esa-sceva/satcom-chunk-collection huggingface dataset:
# Validating Precise Orbit Determination from Satellite-Borne GPS Data of Haiyang-2D
## 3 Results and Discussion
### Orbit Determination Strategies and Analysis of Accuracy
#### 3.2.1 Orbit Determination Strategies  
LEO satellites are mainly affected by conserved and non-conserved forces, and in order to eliminate the influence of these forces on satellite motion, various mechanical models and pseudo-random parameters were added to the RD orbit determination process [32; 33]. XGM2019 (120 \(\times\) 120 orders) was used to eliminate the Earth's gravity; TIDE2000 and FES2004 [34] to eliminate solid and ocean tides [35]; and DE405 to eliminate the effects of multi-body ingress (Sun, Jupiter, Saturn, Uranus, Neptune Venus, etc.) [36]. The strategies used in the POD of HY-2D are shown in Table 3.  
This study used HY-2D spaceborne GPS data for 7 days from 19 to 24 September 2021 (DOY 262-DOY 268) for POD [37]. Arc lengths of 24 h were selected, and the pulses were estimated every 6 min. The RD orbits were validated using internal and external validation methods. Using observations collected by the HY2 receiver to analyze the accuracy of orbits in internal validation, the carrier-phase residuals were used to assess the accuracy of HY-2D RD orbit in experiments. The external validation methods used data independent of the POD to assess the accuracy of orbits. In addition to the SLR range validation, the proposed three methods including carrier differential validation, inter-satellite differential validation, and SLR 3D validation were also used, and results indicated that these three methods were reliable.
## 2 Data and Methods
### Evaluation Indicators  
The effectiveness of landslide area identification methods is evaluated by generating a confusion matrix, which is then used to calculate the producer's accuracy and user's accuracy. In the multi-category problem, each category is considered \"positive\", and the rest is considered \"negative\".  
Accuracy: The proportion of accurately classified samples among the total samples.  
\[Accuracy=\frac{TP+TN}{TP+FN+FP+TN} \tag{7}\]  
where \(TP\) denotes the number of correctly classified positive samples, \(TN\) is the number of correctly classified negative samples, \(FP\) is the number of negative samples that aremisclassified as positive, and \(FN\) is the number of positive samples that are misclassified as negative.  
Precision: The proportion of positively classified samples that are accurate among the entire set of positive samples.  
\[Precision=\frac{TP}{TP+FP} \tag{8}\]  
Recall: The proportion of positive samples correctly classified out of the total positive samples.  
\[Recall=\frac{TP}{TP+FN} \tag{9}\]
## 3 Results and Discussion
### Bottom Sediment Characterization
In this study, oxidized forms of Al, Fe, Mn and S were detected in the bottom sediment, with aluminum oxide (Al\({}_{2}\)O\({}_{3}\)) being the most prevalent, followed by iron oxide (Fe\({}_{2}\)O\({}_{3}\)). Research on sedimentary metals and sulfur in natural wetlands exposed to AMD has shown a predominance of oxides of Al, Fe, and Mn, especially in oxic sediments, with a decrease in these oxides in the anoxic zones [70; 72; 73]. Figure 4: Relationship between the mean OC %, OM %, ORP, and pH in the bottom sediment of each site in the dry season (May 2022). Ref. [73] noted that the accumulation, distribution, and oxidative status of metals and sulfur in wetland sediments are influenced by the availability of oxygen (redox profile) in various sedimentary layers (e.g., 0-5 cm, 2.5-7.5 cm, and \(>\)7.5 cm). They also found a positive correlation between OM and these elements. Ref. [74] observed a similar pattern in their inlet-outlet study of a natural wetland receiving AMD, where the OM% increased from the inlet to the outlet (10%-25.4%), and the metal accumulation increased for Fe (212-255 \(\mu\)g/g) and Mn (1.6-4.2 \(\mu\)g/g) but not for Al (1.21-1.05 \(\mu\)g/g). In the current study, the highest concentrations of Fe and Al in bottom sediments were recorded at S2, where OM was the most abundant, consistent with the findings from other studies. The relationship between OM and ORP is critical in wetlands. [PERSON]. [75] explained that OM acts as the terminal electron acceptor under highly reduced redox potentials. The pH, ORP, texture, and OM% in bottom sediments may also be influenced by emergent vegetation and microbiological activities, and vice versa. This interconnected and complex nature of the biogeochemical reactions within the bottom sediment cannot be fully understood in isolation [76].
# Agriculture 5.0 in Colombia: Opportunities Through the Emerging 6G Network
###### Abstract  
Agriculture 5.0 represents a shift towards a more sustainable agricultural model, integrating Artificial Intelligence (AI), the Internet of Things (IoT), robotics, and blockchain technologies to enhance productivity and resource management, with an emphasis on social and environmental resilience. This article explores how the evolution of wireless technologies to sixth-generation networks (6G) can support innovation in Colombia's agricultural sector and foster rural advancement. The study follows three main phases: search, analysis, and selection of information. In the search phase, key government policies, spectrum management strategies, and the relevant literature from 2020 to 2025 were reviewed. The analysis phase addresses challenges such as spectrum regulation and infrastructure deployment within the context of a developing country. Finally, the selection phase evaluates technological readiness and policy frameworks. Findings suggest that 6G could revolutionize Colombian agriculture by improving connectivity, enabling real-time monitoring, and facilitating precision farming, especially in rural areas with limited infrastructure. Successful 6G deployment could boost agricultural productivity, reduce socioeconomic disparities, and foster sustainable rural development, contingent on aligned public policies, infrastructure investments, and human capital development.  
artificial intelligence; Agriculture 5.0; smart agriculture; 6G Networks; spectrum; Internet of Things (IoT); sustainable farming +
Footnote †: journal: _Nature_  
0000-0002-4000-0002-9999-22000][PERSON]  
1 Manglar Research Group, Universidad de Sucre, Sinchejolo 700001, Colombia; [EMAIL_ADDRESS]  
2 Faculty of Engineering, Universidad de La Guajria, Riohacha 440002, Colombia; [EMAIL_ADDRESS]  
3 Faculty of Economy, Universidad del Atlantico, Puerto Colombia 081007, Colombia; [EMAIL_ADDRESS]
# Personalized Contextual Information Delivery Using Road Sign Recognition
## 3 Proposed System
### Modular Components for Road Sign Recognition  
The proposed system adopts modular architecture to efficiently process road sign images and extract meaningful information. This design ensures that each module handles a specific aspect of road sign recognition, including object detection, text recognition, and directional information extraction, providing a foundation for further contextual integration and user-specific outputs [1; 8].  
The modular system consists of three primary components: object detection, text detection and recognition, and directional information grouping. These modules work sequentially to analyze road sign images, beginning with identifying objects within the image, followed by recognizing textual content, and finally establishing directional relationships among detected objects [6; 7].
## 2 Background
### Load Balancing
Both academia and industry are paying close attention to the congestion of traditional lossy data center networks and designing a series of congestion control mechanisms. The well-known DCTCP protocol can effectively control the switch queue length when network congestion is not serious [20], but DCTCP is still difficult to avoid overflow of the switch buffer when a large number of concurrent links occur. Therefore, these mechanisms are also difficult to avoid congestion and packet loss. 5. Switch Congestion (Switch Buffer Overflow) Whether the network uses electrical signal transmission or optical signal transmission, when the data packet enters the switch, data basically needs to be buffered. Usually, the optical signal also needs to be converted into an electrical signal for processing, and it is difficult to directly process the optical signal. The electrical signal processing speed of the switch is limited by the processing capability of the device. As shown in Figure 2, when forwarding the data packet, the packet needs to be parsed, repackaged and forwarded. These operations need to be performed in the memory. Moreover, caching the data packets requires a large amount of storage space. When different types of flows compete for the switch cache, the long flow will occupy most of the switch cache queues space, making the latency performance of the short flow become worse. Figure 2: Shared memory switch mode in data center network. With the parallel processing, shared buffer space, and buffer management scheduling technology, such large memory is often not needed. Even so, data center switches are designed with large memory features. In conclusion, packet loss is the most direct cause of the increase of FCT, and the main reasons for packet loss are link congestion and switch congestion. It is not appropriate to only minimize the maximum link utilization for the entire network, and we should consider both link resources and switch resources. Link resources can be represented by link utilization. Switch resources can be represented by switch load. This paper tries to use link remaining bandwidth and switch idle memory size to represent link resource and switch resource. Further, we refer to link resources and switch resources collectively as network resources. In this paper, data center load balancing can be described as the matching problem between traffic and network resources, that is, the overall load balancing of the network is realized on the basis of minimizing the network packet loss rate. It also naturally achieves the traditional goal of minimizing the maximum link utilization in this case.
## 5 Calculus Analysis
### Calculation Results and Analyses  
#### 5.2.1 Influence of Information System on System Reliability Assessment  
In order to study the impact of information system failure on system reliability, this study analyzed the following two aspects. 1 The impact of overall information system failure on system reliability. 2 The impact of information component failure on system reliability.  
In the study of the impact of information system failure on the reliability of the distribution network system, this research adopted the method of comparative analysis to analyze the average power supply availability (average service availability index, ASAI), the expectation of shortage of electricity supply (expected energy not supplied, EENS), average system outage frequency (system average interruption frequency index, SAIFI), average system outage time (system average interruption duration index, SAIDI), and average duration of outages for customers (customer average interruption duration index, CAIDI), with both no consideration and consideration of information system failure. The results are shown in Table 1.  
From the results in Table 1, it can be seen that although information system failures have a certain negative impact on the reliability of a power system, the impact is relatively small. In order to further investigate the impact of information system component failures on system reliability, the failure rate of each type of information component was reduced to 0 or increased to 200%, with a step size of 10%. At the same time, we kept the failure rates of other components constant and conducted multiple simulations to take the average. We fitted the data points to obtain reliability index curves for four types of information IEDs, SDH switches, SDH lines, and server communication nodes under different failure rates. The reliability index curves of components under different failure rates are shown in Figure 4.  
From Figure 4, it can be seen that changes in the failure rate of information equipment have different degrees of impact on the reliability of CPDSs, in descending order of IEDs, synchronous digital hierarchy SDH lines, SDH switches, and server communication nodes. With the increasing failure rate of components, IEDs have the greatest impact on system reliability. Due to their large number and high failure rate, a further increase in the failure rate will result in a large number of IEDs failing, thus reducing the reliability of the active distribution network. In comparison, servers have the least impact on system reliability due to their extremely low failure rate due to the presence of a backup mechanism. In summary, the development and use of high-quality IEDs, the addition of standby for SDH switches, and the regular maintenance of lines are all effective measures to reduce the failure rate of information systems and increase the reliability of active distribution networks.
## 3 Materials and Methods
### Assessment Approaches  
The scenarios or configurations evaluated in this study represent two completely different approaches to integrating computer vision processing into a simulator developed in Unity, as they face different modes of communication between the main program and the computer vision process, different modes of execution and, of course, different programming languages, with all of the pros and cons that this implies.  
The first approach takes advantage of one of the functionalities offered by Unity and its C# language, namely the possibility of adapting libraries that implement extra functionalities that run from the C# code independently of the language the library was developed in. Since all the experiments are carried out on the Windows 10 operating system, the native library has been implemented in the programming language that best suits it, so it has been done in C++. It should be mentioned here that the native plugin serves only as a means of information exchange, since video processing would always run on the same Unity thread. In contrast, the second approach uses the MQTT message protocol to exchange information with an external Python program that also runs in parallel  
Figure 1: Video processing in Unity using **(a)** a native C++ DLL, **(b)** an external Python module communicated via MQTT.  
Figure 2: Virtual indoor environment.  
to Unity. In this case, an MQTT broker is needed to link the two programs and manage the communication between them.  
As for the differences between the programming languages themselves, C++ is a cross-platform compiled language, while Python is also cross-platform but is an interpreted language. The former is widely used thanks to its high performance in real-time applications [42] and also in embedded systems processing images [43]. In addition, C++ allows a better native integration with OpenCV libraries than Python. However, the latter is characterised by its significant number of compatible libraries and its ease of use. In this regard, it is worth noting that, although OpenCV was developed entirely in C and C++, it is much easier to implement OpenCV in Python than in C++. Moreover, there are many OpenCV-based libraries in Python with ready-to-use advanced processing functions that are not available in C++.
# Fast 50 Hz Updated Static Infrared Positioning System Based on Triangulation Method
## 2 Materials and Methods
### Angle Determination Method
#### 2.2.2 Weighted Mean of Angles Method  
In the case of circular quantities, the so-called Weighted Mean of Angles method to determination of the \(\varphi_{i}\) angle can be used [50]. In the case under consideration, the intensity of the measured IR radiation can be represented as a two-dimensional vector in Cartesian space \(\vec{I_{D_{j}}}=(I_{D_{j}}\cos D_{j},I_{D_{j}}\sin D_{j})\), where \(I_{D_{j}}\) is the length of this vector (this is what the diode \(D_{j}\) measures), and the angular position of the measuring photodiode of the receiver \(D_{j}\) is the angle of inclination of the vector from the x-axis. In order to find the weighted mean value of the angle (i.e., the \(\varphi_{i}\) angle), it is necessary to determine the sum of all 32 vectors \(\vec{I_{D_{j}}}\), and then to determine what angle this resultant vector forms with the x-axis. The value of the angle \(\varphi_{i}\) is therefore described by Equation (5).  
\[\varphi_{i}=arctan\frac{\sum\limits_{j=1}^{32}I_{D_{j}}\sin D_{j}}{\sum \limits_{j=1}^{32}I_{D_{j}}\cos D_{j}} \tag{5}\]  
Unlike approximation, this method does not require a pre-modification of the recorded spectrum \(I_{D_{j}}\) because the zero IR intensity vector contributes nothing to the vector sum. This method is much less computationally complex than the approximation method and much easier to implement on a microcontroller.
# Inferring Urban Social Networks from Publicly Available Data
## Appendix B Social Contact Data
### Initial Data  
We assume that social contact patterns among individuals of our age-stratified population are available in the form of a \(n\times n\) social contact matrix \(\Gamma=\{\gamma_{i,j}\}_{i,j\in\{0,\dots,n-1\}}\), where \(n\) is the number of considered age groups. Following a systematic literature review, the project SOCRATES [16] recently released an online tool ([[https://lwillem.shinyapps.io/socrates_rshiny/](https://lwillem.shinyapps.io/socrates_rshiny/)]([https://lwillem.shinyapps.io/socrates_rshiny/](https://lwillem.shinyapps.io/socrates_rshiny/)) (accessed on 23 April 2021)) to extract and analyze social contact patterns for a wide range of countries based on the best publicly available survey datasets. The tool allows to select a number of parameters upon which social contact matrices are usually dependent, such as age breaks, gender, day of the week, duration, or location of the contact. Once the preferred dataset and the aforementioned parameters are selected, the tool produces the desired square matrix with \(\gamma_{i,j}\) measuring the average number of daily contacts. For the scope of this paper, we relied upon the SOCRATES tool (v1.32), using the Polymod [15] dataset for Italy and the following set of parameters:  
* Age breaks: 0, 18, 35, 65 (the choice was dictated by the available data from ISTAT to build households);
* Type of day: all contacts;
* Contact duration: more than 15 min;
* Contact intensity: all contacts;
* Gender: all;
* Reciprocity: yes;
* Weigh by age: yes;
* Weigh by week/weekend: yes;
* Locations: all except Home (because we use households for home contacts).  
While we have no particular reason to recommend the use of the SOCRATES tool to determine \(\Gamma\), we believe it is a valuable resource that perfectly fits our needs. More generally, the construction of a social contact matrix \(\Gamma\) is a typical problem in computational social science and our simulator simply assumes that \(\gamma_{i,j}\) is an estimate of the volume of interactions/relationships that an individual of age group \(i\) has with any other individual of age group \(j\) within a given time span. It goes without saying that the type of contacts described by \(\Gamma\) is partially reflected on the final structure of the graph.
## 5 Proposed Method
### DDoS Detection  
Once the individual and ensemble classifier models are trained in the previous phase and the best-performing supervised and unsupervised models are identified, these models then can be deployed on the perimeter of a real-world network to detect DDoS intrusions as part of an IDS. Figure 3 depicts the flowchart of the DDoS detection mechanism using these models in the ensemble framework. The network traffic undergoes data preprocessing steps, where they are sanitized, feature-reduced, and then classified by the ensemble framework that detects any DDoS attack and provides an alert regarding it.  
In our experimental setup, we used verification data (described in Section 5.1) to mimic the real-time network traffic.  
Figure 3: DDoS detection using the combined best performing supervised and unsupervised models.  
```
Input :\(train\_X\) : train features, \(train\_y\) : train target, \(test\_X\) : test features, \(test\_y\) : test target Output :\(m1\), \(m2\) : best-performing supervised and unsupervised models
1\(ml\_si\) = ['LR', 'DT', 'NB', 'NN', 'SVM']
2\(ml\_ui\) = ['COCSL', 'LOF', 'ISOF', 'ELE', 'COSP']
3\(ml\_e\) = ['Ens_LR', 'Ens_DT', 'Ens_NB', 'Ens_NN', 'Ens_SVM']
4\(resultdf\_s\) = ['LR_Target', 'DT_Target', 'NB_Target', 'NN_Target', 'SVM_Target', Actual_Target]
5\(resultdf\_u\) = ['OCSSL_Target', 'LOF_Target', 'ISOF_Target', 'ELE_Target', 'OCSP_Target', Actual_Target]
6 foreach\(m1\in ml\_si\)do
7 Train \(m1\) on \(train\_X\) and \(train\_y\)
8 Evaluate performance of \(m1\) on \(test\_X\) and \(test\_y\)
9 Store performance metric
10\(predicted\_target\) = a list of predicted class on \(test\_X\) using \(m1\)
11 Append \(predicted\_target\) in \(resultdf\_s\)
12
13 end foreach
14 Append \(test\_y\) in \(resultdf\_s\)
15\(new\_train\_X\), \(new\_train\_y\), \(new\_test\_X\), \(new\_test\_y\) = split \(resultdf\_s\) by 70%/30%
16 foreach\(m1\in ml\_e\)do
17 Train \(m1\) on \(new\_train\_X\) and \(new\_train\_y\)
18 Evaluate performance of \(m1\) on \(new\_test\_X\) and \(new\_test\_y\)
19 Store performance metric
20 end foreach
21 Analyze the stored performance to determine **best performing supervised model \(m1\)**
22 foreach\(m2\in ml\_ui\)do
23 Train \(m2\) on \(train\_X\) and \(train\_y\)
24 Evaluate performance of \(m2\) on \(test\_X\) and \(test\_y\)
25 Store performance metric
26
27 end foreach
28 Aprend \(test\_y\) in \(resultdf\_u\)
29\(new\_train\_X\), \(new\_train\_y\), \(new\_test\_X\), \(new\_test\_y\) = split \(resultdf\_s\) by 70%/30%
30 foreach\(m2\in ml\_e\)do
31 Train \(m2\) on \(new\_train\_X\) and \(new\_train\_y\)
32 Evaluate performance of \(m2\) on \(new\_test\_X\) and \(new\_test\_y\)
33 Store performance metric
34 end foreach
35 Analyze the stored performance to determine **best performing unsupervised model \(m2\)**
```  
**Algorithm 1**Combined ensemble (supervised and unsupervised) classification
# Line Structure Extraction from LiDAR Point Cloud Based on the Persistence of Tensor Feature
## 3 Methodology  
Suppose a point \(p\) is consisted of \(N\)-dimensional coordinate and without normal vector information, which can be denoted as \(p(x_{1},x_{2},\ldots,x_{N})\). The line feature is the geometric information of each point, and the higher value of the line feature, the more probability of the point to be a part of lines. Hence, the line segment can be constructed by connecting each point with the highest local line feature value. The line structure is built using line segments and their structure connections. To get the line structure from the unstructured point cloud, the geometric feature to express line feature needs to be constructed, which is computed based on the tensor voting, then line datasets to extract line segments is generated. The next step is extracting consecutive line segment from the subset, which is conducted based on the persistence of tensor feature, using topological relations calculated by the persistent homology. Hence, the line structure extraction process consists of 4 steps:  
1. initialize the tensor for unstructured point and get geometric features in different dimensions;
2. revote the tensor feature in different dimensions and compute the refined geometric feature;
3. represent the geometric feature and construct the line candidate subset (referred to as _LCS_);
4. construct the Morse-Smale complex and extract line structures based on the discrete Morse theory.  
The pipeline of the line structure extraction framework is depicted in Figure 1.