esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Causes of Damage to and Conditions of Use of Main Helicopter Blades
### Environmental Factors  
Temperature is one of the environmental factors affecting the durability of an adhesive bond [13]. Even small changes in the temperature of bonded elements with different coefficients of thermal expansion can cause microcracking in the bonding ply [14], accelerate the diffusion of contaminants, and accelerate creep [15]. Thermo-physical transformations occurring in polymer adhesives are equally dangerous for bonded structures. Thermophysical transformations occur at particular temperatures and change the mechanical properties of the material [16; 17].  
The manufacturer has not set up a strictly defined operating temperature range for Mi helicopters.  
The Certification Specifications for Large Rotocraft CS-29 [18] do not explicitly define the maximum operating temperature of main rotor blades. However, since temperature can play a part in the occurrence of damage in factory-made adhesive joints both during operation and during the process of repair of damaged parts, it was decided to take temperature into account in the testing of bonds during the development of repair technology. The range of operating temperature for the research was adopted based on the helicopter flight profile in the Polish Armed Forces (flight altitude and places of operation--environment) and the results of temperature measurements of the leading edge anti-icing device heating system.  
The ceiling for Mi helicopters is 4.5 km above sea level, which, according to the ISA [19], translates into a minimum temperature of approx. \(-20\)\({}^{\circ}\)C. The locations where the Polish Armed Forces helicopters fly is classified information, but the users of the helicopters stated that they operate within the ambient temperature range of \(-40\div+50\)\({}^{\circ}\)C.  
Publication [20] presents the results of thermal imaging tests of the Mi-8 helicopter rotor blade with the leading edge anti-icing device system activated. According to the measurements, the anti-icing device system can heat the leading edge up to 40 \({}^{\circ}\)C. This is, therefore, lower than the maximum operating temperature resulting from environmental effects.  
Therefore, it can be assumed that the operating temperature of the blades is within the range of \(-40\div+50\)\({}^{\circ}\)C.  
The second environmental factor that particularly negatively impacts adhesive joints is moisture, as was shown by the research presented in [21]. As in the case of temperature, the manufacturer did not indicate the permissible level of ambient humidity during the operation and storage of the blades. Therefore, the Certification Specifications for Large Rotocraft CS-29 were used [19]. Subsection CS29.45 states that the maximum ambient relative humidity during operation is 80%. Based on the aforementioned study [21], it is surmised that prolonged exposure to 80% humidity can significantly contribute to adhesive bond failures. Therefore, the effect of humidity on adhesive bonds should be considered in qualification tests.
## 4 Experimental Evaluation
### Performance Evaluation of CPLEX and S-GAIPSO  
In general, as the scale of the problem increases, its complexity will increase. As an advanced solver CPLEX can solve VRP models of general complexity, the S-GAIPSO proposed in this paper can obtain the optimal solution in a short time. In order to test the calculation and optimization capabilities of the proposed S-GAIPSO algorithm, we conducted experiments on numerical cases with tasks of 5, 10, 20, and 30 in different scales. Table 3 shows the corresponding results and compares the calculation time and optimized gap value of CPLEX and S-GAIPSO. Figure 8 compares the average target values for different scale tasks in the form of a bar graph. As the number of tasks increases, the difference between the function values (the total travel cost of the robot used and the penalty cost of exceeding the customer's service time) obtained by the algorithm and CPLEX becomes larger and larger. The calculation time of the SR MILP increases rapidly with the increase of the number of tasks. In the experiment to solve a scale of 20 tasks, it appeared that CPLEX could not generate the best solution for 2 cases. As the scale of the experiment continues to increase, for example, and each case has 30 tasks, CPLEX can no longer be used to provide a feasible solution, and our proposed algorithm also shows strong computing power and requires less than 0.35 s CPU time to obtain the optimal or close-to-optimal solution. Compared with CPLEX solvable solutions, S-GAIPSO achieved a gap of 0.157%, 1.097%, and 2.077%, respectively, when there were 5, 10, and 20 tasks on average. The optimality gap is determined by the Formula (40) calculations.  
\[G\text{ap}=\frac{Obj.value(CPLEX)-Obj.value(CPLEX-S-GAIPSO)}{Obj.value( CPLEX)}\times 100\% \tag{40}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Station** & **Robot** & **Optimal Scheduling** \\ \hline  & 1 & station1\(\rightarrow\)2\(\rightarrow\)8\(\rightarrow\)5\(\rightarrow\)station1 \\
1 & 2 & station1\(\rightarrow\)10\(\rightarrow\)4\(\rightarrow\)9\(\rightarrow\)station1 \\  & 3 & Station2\(\rightarrow\)3\(\rightarrow\)7\(\rightarrow\)6\(\rightarrow\)1\(\rightarrow\)station2 \\
2 & 4 & station1 \\ \hline \multicolumn{2}{c}{Obj. value} & 5496.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Optimal schedule of SR.  
Figure 8: Comparison chart of the average target value under different task scales.
## 2 Materials and Methods
### Characterisation  
The FT-IR spectrum for chitosan, alginate, and the synthesized materials were collected using a Bio-Rad-FTS-40 instrument (Bio-Rad Laboratories, Inc., Santa Clara, CA, USA). The samples were collected in a 1:10 (wt. ratio) with IR grade KBr with co-grinding into a fine powder for spectral analysis.  
PZC measurements were obtained for Al-TMC, Cu-TMC, Fe-TMC, sodium alginate, and chitosan using the pH-drift method. In brief, 60 mg of sample were added to 25 mL of 0.01 M NaCl (_aq_) with a starting pH ranging between 3 to 8. The NaCl concentration was held constant to ensure consistent sensitivity of the pH meter. After shaking at 298 K for 48 h, the final pH for each solution was measured to plot the change in pH (\(\Delta\)pH) vs. initial pH [19].  
SEM-EDX analysis was carried out on a Hitachi SU8010 Scanning Electron Microscope equipped with 170 mm\({}^{2}\) Ultim max Energy Dispersive Detector from Oxford instruments. All EDX scans were obtained at an operating voltage of 17 kV. AZTEC 4.3 data acquisition software was employed to obtain the EDX patterns.  
For the surface area (SA) determination of the TMC materials in their dry state, an ASAP 2020 surface area and porosity analyzer with N\({}_{2}\) as the backfill gas at \(-\)196.2 \({}^{\circ}\)C (BET surface area determination) was employed. To investigate the surface area or accessible surface area for dye adsorption in water, PNP solutions with variable concentrations ([PNP] = 2 to 16 mM) were used; 10 mg of each adsorbent material was added to 10 mL to shake in glass vials at 23 \({}^{\circ}\)C for 24 h. Then, aliquots of supernatant solution were centrifuged for 1 h before absorbance measurements. The uptake of PNP by the TMCs was measured at pH 8.5 (50 mM sodium bicarbonate buffer) and 508 nm [20; 21].
###### Abstract  
The localization of multiple interference sources in Global Navigation Satellite Systems (GNSS) can be achieved using carrier-to-noise ratio (C/N\({}_{0}\)) information provided by GNSS receivers, such as those embedded in smartphones. However, in increasingly prevalent complex scenarios--such as the coexistence of multiple directional interferences, increased diversity and density of GNSS interference, and the presence of multiple low-power interference sources--conventional localization methods often fail to provide reliable results, thereby limiting their applicability in real-world environments. This paper presents a multi-interference sources localization method using object detection in GNSS C/N\({}_{0}\) distribution maps. The proposed method first exploits the similarity between C/N\({}_{0}\) data reported by GNSS receivers and image grayscale values to construct C/N\({}_{0}\) distribution maps, thereby transforming the problem of multi-source GNSS interference localization into an object detection and localization task based on image processing techniques. Subsequently, an Oriented Squeeze-and-Excitation-based Faster Region-based Convolutional Neural Network (OSF-RCNN) framework is proposed to process the C/N\({}_{0}\) distribution maps. Building upon the Faster R-CNN framework, the proposed method integrates an Oriented RPN (Region Proposal Network) to regress the orientation angles of directional antennas, effectively addressing their rotational characteristics. Additionally, the Squeeze-and-Excitation (SE) mechanism and the Feature Pyramid Network (FPN) are integrated at key stages of the network to improve sensitivity to small targets, thereby enhancing detection and localization performance for low-power interference sources. The simulation results verify the effectiveness of the proposed method in accurately localizing multiple interference sources under the increasingly prevalent complex scenarios described above.  
GNSS interference; interference localization; target detection; C/N\({}_{0}\) distribution map 2025 IEEEexample:BSTcontrol  
1  
[PERSON] [PERSON] [PERSON] [PERSON] [PERSON] [PERSON] [PERSON] Localization of Multiple GNSS Interference Sources Based on Target Detection in C/N\({}_{0}\) Distribution Maps. _Remote Sens._**2025**, 17, 2627.  
[[https://doi.org/10.3390/rs17152627](https://doi.org/10.3390/rs17152627)]([https://doi.org/10.3390/rs17152627](https://doi.org/10.3390/rs17152627))
# Surface-Modified Compounds Improve the Detection Sensitivity of Terahertz Metasurface Biosensors
## 1 Introduction
The embedded vanadium dioxide (VO\({}_{2}\)) phase-change material (PCM) is capable of exciting a hybrid Fano resonance with dynamic spectral tuning. [PERSON] et al. [26] proposed a dual band multifunctional encoded metasurface that controls the transmission and reflection of the entire space by changing the frequency and direction of the incident wave. proposed a dual band multifunctional encoded metasurface that controls the transmission and reflection of the entire space by changing the frequency and direction of the incident wave. By arranging specific encoding sequences on different frequencies, the functions of reducing RCS, beam splitting, and OAM generation are achieved. [PERSON] et al. [27] proposed the metasurface based on liquid crystal programmable has the functions of beamforming and beam steering, and realizes the dynamic wave control. More elaborate control of meta-unitand quasi-2-bit modulation are realized. [PERSON] et al. [28] achieved a hyper-dispersive metalens in the range of 2.40 to 2.61 THz by simultaneously controlling the frequency-dependent phase, group delay, and group delay dispersion of the superlattice lens. Its dispersion was 1.76 times larger than that of conventional diffractive hyper-dispersive metalens. [PERSON] et al. [29] successfully deposited terahertzMetamaterial on LCE films by using the thermal evaporation and high-efficiency cooling system of a programmable electronic shutter. Achieved a large switching contrast of 277 when modulating the transmission ratio. In order to verify the detection effect of the double-opening ring THz metasurface sensor and the performance improvement effect after modified, three common cancer markers CEA, NSE and SCC were selected as the research objects. First, the unmodified sensor was detection different concentrations of the three markers, and the sensitivity of the sensor was obtained by calculating the change in the resonance frequency of the sensor and establishing the relationship between the concentration of the marker and the frequency shift. Second, the surface modification work was carried out, and WS\({}_{2}\) and HNT were modification to metasurface sensor, respectively. After that, using the modified sensor detection the cancer markers mentioned above. Finally, the detection result of modified sensor was compared with the unmodified sensor, to explore the improvement of the detection ability by different compounds. According to the analysis of experimental results, we found that compared the sensor modified with WS2 and blank sensor, the sensor modified with HNT has the highest sensitivity for the above three cancer markers. This provides a new scheme for THz metasurface sensors in the detection of trace biomarkers, which is suitable for biomedicine, human health monitoring and other fields.
## 5 Simulation Results
### Parametric Evaluation of the Proposed Method  
#### 5.2.1 \(K_{b}\) Gain  
The proportional gain \(K_{b}\) modulates BESS response when local SOC regulation is activated. Selecting a smaller gain will enhance the participation of the battery in aFRR provision but will also lead to operating closer to the edge of its SOC range. In Figure 11, the frequency, BESS response, SOC and participation factor are shown for three different \(K_{b}\) values (0.328, 0.061, 0.032), resulting from the tuning process described in Section 2.1, by requiring \(P_{b,soc}^{*}=-1\) pu for SOC 24.5% (red/dash-dotted), 22.5% (blue/continuous) and 20.5% (green/dashed), respectively.  
Transition from providing secondary reserves to idling is smoother when smaller \(K_{b}\) values are used, thus minimizing the small transients in frequency noted in Figure 11a when entering the minimum SOC activation zone. However, a smaller \(K_{b}\) value leaves SOC closer to its minimum limit, even though this is never exceeded (Figure 11c). For this reason, a minor increase in BESS cycles takes place at low \(K_{b}\) values (cycle range: 0.40-0.45). Since smaller \(K_{b}\) values result in lower SOC levels in real-time operation, this also leads to reduced \(pf_{b,up}\) values (as in Figure 11d), according to Equation (1) and the calculation of \(c_{b,up}\) (Figure 2).  
Overall, lower \(K_{b}\) values seem preferable, taking into account the improved exploitation of BESS energy reserves in real-time operation and the smoother transition to idling. However, higher \(K_{b}\) values will result in a slightly increased SOC level following a contingency, thus allowing provision of FCR to be sustained longer, if necessary. A relevant requirement is set in ENTSO-e's proposal concerning limited-energy resources' participation in frequency regulation [57].  
Figure 11: Response following a diesel unit outage at 150 s and synchronization of another at 900 s, for different \(K_{b}\) values. (**a**) Frequency, (**b**) BESS active output power (positive: injection, negative: absorption), (**c**) SOC, (**d**) BESS aFRR-up participation factor.
## 4 Proposition of Signal Classification Algorithms
### The Classification System Based on the Classic Expert System  
In order to design a classic classifying algorithm based on the expert approach, a good knowledge of the properties of the object, signal or phenomenon under study is required. It is no different in the case of recognition of the type of the EEG signal. In order to determine a proper structure of a classic algorithm, it is necessary to get to know well the properties and the range of values of the EEG signal at rest, They should be determined for each electrode separately, as the voltage value for all channels is different. Thus, a few signals corresponding to the restful state were collected and the range of changeability of their values was determined. Next, the deviations from standard for the particular channels when evoking moves forward, backward and to the left were to be investigated.  
Based on the gathered knowledge on the activity of the particular channels during various types of evoking movement, an algorithm which will classify future signals containing information on evoking an order of a given movement may be designed. This way it is known that an exemplary move to the right will be characteristic of deviation from standard (not always simultaneous and the same) of channels: T8, FT10, FC6, C4, FC2, F8 and F4. The move to the left, however, will be characteristic of the activity (not necessarily simultaneous) of channels: F7, F3, FC1, C3, FC5, FT9 or T7. The details pertaining to the activity of the channels during various types of movements can be seen on the figure legends in Section 3.  
Detection of the particular types of channel activities makes it possible to expose the right signal which will make it possible to control a mobile robot. In the following subsection, an algorithm basing on the use of artificial intelligence for the EEG signal classification will be suggested.
# Transferring Sentiment Cross-Lingually within and across Same-Family Languages
## 4 Related Work
### Sentiment Analysis
This method necessitates a parser for the tree structure. In addition to supervised and unsupervised techniques, research also focuses on semi-supervised methods. [PERSON] and [PERSON] [40] created domain-independent polarity classifiers using word similarity techniques in a semi-supervised setup. The authors described numerous matrices of word similarity. First, the lexical association is calculated using PMI to determine the similarity between two words. Second, semantic spaces represent a collection of conceptually similar words. Last but not least, distributional similarity defines the similarity between two words based on the words in their context. A large unsupervised dataset was utilized to compute the co-occurrence and occurrence frequencies required for the aforementioned matrices. [PERSON] et al. [41] compared the performance of SVMs and ANNs (artificial neural networks). The authors discovered that ANNs statistically outperformed SVMs when combined with the information gain-based feature selection method. Nonetheless, the results demonstrated that SVMs are less susceptible to noisy terms in the presence of data imbalance. Other authors [42; 43; 44] investigated recursive-style neural networks for learning vector representation for a sentence. These methods abandon single-word features in favor of a vector-based strategy. The authors' proposed recursive neural network learns the vector representations of phrases in a tree structure. It assigns a vector and a matrix to each node in a parse tree in order to capture its influence on the surrounding words. The recursive neural tensor network computes higher node representation using leaf-level word vectors. These procedures utilized parse trees. CNN's semantic modeling of sentences was investigated [45; 46]. The CNNs presented by the author are not parse tree-based. Utilizing filter pooling operations, relations between discontinuous phrases were captured. In addition to using a single neural schema such as unidirectional LSTM [47] or bidirectional LSTM [48], authors have mixed and matched networks such as CNN-LSTM [49], CNN, and RNN [50]. CNN is used to acquire regional characteristics, while the recurrent network learns the interdependencies between these regional characteristics. These methods consistently outperform feature engineering techniques. During back-propagation, which retrofits these representations for sentiment analysis, the word embedding used as input layers is also fine-tuned. The task-specific knowledge is eventually helpful during the time of inference. To prevent overfitting, they require an extensive training set.
# Ultrasound Elastography: Methods, Clinical Applications, and Limitations: A Review Article
## 5 Clinical Applications of Ultrasound Elastography
### Breast
#### 5.3.1 Elastography Techniques in Breast Disease Diagnosis
Maximum stiffness had a sensitivity of 93% and a specificity of 81%, while mean stiffness had a similar sensitivity of 94% and a specificity of 71% in these studies. SWI can improve the performance of the B-mode ultrasound and its function in assessing breast lesions, as it improves the specificity of the B-mode ultrasound (from 61.1% to 78.5%), reducing the need for unnecessary invasive biopsies and improving patient management [107; 108].
## 3 Results and Discussion
### Catalytic Mechanisms in \(\mathrm{Cat{-}2/H_{2}O_{2}/Fe(VI)}\) System
Figure 9: EPR spectra of (**a**) high valent iron, (**b**) \({}^{1}\)O\({}_{2}\), (**c**) O\({}_{2}\)\({}^{\bullet-}\) and (**d**) \({}^{\bullet}\)OH in the Cat\(-2/\)H\({}_{2}\)O\({}_{2}\)/ Fe(VI) system. further facilitates the degradation of organic pollutants. Graphitic nitrogen can act as a site for the generation of \({}^{1}\)O\({}_{2}\), thereby promoting the degradation of organic pollutants. H\({}_{2}\)O\({}_{2}\), as an oxidant, can promote the conversion of Fe(VI) to high-valent iron through electron transfer, thus facilitating the Fe cycle [14]. Moreover, the abundant O\({}_{2}\)\({}^{\bullet-}\) and \({}^{\bullet}\)OH radicals generated in the system can be converted into \({}^{1}\)O\({}_{2}\)[24], which further aids in COD degradation. In summary, the various reactive species continuously generated in the system can progressively degrade organic pollutants into small molecular intermediates, ultimately mineralizing them into H\({}_{2}\)O and CO\({}_{2}\). Comparison Between Cat\(-\)2/H\({}_{2}\)O\({}_{2}\)/Fe(VI) System and Fenton or Ozonation System  
Based on the sequential batch experiment, the maximum COD removal rate (46.2%) was achieved when the concentrations of H\({}_{2}\)O\({}_{2}\), potassium ferrite, and catalyst were 0.8 mM, 0.1 mM, and 0.5 g/L, respectively, and 32.34 mg of COD can be removed. According to Table S2, the cost of the Cat\(-\)2/H\({}_{2}\)O\({}_{2}\)/Fe(VI) system for removing COD is USD 0.63/kg COD. Previous reports have found that the costs of the Fenton and ozone processes for COD removal are USD 1.77/kg COD and USD 1.96/kg COD, respectively [25]. The Cat\(-\)2/H\({}_{2}\)O\({}_{2}\)/Fe(VI) process has great potential in cost for future applications. In addition, the Fenton and ozone processes have been used in wastewater treatment plants. Therefore, all three processes have scalability.