esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Routing Protocol for LLNs  
RPL is a distance-vector routing protocol. It establishes a tree-like routing topology in the form of a Destination-Oriented Directed Acyclic Graph (DODAG) converging at a root. Each RPL node tracks three logical sets of neighboring nodes (Figure 1): (1) the candidate neighbor set; (2) the parent set; and (3) the preferred parent, i.e., the default next hop towards the root.  
Maintaining these sets requires information contained in DODAG Information Objects (DIOs) (Figure 2), destined to either: (1) the link-local all-RPL-nodes IPv6 multicast address (ff02::1a16); or (2) the source of a DODAG Information Solicitation (DIS). It contains (amongst others) four values used for topology identification and maintenance: the RPLInstanceID, DODAGID, DODAG Version Number, and the Rank advertised by the DIO source. RFC 6550 [4] (Section 3.1.2) provides more information.  
While mandatory only for solicited DIOs, a DODAG Configuration option (Figure 3) is often included in every DIO. It contains (amongst others) two values used for rank computation/comparison: MaxRankIncrease and MinHopRankIncrease (see [4] (Section 6.7.6)). RFC 6550 notes the following:  
_\"This information is configured at the DODAG root and distributed throughout the DODAG with the DODAG Configuration option. Nodes other than the DODAG root MUST NOT modify this information.\"_ [4] (p. 52)  
Figure 1: Relationship between three logical Routing Protocol for Low-power and Lossy Networks (LLNs) (RPL) sets tracked by each network node. Note that the formation of these sets is governed by the RPL Objective Function (OF) tied to the RPL instance to which a given node belongs.  
Figure 2: Format of an RPL Destination-Oriented Directed Acyclic Graph (DODAG) Information Object (DIO). Note that a DIO is an Internet Control Message Protocol (ICMP) Version 6 (ICMPv6) informational message with \(\text{Type}=155\) (RPL Control Message) and \(\text{Code}=0\text{x}01\) (DIO).
# Leveraging Hybrid RF-VLP for High-Accuracy Indoor Localization with Sparse Anchors
## 2 Hybrid RF-VLP-Enabled Positioning System  
### System Overview  
As illustrated in Figure 1, the proposed system is a hybrid RF-VLP system, combining the benefits of RF and optical signals for robust indoor positioning. The system consists of LED anchors deployed on the ceiling, with non-overlapping coverage areas, and each LED broadcasts a unique ID. The receivers, placed on robots or automated guided vehicles, are equipped with RF transceivers, enabling communication with each other via a mesh network. The RF component of our system is based on BLE, but the system is designed for flexibility, enabling the use of other wireless technologies such as Wi-Fi or Zigbee depending on the specific requirements of the application, such as range, power efficiency, and network topology. Each receiver typically receives a signal from one LED at a time. The hybrid transceiver measures the received signal strength (RSS) of the optical signal to estimate the distance to the LED. Additionally, inter-receiver RSS measurements are used to estimate the relative distances between receivers. By combining these measurements, an integrated positioning algorithm computes the precise locations of all receivers. A potential application of this system could be in a smart building where accurate indoor navigation is essential. For example, in a large warehouse, this hybrid RF-VLP system could provide the real-time positioning of inventory and workers, improving operational efficiency and safety. Section 2.2 will describe the proposed localization algorithm in detail.
# Seamless Indoor-Outdoor Localization Through Transition Detection
###### Abstract  
Indoor localization techniques operate independently of Global Navigation Satellite Systems (GNSSs), which are primarily designed for outdoor environments. However, integrating indoor and outdoor positioning often leads to inconsistent and delayed location estimates, especially at transition zones such as building entrances. This paper develops a probabilistic transition detection algorithm to identify indoor, outdoor, and transition zones, aiming to enhance the continuity and accuracy of positioning. The algorithm leverages multi-source sensor data, including WiFi Received Signal Strength Indicator (RSSI), Bluetooth Low-Energy (BLE) RSSI, and GNSS metrics such as carrier-to-noise ratio. During transitions, the system incorporates Inertial Measurement Unit (IMU)-based tracking to ensure smooth switching between positioning engines. The outdoor engine utilizes a Kalman Filter (KF) to fuse IMU and GNSS data, while the indoor engine employs fingerprinting techniques using WiFi and BLE. This paper presents experimental results using three distinct devices across three separate buildings, demonstrating superior performance compared to both Google's Fused Location Provider (FLP) algorithm and a GPS.  
seamless tracking; machine learning; indoor-outdoor localization; particle filter; Kalman filter; GNSS 202520252542598  
20252025142598
# Elevating Academic Advising: Natural Language Processing of Student Reviews
## 4 Results
### Demographic Influences on Student Sentiment and Academic Advisor Evaluations  
To assess the influence of demographic variables on students' sentiment towards academic advisors, we conducted a series of statistical tests. Firstly, a student \(t\)-test was employed to determine if there was a significant difference between the sentiment analysis scores provided for male and female academic advisors (Table 1). The analysis showed that gender had an insignificant effect on sentiment scores (males = +0.61, females = +0.63; \(p\) = 0.112). Similarly, the comparison of mean Likert scores with gender revealed no significant differences (males = 4.52, females = 4.41; \(p\) = 0.371).  
Additionally, a one-way ANOVA was conducted to explore potential variations in sentiment analysis scores across different age groups of academic advisers. The results revealed a significant distinction among the four age groups, with average sentiment scores of +0.73 for those under 40, +0.58 for individuals aged 40-49, +0.41 for those between 50-59, and +0.37 for those above 59 (\(p\) < 0.01). This suggests that in our research, academic advisors of a younger age generally tend to convey more favorable sentiments overall. Similarly, the mean Likert scores for the four age groups exhibited significant differences, with scores of 4.72, 4.31, 4.11, and 3.89, respectively (\(p\) < 0.01) (see Table 2 for details).  
Finally, we conducted a university analysis by categorizing academic advisors into five geographical subgroups based on their affiliated universities: University of Thessaly, Greece (UTH); Slovak University of Agriculture, Slovakia (SUA); University of Agribusiness and Rural Development, Bulgaria (UARD); Karelia University of Applied Sciences, Finland (KARELIA); and Van Hall Larenstein University of Applied Sciences, Netherlands (VHL). To explore the potential impact of university on the relationships between average Likert ratings and sentiment analysis scores, we employed one-way ANOVA tests. In contrast to age, no statistically significant differences were observed in Likert ratings and sentiment analysis scores across the diverse universities (refer to Table 3).  
These analyses provide insights into how demographic factors may or may not influence students' sentiments and evaluations of academic advisory services.
# Using Machine Learning for Web Page Classification in Search Engine Optimization
## 5 Research Methods
### Data Set Forming  
The data set for this research was formed by random sampling of 600 web pages from the DMOZ directory (the original DMOZ, [[http://dmoz.org](http://dmoz.org)]([http://dmoz.org](http://dmoz.org)), has been out of service since March 2017, but there are several websites with saved archives that can be used for data download: [[https://dmoz-odp.org/](https://dmoz-odp.org/)]([https://dmoz-odp.org/](https://dmoz-odp.org/)), [[https://github.com/SongweiGe/Dmoz-Dataset](https://github.com/SongweiGe/Dmoz-Dataset)]([https://github.com/SongweiGe/Dmoz-Dataset](https://github.com/SongweiGe/Dmoz-Dataset))). DMOZ is often used in the field of information retrieval and web pages' classification [49; 50; 51], summarization of web page text [52; 53; 54], and keyword extraction [55].  
This research focused only on web pages in the English language, but the developed models can be applied to web pages in any language.  
The DMOZ directory contains a list of websites in hierarchical categories and subcategories, depending on the website topic. There are 16 categories at the top level: \"Arts\", \"Business\", \"Computers\", \"Games\", \"Health\", \"Home\", \"News\", \"Recreation\", \"Reference\", \"Regional\", \"Science\", \"Shopping\", \"Society\", \"Sports\", \"Kids & Teens Directory\",and \"World\". Since, in our research, the category title was used as a keyword, the sample was pulled from the category at a minimum of the third level of hierarchy. This was done to avoid broadening of keywords that could not describe the page at the desired level of detail. Also, since this research targeted pages in the English language, the categories \"Regional\" and \"World\" were not included in the sampling. The \"News\" category was also excluded since the websites it contains are mostly news portals, which would make it difficult to determine keywords from the content. The number of web pages in the sample by DMOZ categories is given in Table 1.  
Based on the category title (minimally third level), a list of keywords for each web page was formed. In some cases, in which the category title contained a few words, they were split, as in the examples:  
Kids_and_Teens/Entertainment/Magazines_and_E-zines/, which were transformed into keywords: Kids, Teens, Entertainment, Magazines, E-zines.  
Stop words were excluded from the category titles. If a web page was contained in more than one category, only the first one was used (from the third level or below). The result of this phase was a data set with 600 URLs and keywords for each of them.
# Overview of Methods for Large-Scale RNA Synthesis
## 2 Methods of RNA Synthesis  
RNA can be obtained in three ways: purification from biological sources, chemical synthesis using a solid-phase method, or enzymatic synthesis by in vitro transcription. Isolation from cells is suitable for complex or large and highly abundant RNAs (ribosomes or tRNAs), resulting in native samples having all the important posttranscriptional modifications. Shorter or less-abundant RNAs can be synthesized chemically or enzymatically. These methods are more universal and preferred for structural studies of RNA molecules.
## 3 Classification of Systems
### Number of Power Transmission Paths Parallel to the CVU
#### 3.3.2. Bypass-Type Dual-Path Systems  
For a bypass-type dual-path system, the power is transmitted by either the CVU or the constant ratio path. The power cannot be transmitted through both simultaneously unless the ratios of the CVU and the constant ratio path are exactly the same. As shown in Figure 10(a), the power is transmitted through the constant ratio path (gear drive) with higher efficiency and higher torque capacity. In Figure 10(b), the power flows through the CVU with a seamlessly variable ratio. This kind of system does not need an MDOF mechanism to split or merge the input power but requires clutching or braking to direct the power through the CVU or through the constant ratio path. Therefore, during the CVU drive mode, the maximum allowable torque of the system is still confined by the CVU. The system ratio coverage is the same as that of the CVU if the ratio of the constant ratio path does not exceed that of the CVU.  
In the BPD, bypass-type dual-path systems have a loop structure comprising only connection lines and a CVU, as shown in Figure 12. In this configuration, there is another transmission path parallel to the CVU, and they are joined together directly. The actual  
Figure 11. Power flow in a bypass-type dual-path system under different drive modes.  
Figure 10. Example of a loop structure for power-split-type dual-path systems in the BPD.  
Figure 9. Power flows in power-split-type dual-path systems.  
ratio of the system can be set by combining clutches, brakes or gearsets, which are ignored in the BPD.
## 5 Discussion  
### Relationship between Rainfall and Landslides  
The studied landslide was located in Hongya Village, Weiyuan Township, Tu Autonomous County, Huzhu, and it had a width of 745 m and a length of 883 m. Optical remote sensing imagery from Google Earth 7.3.6 indicated that the base of the landslide was adjacent to man-made structures such as schools and residential buildings. Figure 7a presents the yearly average deformation rate of the Hongya Village landslide from July 2021 to September 2022, obtained using the SBAS-InSAR technique based on the ascending orbit data of the Sentinel-1 satellite. Figure 7b,c illustrate the time-series deformation at points P1 and P2 (indicated by the white circles in Figure 7a) in response to the monthly rainfall. Figure 7a reveals that the areas of greater deformation were situated in the central and rear sections of the Hongya Village landslide, with a maximum deformation rate of approximately -13 mm/yr.  
Figure 7b indicates that the landslide-induced deformation was strongly influenced by precipitation, and it increased as the amount of rainfall increased. The correlation values between the time-series deformation of the points marked P1 and P2 and the monthly rainfall are 0.64 and 0.72, demonstrating that rainfall had a significant influence on the  
Figure 6: Red Bluff Village landslide zoning map.  
landslide's movement [43; 44]. For instance, the Hongya Village landslide experienced linear deformation from July 2021 to September 2022. Morphing acceleration occurred between July 2021 and November 2021, when the rainfall exceeded 100 mm, while it decreased between December 2021 and March 2022 due to reduced rainfall. However, when precipitation exceeding 40 mm reoccurred in April 2022, the deformation accelerated markedly. In conclusion, the analysis confirmed that rainfall was a critical factor influencing the landslide activity in Hongya Village. Moreover, the increment in rainfall can be seen in Figure 7b in terms of summer precipitation, especially in the month of August, when comparing 2021 to 2022. This phenomenon may result from the effects of climate change, and it will have a lasting influence on the occurrence of geological disasters in the near future.
## 2 System Architecture and Problem Description  
### Architecture of the Integrated Heat Dissipation Module  
Based on AESA radar, a high-performance heat dissipation system is designed with a limited size and weight. The heat dissipation mechanism of the whole system is composed of two sub-systems, namely, the Integrated Heat Dissipation Module (IHDM) and the Heat Exchange Module (HEM), as shown in Figure 1. The phase array radar T/R module, which is the subject of this study, is designed as the Quad Transmit Receive Module (QTRM). The heat exchanger of these modules cools the coolant flowing into the heat exchanger and transmits the heat energy outside of the system, while the pump provides the flow power of the fluid circulation. The T/R module is the main heat-source-generating module of the system (shown in the red area in Figure 1). Since each T/R module is composed of several arranged electronic components, the temperature difference between each electronic component must be maintained within a certain range, to ensure the consistent operational function of each electronic component. Therefore, it is necessary to control the heat dissipation of the cold plate by the heat dissipation assembly in order to maintain the overall operating temperature of the T/R module within an appropriate range, which is also the purpose of this study. In order to meet the requirements of effective heat dissipation for high-power modules in a narrow space, this study combined the fluid circulation-type cold plate and the phase array radar T/R module to develop an integrated heat transfer module, and it then optimized its design. The operational mode of the whole system uses the pump to pressurize the coolant in the storage tank and sends it out to the fluid pipeline (Cool-out); it then adjusts the flow rate through the throttle, so that the cold water (Cool-in) flowing into the four cold plates can maintain a uniform speed and volume, and the coolant flowing into the cold plates can be used to dissipate heat to the T/R module. The coolant absorbs heat energy inside the cold plate, then flows out through the hot water outlet (Hot-out), and is guided to the hot water inlet (Hot-in) of the heat exchanger through a pipeline, where the hot water is cooled by the heat exchanger, thus forming an active high-performance circulating heat dissipation system.  
The above-mentioned sub-system components all play an important role in the heat dissipation mechanism of the overall system, and the design of the IHDM is a vital and key module that affects the heat dissipation performance of the whole system. Among them, the temperature equalization and heat dissipation performance of the fluid circulation-type cold plate are the key components that directly affect the performance of the radar system. Therefore, this study focused on the optimal design of the fluid circulation-type cold  
Figure 1: Fluid circulation heat dissipation system.  
plate so that the radar system components can operate within an appropriate temperature variation range, which ensures that the radar can perform its expected functions.
## 3 Materials and Methods
### 2D and 3D Geophysical Microgravimetry Surveys  
Microgravimetry is a method used for the search and recognition of shallow underground forms of small size, often of anthropogenic origin. The results of microgravimetric studies are used to detect voids and discontinuous deformations. Conclusions concerning geological structure of the studied area are developed in the form of maps and density models showing subsurface distribution of rock masses. Details of the method can be found in [37; 41; 42; 54], and example applications of the method in studies of post-mining areas are stated in the Introduction section.  
Based on the analysis of the location of former underground operations, in relation to present-day topography and archival geological profiles, a geophysical survey network for 2D and 3D microgravity surveys was designed. This included profile lines that were set out at an interval of approximately 100 m, and approximately 10 m spacing in a given line in the area of test field three. Furthermore, three detailed test sites were set out for 3D gravimetric surveys in a quasi-regular 6 \(\times\) 6 m grid, including one site where a sinkhole was spotted in late 2019. The location of the profile lines and test sites is shown in Figure 5.  
The gravimetric field surveys consisted of the establishment of 4917 measurement points and GNSS measurements of their location; 3900 points were located along the 2D profiles, and 1017 in the detailed test sites. For the microgravity surveys, two Scintrex CG-5 Autograv gravimeters were used. The gravimetric measurements, at scattered points, were made with the method of intermediate points. Thus, each measurement sequence began and ended with measurement on a base point with a known value of the acceleration of gravity g. To determine the accuracy of measurements, acceleration of gravity g values was determined twice at selected positions, distributed evenly over the entire area of study. The mean square error of a single measurement amounted to \(\pm\)0.006 mGal. For data processing, Schlumberger Petrel (Grav&Mag plug in) and ZOND software were used.  
Figure 4: An example of source documentation; a section of mining map depicting underground workings.  
The results of interpretation of the obtained geophysical data are presented in the form gravimetric maps in the Results and Discussion section. The prepared gravimetric maps highlighted the geostructural forms, and helped to define the range of possible land deformations that were further studied with the Electrical Resistivity Tomography (ERT) surveys.
## 5 Results and Discussion
### Experimental Tools and Setup  
Preprocessing and slice selection are performed using pyRadiomics v3.1 and keras preprocessing packages, while ALT-CNN-DENSE _Net_ implementation, training, testing and validation are done using Python language v3.7.6 with Keras package v2.3 (TensorFlow v2.1 backend). Also, numpy v1.18.4 and OpenCV v4.2.0 packages are used. Experiments are conducted on core i7, 2.21 GHz processor with 16 GB RAM and NIVIDIA TESLA v100-sxm2-16 gb. Curves and diagrams have been created and exported using matplot and Microsoft Visio.  
The performance of the simple HU-based segmentation approach is evaluated against the thoracic cavity segmentation masks provided with the datasets. Dice similarity coefficient [37] is used for segmentation evaluation. It quantifies the overlap between two binary segmentation masks, where a value of 0 indicates no overlap and a value of 1 indicates complete overlap. For the clustering-based slice selection phase, the quality of the produced partitions is evaluated by two well-established clustering evaluation indices. The performance metrics used are Silhouette index (Sil) and Davies Bouldin (DB) index [38], which measure the intra-clutser and inter-cluster distances. The purpose is to select the clusters with the highest compactness and best separability from the other clusters (i.e., higher Silhouette index and lower Davies Bouldin value). A range of cluster numbers are tested with the different clustering algorithms to determine the best suited algorithm and number of clusters based on Sil and DB values. Then, the appropriate number of clusters is affirmed through the elbow method [39].  
All CNN architectures are trained from scratch using Adam optimizer with starting learning rate of 0.0001. ReLU activation is used to help overcome the vanishing gradient problem. The percentage splits of the datasets into training, validation and testing are 0.55, 0.15 and 0.3 respectively. Inputs are divided into batches of size 5. Validation accuracy and mean-squared-error are monitored for each epoch. In addition, the learning rate is reduced by almost a factor of 0.15 for every two consecutive epochs without improvement in validation. The best model is defined as having the maximum validation accuracy then it is stored and applied on the testing. Boot strapping with replacement is applied and the reported results represent the average of 10 runs. The performance of the CNN classification models is evaluated using four performance measures and confusion matrix [40]. The metrics are accuracy (Acc), sensitivity (Sn), F1-Score and AUC. Confusion matrices are displayed to show the classification distribution across class labels to aid the visualization of the pipeline performance.