esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning for Bankruptcy Prediction in the American Stock Market: Dataset and Benchmarks
## 9 Conclusions  
In this research work, we deeply investigated the performance of several machine-learning techniques concerning predicting bankruptcy in the American stock market. We compared the models over two different tasks: (a) default prediction using time series accounting data; (b) survival probability prediction. We performed the tasks using a dataset with 8262 companies in the period between 1999 and 2018. The dataset is also one of the contributions of this paper since it has been publicly released. We used temporal criterion to divide the dataset into training, validation, and test sets. For both tasks, Neural Networks achieve the absolute best results despite exhibiting a great variance in their results, leading to the conclusion that these models can be superior only when opportunely designed and trained with higher computational costs. Finally, we critically discuss the general use of the Area Under the Curve as a common metric to evaluate bankruptcy prediction tasks since in most cases computing precision, recall for the single classes, and the macro-\(F_{1}\) score would better define the models' performance. Moreover, we highlighted that using more fiscal years for the prediction can improve the performance for both tasks, as has been proved in the past only for small datasets. In light of this, future works are to be related to the use of Recurrent Neural Networks and attention-models to better exploit the time-series information, considering the possible trade-off of using deep learning models with such short time series and relatively small datasets. Moreover, bankruptcy prediction could also be evaluated with unsupervised models like Isolation Tree and anomaly detection models. The current dataset could also help in that case. Finally, future works are to be related to the possible limitations of this research work. The main issue to be deeply investigated is related to the temporal dimension of the study: we were able to collect reliable data until 2018 and testing on previously unseen example has been promising. However, it would be interesting to evaluate if the current models could also generalize on different economic situations like the ones that come up with the COVID-19 pandemic and the resource crisis. Another limitation of this work is related to the class imbalance. Several techniques of effective sampling methods should be considered in the study in order to evaluate a balanced scenario as long as there is synthetic data generation.  
Conceptualization, G.L.; Methodology, G.A.; Software, M.P.; Supervision, P.M.P. and A.P.; Writing--review & editing, S.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Real-Time Computing Strategies for Automatic Detection of EEG Seizures in ICU
## 3 Results
### 'Convulsive Seizure' Subgroup  
In assessing seizure detection performance among patients with convulsive seizures compared to those with normal recordings, the k-nearest neighbours (KNN) model with a k value of 9 was found to be most effective. The mean F1 score for all features was 0.84. Focusing on the most influential variables, including channel means and greatest local differences in various transforms, the mean F1 score was 0.88 with a standard deviation of 0.04. Sensitivity was calculated at 90.7%, and specificity was 100%, as detailed in the confusion matrix (Figure 3H). False positives and negatives primarily originated from patients with brain haemorrhage, epilepsy, and CET, with a higher incidence of GS and FS.
# Towards Digital-Twin Assisted Software-Defined Quantum Satellite Networks
## 3 System Model
As reported in [57; 58], the attenuation between the GS and the satellite in contact is modeled as follows:  
\[\mathcal{A}_{u,\varphi}(t)=\frac{\mathcal{Z}_{u,\varphi}^{2}(t)(\Theta_{T}^{ 2}(t)+\Theta_{med}^{2}(t))}{\mathcal{D}_{R}^{2}\mathcal{T}_{T}(1-\mathcal{L}_ {P})\mathcal{T}_{R}\mathcal{T}_{OPT}PDE}10^{\frac{\mathcal{A}_{med}(t)}{10}} \tag{2}\]  
where \(\mathcal{Z}_{u,\varphi}(t)\) is the distance between the GS and the satellite telescopes or between two telescopes belonging to two neighboring satellites and \(\Theta_{T}(t)\) and \(\Theta_{med}(t)\) are, respectively, the divergence angle of the transmitting telescope and the divergence angle caused by the atmospheric turbulence, expressed as follows:  
\[\Theta_{T}(t)=1.27\frac{\lambda}{\mathcal{D}_{T}} \tag{3}\]  
\[\Theta_{med}(t)=2.1\frac{\lambda}{r_{0}} \tag{4}\]  
Moreover, \(\mathcal{D}_{T}\) and \(\mathcal{D}_{R}\) are the diameters of the transmitter and receiver telescopes apertures, \(\lambda\) is the wavelength, \(\mathcal{T}_{T}\) and \(\mathcal{T}_{R}\) are the transmission factors of the transmitter and receiver telescopes, \(\mathcal{T}_{OPT}\) models the optics between the entangled pair sources and the satellite's telescopes, \(\mathcal{L}_{P}\) is the pointing loss due to their misalignment, \(PDE\) is the single photon detector efficiency, and \(\mathcal{A}_{med}\) is the medium attenuation, calculated as in [12]. Considering that LEO constellations operate above the Karman line [59], which is the conventional definition of the edge of space, for the inter-satellite links, the value of \(\mathcal{A}_{med}\) was considered to be zero. Therefore, in this case, the geometric component prevails in Equation (2). Another parameter typically measured in QNs which has been analyzed in Section 5 is fidelity. As explained in [2], the fidelity between two quantum states described by the density matrices \(\rho\) and \(\sigma\) is given by the following formula:  
\[\mathcal{F}(\rho,\sigma)=\left(\mathrm{Tr}\sqrt{\sqrt{\rho}\sigma\sqrt{\rho}} \right)^{2} \tag{5}\]  
Figure 2: A detail of the TG of the constellation. Specifically, the instants of time from which the links stop being available are represented. This formula is used to measure how close two quantum states are to each other, with values ranging from 0 to 1, where 1 indicates identical states. The E2E flows between the two GSs can be optimized by the DT through the application of the optimization method defined in [18], also taking into account parameters such as fidelity among the problem constraints. Furthermore, since the management of the swapping process can cause an increase in terms of overhead, it is appropriate to minimize it, especially in the case of distributed computing operations. This target can be achieved with an effective control of entanglement generation and swapping procedures by the CP, an objective that can be achieved through the optimization problem defined in [18].
# DBCW-YOLO: A Modified YOLOv5 for the Detection of Steel Surface Defects
## 4 Experiment
### Ablation Experiment  
According to Table 4, we know that our improvement is useful. The mAP value of YOLOv5m is 75.3%, and the mAP value of DBCW-YOLO is 81.1%, which has improved the effect on all six types of defects. Cr increased by 11.2%, In increased by 7.3%, Pa increased by 1.4%, PS raised by 2.3%, RS raised by 11.4%, and Sc raised by 1.1%. For the function of each module, ablation experiments were conducted in this article, respectively, and the mAP value was significantly improved by each module, while the increase in the mAP value by module superposition was still 4% and 4.3%. Therefore, our experiments proved the usefulness of every module. For the two types of defects in the detection effect of the benchmark model, Cr and Sc both increased by more than 10%. Compared to YOLOv5m, the overall mAP value of DBCW-YOLO increased by 5.8%, which verified the detection capability of DBCW-YOLO.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Methods** & **mAP 0.5** & **Cr** & **In** & **Pa** & **PS** & **RS** & **Sc** \\ \hline YOLOv5m & 75.3\% & 39.8\% & 79.7\% & 91.6\% & 90.5\% & 58.6\% & 91.8\% \\ W-YOLO & 76.8\% & 39.3\% & 80.0\% & 91.0\% & 96.0\% & 61.1\% & 93.5\% \\ BC-YOLO & 77.3\% & 40.6\% & 80.7\% & 95.1\% & 96.8\% & 61.7\% & 88.9\% \\ D-YOLO & 78.5\% & 47.3\% & 80.6\% & 93.4\% & 94.4\% & 61.5\% & 94.1\% \\ DW-YOLO & 79.3\% & 46.9\% & 88.9\% & 90.2\% & 90.5\% & 65.5\% & 93.8\% \\ BCW-YOLO & 79.6\% & 50.4\% & 82.1\% & 93.9\% & 94.5\% & 65.8\% & 91.2\% \\ DBCW-YOLO & 81.1\% & 51.0\% & 87.1\% & 93.0\% & 92.8\% & 70.0\% & 92.9\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation experiments on NEU-DET.  
Figure 9: AP comparison of various types.  
Experiments indicate that compared with the base model and other models of the network, our method improves the accuracy of defect detection in steel structures, which further proves the superiority of the DBCW-YOLO algorithm.
# High-Performance Computing and Parallel Algorithms for Urban Water Demand Forecasting
## 3 Results and Discussion
### Parallel Pipeline Performance and Scalability
#### 3.5.1 Efficiency of Parallel Execution  
Parallel efficiency, defined as the ratio of speedup to the number of cores used, provides insight into how well computational resources are utilized. The Bi-LSTM Optimization step achieves 71.12% efficiency, suggesting that task parallelism is well-balanced with minimal communication overhead. In contrast, Outlier Detection exhibits the lowest efficiency at 21.25%, likely due to a lower computational load and increased synchronization costs among MPI ranks.  
Figure 8 illustrates the efficiency trends across different processes, highlighting the computational phases where additional optimization may further enhance parallel performance.
# Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review
## 6 Energy Efficiency and Resource Allocation Algorithms
### Discussion  
There are several network EE issues that should be taken into consideration for management in optical and mmWave radio networks. The massive small cell deployments increase the signalling cost as mmWave bands have smaller coverage radii. In addition, antenna processing for massive MIMO antenna systems consumes extra power [84]. The selected papers in Table 5 reveal that the use of sleep modes and information exchange among small cells are some of the methods to respond to EE requirements of mmWave UDN deployments. The EE optimization methods identified for optical and mmWave radio networks can be enhanced with cognitive networking methods, where each node seeks to \"minimize its energy\" by minimizing the cumulative neighborhood energy function, as in [85]. Adapting cluster-based protocols used to harvested energy utilization for wireless sensor networks [86] for UDN deployments can also provide a novel research direction for converged optical and radio networks.  
Apart from these operation and maintenance level for EE, such as the sleep modes, the reduction in energy related costs should also be a target during the planning and pre-deployment phases [87], which can be optimized by selecting the optimal AP density and distribution. The relationship between the transmit power and the AP density is defined as a function with the use of stochastic geometry in [88], and solving this non-linear function gives the unique transmit power and density that maximizes the energy efficiency. Modeling the energy performance of APs and optical transport network together should also be considered in the planning phase [89] to increase the overall EE of the network.  
Finally, creating SDN applications that aim to achieve energy minimization as in [81] can be considered as strategy to overcome EE issues; however the architectural changes for network management by implementing SDN can also increase power consumption as it introduces new components with controllers and SDN switches. To analyze the power consumption impact of the architectural changes in the C-RAN fronthaul, Ref. [90] measures the power consumption of SDN switches, RRHs, optical transceivers and control components, and the results show that this architecture increases the total power consumption of the network by about 20%. The optimal EE solution therefore also requires planning of the power consumption of the SDN components in the pre-deployment phase. Finally, the limitations of the SDN-based solutions in Table 5 show that used algorithmshave diminishing power gains for high arrival rate [81], have complexity issues with the increasing number of users [49], and do not consider high resource demanding multimedia services [83]. An SDN-based energy consumption solution for beyond-5G dense mmWave deployments providing eMBB services to users is therefore a topic to consider for future research.
## 4 Simulation and Analysis
Figure 4: The convergence in term of different initial values of users for Algorithm 1. Figure 5: The energy efficiency via the weight coefficient. Figure 6 shows the relationship between the network EE and spectrum efficiency where the number of antennas installed on the MBS is different. The simulation setting of the number of users \(K\) and the initial value of each BS's transmit power \(p_{j}\), \(\forall j\) are 30 and \(p_{j,\max}\), respectively. The horizontal and vertical axes in Figure 6 represent the spectral efficiency and EE values of the network calculated by Algorithm 2 with different values of given weight coefficient \(\omega\), respectively. It can be obviously seen in Figure 6 that the network EE is a concave function on the spectrum efficiency. In addition, the maximum value of the network EE and spectral efficiency increase and reduce with the number of antennas installed on the MBS increasing from 50 to 200. This point is interesting and we have some discussion about it. On the one hand, the number of available spatial channels increases with the more antennas existing in the network. The network obtains greater throughput and spectrum efficiency. On the other hand, the energy consumption of the network also increases as the number of antennas increases. To some extent, the network EE reduces, correspondingly. Figure 7 shows the network EE calculated by different algorithms when the number of antennas installed on the MBS increases from 10 to 170. The simulation setting of the number of users \(K\) and the initial value of each BS's transmit power \(p_{j}\), \(\forall j\) are 30 and \(p_{j,\max}\), respectively. We can find two interesting points in Figure 7. Firstly, it can be obviously found that the network EE is a concave function on the number of antennas installed on the MBS. This point can be proven as described above. The second point is that the performance of the network EE obtained by the proposed Algorithm 1 is superior to other single optimization algorithms, which also demonstrates the available effectiveness of the proposed Algorithm 1 to some extent. Figure 6: The energy efficiency via the spectrum efficiency.
## 1 Introduction  
Nowadays, authentication has been adopted in many areas, such as radio frequency identification (RFID), cloud computing, wireless sensor networks, internet of things (IoT), etc. Authentication schemes can be separated into two types: one is individual-oriented authentication, and another is group-oriented authentication. The scenario of individual-oriented authentication is one-to-one communication. There are only two users needing to authenticate each other. In group-oriented authentication, there are more than two users in a group. They build a private network and share messages among the group. Such a cryptographic primitive can be applied heavily to many-to-many network environments, e.g., Internet of Things or RFID.  
However, most of these authentication schemes are built on traditional cryptographic primitives, e.g., RSA and ElGamal, where the cryptosystems are constructed based on discrete logarithm or factorization. It is widely believed that such primitives are not resistant to quantum algorithms. For instance, the algorithm proposed by [PERSON] [1] in 1994 is a quantum algorithm that solves discrete logarithm problems and factorization problems in subexponential time complexity. To deal with those quantum attacks, lattice-based cryptography was introduced by [PERSON] [2] in 1996. A lattice can be represented as a matrix which has a periodic structure in dimensional space. There are two central hard problems in lattice-based cryptography: the shortest vector problem (SVP) and the closest vector problem (CVP). The shortest vector problem (SVP) is, given a base of a lattice, to find the smallest possible nonzero vector of the lattice. The closest vector problem (CVP) is, given a base of a lattice and a vector which does not belong to the lattice, to find a vector belonging to the lattice that is the closest vector to the given vector. Both SVP and CVP are believed to be invulnerable to quantum attacks such as [PERSON]'s algorithm. As a powerful and promising quantum-resistant primitive, it has been adopted in lots of applications, such as public key cryptosystems and sieve algorithms.  
To the best of our knowledge, the existing lattice-based authentication schemes [3,4] are based on a lattice-based public key encryption called NTRU encryption [5], proposed by [PERSON], [PERSON], and [PERSON] in 1998. However, these two schemes support only the case of a single user. Another intuitive way to achieve group authentication is by using a lattice-based group signature. Nevertheless, a signature scheme is an asymmetric cryptographic primitive, which is usually more costly than a symmetric one. Besides this, group signatures support additional but unnecessary properties, such as the anonymity of signers, which may not be a desired property in RFID or IoT. Therefore, in view of the aforementioned issues, we will propose a new construction for lattice-based authentication. The proposed scheme also supports group authentication.
## 4 Adsorption Study
### Effect of Adsorbent Dosage on the Removal of RB  
Figure 4 illustrates the effect of adsorbent dosage on the adsorption of RB dye using CC biochars by plotting the percentage adsorption of RB as a function of the adsorbent dosage in the 0.01-0.8 g range. The adsorbent dose is a key parameter affecting the removal efficiency [40].  
The results show that the removal efficiency increases from 59.94 to 68.50 wt% for CCB500, from 70.34 to 100 wt% for CCB600, with the increment of the adsorbent dose from 0.01 to 0.8 g, and from 59.83 to 100 wt% for CCB700 for an increase from 0.002 to 0.08 g. Generally, an increasing biochar dose means more active sites, as well as more surface area being available for interactions between the adsorbent and the dye. The same trend was observed in a previous study using biochars derived from different biomasses in order to remove organic and inorganic pollutants [38; 42; 43; 44].  
Indeed, for CCB500, the dye removal efficiency increases with adsorbent dose until 0.025 g and then decreases to reach constant efficiency. This behaviour could be explained by the fact that the high adsorbent accumulation and high availability of active sites lead  
Figure 5: pH\({}_{\text{IPZC}}\) of the studied biochar samples.  
to a decrease in adsorption capacity. At a constant dye concentration and with increasing amounts of adsorbent, dye removal is limited [45].
# Investigating Models for the Transcription of Mathematical Formulas in Images
## 5 Results
### Mathematical Expression Recognition Models  
To convert formula images into LaTeX code, we evaluated the MER models _LaTeX-OCR_[38] and _TrOCR_[36] by comparing their resulting LaTeX codes with the LaTeX codes in the references of the _Formula2 Text_ test set.  
Figure 10 visualizes the performances of the analyzed MER models in terms of TER. The blue bars show the performances of the original pre-trained models. The green bars represent the performances of the models that we fine-tuned using the _Formula2 Text_ validation set. We observe that the fine-tuned _LaTeX-OCR_ achieves, with 23.45%, a notably lower TER compared with the fine-tuned _TrOCR_'s TER of 27.71%, which is 18.16% relative.  
Table 1 shows the relative improvements with the fine-tuning. We see that the fine-tuning had a significantly higher effect on _TrOCR_, which is explained by the fact that _LaTeX-OCR_ had already been trained with printed and handwritten formula images. The fine-tuning process resulted in a substantial relative improvement of 202.24% for _TrOCR_ and only in a relative improvement of 5.97% for _LaTeX-OCR_.
## 6 Conclusions  
The present work emphasizes on the development of a sequential coupling mathematical model for unsteady flow field-rotordynamics of the centrifugal pump as well as the corresponding numerical calculation program. A horizontal split centrifugal pump with double-entry impeller was chosen for research, and the validity and accuracy of the computation were verified through its performance test and vibration measurement. The coupling model presented in this work provided a series of interconnected computation information including the inner flow field and deformation and vibration behaviors of the external rotor system in a flexible high efficiency way, and some conclusions can be drawn as follows:  
(1) \(f_{\text{BPF}}\) is the dominant frequency in the fluctuation of hydraulic radial force on the impeller, pressure pulsation in the blade tip clearance, and vibration of main shaft, which are closely related to the RSI of the centrifugal pump.  
(2) Operating stability indexes like fluctuation of hydraulic radial forces on the impeller, vibration, and deformation levels of the main shaft are predominated by the unsteady flow process in the centrifugal pump. Those indexes are at the optimal level around the rated flow rate, but get deteriorated when it deviates from the best work condition, which is more serious in part-load conditions.  
(3) Under large flow rates, although the radial forces on the impeller are larger than other conditions, the vibration levels can be controlled better than those under part-load conditions due to their relatively stable inner flow field.  
In sum, this work mainly discusses the rotor vibration behaviors induces by inner flow process of centrifugal pump. A more precise unsteady CFD model should be conducted in future work to obtain more accurate time-varying laws of the hydraulic exciting forces. Moreover, more measuring points, including other non-intrusive sensors, needed to be supplemented in the experimental validation work.