esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Literature on Vehicle Routing Problems of Last-Mile Delivery in Urban Areas
## 3 Framework of the Literature Survey  
The VRP seeks to find the best fleet and its routes to deliver parcels to customers. The conventional VRP models mostly consider the routes as a closed loop, in which the fleet returns to its depot after delivery, as shown in Figure 2. These models also consider relatively homogenous fleets run by professional operators.  
Figure 1: Applied methodology for this review.  
Compared with them, the vehicle routing in LMD considers more diverse issues in city logistics, reflecting the new needs and conditions given to it. To better respond to these issues, LMD planning mostly begins with segmentation or grouping of delivery points. It is then followed by parcel packaging to ensure safe and convenient handling. Subsequently, the delivery fleet is chosen along with its delivery paths. The details of these steps vary depending on the conditions of the delivery requests and the available resources. The following sections provide the categories and nature of the LMD problems considered in the literature.
# Measurement and Prediction of Wind Fields at an Offshore Site by Scanning Doppler LiDAR and WRF
## 2 Measurement and Prediction Methods  
This section first describes the test facility and the measurement devices. This is followed by discussions about three scan modes and the methods for the retrieval of velocity vectors from LOS velocity data. Finally, the details about the WRF (Weather Research and Forecasting) for the wind field prediction of the site is presented.
## 2 Route Generation Methodology  
The developed methodology uses a multi-level approach that pre-processes routes in sub-areas on the lower level and schedules the vehicles to create the total path on the higher level. The graph formulation and the lower and higher-level path generation algorithms will be discussed in detail in this section. A pseudocode representation of the developed approaches can be seen in Algorithm 1.  
```
Input: Sub-area graphs with candidate start locations, total area graph, current vehicle locations, and vehicle route progress
```  
```
Run lower-level path generation on all sub-areas to create several routes for each area for sub_area in sub_areas do
```  
for start_point in sub_area do ```
run lower-level path generation algorithm to create route
```

resolve infeasible turns in route ```
# Run higher-level path generation to create the total route for each vehicle
```  
The higher-level path generation waits for vehicles to finish the assigned route, or for a vehicle to breakdown so it can re-generate the routes ```
while True do
```

run higher-level path generation algorithm to create total paths ```
wait for all_vehicles_finished or vehicle_breakdown
```  
if all_vehicles_finished do ```
set False
```

update current_vehicle_locations and vehicle_route_progress ```
continue
```  
Output: coverage paths for each vehicle ```
**Algorithm 1** Total Algorithmic Flow: Lower-Level and Higher-Level Path Generation
## 3 Results and Discussion
### Modifications in the Volatile Profile
Further studies using liquid chromatography coupled to a mass spectrometer would be required to fully understand the formation of these sesquiterpenoids in orange juice. Regarding the goal of this study, germacrene D and eremophila-1(10),11-diene do not contribute to orange juice aroma (undetermined odor threshold) and can be considered inert compounds. The juice presented small concentrations of dodecanal, decanal, nonanal, octanal, and 1-octanol, accounting for 5.9% (_w/w_) of the pasteurized orange juice volatiles. The contents of the aldehydes increased during plasma application, with higher increases at fluences above 400. These substances are typical green leaf volatiles (GLVs) that are naturally formed in fruits, vegetables, and other plants, being formed from fatty acids through the lipoxygenase (LOX) enzymatic route [33]. The lipoxygenase (LOX) enzymatic route produces aldehydes by fatty acid oxygenation, which can further reduce the aldehydes producing alcohols [33]. Orange juice pasteurization reduces LOX activity, but does not fully inhibit it [34]. The formation of aldehydes during plasma treatment may have been caused by the LOX enzymatic route or by the direct oxidation of fatty acids by reactive oxygen species of palmitoleic, oleic, and linoleic acids, which are present in small concentrations in orange juice [35]. The increase in the concentration of these aldehydes followed the order: decanal > dodecanal > nonanal > octanal. This order partially agrees with the reported concentrations of the fatty acids in orange juice (oleic > linoleic > palmitoleic > eicosenoic) that could form such aldehydes. Oleic and linoleic acids can form either decanal or nonanal, depending on the double bond that is cleaved and oxidized. Herein, a higher selectivity toward decanal was observed. Palmitoleic and eicosenoic acids tend to generate octanal and dodecanal, respectively. The results obtained herein indicate that glow discharge plasma tends to preferably cleave fatty acids with more double bonds, which would explain the more significant changes in decanal and dodecanal. The oxidation of octanal to 1-octanol was not significant, and nor was the oxidation of the other aldehydes into their respective alcohols. Decanol, nonanol, and dodecanol were not identified in the volatile compounds of orange juice, probably due to their absence or low concentration in the mixture.
## 2 Experimental Setup
#### Instrumentation  
To measure the irregularity in the blade passing pressure signature, two fast response pressure transducers (MEGGITT 8507C-1, Meggitt PLC, Christchurch, UK) were flush mounted in the casing wall near the rotor tips. Those two pressure sensors were fitted 3 mm upstream of the rotor leading edge plane. The circumferential displacement of the sensors is shown in Figure 1. The total-to-static pressure rise characteristic was determined using a Prandtl-tube upstream of the rotor and 20 circumferentially distributed static pressure taps downstream of the compressor stage. Measurement data was sampled at 24 kHz and a hardware low-pass filter at 10 kHz was used in order to avoid aliasing.
# Design of an Open Remote Electrocardiogram (ECG) Service
## 5 Experimental Results
### Server and Network Infrastructure  
To analyze the timing of data delivery from patient's sensor to doctor's display, we split it into two components. One, outside our control, is related to the performance of the Wide Area Network (WAN) between the relay server and the edge devices. The other is related to the timing of the pipeline made by the three components in our design.  
To evaluate the latter component, i.e., including the WebSocket-based pipeline but disregarding WAN links, we prepared a laptop hosting both the WebSocket relay server and doctor's interface. Next, we attached this device to the same WiFi AP of patient's sensor (see Figure 10). The distance between the ECG sensor and the AP is below ten meters; they do not need to be in their line of sight, but solid walls may reduce the quality of the ECG. To obtain a report about the performance of the whole pipeline, we instrumented the code of the Javascript application to measure transfer/loss rates. We observed a 0% loss rate, meaning that there is no harmful bottleneck; in particular, the ESP12e WebSocket performs timely. An even more relevant result since the pipeline transports the six data needed by a diagnostic ECG, although our test considers only one of them.  
The test configuration with WAN communication is shown in Figure 2 and introduces a remote Heroku dyno in Europe that implements the relay server. According to IP geolocation services, the server is 1500 km away from both the sensor and the display. The results are still good for this configuration since the observed loss rate of circa 4% does not affect the readability of the trace, which is the one shown in Figure 1.  
The results drastically degrade when using a US server. In that case, the loss rate rises to 40% and the ECG is no longer readable.  
Figure 9: The lower trace shows the last data acquisition operation in Figure 8 (first pulse on the left) in a sequence of ten, the upper one shows the time spent sending a packet on the WebSocket.  
A similar result is obtained using a tethering smartphone instead of the AP on patient's side: with the relay server in Europe, the loss rate was 36%.
## 6 Conclusions  
This study developed a performance-based design technique for an axial-flow fan unit using the cascade of airfoils. The existing target fan system was experimentally analyzed to establish a performance prediction baseline. Three-dimensional CFD techniques were used to conduct numerical studies, and the results were compared with the experimental results to validate the numerical analysis. The present study both quantitatively and qualitatively established that the mid-span section plays a pivotal role in influencing the overall aerodynamic performance, spanning from the hub to the tip of the fan blade. It underscored the critical importance of optimizing the cross-sectional airfoil shape in this mid-span region. Subsequently, additional numerical analysis was conducted, utilizing a two-dimensional cascade cross-section to optimize the airfoil shape within the cross-section of the blade. Through rigorous investigation, optimal values for each design parameter were derived, ultimately leading to the realization of an optimized three-dimensional axial-flow fan by applying these refined airfoil shapes. Three-dimensional numerical analysis yielded compelling results, showing a notable enhancement in the flow rate performance of approximately 2%, a significance increase in the efficiency of 17%, and a remarkable reduction in the noise levels of 10.8 dBA. A redesigned three-dimensional axial-flow fan was manufactured, and comprehensive experiments were carried out to provide experimental validation. These experiments consistently affirmed the improvements in the flow performance, demonstrating an increase of approximately 1%, and a reduction in power consumption of 8%, along with a reduction in the noise levels of 1.5 dBA. These experimental findings confirmed the outcomes of the three-dimensional numerical results. Collectively, these findings unequivocally underscore the feasibility and applicability of our axial-flow fan design methodology, employing the two-dimensional cascade approach to enhance the performance of three-dimensional axial fans. This study opens up new avenues for optimizing the performance of axial-flow fans and contributes significantly to the field of fan design and aerodynamics.  
Conceptualization, methodology, supervision, writing--review and editing, C.C.; software, validation, investigation, writing--original draft preparation, S.-Y.R.; resources, data curation, visualization, J.W.K.; resources, data curation, visualization, B.I.P. All authors have read and agreed to the published version of the manuscript.  
Following are results of a study on the \"Leaders in INdustry-university Cooperation 3.0\" Project, supported by the Ministry of Education and National Research Foundation of Korea.  
Not applicable.  
The data presented in this study are available in the article.  
Authors [PERSON] and [PERSON] were employed by the company LG Electronics. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 3 Application in the Reconstruction of the HWRR Decommissioning Model
### Point Cloud Data Processing for the HWRR Decommissioning Area  
The acquired point cloud data underwent processing through the dedicated point cloud management software SCENE ([[https://www.faro.com/zh-cn/Products/Software/SCENE-Software](https://www.faro.com/zh-cn/Products/Software/SCENE-Software)]([https://www.faro.com/zh-cn/Products/Software/SCENE-Software](https://www.faro.com/zh-cn/Products/Software/SCENE-Software)) (accessed on 24 February 2024)), encompassing essential tasks such as point cloud filtering, registration, coloring, and optimization [32]. Upon importing the single-station point cloud data into the SCENE software, once the data are loaded, the software autonomously eliminates impractical noise points. Furthermore, the software offers a manual option for the removal of noisy point cloud data arising from factors such as pedestrians, sandries, and other elements encountered during the scanning process. Notably, the software exhibits the capability to automatically recognize and pinpoint target spheres, achieving the automatic registration of point cloud data originating from two adjacent scanning stations.  
The registration of point cloud data for the 572 scanning stations in the HWRR plant is based on target spheres between adjacent scanning stations. These target spheres are adjusted in 3D point cloud processing software to meet the required project accuracy before registration. The combination of control point data and target point data enables automated real-coordinate transformations, and the use of the least-squares method enhances the accuracy of large-scale data stitching. The scanner's integrated image capture system facilitates automatic colorization of the point cloud, resulting in a colored and measurable panoramic point cloud. The point cloud model for the HWRR plant is illustrated in Figure 7.
# StegNet: Mega Image Steganography Capacity with Deep Convolutional Network
## 2 Related Work
### JPEG RAR Steganography  
The JPEG RAR Steganography is a kind of file-format-based steganography, which uses a feature in these two file format specifications (JPEG [9] and RAR [10]).  
After the JPEG file has scanned the segment of EOI (End Of Image) (0 xd9 in hex format), all the remaining segments are ignored (skipped), and therefore any information is allowed to be appended afterward. A RAR file [10] has the magic file header \"0x52 0x61 0x72 0x21 0x1a 0x07 0x00\" in hex format (\"Rar!\" as characters) and the parser will ignore all the information before the file header. It is possible to dump the binary of the RAR file after the JPEG file, and it'll apparently act as if it is a JPEG image file while it is actually also a RAR archive. However, the method is very fragile to any file alterations. Third-party surveillance might truncate useless information to save transmission resource or apply some image alterations to attack potential steganography. Any alteration will crash the steganography, and all hidden information is lost.  
Figure 2: StegNet and 3-bit LSB Comparison Embedded-Cover-Diversity = 0.76%, Hidden-Decoded-Diversity = 1.8%, Payload Capacity = 23.57 bpp.
# Data Fusion Based on Adaptive Interacting Multiple Model for GPS/INS Integrated Navigation System
## 5 Conclusions  
The major contribution of this work is to propose an AIMM filter approach which is the integration of INS/GPS in sensor data fusion for sea trial in Dalian port, China. It is employed to solve the problem of precision decrease affected by noise nondeterminacy of EKF. The estimation accuracy was enhanced as the AIMM filter ought to separately adjust the covariance between process noise and measurement noise. In addition, in order to decrease computational load, a measurement particular sequential update method was adopted. The results gained from real ship experiments and simulations demonstrate that GPS/INS integration method can be achieve a better position precision.  
In the future, this topic will be concentrated in the integration of multi-GNSS and INS for enhancing positioning precision and eliminating equivocal resolution ability. Moreover, the advanced algorithm of data fusion will be considered.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] analyzed the data; [PERSON] contributed analysis tools; [PERSON] wrote the paper.  
This research was funded by [the National Nature Science Foundation of China] grant number [51579024, 51879027], [the National Science Fund of Liaoning Province] grant number [20170520243], [the Fundamental Research Funds for the Central Universities] grant number [3132018154].  
The authors declare no conflict of interest.
## 4 Discussion
### Limitations of the Study  
It should be noted that the proposed approach has a limitation. It does not allow for the precise position of the defect but only indicates the line where the defect is located between the transmitting and receiving transducer. However, using such 1D scanning, evaluating more detailed information about defect characteristics like shape, depth, and orientation is impossible; the advantage is that the scan is fast, sensitive to defect presence, and can be applied for rough quality control on the boards' production line.  
At the moment, the ML system's responses only show values. In this ML-based system, the input values represent location and the measured amplitudes of [PERSON]'s ultrasonic waves, which interact with the material, recording data under the influence of any defects present. The output values indicate the presence and characteristics of defects, such as their size, derived from the wave amplitude patterns. This relationship between input and output enables the system to predict defect details in real time, supporting automated quality control processes. These values are normalized for the system to equal value ranges (e.g., 0-1) to eliminate the possible impact of their original value disparities on the system's validity (e.g., differences between input variables counted in the hundreds). Visualization of probable defects may be one of the next steps in the development of the system.
## 4 Experimental Results
### Experiment Results and Analysis
#### 4.3.1 Results on the THUCNews Dataset  
We used several advanced pre-training model methods in NLP to conduct experiments on the THUCNews dataset. The detailed comparison results are shown in Table 1. All of the pre-training models on display are based on the Transformer, and all of them load the relevant Chinese pre-training parameters. The training process maintains the same strategy (e.g., learning rate decay) to ensure the fairness of comparison. All methods are based on the Transformer architecture, but the models show diverse effects on the task due to the different architectures of the models themselves. Although T-PTLM [32] has iterated numerous methods, BERT [10] still achieves good results on the training set, which may be due to the reinforcement of the pre-training parameters. ERNIE [48], as a Chinese pre-training method, also achieves good results on the task because it combines different training strategies, enabling the model to continuously expand and adjust parameters. XLNet [12] achieves impressive performance on the task, indicating that two-stream attention is effective enough. In comparison with these methods, APTrans has achieved the best results on this task, with the best accuracy on the validation and test datasets (Dev: 98.55%, Test: 97.89%). This result indicates that our method successfully uses the key information relationship and the inline relationship of word groups in the text, realizes the fusion of location information and semantics, and achieves good results.
# An Affordable Fast Early Warning System for Edge Computing in Assembly Line
## 3 Proposed Affordable Fast Early Warning System
### Edge Devices  
Edge devices were attached to relevant workstations, so that sensors could collect various environmental condition data, such as gyroscope, accelerometer, temperature, humidity, ambient light, and air quality, during process. The SBC provided general-purpose input and output (GPIO) ports, low cost, and low power consumption [33]. There were several suitable SBC options, including Raspberry Pi [34] and BeagleBoard [35]. We selected Raspberry Pi for the present study due to its  
Figure 1: Proposed affordable fast early warning system (AFEWS) architecture.  
Figure 2: Example assembly line excerpt with edge devices attached to workstation panels.  
lower cost, support availability, and reasonable performance [36]. A local database was installed in each edge device to store collected sensor data and analysis results.  
The Raspberry Pi was approximately \(8\times 5\times 1\) cm (width/length/thickness) and provided micro USB power, micro SD card, display serial interface (DSI), USB, camera serial interface (CSI), LAN, HDMI, audio, and video ports, along with 40 pin GPIO connectors for sensor interfaces. The Raspberry Pi used in this study was as follows:  
* Model name: Raspberry Pi 3 Model B
* CPU: quad core 1.2 GHz Broadcom BCM2837, 64 bit
* RAM: 1 GB
* Storage: micro SD, 16 GB
* Network: BCM43438 wireless, LAN, and Bluetooth Low Energy  
We employed several sensor devices, including accelerometer and gyroscope: Sense Hat [37], humidity and temperature: DHT11 [38], ambient light: BH1750 FVI [39], and air quality: ZP01-MP503 [40], as detailed in Table 1.  
We used MongoDB as the local edge device database for this study. MongoDB is a Non-SQL type database, which offers flexible data-schema compared to relational databases. Previous studies have shown it can be efficiently used to store continuous generated sensor data [29; 41]. Several client libraries, including C, C++, Python, Java, Node.js, etc., were also available to simplify direct communication with MongoDB [42], ensuring the development community can rapidly develop MongoDB based applications [43].  
We installed the official supported operating system (OS) for the Raspberry Pi (Raspbian Stretch with Desktop OS) [44], with MongoDB V3.2, Python V3.6.5 and PyMongo V3.7.2 [45] to collect sensor data and store them to MongoDB. Incoming sensor data were analyzed using the proposed hybrid fault model to predict workstation conditions and this result was also stored to MongoDB.
# Retinal Vessels Segmentation Techniques and Algorithms: A Survey
###### Abstract  
Retinal vessels identification and localization aim to separate the different retinal vasculature structure tissues, either wide or narrow ones, from the fundus image background and other retinal anatomical structures such as optic disc, macula, and abnormal lesions. Retinal vessels identification studies are attracting more and more attention in recent years due to non-invasive fundus imaging and the crucial information contained in vasculature structure which is helpful for the detection and diagnosis of a variety of retinal pathologies included but not limited to: Diabetic Retinopathy (DR), glaucoma, hypertension, and Age-related Macular Degeneration (AMD). With the development of almost two decades, the innovative approaches applying computer-aided techniques for segmenting retinal vessels are becoming more and more crucial and coming closer to routine clinical applications. The purpose of this paper is to provide a comprehensive overview for retinal vessels segmentation techniques. Firstly, a brief introduction to retinal fundus photography and imaging modalities of retinal images is given. Then, the preprocessing operations and the state of the art methods of retinal vessels identification are introduced. Moreover, the evaluation and validation of the results of retinal vessels segmentation are discussed. Finally, an objective assessment is presented and future developments and trends are addressed for retinal vessels identification techniques.  
Keywords:retinal vessels segmentation; matched filters; fuzzy expert systems; fuzzy c means; machine learning; adaptive thresholding; mathematical morphology; level set; vessel tracking; multi-scaling +
Footnote †: journal: Medical Imaging