esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
### Data Preparation  
In this section, we present three widely used hyperspectral datasets--namely the Pavia University (PU) dataset, the Kennedy Space Center (KSC) dataset, and the Indian Pines (IP) dataset--selected to validate the effectiveness of the proposed method, as summarized in Table 1. The PU dataset contains a relatively small number of classes, with each class comprising a sufficient quantity of labeled samples, and the differences in the number of labeled samples among the various classes are minimal. In contrast, the KSC dataset features a greater number of labeled classes compared to the PU dataset, with a balanced distribution of labeled samples across different categories. The IP dataset, on the other hand, encompasses a more extensive range of land cover classes, revealing significant variations in the number of labeled samples associated with these classes. An analysis of the spectral correlations among different land cover categories in the three datasets reveals that there are certain classes within each dataset that exhibit similar spectral characteristics. For example, within the PU dataset, classes 3 and 8 demonstrate spectral similarity; the KSC dataset reveals spectral resemblance among classes 3, 4, 5, 6, and 7; while in the IP dataset, classes 3, 11, and 12 also show comparable spectral features. The differences in the number of labeled sample categories across these datasets, along with variations in the number of labeled samples for different land cover categories and the presence of similar spectral characteristics among certain land cover classes, provide a strong basis for evaluating the model's effectiveness and robustness.  
The training process of the model is considerably dependent on labeled data, with the number of labeled samples directly influencing the model's performance. To effectively illustrate the practical applicability of the proposed method, we adopt the approach described in reference [23] by selecting a reduced number of training samples. In the PU dataset, we randomly select 1% of the labeled samples from each category to constitute the training set, with an additional 1% designated as the validation set, while the remaining 98% is utilized to evaluate the model's classification performance. For the IP and KSC datasets, we randomly select 5% of the labeled samples from each category as training data, another 5% for validation, and the remaining 90% to assess the classification efficacy of the model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Geometric Resolution** & **Size** & **Bands** & **Number of Categories** \\ \hline PU & 1.3 m & \(610\times 340\) & 103 & 9 \\ KSC & 18 m & \(512\times 614\) & 176 & 13 \\ IP & 20 m & \(145\times 145\) & 200 & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of Dataset Information.
# Time Series RUL Estimation of Medium Voltage Connectors to Ease Predictive Maintenance Plans
## 2 The Role of the Electrical Resistance on Ageing  
The electrical resistance is known to play an important role on the degradation of power connectors [23], being an excellent indicator of their health condition [24]. Any rise of the electrical resistance is translated in an increase of the connector's operating temperature, which further rises the electrical resistance, thus degrading the thermal behavior of the connector and reducing its expected useful life [3].  
Despite its importance, it is not possible to measure electrical resistance directly, so indirect methods are required. The common method to measure the electrical resistance is by measuring the voltage drop across the connector terminals and the electrical current circulating in the connector. However, this method is valid in direct current (DC) circuits but it cannot be applied directly in alternating current (AC) circuits, since under AC supply, the impedance of the connector \(Z_{\text{C}}\) is obtained instead of the resistance,  
\[Z_{t,T}=\frac{\Delta V_{t,T}}{I_{t}}, \tag{1}\]  
where \(\Delta V_{t,T}\) is the instantaneous value of the voltage drop across the terminals of the connector measured at time \(t\) when its temperature is \(T\), and \(I_{t}\) is the instantaneous value of the current flowing in the connector at the same time \(t\). It is noted that for an on-line monitoring of the resistance of the connector, \(I_{t}\) is the AC current flowing in the loop or installation where the connector is placed.  
In order to determine the electrical resistance of the connector, the phase shift between the voltage drop and the current waveforms must be measured. It can be done by applying the properties of the phasors as,  
\[\overline{Z}_{t,T}=\frac{\Delta\overline{V}_{t,T}}{\overline{I}_{t}}=\underbrace {\frac{\Delta V_{t,T}}{I_{t}}\times\cos\varphi}_{R_{t,T}}+j\times\underbrace {\frac{\Delta V_{t,T}}{I_{t}}\times\sin\varphi}_{X_{t,T}}, \tag{2}\]  
where \(R_{t,T}\) and \(X_{t,T}\) are, respectively, the instantaneous values of the resistance and the reactance of the connector, and \(\varphi\) is the phase shift between the voltage drop and the current waveforms.  
It is known that the electrical resistance changes with the temperature of the connector, so this effect has to be considered in the measurements. To deal with the effect of temperature, the resistance of the connector is often referred to 20 \({}^{\circ}\)C (\(R_{t,20}\)\({}^{\circ}\)C) and thus from (2),  
\[R_{t,20^{\circ}\mathrm{C}}=\frac{R_{t,T}}{1+\alpha\times(T-20)}=\frac{\Delta V _{t,T}\times\cos\varphi}{I_{t}\times[1+\alpha\times(T-20)]}, \tag{3}\]  
where \(\alpha\) is the coefficient of temperature coefficient, its value being around 0.004 \({}^{\circ}\)C\({}^{-1}\) for pure aluminum and copper materials.  
As mentioned before, the electrical resistance of medium-voltage connectors is in the range of a few micro-ohms, and thus, special care must be taken when measuring this magnitude.
# Improving RE-SWOT Analysis with Sentiment Classification: A Case Study of Travel Agencies
## 2 Related Works
### Sentiment Analysis  
Sentiment analysis is a method of opinion detection. The composition of opinions includes the source of the opinion (that is, the person who expresses the opinion), the target object of the opinion, the polarity of the opinion, and the text containing the opinion. The simplest sentiment analysis simply judges the polarity of the opinion; a more advanced analysis will give a quantitative value to the intensity of the opinion's polarity. A more complex sentiment analysis will try to judge the source of the opinion and the target object mentioned and may even analyze which aspect of the target object the comment refers to as well as the emotions implicit in the opinion.  
Research on sentiment analysis has a wide range of applications, covering film, catering, tourism, and other reviews. For example, [PERSON] [20] collected reviews from the Bulletin Board System PTT and analyzed the sentiment score of the adjective terms to recommend the movie. There are currently ready-made sentiment analysis tools that can read text messages and determine the implied emotional polarity and intensity of the message. Of these, VADER (Valence Aware Dictionary and sEntiment Reasoner) is one of the most widely used tools [21; 22; 23]. VADER is a rule-based sentiment analysis tool that has been proven to be more accurate than other sentiment analysis methods when dealing with text from social media, movie reviews, and product reviews. VADER can not only identify emotional polarity, it can also give a quantitative value of the intensity of emotional polarity [13; 24].  
The advantages of VADER include [13]  
1. It performs well with social media text covering various fields;
2. It does not require any training data but uses a generalized sentiment dictionary based on the psychological valence and gold-standard sentiment lexicon;
3. It has a satisfactory level of efficiency for online streaming data and does not seriously impact the balance between speed and performance.  
Given a sentence, VADER gives scores in four categories: positive, negative, neutral, and compound. The compound score is derived by normalizing the sum of the positive, negative, and neutral scores. This score ranges from \(-1\)(most extreme negative) to \(+1\) (most extreme positive) [25]. [PERSON] and [PERSON] [21] proposed a method to transform the VADER score into a rating on a scale of \(-2\) to \(2\). The transformation rule is as follows:  
_If compound score > 0.001, then_  
_If positive score > 0.5, the rating is set to \(+2\); else, the rating is set to \(+1\)._  
_If compound score < \(-0.001\), then_  
_If negative score > 0.5, the rating is set to \(-2\); else the rating is set to \(-1\)._  
_If compound score is between \(-0.001\) and \(0.001\), the rating is set to 0._  
We can now propose a solution to the RE-SWOT problem that we posed in the previous subsection. If the user ratings are required but are lacking, we can utilize VADER to calculate the sentiment scores of user reviews and use [PERSON] and [PERSON]'s method to generate user ratings.
# Energy Consumption Analysis of LPWAN Technologies and Lifetime Estimation for IoT Application
## 6 Battery Life Estimation
### Transmission Power Limitations  
In addition to the limitations imposed by the duty cycle, there is a limit on the transmission power which affects the total battery life. Transmission power is expressed in milliwatt (mW) or decibel-milliwatt (dBm) that ranges from 5 to 2000 mW. For example in Europe, Sigfox has a duty cycle of 1% and maximum output power of 25 mW [40]. For each LPWAN technology there is a configuration setting for power adaptation which results into different energy consumption. The power configuration is tightly coupled with the distance of end-device from the gateway. Most of the LPWAN technologies operating in the ISM band has a power setting of 14 dBm whereas transmission power in cellular are around 20 dBm. For example, LoRaWAN end-device has the option to configure the data rate and transmit power or use the Adaptive Data Rate (ADR) mechanism that is managed by the network server. The paper [41], demonstrates the impact of LoRa transmission parameter selection on communication performance and energy consumption. Also, it shows the significance of transmission settings to satisfy performance requirements. Thereby, for calculating the battery life it is vital to consider the configuration parameters for the end-device, need for retransmissions caused due to collisions, and scenario where acknowledgement is expected by the end-device.
## 3 Coupled Modal Analysis of Ring-Stiffened Conical Shell with Bases  
### Establishment of Structural Model  
A ring-stiffened conical shell with bases is built in Ref. [36], as shown in Figure 1. The full length is 3200 mm, and the thickness is 7 mm. The diameters of the front-end and back-end are 1750 mm and 840 mm, respectively. The rib interval is defined as 150 mm and the rib thickness is 5 mm. The overall material is steel. The relevant parameters, i.e., elastic modulus, Poisson's ratio, density and loss factor, are defined as 210 GPa, 0.3, 7800 kg/m\({}^{3}\) and 0.001, respectively. The bases are mainly composed of plates, webs and ribbed slabs. The plate thickness is 10 mm, and the thicknesses of web and ribbed slabs are all 5 mm. The length of the base is 1200 mm. The width of the web is 100 mm, and the rib interval is 150 mm. The distances of a plate's front-end and back-end to shell surface are 370 mm and 532.5 mm, respectively. The distance from web to the longitudinal center plane of shell is 325 mm.  
In this paper, a combination of FEM and BEM is applied to deal with the underwater vibration and acoustic radiation of the ring-stiffened conical shell. Since the coupling between the conical shell and water is analyzed by applying the FEM in this section, the finite element model of the fluid medium is established in Figure 2. The conical shell in Figure 2 is modeled by the structural finite element quadrilateral meshes, and the water is modeled by the acoustic finite element tetrahedral meshes. To achieve a compromise between computation efficiency and accuracy, the size of the finite element model of water is 10 m long \(\times\) 10 m wide \(\times\) 10 m high, and each side is divided into 30 tetrahedral elements. The mesh size of the conical shell is 50 mm \(\times\) 50 mm and the mesh size of the base is 50 mm \(\times\) 25 mm. The fluid density \(\rho_{0}\) = 1000 kg/m\({}^{3}\). The sound velocity \(c\) = 1500 m/s. Since the whole conical shell is immersed in the water, the coupling effect outside the conical shell is mainly caused by external flow field, while the inside of the conical shell is considered as a vacuum.
# Application of Molecularly Imprinted Polymers in the Analysis of Waters and Wastewaters
## 5 Application of MIPs for the Detection of Different Classes of Contaminants
### Heavy Metals
#### 5.1.3 Copper  
Copper is found to be harmful to the environment and organisms in large quantities. It is well known that copper pollution has a negative impact on urban ecosystems, as well as affecting aquatic animals' chemoreception and chemosensory [43]. WHO indicated 2 mg L\({}^{-1}\) as acceptable level due to its hazardous effect on human health, leading to hypochromic anemia, osteoporosis, liver, and kidney failures and immunotoxicity [45; 50].  
A novel copper (II)-selective potentiometric sensor based on graphite oxide/imprinted polymer composite was developed. 5-methyl-2-thiozymethacrylamide was used as a functional monomer and the produced Cu IIP/multiwalled carbon nanotubes/graphene oxide was used as a modifier for a carbon paste electrode. A detection limit of 4 \(\times\) 10\({}^{-7}\) M was reported with a linear response range 1 \(\times\) 10\({}^{-6}\)-1 \(\times\) 10\({}^{-1}\) M. The electrode was applied in the analysis of different water samples (tap, river, dam) and they were in good agreement with those obtained by using inductively coupled plasma-mass spectrometry (ICP-MS) [51].  
Chitosan (C), gelatin (G), and 8-hydroxyquinoline (HQ) were used as a monomer to prepare green imprinted IIPs for Cu(II). Multi-point interactions from complementary functional monomers, as shown in Figure 4, indicate that the optimal mass ratio for G:HQ was 2:1, and that the optimal G-HQ:C: Cu(II) ratio was 150:150:5. The maximum adsorption capacities of G-HQ-C IIPs toward Cu(II) reached up to 111.81 mg g\({}^{-1}\) at room temperature and pH 6. G-HQ-CIIPS are applied to selective Cu(II) in different water samples [52].
## 3 Results
### Model Predictions on the Image
#### 3.4.2 Spatial Resampling  
As mentioned earlier, the original Fenix hyperspectral image was acquired with 1 m GSD, which is obviously not the same as all other sensors in this study. Hence, to examine the potential of the native GSD of the various sensors, the original 1 m GSD was resampled into four GSDs, namely: 5, 7.5, 15, and 30 m. Then, the models, which were developed per sensor in the previous section were applied to the different GSD images and the results are shown in column D in Figure 8. Additionally, Figure 8 also shows (for comparison purposes) three spatially resampled images: the true-color Fenix image, the EROS image, and the model predictions (from Figure 7A) in the columns A, B, and C, respectively. For illustration simplicity, we do not show here the spatially resampled maps of all of the sensors, but rather only for the Fenix. Naturally, as the GSD increases, the ability to classify the pixels correctly decreases, and the larger the area contaminated by the spill, the coarser the GSD that is needed for detection. With that being said, the spatial pattern of the spill outlined in the 1 m GSD is clearly observable in the 5 m map (column D), while this fades away in the 15 and 30 m maps. The 7.5 m GSD seems to be a transition GSD, with the ability to properly follow the oil spill pattern. Although it is not as good as the spectrally resampled 1 m prediction (column C), it does seem to enable following the oil spill.  
Figure 8: Model prediction (only for the Fenix sensor) resulted from the spatial resampling. (**A**)—Evrona true color composite, resampled to different GSD. (**B**)—EROS image, resampled to different GSD. (**C**)—Predictions based on the original GSD, resampled to various GSDs. (**D**)—Prediction made on the spatially resampled hyperspectral image.  
The 15 and 30 GSD maps center on the larger area of the spill (the north-west part of the image). This does not mean that a GSD of 15 or 30 m are irrelevant to detect PHC, it just means that in our case study, where the oil flowed in narrow drainage channels, 15 or 30 m GSD will not be effective. It means that sensors with this GSD (e.g., LS8 and ASTER) are not suitable for this mission. This is somewhat compatible with the study done by [18], where they examined the ability to map minerals related to acid mine drainage and found the 15 m images to be comparable to the 2 m images, while the 30 m images lost the ability to identify smaller spectral features.
## 1 Introduction
It shallmeasure VOD for the detected clouds with an accuracy equal to or better than 0.03 root mean square deviation (RMSD) for each laser wavelength. It shall also measure base and top heights for the detected clouds with an accuracy equal to or better than 300 m RMSD. **PBL properties:** The RL shall detect the PBL with heights ranging from 0.5 km to 9 km above ground and Vertical Aerosol Optical Depths (VAODs) ranging from 0.03 to 0.7. It shall provide VAODs for the detected PBL with an accuracy equal to or better than 0.03 RMSD for each laser wavelength. It shall also measure the heights of detected PBLs with an accuracy equal to or better than 300 m RMSD. It shall measure the extinction Angstrom exponent with an accuracy better than 0.3 RMSD. **Pointing capability:** The RL shall have a range of pointing directions starting from 25\({}^{\circ}\) or lower elevation angles up to zenith and be applicable to all azimuth angles. **Measurement time:** To limit interference with CTAO science observations, the RL shall measure the aerosol extinction profile to the required accuracy along any line-of-sight within the pointing limits within one minute or less. These requirements may already be achieved with elastic lines only if reasonable efforts are made to continuously maintain an absolute LIDAR calibration [9; 22], and external information is available about the size distribution of aerosols [20; 23; 24]. However, the availability of Raman lines allows for improved accuracy obtained directly from the data rather than on the basis of regular calibration procedures. This led us to develop a custom-designed four-wavelength RL [16], which is described hereafter.
# Toward Generative AI-Based Intrusion Detection Systems for the Internet of Vehicles (IoV)
## 4 Generative AI-Based IDS for IoV
### Contextual Suitability of Generative Models
These limitations can lead to high false negatives, especially when trained on low-diversity or poorly normalized datasets. The unsupervised nature of many VAE-based models further complicates detection in real-world traffic where anomalous patterns are subtle and not well represented in training data. Choosing the right generative model for an IoV IDS requires balancing detection performance against resource availability. GANs, while resource-intensive, typically achieve high detection accuracy, especially for rare or previously unseen attacks. They are excellent in training-phase augmentation but may not be viable for real-time, low-power applications without extensive optimization. Transformers strike a balanced trade-off. When pretrained and fine-tuned appropriately, they provide both high accuracy and adaptability to evolving threats. However, their training and deployment still require moderate computational resources, which may limit applicability in ultra-constrained environments unless paired with edge-optimized architectures. VAEs, though often yielding slightly lower detection precision compared to GANs and Transformers, offer superior efficiency and deployability. Their ability to generalize from normal data makes them highly effective in environments with limited labeled datasets and constrained hardware. In conclusion, each generative model brings a distinct set of advantages and limitations. GANs excel in accuracy and data generation; Transformers offer robust, context-aware modeling; and VAEs shine in low-resource, edge-compatible scenarios. The optimal choice \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Criteria** & **GANs** & **Transformers** & **VAEs** \\ \hline Real-Time Latency & \begin{tabular}{c} Poor (slow training \\ and inference) \\ \end{tabular} & \begin{tabular}{c} Good (fast inference \\ with attention) \\ \end{tabular} & \begin{tabular}{c} Good (fast reconstruction-based scoring) \\ \end{tabular} \\ \hline Robustness Under & \begin{tabular}{c} Sensitive to noise \\ and imbalance \\ \end{tabular} & \begin{tabular}{c} High (context-aware \\ sequence modeling) \\ \end{tabular} & \begin{tabular}{c} \(\mathcal{V}\) Moderate \\ (probabilistic generalization) \\ \end{tabular} \\ \hline Hardware Compatibility & \begin{tabular}{c} Low (GPU required) \\ \end{tabular} & \begin{tabular}{c} \(\triangle\) Moderate \\ (requires optimization) \\ \end{tabular} & \begin{tabular}{c} High (lightweight \\ and deployable) \\ \end{tabular} \\ \hline Memory and & \begin{tabular}{c} High (dual models, \\ unstable training) \\ \end{tabular} & \
\begin{tabular}{c} \(\triangle\) Moderate–High \\ (manageable with pruning) \\ \end{tabular} &
\begin{tabular}{c} Low (suitable for \\ edge devices) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparative evaluation of GANs, Transformers, and VAEs based on key IoV criteria. depends on the specific operational context, threat model, and system constraints of the targeted IoV application.
## 3 Results
### Maximum 1-Day, Consecutive 3-, 5-, and 7-Day Precipitation Extremes  
Index maximum 1-day precipitation (RX1 day) informs the maximum daily (extreme event) precipitation of a year at that location. However, such an event is highly localized [80]. The spatial mapping of RX1 day reveals that higher extreme values are concentrated across the country's southern belt, with the highest intensity in the central region (Figure 3 top panel). During the monsoon season, the moisture-bearing monsoonal wind approaches the country; heavier precipitation falls in the southern foothill, the first windward side of low-elevated hills (Churia) across the region [80]. As previously mentioned, IMERG_Uncal shows higher precipitation extremes than IMERG_Cal; the country-averaged RX1 day by IMERG_Uncal (IMERG_Cal) is 112 (93) mm. Another index, RX1 dayTOT (ratio of RX1 day to the PRCPOT), updates the contribution of max 1-day precipitation to the total precipitation, where higher RX1 dayTOT indicates high severity of extremities in a day with respect to total annual precipitation.  
The spatial distribution of RX1 dayTOT (Figure 4) is similar to that of r95 pTOT and r99 pTOT (Figure 2), indicating two pockets with high contribution from extreme precipitation: (i) southern lowlands because of the higher absolute value of extreme precipitation events and (ii) northwestern high-altitude region because of lower annual precipitation and a smaller number of wet days. The spatial distribution of maximum consecutive 3-, 5-, and 7-day precipitation (RX3 day, RX5 day, and RX7 day in Figure 4) are similar to the spatial distribution of RX1 day (Figure 3 top panel). This similar pattern is evident for RX3 dayTOT, RX5 dayTOT, and RX7 dayTOT, but the absolute value differs (Figure 4). The country-averaged RX3 day, RX5 day, and RX7 day are 173 mm, 201 mm, and 224 mm based on IMERG_Uncal, and 146 mm, 176 mm, and 202 mm based on IMERG_Cal, respectively. Regions with a higher extreme precipitation contribution to PRCPOT possess a higher chance of waterlogging, inundation, and other geo- and hydrometeorological hazards [96].