esa-sceva/satcom-chunk-collection huggingface dataset:
# The Big Picture: An Improved Method for Mapping Shipping Activities
## 2 Background
### Density Maps  
Together with being a widely used visualization tool for mobility data, density maps allow for a further understanding of the traffic and subsequent activity in a specific area of interest [52; 53]. Analyzing density maps as they evolve over time also supports traffic changes and pattern distribution interpretation. Moreover,  
The term \"vessel density\" has several co-notations and is thus used with several meanings in the maritime domain. Therefore, vessel density can refer to:  
1. The average number of vessels detected within a defined geographical area (spatial grid) in a given timeframe;
2. The average number of crossings within a defined geographical area (spatial grid) in a given timeframe (often also referred to as \"vessel traffic density\");
3. The total time of the presence of a vessel within a defined geographical area (spatial grid) in a given timeframe;  
There is a considerable difference in the methods used for the creation of density maps according to the definition used, including calculations based on the number of vessel positions detected, the number of vessel tracks, their length crossing a given area and many more variations.
# Leveraging LoRaWAN Technology for Precision Agriculture in Greenhouses
## 3 Materials, Methods and Field Tests in a Greenhouse
### Steps for WSNs Deployment in a Greenhouse  
The sequential steps used in this WSN setup for both deployment and research purpose are shown in Figure 3. Based on the challenges in the greenhouse, discussed earlier in Table 1, we propose the ordered steps and the potential research accomplishment in each step for monitoring in greenhouse. The prerequisite for greenhouse monitoring is, to do a field visit and retrofit the requirements of the sensors and their wireless connectivity. The initial study involves analysis of greenhouse complex topology, crop cycle period and plant growth. It gives an idea of what all crop-specific parameters needs to be monitored. There needs to be a trade-off between the frequency of data transmission and the expected battery lifetime of the sensor. In parallel, it is vital to pick the right wireless communication technology concerning distance between sensor node and gateway, power source and number of message transmission. Once the fundamental WSN setup is established, energy optimization can be done in different ways. Most greenhouses follow strict hygiene checks so instead of frequent intrusion for battery replacement or maintenance, it is better to opt for energy harvesting, self-adaptation, and self-maintenance techniques. We will include aforementioned concepts in the solution as part of our future work. There are considerable checkpoints before deployment such as: (1) The installation location of the sensor. (2) Types of sensors that are required to monitor a specific crop. (3) We needed a wireless technology for reliable communication, for which we selected LoRaWAN. (4) Access to the sensor readings using a mobile application or dashboard. (5) Provision for an end-to-end data flows from sensors along with the sensor box to get the accurate readings from sensors.  
Figure 3: Sequential steps for WSN deployment and monitoring in a greenhouse.  
Figure 2: Greenhouse deployment setup.
## 5 Performance Analysis
### Simulation Results and Discussions
The PhaT protocol preserves effective SLP protection with better communication overhead than its contender TreeR protocol. Similarly, the PhaP protocol preserves stronger SLP protection than its contender ProbR protocol with controlled communication overhead. An additional superior feature of the PhaP protocol is that, it achieves minimized energy consumption in the near-sink region where the sensor nodes experience exhaustive energy consumption. High energy consumption for the sensor nodes in the near-sink region greatly affects the network lifetime [3,4]. Thus, PhaP may be considered as a better candidate than ProbR when network lifetime maximization is an important requirement. Figure 9: End-to-end delay (EED) of the protocols. (**a**) EED against varied source–sink distance. (**b**) EED against varied source packet rate.
# Sentiment Analysis of Arabic Course Reviews of a Saudi University Using Support Vector Machine
## 5 Methods
### 5.1 _Svm-Saa-Scr_
#### 5.1.1 SVM-SAA-SCR Preprocessing and Classification  
The proposed technique for preprocessing and classifying students' Arabic course reviews includes the following steps:  
1. Collect a dataset of students' Arabic course reviews from various sources, such as online forums, social media, and surveys.
2. Preprocess the reviews by removing any irrelevant information, such as URLs, user-names, and special characters.
3. Tokenize the reviews by splitting them into individual words or phrases.
4. Remove stop words, such as \"and\" and \"the\", which do not carry much meaning in the analysis.
5. Perform stemming or lemmatization to reduce words to their base form and improve consistency in the dataset.
6. Use pre-trained sentiment analysis models, such as SentimentIntensity Analyzer or TextBlob, to classify each review as positive, negative, or neutral.
7. Use the resulting sentiment scores to create visualizations, such as bar charts or pie charts, to show the distribution of sentiments among the reviews.
8. Analyze the results to identify patterns and trends in the students' sentiments towards the course.
9. Use the insights gained from the analysis to improve the educational process and address areas of improvement.  
Please note that this is a general approach and it may require some adjustments to suit the specific requirements of the task. Also, you may need to preprocess the data more deeply to handle the Arabic language specifics such as diacritics, or even use specialized libraries for Arabic text processing.
## 2 State of the Art
### Autonomous Driving and Influence on Vehicle Concepts  
The SAE Standard J3016 [15] differentiates between six levels of automated vehicles, ranging from Level 0 vehicles that are driven by the driver without any support of a driving assistant to Level 5 vehicles that handle the driving task without any support from vehicle occupants. In other words, AVs (Levels 4 and 5) are designed such that they do not need any input from the driver. The driver is no longer needed, and the driver's place does not have to be provided in the vehicle concept. This results in a change in the role of the occupants from driver to passenger. The passenger is an occupant who can carry out secondary activities during the ride, e.g., reading, working, and sleeping [16].  
This changes the control loop of AVs (Figure 3):  
Figure 2: Conventional control loop of the driving guidance, adapted from [14].  
Compared to the conventional control loop of driving guidance, the vehicle replaces the driver. The vehicle has to handle the driving task by itself. The passenger can only influence the driving experience by providing requirements or targets to the vehicle.
# A Tour Recommendation System Considering Implicit and Dynamic Information
## 5 Experiment Design and Discussion
### Sensitive Analysis of Parameters
#### 5.4.1 The Number of Clusters  
As mentioned in Section 3, the proposed system divides users into clusters based on their visiting sequence similarity to increase the recommendation performance. A set of experiments is conducted to know how the number of clusters affects the recommendation result when performance metrics of P@K, MAP@K, MRR@K, and NDCG@K are applied. Since a similar trend is found for all K values, only the result of K = 5 is shown and discussed. Figure 7a shows the comparison result for P@5 and MAP@5, while Figure 7b shows the effect of MRR@5 and NDCG@5. It is found that most of the metrics increase when the number of clusters grows because the similarity of users in the cluster will be higher when the number of clusters increases. However, when the number of clusters is five and six, the increase is not significant. Note that too many clusters might make the number of users in the cluster few, which is unsuitable for the following user preference evaluation and dynamic staying time prediction.
## 3 Results and Discussion
### Effect of NaSt on the Hydration Products and Pore Structure of AASRSS
#### 3.3.2 FTIR  
Due to the poor crystallinity of most of the products of the alkali slag-red mud-steel slag, which is difficult to detect using XRD, FTIR tests were carried out. By distinguishing between the typical wave numbers and the corresponding transmittance, the relative amounts of chemical reaction products can be estimated. The results of the AASRSS FTIR at 28 days are shown in Figure 7. The absorption peaks of the [O-H] stretching vibration of the water molecule are shown at 3460 cm\({}^{-1}\) and 1631 cm\({}^{-1}\)[30]. Three bands are present at 1471 cm\({}^{-1}\), 1419 cm\({}^{-1}\) and 876 cm\({}^{-1}\), corresponding to the antisymmetric stretching vibrations and out-of-plane bending vibrations of the CO3\({}^{2-}\) ion [30]. The peaks at about 2918 cm\({}^{-1}\) and 2850 cm\({}^{-1}\) correspond to the asymmetric stretching vibrations and symmetric stretching vibrations of the CH2 group of NaSt [31]. The bending vibrational absorption peak and the stretching vibrational absorption peak of [Si-O] were found at 987 cm\({}^{-1}\) and 466 cm\({}^{-1}\), respectively [32]. Combined with the results of the XRD pattern, we could attribute these absorption bands to the C-A-S-H gel in the hydration products. In summary, the intensity of the [Si-O] absorption peaks became slightly lower with the addition of NaSt, meaning that the amount of C(N)-A-S-H in the AASRSS was reduced, expectedly. Moreover, the wave number of [Si-O] shifted from 987 cm\({}^{-1}\) to the slightly higher value of 994 cm\({}^{-1}\) with the addition of 5 wt.% NaSt, which means that the structure of C(N)-A-S-H was probably changed. To explain this, further research is needed.
## 4 Resulting Use Cases
### Dynamic Program Visualization  
The students who were learning to program hardly used the debugging tools offered by current programming environments because their use still required a high level of abstraction, among other reasons. These tools helped to understand the execution of a program or to detect and correct errors at runtime.  
In this context, the proposed ITS was used to build an interactive AR debugging environment where the teachers could teach programming concepts or demonstrate the use of a debugger in a real situation. Such an environment was built on a physical surface of the real world, from the 3D graphic representations presented in Figure 2.  
Through a menu associated with the visualization, the user was in charge of starting the program debugging. Through this process, the Eclipse debugger was launched, which executed the actions performed in the visualization by sending orders through network sockets to the plug-in that acted as a server. Once the debugger was launched, the vehicle appeared, placing itself over the starting point of the visualization, which was initially the function definition statement.  
The actions that the user could perform were the classic ones that a debugger integrates, which were executed through gesture interaction mechanisms supported by the visualization device. Among the most relevant actions, it is worth mentioning the possibility of checking the value of the variables when the user selected a sentence through which the vehicle passed, executing the program step-by-step through discrete vehicle movements or even adding breakpoints that enabled its movement without pausing until the statement that contained such an element. On the other hand, the user also had the possibility of checking the output of his/her program through a console in the shape of a billboard, which was placed next to the representation that printed the states through which it passed. Additionally, the comment system provided explanations to the user about what happened during the execution of the program, in order to facilitate the understanding of the source code.  
This process ended when the vehicle reached the return sentence, disappearing from the display and stopping the process in both the device and the debugger in the Eclipse environment.
## 2 Materials and Methods  
### Mine Image Processing  
According to the geological structure characteristics of the Gaosong ore field, we selected the Landsat 8 Oli image and DEM elevation image to extract the lineaments in the mining area to be processed. The preprocess of the Landsat 8 Oli image mainly includes RGB color synthesis, image sharpening, geometric correction, and median filtering.  
The Landsat 8 Oli image used in this experiment was taken in October 2014, and the solar altitude angle was about 145\({}^{\circ}\). The overall vegetation coverage of the image was high, but the bedrock in the mining area image was exposed obviously, the number of clouds was two, and the coverage was low. As shown in Figure 2, it is suitable for a lineament interpretation of remote sensing images.  
Figure 1: Distribution of fracture structure in the Gaosong ore field.  
Figure 2: Original remote sensing image.  
First, the original remote sensing image was synthetically processed using 321 band RGB color composite, which conduces to expand the monochromatic band color space, make the original gray image into a color image, and increase the object information. The image processing results are shown in Figure 3. We then needed to clip it and select the area of the Gaosong ore field for automatic extraction and analysis of lineaments because the original image was too large.  
The study area ranges from 103 deg9'30'' to 103 deg18'7'' E in longitude and 23 deg9'30'' to 23 deg27'23'' in latitude. The Landsat 8 Oli image and DEM image are clipped by an image clipping tool. The results of the study area are shown in Figures 4 and 5.  
To make the lineament features in the mining area more obvious, this paper conducts geometric correction, image enhancement, and image sharpening on Landsat 8 Oli images based on cubic polynomials [33; 34; 35]. Among them, the geometric correction is to eliminate the tensile change caused by the orbital height, the flight attitude, and the speed of the satellite sensor. The image enhancement and image sharpening are to strengthen the pixel features in the image, which can then be more clearly displayed in the remote sensing image. The enhancement remote sensing image is shown in Figure 6. For DEM images, a median filter is used to smooth the image and remove most of the noise. That is, select the pixel values of the pixels in the remote sensing image and their surrounding adjacent pixels (a total of odd pixels), sort these pixel values, and then use the pixel value in the middle position as the pixel value of the current pixel, so that the surrounding pixel value is closer to the real value, thereby eliminating isolated noise points. The edge characteristics of the image were maintained so that the image does not produce significant blur [36; 37; 38], and the principal component analysis method was used to combine the different bands in the image.  
Figure 5: DEM clipping image.  
Figure 6: Enhancement of the remote sensing image.
# Optimum Slot and Pole Design for Vibration Reduction in Permanent Magnet Synchronous Motors
## 2 Materials and Methods  
In this section, the theoretical calculations and the procedure for the experimental analyses are presented.  
First, the calculation of the magnetic pressure is explained and the spatial distribution of the pressure waves is determined according to the number of slots and poles. The vibration response calculation is then detailed, which shows the vibration modes that are excited by each spatial harmonic and, thus, which harmonics of the magnetic pressure are more sensitive to vibrations. This determines the optimum combinations of slots and poles.  
In addition, the Finite Element models developed to analyse the influence of the number of slots on structural behaviour are explained.  
Finally, the experimental measurements to obtain the vibration modes and natural frequencies are explained as well as the measurements of the operational vibration response.
## 3 Results and Discussion
### Numerical Characterization of Electrothermal Induced Convection
#### 3.1.2 Traveling-Wave Induced Electrothermal Flow
At intermediate frequencies, it can be seen that the overall electrothermal flow rate is almost consistent with the horizontal pump flow rate, which attains an out-of-phase relaxation peak with the largest pump performance in the absence of lateral convective stirring. This frequency point of maximum pump motion is called the ideal pump frequency f = f MW by taking into account the out-of-phase smearedstructural polarization of inhomogeneous liquid solution subjected to a TW field. On the contrary, when the horizontal pump flow rate is almost zero at low TW field frequencies, there is a strong lateral stirring flow rate, and the vorticity magnitude becomes much higher. However, it only reaches 500, compared to the SWET flow with vorticity up to 1800. At high frequencies, both the horizontal and lateral TWET velocity are weak, and the vorticity is low as well due to the occurrence of bulk ionic screening. Accordingly, the TWET actuation mode is more likely to pump for unidirectional sample delivery at intermediate frequencies, and the SWET counterpart is more likely to produce eddies for chaotic analyte stirring in the low-frequency range. To conclude, when the frequency of the imposed TW voltage wave equals f MW, the pumping effect of TWET fluid motion is the most apparent, and electrothermal eddies no longer exist. This conforms to the expectation of boosted analyte delivery in microfluidic immunoassay to the best extent.
# Embedded Deep Learning for Ship Detection and Recognition
## 3 Designing a Recognition Neural Network-DCNet
### CNet  
Ship recognition is challenging, and we can make use of the fact that there is only a limited number of ships in a port. CNet model considers the recognition problem as a classification problem, which is connected to the end of the DNet. We set the output boxes and classes as the input and share the first three layers' feature maps of the DNet. The boxes is resized to 14 \(\times\) 14 by a ROIPool layer as shown in Figure 3, which was proposed in [8]. Two extra convolutional layers followed by the ROIPool layer are added, and, finally, two fully connected layers and a softmax layer are used to predict the output probabilities.  
Finally, CNet outputs three ship classification scores of bow, cabin and stern. We design the voting strategy as  
\[S_{i\in(\mathit{probabilities})}=\lambda_{b}\mathit{Score}_{\mathit{box}}^{i}+ \lambda_{c}\mathit{Score}_{\mathit{cabin}}^{i}+\lambda_{s}\mathit{Score}_{ \mathit{stern}}^{i}.\]  
\(\mathit{Score}^{i}\) denotes the output score of probabilities \(i\). It weights the score of cabins equally with scores of bow and stern which may not be ideal. To resolve this, we use \(\lambda\) to weight the scores.
## 1 Introduction
Clinicians need to be aware of which parts of the explanation are less certain, as this can help reduce diagnostic errors and improve the overall diagnosis process. To address these concerns, researchers have started to evaluate explanation methods that incorporate uncertainty quantification. These methods provide a more comprehensive and nuanced understanding of how deep learning models make decisions, accounting for the inherent uncertainty involved in these models. Although much research has focused on developing explanation methods for deep learning models, there is limited research regarding their clinical effectiveness. To address this issue, this paper aims to investigate the clinical effectiveness of deep learning explanation methods, specifically focusing on prostate lesion classification in MRI images. This study evaluated the effectiveness of post hoc explanation methods and uncertainty-based explanation method quantification in this context  
assist clinicians in identifying and diagnosing abnormalities, thus extending the benefits observed in prostate imaging to a wider range of medical fields. The primary objective of this study was to identify which explanation methods are beneficial for radiological performance and which ones are not. Additionally, this study aimed to evaluate the effectiveness of uncertainty-based explanations and determine what characteristics radiologists consider explainable. To achieve these goals, this study employed an exploratory pilot design and involved a group of radiologists. Specifically, this study sought to (1) assess whether post hoc and uncertainty-based explanation methods improve lesion detection accuracy, (2) determine whether post hoc and uncertainty-based explanations align with radiologists' explanations, and (3) gain insights into what features radiologists consider explainable.
## 3 Results and Discussion
### Variation of VOC Profile of MT and FMT  
In order to investigate the changes in the aroma components after fermentation, the VOCs of MT and FMT were extracted by HS-SPME and analyzed based on GC\(\times\)GC-TOF-MS. This method allowed for the detection of a wider range of VOCs [31]. It was found that the signal nature and intensity of MT and FMT varied significantly in the three-dimensional total ion flow chromatogram (Figure S1).  
A total of 1466 VOCs were detected in the MT samples, and 1538 VOCs were detected in the FMT samples. Among them, 812 typical compounds were identified in MT, while 884 typical compounds were identified in FMT. The identified VOCs were classified into eight categories, and the number and relative content of each category were obtained (Figure 2). Heterocyclic compounds were the most abundant category in FMT, with 239 kinds; the others were, in turn, hydrocarbons (218 kinds), ketones (167 kinds), alcohols (100 kinds), esters (70 kinds), aldehydes (45 kinds), carboxylic acids (31 kinds), and other compounds. In MT, seven classes of volatile compounds were identified. Among them, there were 226 types of hydrocarbons, 197 types of heterocyclic compounds, 138 types of ketones, 93 types of alcohols, 83 types of esters, 43 types of aldehydes, 27 types of carboxylic acids, and other compounds. After fermentation, the relative content of aldehydes decreased from 7.66% to 3.41%, and hydrocarbons decreased from 13.14% to 8.37%. Meanwhile, the relative content of carboxylic acids (1.20-2.92%), esters (3.36-7.86%), ketones (10.77-16.76%), and heterocyclic compounds (17.28-25.40%) were significantly increased. These results indicated that fermentation by _E. cristatum_ altered the composition of VOCs of MT.  
Principal component analysis (PCA) was used to obtain the interrelationships between different samples. As shown in Figure 3A, the PCA indicated that the two principal components accounted for 51.6% and 8% of the overall variances in the mode, with a total explained variance of 59.4%. MT and FMT clustered within the group, and the difference in volatile profiles between the groups was significant. Based on the PCA, an orthogonal  
Figure 2: Relative abundance of different types of volatiles.  
projections to latent structures discriminant analysis (OPLS-DA) model was constructed to analyze the differences in VOCs of MT and FMT (Figure 3B).  
Based on the OPLS-DA model, the Variable Importance for the Projection (VIP) diagram was obtained. Two conditions were used to select the relevant differential compounds among MT and FMT: \(p<0.05\) and VIP \(>1\). We screened 200 VOCs as differential compounds, of which 144 were upregulated and 56 were downregulated in FMT compared to MT. To further analyze the differential compounds, heat maps were drawn with fold change (FC) \(>2\) or FC \(<0.5\) as the screening conditions to show the differences between the MT and FMT groups (Figure 4). The cluster heat map showed the potential differences and associations among the VOCs in MT and FMT. According to the heat map analysis, the relative contents of most VOCs were upregulated after fermentation.