esa-sceva/satcom-chunk-collection huggingface dataset:
# One-Class LSTM Network for Anomalous Network Traffic Detection
## 2 Background and Related Work
### Deep Learning for Unsupervised Anomaly Detection  
Anomaly detection is an important topic in data science research [17]. The aim in unsupervised anomaly detection is to find a separation rule between anomalous and normal data without labels. In recent years, with the unprecedented success of deep neural  
Figure 2: Schematic diagram of the classic single classification algorithm.  
networks as feature extractors for processing image, audio, and text data [18], several hybrid models that combine deep learning and the OC-SVM have emerged [19, 20]. The hybrid model uses a pretrained deep learning model to acquire rich representational features and is then fed into shallow anomaly detection methods, such as the OC-SVM. However, these hybrid OC-SVM methods are decoupled because their feature learning is task-agnostic and is not customized for anomaly detection [21].  
In addition to hybrid approaches, another common method of anomaly detection is based on the use of deep auto-encoders such as the robust deep autoencoder (RDAE) [2] and robust convolution autoencoder (RCAE) [22]. The input data \(X\) of a deep autoencoder is decomposed into two parts, \(L_{D}\) and \(S\), where \(L_{D}\) represents the latent representation of the hidden layer and \(S\) represents the noise and outliers that are difficult to reconstruct. Therefore, the optimization objective function is:  
\[\begin{split}\underset{\theta,\,S}{\text{min}}+\left\|L_{D}-D_{ \theta}(E_{\theta}(L_{D}))\right\|_{2}+\lambda\cdot\left\|S^{T}\right\|_{2,1} \\ s.t.\,\,\,\mathbf{X}=L_{D}+S\end{split} \tag{4}\]  
Back-propagation and the alternating direction method of multipliers (ADMM) can be used to solve the above optimization problems, and the reconstruction error is employed as an anomaly score [23].  
Auto-encoders have the objective of dimensionality reduction and do not target anomaly detection directly. However, the main difficulty of this approach is the question of how to choose the right degree of compression. Apart from auto-encoders, some deep models train neural networks by minimizing the volume of the hypersphere surrounding the data or the distance between the data and the hyperplane [20, 24]. However, such experiments are still based on features extracted via deep auto-encoders or pre-trained models [25, 26]. In our experiments, we carried out a detailed comparison between the proposed OC-LSTM and the abovementioned anomaly detection methods.
###### Abstract  
Since the 1990s, mobile telecommunication networks have gradually become denser around the world. Nowadays, large parts of their backhaul network consist of commercial microwave links (CMLs). Since CML signals are attenuated by rainfall, the exploitation of records of this attenuation is an innovative and an inexpensive solution for precipitation monitoring purposes. Performance data from mobile operators' networks are crucial for the implementation of this technology. Therefore, a real-time system for collecting and storing CML power levels from the mobile phone operator \"Telecal Fase\" in Burkina Faso has been implemented. This new acquisition system, which uses the Simple Network Management Protocol (SNMP), can simultaneously record the transmitted and received power levels from all the CMLs to which it has access, with a time resolution of one minute. Installed at \"Laboratoire des Materiaux et Environnement de l'Universite Joseph KI-ZERBO (Burkina Faso)\", this acquisition system is dynamic and has gradually grown from eight, in 2019, to more than 1000 radio links of Telecel Fase's network in 2021. The system covers the capital Ouagadouigou and the main cities of Burkina Faso (Bobo Dioulasso, Ouahigouya, Koudougou, and Kaya) as well as the axes connecting Ouagadouigou to these cities.  
commercial microwave links; power level; SNMP protocol; acquisition system; rainfall estimation
## 1 Introduction
* The MODMSA is evaluated by extensive simulation experiments. The results demonstrate that the MODMSA outperforms other comparison algorithms especially in the diversity of non-dominated solutions. The remainder of this paper is oriented as follows: In Section 2, the EOS dynamic mission-scheduling problem is described and the multi-objective mission scheduling mathematical model is formulated. In Section 3, the MODMSA is proposed for the DMP-PP. The simulation experiments and discussion are introduced in Section 4 and Section 5 respectively. Finally, the conclusions are described in Section 6.
# Progress on Single-Feed Quality Wideband Linear Wire Array
## 4 Reflectors and Directors  
To generate a unidirectional radiation pattern, a flat reflector was used [32]. The distance between the reflector and the driven element can impact antenna gain and match, so a stepped reflector was used to achieve a wider bandwidth [33]. These results have inspired further research into designing wider-bandwidth reflectors. The use of a W-shaped reflector resulted in a bandwidth of over 70%. The W-shaped reflector not only formed a unidirectional beam but also improved the impedance bandwidth while optimizing the distance in the feeding zone. In order to reduce the antenna's wind load, a wire array arranged in a W shape was used to replace the metallic W plate, and similar results were obtained [27, 34].  
In addition to reflectors, directors can also be applied to the WHEMS structure to achieve high directional antenna gain [35]. Three simple wire directors can be used to enhance array antenna gain. The positions of the directors are set to match the equivalent source of the driven WHEMS element. The directors exhibit capacitive behavior at lower frequencies and maintain this capacitive behavior across the entire operating frequency range, while the reflectors display inductive characteristics throughout the frequency range of interest. The arrayed directors and reflector improved the aperture efficiency of the QWL antenna. The combination of a wideband driven element, W-shaped reflectors, and directors resulted in a wideband high-gain linear array, as shown in Figure 6, where the arrows in the Figure 6d point to the y-axis corresponding to the values referenced by each curve [36].  
Figure 5: **(a)** The WHEMS antenna, **(b)** its equivalent currents model, and **(c)** the calculated 3D radiating pattern of the WHEMS antenna at its lowest working frequency.
# Pro-Health Potential of Selected Uncommon Oilseed Plants
## 3 Pro-Health Potential of Selected Uncommon Oilseed Plants
### Nigella sativa L.  
_Nigella sativa_ L., also known as black cumin, black caraway, or kalonji, and less commonly as funnel flower or devil in the bush, holds a significant place as one of the most important medicinal plants in history. It has been referenced in various historical and religious texts [75; 76]. Black cumin, an annual flowering plant of the _Rannuculaceae_ family, typically attains a height ranging from 20 to 45 cm. The plant is characterized by its narrow, lancoleate leaves, measuring 2.5 to 5 cm in length. The delicate flowers of the black cumin plant consist of 5 to 10 petals, and their coloration may vary, exhibiting shades of yellow, white, pink, pale blue, or pale purple. The fruit manifests as a substantial, swollen capsule, comprising three to seven fused follicles, within which numerous seeds are contained. Measuring approximately 0.2 cm in length and 0.1 cm in width, the small, black, flattened, oblong, and angular seeds feature a funnel-like shape, with a black outer part and a white inner part. Seeds possess a discernible bitter taste and an astringent aroma [75; 76; 77]. The precise origin of black cumin is not extensively documented; however, it is widely believed to have originated in the Mediterranean region. Black cumin is a medicinal plant that has been used since ancient times. It has been used to treat fever, headache, diarrhea, indigestion, and asthma, among many other conditions. Currently, extensive research is underway regarding the pharmacological effects of _Nigella sativa_ L. in treating a wide range of diseases and ailments. Its health-promoting properties are attributed to the abundance of various phytochemicals [75; 77]. The seeds and oil of black cumin are rich in a variety of bioactive compounds. Terpenes and terpenoids form the largest group of chemical compounds present in black cumin. Among these, thymoquinone (TQ) and its derivatives, including carvacrol, 4-terpineol, \(\alpha\)-pinene, thymol, and p-cymene, are particularly noteworthy. Thymoquinone, the most extensively studied compound, accounts for many of the pharmacological properties associated with black cumin, such as its anti-inflammatory, antibacterial, and anticancer effects [77; 78; 79]. Black cumin oil also contains a significant quantity of phytosterols, with \(\beta\)-sitosterol constituting a substantial portion [77; 78; 79]. Several alkaloids with potential pharmacological activity have been identified in black cumin, including nigellicimine, nigellicimine-N-oxide, and indazole alkaloids like nigellidine and nigellicine. Tocopherols, with \(\gamma\)-tocopherol being the most predominant, are also present in black cumin. Polyphenols such as quercetin, kaempfer, ferulic acid, and chlorogenic acid exhibit robust antioxidant activity [75; 76; 77; 78; 79]. Black cumin seeds are abundant in fatty acids, including linoleic (55.6%) and oleic (23.4%) acids [75; 77; 78; 79]. Table 2 shows a summary of the chemical composition of the selected uncommon oilseed plants based on the collected data.
# Enhanced and Combined Representations in Extended Reality through Creative Industries
## 4 Results and Discussion
### Discussion
#### 4.2.1 Training Purposes  
The proposed solution could be a useful interpretive tool in the educational process (RQ3). In specific specialties, the study of tomography is a basic means of providing information. Integrating digital multimodal applications into the education of future professionals is likely to better prepare them as scientists (RQ3). For example, in medicine, a diagnosis can be based on and validated according to the correct interpretation of a patient's CT scans [20]. The application of the proposed digital solution in the education of medical students could simulate the process of diagnosis of diseases based on tomography (RQ3). Accordingly, in archaeology, the analysis of CT scans of burial remains [22] or excavation finds at risk through friendly and easy-to-use digital tools for an audience unfamiliar with volumetric graphics technologies could provide useful directions on how to preserve and manage a rare and fragile object. The same digital multimodal application could be used to train students in archaeology (RQ3).  
Industry is a field in which the multimodal representation of 3D objects through the proposed digital solution could be widely applied [32]. On the one hand, most machines are usually three-dimensional and, in several cases, have a complex internal structure. On the other hand, there is a constant need for initial training of workers in the industry to update their knowledge in new production processes or for training in the use of a new industrial system. In some cases, training can be undertaken by demonstrating the machine's functionalities, while in other cases, this is impossible or dangerous to implement [33]. Therefore, easy-to-use digital applications based on the widespread immersive technologies of extended reality can holistically represent a machine, both for its external form and internal structure and function, offering safe and effective training to its operators (RQ3).
# Surface Reconstruction Planning with High-Quality Satellite Stereo Pairs Searching
## 6 Discussion
### Further Planning Using Simulated Constellation  
When verifying the effectiveness of the proposed planning algorithm in selecting imaging pairing opportunities to generate higher-quality stereo surface, the previous evaluation is carried out using data from a single satellite (WorldView-3). With the development of satellite constellation, multi-satellite collaborative planning has become an important means to further improve observation efficiency and data quality. Therefore, in order to further verify the performance of the algorithm in more complex and practical application scenarios, we simulated a constellation consisting of multiple satellites (including WorldView-3, WorldView-1, WorldView-2, and GeoEye-1), as shown in Figure 5, based on which more experimental evaluation is carried out in this section.  
Similar to the case in Section 5.3, the imaging time windows are localized for each satellite based on its orbit. For each potential image pair, the quality of the DSM is assessed to identify stereo pairs with high reconstruction qualities in the planning algorithm. The average reconstruction quality of the selected imaging pairs is presented in Figure 10 for different objectives.  
As shown in Figure 10, the quality-driven planning method still returns the best overall reconstruction performance on the quality objective (\(Q_{2}\)). This result is consistent with the single satellite result in Figure 7, for planning within the time span of both the whole year and a half of the year. Because more opportunities are available for Earth imaging using multiple satellites, the planning is more computationally complex. However, the quality-driven method can still return the searched stereo pairs in seconds, showing the promising scalability to large-scale problem settings.  
Figure 9: Three-dimensional reconstruction results (top to bottom: \(Q_{1}\), \(Q_{2}\) and \(Q_{3}\)). The results show that the 3D reconstruction results based on quality-driven planning using \(Q_{2}\) (reconstruction quality) are superior to those of the other objectives (\(Q_{1}\) and \(Q_{3}\)) in terms of DSM error and visual smoothness.
## 2 Materials and Methods
### Machine Learning Models for Estimation of Sailing Losses
#### 2.6.1 k-Nearest Neighbours Model  
The k-nearest neighbours algorithm is a supervised learning method that is used for both classification and regression tasks. This algorithm does not generate an internal model; rather, its training involves measuring distances within the stored training dataset. For regression tasks, the prediction consists in the average of k values (or neighbours) closer to the new instance regarding its features. This algorithm has already been tested in regression tasks such as pollution prediction [27]. The selection of the parameter k is crucial in the development of the model. The higher the number of neighbours (or k), the more instances are taken into account when calculating the prediction, and thus the variance of the model is reduced. However, this will increase the bias error. On the contrary, a low value of k will make the model too sensitive to small changes, increasing the variance, yet reducing bias error.  
In kNN for regression, features with larger ranges can dominate distance calculations, leading to biased predictions. Standardising the data ensures that all features contribute fairly. Standardisation involves transforming each feature to fit a standard distribution with a mean of 0 and a standard deviation of 1. Equation (3) shows how standardisation has been conducted. \(Z\) represents the standardised data by removing the mean (\(\mu\)) and scaling to unit variance dividing it by the standard deviation (\(\sigma\)) of that feature.  
\[Z=\frac{X-\mu}{\sigma} \tag{3}\]  
With the aim of determining the parameter k, K-fold cross-validation and grid search have been performed. Five-fold cross-validation involves dividing the training dataset into five subsets, using four for training and one for testing in rotation to assess model performance, as shown in Figure 5. For grid search, GridSearchCV algorithm was used. This algorithm systematically searches through a specified parameter grid to find the optimal hyperparameters for a model by using cross-validation. By utilizing this algorithm to vary the number of neighbours (parameter k in kNN) from 2 to 12, a cross-validation RMSE error ranging between 0.92% and 0.99% was obtained. The lowest RMSE was achieved when k was set to 3, as illustrated in Figure 6. Therefore, 3 was selected as the optimal value for this parameter. The selected metric for assessing distance calculations is Euclidean. Predictions for kNN with k set to 3 are depicted in Figure 7. Each axis represents an indeterminate feature. To estimate SL, the algorithm identifies the three nearest neighbours by measuring the Euclidean distance between instances based on these features. The instances closest to the star shape are the nearest neighbours (shown as yellow circles). The estimated SL value is determined by averaging the SL values of these three neighbours.
# A Thermogravimetric Analysis of Biomass Conversion to Biochar: Experimental and Kinetic Modeling
## 3 Results and Discussion
### Thermal Decomposition Kinetic Modeling
In relation to (2)-(6), the following variables are involved: \(\frac{d\alpha}{dt}\)--the rate of reaction conversion; \(k_{0}\)--preexponential factor \(\big{(}s^{-1}\big{)}\); \(R\)--ideal gas constant (\(8.314\,\mathrm{J}\cdot\mathrm{mol}^{-1}\cdot\mathrm{K}^{-1}\big{)}\); \(f(\alpha)\), \(g(\alpha)\)--functions characterizing the reaction mechanism; \(m_{0}\), \(m_{i}\), \(m_{\infty}\)--initial, instantaneous, and final normalized mass in %; \(\beta\)--heating rate (\({}^{\circ}\mathrm{C}\cdot\mathrm{min}^{-1}\)); \(T\)--temperature (K). Different kinetic model functions \(f(\alpha)\) and their corresponding \(g(\alpha)\) are usually employed for the solid-state reactions, the most important among them being presented in Table 3. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & \multicolumn{2}{c}{**Differential Form—_f(\(\alpha\))_**} & **Integral Form—g(\(\alpha\))** \\ \hline \multicolumn{4}{c}{Power/Exponential} \\ \hline \multirow{4}{*}{Power law} & P\({}_{2}\) & \(2\cdot\alpha^{\frac{1}{2}}\) & \(\alpha^{\frac{1}{2}}\) \\ \cline{2-4}  & P\({}_{3}\) & \(3\cdot\alpha^{\frac{3}{2}}\) & \(\alpha^{\frac{1}{2}}\) \\ \cline{2-4}  & P\({}_{4}\) & \(4\cdot\alpha^{\frac{3}{2}}\) & \(\alpha^{\frac{1}{2}}\) \\ \hline \multicolumn{4}{c}{Random nucleation and nuclei growth} \\ \hline \multirow{4}{*}{Avrami-Erofeev} & A\({}_{2}\) & \(2\cdot(1-\alpha)\cdot[-\ln(1-\alpha)]^{\frac{1}{2}}\) & \([-\ln(1-\alpha)]^{\frac{1}{2}}\) \\ \cline{2-4}  & A\({}_{3}\) & \(3\cdot(1-\alpha)\cdot[-\ln(1-\alpha)]^{\frac{3}{2}}\) & \([-\ln(1-\alpha)]^{\frac{1}{2}}\) \\ \cline{2-4}  & A\({}_{4}\) & \(4\cdot(1-\alpha)\cdot[-\ln(1-\alpha)]^{\frac{1}{2}}\) & \([-\ln(1-\alpha)]^{\frac{1}{2}}\) \\ \hline \multicolumn{4}{c}{Geometrical contraction models} \\ \hline Contracting area & R\({}_{2}\) & \(2\cdot(1-\alpha)^{\frac{1}{2}}\) & \(1-(1-\alpha)^{\frac{1}{2}}\) \\ \hline Contracting volume & R\({}_{3}\) & \(3\cdot(1-\alpha)^{\frac{1}{2}}\) & \(1-(1-\alpha)^{\frac{1}{2}}\) \\ \hline \multicolumn{4}{c}{Diffusion models} \\ \hline
1D diffusion & D\({}_{1}\) & \(1/(2\cdot\alpha)\) & \(\alpha^{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Theoretical forms of kinetic models both in differential and integral form [58; 59].
###### Abstract  
Silk mat originates from the cocoon of the silkworm and is prepared by a simple method. The material has been used for guided bone regeneration (GBR) in animal models. In this study, the silk mat used for a clinical application was compared with a commercially available membrane for GBR. A prospective split-mouth, randomized clinical trial was conducted with 25 patients who had bilaterally impacted lower third molars. High-density polytetrafluoroethylene (dPTFE) membrane or silk mat was applied in the extraction socket randomly. Probing depth (PD), clinical attachment level (CAL), and bone gain (BG) were measured at the time of extraction (T0) and then at three months (T1) and six months after extraction (T2). There was no missing case. GBR with silk mat was non-inferior to GBR with dPTFE for PD reduction at T1 and T2 (\(p_{\text{non-inferiority}}<0.001\)). PD and CAL were significantly decreased at T1 and T2 when compared with those at T0 in both membrane groups (\(p<0.001\)). BG at T2 was 3.61 \(\pm\) 3.33 mm and 3.56 \(\pm\) 3.30 mm in the silk mat group and dPTFE group, respectively. There was no significant complication from the use of silk mat for the patients. The results for patients undergoing GBR with silk mat for third-molar surgery were non-inferior to GBR with dPTFE for PD reduction.  
guided bone regeneration; silk mat; polytetrafluoroethylene; probing depth; bone gain
## 5 Hypothesis Testing and Discussion
### Testing Hypothesis H5  
**Proof of Hypothesis 5 (H5).** Negative prices occur primarily when the combined RES generation exceeds a certain threshold at a specific time (e.g., weekend afternoons), and this phenomenon is deterministically predictable. \(\square\)  
Clear patterns were identified by analysing the occurrence of negative prices as a function of hourly solar and wind generation, month, and day of the week. In Germany, Estonia, and France, negative prices regularly appear during spring and summer weekend afternoons when combined RES generation is high and demand is low. In countries like Slovakia, these phenomena are rare and do not show a stable pattern.  
The rules developed (e.g., \"if solar + wind generation is >35 GW on Saturday between 13:00-15:00, probability of negative prices > 30%\") provide a practical and interpretable basis for decision making by market actors. These results clearly confirm Hypothesis H5.
## 4 Evaluation and Discussion
### Image Quality Analysis
#### 4.4.1 Payload Analysis  
Payload refers to the amount of secret information that can be embedded into a cover image [29]. This quantity largely depends on the embedding technique and the structure of the cover image. For instance, with the LSB technique, the payload can be calculated mathematically. The fewer cover image pixels required to embed one secret image pixel, the larger the maximum payload of the LSB embedding.  
In LSB-1, \(24/1=24\) pixel channels are required to accommodate every \(8\times 3=24\) bits of the secret image. Thus, it requires \(24/3=8\) RGB pixels of the cover image for each secret image RGB pixel. In LSB-2, \(24/2=12\) pixel channels are needed to accommodate every \(24\) bits of the secret image, requiring \(12/3=4\) RGB pixels of the cover image for each secret image RGB pixel. In LSB-4, \(24/4=6\) pixel channels are necessary to accommodate every \(24\) bits of the secret image, requiring \(6/3=2\) RGB pixels of the cover image for each secret image RGB pixel. Finally, for LSB 3-3-2, it requires one pixel to accommodate one pixel channel (leaving one channel unused for the third pixel) of the secret image, thus requiring three pixels of the cover image for each pixel for each secret image RGB pixel.  
Therefore, for an RGB secret image scheme, the LSB 3-3-2 embedding technique has a payload approximately 2.67 times larger than LSB-1 and approximately 1.33 times larger than LSB-2. While LSB 3-3-2 has a payload 1.5 times smaller than LSB-4, this smaller payload is compensated for by the higher quality of the stego image.
## 4 Results and Analysis
### Metrics and Scenarios  
The performance of the optimization model was evaluated using four key metrics:  
1. Direct Picking: This represents the percentage of items picked directly from the reserve area, bypassing forward and buffer areas. A lower percentage indicates a more streamlined replenishment process.
2. Replenishment: This tracks the frequency of stock restocking activities, ensuring forward areas remain adequately stocked to meet demand.
3. Buffer Area Usage: This measures the percentage of inventory stored temporarily in buffer areas. Lower usage reflects improved stock flow without reliance on intermediary storage.
4. Forward-to-Buffer Transfers: This reflects the movement of inventory from forward areas to buffer areas due to capacity constraints or imbalances.