esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Mitigation of RIrNLs' effects, including XPM, FWM and SPM, are analyzed in this paper with an analytical and simulation model, in order to design an optimal OTS using FD-MIMO equalizer-based enhanced DSP receiver and microstrip Chebyshev low pass filter for long-haul transmission. These impacts are investigated for distinct values of channel spacing, transmission length, number of channels, input and received power, nonlinear refractive index, nonlinear dispersion and nonlinear effective area, in a 32 channel WDM system. The simulations are performed using OSNR and BER as performance metrics. Results show that the proposed design maintains its continuity at 2.6 \(\times 10^{-17}\) m\({}^{2}\)/W refractive index, 80 \(\upmu\)m\({}^{2}\) effective area, up to 500 km fiber length with the conventional 6 dBm input optical power. The impacts of RIrNLs are minimized through implementation of the proposed optimum design and using duo-binary modulation, semiconductor optical amplifier and filters. The BER achieved in this work is \(<\)10\({}^{-5}\) till 500 km range, for a maximum number of 32 channels, with 100 Gbps aggregate data rate. This shows the feasibility and effectiveness of the proposed model.  
Conceptualization, M.I., F.A., A.S.A. and F.M.; methodology, F.A., H.K. and M.I; software, F.A., and F.M.; validation, F.A., M.I., R.M., I.G. and F.S.A.; formal analysis, F.A., A.G., F.M., R.M. and H.K.; investigation, F.A., and F.M.; data analysis, F.A.; writing--original draft preparation, F.A.; writing--review and editing, F.M., M.I., A.S.A., R.M., F.S.A. and H.K.; visualization, F.M.; supervision, M.I. and F.A.; project administration, F.A., F.M. and M.I.; obtaining financing, M.I. and A.S.A., A.G., R.M. and F.S.A. All authors have read and agreed to the published version of the manuscript.  
EMMAT E-mobility and sustainable materials and technologies (E-mobilnosc oraz zrownowazone materialy i technologie) Projekt nr PPI/APM/2018/1/00027.  
Not applicable.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
This work was supported by the Ministry of Education Kingdom of Saudi Arabia and the Deanship of Scientific Research, Najran University Kingdom of Saudi Arabia, under code number NU/ESCI/17/007.  
The authors declare no conflict of interest.
# Machine Learning for Bankruptcy Prediction in the American Stock Market: Dataset and Benchmarks
## 8 Results
### Best Model and Temporal Windows  
In light of our results, we can assert that Neural Networks should be preferred among all the ML models for the bankruptcy prediction tasks since they present the best ability to generalize on new and unseen cases with every metric adopted. However, the training and design of ANNs is usually harder and requests a larger computation time, higher costs, and more experiments with respect to the other models. When computation time and costs are constraints to be kept in account or when the model should be used in high dynamic contexts, the Random Forest algorithm should be preferred since it presents almost similar performance, but it requires fewer parameters, and thus it takes less time to be designed and trained.  
The temporal window analysis for tasks T1 and T2 led to the following conclusions:  
* For the default prediction task (T1), the general performance increases when considering more than one year of accounting variables and this is true for both ANNs and RandomForest. Indeed, the 5-year temporal window exhibits the best results in terms of AUC. However, in light of the discussion about the metrics, the temporal window of three years achieves the best trade-off between AUC and macro-\(F_{1}\) score using fewer variables.
* For the survival probability prediction task (T2), the best performance is achieved when trying to predict the company status three years in advance (LAW = 3) with an AUC = 0.87 with the ANN. It should be highlighted that ANN reached for LAW = 5 a considerable AUC = 0.86. However, all the models exhibit a really low precision on the bankruptcy class except for SVM and XGBoost. Indeed the best model in terms of macro-\(F_{1}\) score is XGBoost (LAW = 5)  
In general, learning from temporal variables seems to lead to better performance, especially when the model learning capacity can learn complex patterns as happens with Neural Networks.
# JorGPT: Instructor-Aided Grading of Programming Assignments with Large Language Models (LLMs)
## 3 Materials and Methods
### Dataset and Data Preprocessing  
The programming exercises used in this study came from an undergraduate \"Introduction to Programming\" course; these were real student assignments. The dataset comprised 672 student submissions. Of these, 80% were allocated for model training using cross-validation, and the remaining 20% were reserved for validating the proposed approach. The exercises are designed to assess fundamental programming concepts. The specific types of exercises include loops, conditional statements, functions, and basic data structures,  
Figure 1: Data flows between the different system components and the LLMs.  
among others. The programming language used for the exercises was C, but it could be any other language, such as C++, Java, or Python. The student submissions were evaluated and graded by the course instructors, and these grades serve as the reference for evaluating the LLM-based assessment system.  
The student submissions were exported from the Canvas virtual classroom as a CSV file. The system processes the data and generates results in JSON format and an Excel file.  
The student code is read from a CSV file. The names of the comments are removed from the source code, while in the CSV they are used for internal management but are not passed on to the LLM to ensure anonymity. Exercises that are blank are skipped to avoid unnecessary API requests. To minimize the risk of prompt injection [28], the system adds <CODE></CODE> tags around the code.  
Prior to submission to the LLM, the students' code undergoes minimal preprocessing. No tokenization or whitespace normalization is applied, as the goal is to preserve the original formatting for pedagogical evaluation. The programs are represented as plain source code.
# Analysis of Acoustic Flow Field Characteristics of Key Components of High-Speed Train Bogies
## 2 Calculation Model
### Theoretical Basis of Acoustic Flow Field Calculation
#### 2.1.2 Acoustic Finite Element Method (AFEM)  
Further development of the acoustic finite element method was based on the hydrodynamic control equations.  
Acoustic Finite Element Method (AFEM) is a numerical calculation method developed on the basis of classical acoustic equations. It employs numerical computation techniques to help us analyze and solve complex acoustic field problems, such as acoustic wave propagation, sound system design, noise control, and so on. Integrating the classical acoustic equations gives [33] (Volume 6):  
\[\int_{V}p\Big{(}\
abla^{2}p(x,y,z)-k^{2}p(x,y,z)+j\rho_{0}\omega q_{0}(x,y,z) \Big{)}dV=0, \tag{7}\]  
where \(p\) denotes the weight function, and \(V\) is the computational domain.  
According to Gaussian theory, the conversion relationship between volume and area fractions transforms the above equation into the following relationship:  
\[\int_{V}(\
abla p\cdot\
abla p)dV-\omega^{2}\int_{V}\left(\frac{1}{c^{2}}p\cdot p \right)dV=\int_{V}jp\rho_{0}\omega q_{0}dV-\int_{S}j\rho_{0}\omega p\cdot ndS, \tag{8}\]  
where \(S\) denotes the boundary of \(V\).  
The above equation is discretized for a finite element mesh and collapsed to obtain a system of equations in numerical form:  
\[\Big{(}K_{a}+j\omega C_{a}-\omega^{2}M_{a}\Big{)}\cdot p_{i}=Q_{i}+V_{ni}+P_{ i}=F_{ai}, \tag{9}\]  
where \(Q_{i}\) is the input sound source vector; \(V_{ni}\) is the input velocity vector of the acoustic plenum, i.e., the velocity boundary condition of the acoustic plenum; \(P_{i}\) is the input sound pressure vector, i.e., the boundary condition of the acoustic pressure; \(F_{ai}\) is the acoustic excitation; \(p_{i}\) is the acoustic pressure of the grid node where the solution is to be solved; and the \(K_{a}+j\omega C_{a}-\omega^{2}M_{a}\) matrix of equations is a sparse matrix.  
The finite element method is well-suited for addressing nonlinear and inhomogeneous problems, as it can effectively handle complex geometries and boundary conditions. On the other hand, the boundary element method is more suitable for solving linear and homogeneous problems, but it only adapts to regular regions and boundary conditions. In terms of accuracy, the finite element method outperforms the boundary element method in narrow regions, while the latter exhibits higher accuracy in other cases. Considering that the bogie of a train set features a complex structure and falls within a narrow region, this study opts to employ the finite element approach for sound field simulation and analysis.
# Detecting Cyber Threats in UWF-ZeekDataFall22 Using K-Means Clustering in the Big Data Environment
## 2 Related Works
### Addressing the Gap  
While all the previous studies have employed various methods including enhancing centroid-based clustering, grid and density-based clustering, and the optimization of clustering methods, our paper highlights its uniqueness in several ways. First, through its use of the UWF-ZeekDataFall22 dataset, which provides non-redundant, current, and relevant network traffic data rather than the widely used but 25-year-old KDD-Cup99 dataset that many of the above-mentioned studies employ. Second, in contrast to other works, this paper applies K-means clustering to detect evolving cyber threats without requiring labeled data. The labeled data is only used to compare the results. Since no prior knowledge of specific attacks is required, this makes our work more adaptable for real-time intrusion detection, allowing the detection of new and previously unknown attack patterns. Third, none of the previous works have focused on using just the connection-based features of network traffic for clustering.
# Survey on Ranging Sensors and Cooperative Techniques for Relative Positioning of Vehicles
## 3 Non-Cooperative Relative Positioning
### Laser Scanners  
Light Detection and Ranging (LIDAR) devices are laser-based ranging systems that, similar to radar, are based on the time-of-flight of reflected light pulses and are able to measure the distance towards an object. They usually work in the near infrared region of the electromagnetic spectrum at 905 nm. Their transmit power is limited to complying with eye-safety regulations, which imposes a practical limit on the measuring range of the sensor. Usually, a rotating device makes it possible to use a few laser light sources to scan points in space. These devices are called laser scanners. 2D laser scanners measure points in one plane, while 3D laser scanners are able to also vary the elevation angle. Many available products use different scanning planes in order to achieve a small range of elevation angles to account for the inclination of the vehicle.  
The use of laser scanners is mainly in the field of obstacle detection, collision mitigation and stop-and-go assistance. Nevertheless, the use of laser scanners for ACC is already finding its place in ITS. Future autonomous cars will likely rely on laser scanner information to get information from surrounding obstacles [27]. Unfortunately, laser scanners are not able to measure the relative speed between ego and target vehicle directly, but differentiating the range of successive scans [27].  
Table 2 summarizes the performance of four commercially available laser scanners in terms of dimensional resolution, maximum range, azimuth angle, accuracy and measurement cycle. The accuracy is divided into range, radial speed and azimuth angle. As the output power of automotive laser scanners is limited, their maximum perception range lies between 80 m and 200 m and strongly depends on the light reflectivity index of the object. Short-range laser scanners for object detection have ranges of around 10 m. With values around 100\({}^{\circ}\), the field of view of laser scanners is generally broader than that of long-range radar sensors. Even 360\({}^{\circ}\) laser scanners that scan the whole surrounding at high revolutions with several laser beams exist. These have been used in autonomous vehicles as Junior from Stanford University or the Google self-driving car.  
Laser scanners feature good range accuracy between 0.02 m and 0.5 m. The lateral accuracy is limited by their angular resolution of around 0.1\({}^{\circ}\). The speed accuracy of 0.5 m/s is slightly worse, but comparable to that of radar sensors. The availability of laser scanners is importantly limited by environmental conditions. Fog, rain, dust, dirt and water heavily detriment the performance of the sensor. Furthermore, incident sunlight in the morning and afternoon hours can cause important disturbances on the laser detecting device. The price is still high for laser scanners being incorporated into commercial vehicles. Nevertheless, they are extensively used by many research groups to test novel advanced driver assistant systems or self-driving vehicles.
## 4 Discussion  
The developed Cox CT model applied on CT features obtained a CI of 0.707. With recurrence as an additional input variable, it obtained a CI of 0.776, outperforming all the other proposed models. The RSFs performed poorly (CI \(\approx\) 0.5), and the addition of recurrence did not produce a significant improvement. The RSFs' results proved the importance of the feature selection task in radiomic applications.  
The developed Cox PET model without a recurrence feature obtained a nonsignificant (<0.5) CI score. The low efficiency of the Cox PET model was probably due to the limited PET scanner resolution. It was shown [23] that for small tumors (volume \(\lesssim\) 10 cm\({}^{3}\)), textural PET radiomic features are highly correlated with the tumor volume, adding almost no further information, making the quantification of intratumoral heterogeneity inadequate [3]. The inclusion of recurrence to the Cox PET model produced a considerable improvement (CI = 0.62, i.e., +30%) in performance. The Cox models, contrary to the RSF models, were built using smaller subsets of features, so the addition of a strong predictor had a greater impact on the performance. Nevertheless, the performance of the Cox PET model was not as good as that of the Cox CT model. The CT model also performed better than the model with combined PET and CT features.  
The inclusion of recurrence along with standard radiomic features produced a significant improvement in predictive performance. The radiomic features quantified the patient state at a particular time point, thus losing the information about patient history. The recurrence score allowed for the inclusion of this history information in survival evaluation. The recurrence clinical feature could not be directly evaluated by radiomic analysis; therefore, it may provide a significant boost to the survival models' predictions.
## 2 Related Work  
Computer-assisted Diagnosis (CAD) systems can analyze breast ultrasound images and provide auxiliary diagnosis results for benign or malignant tumors. Numerous research efforts have focused on the segmentation, feature extraction, feature selection and classification of breast images [11; 12; 13; 14]. [PERSON] et al. [12] proposed a technique that integrates region growing and edge detection for segmenting magnetic resonance (MR) images. High feature counts increased computational costs and decelerated the classification process. Feature selection methods decreased feature space to improve process accuracy and minimize computation time by removing redundant, irrelevant and noisy features [13]. The classification stage, the core of the CAD system, categorized ROI identification data into predefined classes, typically binary classification as positive or negative classes. Support vector machines (SVM) [15; 16] are important methods in classification tasks. A breast cancer diagnosis approached utilizing an SVM-based method was suggested by [PERSON] et al. [14].  
Recently, due to neural network advancements, a deep convolutional neural network (CNN) [17] has been developed, achieving high accuracy in complex image classification tasks such as ImageNet Challenger [18], enhancing classification and detection progress. Deep learning techniques facilitate automatic feature learning via neural networks, bypassing the need for manual design and feature extraction processes typically involved in conventional machine learning approaches. They can handle more complex data and have a stronger generalization ability and scalability. [PERSON] et al. [19] proposed using deep feature extraction and transfer learning methods to complete the breast ultrasound image classification task. [PERSON] et al. [20] initially trained a small network from scratch as a baseline model, then employed two more complex network models, the first being VGG16 [21], which was trained on over 14 million images, and the second being a fine-tuned model derived from features saved by pre-trained CNN models of VGG16. [PERSON] et al. [22] utilized eight pre-trained CNN models and one custom model, all of which were trained on the ImageNet dataset. The convolutional layer weights of these pre-trained models were employed as feature extractors for breast cancer classification. [PERSON] et al. [23] first used ViT to classify breast ultrasound images using different enhancement strategies. [PERSON] et al. [24] developed five new breast cancer detection frameworks based on deep hybrid convolutional neural networks. The proposed hybrid scheme outperformed the respective base classifiers while maintaining the overall advantages of both networks. Table 1 compares existing methods for breast ultrasound image classification.
# Engineering of Ti:Sapphire Lasers for Dermatology and Aesthetic Medicine
###### Abstract  
This review describes new engineering solutions for Ti:Sapphire lasers obtained at Laseroptek during the development of laser devices for dermatology and aesthetic medicine. The first device, PALLAS, produces 311 nm radiation by the third harmonic generation of a Ti:Sapphire laser, which possesses similar characteristics to excimer laser-based medical devices for skin treatments. In comparison to excimer lasers, Ti:Sapphire laser services are less expensive, which can save \(\sim\)10% per year for customers compared to initial excimer laser costs. Here, the required characteristics were obtained due to the application of a new type of diffraction grating for spectral selection. The second device, HELIOS-4, based on the Ti:Sapphire laser, produces 300 mJ, 0.5 ns pulses at 785 nm for tattoo removal. The characteristics of HELIOS-4 exceed those of other tattoo removal laser devices represented in the medical market, despite a simple and inexpensive technical solution. The development of the last laser required the detailed study of a generation process and the investigation of the factors responsible for the synchronization of the generation in Ti:Sapphire lasers with short (several millimeters) cavities. The mechanism that can explain the synchronization in such lasers is suggested. Experiments for the confirmation of this concept are conducted and analyzed.  
solid-state lasers; Ti:Sapphire lasers; tunable lasers; subnanosecond lasers; induced scattering in crystals +
Footnote †: journal: Physics Letters B
# Linking Singular Spectrum Analysis and Machine Learning for Monthly Rainfall Forecasting
###### Abstract  
Monthly rainfall forecasts can be translated into monthly runoff predictions that could support water resources planning and management activities. Therefore, development of monthly rainfall forecasting models in reservoir watersheds is essential for generating future rainfall amounts as an input to a water-resources-system simulation model to predict water shortage conditions. This research aims to examine the reliability of linking a data preprocessing method (singular spectrum analysis, SSA) with machine learning, least-squares support vector regression (LS-SVR), and random forest (RF), for monthly rainfall forecasting in two reservoir watersheds (Deji and Shihmen reservoir watersheds) located in Taiwan. Merging SSA with LS-SVR and RF, the hybrid models (SSA-LSSVR and SSA-RF) were developed and compared with the standard models (LS-SVR and RF). The proposed models were calibrated and validated using the watersheds' observed areal monthly rainfalls separated into 70 percent of data for calibration and 30 percent of data for validation. Model performances were evaluated using two accuracy measures, root mean square error (RMSE) and Nash-Sutcliffe efficiency (NSE). Results show that the hybrid models could efficiently forecast monthly rainfalls. Nonetheless, the performances of the hybrid models vary in both watersheds which suggests that prior knowledge about the watershed's hydrological behavior would be helpful to implement the appropriate model. Overall, the hybrid models significantly surpass the standard models for the two studied watersheds, which indicates that the proposed models are a prudent modeling approach that could be employed in the current research regions for monthly rainfall forecasting.  
Keywords:rainfall forecasting; machine learning; least square support vector regression; random forests; singular spectrum analysis +
Footnote †: journal: Applied Sciences
## 7 Evaluation
### First Study
One respondent considered that it might not be \"very comfortable to have to hold a book in the same position to watch a long video\". Two respondents considered that the sphere used to represent the portal was not intuitive: \"I don't understand immediately what that sphere is for, it is not intuitive\". Another respondent considered that the visual elements were not updating fast enough, which caused distraction. One respondent was also very critical regarding the value of the VR Book: \"I don't see the advantage of this solution over just looking at a 2D page\". Respondents also provided several suggestions: being able to see the video in 360\({}^{\circ}\) instead of just on the book's page; using animated, more lively 360\({}^{\circ}\) backgrounds; being able to \"zoom in\" in the information on the book's pages by displaying the information in \"a larger floating option in front of the user\"; using not just 360\({}^{\circ}\) backgrounds but immerse the user in an explorable architectural 3D space; adding thickness to the virtual book  
Some respondents also expressed their appraisal (\"That sis [sic] exactly what I am looking for, to be able to see what is not there anymore\") regarding the prototype and suggested further aspects for consideration. For example, one respondent highlighted the challenge of navigation within the 360\({}^{\circ}\) environment and wished (s)he could share the experience with other users; another suggested the use of the VR Book in a graphical adventure type of game; another suggested incorporating voice commands. Overall Impression  
When asked about their general opinion regarding the prototypes they saw while filling in the questionnaire, 20 respondents answered they liked it (10 respondents) or liked very much (10 respondents). Only one respondent disliked the Tangible VR Book prototype.