esa-sceva/satcom-chunk-collection huggingface dataset:
# Noninvasive Deep Learning Analysis for Smith-Magenis Syndrome Classification
## 3 Methods and Materials
### Metrics  
In this context of SMS classification, a true positive (TP) refers to an SMS individual that is correctly identified as an SMS. Conversely, a true negative (TN) signifies a non-SMS participant that is accurately classified as such. However, classification errors can occur. A false positive (FP) denotes a non-SMS individual mistakenly categorized as an SMS, and a false negative (FN) represents an SMS individual incorrectly classified as a non-SMS. Understanding these terms is crucial for evaluating the performance of SMS classification algorithms.  
The performance of a classifier in binary classification can be evaluated using three key metrics: sensitivity, specificity, and accuracy.  
Sensitivity, known as the true positive rate, reflects the proportion of positive cases that the model correctly identifies. It essentially calculates the ratio between true positives (correctly classified positive examples) and all the samples that were originally labeled as positive.  
\[\text{Sensitivity}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{1}\]  
Specificity, however, focuses on the model's ability to correctly classify negative cases. It is calculated as the ratio between true negatives (correctly classified negative examples) and all the samples initially labeled as negative.  
\[\text{Specificity}=\frac{\text{TN}}{\text{TN}+\text{FP}} \tag{2}\]  
Balance accuracy, while seemingly straightforward, can be misleading in imbalanced datasets. It is simply the sum of true positives and true negatives divided by 2.  
\[\text{Balance Accuracy}=\frac{\text{Sensitivity}+\text{Specificity}}{2} \tag{3}\]
## 3 Results
### Model Error Distribution  
The Root-Mean-Squared Error (RMSE) and Normalized RMSE (NRMSE) are employed as evaluation metrics to assess the performance of the trained ANN model. These metrics provide valuable insights into the accuracy of the model predictions across various wave parameters and vessel forward speeds. The RMSE and NRMSE are calculated for the discrete spectral density for each case using Equations (14) and (15), respectively. Figure 12 presents the error distributions observed along the wave parameters and vessel forward speeds. The error distributions are visualized using box plots, which depict the quartiles of the dataset. The whiskers of the box plots (1.5 \(\times\) IQR) extend to show the rest of the distribution, excluding points identified as outliers. Outliers are identified using a method based on the inter-quartile range (IQR), ensuring robustness in the analysis of the error distributions.  
The ANN model demonstrates an RMSE accuracy of less than 0.4, indicating low overall error rates. However, the NRMSE values indicate higher errors with low significant wave heights (\(H\)s) since the magnitude of the spectral density is small in this condition. Notably, for significant wave heights (\(H\)s) greater than or equal to 3 m, the model accuracy is less than 7% in terms of NRMSE. Conversely, for \(H\)s values less than 3 m, the NRMSE values tend to be higher. Other parameters do not show significant variability in the RMSE and NRMSE distribution, but it could be seen that the errors slightly increase at low peak periods (\(Tp\)), low peak enhancement factors (\(\gamma\)), high forward speeds (\(V\)), and low wave headings (\(\beta\)).  
Figure 11: Estimated original wave spectra at different peak enhancement factors \(\gamma\) (Model 3, \(H\)s = 3 m, \(Tp\) = 15 m, wave headings \(\beta\) = 30 deg, and forward speed \(V\) = 10 knots).  
Figure 13 presents a test case that utilizes the estimated encounter wave spectrum obtained via the Kalman filter (KF) algorithm, using real-time motion sensor signals as  
Figure 12: Error distribution of Model 3 for wave parameters.  
illustrated in Figure 1. This example demonstrates a more realistic application of ML Model 3, which converts encounter wave spectra experienced by vessels into original wave spectra. The onboard KF-estimated encounter wave spectrum fluctuates and contains some error, as shown in Figure 12(a), and therefore requires conversion to the original wave spectrum using the ANN. As a result, the overall accuracy of the reproduced original wave spectrum is slightly reduced compared to the theoretical curves, as shown in Figure 12(b). Nevertheless, the overall spectral shape is reconstructed with reasonable accuracy. This result highlights the model's potential for practical use in real-time onboard applications, even though the initial training was conducted using various theoretical spectra.
# Limitations and Performance Analysis of Spherical Sector Harmonics for Sound Field Processing
## 6 Discussion  
Although SSHs were derived from SHs and share high similarities in their formulations, according to the analysis in Sections 3-5, it is evident that SSHs have essential limitations compared to SHs. The first limitation arises from violating the Helmholtz equation, a fundamental requirement for many sound field processing applications. Consequently, SSHs cannot accurately conduct sound field radial extrapolation. As a result, SSHs remain suitable solely for analyzing measurement regions and are incapable of extending sound field analysis into the broader space. The simulation results have shown significant errors in extrapolating the sound field using SSHs, except under specific conditions such as the exterior case with low frequencies, large sector sizes, and small extrapolation distances. Furthermore, the distortion problem affects the reconstruction accuracy over the edge areas of the sector region. Overall, SSHs remain reliable tools for decomposing and reconstructing the sound field within the measured spherical sector region, with applications in beamforming and localization. However, users should be mindful of the noted limitations to ensure they are applied appropriately.  
To improve the performance of SSHs, it is crucial to ensure that the measurement region is larger than the target sector region both longitudinally and latitudinally to mitigate the distortion issues and improve the accuracy of sound field reconstruction. Considering these limitations, future implementations of spherical sector harmonics should primarily focus on processing and analyzing the measurement sector region without any extrapolation process. Alternatively, if a dual-array setup is available, the method proposed in [28] can be employed to enhance the extrapolation performance. Additionally, for the interior case, an improved reconstruction accuracy can be achieved when the source is oriented towards the sector region.
###### Abstract  
The accuracy assessment of mobile mapping system (MMS) outputs is usually reliant on manual labor to inspect the quality of a vast amount of collected geospatial data. This paper presents an automated framework for the accuracy assessment and quality inspection of point cloud data collected by MMS operating with lightweight laser scanners and consumer-grade microelectromechanical systems (MEMS) sensors. A new, large-scale test facility has been established in a challenging navigation environment (downtown area) to support the analyses conducted in this research work. MMS point cloud data are divided into short time slices for comparison with the higher-accuracy, terrestrial laser scanner (TLS) point cloud of the test facility. MMS data quality is quantified by the results of registering the point cloud of each slice with the TLS datasets. Experiments on multiple land vehicle MMS point cloud datasets using a lightweight laser scanner and three different MEMS devices are presented to demonstrate the effectiveness of the proposed method. The mean accuracy of a consumer grade MEMS (<$100) was found to be 1.13 \(\pm\) 0.47 m. The mean accuracy of two commercial MEMS (>$100) was in the range of 0.48 \(\pm\) 0.23 m to 0.85 \(\pm\) 0.52 m. The method presented here in can be straightforwardly implemented and adopted for the accuracy assessment of other MMS types such as unmanned aerial vehicles (UAV)s.  
mobile mapping systems; lightweight lidar; accuracy assessment; registration +
Footnote †: journal: _sciences_
# Assessing the Techno-Economic Feasibility of Bamboo Residue-Derived Hard Carbon
###### Abstract  
Bamboo residues represent an abundant, renewable biomass feedstock that can be converted into hard carbon--an emerging anode material for sodium-ion batteries. This study presents a detailed techno-economic analysis of hard carbon production from bamboo residues across China's ten most bamboo-rich provinces. Regional feedstock availability was estimated from provincial production statistics, while average transportation distances were derived using a square-root-area-based approximation method. The process includes hydrothermal pretreatment, acid washing, carbonization, graphitization, and ball milling. Material and energy inputs were estimated for each stage, and both capital and operating expenses were evaluated using a discounted cash flow model assuming a 15% internal rate of return. The resulting minimum selling price of bamboo-derived hard carbon ranges from 14.47 to 18.15 CNY/kg. Assuming 10% of bamboo residues can be feasibly collected and processed, these ten provinces could collectively support an annual hard carbon production capacity of approximately 1.04 million tons. The results demonstrate that bamboo residues are a strategically distributed and underutilized resource for producing cost-competitive hard carbon at scale, particularly in provinces with existing bamboo industries and supply chains.  
hard carbon; biomass; pyrolysis; economic analysis; sodium ion battery; bamboo residue 2025
## 5 Discussion
### Global Involvement and Interdisciplinary Collaborations  
The research on cybersecurity utilising ML and DL reaches global scope because authors participating from 114 nations collaborated mutually, as excerpted in Figure 7. The current complexity of modern cyber threats exceeds technical difficulties; hence, cross-border and interdisciplinary partnerships prove indispensable. The combination of international organisations enables scientific teams to share talents with resources and research materials, resulting in the faster discovery of security innovations for identifying threats, evaluating weaknesses and enhancing privacy measures.  
Social sciences and ethical and legal frameworks should unite with technical perspectives to address fundamental socio-technical matters, including AI decision accountability standards and ethical rules for extensive user data collection and privacy compliance frameworks. Various documented cooperative programs, such as global cyber operations ranges and joint protection systems for threat detection, demonstrate how expertise in technical and policy domains generates technologically solid and societally harmonised solutions. The combination of cybersecurity specialists and legal scholars has led to clear data protection definitions, and sociologists and psychologists have brought forward strategies to handle insider threats. Effective cybersecurity surpasses technical know-how since it needs comprehensive solutions, which unite ethical, legal, and societal perspectives.
###### Abstract  
Land remote sensing capabilities in the optical domain have dramatically increased in the past decade, owing to the unprecedented growth of space-borne systems providing a wealth of measurements at enhanced spatial, temporal and spectral resolutions. Yet, critical questions remain as how to unlock the potential of such massive amounts of data, which are complementary in principle but inherently diverse in terms of products specifications, algorithm definition and validation approaches. Likewise, there is a recent increase in spatiotemporal coverage of in situ reference data, although inconsistencies in the used measurement practices and in the associated quality information still hinder their integrated use for satellite products validation. In order to address the above-mentioned challenges, the European Space Agency (ESA), in collaboration with other Space Agencies and international partners, is elaborating a strategy for establishing guidelines and common protocols for the calibration and validation (Cal/Val) of optical land imaging sensors. Within this paper, this strategy will be illustrated and put into the context of current validation systems for land remote sensing. A reinforced focus on metrology is the basic principle underlying such a strategy, since metrology provides the terminology, the framework and the best practices, allowing to tie measurements acquired from a variety of sensors to internationally agreed upon standards. From this general concept, a set of requirements are derived on how the measurements should be acquired, analysed and quality reported to users using unified procedures. This includes the need for traceability, a fully characterised uncertainty budget and adherence to community-agreed measurement protocols. These requirements have led to the development of the Fiducial Reference Measurements (FRM) concept, which is promoted by the ESA as the recommended standard within the satellite validation community. The overarching goal is to enhance user confidence in satellite-based data and characterise inter-sensor inconsistencies, starting from at-sensor radiances and paving the way to achieving the interoperability of current and future land-imaging systems.  
calibration; validation; optical; land; interoperability; traceability; metrology; Fiducial Reference Measurements (FRM) 2021
## 2 Literature Review
### Machine Learning-Based VPN Classification  
The very first crucial step of the machine learning-based classification process begins with the extraction of features. Numerous features obtained from flow statistics, packet headers, and payload information have been tested by researchers. Packet size, flow duration, inter-arrival time, and protocol dispersion are a few typical features [3]. Entropy-based characteristics [9] and features obtained from encrypted payloads using statistical analysis have both been investigated in recent publications [7].  
For VPN categorization, a variety of machine learning algorithms, including supervised, semi-supervised, and unsupervised techniques, have been used. Typical supervised algorithms include support vector machines (SVMs), decision trees, neural networks, and k-nearest neighbors (k-NNs) [3]. Without labeled data, unsupervised approaches like clustering have also been used to categorize VPN traffic [28]. To further enhance classification performance and minimize annotation work, semi-supervised techniques that make use of both labeled and unlabeled data have been proposed [7].
# Artificial Neural Networks for Navigation Systems: A Review of Recent Research
## 4 Navigation Integration Enhanced by the ANN
### More Recent Development of NNs for Navigation System Designs
#### 4.3.4 Gated Recurrent Unit (GRU) NN  
The majority of neural networks, including RBF and MLP, are static networks. These networks' primary drawback is that they can only store a limited amount of information from the past and can only use data from the most recent phase. The gated recurrent unit (GRU) was employed for the INS/GPS since the GRU is used for events connected to time series [88; 111; 112; 113; 114]. Figure 18 displays a hybrid method based on a GRU and an IMM-ARCKF. To address the uncertainty of the system model and measurement noise statics in the road applications of INS and GPS, the IMM-ARCKF technique is first presented. Then, a GRU neural network with two training and prediction modes is added to the INS/GPS. First, the GRU neural network operates in training mode when a GPS signal can be received. When there are GPS outages, the GRU neural network forecasts the GPS position increment. The experiment and analysis also assess the algorithm's performance. The suggested algorithm can increase location accuracy during GPS outages, according to the experiment's results.  
Figure 17: Basic schematic of ELM structure.  
A hybrid algorithm based on GRU and AKF can be created to lessen the degradation, as shown in Figure 19. The GRU network is designed to forecast location changes during GNSS outages and has the advantages of high accuracy and efficiency. The GRU-predicted error accumulation is also taken into account in this research, and AKF is included as an additional method of enhancing navigation performance [101]. The basic KF, MLP-aided KF, LSTM-aided KF, and GRU-aided KF methods are all compared to the proposed hybrid algorithm, which is trained and tested on real-world road datasets. A useful solution for overcoming GNSS disruptions is the neural network. The performance analysis of different forms of neural networks indicates that both RNNs overcome the MLP in forecast positional variance, while the GRU surpasses the LSTM in model performance and training effectiveness.
## 5 Experimental Results and Discussion
### Comparison of Previous Work  
Key parameters of recent reported works on short-reach DD links are summarized in Table 1. Although the capacity and reach can be further improved using complex-valued modulation, the hardware complexity of transmitter was almost doubled owing to the IQ modulator and another DAC, which is not suitable for such a cost-sensitive scenario. Meanwhile, the transmitter structures proposed in References [26; 27] are also complicated, and the subcarrier allocation algorithm was designed only for multi-carrier modulation, such as the orthogonal frequency division multiplexing (OFDM) signal, which is not compatible with PAM. The main contribution of our work is to offer a cost-effective solution employing imbalanced MZM with single DAC. The hardware complexity is almost the same as standard IM-DD configuration. Another advantage is that all three imbalances are compatible with various modulation schemes, including PAM, carrier-less amplitude and phase modulation (CAP), subcarrier modulation (SCM), and discrete multi-tone (DMT). Moreover, as mentioned in Section 5.5, although 20 km was chosen for fair comparison, the differential skew scheme has the potential for longer transmission, up to 80 km. Figure 12: Typical eye-diagrams and amplitude histograms of PAM-6/8 signals with FFE/VNE at BTB and after 20 km SSMF transmission operating at different modes, respectively. w/ diff., conventional IM with differential driven mode; w/bias, with bias deviation in single-arm driven mode; w/amp., with amplitude mismatch in differential driven mode; w/skew, with time skew in differential driven mode. Figure 13: Calculated transfer function operating at different modes after (**a**) 20 km, (**b**) 40 km, (**c**) 60 km, and (**d**) 80 km SSMF transmission, respectively. IM: conventional IM with differential driven mode; Bias (40\({}^{\circ}\)), with 40\({}^{\circ}\) bias phase in upper-arm single driven mode; Amp (\(-20\%\)), with \(-20\%\) amplitude mismatch in differential driven mode; Skew (20 ps) with 20 ps time skew in differential driven mode.
## 2 Attitudes towards Using Sports Supplements
#### 2.1.2 Nutrition in the Training Period  
The training period for combat players is composed of the accumulation and intensification phases according to the training periodization concept, and nutrition interventions have to be in accordance with them. Carbohydrates are considered to be the primary source of energy for aerobic exercise. Considering their limited storage capacity in the form of glycogen, carbohydrates play a vital role in sports performance [26]. High-glycemic-index carbohydrates, due to their rapid availability, are recommended before and during exercise to support the effort energy demand. Fast recovery can be achieved by ingesting high-glycemic-index carbohydrates immediately after the effort [27]. According to the International Society of Sports Nutrition, the recommended quantity of carbohydrates for athletes ranges from 8-12 g/kg/day [26]. In combat sports, CHO are, along with hydration, the main strategy for recovery after rapid weight loss. The proposed protocol for rapid glycogen restoration is 1.2 g/kg/h of high-glycemic-index CHO or 0.8 g/kg/h CHO combined with 0.2-0.4 g/kg/h protein in combination with 3-8 mg/kg of caffeine [26].  
Protein intake in the athlete diet should be higher compared with the rest of the population (0.8-0.9 g/kg/day), ranging from 1.2-2.1 g/kg/day with a variation of up to 3.1 g/kg/day in calorie-restricted diets. Optimal protein ingestion shows multiple benefits, such as muscle gain and anabolic effects, anticatabolic effects, injury prevention and fast recovery, and plays an important role in weight loss strategies [25; 28; 29].  
There are no major differences between athletes and the general population in terms of lipid consumption recommendations. Between 20 and 35% of daily calories should come from healthy fats. Fat-restricted diets are not recommended, taking into consideration the role of fat in many processes besides energy production, such as cell membrane synthesis, absorption of fat-soluble vitamins, hormone production and proper brain function. Considering this aspect, the combat player's diet is often supplemented with omega-3 polyunsaturated fatty acids, with satisfactory results [30]. Lipids were proposed in many studies as the prime energy source for performance due to their high caloric densities, but the results are inferior compared with CHO in intense near-VO2 max activities [31; 32]. Lipids remain an important source of energy in low-intensity activities and are an essential component in every nutrition plan.
# An Application of an Unequal-Area Facilities Layout Problem with Fixed-Shape Facilities
## 1 Introduction
### Contribution and Organization of Paper  
Our contribution is the application of the UA-FLP with fixed facilities dimensions to a layout problem encountered at the Toyota Motor Manufacturing West Virginia (TMMWV) plant in Buffalo, WV. Also, an effective matheuristic is presented for the proposed problem which consists of a simple, but effective GA, which is able to produce high quality alternative layouts. More specifically, a GA is used to generate a population of solutions (i.e., permutations of facilities) to determine the order in which facilities should be placed on the plant floor. Next, a boundary search heuristic (BSH), available in the literature, is used to place the facilities on the plant floor to obtain the actual layouts and the fitness of each layout (i.e., total weighted distance). Last, the layouts obtained are used to set the binary variables in a mixed integer linear program, and a dual simplex method is used to solve the corresponding linear program efficiently.  
The remainder of the paper is organized as follows. In Section 2, a mathematical programming formulation is presented for the proposed UA-FLP, and a small problem instance is solved using the mixed integer linear programming (MILP) model. The proposed GA is presented in Section 3. In Section 4, some computational results of the proposed techniques on several test problems are given. Finally, Section 5 provides conclusions.