=1,2,\ldots,n-a\] (1) \[W_{S_{i}}=\frac{1}{S_{i}}\cdot n\cdot\frac{1}{\sum_{k=1}^{a}\frac{1}{S_{k}}}, \forall i=1,2,\ldots,a\] (2) \[W_{i}=\max\bigl{(}W_{S_{i}},\;W_{F_{i}}\bigr{)},\;\forall i=1,2,\ldots,a\] (3)  
For a deeper understanding of our algorithm, please refer to Section 4.4.4 for a simple example.
## 4 Machine Learning Problems
### Ranking  
The ranking creates a hierarchy of importance for a set of information retrieved. ML ranking applications can be prepared through semi-supervised, supervised, or reinforced learning. The ranking is distinguished by type, either pointwise, pairwise, or listwise. Key to these is the application of a metric to score retrieved items; commonly, the mean average precision (MAP) is used.  
\[MAP=\sum\frac{\sum_{q=1}^{Q}AveragePrecision(q)}{Q} \tag{2}\]  
There are numerous models for ranking, including ANN, SVM, extreme gradient boost, and other custom or hybrid models. In the point-wise approach, ranking is handled as a regression problem. Pairwise pairs of retrieved documents are compared in a binary classification problem. Whereas listwise, the loss is computed on a list of documents' predicted ranks. In pairwise retrieval, binary cross entropy (BCE) is calculated for the retrieved document pairs utilizing \(y_{ij}\) is a binary variable of document preference \(y_{i}\) or \(y_{j}\) and \(s_{ij}=\sigma(s_{i}-s)\) is a logistic function:  
\[BCE=-\sum_{i,j=1}^{n}y_{ij}log(s_{ij})+(1-y_{ij})log(1-s_{ij}) \tag{3}\]  
In the listwise ranking, discounted cumulative gain (DCG) or variants of this form are used; it grades the relevance of documents set to evaluate the gain of a document based on its position; the DCG is defined as:  
\[DCG_{p}=\sum_{i=1}^{p}\frac{relevance_{i}}{log_{2}(i+1)} \tag{4}\]  
The ranking is highly implemented in our modern world, and ranking systems are trained for online systems that take user clicks as input in determining the precision of the ranking of retrieved items. Just some of the applications for ML-based ranking include:  
* Web search results ranking
* Writing sentiment analysis
* Travel agency booking availability based on search criteria
# Towards Explainable Artificial Intelligence for GNSS Multipath LSTM Training Models
## 3 Methodology: LRP for LSTM-Based GNSS Multipath Detection
### Brief Introduction to LRP
LRP has been successfully applied across various domains, including image classification, text analysis, and time-series prediction, demonstrating its versatility and effectiveness in making deep learning models more interpretable and trustworthy. Through the careful propagation of relevance scores, LRP creates a comprehensive explanation framework that bridges the gap between the black-box nature of deep neural networks and the need for interpretable AI systems in critical applications.
# A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning
## 4 Conclusions  
In this paper, we have proposed a fast indoor/outdoor transition detection algorithm based on machine learning without any infrastructure. We extracted statistical and time sequence features under different sliding window lengths and spatial geometry distribution features from GNSS measurements. To evaluate IO detection accuracy and transition delay of the proposed algorithm, we conducted experiments in the complex indoor and outdoor environments in urban Beijing. The evaluation results demonstrate that the IO detection accuracy was 99.11% in indoor and outdoor scenarios where we have collected data and 97.02% in new indoor and outdoor scenarios. Furthermore, IO detection accuracy was 94.53% in indoor and outdoor transition scenarios where we have collected data and the probability of switching delay within 3 s exceeds 80%. In the new scenarios, IO detection accuracy was 92.80% and the probability of switching delay within 4 s exceeds 80%. The proposed algorithm outperforms other existing IO detection methods and satisfies the requirement of indoor and outdoor seamless navigation and positioning.  
In our future work, we will further improve detection accuracy, shorten the switching delay and expand the scale of the experiment. Also, we will consider extracting more universality features to eliminate device heterogeneity. Furthermore, we will design an indoor and outdoor seamless navigation and positioning system based on smartphone for pedestrians.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Training Time(s)** \\ \hline RF & 20 \\ SVM & 819 \\ AdaBoost & 356 \\ XGB & 9 \\ LGB & 2 \\ Stacking Based Model Ensemble & 1133 \\ Stacking Model \& HMM & 1135 \\ \hline \hline \end{tabular}
\end{table}
Table 10: The comparison of different algorithm training time.
# An Overview on Atomization and Its Drug Delivery and Biomedical Applications
## 7 Electrospray Atomization and Its Characteristics  
Electro-hydro dynamic atomization is also known as electrostatic/electrospray atomization and it is usually considered as an alternative methodology to other techniques utilizing heat and some chemical substances. Electrospray atomization is the basic principle behind electrospinning (which gives rise to fibres) and electrospray (initiating fine spray containing particles). Electrospraying is the most widely used procedure in which a very high voltage electric current is applied to influence the liquid stream coming out of an orifice to either form a cone-jet which is the most common, dripping or multi-jet and ultimately generating long fibres or small droplets based on its application [77]. Even though  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Types** & **Applications** & **Advantages** & **Disadvantages** & **Ref** \\ \hline Electro hydrodynamic atomization & Bodywide delivery of & Can control the size of & Poor yield, usage of & \\ atomization & hypoglycemic agents & the drops & electric field & [65] \\ \hline Dry powder nebulizer & Delivery of hypochloride salt & Transferable, stable, free & \begin{tabular}{c} Larger drop size yield, \\ restricted for powdered \\ medicines, accumulation \\ in lungs \\ \end{tabular} & [66] \\ \hline Pressurized metered & Delivery of Flovent, & Transferable, manageable & \begin{tabular}{c} Uses chemicals to produce \\ pressurized air, accumulation \\ in upper lung \\ \end{tabular} & [67] \\ \hline Liquid atomization & \begin{tabular}{c} Delivery of Cetraxal, \\ bclometasone dipropionate, \\ Rubex \\ \end{tabular} & \
\begin{tabular}{c} Drug processing is \\ not essential \\ \end{tabular} &
\begin{tabular}{c} Comparatively larger drops, \\ can degrade the drug, bulky \\ \end{tabular} & [68] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Types of aerosol initiating techniques.  
the experimental apparatus and the parameters are the same for electrospinning and electrospray, the results obtained are different in its structure [78]. Micro/nanofibers are the result of electrospinning which has a range of applications in the field of tissue engineering [79], cancer therapy [80], antibacterial coating [81], etc. micro/nanobeads are the product of electrospray and its application is in the field of pharmaceutics and drug delivery. Figure 5 represents the schematic of the working apparatus of both electrospinning and electrospray.  
Electro hydrodynamic atomization was first studied in the year 1600 by the scientist [PERSON] [83] and later [PERSON] theoretically and experimentally calculated the maximum electric charge that a liquid droplet can carry and he named it as Rayleigh limit after his name [12]. In 1960, [PERSON] mathematically explained the development of cone-jet in the liquid stream in the action of an electric current and the cone is known as Taylor cone [84]. Further modification in the experimental parameters lead to the development of biomolecules [85] with a range of applications in different fields.
## Nomenclature  
\(e_{g}\)The grid voltage vector  
\(E_{g}\)Amplitude of the grid voltage vector  
\(V_{g}\)The output voltage of the grid-side converter  
\(i_{g}\)Grid current vector  
\(R_{f}\)Equivalent resistance between the grid-side converter and the grid  
\(L_{f}\)Equivalent inductance between the grid-side converter and the grid  
\(\omega_{ac}\)Angular frequency of the AC collection line  
\(P_{WF}\)Active power output of the grid-side converter  
\(Q_{WF}\)Reactive power output of the grid-side converter  
\(v_{dc}\)DC bus voltage measured value of the PMSG  
\(V_{dc0}\)DC bus voltage rated value of the PMSG  
\(J\)Inertia of the PMSG  
\(T_{e}\)Electromagnetic torque  
\(T_{m}\)Mechanical torque  
\(P_{M}\)Input mechanical power of the prime mover  
\(P_{E}\)Output electromagnetic power of the motor  
\(C_{w}\)The capacitance value of the DC bus line  
\(\omega_{r}\)Rotor speed of the PMSG  
\(\omega_{0}\)Rated speed of the PMSG  
\(H_{J}\)Inertia time constant of the permanent magnet synchronous motor  
\(H_{C}\)Capacitance inertia time constant of the DC capacitor  
\(n_{p}\)The number of pole pairs  
\(\psi_{f}\)Permanent magnet rotor flux  
\(P_{e}\)Electromagnetic power  
\(\delta_{G}\)Power angle of the synchronous motor  
Phase difference between the output voltage vector of PMSG grid-side  
converter and the grid voltage vector
## 7 SOFC Fuels  
The key superiority of SOFC over other fuel cells is the flexibility in choosing fuel for its operation. One can utilize either hydrogen or hydrocarbon or hydrocarbon derived fuels. The high temperature SOFC also has another important advantage that it can operate directly on practical hydrocarbon without any complex or sophisticated external fuel reformer systems. The hydrogen and carbon monoxide are the typical fuels for high temperature fuel cell which then get oxidized at anode into water and carbon dioxide along with electrical power and large heat [12; 103]. The generation of hydrogen can be done by electrolysis of water through renewable energy [112]. Natural gas is considered to be the most common fuel for stationary application. Its composition is mainly based on methane but it also contains higher hydrocarbons along with sulphur containing compounds. Both higher hydrocarbons and sulphur containing compounds result into carbon deposition and sulphur poisoning of the electrodes during SOFC operation. In this case excess of oxygen or steam is usually given in order to avoid coking and carbon deposition. Another advantage of using natural gas is that it can also be used as fuel without any reformer system in high temperature SOFC as it can internally be reformed through oxygen or co fed-steam in SOFC stacks due to high temperature. For remote and small scale applications, bottled gas such as butane or propane are used rather than methane. However, carbon deposition is difficult to avoid in the case of using bottled gas as fuel [103]. Other hydrocarbon fuels such as LPG, Napatha, kerosene, diesel, gasoline can also be used as SOFC fuel but a reformer or fuel cleaning unit is required to clean the fuel [8]. There are a wide range of practical fuels that can be used in SOFC, some of which are shown in Figure 9.
# Automatic Detection Method of Sewer Pipe Defects Using Deep Learning Techniques
## 2 Literature Review
### Sewer Defect Detection Based on Deep Learning Techniques
#### 2.2.2 Sewer Defect Detection Based on Object Detection Techniques  
Object detection technology can classify multiple targets appearing in a single image and obtain their precise positions, which is more widely applicable. Object detection techniques are mainly divided into two categories, one is two-stage networks such as RCNN [14] and Faster R-CNN [15]. The other is one-stage networks such as Single Shot MultiBox Detector (SSD) [16] and YOLO [17].  
[PERSON] & [PERSON] [2] first applied the two-stage network Faster R-CNN to the CCTV image detection task. This method uses 3000 images for training and realizes the accurate detection of four kinds of defects: root, crack, infiltration and deposit. The mean average precision (mAP) of this method can reach 83%. However, the detection speed of this method is slow and it is only suitable for offline detection. [PERSON] et al. [18] used three methods, SSD, YOLOv3 and Faster R-CNN, to detect sewer pipe defects, and compared the performance between different methods. [PERSON] et al. [19] proposed an improved Faster R-CNN model, which combines global context features with local defect features to achieve sewer pipe defect location and fine-grained classification. [PERSON] et al. [6] developed a real-time automated defect detection system based on YOLOv3, which can detect six types of defects. Due to the good detection speed of YOLOv3, this method can use CCTV video as input and generate defect-marked video, with an mAP of 85.37%. [PERSON] et al. [3] proposed an improved YOLOv3 model with reference to YOLOv5, which achieved accurate detection of four types of defects, and the mAP can reach 92%. Most current research on sewer defect detection is based on YOLOv3 or Faster R-CNN algorithm [20]. The detection speed of the YOLOv3 is fast, but its feature fusion ability is weak; furthermore, it is easy to produce miss or false detection for small targets. Faster R-CNN usually has high accuracy, but the detection speed is very slow.  
In this work, we focus on improving the detection accuracy of sewer defects. Specifically, we propose an improved YOLOv4 model to detect sewer defects. By introducing the SPP module to improve the multi-scale feature fusion capability, the improved model can obtain richer global information and improve the detection accuracy of sewer defects, especially for small defects such as cracks.
# A Pipeline Approach to Context-Aware Handwritten Text Recognition
## 3 Proposed Method
### The Proposed Context-Aware HTR Pipeline
#### 3.2.1 Pre-Processing  
During the data pre-processing stage, various procedures were applied to ensure that the data were in an appropriate form for further model training. Considering different noises that might occur in a real-life document image, we took several aspects into consideration. We identified two problems: (a) the image is taken at a slanted or skewed angle; (b) there are lines in the image that could affect classification performance. Progressive Probabilistic Hough Line Transform (PPHLT) [17,18] was applied to rotate the image to the correct angle. The method first detects the image's edges by applying canny edge detection. The image is then rotated accordingly based on the computed angle from the PPHLT algorithm. Next, morphological operations and image inpainting [19] are applied to remove the lines from the image. After that, trimming is used to remove excessive white pixels, such as in the border regions in the image.  
A YOLOv5 model has been trained to identify the text region of interest (ROI). A text ROI is categorized into three types: printed texts, handwritten texts, and non-text. More information about ROI localization and categorization is given in the next section. The segmented regions are then padded into the same size. For model training, the text ROIs are labeled manually as printed, handwritten, and others. There are a total of 5099 handwritten text segments, 7445 printed text segments, and 261 non-text segments in this study. Figure 3 shows a sample of the labeled image.  
Figure 3: Sample labeled image.  
Figure 2: HTR pipeline process and architecture.  
The text segment is augmented randomly by reducing or adding line width, adding Gaussian noise, and blurring the images. The final dataset is composed of 15,297 segments of handwritten text images. Figure 4 presents the flow of the proposed pre-processing approach. Some samples of the text segment and the corresponding augmented images are shown in Figure 5.
# Evaluating the Reliability of Powder Bed Fusion for Biomedical Materials: An Experimental Approach
## 3 Results
### Real Parts Manufactured by PBF  
Successful and stable PBF trials that do not require continuous observation were achieved. The microstructures and mechanical properties of the PBF parts meet the specified requirements. All the parts were designed and analyzed using the physical-mechanical characteristics determined in this work. For example, the maxillofacial implants illustrated in Figure 3a,b were fabricated from pure Ti powder (type TILOP 45) using the following PBF parameters: 120 W of laser power, 500 mm/s of scanning speed, and 0.12 mm of hatch distance (processability rank 4). In this case, the zygomatic reconstructions have 441 MPa of mechanical resistance, 103 GPa of elasticity modulus, 0.3 Poisson ratio, and 260 HV of surface hardness.  
Figure 3c,d show different dental prosthetics such as bridges, complete protheses, and partial dentures (frames) made of CoCrWMo alloys. After printing, the dental bridge from Figure 3c was post-processed on external surfaces by alumina sandblasting at 2-3 bars for 10 min, and, then, it was manually polished with carborundum disks.  
There are standardized implants for all the human bone systems, but the ideal is to have a customized one to patient needs, both from a design perspective and a physical-mechanical response. Recent advancements in the PBF process allow for the development of multi-material implant structures. Figure 3e,f reveal some hip orthopedic implants developed according to patent RO 132908B1/2024 with title \"Process of Manufacturing Customized Multi-Structure Medical Implants by Additive Manufacturing Technologies\" [46]. Knowing that we were using 70 W laser power and 400 mm/s scanning speed, with the elastic modulus of PBF implants made of Ti6 Al7 Nb similar to cortical bone (38 GPa), we were able to develop novel multi-material implants. In this case, to mimic the mechanical behavior of the cortical bone, the outer boundary of the hip implant was made of Ti6 Al7 Nb using the PBF parameters detailed above (2-3 mm thickness), and the final implant was infiltrated using an organic biocomposite (BISGMA and TEGDMA).  
A knee replacement implant (tibial component) was possible to fabricate having a lattice structure inside of it (Figure 3g). The purpose of these scaffolds was to support bone ingrowth. After it was post-processed, the average dimensional deviation was +0.07 mm. Many designs of macro-porous grafts for regenerative medicine were successfully fabricated from CoCr alloy using the following PBF parameters: 100 W of laser power, 500 mm/s of scanning speed, and 0.10 mm of hatch distance (processability rank 4).  
Figure 3: Real parts fabricated by SLM: (**a,b**) Maxillofacial implants—pure Ti, (**c,d**) dental applications—CoCrWMo, (**e,f**) orthopedic implants—Ti6 Al7 Nb, (**g**) orthopedic implants, and (**h**) lattice grafts—CoCr.
## 5 Evaluation and Analysis
### Experimental Setup
We compared Asymmetric Programming with a representative Meta-Cure scheme [17] and the default error correction scheme in the Linux kernel (labeled as \"Baseline\"). First, we use the real workload of the sensor system of Zynq board to evaluate the effectiveness of Asymmetric Programming scheme. Then, we apply IOMeter [39], which is widely used as a standard and configurable workload generator, to conduct more comprehensive experiments to further analyze the proposed scheme. Figure 9: The firmware architecture of the experimental platform.