esa-sceva/satcom-chunk-collection huggingface dataset:
# Sequential Brain CT Image Captioning Based on the Pre-Trained Classifiers and a Language Model
## 4 Experiments
### Experiment Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \multicolumn{2}{c}{**Models**} & & & & \\ \multicolumn{2}{c}{**(+GPT-2)**} & **ST** & **EA** & **VE** & **GM** \\ \hline \multirow{2}{*}{ResNet-50V2} & B & 0.51 & 0.69 & 0.44 & **0.63** \\  & G & 0.51 & 0.69 & 0.44 & **0.63** \\ \hline \multirow{2}{*}{DenseNet-121} & B & **0.54** & **0.71** & **0.46** & **0.63** \\  & G & **0.54** & **0.71** & **0.45** & **0.63** \\ \hline \multirow{2}{*}{VGG-16} & B & 0.51 & 0.66 & 0.42 & 0.59 \\  & G & 0.51 & 0.66 & 0.42 & 0.60 \\ \hline \multirow{2}{*}{VGG-19} & B & 0.50 & 0.66 & 0.41 & 0.59 \\  & G & 0.51 & 0.67 & 0.44 & 0.59 \\ \hline \end{tabular}
\end{table} 
Table 3: The embedding-based metric evaluation scores of the final model. (ST: skip-thought, EA: embedding average, VE: vector extrema, GM: greedy matching, B: beam search (n = 3), G: greedy search). N-gram-based evaluation metrics are effective in capturing local patterns but have limitations in handling long sentences or diverse vocabularies, posing challenges in terms of vocabulary diversity and context representation. Moreover, due to a fixed number of previous tokens, they might struggle with capturing long-term dependencies, considering only partial sequence information, and potentially limiting the model's understanding and prediction of context. Embedding-based evaluation metrics measure performance on specific tasks but do not provide insights into how well a model performs in different tasks or domains. This limitation hinders the evaluation of a model's generalization ability. Embeddings are learned automatically based on the training data, and if the data are biased or contain limited information, the learned embeddings may reflect this bias, leading to a decrease in model performance when applied to new data or domains. BERT score is an evaluation metric designed to leverage the strengths of both metrics while addressing their shortcomings. Table 4 presents the evaluation scores using BERT score. The employed BERT model is PubMedBERT [50], and the results show high performance across precision, recall, and F1 scores, all reaching 80%, in evaluating sentence similarity based on contextual information. Table 5 displays the text generated from 3D ICH CT images by the final model, combining each CNN encoder and GPT-2. In the generated sentences, words unrelated to the reference sentences are marked in red, words semantically similar to ICH are marked in blue, and accurately generated words are marked in purple. When comparing the reference sentences with the generated ones, ResNet-50V2 failed to produce sentences mentioning the incorrect location and ICH, but accurately generated key information about subdual hematoma (SDH) and brain herniation. Both DenseNet-121 and VGG-16 did not generate sentences related to ICH but produced text related to lacunar infarctions and SAH, resembling ICH in the CT images. VGG-19 generated sentences related to ICH but appeared to confuse SDH with ICH and generated ICH as lacunar infarctions.
# Hybrid-Based Analysis Impact on Ransomware Detection for Android Systems
## 1 Introduction
The goal is to benefit from the advantages of both types of analyses significantly and apply each one of them smartly and effectively. The hybrid system examines the Android app by running the static analysis first. Then, based on the results of this analysis, the system decides to pass the app to the dynamic analysis or not. We used more than 70 antivirus engines to scan the Android app to guarantee an efficient static analysis process. Based on the detection accuracy of the engines, the system could forward the app to the dynamic analysis, which is executed in an isolated environment from the user device. Furthermore, we performed an exhaustive study to explore and test the state-of-the-art dynamic malware analysis tools to choose a suitable tool to integrate into the hybrid system. Moreover, the features extracted during the execution of both types of analysis were compared and stored in a database. Consequently, this research highlighted the classes, methods, and permissions most used by Android Ransomware apps. Moreover, this dataset can be offered to researchers and developers to perform further analysis, using different machine learning techniques. Hence, the main contributions of this paper can be summarized as follows:  
* Present the state of the art of ransomware detection systems, whether static based or dynamic based. * Provide a systematic review of the available dynamic analysis tools and methodologies. * Build a new dataset of ransomware apps. * Propose a hybrid system to detect Android ransomware by applying both static and dynamic analyses. * Study the impact of dynamic analysis on the ransomware detection accuracy. Evaluate the proposed hybrid system by examining hundreds of samples belonging to different ransomware families, using various evaluation metrics. * Showcase studies of encountered ransomware samples. * Discuss the observations and suggest recommendations regarding the dynamic analysis process and the ransomware patterns. The rest of this paper is organized as follows: Section 2 presents an overview of the existing works on ransomware analysis approaches. Section 3 describes the proposed hybrid-based ransomware detection approach. In Section 4, the experiment preparation and implementation are discussed. The results and discussion are presented in Section 5. Finally, the paper is concluded and future work is presented in Section 6.
## 3 Materials and Methods
### Apparatus and LC-ESI-MS/MS Conditions  
Instrumental analysis was conducted using a Waters LC system with 1525 \(\mu\) binary pump, equipped with an autosampler from Leap Technologies (Minneapolis, MN, USA). The separation column was Phenomenex Gemini C18 (150 mm length \(\times\) 2.0 mm i.d., 3\(\mu\)m particle size, pore size 100 A) connected to a pre-column Phenomenex Gemini C18 (4 mm length \(\times\) 2.0 mm i.d.). The flow rate was 0.2 mL/min and the injection volume was 5 \(\mu\)L. The mobile phase gradient used was 70/30 v% methanol/water (no additives) at time 0 min and increased linearly to 85 v% methanol from 0.00 to 4.00 min, held at 85 v% methanol for 3 min; increased to 100 v% methanol from 9.00 to 14.50 min. The re-equilibration of the column to the initial mobile phase took place for 10.10 min such that the total run time was 25 min. The target analytes eluted within 14 min.  
The LC system is connected to the Waters Micromass Quattro premier tandem mass spectrometry (Milford, MA, USA). The software includes MassLynx for instrument control and QuanLynx for data processing. The source temperature is set to 120 \({}^{\circ}\)C and the desolvation temperature was 390 \({}^{\circ}\)C. The desolvation N\({}_{2}\) gas flow was 800 L/h, and the cone N\({}_{2}\) gas flow was 100 L/h. Electrospray ionization was used in negative ion mode with the capillary voltage set to 2.90 kV. The collision gas was argon at 0.18 mL/min or 1.6 \(\times\) 10\({}^{-3}\) mbar. Infusion experiments were carried out in ESI mode using pure standards of each target analyte at 1 \(\mu\)g/mL and a syringe pump infusion rate of 50 \(\mu\)L/min. This was used to determine the SRMs and optimal cone and collision energy. SRMs for internal and surrogate standards were as follows: BPA-d\({}_{16}\) (241\(\rightarrow\)223, 241\(\rightarrow\)241); E1-d\({}_{4}\) (273\(\rightarrow\)147, 273\(\rightarrow\)145); 4 NP-d\({}_{4}\) (223\(\rightarrow\)110, 223\(\rightarrow\)223); equilin (268\(\rightarrow\)143, 268\(\rightarrow\)224); gemfibrozil-d\({}_{6}\) (255\(\rightarrow\)121, 255\(\rightarrow\)255) and retention times were 5.44, 6.84, 13.80, 6.84, and 11.58 min, respectively.
To facilitate the determination of the characteristics of GDOP variation with observation geometry, it is necessary to describe the relative positional relationship with fewer parameters. The eccentricity of most space targets is small, and can be approximated as 0. To highlight the characteristics of the variation, the target and the optical sensor orbital radius can be considered to be a constant value. In this paper, appropriate coordinate system transformation is performed. Only three parameters--the right ascensions of the target, the optical sensor in the new coordinate system, and the angle between the orbital plane of the target and the optical sensor--are used to represent the arbitrary relative geometric positional relationship between the target and the optical sensor. Simulationexperiments are designed to calculate the GDOP for different observation geometries, and it is found that the worst orbit solution accuracy is obtained when the target and the optical sensor orbits are coplanar, or when the target is close to the optical sensor's right ascension and declination. It also explains the variation in the initial orbit determination accuracy in cycles when the optical sensor observes a single target over a long period. The structure of the rest of this paper is as follows. The second section briefly introduces the UVM; the third section uses the UVM to derive the GDOP as a parameter to compare the initial orbit determination accuracy; the fourth section describes the representation method for the observation geometry of arbitrary scenarios; the fifth section designs simulation experiments to verify whether the approximation method of the derived GDOP is reasonable and for analysing the effect of the observation geometry on short-arc angles-only initial orbit determination.
## 4 Results and Discussion
### Structural Setting  
Magnetic lineaments are defined as disruptions in the contour patterns, with the disruption caused by association of blocks of rocks of varying composition (of varying magnetic susceptibility) at various places along opposite side of the lineament. The magnetic anomalies are based on the amplitude, wave length, and the shape of contour patterns. We can conclude that trend analysis and the distribution of its pattern can be used to define magnetic provinces which reflect the structural tectonic lineament of the area under study [32]. Trend analysis techniques have been frequently used in various fields of geology and geophysics for purposes of defining structural problems. The interpreted trends are represented by peaks exceeding the significant frequency of the maps. The study area is mainly represented by igneous and sedimentary rocks. These rocks are affected by various regional and local geologic structures of varying types, trends, and locations. The regional and residual magnetic anomaly maps were interpreted to determine the common  
Figure 12: Depth of contacts using Euler deconvolution method (SI = 0) as deduced from RTP map of the study area.  
structural trends affecting the study area (Figure 13). The subsurface structures in the studied area were traced from the interpreted aeromagnetic maps including the regional and residual maps. The most predominant magnetic fault trends deduced from the analysis of the regional anomaly map have NW-SE, NNE-SSW, NE-SW and N-S trends. On the other hand, the most prominent magnetic fault trends deduced from the analysis of the residual anomaly map have NNE-SSW, NNW-SSE, ESE-WNW and N-S trends. Through the analysis of the regional (deep) and the residual (shallow) anomaly maps, the NW-SE, NNE-SSW and NNW-SSE trends seem to be the most important and significant, and play an effective role in the structural framework of the study area.
# Multi-Model Identification of HVAC System
## 3 Gap Metric
### Proper Number of Mode Selection  
It is possible that some of the identified local models using the proposed method in the previous section are similar, i.e., the gap metric distance between these models is neglectable. Hence, the Gap metric is employed to merge the linear models that are similar. Using this strategy, a plentiful number of local models can be constructed.  
The algorithm for constructing local models is as follows:  
1. Initialize the operation regions and calculate the nonlinear measure index for each operating region. Select enough operation points in each operating region.
2. Collect enough data from the system around the operating point OPi, use the proposed Algorithm 1 and get the linear models Pi.
3. Compute the gap metric between all pairs of linear models \(P_{i}\) and \(P_{j}\) (i.e., \(\delta\left(P_{i},P_{j}\right)i,j=1,\ldots,N\)).
4. Prescribe a threshold level \(\tau\). Cluster the local models that satisfy \(\delta j\leq\tau\).
## 4 Discussion  
### Difference in \(c_{s}\) Estimates between Machines In Vitro  
Two studies have compared Siemens S2000 (or S3000) point SWE (pSWE) or 2D-SWE and Aixplorer \(c_{s}\) estimates _in vitro_ using the same transducers that were used in the current study [15,18]. Using a phantom with nominal \(c_{s}\approx 2.3\) ms\({}^{-1}\) [PERSON] et al. [15] found S2000 pSWE gave statistically significantly lower \(c_{s}\) estimates than Aixplorer by 0.1 ms\({}^{-1}\) and 0.4 ms\({}^{-1}\) at depths of 3.0 cm and 4.0 cm, respectively. [PERSON] et al. [18] compared S3000 pSWE with S3000 2D-SWE and Aixplorer 2D-SWE, at depths of 1.0, 2.5 and 4.0 cm in soft (\(c_{s}\approx 0.9\) ms\({}^{-1}\)) and hard (\(c_{s}\approx 2.1\) ms\({}^{-1}\)) phantoms and found small (<0.2 ms\({}^{-1}\)) statistically significant differences between them, however, no consistent bias between machines was observed.  
[PERSON] [14] compared S2000 pSWE and Aixplorer 2D-SWE using curvilinear transducers at depths of 3.0, 4.5 and 7.0 cm across multiple machines at multiple sites. The Aixplorer gave significantly greater \(c_{s}\) estimates than the S2000, by up to -0.2 ms\({}^{-1}\) in a soft phantom (\(c_{s}\approx 1.0\) ms\({}^{-1}\)). However, the use of a phased array transducer with the S2000 at one of the test sites may have biased the results; the S2000 \(c_{s}\) estimates at that site were significantly lower than the S2000 estimates at the other 4 sites. There was no statistically significant difference between the two machines in mean \(c_{s}\) estimates in a harder phantom (\(c_{s}\approx 2.1\) ms\({}^{-1}\)). Similar to the above studies, the phantom data obtained in this study revealed only small differences in \(c_{s}\) between the machines, which were on the order of the interobserver variation. Estimates were obtained at a depth of 1.5 cm using linear transducers for both systems and therefore no bias due to depth or transducer geometry was expected.
# Effect of Extraction Method and Thermosonication on Apple Juice Quality
## 3 Results and Discussion
### Anthocyanin Content  
Anthocyanins are natural plant pigments widely distributed in the environment, classified among flavonoids [61]. Anthocyanin content is an important determinant of the color of various fruits. Cyanidin is one of the most common pigments found in apples and is primarily responsible for the red coloration of their skin [62; 63]. Anthocyanins exhibit antioxidant properties through quenching of free oxygen radicals, including singlet oxygen, superoxide anion radical, and hydroxyl radical. They also exhibit detoxification, anti-angiogenic, and anti-inflammatory activity; they can also induce apoptosis and inhibit digestive enzymes [64].  
Results concerning the influence of various types of treatment of apple juice on anthocyanin content are presented in Figure 8. All applied methods of juice preservation resulted in a considerable decrease of anthocyanin content. The most unfavorable influence was exhibited by pasteurization, which showed as much as a 97% decrease of anthocyanin content. In the case of ultrasound treatment, the decrease of anthocyanin content depended on the force of ultrasound and amounted to 92% for 17.5 W\(\cdot\)cm\({}^{-2}\) intensity (treatment U1) and 82% for 25 W\(\cdot\)cm\({}^{-2}\) intensity (treatment U2).  
The process of anthocyanin degradation under the influence of heat treatment is widely known [65; 66]. Anthocyanin breakdown is mainly caused by covalent bond cleavage or acceleration of the oxidation process as a result of increased temperature [66].  
The results regarding the effect of ultrasonic treatment on the stability of anthocyanins in apple juice differ from those obtained by other authors. During ultrasound treatment of apple juice, [PERSON] et al. [46] did not observe a statistically significant reduction of anthocyanin content. However, it should be noted that the authors used considerably lower intensity of the ultrasound field (2.0 W\(\cdot\)cm\({}^{-2}\)). Moreover, the unfavorable influence of ultrasound on anthocyanin stability can also be partially explained by the lack of correlation between total polyphenol content and antioxidant activity.  
Figure 8: Effect of preservation method on anthocyanin content in apple juice. Error bars represent standard deviations. Values that share the same letter are not significantly different at P \(<\) 0.05.  
On the other hand, [PERSON] et al. [67] observed a statistically significant decrease in anthocyanin content in ultrasound processed strawberry juice. Degradation of anthocyanin during ultrasound treatment could be explained by oxidation reaction promoted by the interaction of free radicals, which leads to destruction by the opening of rings and formation of chalcone [68].
# Numerical Study of the Filtration Performance for Electrospun Nanofiber Membranes
## 2 Numerical Simulation Method  
### Introduction to Simulation Software  
The GeoDict software used in this study is a professional numerical simulation tool for porous media and fiber materials developed by Math2 Market GmbH, Germany (version number: GeoDict 2023.1). Its core functions include three-dimensional structure reconstruction, flow field simulation, and filtration performance prediction. Filtration performance refers to the comprehensive evaluation of the filter medium's ability to capture particles, including filtration efficiency, pressure drop, dust holding capacity, etc. It is widely used in academic research and industrial design in the fields of filter materials, battery electrodes, and porous membranes. The FiberGeo, FlowDict, and FilterDict modules of the software are all built-in toolboxes: FiberGeo is used to construct the three-dimensional structure of virtual fiber materials, FlowDict focuses on fluid flow and resistance simulation, and FilterDict focuses on the numerical calculation of particle filtration efficiency.  
The three work together to support the whole process analysis from material modeling to performance prediction.
# Review of Remote Sensing Methods to Map Coffee Production Systems
## 4 Discussion
### Considerations for Choosing the Best Method
(2019) classified five systems that are present in Lampung Province, Indonesia (closed canopy cover (\(>\)50% cover), shade polyculture (25-50% cover), sun monoculture (25-50% cover), sun coffee (25% cover), and newly planted (sparse cover) using an integrated pixel-based analysis and object-based approach, with an overall accuracy up to 85.4%. Researchers should heavily weigh the efficacy of the approaches with multiple coffee classes into the decision to determine the best approach to develop scalable, replicable methodologies. Researchers should develop a validation approach that emphasizes scalability and considers the accuracy assessment best practices identified by [PERSON] et al. (2019). In the early stages of a study, researchers must create a robust sampling design to assess the accuracy, preferably based on stratified sampling to ensure a representative spatial distribution and accounting for the size of the study area, while identifying and integrating the limitations of the region of interest, such as study area accessibility/fieldwork cost and reference data availability. [PERSON] et al. (2015) also recommended that the reference dataset used in the response design should be a higher spatial resolution than the imagery used for the classification. In the absence of local independent validation data, many of the studies in this review used high-resolution optical imagery, such as WorldView 2 ([PERSON] et al., 2016) and aerial photographs ([PERSON] et al., 2017). Analysts could use Light detection and ranging (LiDAR) data in conjunction with high-resolution imagery in agroforestry coffee systems. [PERSON] et al. (2016) detailed and compared methods for understory density metrics and [PERSON] et al. (2017) presented methods for estimating understory density in Kalimantan, Indonesia. These studies detailed the natural variability observed in Indonesian forests and outlined the optimal configuration of sensors for accurate understory density estimation. Similar methods could be applied to validate the classification of shaded systems where a thick canopy obscures coffee plants. High-resolution satellite data, aerial photographs, and LiDAR data would significantly increase the project costs, therefore, limiting scalability. [PERSON] et al. (2018) instead used high-resolution imagery freely available on Google Earth as reference data. Similar methods using open source software, such as Collect Earth (2018), have gained traction in recent years, and offer the opportunity to quickly, easily, and sustainably implement accuracy assessments in scalable classification methodologies.
# An Aircraft Object Detection Algorithm Based on Small Samples in Optical Remote Sensing Image
## 2 Theoretical Analysis and Methods
### Fusion Algorithm  
As shown in Figure 5, the use of weak and small object detection algorithms to detect aircraft objects will mark multiple bounding boxes near the center of the aircraft. Since the bounding boxes are concentrated near the center of the aircraft, only one bounding box can be left on each aircraft by using the mean shift cluster.