esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods  
### Reagents  
Fmoc-L-phenylalanine, Fmoc-L-glutamic acid 5-tert-butyl ester, and N-\(\alpha\)-Fmoc-N-\(\beta\)-trilyl-L-asparagine were procured from Fluka (Buchs, Switzerland), while Fmoc-O-tert-butyl-L-tyrosine was sourced from Roth (Karlsruhe, Germany). Furthermore, Rink amide resin (4-(2\({}^{\prime}\),\({}^{\prime}\)-dimethoxyphenyl-Fmoc-aminomethyl)phenoxy resin, PEPTIPURE(r), 100-200 mesh, 0.3-0.8 mmol\(\cdot\)g\({}^{-1}\), 1% DVB) purchased also from Roth was used as a solid support. Various chemical reagents essential for experimental procedures were obtained from reputable suppliers. Dimethyl sulfoxide (DMSO, EMSURE ACS), diethyl ether (EMSURE ACS), N,N-dimethylformamide (DMF), piperidine (PYP), trisopropylsilane (TIS), acetic acid, benzotriazol-1-yl-oxytripryrolidino phosphonium hexafluorophosphate (PyBOP), N-methylmorpholine (NMM), 2,5-dihydroxybenzoic acid (DHB), \(\alpha\)-cyano-4-hydroxycinnamic acid (HCCA), and NaH\({}_{2}\)PO\({}_{4}\) were acquired from Merck (Darmstadt, Germany). Dichlormethane (DCM) was sourced from Scharlab (Barcelona, Spain), while methanol, acetonitrile (ACN, HPLC grade), and trifluoroacetic acid (TFA) were obtained from [PERSON] (Karlsruhe, Germany). All solutions were prepared meticulously using water of high purity with a resistivity of 18.2 M\(\Omega\cdot\)cm, generated through a Millipore Milli-Q system (Bedford, MA, USA).
# A Comprehensive Survey of Recent Routing Protocols for Underwater Acoustic Sensor Networks
## 3 Routing Protocols for UWSNs
### P-Auv  
P-AUV [34] is a routing protocol designed for _ad hoc_ AUV-based networks. This aims to achieve energy efficiency and higher packet delivery ratios by minimizing collisions. The proposed protocol uses locations of nodes for forwarding decisions, and creates loop-free paths in which nodes near the sink nodes have priority to be chosen as relays. Collisions are reduced by configuring every sensor node to select a back-off value, based on its location. All AUVs are equipped with the necessary apparatus for navigation. Thus information about initial position combined with navigation enables AUVs to know their current position at any point of time. Moreover, synchronization between AUVs is not required, hence reducing synchronization-related communication overhead. An AUV that is acting as a relay makes its forwarding decision based on its location. It selects a back-off value based on its distance from the source node i.e., the AUV with greater distance from a source node has priority to decrease its packet delivery delay, compared to the AUV with closer distance from its source node. Besides reducing collisions, communication overhead is further reduced by avoiding the exchange of state information, synchronization, neighborhood, and location details. By reducing communication overhead, P-AUV not only reduces energy consumption, but also achieves improved delivery ratio and small end-to-end (E2E) delays. P-AUV does not use GPS hardware. Moreover, sensor nodes are not required to surface, in order to refresh their coordinates. Initial position information combined with displacement information from tracking apparatus is used by AUVs to update their positions. However, this introduces position estimation error that may affect routing, as routing decisions are based on the self-location estimation of a node.
# Travel Time Prediction on Long-Distance Road Segments in Thailand
## 4. Results and Discussions
### Computation Time  
This was an additional experiment that observed the computation time of each method from the previous experiments. This experiment was performed on a computer with a four-core central processor, 8 gigabyte of memory, and a Linux operating system. The results are shown in Table 7. For the deep learning approaches from the ConvLSTM onwards, it was evaluated by 50 epochs. The SA-LSTM with Butterworth low-pass filter had a computation time similar to that of the other LSTM-based approaches.
# Evolutionary Approach to the Euclidean Steiner Tree Problem in n-Space
## 2 Proposed Algorithm  
The proposed solution is based on a genetic algorithm. A genetic algorithm is a meta-heuristic often used in challenging optimization problems, including combinatorial ones. The general principle of operation is based on simulating the Darwinian process of natural selection by promoting better solutions from the current population. This selective pressure promotes more promising solutions, which, after undergoing the selection process, serve to generate new, potentially better solutions as a result of applying the appropriate operators. These operators typically include a crossover operator, which generates a new solution based on two selected ones, and a mutation operator, which creates a new solution by randomly altering an existing one. Repeatedly performing selection, crossover, and mutation steps (combined with a procedure for evaluating the quality of each new solution) has significant practical potential for locating areas in the search space with high-quality solutions. The genetic algorithm is a heuristic and, therefore, does not guarantee finding the optimal solution; however, in practice, it often provides satisfactory solutions within a reasonable time frame. The genetic algorithm can also be called a meta-heuristic, meaning it is a general approach to solving optimization problems and needs to be adapted to a specific problem. To carry this out, it is essential, first and foremost, to choose an appropriate data structure capable of representing a solution in the given optimization problem. The data structure should allow for some way of encoding the solution it represents. The chosen data structure also dictates the possible crossover and mutation operations. A schematic representation of the genetic algorithm is shown in Figure 5. In the following sections, we describe the individual steps and assumptions made to adapt the genetic algorithm to solving the ESTP in Euclidean space of any dimensionality.
# Gaussian Mixture Modeling for Detecting Integrity Attacks in Smart Grids
###### Abstract  
Thehematics focusing on inserting intelligence in cyber-physical critical infrastructures (CI) have been receiving a lot of attention in the recent years. This paper presents a methodology able to differentiate between the normal state of a system composed of interdependent infrastructures and states that appear to be normal but the system (or parts of it) has been compromised. The system under attack seems to operate properly since the associated measurements are simply a variation of the normal ones created by the attacker, and intended to mislead the operator while the consequences may be of catastrophic nature. Here, we propose a holistic modeling scheme based on Gaussian mixture models estimating the probability density function of the parameters coming from linear time invariant (LTI) models. LTI models are approximating the relationships between the datastreams coming from the CI. The experimental platform includes a power grid simulator of the IEEE 30 bus model controlled by a cyber network platform. Subsequently, we implemented a wide range of integrity attacks (_replay_, _ramp_, _pulse_, _scaling_, and _random_) with different intensity levels. An extensive experimental campaign was designed and we report satisfying detection results.  
critical infrastructure protection; linear time invariant modeling; hidden Markov model; fault diagnosis; cyber-physical system; Gaussian mixtures +
Footnote †: journal: Electronics
###### Abstract  
Linear spatial data are primarily used in Geographic Information Systems (GISs) to represent spatial data in the form of roads, rivers, railways, and utility lines. Linear spatial data are mostly composed of one-dimensional linear elements, incorporating geometric attributes such as location, direction, and length, as well as the interconnections of these elements. In the case of roads, this information is used to map and analyze traffic data, such as vehicle movements, on the road network. This study aims to propose an area-based spatial analysis method that allows for the flexible application of analysis scales using individual vehicle data, as opposed to node and link generation for linear road networks. The analysis focused on nine expressways, conducting a microscopic analysis of speed-homogeneous sections. The final analysis showed that out of 375 cells, 91 cells in the final 12 division cells did not meet the homogeneity criteria. This discrepancy was ascertained to be due to vehicles decelerating or accelerating when entering or exiting highways at ramps or interchanges, not due to directional speed differences but lane-specific speed variations. The final cells with large speed deviations were found to be influenced by connections to highway on-ramps or off-ramps. In contrast, sections with small speed variations within a cell were influenced by traffic factors such as connection points and traffic volume, which hindered normal driving. As a result, this study validated that traffic information from highways, typically provided as linear data, could be divided into cells based on real-time GPS speed data and presented on an area-based scale. While dividing regions based on fixed intervals does not pinpoint exact speed change points, this study found that reasonable segmentation is possible based on spatial size and speed-homogeneous sections.  
geohash; cell; spatial analysis; individual vehicle data; space mean speed; time mean speed; linear information +
Footnote †: journal: Applied sciences
## 1 Introduction  
With the further development of infrared focal plane arrays (IRFPA), infrared remote sensing techniques play an important role in many fields, especially in multiple military cases for aerospace vehicles [1; 2; 3; 4]. Infrared signature has been demonstrated much during the last few years in feature acquisition and recognition of targets, which can provide signature information of targets such as radiance and intensity [5; 6; 7], for different vehicles usually represent different infrared signature.  
At present, the research on the infrared radiation characteristics of targets mainly includes two ways, namely the simulation and the experiment. In the simulation, the mathematical radiation model is first established based on the state and the environment of targets. Then, according to the radiation formation mechanism of targets, each component that influences the radiation characteristic of the target is analyzed and calculated theoretically [8; 9; 10; 11; 12; 13]. Although the simulation is widely used in the study of the infrared characteristics, due to the fact that they are not subject to the site and cost, the accuracy and effectiveness cannot be easily verified. In contrast, the experiment that measures and inverses the actual radiation of the target with a radiometrically calibrated infrared measuring system is the direct and the only means to obtain and verify the radiation characteristics of the measured target [14; 15]. In experiments, infrared signature measurement of targets usually consists of three steps: (1) radiometric calibration for the infrared thermal imager, which aims at quantifying the relationship between the radiation received and the output digital number (DN); (2) estimation of the emission and reflection from the ground and the atmospheric effects, such as atmospheric transmittance and path radiation, which can be predicted by software like MODTRAN; (3) inversion of the radiation characteristics using a model of radiation measurement, according to the target material surface emissivity, transmittance and so on. In both approaches mentioned above, the influence caused by the atmosphere must be inevitably corrected, especially for long range targets. However, the estimation of atmospheric parameters and the atmospheric radiation, nowadays, mainly depends on atmospheric transmission radiative transfer software such as LOWTRAN, MODTRAN, and FASCODE, of which the uncertainty is merely approximately 20-30% which is far from enough in engineering application.  
In this work, a method of infrared radiation measurement based on blackbody or an object served as a blackbody for cooperative targets is proposed, based on which the atmospheric transmittance and the atmospheric radiation are estimated and the actual radiation of the measured cooperative target is calculated with lower uncertainty. The proposed method can be considered as an alternative for practical scenes when measuring cooperative targets in engineering applications.
## 5 Simulation Experiment and Analysis
### The Second Simulation Environment  
To further verify the applicability and stability of the proposed algorithm (IEHA) in diverse scenarios and bolster theoretical support for its real-world applications, we introduced the third simulation environment. We transformed the layout of the actual port into a raster map by grayscale processing, as in Figure 11, for use in the simulation. Based on Figure 12 and Table 3 from the simulation experiments, the IEHA algorithm shows a clear edge in path planning within the third simulation set. Against the A* algorithm, IEHA cuts down average turns by 38.46%. Compared to the IEA algorithm, it slashes average turns by 57.89% and shortens the average path length by 6.48%. Though facing a 1.96% increase in average path length relative to the ACO algorithm, IEHA delivers a 33.33% drop in steering and eradicates collisions. Considering all factors, IEHA surpasses other algorithms in minimizing steering, preventing collisions, and optimizing path length. It achieves complete collision avoidance, remarkably boosting path planning safety and cutting potential collision-induced environmental costs and resource waste.
# Memory-Efficient Batching for Time Series Transformer Training: A Systematic Evaluation
## 5 Experimental Setup and Evaluation
### Experimental Setup
#### 5.2.1 Parameter Variations  
To assess the generality and robustness of our method, we systematically varied the following training parameters:  
* **Window Size:** Different sliding-window lengths were tested to assess their impact on performance.
* **Batch Size:** The relationship between batch size and memory/runtime efficiency was explored.
* **Feature Dimension:** We varied the number of input features to test scalability in multivariate settings.
* **Model Architecture:** We selected five time series model architectures to examine how our optimization interacts with varying model complexity.  
We focused on four transformer-based architectures--Informer, Autoformer, PatchTST, and a vanilla Transformer--as well as a linear forecasting model (DLinear) for lightweight comparison. Each model was trained using its default configuration. The number of parameters in each model and the evaluation setup details are summarized in Table 1.
## 3 Results
### Thermal Properties
#### 3.4.1 Thermal Conductivity  
Figure 6 shows that mixes N10/0 and N25/0 exhibited lower thermal conductivity (\(\lambda\)) than NAC and mixes R10/50 and R25/50 lower \(\lambda\) than R0/50, inferring that the recycled material concrete would afford more effective thermal insulation than conventional materials. The use of GRC lowered \(\lambda\) by 2.5% in N10/0 and 6.9% in N25/0 relative to NAC and by 3.1% in R10/50 and 7.2% in R25/50 relative to R0/50. Those values lay in the 2.8% to 6.8% range observed for concrete made with up to 25% RA to  
Figure 5: Open porosity versus electrical conductivity.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**NA mix**} & \multicolumn{3}{c}{**MRA mix**} \\ \cline{2-7}
**Parameter/Differential** & **NAC** & **N10/0** & **N25/0** & **R0/50** & **R10/50** & **R25/50** \\ \hline \(\lambda\) (W/m-K)\({}^{1}\) & 2.064 & 2.013 & 1.922 & 1.963 & 1.901 & 1.821 \\ \(\lambda\) with GRC (\%) & - & -2.5 & -6.9 & - & -3.1 & -7.2 \\ \(\Delta\) with MRA (\%) & & & & -4.9 & -5.6 & -5.3 \\ \(\Delta\) with GRC (\%) \(\times\) MRA (\%) & & & & -7.9 & -11.8 \\ Volume heat capacity (M/m-K)\({}^{1}\) & 1.864 & 1.839 & 1.828 & 1.781 & 1.771 & 1.768 \\ \(\varphi\) (R/m-K)\({}^{3}\) & 0.814 & 0.826 & 0.847 & 0.848 & 0.863 & 0.888 \\ \(\Delta\) with GRC (\%) & - & 1.4 & 4.1 & - & 1.8 & 4.8 \\ \(\Delta\) with MRA (\%) & & & & 4.1 & 4.5 & 4.9 \\ A with GRC (\%) \(\times\) MRA (\%) & & & & 6.0 & 9.1 \\ \hline \hline \end{tabular}  
* Standard deviation was \(<\)0.003 for all parameter values. Notes: \(\Delta\) with GRC (%) compares NAC to N10/0 and to N25/0 in mixes with NA; and R0/50 to R10/50 and to R25/50 in mixes with MRA; \(\Delta\) with MRA (%) compares NAC to R0/50, N10/0 to R10/50 and N25/0 to N25/50; \(\Delta\) with GRC \(\times\) MRA (%): compares NAC to R10/50 and to R25/50.  
\end{table}
Table 9: Thermal properties of the concrete mixes.  
replace NA fines [9] and concrete prepared with 10% ground granulated blast furnace slag as an OPC substitute [58].  
The inverse linear relationship (R\({}^{2}\geq\) 0.997) between \(\lambda\) and GRC in NA and MRA mixes is plotted in Figure 6a. The mean decline of 5.3% in \(\lambda\) (determined as the vertical distance between the two trend lines: the MRA effect) attributed to the use of 50% MRA was very close to the 5.5% decline reported for concrete with 50% RA of a type similar to the RA used in this study [12]. The joint use of GRC and MRA lowered thermal conductivity by 7.9% in R10/50 and by 11.8% in R25/50 relative to the reference NAC.  
All the recycled material mixes exhibited \(\lambda\) in the 2.01 W/m\(\cdot\)K to 1.60 W/m\(\cdot\)K range found for concrete with up to 50% RA fines and up to 50% RA coarse recycled CDW aggregate [12], as well as within the range recommended by Eurocode 2 [59]: 1.95 W/m\(\cdot\)K to 1.33 W/m\(\cdot\)K.
## 2 Preliminaries
### Ipfs  
IPFS is a distributed and decentralized peer-to-peer file-sharing system that aims to create a permanent and decentralized method of storing and sharing files on the internet [17]. IPFS stores files based on their content instead of their location, using a global network of interconnected nodes for faster and more efficient content distribution.  
One of the key benefits of IPFS is that it allows for decentralized and censorship-resistant file sharing, making it useful for applications, such as content distribution and file archiving. It can also be used as a building block for decentralized applications, providing a secure and reliable method of storing and sharing data in a distributed network.
# High-Pressure Technologies for the Recovery of Bioactive Molecules from Agro-Industrial Waste
## 2 Presurized Liquid Extraction Processes
### PLE of Bioactive Compounds from Waste
#### 2.2.3 Lipids  
Lipids are categorized as either neutral or complex. Neutral lipids include glycerides, wax esters, free fatty acids, free fatty alcohols, carotenoids, sterols,tocopherol and lignin-type antioxidants, and biologically derived hydrocarbons. Complex lipids include phospholipids, glycolipids, ceramides, cerebrosides, and gangliosides.  
Many well-known extraction methods have been applied for the extraction of lipids from food waste, including Soxhlet, Bligh and Dyer, and Folch processes, and SFE; more recently, PLE have been successfully developed to enhance lipid extraction. Many applications of PLE methods have been reported, such as the extraction of compounds from plants [28; 99; 100; 101], animals [102; 103; 104; 105], soil [106; 107], and algae [108], but few from yeast [109; 110]. According to the large potential of lipid production with oleaginous microorganisms, the application of PLE methods to lipid extraction from yeast is a major challenge but with promising perspectives [111; 112].  
[PERSON] et al. [113] used a PLE process to extract microalgal lipids from Isochrysis biomass. Compared with the Soxhlet and Folch methods and PLE using n-hexane, PLE using ethanol exhibits excellent lipid extraction performance. [PERSON] et al. [114] used PLE to extract total lipids from oleaginous yeast (_Rhodotorula glutinis_) and compared it with two traditional methods: the Soxhlet method and the improvement of the Bligh and Dyer method. The results showed that the extraction time of PLE was reduced by 10 times, the amount of solvent was reduced by 70%, and the lipid extraction rate was significantly higher than the reference method (Soxhlet method and Bligh and Dyer method). [PERSON] et al. [115] used PLE to extract 3-rich oil from chia seeds by using pressurized food grade ethanol at 60 \({}^{\circ}\)C. In a short extraction time (10 min), the recovery rate of the seeds was close to 100% [116; 117; 118; 119; 120; 121]. However, since lipids are soluble in non-polar solvents such as hexane, extraction using PLE cannot be performed applying green solvents. For this class of compounds, supercritical fluids extraction is more advantageous.
# Along-Track Multistatic Synthetic Aperture Radar Formations of Miniatellites
## 4 Control of Sensor Position
### Mitigation of System Performance Degradation
#### 4.2.3 System Sensitivity  
As shown in Equation (24), a little change in the PRF may produce large displacement in the phase center locations with a consequent change of the system performance that can be optimized by choosing the best PRF for the current situation. This optimization, however, needs a perfect knowledge of the satellite positions. In the following, we have analyzed the variation of the system performance, increasing the PRF interval values within which the PRF value can be optimized, once given the locations of the satellites. The system performance is measured by the probabilities (in the abscissae) of the losses of SNR (in dB, ordinates).  
Figure 7 addresses the probabilities of the losses of SNR for a formation with N = 4 satellites randomly spaced, with a reconstruction multiple equal to \(R=2\), 3, and 4 (corresponding to the full reconstruction capability). In each figure, the cases with no twin antenna or multiple twin antennas have also been presented. As it can be seen, the case with \(R=4\) is the most difficult to treat and three out of four possible double antennas are needed to achieve reasonable performances. As an alternative, a larger deviation of the PRF or a larger formation can be used but with more problems of across-track baseline, as to be discussed in the next section.
## 5 Discussion
### Analysis of Results  
The experimental results underscore the effectiveness of the joint model architecture in improving SER performance. By integrating both discrete and dimensional emotion representations, the joint model consistently outperformed baseline models across various configurations, achieving higher Weighted Accuracy (WA), Unweighted Accuracy (UA), and Concordance Correlation Coefficient (CCC) values. The parallel feature extraction method, combining multiple feature encoders such as Wav2 Vec 2.0 and HuBERT, significantly contributed to the model's ability to capture diverse emotional cues from audio signals. The inclusion of self-attention and co-attention mechanisms further enhanced the model's capacity to focus on the most informative parts of the input data. Moreover, the dynamic and joint weighting schemes proved particularly effective, allowing the model to balance the importance of discrete and dimensional tasks dynamically during training. This adaptive approach led to a more robust and generalizable model, capable of accurately predicting both types of emotion representations.