esa-sceva/satcom-chunk-collection huggingface dataset:
# An Admission-Control-Based Dynamic Query Tree Protocol for Fast Moving RFID Tag Identification
## 2 Related Works
[PERSON] also modeled the dynamic RFID system in [13]. However, these works only consider a basic aloha-based algorithm in which the identification efficiency is low. In [28], [PERSON] et al. developed a schedule-based RFID anti-collision protocol to maximize tag moving speed. They considered the identification deadlines of moving tags and scheduled an optimal number of tags to compete for the channel accordingly. With a schedule strategy, the identification efficiency is greatly improved. In recent research, some new strategies have been developed for mobile RFID systems. In [14], [PERSON] et al. proposed a blocking collision tracking tree algorithm to divide the identification process into two phases, one for staying tags and one for arriving tags. Making use of bit tracking to split tags into appropriate subsets, the identification efficiency is increased. In [6], [PERSON] et al. further improved the tag identification efficiency with a bit tracking technique. They developed an efficient bit-detecting (EBD) algorithm to quickly recognize unknown tags in each reading round. Thus, the proposed algorithm can support a much higher tag flow rate in mobile systems. Later on, [PERSON] proposed a dynamic collision tree protocol to deal with arriving tags [29]. [PERSON] et al. developed a dynamic tag population estimation algorithm with a Kalman filter [30]. In [31], [PERSON] et al. designed a rate-adaptive algorithm to revisit the reading rate with mobility. [PERSON] et al. also proposed a collision avoidance algorithm for mobile RFID devices [32]. However, the control strategies and reading methods of these works need further improvement to support higher tag moving speeds. To sum up, Table 1 compares different RFID tag identification protocols in static and moving situations.
## 4 Application of APD to Other Sites  
To illustrate the generalisability and adaptability of the Automated Parameter Determination (APD) system, this section showcases its application at three Norwegian GeoTest Sites (NGTS), each representing a different soil type: clay, silt, and sand. These case studies underscore APD's capability to process site investigation data across varied geological conditions. The analyses demonstrate the system's reliability in addressing diverse ground conditions and its usefulness in early-stage design and site assessment tasks. In all three cases, the required in situ test data were obtained via the online platform Datamap [51]. The findings reinforce APD's effectiveness in deriving soil parameters directly from field-based measurements.
## 4 Discussion
### Finding 2: Significant Effect of Initial Temperature Load  
From the data presented in Figures 13 and 14, it is evident that for noncalibrated samples, the most significant deformation occurs during the initial temperature loading. Figure 13 illustrates the evolution of total runout deviation for noncalibrated samples and encompasses all four rounds of measurement. Meanwhile, Figure 14 presents the same data, excluding the first round of measurement, and considers the state after the initial temperature loading as the starting point.  
The results affirm that the majority of deformation takes place during the initial temperature cycle. This observation is quantified in Table 6, which indicates that, on average, 77.6% of the maximum deflection value occurred after the first temperature test. In contrast, the final step, which involved mechanical testing, resulted in an average deflection increase of 31.4% (calculated relative to the previous condition). These findings highlight the most significant changes occurring during the initial thermal loading. Consequently, incorporating a technological heat treatment during the manufacturing process could enhance the dimensional stability of the product.  
Figure 13: Deviation of total runout during the environmental tests.
## 2 Materials and Methods
### Thermal Performance Evaluation  
The heating performance of the ENMH was evaluated by applying DC voltage using a regulated power supply (2231A-30-3 Triple Channel; Keithley Instruments, LLC., Solon, OH, USA). Surface temperature and distribution were monitored with an infrared thermal camera (PI 640i; Optris GmbH, Berlin, Germany) and analyzed using PIX Connect software (version Rel 3.21.3113.0). Conductive silver paste was applied to both ends of the Cu-coated nanofiber web to enable wire attachment. Temperature measurements were taken from a central rectangular region, excluding the electrode areas, while the sample was maintained within the camera's focal range. The thermal response was recorded at a frame rate of 32 frames/s.
## 3 Experimental Verification
### Graffiti Detection on Orthophotos  
After generating orthophotos from all four facade planes of the TOC, the graffiti detector we trained in Section 3.1 was then applied to each orthophoto. The results of the graffiti-detection process on each facade plane on the Church of Agios Nikolaos are shown in Figure 10. The detected graffiti are marked with green rectangular bounding boxes. All graffiti presented on the facades were successfully identified. Since the individual images were collected at various locations and viewpoints, causing significant brightness  
Figure 9: Steps in the orthophoto generation process for the Church of Agios Nikolaos: (**a**) the point cloud generated and its four major facade planes; and (**b**) a representative orthophoto generated from \(\mathsf{S}_{1}\).  
or exposure discrepancies, the lighting differences over the region of the orthophoto were not fully compensated. In addition, the overall quality of the orthophotos was inconsistent because they were highly dependent on the quality of the input images. Nevertheless, graffiti markings were still observed and identified by the graffiti detector. We conducted data collection in two different time windows. We noticed that all graffiti markings were successfully detected on each of orthophotos, and readily knew that there was no addition or removal. This demonstrated that the trained detector was robust enough, and was insensitive to the environmental variation in the background scene. The graffiti distribution was shown in a single view, which will also assist in assessing the overall exterior condition and directing maintenance teams to the proper location. The processing for this step took an average of 1.4 min for each of the four orthophotos.  
Figure 10: Graffiti-detection results from the orthophotos generated for the four facades of the Church of Agios Nikolaos based on data collected in (**a**) May 2018 and (**b**) January 2019.
# Developing Machine Learning-Based Models for Railway Inspection
## 2 Overview of Railway Defect Detection Techniques and Acceleration Data
### Acceleration Data  
An accelerometer is a sensor capable of detecting the impact of rail surface defects to wheel or vehicle body when mounted on the wheel or the bogie of an in-service railway vehicle. When a train passes over track with rail surface irregularities such as joints and surface defects, the impact force that occurs at the wheel/rail interface will generate a peak of high acceleration to the wheelsets, axle box, bogie, and vehicle body. The abnormal motion can be monitored by the accelerometer.  
In this study, the investigation uses the acceleration data collected by two accelerometers installed on a rail inspection vehicle (as shown in Figure 1) owned by Transport Canada. The two sensors measure acceleration signals in vertical directions at two locations--i.e., left and right--as shown in Figure 2. A segment of track with many rail joints was selected to collect the acceleration data.  
In order to collect enough data points during the high frequency impact due to the joint discontinuity, a sampling rate of 464 Hz was used in the data acquisition system. This particular sampling rate was set by the acceleration data system. Figure 3 shows a typical piece of the time series data collected through a test run. As multiple accelerometers are mounted on wheels of 'right' and 'left' sides, a joint (or a surface defect) on one side of the track can also be observed in the data of the other side and vice versa [21; 22]. If the data for the right and left rails is analyzed separately for the defect detection on each side, extra verification tests have to be considered to decrease the false alarm rate. Therefore,  
Figure 1: Transport Canada rail inspection vehicle.  
Figure 2: Test vehicle and accelerometers.  
a robust algorithm should take the data of both sides into account and identify if there is a joint (or defect) at the location and on which side of the track the joint (or defect) is located.  
Other data collected during the test runs include vehicle speed by wheel axle encoder, as well as the latitude and longitude coordinates by a GPS sensor. The speed channel will be used later to convert the acceleration data into the distance domain, and the GPS data is useful to map the locations of the identified joints (or defects). The Figure 3 shows an example of the acceleration data collected from the rail inspection vehicle and Figure 4 shows a rail inspection technician inspecting a rail joint in the field. It is a time consuming task to manually inspect the rail joint for maintenance decision-making.
## 6 Key Challenges and Future Directions
### Limitations of Current WebAssembly Implementations
* **Testing Challenges*
* Similar to debugging, testing Wasm modules, especially in the context of IoT applications, presents its own set of challenges. Considering the wide variety of devices and environments in IoT, it can be difficult to effectively test Wasm applications in order to ensure they work correctly in all potential scenarios. * **Restricted Networking Capabilities*
* In its current state, Wasm lacks direct access to networking APIs, limiting its capabilities for networking tasks without interaction with JavaScript, which can introduce complexity and performance drawbacks. As Wasm's use expands in the world of IoT, it encounters unique challenges, one of which is its restricted networking capabilities. This section highlights these challenges and discusses their impact on the use of Wasm in IoT applications. * **Wasm's Networking Model*
* Wasm currently operates within the constraints of the same-origin policy, meaning that it can only make network requests to the same source from which the Wasm module was loaded. While this policy is a crucial security feature in web environments, it can limit the direct networking capabilities of Wasm, particularly
in the context of IoT, where devices often need to communicate with different servers and devices. * **Lack of Direct Access to Networking APIs** As of now, Wasm does not have direct access to networking APIs. This means that Wasm applications must rely on the host environment, typically a JavaScript environment in a web browser, to make network requests on their behalf. This lack of direct access can lead to inefficiencies and makes it more difficult to implement networking functionality in a straightforward manner. * **Implications for IoT** These networking restrictions can be particularly problematic for IoT applications. IoT devices often need to communicate with various servers, devices, and services, often across different origins. The lack of direct access to networking APIs presents a challenge, as it means that all networking operations must go through the host environment, which could potentially impact performance and introduce additional complexity. * **Potential Solutions** Proposed solutions to this challenge involve extending Wasm's capabilities either through new Wasm APIs or via the WASI. In particular, the latter aims to provide Wasm applications with more direct access to system capabilities, including networking. This evolution could significantly enhance the utility and power of Wasm in the context of IoT.
## 4 Conclusions  
In this work, HAR trenches with widths from 2 \(\upmu\)m to 30 \(\upmu\)m and depths from 20 \(\upmu\)m to 440 \(\upmu\)m were measured by a CNN model-based TSOM method. The TSOM image was mapped to a normalized image of appropriate size, and the width and depth measured by SEM were taken as labels for iterative training to obtain a DL regression model that was capable of accurately predicting the width and depth. The standard deviation and error were around a hundred nanometers or less. The superiority of the convolutional neural network model-based TSOM compared to machine learning-based TSOM was experimentally investigated and discussed. This method has the potential to achieve nanometer precision, and its application fields can be extended to integrated circuit metrology, such as through-silicon via.  
Conceptualization, J.S. and X.C.; methodology, G.L. and J.S.; software, G.L.; validation, G.L. and C.G.; formal analysis, S.H.; investigation, G.L.; resources, C.C.; data curation, G.L.; writing--original draft preparation, G.L. and J.S.; writing--review and editing, J.S. and X.C.; visualization, X.J.; supervision, W.Z.; project administration, X.C.; funding acquisition, W.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (No. 2019 YFB2005600), National Key Laboratory of Precision Testing Techniques and Instrument, Tsinghua University (No. TH20-01), National Natural Science Foundation of China (No. 51905528), State Key Laboratory of Precision Measuring Technology and Instruments (Tianjin University) (No. pilab2102), and Key Laboratory of Microelectronic Devices Integrated Technology, Chinese Academy of Sciences.  
Not applicable.  
The data presented in this study are available from the corresponding author, [PERSON], upon reasonable request.  
Figure 9: Standard deviation and error of depth results for widths of (**a**) 1.93 \(\upmu\)m, (**b**) 10.3 \(\upmu\)m, and (c) 30.3 \(\upmu\)m, predicted by machine learning model (dot line) and deep learning model (solid line). ML, machine learning; DL, deep learning; SD, standard deviation.
# MANET Routing Protocols' Performance Assessment Under Dynamic Network Conditions
## 5 Results and Comparative Analysis
### Scenarios with Homogeneous Nodes
#### 5.1.7 Packet Loss Rate  
All protocols show comparatively significant packet loss rates at 20 nodes, as shown in Figure 7, providing a baseline against which to compare. Because proactive protocols maintain their routes continuously, they exhibit a moderate packet loss rate, which makes them more effective for smaller networks. However, the overhead of maintaining routing tables makes their performance less than ideal for very large networks. The overhead of route discovery under moderate traffic conditions causes reactive protocols to noticeably reduce their packet loss rate as the network capacity grows to 80 nodes. Packet loss rates are comparable in smaller networks (20 and 80 nodes) due to less congestion and stable pathways. Higher connectivity enhances redundancy as the network expands to 200 and particularly 500 nodes, enabling dropped packets to be promptly diverted and minimizing packet loss.  
At 200 nodes, the packet loss rate shows the biggest improvement, underscoring the difficulties in scalability and adaptation that come with increasing node density. Because hybrid protocols exhibit balanced performance by combining proactive and reactive techniques, they are especially well-suited for very high-density networks (500 nodes), where they can effectively handle the lower packet loss rate. Most protocols exhibit a large decrease in packet loss rates at 500 nodes, suggesting a return to efficiency at the highest network densities. The effectiveness and scalability of each protocol category are highlighted in this analysis, which also offers important insights into their operational performance and possible limitations in a range of network scenarios. The protocol's suitability for each of the four cases, taking into account the packet loss rate, is summarized in Table 11 below.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Network Size** **(Nodes)** & **Proactive Protocols (OLSR, DSDV)** & **Reactive Protocols (AODV, DSR, AOMDV, TORA)** & **Hybrid Protocols (ZRP, GRP)** \\ \hline
20 Nodes & Moderate jitter, efficient for smaller networks & Varying jitter, generally higher than proactive protocols & Moderate jitter, balanced performance \\
80 Nodes & Noticeable increase, consistent performance & Significant increase due to route discovery overhead & Balanced increase, effective management of increased jitter \\
200 Nodes & Sharp rise, high jitter & Peak jitter, highlighting scalability challenges & Peak jitter, balanced performance, effective integration of strategies \\  & Decrease or stabilizes, less optimal due to maintenance overhead & Stabilizes, indicating return to efficiency & Stabilizes, maintains efficient performance, most suitable for high-density networks \\ \hline \end{tabular}
\end{table}
Table 10: Protocol Suitability for Different Scenarios Considering Jitter.
## Processing Setup and Description
### Satellite Laser Ranging  
Laser ranging to LEO satellites is a highly valuable complementary technique for validation of GNSS-derived orbits, since it uses completely independent measurements from the global geodetic ground station network of the ILRS [44; 95]. SLR observations are obtained by measuring the two-way signal travel time from an SLR ground station to a satellite equipped with a Laser Retroreflector (LRR), which passively reflects the short laser pulses emitted. For most ILRS stations a one-way range precision typically ranging from 0.3-1.8 cm is achieved [58; 96]. The signal travel time, corrected for propagation effects and translated into a range \(R\), is then compared to the range from the orbit determination:  
\[\Delta R_{\text{SLR}}=R_{\text{SLR}}-R_{\text{POD}}. \tag{1}\]  
Due to the measurement geometry the SLR residuals \(\Delta R_{\text{SLR}}\) for high-orbiting GNSS satellites are most sensitive to the radial component of the orbit solution. For satellites in LEO, which are in general tracked at lower elevation angles, however, SLR range is sensitive to all orbital components. In an idealized concept of zero mean error and even distribution of the standard deviation in all directions the 3D position standard error can be approximated by \(\sigma_{\text{pos}}=\sqrt{3\sigma_{\text{SLR}}}\)[44]. This idealization disregards that LEO passes are more sensitive in north-south direction to the along-track component and in east-west direction to cross-track component [97].  
The SLR residuals then serve in our context as a measure for orbit accuracy, supporting the detection and estimation of potential systematic errors. Station coordinates for the observation  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Solution Identifier** & **Analysis Center** & **Software** & **GNSS Data** & **Remark** \\ \hline _Internal Solutions_ & & & & \\ WEGC-BC & WEGC & Bernese 5.2 & CODE & primary solution \\ WEGC-BI & WEGC & Bernese 5.2 & IGS-JPL & control run \\ WEGC-NC & WEGC & NAPEOS 3.3.1 & CODE & control run \\ \hline _External Solutions_ & & & & \\ UCAR & UCAR & Bernese & & \\ EUM & EUMETSAT & NAPEOS & & \\ AIUB & AIUB & Bernese & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Orbit solutions and metadata used in this study.  
modeling were taken from the Satellite Laser Ranging Frame 2008 (SLRF2008, [[https://ilrs.cddis.eosdis.nasa.gov/science/awg/SLRF2008.html](https://ilrs.cddis.eosdis.nasa.gov/science/awg/SLRF2008.html)]([https://ilrs.cddis.eosdis.nasa.gov/science/awg/SLRF2008.html](https://ilrs.cddis.eosdis.nasa.gov/science/awg/SLRF2008.html))) in alignment with the GNSS orbit products used in the POD (Table 1).
# Two Medoid-Based Algorithms for Clustering Sets
## 5 Clustering Results  
The effects of the seeding methods were preliminarily studied using the \(data\_1200\_100\_16\_5\_1 dataset (see Table 1). The results are reported in Table 2.  
Simulation experiments were carried out on a Win11 Pro, Dell XPS 8940, Intel i7-10700 (8 physical cores), CPU@2.90 GHz, 32 GB Ram, and Java 17. As one can see from Table 2, all the four seeding methods agree on the best \(SDM\), \(ARI\), \(CI\), and \(SI\). However, they significantly differ in the \(success\ rate\) and the average \(ARI\) and the average \(CI\). The best results follow from the careful seeding ensured by G-K-means++ where a \(success\ rate\) of about 40%, an average \(ARI\) of 0.93, and an average \(CI\) of about 0.69 were observed, although with an increased computational time. Table 2 also confirms that maximin, after G-K-means++, is capable of offering better results than uniform random or K-means++ seeding. The Silhouette index (\(SI\)) mirrors the limited overlapping percentage (\(o=5\%\)) present in the dataset. The dataset \(data\_1200\_100\_16\_5\_1 was also studied by using the parallel version of the implemented random swap algorithm (\(PRS\)) (see Algorithm 2) separately fed by each one of the four seeding methods. At most, 100 swap iterations were set. The minimal number of swaps required to detect the \"best\" solution (minimal \(SDM\) cost) and corresponding \(ARI\), \(CI\), and \(SI\) indexes are reported in Table 3.  
From Table 3 it emerges that even with the uniform random seeding, \(PRS\) was able to find the best solution with few iterations. The effect of seeding methods on the \(data\_1200\_100\_16\_5\_1 dataset was also observed with the other datasets. Therefore, in the following, for simplicity, only the results gathered by using repeated K-medoids and parallel random swap when seeded, respectively, by uniform random and by G-K-means++ are reported in Tables 4-7. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Seeding** & **bSDM** & **bARI** & **bCI** & **SR** & **bSI** & **aARI** & **aCI** \\ \hline Uniform Random & 1513 & 0.99 & 0 & 6.0\% & 0.71 & 0.72 & 2.71 \\ Maximin & 1513 & 0.99 & 0 & 17.5\% & 0.71 & 0.89 & 1.14 \\ K-Means++ & 1513 & 0.99 & 0 & 14.5\% & 0.71 & 0.86 & 1.50 \\ G-K-Means++ & 1513 & 0.99 & 0 & 39.6\% & 0.71 & 0.93 & 0.69 \\ \hline \end{tabular}
\end{table} 
Table 2: Results of repeated K-medoids (1000 runs) under different seeding methods, on the \(data\_1200\_100\_16\_5\_1 dataset with weighted Jaccard distance. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Seeding** & **SDM** & **ARI** & **CI** & **SI** & **Iterations** \\ \hline Uniform random & 1513 & 0.99 & 0 & 0.71 & 26 \\ Maximin & 1513 & 0.99 & 0 & 0.71 & 4 \\ K-means++ & 1513 & 0.99 & 0 & 0.71 & 18 \\ G-K-means++ & 1513 & 0.99 & 0 & 0.71 & 4 \\ \hline \end{tabular}
\end{table} 
Table 3: Results of parallel random swap (max 100 iterations) under different seeding methods, on the \(data\_1200\_100\_16\_5\_1 dataset with weighted Jaccard distance.