esa-sceva/satcom-chunk-collection huggingface dataset:
# ChaMTeC: CHAnnel Mixing and Temporal Convolution Network for Time-Series Anomaly Detection
## 5 Experimental Results and Performance Evaluation  
To rigorously evaluate the proposed method, we conduct extensive experiments against five recent and widely adopted benchmark models in the field of time-series anomaly detection:  
* LMSAutoTSF [20]: A learnable multi-scale architecture based on trend-seasonal decomposition.
* iTransformer [21]: Inverted transformer architecture separating variable and temporal tokens.
* TimeMixer [22]: A decomposable multi-scale mixing architecture.
* PatchTST [23]: A transformer variant optimized for long-term forecasting with patch-based input encoding.
* AnomalyTransformer [5]: A transformer-based model leveraging self-attention and an association discrepancy criterion for anomaly scoring.  
All baseline models were retrained on the same datasets using their officially released code and configuration guidelines. The comparison metrics used are the following:  
* F1-Score: The traditional harmonic mean of precision and recall in pointwise anomaly detection.
* (F1\({}_{PA}\)) (Point-Adjusted F1): A metric that considers a detection correct if it overlaps with a true anomaly window.
* (F1\({}_{CPA}\)) (Coverage-based F1): A stricter metric that requires a certain level of overlap (coverage) with the ground-truth anomaly duration.  
In addition, we apply our sliding window dynamic thresholding technique not only to ChaMTeC but also to the baseline models. This ensures that the performance gains are due to architectural innovation and not simply better thresholding.
###### Abstract  
The incidence of superficial infections, including oral candidiasis, has recently increased significantly. Their treatment is quite difficult due to the growing resistance of _Candida_ spp. to antifungal agents. Therefore, it is necessary to search for novel antimyoctics or alternative antifungal therapies. The purpose of the study was to evaluate the antifungal activity of natural terpene--linalool (LIN)--against both reference fungi belonging to yeasts and _Candida_ spp. isolates from the oral cavities of immunocompromised, hemato-oncology patients. Moreover, its mechanism of action and interactions with selected antifungal drugs or antiseptics were investigated. The broth microdilution technique, ergosterol or sorbitol tests, and a checkerboard method were used for individual studies. The LIN showed potential activity toward studied strains of fungi with a minimal inhibitory concentration (MIC) in the range of 0.5-8 mg/mL and fungicidal effect. This compound was also found to bind to ergosterol in the yeast cell membrane. Additionally, the interactions between LIN with antiseptics such as chlorhexidine, cetylpyridinium, and trickson showed beneficial synergistic effect (with FIC = 0.3125-0.375), or an additive effect with silver nitrate and chlorquinaldol (FIC = 0.625-1). Moreover, statistically significant differences in MIC values were found for the synergistic combinations of LIN. No interaction was indicated for nystatin. These results confirm that the LIN seems to be a promising plant component used alone or in combination with other antimyoctics in the prevention and treatment of superficial fungal infections. However, further clinical trials are required.  
linalool; synergism; interaction; mode of action; _Candida_ spp.
## 3 Methodology
### Calculation of Camera Parameters  
When using the deep-learning-based MVS method to reconstruct the UAV image in the earthquake area, the COLMAP based on incremental SfM (structure-from-motion) [31] technology is used to complete the sparse reconstruction part to calculate camera parameters [20]. Incremental SfM is a processing method for sequential iterative reconstruction of 3D scenes. As shown in Figure 3, it usually starts with feature extraction and feature matching and then generates 3D models of scenes through geometric verification iteration. Next, the selected two-view is used as the basis of the model, and the registration of the new image is gradually added and the reconstruction is refined by triangulation and bundle adjustment (BA). After sparse reconstruction, the camera parameters of each image and the horizontal depth range of the model from the camera are output.  
The processed data are calibrated by the image and camera parameters, and there will be a slight deviation from the original image. In order to adapt to the input of each multi-view semantic stereo network, the image processed by COLMAP is preprocessed to a uniform size, and the image is restored to the original size. According to the image scaling ratio, the same scaling is performed on the camera internal parameters, including the main point offset coordinates and the camera focal length.  
\[\frac{W}{W_{0}}=\frac{H}{H_{0}}=\frac{u}{u_{0}}=\frac{v}{v_{0}}=\frac{f}{f_{0}} \tag{1}\]  
In Formula (1), \(W\) is the width of the image, \(H\) is the height of the image, \(u\) and \(v\) are the coordinates of the principal point of the image, and \(f\) is the focal length of the camera. Others are scaled corresponding parameters.  
Figure 3: Principle of COLMAP [31].  
Figure 2: Workflow of building damage classification based on 3D model.
## 2 Serial Detection
### Soft Output for Serial Detection  
To improve the serial detection, in [19], the authors added the noise information to the signal \(\mathbf{s}[j,k]\) and created a soft output. To explain this, we consider a simple example. Assume that the data have two bits \(\mathbf{x}=[x[0]\ x[1]]^{T}\); \(x[k]\in\{-1,\ 1\}\), that is, \(\mathbf{x}\in\{-1,\ 1\}\times\{-1,\ 1\}\), and the channel interference is \([0.2\ 1\ 0.2]\). Based on the trellis diagram in Figure 2 with the starting status \([-1\ -1]\), we have the channel interfered signal \(\mathbf{z}\) as in Table 1. In Figure 2, the dashed line represents assigning input \(1\), and the solid line represents assigning input \(-1\).  
If the original signal \(\mathbf{x}\) is \([1\ -1]^{T}\), the noise \(\mathbf{e}\) is \([0.1017\ 0.4458]^{T}\). The received signal \(\mathbf{z}_{e}\) is \([-0.8983\ 1.0458]^{T}\), calculated using (3) and (4). \(\mathbf{y}_{e}\) is decoded using the VA, and the trellis diagram is shown in Figure 3.  
\[\mathbf{z}=\mathbf{x}*[\ r\ \ \ 1\ \ \ \ r\ \ ]^{T}, \tag{3}\]  
\[\mathbf{z}_{e}=\mathbf{z}+\mathbf{e}, \tag{4}\]  
In Figure 3, we can observe that the path with metric (0.1017, 0.0458) is the maximum likelihood path. Therefore, the detected result is \([1\ 1]^{T}\).  
\begin{table}
\begin{tabular}{c c} \hline \hline \(\mathbf{x}\) & \(\mathbf{z}\) \\ \hline \([-1\ -1]\) & \([-1.4\ -1.4]\) \\ \([-1\ 1]\) & \([-1.4\ -1]\) \\ \([1\ -1]\) & \([-1\ 0.6]\) \\ \([1\ 1]\) & \([-1\ -1]\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The values \(\mathbf{z}\) corresponding to the values \(\mathbf{x}\).  
Figure 2: Branch metric of the trellis for the channel interfered signal.  
Further, it is known that VA finds the sequence values \(\mathbf{z}\), which is similar to the received sequence \(\mathbf{z}_{e}\). This is represented in the formula as follows:  
\[\underset{z[k]}{\text{argmin}}\sum_{k=0}^{L-1}{(z_{e}[k]-z[k])}^{2}, \tag{5}\]  
where \(L\) is the number of bits of the data sequence, and \(z[k]\) is the output of the paths in the trellis. Usually, the output of the detector is a hard (discrete value) output. However, if this result is used in serial processing, the next detection process has no margin to correct the result because the signal [1 1]\({}^{T}\) is a right vector. Thus, to correct this result in the next detector, we should add the noise (i.e., interference) information to create a soft (continuous value) output.  
We propose a neural network to predict the noise vector \(\mathbf{e}\) that is supplied for serial detection to create the soft output after the inner detection. In the next section, we investigate the structure of the neural network suitable for predicting the noise vector \(\mathbf{e}\) from \(\mathbf{z}_{e}\).
# An Architecture for Reliable Transportation of Delicate Goods
## 3 Technical and Domain Contextualization
### Technical Options
Therefore, it is quite important that the cost of the monitoring device be reasonable in comparison to the cost of the merchandise. The main reason is that the transportation enterprises will, potentially, need many of these devices, once millions of products are moved every day. Even if not all merchandise requires this kind of monitoring, probably most would benefit from this technology. Therefore, to minimize costs, the devices should be reusable and, if possible, have a significant lifetime, including the battery. Another concern is the full compliance of the control process by the involved players. As already explained, the transportation may involve many companies and even individual actors. It is very important to promote everyone's commitment, to ensure the quality of the transportation process. In the authors' conception, this can be reinforced if a protocol, sustained by facts, is defined to be applied whenever the merchandise passes from one operator to another. The operator who passes the goods wants to be certified that they delivered the merchandise in good condition; and the receiving operator wants to have the guarantee that the merchandise was passed to them in good condition. Everyone is thus aware that will be controlled by whoever passes and receives the goods. This works as a self-control solution that encourages everyone to comply with the agreed conditions. By accepting this beforehand, there will be no exemption from responsibilities in case of non-compliance. In addition to the technical perspective, that aims to achieve an effective solution, the authors are also focused on supplying as many arguments as possible, to promote the effective adoption of the purposed architecture. In this sense, the authors are invested in creating a solution capable of supporting innovative services with high added value.
## 1 Introduction
### Study Site  
The Paclele Mici mud volcano, part of the Berca Mud Volcano (Noroiosi de la Paclele Mici in Romanian), constitutes a remarkable geological and botanical reservation located in the Scortoasa commune. Geologically, the Paclele Mici mud volcano is a consequence of the eruption of mud and natural gases (95% methane per [14]), from 3000 m beneath the Earth's surface. The volcano produces a brine that shows remarkable stability in two analyses conducted 25 years apart [15; 16]. This brine exhibits contents of Na\({}^{+}\) and Cl\({}^{-}\) on the same order of magnitude as corresponding seawater concentrations, which suggests a halite leaching origin [17].  
Botanically, the badlands around the Paclele Mici mud volcano provide a unique environment characterized by high soil salinity because of this brine. This condition results in sparse highly specialized vegetation, creating a particular landscape. Despite the scarcity of plant life, this environment is conducive to the survival of some rare species of plants such as _Nitraria schoberi_ and _Obione verrucifera_[18], which are capable of withstanding the high salt levels present in the soil. The site is currently carefully managed to ensure the preservation of its unique environment.  
All of that makes Paclele Mici an important study site. However, like many other mud volcanoes, Paclele Mici is a challenging task to study geophysically. In [19], the electrical signature of a different mud volcano area in Romania (the Homorod area) is presented, with values of 1.7-28.7 \(\Omega\)m. However, at the Paclele Mici site, values were expected to be even lower given the high level of activity and levels of salt. The geoelectrical data in [19], also from the Paclele Mici volcano, suggest somewhat lower values. This is challenging because high conductivity [20] is often associated with reduced contrast, higher attenuation, and challenges in data interpretation. As geoelectrical surveys rely on the propagation of electrical currents through the subsurface, high conductive subsurface materials allow easy flow of these currents but also quickly dissipate the electrical energy. As a result, the depth of penetration of the electrical signals is reduced, making it difficult to investigate deeper subsurface structures. Differentiating between different objectives also becomes more challenging as high conductivity can mask and blur geoelectrical boundaries. In addition, standard geoelectrical equipment is not optimized for highly conductive environments. This can lead to technical challenges, such as the need for more powerful transmitters or more sensitive receivers, to adequately conduct surveys in such settings. In addition to the resistivity values, the Paclele Mici site also features important topographic variations that can pose additional challenges to data acquisition and processing. The inversion of geoelectrical data with steep topography is challenging as such topography can affect the measurements severely [21].  
Here, however, we focus on a larger area, using nonconventional geoelectrical surveys that can address both these issues.
## 5 Results
### Spillover Effects Analysis
#### 5.3.2 Selection of Spatial Econometric Models  
We apply the Lagrange Multiplier (LM) test to examine spatial dependence, with results reported in Table 7. The spatial lag test reveals statistically significant and robust LM statistics across all model specifications, which confirms the presence of spatial lag effects. By contrast, the Spatial Error test yields significant results at the 1% level under random effects and time-fixed specifications, consequently leading to the rejection of the null hypothesis of \"no spatial error effects.\" However, under individual fixed- and two-way fixed effects, the robust LM statistics show mixed significance (327.35 *** and 2.391 in Table 7, respectively), thereby implying that fixed effects may partially account for the spatial error correlations. Following [PERSON] and [PERSON] (2010), the SDM is the preferred specification when significant spatial lag effects coexist with insignificant spatial error effects ([PERSON], 2015).  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multicolumn{1}{c}{**Spatial Model Testing**} & & \multicolumn{1}{c}{**Random**} & \multicolumn{1}{c}{**Temporal Fixed**} & \multicolumn{1}{c}{**Individual**} & \multicolumn{1}{c}{**Two-Way Fixed**} \\  & & **Effects** & **Effects** & **Fixed Effects** & **Effects** \\ \hline \multirow{3}{*}{LM} & Spatial lag & Lagrange Multiplier & 389.801 *** & 717.263 *** & 30.827 *** & 84.792 *** \\  & Robust Lagrange Multiplier & 4.283 *** & 46.373 *** & 82.180 *** & 57.894 *** \\ \cline{1-1} \cline{2-7}  & Spatial error & Lagrange Multiplier & 620.928 *** & 689.380 *** & 276.002 *** & 29.289 *** \\ \cline{1-1} \cline{2-7}  & Robust Lagrange Multiplier & 235.410 *** & 18.490 *** & 327.354 *** & 2.391 \\ \cline{1-1} \cline{2-7}  & LR-SDM/SAR & & 100.17 *** & 125.85 *** & 104.21 *** & 25.81 *** \\ \cline{1-1} \cline{2-7}  & LR-SDM/SEM & & 7290.77 *** & 109.84 *** & 72.20 *** & 22.17 *** \\ \hline \hline \end{tabular}  
Notes: ***\(p\) < 0.01.  
\end{table}
Table 7: Results of spatial econometric model specification.  
Figure 5: Moran’s I scatter plot of urban carbon emissions intensity from 2015 to 2023.  
The appropriate use of the SDM is assessed using LR tests and compared to simpler specifications such as the spatial autoregressive model (SAR) and the spatial error model (SEM). Both the LR-SDM/SAR and LR-SDM/SEM tests yield statistically significant results, thus rejecting the null hypotheses that \"SDM can be reduced to SAR\" and \"SDM can be reduced to SEM.\" These findings confirm that the SDM provides a superior fit by incorporating spatially lagged independent variables in addition to the dependent variable. Consequently, adopting the SDM allows for a more comprehensive analysis of spatial interactions between independent and dependent variables, thereby reducing potential model misspecification bias.
## 3 The Selection of DEMATEL Method for GenAI Tools Criteria Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Number of Comparisons Required** &
\begin{tabular}{c} **Criteria Dependency** \\ **Consideration** \\ \end{tabular} & **Scale Used** \\ \hline AHP & \(n(n-1)/2\) & None & Saaty’s 1–9 Scale \\ ANP & \(n(n-1)/2\) & Interdependencies & Saaty’s 1–9 Scale (or extensions) \\ DEMATEL & \(n(n-1)\) & Causal Relationships & Integer Scale (e.g., 0–4 or 0–5) \\ BWM & \(2n-3\) & None & 1–9 Scale \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of a set of subjective weighting multi-criteria methods for number of comparations, criteria dependency, and scale used. ships between criteria. This helps decision-makers understand the dynamics of the system and prioritize critical factors more intuitively. While AHP is good for hierarchical structures and consistency checks, it does not handle interdependencies well. ANP accounts for interdependencies but is more complex and computationally intensive compared to DEMATEL. BWM reduces the number of comparisons but does not explore causal relationships between criteria. DEMATEL stands out because it focuses on analyzing causal relationships and interdependencies among criteria, offering a holistic approach to decision-making. The DEMATEL method is a valuable tool for analyzing complex systems and decision-making processes. It has been applied to prioritize factors affecting AI adoption in banking [41] and identify challenges in implementing AI in public manufacturing sectors [42]. The DEMATEL method has been successfully used in various fields, including waste recycling analysis, project selection, and e-learning program evaluation [43]. In the case of the addressed problem of prioritizing and analyzing the importance of the evaluation criteria used in choosing a GenAI tool for academic research, the choice of the DEMATEL method is justified by various criteria such as performance, cost, user interface, and integration capabilities that are often interconnected.
## 5 Applications and Research Challenges
To make this a reality, the SDR technologies would have make a to leap in capacity compared to what they offer now, since at the moment, the bands they are capable of covering do not yet include millimeter wave (mmWave) and bandwidths beyond 20 MHz. Therefore, there is still a critical hardware development challenge to be overcome. Furthermore, even if large pools capable of supporting the instantiation of vUEs are available, their management should be integrated with the actual OSS. Therefore, the heterogeneity present in the ecosystem of network management technologies would be increased along with its complexity. Linked to this challenge is the necessity to consider the products currently performing similar tasks of study of mobile networks through drive tests, which would constitute direct competition for the entry of vUEs into the market. Third parties must enter the business so that this type of service could be outsourced. A further important consideration is the diverse nature of the information to complete the contextual sources, such as sensors and the easiness of its availability. In this way, the expected increase of computing capabilities in the cloud (or on the cloud edge) could provide resources for the vast amount of information that a massive deployment of vUEs would imply. Therefore, a horizon is opening up in which all the aspects, as mentioned earlier, will gradually come together for an automatic management setup capable of tackling the most challenging problems with a precision never seen before. Last but not least, there would be a multitude of technical challenges to overcome in order to integrate the output in the form of logs into the big data platforms that feed the machine learning algorithms under development [43]. Among them, the diversity of periodicities of each of the measurements can be highlighted [44] as a field to be explored with the implementation of vUE probes. Besides, and based on the timestamped measurements gathered, specific novel time-series processing techniques would have to be applied [45; 46] to obtain greater precision in the analysis and its inclusion in the network management algorithms.
# Design of CubeSat-Based Multi-Regional Positioning Navigation and Timing System in Low Earth Orbit
## 3 Results
### Thermal Analysis
#### 3.7.7 Thermal Simulation Results  
The simulations are run for a duration of 5 days for the two worst-case scenarios: 50% and 100% electrical power-to-heat conversion ratio. The results of the simulations are shown in Figure 13, and their combined temperature range is 17-36 \({}^{\circ}\)C.  
Among all components of the CubeSat BUS and the PNT Payload, the CSAC is regarded as the most temperature sensitive. However, as illustrated in Figure 14, its frequency errors remain minimal when operating within the 17 \({}^{\circ}\)C to 36 \({}^{\circ}\)C temperature range.
# Machine Learning for Network Intrusion Detection--A Comparative Study
## 5 Methodology
### Model Training and Cross-Validation  
After the dataset has been processed and converted into an appropriate format to fit the supervised machine learning models, the actual learning process starts. In this study, the training and validation datasets were used to train the chosen algorithms. In the beginning, the default parameters of the algorithms were used to create the models. The model training and cross-validation phases were designed as parallel processes. The reason for this is that these steps can be performed independently for each model. Different machine learning algorithms allow the use of a set of varying parameters to maximize the performance of the models. Once the models are created, the search for the best-performing hyperparameters of the models needs to be carried out. To find the best parameters, we employed randomization followed by a grid search combined with stratified k-fold cross-validation (CV) and the Bayesian Optimization with Tree-Structured Parzen Estimator. Grid search is an exhaustive method where multiple models are trained with different given sets of parameters before it returns the best-performing ones. To reliably identify the best-performing ones, we used stratified k-fold cross-validation (CV). K-fold CV ensures that each data instance is used for training and testing. This technique allows the comparison of the performance of models trained with different parameters as each one predicts every data point. Moreover, we stratified the folds to ensure that every fold had the same class distribution as the entire dataset, so that every class was relatively as often represented as in the whole dataset. The number of folds was set to 5, which meant that one fifth of the data served as the validation set, while a total of five models were trained per parameter set. The grid search method returns the best parameters based on the selected scoring methods. As our research aimed to achieve high recall rate for attack classes while maintaining high precision for benign traffic, we used the recall macro measure for this purpose, instead of the default accuracy method. At the end of this phase, we found the best combination of parameters for each model, which were then used to test them.