esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Failure Criterion of Granite Samples After High-Temperature and Water-Cooling Treatment  
The above-mentioned studies have provided the variation behaviors of cohesion, the internal friction angle and tensile strength of granite samples after different high-temperature and water-cooling treatments, and the empirical equations have been proposed based on laboratory experimental results. Accordingly, a modified Mohr-Coulomb criterion with a tension cut-off can be built for granite considering the effect by different high-temperature and water-cooling treatment. The modified Mohr-Coulomb criterion is shown in Equation (8):  
\[\tau=\sigma_{\text{n}}\text{tan}\,\varphi_{T}+c_{T}, \tag{8}\]  
where \(\sigma_{\text{n}}\) and \(\tau\) are the normal stress and shear stress, respectively. \(c_{T}\) and \(\varphi_{T}\) are cohesion and the internal friction angle dependent on treatment temperature, respectively. This equation can be converted to the formula expressed with principal stresses \(\sigma_{1}\) and \(\sigma_{3}\) as shown in Equation (9):  
\[\sigma_{1}=\sigma_{3}\frac{1+\text{sin}\sigma_{T}}{1-\text{sin}\sigma_{T}}+2c _{T}\frac{\text{cos}\varphi_{T}}{1-\text{sin}\varphi_{T}} \tag{9}\]  
In addition, Equation (10) is required for the \"tension cut-off\" as follows:  
\[\sigma_{\text{t}}=\sigma_{\text{t}T} \tag{10}\]  
Consequently, the modified Mohr-Coulomb criterion with a tension cut-off consists of Equations (9) and (10), and it can be applied for granite considering the effect by different high-temperature and water-cooling treatment. In these two equations, \(\sigma_{\text{t}T}\) can be obtained according to Equations (6) and (7). \(c_{T}\) and \(\varphi_{T}\) can be determined by Equations (11) and (12) as follows:  
\[c_{T}=c_{\text{R}}c_{\text{N}}=\frac{c_{\text{R}}}{1+a\Big{(}\frac{T}{100}-0.2 \Big{)}+b\Big{(}\frac{T}{100}-0.2\Big{)}^{2}} \tag{11}\]  
\[\varphi_{T}=\varphi_{\text{R}}\varphi_{\text{N}}=\varphi_{\text{R}}(\frac{1- A}{1+\big{(}\frac{T}{100}\big{)}/x_{0}\big{)}^{p}}+A) \tag{12}\]  
The fitting coefficients in these equations have been obtained in Table 4. The values of \(c_{\text{R}}\), \(\varphi_{\text{R}}\) and \(\sigma_{\text{tR}}\) can also be determined according to the test results under room temperature and presented in Table 6. It should be noted that the tensile strength of Fangshan granite, Dabie granite, Sichuan granite and Rizhao granite samples were not measured in the laboratory experiments, and their corresponding values in Table 6 are estimated by the Hoek-Brown strength envelope of tri-axial compression test results (i.e., \(\sigma_{3}\) is obtained when \(\sigma_{1}=0\)).  
Figure 9: Validation of the failure criterion with the experimental results of (**a**) Fangshan granite, (**b**) Dabie granite, (**c**) Gonghe granite-1, (**d**) Gonghe granite-2, (**e**) Gonghe granite-3, (**f**) Sichuan granite, (**g**) Wuyi granite, (**h**) Rizhao granite-1, and (**i**) Rizhao granite-2.
## 2 Materials and Methods
### Evaluation Metrics  
To evaluate both search methods in the SME use case, the precision as provided via the number of relevant documents retrieved by either method is compared. Since SME resources are limited, only the top 10 documents of either search method are screened by the SMEs after each search. To evaluate the goodness of the result ranking, the mean average precision (MAP) is used [43]. To calculate the MAP of different queries \(q\) with \(n\) documents, we used the established MAP formula based on the precision at document position \(k\) in the results with \(k\in n\):  
\[p@k(q,k)=\frac{number\ of\ relevant\ documents\ in\ q\ at\ top\ k\ positions}{k}, \tag{3}\]  
and the subsequently defined average precision (\(AP\)) with binary relevance of a document at position \(k\) with \(r_{k}\in\{0,1\}\):  
\[AP(q)=\frac{\sum_{k=1}^{n}p@k(q,k)\cdot r_{k}}{number\ of\ relevant\ documents}. \tag{4}\]  
Figure 2: Illustration of the BERT-based search via three steps: (1) the BERT model computes the embeddings of all publications in PubMed, clinical trials in ClinicalTrials.gov, and (2) of the product description; (3) the cosine similarity between the embedding of the product description and each publication/clinical trial is computed to obtain the ranked search results via descending similarity scores.  
Based on these formulas, calculating the mean value of \(AP\) over all queries results in MAP. In the scope of this work, we evaluated each method with two queries, namely a query for the SME A and SME B use case.  
To gain further insights into the content of the retrieved documents of either method, terms rated as relevant and irrelevant by SMEs were compared with the MeSH terms of all identified relevant publications. In addition, the number of the same documents found by both search methods and the overlap of MeSH terms in the result sets of both methods are also contrasted by calculating the Jaccard index. Doing so should help to better understand whether both search methods retrieve the same or a different type of content as expressed via MeSH terms.  
While the recall cannot be determined for the use cases, since the overall number of relevant documents is not known, it will be evaluated with the established CLEF 2018 eHealth TAR dataset for further insights. Both of our search methods will be compared with the recall of the methods of other participating teams in the challenge.  
Since the dataset contains only relevant documents, documents of unknown relevance will be retrieved in the searches. Instead of marking those possibly relevant documents as irrelevant and incorporating them in the feedback loop for the MeSH-based method, only documents known to be relevant, e.g., positive seeds, will be used.
## 2 Establishment of Disturbance Model
### Disturbance Model of Single Support Cylinder
#### 2.2.2 Working Medium
(iii) Because the support cylinder operates at a low pressure, the separation and dissolution of air in hydraulic oil during the pressure change can be ignored. (iv) The air contained in hydraulic oil is regarded as an ideal gas, which satisfies the ideal gas state equation. When hydraulic oil is mixed with air, the density of the mixed medium is:  
\[\rho_{a}=\frac{m_{oil}+m_{air}}{V_{oil}+V_{air}} \tag{17}\]  
where \(m_{oil}\) is the mass of hydraulic oil, \(m_{air}\) is the mass of mixed air, \(V_{air}\) is the volume of air content in working medium, \(V_{oil}\) is the volume of hydraulic oil. \[V_{oil}=\frac{m_{oil}}{\rho_{r1}[1+(1/K)(P-P_{r1})+\alpha_{oil}(T-T_{r1})]} \tag{18}\]  
where \(K\) is the elastic modulus of pure hydraulic oil, \(\rho_{r1}\) is the calibrated density of pure hydraulic oil, \(P_{r1}\) is the calibrated pressure of pure hydraulic oil, \(T_{r1}\) is the calibrated temperature of pure hydraulic oil. According to Equations (17) and (18), the comprehensive density of mixed hydraulic oil can be obtained as:  
\[\rho_{r}=\frac{m_{oil}+m_{air}}{m_{oil}+V_{air}\cdot\rho_{r1}[1+(P-P_{r1})/K+ \alpha_{oil}(T-T_{r1})]} \tag{19}\]\[k=\frac{1}{\rho_{r}}\frac{d(\rho_{r})}{dP} \tag{20}\]  
According to the equation of the volume elastic coefficient, the volume compression coefficient of hydraulic oil can be expressed as:  
\[k=-\frac{1}{\Delta p}\cdot\frac{\Delta V}{V} \tag{21}\]  
Then, when the temperature and air content change, the change in pressure is:  
\[\Delta p=-\frac{1}{k}\cdot\frac{\Delta V}{V} \tag{22}\]
## 4 Discussion  
### Vegetation Index Intercomparison  
A principal finding of this analysis is the observed high correlations of two pairs of vegetation indices: EVI with EVI2, and DVI with NIRv. The similarity between EVI and EVI2 confirms the potential for the EVI2 to serve as a representative proxy for EVI when a blue band is unavailable. The strong correlation and low bias between both these indices and \(\mathrm{F_{v}}\) supports their use for the estimation of vegetation abundance in cases where SWIR bands are not available. Likewise, the similarity of DVI and NIRv, at least for this diverse agricultural mosaic, is striking (\(\rho\) \(>\) 0.99; MI = 2.45, 99% of \(|\) DVI-NIRv\(|\)\(<\) 0.07). The minimal sensitivity to background reflectance trades off with the overall underestimation of \(\mathrm{F_{v}}\). While this can be corrected using a simple linear bias correction, this correction increases sensitivity to background reflectance for sparse vegetation.  
In contrast, NDVI and SR deviate from all four other VIs in important ways. NDVI in particular is challenged by saturation at mid-to-upper values and sensitivity to background reflectance at mid-to-lower values, both of which are clearly more severe than for the other VIs. These findings extend previous results based on datasets collected at coarser spatial and spectral resolution [27; 38; 39; 40; 41] to 3 m imaging spectroscopy, and agree with a large amount of the literature (e.g., [42; 43], including a recent multiscale results correlating to cover crop biomass [24]). Concordance with these studies suggests it is reasonable to expect the results of this analysis--including the striking heteroskedasticity, nonlinearity, and background reflectance sensitivity of NDVI and SR--to generalize to other current and future satellite (and airborne) multispectral sensors such as those on the WorldView, Pleiades, Landsat, and Sentinel-2 platforms. Further, because this analysis simulates SuperDove data directly from AVIRIS-ng, the entirety of the observed relationship between VIs and mixture fractions can be attributed to the spectral modeling step alone (rather than acquisition-specific idiosyncrasies such as view and illumination angle).  
Figure 4: Spectral index distributions for unvegetated spectra. (**Left**): histograms show VI values for all spectra with \(<\)5% photosynthetic vegetation cover, as estimated from inversion of the AVIRIS-ng spectral mixture model. NIRv values are closest to zero with nearly all values \(<\) 0.1 and a mode near 0.05. DVI is characterized by a mode near 0.08 and greater dispersion. EVI and EVI2 both show modes near 0.12. SR modal value is near EVI, but with less dispersion. NDVI shows, by far, the highest mode (\(>\)0.2) and largest dispersion (NDVI for some unvegetated spectra as large as 0.5). (**Right**): histogram of NIRv values after linear regression is applied. For the distribution of regressed NIRv, mean = 0.08 and standard deviation = 0.026.
# Wind Turbine Predictive Fault Diagnostics Based on a Novel Long Short-Term Memory Model
## 4 Results
### Performance Metrics upon Individual Faults
#### 4.2.3 PcsTrip  
As seen in Table 8, the MIP range (0.75342, 1) is expressively upper than the MIR range (0.48148, 0.88889). Hence, the predictions on PcsTrip faults witness relatively lower recall scores than precision scores, resulting in more FNs than FPs. Thus, F-scores are increased by relatively better MIPs. However, SVM still has the worst prediction results on fault cases due to its poorest MIR range. Both LSTM models have satisfying MIPs by surpassing 0.8, but they are outclassed by RF, which has the most correct predictions on fault-free cases due to its highest minimum MIP. Both LSTM models have better MIR ranges and, thereby, more accurate fault predictions. Moreover, T2V-LSTM yields more correct predictions on fault cases with fewer FNs and generates the resultant optimum range on F-scores.  
Regarding the time-domain prediction results in Figure 11, the MIPs under both LSTM models underperform RF, ANN, and KNN, whilst T2V-LSTM has superiority on MIRs in most test cases. Therefore, T2V-LSTM is the best fault predictor for PcsTrip faults with its best MIRs, leading to the fewest FNs and most balanced F-scores in Figure 11c.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Scores**} & \multicolumn{5}{c}{**Classifier (PcsOff)**} \\  & **LSTM** & **T2V-LSTM** & **RF** & **ANN** & **SVM** & **KNN** & **ALL** \\ \hline \multirow{2}{*}{MIP} & MIN & 0.75926 & 0.7884615 & 0.90476 & 0.70833 & 0.67568 & **0.90698** & 0.67568 \\  & MAX & **1** & **1** & **1** & **1** & 0.89362 & **1** & 1 \\ \hline \multirow{2}{*}{MIR} & MIN & **0.76087** & 0.7173913 & 0.58696 & 0.69565 & 0.54348 & 0.63043 & 0.54348 \\  & MAX & **0.97826** & **0.9782609** & 0.93478 & 0.93478 & 0.91304 & 0.84783 & 0.97826 \\ \hline \multirow{2}{*}{F-score} & MIN & **0.82** & 0.7764706 & 0.72973 & 0.7234 & 0.60241 & 0.75325 & 0.60241 \\  & MAX & **0.97778** & 0.9677419 & 0.96629 & 0.94505 & 0.90323 & 0.8764 & 0.97778 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Validation scores for PcsOff.
## 2 Results and Discussion  
Generally speaking, we can affirm that the Cartosat-1 DSM's accuracy decreases as the number of GCPs used decreases, with increasing ground sampling distance and with increasing terrain slope. Moreover, the use of high quality GCPs is fundamental to obtain good DSMs, filtering may help to enhance the elevation accuracy and the generation method used is fundamental for determining the final quality of products. Following, the effect of each of them will be considered.
## 1 Introduction
Triplet loss is derived from the FaceNet [31] architecture published by Google and applied to face recognition systems. We use triplet loss as the loss function during training because it shortens the distance between all samples from the same speaker and increases the distance between different speakers during the training process. We use the cluster training method for model training in all the methods. The cluster training method divides the training data into small groups before training and trains all groups in sequence. The second method, BLSTM-MFCC-TL, uses a BLSTM model, enabling the network to have a bidirectional memory path. Thus, it can obtain features that are more representative of the speaker from the forward and reverse directions of the time series. In the third method, BLSTM-MFCCAE-TL, a set of autoencoder (AE) features is generated using the pretrained AE in [16] and combines the AE features with the MFCC features for input to the model training. Because the research direction of [16] is denoising word recognition and our direction is speaker recognition, the training purposes and datasets of the two are different and cannot be directly compared. Therefore, we refer to the model architecture of [16] and construct a baseline method for speaker recognition, which consists of a Gaussian mixture model and hidden Markov model with mel-frequency cepstral coefficients and autoencoder features (GMM-HMM-MFCCAE) method. The main contributions of this study are as follows:  
* The new cluster training method proposed in this study can solve the problem that hardware equipment cannot be loaded during training. * It is confirmed that using BLSTM can yield more helpful speaker features than using LSTM for speaker recognition. * Extracting a set of AE features through a pretrained autoencoder, combining them with MFCC features, and inputting them into the model for training can effectively improve speaker recognition accuracy. Section 2 describes the three speaker recognition methods proposed in this paper in detail. Section 3 discusses the experimental methods and results of each method proposed in this study. Section 4 presents the conclusions of this paper.
# A Strawberry Ripeness Detection Method Based on Improved YOLOv8
## 2 Materials and Methods
### Network Model Architecture for Strauberry Ripeness Detection  
The YOLOv8 object detection algorithm consists of three core components: the backbone, neck, and head. The backbone is responsible for extracting high-level semantic features from the input image, capturing object shape, texture, and contextual information. The neck fuses multi-scale feature maps to improve detection performance across targets of varying sizes. These aggregated features are then passed to the head for final object classification and localization. Notably, YOLOv8 incorporates the decoupled head architecture from YOLOX, which separates the classification and localization branches to enhance detection efficiency. This decoupling is particularly beneficial for strawberry ripeness detection in complex environments, as it enables more precise identification and localization of strawberries amidst cluttered backgrounds, thereby improving ripeness  
Figure 1: Data augmentation.  
Figure 2: Example of strawberry maturity classification: (**a**) ripe; (**b**) turning; (**c**) raw.  
classification accuracy. In addition, YOLOv8 adopts an anchor-free detection mechanism, addressing challenges such as inaccurate localization and the imbalance between positive and negative samples. Inspired by the parallel gradient flow concept in ELAN (used in YOLOv7), YOLOv8 replaces the C3 module of YOLOv5 with the more efficient C2f module, enhancing gradient propagation while preserving the model's lightweight characteristics. Regarding label assignment, YOLOv8 continues the strategy introduced in YOLOv6, further improving detection accuracy and robustness in complex scenes. This strategy also enhances the model's ability to handle uncertainty in target position and scale. As an enhanced single-stage detection algorithm, YOLOv8 integrates multiple architectural innovations, offering strong performance in detecting objects of various sizes. These features make it particularly well suited for strawberry ripeness detection in unstructured, real-world environments. The overall architecture of YOLOv8 is illustrated in Figure 3.
# RBF-Based Monocular Vision Navigation for Small Vehicles in Narrow Space below Maize Canopy
## 1 Introduction
The method fits the 3D points corresponding to the trees into straight lines, and they use a vanishing point detection approach to find the ends of the tree rows. However, navigational problems within confined spaces are still not yet solved. Using the straight-line method cannot successfully extract the crop rows, so the combination of radial basis function (RBF) reference point detection methods and the way to solve the problem of confined space navigation are necessary. The mobile platform of inter-row robots must be provided sufficient space for vehicle equipment to use the walking space as much as possible; workspace should be limited and as effective as possible [28]. The GPS error easily causes the vehicle to drive off rows, especially when GPS is exclusively used to implement vehicle navigation. This results in damage to the crops and the possible failure of the vehicle, which is blocked by the crops, to move further. A local positioning and navigation method is needed for vehicles employed in inter-row maneuvers. In other words, vehicles need a system for local positioning and identifying the course angle. This article proposes a monocular vision positioning system and tested an autonomous navigation mobile platform in maize fields during the late growth stage. The objectives of the study are three-fold: (1) to validate the ability of rapid vehicular positioning and mapping with a monocular vision system; (2) to develop a platform for the real-time operation of remote data transmission tests; and (3) to further achieve robotic precision agriculture operations using the said system.
# Decentralized Documentation of Maritime Traffic Incidents to Support Conflict Resolution
## 2 Background
### Maritime Accident Investigations  
Accidents in the maritime industry have been present since the invention of shipping. Countries typically have their own regulations and processes on when and how accident investigations should be carried out, adapting codes and regulations from the IMO (cf. [5]). Results from the analyses of accidents and incidents are then published in reports. As a basis for relevant accident data, we analyzed accident investigation reports from the authorities of Germany [6] and the United Kingdom [7]. The following information is normally included in investigation reports dealing with accidents that are related to traffic (this does not include accidents on board a single ship):  
* Data related to the involved vessels (name, type, flag state, size, special features, etc.).
* Planned routes of the involved vessels.  
* Type, date, and place of the accident.
* A detailed description of the sequence of events that led to the accident, often including textual descriptions of personnel behavior, navigation decisions, vessel movements, pictures of the involved ships or damage caused, and other information that is specific to the type of accident.
* If available--data dumps from the involved vessels.
* Meta-information on how the investigation was carried out.
* Analysis of the causes of the accident.
* Summary and recommendations.  
Especially the description of the sequence of events that led to the accident is a critical part of a report, as it is the main basis for analyzing the causes of the accident. It often includes eyewitness testimony or may be based on data recorded on board the vessel or other maritime surveillance equipment (e.g., VTS centers).  
Hence, in the discussed scenario of a smaller incident that is not being investigated by a state authority, the involved parties should aim at preserving data that can represent navigation decisions and vessel movements at the time of the accident as a minimum objective reference for its cause. Other information (such as the data related to the vessels, meta-information on the investigation, and further analysis) can still be derived at a later stage of the incident analysis.