esa-sceva/satcom-chunk-collection huggingface dataset:
# Plant-Growth-Promoting Bacteria Mitigating Soil Salinity Stress in Plants
## 5 Role of PGPB in Alleviating Salinity Stress to Plants
### Extracellular Polymeric Substances (EPSs)  
Different biopolymers are secreted by the microbial cell (polysaccharides, polyesters, polyamides) into its surroundings. The biopolymers play an irreplaceable role in plant-microorganisms relationships, especially in the alleviation of plant salinity stress [17; 36; 55; 137], as they are excreted out of the cells and bind cations like Na\({}^{+}\) in decreasing bioavailable concentration, but also serve as signal molecules for the defense response to infections. These EPSs help PGPB to survive in saline environments; thus, most of the halotolerant bacteria possess the ability to extrude this kind of compound [5]. Moreover, they are an instrument that permits microorganisms and plant roots to communicate, establishing a number of interactions. In that sense, EPSs are very useful to microorganisms in order to survive in root surroundings and benefit from the exudates. Of course, the composition of the EPSs excreted by the PGPB cells differs among genera and species and change with time, environmental conditions, and circumstances. There are data that EPSs improve nutrient uptake and water potential in the rhizosphere [138; 139]. PGPB gently adhere to plant roots, secreting EPSs and forming biofilms against desiccation [137].  
Particularly, exopolysaccharide-producing _Enterobacter_ sp. and _Bacillus_ sp. have been found to improve water balance in roots of quinoa under the saline stress of 400 mM NaCl [140]. Many of the EPS-producing PGPB play a vital role in soil fertility and agricultural sustainability, such as _Azotobacter vinelandii_, _Rhizobium_ sp., _Enterobacter_ sp., _Agrobacterium_ sp., _Xanthomonas_ sp., and _Bacillus_ sp.; thus, they indirectly improve soil structure and aggregation in the rhizosphere [55; 138].
# Bridge Assessment under Earthquake and Flood-Induced Scour
## 3 Case Study
### Flood and Earthquake Hazards  
#### 3.2.1 Earthquake Hazard  
In conducting this study, the earthquake hazard is evaluated by referring to the seismic hazard map of Albania (PGA, 10% probability of exceedance in 50 years, and \(V_{\text{S30}}\) site condition of 800 m/s) [21], which provides information about peak ground acceleration for a 475-year RP and ground motion of the 26 November 2019 earthquake in Durres, Albania. The time history analysis focuses on the horizontal orthogonal directions of the earthquake. The bents are not subjected to high bending moments due to vertical permanent loads of superstructure, and so the vertical component of seismic action is not considered [22]. The time history acceleration chosen from a location near the bridge site is adjusted to align with the response spectrum outlined in Eurocode 8-1 [23], and the matched records for each seismic level (\(a_{\text{gR}}=0.05\) g, 0.10 g, 0.20 g, and up to 0.5 g) are generated. The defined target spectrum is Type 1 of the horizontal elastic response spectrum with a damping ratio of 5% and C-ground type. The importance factor for the bridge is equal to \(\gamma_{\text{I}}=1.0\) (importance class II, average importance).  
Seven monitoring stations (www.geo.edu.al (accessed on 8 January 2024)) detected a significant earthquake on 26 November 2019 (M = 6.4), spanning an epicentral distance range of 15 to 130 km [24]. Figure 3 illustrates the recorded ground motions in Tirana, displaying both the (N-S) and (E-W) directions (accelerometric station TIR1, 33.7 km from the epicenter). As shown in Figure 3, the horizontal peak ground acceleration is approximately 0.12 g, and in Durres, near the epicenter, this value was approximately 0.2 g [25].  
Figure 2: Bridge details: (**I**) plan and longitudinal view; (**II**) cross-section of bents and piles; (**III**) cross-section of bridge at bent.
trips involving at least one mode change, connection times (walking + inter-layer waiting) account on average for 23 \(\pm\) 6% of door-to-door travel time. In Montreal, Lachapelle and Boisjoly [4] find that off-vehicle activities (access + waiting) comprise 58.52% of total trip duration (7.09 min of access and 16.68 min of waiting over a mean trip of 43.37 min). As an additional illustration, based on the waiting (5-20 min) and median trip (30-50 min) ranges reported by [PERSON] et al. [5], it can be approximated that waiting represents roughly 17-40% of total trip time (author estimation). These international examples demonstrate that waiting--and transfer--times form a substantial portion of door-to-door journeys and underscore the need to optimize service frequency and coordination.  
This study makes several relevant contributions to public transport research:  
* Integrated methodology: It integrates Exploratory Data Analysis (EDA), global spatial autocorrelation via Moran's I index, and a DBSCAN optimized with KD-Tree indexing and expansion-factor weighting.
* Gap in the literature: The literature documents independent applications of Moran's I (e.g., [PERSON] and Sener [6]) and DBSCAN variants for smart-card data ([PERSON] et al. [7]) or for travel area clustering with an improved DBSCAN ([PERSON] et al. [8]); however, we found no peer-reviewed studies that combine both approaches in a single analytical workflow, nor any published investigations of waiting times in the Greater Valparaiso Metropolitan Area.
* Original local diagnosis: The only available local assessment [9] treats waiting time as a component within an accessibility index and demand model, without applying spatial autocorrelation or clustering techniques, underscoring the originality of our integrated approach.  
To address this gap, the present work provides a comprehensive analysis of public transport waiting times in Greater Valparaiso, identifying spatial patterns, factors influencing these waiting periods, and the most vulnerable user groups.  
The specific objectives of this study are:  
* To analyze the spatial and temporal distribution of public transport waiting times in Greater Valparaiso.
* To explore the variables that affect the duration of these waiting periods.
* To determine which user groups are most disadvantaged by prolonged waiting times.
* To propose recommendations aimed at improving the efficiency and equity of the public transport system based on the study's findings.  
To achieve these objectives, data from the 2014 Greater Valparaiso Mobility and Transport Survey were utilized, supplemented by exploratory analysis techniques, spatial autocorrelation methods, and clustering algorithms. This approach offers valuable information for planners and policymakers, facilitating the design of interventions that optimize accessibility and encourage broader use of public transport.
## 5 Methodology
### Performance Metrics  
To evaluate the effectiveness of the proposed method, four standard metrics, namely accuracy, precision, recall, and F1, were used. Table 2 details these metrics and their formulas. In the table, TP represents true positive predictions, TN represents true negative predictions, FP represents false positive predictions, and FN represents false negative predictions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Description** & **Formula** \\ \hline Accuracy & Ratio of correctly classified samples to total samples & (TP + TN)/(TP + FP + FN + TN) \\ Precision & Ratio of correctly predicted positive samples & TP/(TP + FP) \\ Recall & Ratio of correctly predicted positive samples to all actual positive samples & TP/(TP + FN) \\  & Harmonic mean of precision and recall metrics & (2 \(\times\) Recall \(\times\) Precision)/ (Recall + Precision) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation metrics.
# Image-Level Anti-Personnel Landmine Detection Using Deep Learning in Long-Wave Infrared Images
## 4 Experimental Results
### Results with MASATI Dataset  
We evaluate the effectiveness of the proposed inpainting-based image augmentation method using another public dataset, MASATI [26]. As outlined in Section 2, images from two classes, \"Coast\" and \"Coast & Ship\", are utilized for the binary classification task. Each class is randomly divided into train and test sets with an 8:2 ratio. Following [27], all images are resized to \(224\times 224\). We adopt the same experimental setup used in the AP landmine experiments. We omit InfMAE in the evaluation as the model is designed to deal with infrared images. Two experiments are conducted, one with and another without the proposed inpainting-based image augmentation. To assess the model's reliance on spurious features, we use all images from the \"Sea\" class along with the synthetic ship-free images (i.e., inpainted version of \"Coast & Ship\" test images). Inpainting masks are generated  
Figure 11: Confusion matrices for models trained with feature maps reduced by mean pooling.  
Figure 12: Confusion matrices when foundation models combined with ABMIL are trained from scratch.  
from the annotated bounding boxes provided in the MASATI dataset. Since the original bounding boxes tightly enclose the ships, we expand each bounding box by five pixels in all directions. The \"Sea\" and synthetic ship-free image sets contain a total of 1022 and 207 images, respectively.  
Our results in Tables 7 and 8 highlight several important observations. First, the models trained with the proposed inpainting-based augmentation show slightly lower classification performance compared to those trained without it. However, the models without the proposed augmentation produce a large number of false positives in both the \"Sea\" and synthetic ship-free image sets. This indicates a strong reliance on spurious features and suggests that these models overfit to the training data. Second, the inpainting-based augmentation significantly reduces the number of false positives. This demonstrates that the proposed inpainting method is effective at mitigating the influence of spurious features and promoting the model to focus on features related to the object of interest. Third, OpenCLIP consistently underperforms compared to other models on both the AP landmine and MASATI datasets. This result suggests that OpenCLIP may be less suitable for classification tasks involving small objects.
## 4 Results and Discussion
### Results Using BoW with the SMOTE Technique  
The second set of experiments involves using BoW on the SMOTE balanced dataset. Experimental results are provided in Table 11, which indicates significantly better performance as compared to results on the imbalanced dataset. On the balanced dataset, the performance of tree-based models improved significantly as well as linear models because of the increase in the feature set. RF, DT tree-based models achieved the highest accuracy score of 0.95, and SVM also shares this highest accuracy score with RF and DT. SGD and LR are just behind them with 0.94 and 0.93 accuracy scores, respectively. Using the SMOTE technique, the performance of ETC is improved from 0.80 to 0.89. Similarly, the performance of RF, DT, KNN, and AdaBoost is improved from 0.86, 0.83, 0.52, and 0.69 to 0.95, 0.95, 0.62, and 0.78, respectively. This significant improvement in models performance is due to class balance and an increase in the feature set after balancing. The use of SMOTE for data balancing also reduces the probability of the model over-fitting on the majority class and helps to improve the performance.
## 4 Discussion
By introducing synchronization of the signal acquisition and generation process in each measurement, it was possible to \"extract\" the components of the sound intensity vector from the classical sound pressure measurement at discrete points distributed on a plane, eliminating the need for an intensity probe. In addition, by definition, by making measurements with only one microphone, one of the main error characteristics of the PP probe due to differences in the frequency characteristics of microphones (phase mismatch error) was eliminated. In the proposed method, the signals recorded at the corresponding measurement points a priori correspond to those recorded by the microphones of the classical PP probe, so all the well-developed theory on the metrological side of the measurement issue (e.g., the error of approximation of the derivative by the differential quotient) is analogous here. The error associated with the constancy of the measurement conditions appears to be small (changes in temperature and atmospheric pressure are more in the nature of drift) and can be minimized by optimizing the motion trajectory of the measurement microphone traversing the full range of points belonging to the assumed measurement grid. This will be the subject of an in-depth analysis in the authors' further work. Thus, the proposed method with direct synchronization wherever it is possible to repeatedly measure the distribution of the acoustic signal (e.g., generated by a dynamic speaker), is possible without using a specialized intensity probe, but using only a classical microphone and synchronization system to determine the distribution of the vector field of sound intensity in a specific space. This opens access to intensity methods to a wider range of interested users.
# RoPT: Route-Planning Model with Transformer
## 5 Experiments
### Ablation Studies  
To verify the effectiveness of the GCN, Transformer, and destination information, ablation experiments are conducted on the Porto and Chengdu datasets, with results shown in Table 4. The method marked as w/o \(M_{Des}\) represents the variant of removing destination, w/o Transformer represents the variant of replacing the Transformer with an RNN, and w/o GCN represents the variant of replacing the GCN with a linear layer. The results reveal that destination information exerts the most significant impact. Destination removal leads to a performance degradation of 9.06% (Acc) and 11.09% (Recall) on Porto, and 4.32%/6.17% on Chengdu, correspondingly. The biggest reason may stem from the fact that, when taking a taxi, passengers like to take the expressway to reach their destination as soon as possible. As we know, the number of forks on the expressway are few, so the choice of route is simple, which increases the impact of the destination. After the removal of the GCN and Transformer, the metrics are also decreased, which prove the effectiveness of the combination of the GCN and Transformer. Notably, the GCN demonstrates greater influence, aligning with the well-established principle that spatial road network correlations are prioritized in route planning.
## 5 Importance of Ubiquitous Cellular Coverage in Hospitals
### Proposed Solutions for Coverage Improvement
The comparative economic analysis conducted by [32] further supports this decision, demonstrating that in small to medium-sized buildings with high traffic demands, as is the case in hospitals, femtocells present a significantly lower total cost of ownership (TCO) compared to DAS systems. As long as the cost per FAP remains below EUR 440, femtocells are consistently the most economical solution. The advantage of DAS systems only becomes evident in very large buildings with low traffic density and when installation and infrastructure costs can be amortized over extensive areas [32]. Implementing these solutions will not only enhance connectivity but also support the growing demand for wireless medical systems and telemedicine services, ultimately improving patient care and operational efficiency in CHUCB.
# Concise Robust Control of the Nonlinear Ship in Course-Keeping Control
## 4 Design of a Nonlinear Robust Controller for Ship Course Control
### Design of Nonlinear Ship Course Controller Based on Backstepping  
The mathematical model of ship motion has a certain nonlinearity, and the backstepping method can deal with the nonlinear term effectively. The Lyapunov function of the whole system is constructed recursively through the structural characteristics of the system, which solves the problem that the Lyapunov method lacks the construction method. Moreover, it does not eliminate all the nonlinear terms of the system as other feedback methods do so that the designed controller is both flexible and robust. Moreover, it can reduce the control difficulty and energy consumption of the system.  
Figure 4: Steady-state turning test (velocity of 15 km).  
Figure 5: Steady-state turning of mariner (velocity of 15 km).  
The backstepping method and precise feedback linearization method are constructed by introducing nonlinear feedback or coordinate transformation, but the backstepping method constructs the Lyapunov function and stabilization controller simultaneously through the systematization method, so as to avoid eliminating the nonlinear term of the system. It can also overcome the shortcoming that the precise feedback linearization method needs an accurate system of the mathematical model.  
Define:  
\[\left\{\begin{array}{c}z_{1}=\psi-\psi_{d}\\ z_{2}=r\end{array}\right. \tag{15}\]  
where  
\[\dot{r}=f_{3}=N_{1}+N_{2}\delta \tag{16}\]  
Define the Lyapunov function:  
\[V=\frac{1}{2}k_{1}z_{1}^{2}+\frac{1}{2}z_{2}^{2} \tag{17}\]  
Yield:  
\[\begin{array}{c}\dot{V}=k_{1}z_{1}\dot{z}_{1}+z_{2}\dot{z}_{2}\\ =k_{1}z_{1}(r-r_{d})+z_{2}\dot{r}\\ =k_{1}z_{1}(z_{2}-r_{d})+z_{2}\dot{r}\end{array} \tag{18}\]  
where \(\psi_{d}\) and \(r_{d}\) are the setting course and its derivatives, respectively. In navigation practice, since the target course \(\psi_{d}\) to be tracked is usually a fixed value or its change is slow, it is assumed that \(r_{d}=0\) here. It should be noted that although this assumption is made here, it was found in the subsequent course tracking test that this method can also realize the course tracking control for the case \(r_{d}\
eq 0\). By substituting Equation (16) into Equation (18), we obtain the following results:  
\[\begin{array}{c}\dot{V}=k_{1}z_{1}(z_{2}-r_{d})+z_{2}(\dot{r}+k_{1}z_{1}-k_{ 1}z_{1}+k_{2}z_{2}-k_{2}z_{2})\\ =k_{1}z_{1}z_{2}+z_{2}(N_{1}+N_{2}\delta+k_{1}z_{1}+k_{2}z_{2}-k_{1}z_{1}-k_{ 2}z_{2})\end{array} \tag{19}\]  
Assuming:  
\[N_{1}+N_{2}\delta+k_{1}z_{1}+k_{2}z_{2}=0 \tag{20}\]  
Then, the control law is obtained:  
\[\delta=-\frac{1}{N_{2}}[N_{1}+k_{1}z_{1}+k_{2}z_{2}] \tag{21}\]  
Yield:  
\[\dot{V}=-k_{2}z_{2}^{2} \tag{22}\]  
where \(k_{1},k_{2}>0\) is the feedback coefficient. Thus, for \(z_{2}\
eq 0\); hence:  
\[\dot{V}<0 \tag{23}\]  
In this case, the system can be stabilized.
# Spinning of Endless Bioactive Silicate Glass Fibres for Fibre Reinforcement Applications
## 3 Results
### Effect of Silanes  
Sizing agents are indispensable for textile fibres. These aqueous solutions often consist of up to 20 individual components. The most important chemical is the organofunctional silane, which acts as a coupling agent due to functional groups that bond with both organic and inorganic materials. The silane is selected according to the organic component, i.e., so that reactive group can effectively bind to, for example, a polymer via the vinyl, amino or epoxy group of the silane. In water, it is first hydrolysed to silanol. When such aqueous solutions are applied to glasses, the unstable silanol condenses into a siloxane/poly(siloxane) network, which is then partially covalently bonded to the glass fibre surface [22]. Since the polysiloxane network is able to bridge small flaws in the glassy structure, the silanes lead to an increase in the strength. This effect was also clearly shown in the present study (Figure 13).  
The most stable glass, 18-06, was selected for initial silane coating tests. These fibres, after being stored dry in a desiccator for 8 months, were treated with four silanes commonly used in the industry: 3-Glycidoxypropyltrimethoxysilane (GTPMS, Dynasylan(r) GLYMO), 3-Aminopropyltriethoxysilane (APTES, Dynasylan(r) AMEO), Propyltrimethoxysilane  
Figure 11: Fibre cross-section of glass 18-06 after 14 days storage in SBF (scale bar 2 μm).  
Figure 12: Decrease of the tensile strength of glass 18-06 during storage in a desiccator.  
(Dynasylan(r) PTMO) and N-2-Aminoethyl-3-aminopropyltrimethoxysilane (Dynasylan(r) DAMO). The silanes improved the strength from an initial value of 922 MPa to values between 1062 MPa and 1203 MPa. This corresponds to a strength increase of 15-30 %. Due to the observed variations, the differences in the average strength values determined for the individual silanes are not significant.  
The silanes also significantly increased the Weibull modulus from 3.4 to over 5. The higher Weibull modulus implies that the defects in the glass surface should now be more uniformly distributed in addition to the mechanical properties of the bioactive glass fibres being more reproducible. While the Weibull modulus for GLYMO (GTPMS), PTMO and AMEO (APTES) were at the same level of about 5.5, the modulus of 7.8 after treatment with DAMO is considerably higher. DAMO is a bifunctional organosilane that, like all other silanes applied in the study, has three hydrolysable inorganic silicon-functional methoxy groups. They react with water under hydrolysis and cleavage of methanol to form the corresponding silanols, which can be bound to the inorganic glass surface. In contrast to the other three silanes, DAMO has an organophilic diamino group. The reason why the binding to the glass surface of DAMO is apparently most effective for 18-06 glass requires clarification in further studies.
# Detecting Selected Instruments in the Sound Signal
## 5 Used Architectures and Experimental Results
### Dense Neural Networks

Table 2: The impact of the _drop-out_ value on the efficiency of a dense network. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Model** & **Normalization** & **Accuracy** & **Sensitivity** & **Precision** & **F1-Score** \\ \hline ANN1 & No & 0.63 & 0.61 & 0.61 & 0.61 \\ \hline ANN2 & No & 0.65 & 0.62 & **0.64** & 0.63 \\ \hline ANN3 & Yes & 0.65 & **0.67** & 0.63 & **0.65** \\ \hline ANN4 & Yes & **0.68** & **0.67** & **0.64** & **0.65** \\ \hline \end{tabular} For each evaluation parameter, the best result obtained is presented in bold.  
\end{table} 
Table 3: Efficiency of various dense neural networks. Dying _ReLU_ appears when the sum of inputs for a large number of neurons has negative values. Considering its characteristics, the _ReLU_ turns off such neurons by resetting their outputs. This results in the loss of valuable information encoded in negative values. The _MFCC_ matrices obtained from the analyzed instruments may indeed contain negative values. Therefore, the _dying ReLU_ problem may indeed concern the built model. One solution to this problem is to minimize or completely exclude negative values. This can be obtained, among others, by normalizing the data to the range 0-1 using the _min-max_ method. Such normalization was performed separately for the _MFCC_ coefficients of all rows, taking into account only the training set. The previously described ANN2 Model was retrained this time based on the normalized set and was marked as the ANN3 Model in Table 3. The accuracy obtained was 65%, which is the same as in the previous case, but this time, it was spread over more instruments, indicating better model precision. The network maintained its tendency to recognize guitar, vocals, piano, and violin well and also improved its performance for saxophone, clarinet, and especially trumpet. Another technique that is effective in solving the dying _ReLU_ problem is to use the _LeakyReLU_[25] activation function. The test was performed again on a network with such an activation function and marked as Model ANN4 in the Table 3. The accuracy obtained was 68%, and the confusion matrix indicates improved performance--particularly for the violin. Table 3 summarizes the quality of models ANN1 to ANN4. Figure 2: Confusion matrix (dense network, ANN1).