esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Hydrocarbon Accumulation Rules
### Depressed Basins  
The depressed basins form a \"single-source fan group type\" hydrocarbon accumulation model (Figure 9). The Karoo rift sequences in the Angoche Basin are not developed. The source rock is the Upper Jurassic--Upper Cretaceous strata; mainly marine mudstone and marine shale. The organic matters are Type II and Type III, with high organic matter abundance, TOC between 2 and 18%, geothermal gradient between 25 and 35 \({}^{\circ}\)C/km, and the major thermal model of 35 \({}^{\circ}\)C/km. In terms of reservoir, it has been confirmed that the onshore and shallow-water regions mainly develop Upper Cretaceous coastal and shallow sea sandstone, while the deep-water regions mainly develop sandstone and a small amount of carbonate rock. Benefiting from the development of modern water systems, the erosion of the Precambrian basement provides sufficient provenance. Two sets of deep-water gravity-flow sandstone reservoirs from the Cretaceous, Paleogene, and Neogene are developed in the deep-water region. Seismic data reveal that deep-water sedimentation is dominated by channels, slope fans, and basin floor fans. It is speculated that the traps of the Angoche Basin are mainly lithologic-structural composite traps, and the Cretaceous and Paleogene deep-water sandstone plays are generally developed in the basin. Due to the lack of inherited growth faults in the lower rifts, both the upper and lower transverse rifts and the upper depressions have good oil and gas connectivity.  
Figure 9: Hydrocarbon accumulation model of passive continental margin basins of “depressed type”. Single-source fan group type.  
The main exploration target of this type of basin is mainly the depression sequences since the Cretaceous, and the main target is the large lithologic plays formed by the deep-water gravity flow sand bodies of the Upper Cretaceous.
# End-to-End Multi-Modal Speaker Change Detection with Pre-Trained Models
## 4 Experiments
### Baselines  
To check how well the proposed models work, we compare them with these baselines: (i) Neural Network (NN) Baseline: This model takes a flattened vector of 13-dimensional MFCC features as input. It has fully connected layers with non-linear activation functions. Then, a sigmoid output layer predicts the chance of a speaker change. This baseline captures feature patterns in each segment but does not model time relations between segments. (ii) Bidirectional Long Short-Term Memory (BiLSTM): This model takes sequences of 13-dimensional MFCC feature vectors. Its bidirectional structure helps capture both past and future time relations. BiLSTM outputs are pooled over time to form a fixed-size representation. Then, a fully connected layer with a sigmoid activation is used for classification. (iii) Fine-Tuned Approach with Multilayer Perceptron (MLP): This model is a strong baseline similar to this work. It uses only audio and has a single linear classification layer. It is referred to as Wav2 Vec2-MLP.
## 2 Review of Existing Studies  
In this section, we review existing studies on irrigation mapping with remote sensing and assess data and methodological features that are common and practical. We have chosen spatial scale to categorize these studies. Here _spatial scale_ identifies the scope of the study area and is defined as _local, regional_, or _global_. Local studies refer to one or more irrigation basins or command areas. Regional studies include large river basins and continental areas, while global studies present attempts to map irrigation worldwide. This conceptual framework is helpful both to understand the processes involved in each category and to classify mapping approaches. Within each category, the discussion is further organized around the nature of remote sensing imagery and methods of processing these images. In tabular format we describe the advantages and disadvantages of satellite sensors that have been used to identify and map irrigated lands for the following imaging systems: Landsat, Satellite Pour l'Observation de la Terre (SPOT), China-Brazil Earth Resources Satellite (CBERS), Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), Medium Resolution Imaging Spectrometer (MERIS), and Indian Remote Sensing Satellite (IRS) (Table 1). Finally, we provide selected examples from the literature for image classification techniques to determine the most successful options in identifying irrigated areas and separating them from other land cover types (Table 2). The accuracy of these classifications is included to give an idea of each method's success.
## 5 Experiments and Results
### Ablation Study
#### 5.1.6 The Analysis of Features  
In this section, we assess the performance of the proposed model on the test data by analyzing the distribution of the features learned by each classification branch. Figure 6 shows how the features are discriminative for each label. This indicates that the proposed model can extract the discriminative features with less overlapping.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Chanel** & \multirow{2}{*}{**F1\%**} & **HL** & **mAP\%** & **RL** & **Cov** & **EM\%** \\ \cline{1-1} ConvNeXt & No & 94.54\% & 0.0364 & 90.88\% & 0.071 & 3.636 & 87.27\% \\ \hline ConvNeXt & Yes & 94.72\% & 0.0355 & 91.33\% & 0.070 & 3.657 & 86.86\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: This table shows the impact of incorporating the attention mechanism into the fusion model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **F1\%** & **HL** & **mAP\%** & **RL** & **Cov** & **EM\%** \\ \hline ConvNeXt & 0.910 & 0.07 & 0.85 & 0.12 & 4.11 & 0.78 \\ \hline Swin transformer & 0.90 & 0.07 & 0.85 & 0.13 & 4.12 & 0.78 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The performance of different backbone models on the DDSM dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**View** & **Model** & **F1\%** & **mAP\%** & **EM\%** \\ \hline \multirow{2}{*}{Single view} & Swin & 90\% & 85\% & 78\% \\ \cline{2-5}  & ConvNeXt & 91.0\% & 85\% & 78\% \\ \hline \multirow{4}{*}{Dual view} & Swin & 94.18\% & 90.15\% & 86.28\% \\ \cline{2-5}  & ConvNeXt & 94.54\% & 90.88\% & 87.27\% \\ \cline{1-1} \cline{2-5}  & ConvNeXt with attention & 94.72\% & 91.33\% & 86.86\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: The influence of the overall performance when comparing the single view and two views of the CBIS-DDSM dataset.
[21] studied the mechanical and tribological characteristics of AMCs fabricated by a vacuum-assisted stir-casting process with 99.5% Al reinforced by sillimanite Al2 SiO5 and B4C with particle dispersity ranging from 55 to 80 \(\upmu\)m. The fabricated AMCs exceeded the aforementioned properties of the initial pure Al several times, and the specific parameters differed based on the weight content of the reinforcing materials and hybridization. [PERSON] et al. [22] attempted to optimize the parameters of the stir-casting process based on the data obtained by analyzing the properties of hybrid composites with \(>\)98.5% Al-multiwall carbon nanotube (MWCNT)-graphene (GR), appropriately determining the level of applicability of the optimization methods used. [PERSON] et al. [23] studied the corrosion resistance of Al 1070-MWCNT composites. However, instead of stir casting, they used a combined two-step method of high-pressure die casting and cyclic extrusion developed by the authors to solve the problem of inhomogeneous mixing of components owing to their density differences and poor wettability. The authors concluded that, compared to Al 1070, the corrosion resistance of the obtained composites did not radically decrease. [PERSON] et al. [24] assessed a 99% Al-nano silicon carbide SiC composite obtained by an ultrasonic-assisted stir-casting process and found a significant improvement in mechanical properties with the addition of SiC nanoparticles. Recently, there has been an interest in applying post-casting processes to improve or refine the quality of cast AMCs. Classic examples include well-known heat treatment (HT) and forming processes. Solid solution treatment [25] is the most common, as it promotes uniform dissolution of the particles contained in Al, and rapid quenching helps to preserve the grain structure. Forming processes [26] enhance the performance of AMCs by improving the microstructure and reducing porosity and voids. Considering the above, this study has attempted to experimentally produce economical AMCs that are minimally reinforced (\(\sim\)0.36 wt. %) with fine B4C particles using ultrasonic-assisted stir casting with and without HT. The purpose was to assess the possibility of improving the mechanical characteristics of pure Al, which were initially very poor. This study was part of a funded research project. The fabricated AMCs will be used as initial workpieces for caliber rolling, whose pass design was developed based on recent studies by the authors [27; 28] and similar developments.
## 3 Part I: Localization of Real and Virtual Sources in the Absence of Visual Cues
### Experiment Design
#### 3.1.5 Subjects and Test Signal  
A total of 14 (self-reported) normal-hearing subjects (nine male, five female; aged between 22 and 54) took part in TestReal and TestVR. Nine of these 14 subjects had already participated in the listening tests in the previous study [13]. For all subjects, individual HRTFs and HPIRs were measured, and the VAH BRIRs V11, V13, V21 and V23, each for 185 head orientations, were prepared as described in Section 3.1.3. Seven subjects started with TestReal, whereas the other seven subjects started with TestVR. For each subject, there was at least a pause of one day between the two tests.  
The test signal was a dry recorded speech of 15 s duration, spoken by a female speaker (the same speech as used in the previous studies of authors [12; 13]). This speech was repeated for a total duration of about three minutes to provide the subjects with enough time to give their responses.
# Ontology-Based Context Event Representation, Reasoning, and Enhancing in Academic Environments
## 4 Experimental Results
### Consistency, Coherence, and Efficiency  
The whole ontology system is correct if each context module is accurate and consistent. For this evaluation, we use the Pellet reasoning engine available in Protege. First, we validate each module by checking consistency among defined axioms, instances, and well-defined classes. Then, to evaluate the quality of the ontology system, the following principles were considered:  
* Clarity. During query execution and reasoning tasks, the ontology did not produce any misinterpretation.
* Coherence. After the execution of the reasoning and enrichment process, all individuals were correctly classified.  
Ontology consistency checking was executed to verify that none of the class definitions or axioms had logical contradictions, as well as the individuals instantiated into the ontology. This activity consists of executing the reasoning tasks of taxonomy classification, compute types, and consistency checking. The time execution for the 30 competence questions shows an average of 2523 ms; such performance is related to the size of the input data. We run the competence questions on a server with 64 GB RAM, 3.30 GHz CPU, and 1 TB storage capacity.  
The principle designs were verified with Pellet incremental reasoning. The verification of them with the corresponding evidence based on the Protege tool. Figure 12 shows that the ontology did not produce any misinterpretations when the reasoner was active. In this figure, the whole ontology consisting of 17,255 Axioms, 91 classes, 36 object properties, 51 data properties, and 1099 individuals.  
Finally, Figure 13 shows that, concerning the ontology after the execution of the reasoning and enrichment process, all inferred individuals were correctly classified. Moreover, this figure indicates that the enrichment process took 2333 milliseconds and 2954 axioms were inferred.  
Figure 11: Results of competence questions with context reasoning.  
Figure 12: Verification of Ontology Clarity.  
Figure 13: Verification of Ontology Coherence.
## 1 Introduction
In addition, since the algorithms do not rely on global features, attacks that destroy the overall shape of the model such as data clipping do not affect the correct detection of the watermark. However, since BIM data have strict topological constraints and a different data structure from 3D mesh models and 3D point cloud models, it is difficult to apply existing algorithms based on local features directly to BIM data. In summary, transform domain-based watermarking algorithms have better imperceptibility but require higher computational costs. Global feature-based watermarking algorithms reduce the computational costs, but it is difficult to resist attacks that destroy the overall shape of the model. Local feature-based watermarking algorithms are more flexible and resistant to attacks that destroy the shape of the model. Therefore, this class of methods is more suitable for BIM data copyright protection. However, due to data structure differences, existing algorithms based on local features for 3D models are not yet available for BIM data. Therefore, in order to solve the above problem and fill the gap in the study of robust watermarking of BIM data, this paper presents a novel approach to addressing the problem of securing BIM data via the use of a robust watermarking algorithm. Our method combines the spatial location and attribute information of elements to generate local features. Considering the data structure characteristics and watermark robustness, we utilized the centroid of the enclosing box to locate elements and establish a mapping mechanism to relate elements to watermarked bits. Then, we perturbed the movable elements and embedded invisible characters into the attribute of the immovable elements. Finally, watermark information was embedded multiple times. The rest of this paper is organized as follows: Section 2 provides an overview of the proposed algorithm, including its basic idea and preliminaries. In Section 3, we describe our proposed method in detail. Section 4 presents the experimental results, while Section 5 offers a discussion of our findings. Finally, in Section 6, we draw conclusions based on our research.
# Education and Training in Applied Remote Sensing in Africa: The ARCSSTE-E Experience
## 1 Introduction
### Other Programmes  
_Master's Programme_: In 2012, ARCSSTE-E signed a Memorandum of Understanding with the Federal University of Technology, Akure (FUTA), Nigeria, for the development and operation of a joint Master's programme in Space Science and Technology Applications. The average length of the programme is 18 months and two course options are currently offered, namely the Remote Sensing/GIS and Satellite Communication (Satcom) options. The first set of students was admitted in 2013. Fifteen of the alumni of the Master's programme had previously participated in the PGD programme of ARCSSTE-E.  
_Continuous Development Programme_: ARCSSTE-E organises short courses and other continuous development programmes in remote sensing. The courses provide the opportunity for participants to update their skills and knowledge. In a recent survey that was conducted in 2019 (see description in Section 3), alumni indicated that they would like to acquire skills in analysing radar and lidar images,  
Figure 4: Distribution of participants of the PGD programme of ARCSSTE-E according to course options from 2001 to 2018.  
writing of scripts and small programmes, and in terrain analysis. These would be organised under the continuous development programme.  
_Research Programme_: In addition to education and training, ARCSSTE-E engages in research, innovation, and grantsmanship. For example, in 2017, ARCSSTE-E led a consortium of institutions in West Africa to bid for the implementation of the Multi-Scale Flood Monitoring and Assessment in West Africa project, under the Global Monitoring of the Environment and Security (GMES) and Africa initiative [15]. The consortium won a contract to develop solutions to mitigate flooding problems in West Africa while using satellite data from the constellation of Sentinel satellites [16]. The GMES & Africa Support Programme initiative is a culmination of a long-standing cooperation between Africa and Europe in space science and technology, which is one of the key priorities of the joint European Union (EU)-Africa Union (AU) strategy. Additionally, in 2017, ARCSSTE-E initiated research in the northern region of Nigeria, titled 'Desertification Monitoring System over the Sahelian Region of Nigeria'. The rationale for the research was that a system of information is vital for an accurate assessment of the processes that lead to desertification and droughs. The objectives of the research include continuously measuring parameters (pressure, temperature, humidity, wind velocity, and rain rate); analysing data obtained and ascertaining whether desertification is occurring or not; modelling the possible rate, volume and direction of future encroachments; and, generating a hazard map for affected areas.
## 3 No-Wait Flow Shop Scheduling Problem (NWFSSP)  
No-wait Flowshop scheduling has set of \"n\" jobs and \"m\" machines. The processing time p(i,j) of every job \"i\" on each machine \"j\" is given. NWFSS has additional constraint of \"no-wait\", which measthat once a job starts at the first machine, it will be processed entirely through all \"m\" machines without waiting in between and without any preemption. To meet this constraint, a job may be delayed at the beginning. So, in order to solve this type of problems, a delay matrix (\(\delta\)) needs to be calculated [17].  
Let \(\sigma=\{\sigma_{1},\,\sigma_{2},\,\ldots,\,\sigma_{n}\}\) represent the sequence of \"n\" jobs to be processed on \"m\" machines, and \(\delta\)(i, s) represent the minimum delay on the first machine between the start of job \"i\" and the start of job \"s\". Also, let p(\(\sigma_{i}\), j) represent the processing time on machine \"j\" of the job at the \"i\" position of a given sequence, and let \(\delta\)(\(\sigma_{i-1}\), \(\sigma_{i}\)) denote the minimum delay on the first machine between the start of two consecutive jobs found in the \"(i - 1)\" and \"i\" position of the sequence. Let C(\(\sigma_{i}\)) be the completion time of the job in the ith position of a given sequence.  
For i = 1, 2, \(\ldots\), n and j = 1, 2, \(\ldots\), m  
\[C(\sigma_{1})\ =\ \sum_{j=1}^{m}p(\sigma_{1},j) \tag{1}\]  
\[C(\sigma_{2})\ =\ \delta(\sigma_{1},\sigma_{2})+\sum_{j=1}^{m}p(\sigma_{2},j) \tag{2}\]  
\[C(\sigma_{i})=\sum_{k=2}^{i}\delta(\sigma_{k-1},\sigma_{k})+\sum_{j=1}^{m}p( \sigma_{i},j) \tag{3}\]  
The formula for total flow time (TFT) is given as:  
\[\mathrm{TFT}=\sum_{i=1}^{n}C(\sigma 1) \tag{4}\]  
\[=\ \sum_{i=2}^{n}\ \ \{\ \sum_{k=2}^{i}\delta(\sigma_{k-1},\sigma_{k})+\sum_{j =1}^{m}p(\sigma_{i},j)\ \ \}+\sum_{j=1}^{m}p(\sigma_{1},j) \tag{5}\]  
\[=\ \sum_{i=2}^{n}\left(n+1-i\right)\,\delta\ (\sigma_{i-1},\sigma_{i})+\sum_{i =2}^{n}\sum_{j=1}^{m}p(i,j) \tag{6}\]  
The delay matrix of size n \(\times\) n provides all the \(\delta\)(i,k) values between the start of any two consecutive jobs i and k, where i \(\
eq\) k in a given sequence of n jobs to determine the objective function value. The delay matrix \(\delta\)(i,k) values are obtained from the following equation:  
\[\delta(i,k)=p(i,1)+\max\{\ \ \sum_{h=2}^{r}p(i,h)-\sum_{h=1}^{m}p(k,h),0\ \ \ \}\qquad 2\leq r\leq m \tag{7}\]  
Given the matrix of size \"n\" (jobs) \(\times\) \"m\" (machines) with processing time p(i,j), it is possible to generate (n!) number of feasible sequence of solutions, denoted as F(\(\sigma\)), from which the optimal sequence, denoted as F(\(\sigma^{*}\)), is to be chosen and can be stated as per the equation given below:  
\[F\ (\sigma^{*})\ \leq\ F\ (\sigma) \tag{8}\]  
The problem is to determine a sequence of \"n\" jobs which gives minimum total flow time (TFT).
## 2 Materials and Methods
### Deviation Measurement  
After implant placement, titanium scan bodies (GEO CAD, Geomedi Co., Ltd., Fukuoka, Japan) were connected to the placed implants and the model was scanned with a desktop model scanner (inEos X5, Sirona Dental Systems GmbH, Bensheim, Germany). These digital datasets, along with the CAD files of the implant and the scan body, were uploaded into a CAD software program (Geomagic Design X, 3D Systems Inc., Rock Hill, SC, USA) so that the placed implant position could be verified in the software (Figure 4).  
Figure 4: (**a**) Simulation implant placement performed on the model fixed to the manikin. (**b**) Scan body connected to the placed implant. (**c**) Implant position verified in the CAD software.  
Figure 3: (**a**) Mouth opening of the manikin was set to 47 mm. (**b**) Laboratory setting of the simulation guided implant surgery.  
Figure 2: Comparison of the 3D surface scans of each model. (**a**) Aside from the interdental spaces with undercuts, only the models with a surface discrepancy of less than 0.05 mm (green area) were selected. (**b**) Models with surface discrepancies larger than 0.05 mm (yellow or blue area) were excluded.  
The digital data of the virtual implant planning and the model were also exported in a Standard Tessellation Language (STL) format to the same CAD software. The pre- and postoperative model images were then superimposed. The digital images of each planned and placed implants could be isolated, and the center of the implant platform/apex, and the implant axis connecting the two center points could be calculated and determined by the software. The superimposed pre- and postoperative model images were then imported to a metrology software program (Geomagic Control X, 3D Systems Inc.), and the deviations between planned and placed implants of each implant site were measured for the accuracy analysis.  
The subsequent three-dimensional and angular deviations were measured: global deviation at the implant platform/apex, lateral deviation at the implant platform/apex, depth deviation, and angular deviation. The global deviation was the three-dimensional distance between the center of the implant platform/apex of planned and placed implants. The angular deviation was the three-dimensional angle between the planned and placed implant axis. The lateral deviation and the depth deviation were divided from the global according to the axis of the planned implant (Figure 5). Figure 6 presented a schematic diagram of the summarized workflow of the accuracy assessment.  
Figure 5: Definitions of the deviations measured at the implant platform: (**a**) global deviation, (**b**) lateral deviation, (**c**) depth deviation, and (**d**) angular deviation. The identical coordinate system (blue arrows) was used to measure the deviations at the implant apex.  
Figure 6: Summarized workflow of the accuracy assessment.