esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
### Existing DSRC-Based VANET  
#### 2.1.1 Different Vehicular Communication Scenarios Using OpenFlow SDN Controller  
The Standalone DSRC IEEE 802.11P supports vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) safety communication. However, the DSRC communication range is relatively limited (1000 m), and it can only enable communication in high-mobility cars, up to 120 miles per hour. It does not provide multi-hop communication between vehicles. By analyzing various communication scenarios, the SDN controller aids in extending the range of communication [24; 25].  
Figure 2 depicts the architecture of the OpenFlow SDN controller to select the networks based on different vehicular communication scenarios. There are two modes of communication mentioned in the architecture, i.e., V-to-V and V-to-I. Mostly in V2V, communication takes place only within a limited 1000-m range. Hop-to-hop communication is supported with the help of DSRC communication, when all the vehicles should be in the range of 1 KM. In the above architecture, vehicle V2 can communicate with vehicle V4 with the help of V3. The range between V2 and V4 is 2 km. In this scenario, DSRC cannot provide direct communication. Therefore, hop-to-hop communication occurs between V2 to V3 and V3 to V4 [26; 27].  
The application layer, control layer, and infrastructure layer are all controlled by the SDN controller. Each layer performs its own set of tasks. The top application layer aids load balancing by filtering unauthorized car users through the firewall. The control layer is responsible for general control of the OpenFlow SDN, whereas the infrastructure layer contains switches and devices that connect vehicles and base stations. The flow table is used to keep track of many communication data, such as the distance between neighboring vehicles and the base station, as well as the direction and speed. Vehicle V1 can use the data in the flow table to seek a nearby network by examining the scenario. Vehicles can support two forms of communication: DSRC to DSRC with the use of a base station (BS) and DSRC to mobile networks hybrid communication (3G, LTE, WiMAX). Mobile networks, on the other hand, offer a wide choice of communication options but do not support significant mobility [28; 29].  
When the vehicle delivers communication over a distance of more than 1 km, the design shown in Figure 2 is used. As a result, Hybrid DSRC/WiMAX allows users to communicate across a distance of up to 10 km using RSU. Without an SDN controller, combining IEEE 802.11P and IEEE 802.16D is a difficult operation. However, the IEEE standards are not the same. Uplink and downlink communication use the same physical layer settings. The transmission of OFDM signals faces several hurdles, including a 10 MHz bandwidth, a common 5.9 GHz RF, and subcarrier matching. The DSRC 64 FFTsubcarriers may be equal to the WiMAX 256 FFT subcarriers in our proposed system if other modulation methods and padding bits are used [30; 31; 31].
# Edge-Computing Meshed Wireless Acoustic Sensor Network for Indoor Sound Monitoring
## 5 WASN Deployment Methodology
### Installation of the Core Node  
As the core acts as the gateway between all the sensor nodes of the local area network and the Internet, it has to be configured first to establish the connection to the rest of the sensors in a later time. To do so, the next steps must be followed:  
1. **Core assembly:** First of all, the heat sink must be integrated to the Rpi. Then, the Raspberry must be placed in the designed box. As this is the core node and will only be used as a gateway, this node will not need a microphone.
2. **Core wiring:** the Raspberry should next be plugged into a power source near the router to enable an Ethernet connection between the Raspberry and the router.
3. **Connection to Ethernet:** Once done, an Ethernet wire should be passed through a hole in the cover of the box and, next, must be connected to the RPi. The box can be closed with a M4 screw.
4. **Connection to the mesh:** The core must, then, create a private network and connect to it through WiFi (following the steps of [46]). Additionally, a static IP must be configured, enabling the nodes to send data to it.
5. **Connection to an external database:** The core node must be configured to send processed data to an external database (e.g., Thingspeak). The bytes sent to the cloud would depend on the application. For the proposed project in this paper (i.e., the generation of a noise map), the bytes to be sent would be the \(L_{eq}\) levels calculated in the rest of the nodes and a timestamp.
## 1 Introduction
In Japan [26], ultra-wideband (UWB) was considered as a potential choice for intra-satellite data connections, and several on-board sensing and data communication networks were proposed [27; 28; 29]. In this article, we study the internal communication system for ORS micro-satellites. We show that with the application of COTS devices in satellite design, data rates can be ramped up to hundreds of Kbps. In particular, compared with other on-board subsystems, the attitude and orbit control subsystem (AOCS) requires lower latency and higher sampling rates. Thus, time-critical communication is ensured. Besides, the number of nodes in the network will be dozens, and the network size is time-varying during different stages of the task. Although Zigbee has been applied in several on-board sensor networks [14; 30; 31], it only provides a finite data rate up to 250 Kbps which is much lower in real application [32]. As an alternative to the wired bus, it is more suitable for applications in pico-satellites and nano-Satellites, which require low data rate intra-spacecraft communication as defined by CCSDS [18]. On the other hand, a limitation of Bluetooth technology lies in the network scale. Usually, the maximum number of nodes in a Bluetooth network is seven. Techniques to increase the nodes number are available, however, they add additional complexity to the network [33]. To briefly summarize, the existing protocols are not appropriate for our project. Therefore, a dedicated wireless protocol needs to be developed. It is desired that such protocol provide an average data rate around 500 Kbps and meet the data delay requirements of the various on-board subsystems. Thus, we present an Intra-Satellite Wireless Network (ISWN), by which both wireless sensor and data transmission are accomplished. Using COTS components, we show that both the complexity and cost of the satellite are reduced, which ultimately increases its reliability [34]. A hybrid TDMA/CSMA protocol is proposed. With an improved channel assessment method and adaptive slot allocation mechanism, the hybrid protocol is expected to improve performance while saving energy. The article is organized as follows: Section 2 introduces the concept and structure of the ISWN. In Section 3, the physics layer hardware design is presented. Requirements analysis and scheme details of the MAC protocol are proposed in Section 4. Then, a Markov model and related calculations are given in Section 5. In Section 6, the simulations are carried out and discussions are presented. At the end, conclusions are given in Section 7.
## 2 System Model and Problem Formulation  
As shown in Figure 1, the system model consists of two users (a transmitter-receiver pair), two agents (one for the user, the other for jammer), which can sense the frequency spectrum by performing full-band detection every \(t_{s}\) with step \(\Delta f\). According to the sensing, agents guide the frequency choice of jammers or user respectively. The transmitter is equipped with one transmission radio interface that can communicate on one frequency band at one time. The communication frequency range of the transmitter is denoted by \(B_{u}\), and transmission signal bandwidth is \(b_{u}\). Channel selection set for transmitter is marked as \(\mathcal{A}_{u}=\{a_{1},a_{2},...,a_{N}\}\) (\(|\mathcal{A}_{u}|=N\)). The agent senses the whole communication band continuously and selects a communication channel over \(N\) (\(N=\frac{B_{u}}{b_{u}}\)) channels for transmitter through control link. The receiver is a full band receiver, it reports to the agent the quality of the received signal through control link or wire link.  
For the jammer, we use \(b_{j}\) to denote the jamming band for the jammer at one time, and a jammer can jam several frequency bands. The jamming frequency range is denoted by \(B_{j}\), we set \(B_{j}=B_{u}\). The number of the jammer's decision set can be calculated as \(M=\frac{B_{j}}{b_{j}}\). The action set of jammer can be denoted by \(\mathcal{A}_{j}=\{a_{1},a_{2},...,a_{M}\}\) (\(|\mathcal{A}_{j}|=M\)).  
To simplify the analysis, we divide the continuous time into discrete time slots (have shown in Figure 3), and assume that both jammer and user consume the same time \(\tau\) for making strategy and Assuming that the decision strategy of users and jammer remains unchanged during the time slot \(\tau\).
# Distributed Graph Diameter Approximation
## 1 Introduction
### Related Work  
#### 1.1.1 Exact Algorithms  
For general graphs with arbitrary weights, an approach for the exact diameter computation requires the solution of the All-Pairs Shortest Paths (APSP) problem. There are many classical sequential algorithms to solve this problem. On an \(n\)-node weighted graph, [PERSON]'s algorithm runs in \(\Theta(n^{3})\) time and requires \(\Theta(n^{2})\) space. On graphs with non-negative weights, running [PERSON]'s algorithm (with Fibonacci heaps) from each node requires \(O(mn+n^{2}\log n)\) time, which is better than \(O(n^{3})\) for sparse graphs. Also, there are some works improving on this latter \(O(mn+n^{2}\log n)\) bound. For undirected graphs, a faster algorithm with running time \(O(mn\log\alpha(m,n))\), where \(\alpha(m,n)\) is the inverse-Ackermann function, is presented in [3]. On unweighted graphs, [PERSON]'s algorithm can be replaced by a simple Breadth First Search, with an overall running time of \(O(mn)\). However, [PERSON]'s algorithm and the BFS are difficult to parallelize. In fact, one may run the \(n\) instances of [PERSON]'s algorithm or BFS (one from each node of the graph) on separate processors in parallel, however this approach is not applicable when the graph does not fit in the memory available to a single processor.  
An alternative approach to solve the APSP problem is to repeatedly square the adjacency matrix of the graph. Using fast matrix multiplication algorithms, this approach can be implemented in \(O(n^{2.3727}\log n)\) time [4]. By applying a clever recursive decomposition of the problem, we can drop the repeated squaring, saving a logarithmic factor in the time complexity [5] (pp. 201-206). The space requirement in any case is \(O(n^{2})\), which rules out matrix-based approaches in the context of large graphs.  
The drawback of approaches that solve the APSP problem is that they are either space inefficient or inherently sequential with at least quadratic running times, making them not applicable to very large graphs.
## 3 Data Denoising
### Threshold Determination (DE) Algorithm  
In signal reconstruction, handling inappropriate IMF components can cause signal distortion and reduce data accuracy. Therefore, dispersion entropy is introduced to assess the proportion of noise in high-frequency and low-frequency components of sample data.  
The DE algorithm is fast and considers the relationship between amplitudes, effectively addressing issues such as abrupt changes in similarity measures and discrepancies between amplitude means and values. The calculation process is as follows:  
1. Map the time series x, x= {x\({}_{i}\) = 1, 2, \(\ldots\), N} to y, y = {y\({}_{j}\)}, using a normal distribution function as shown in Equation (8), where uu represents the mean and \(\sigma\) represents the variance. \[y_{i}=\frac{1}{\sigma\sqrt{2\pi}}\int_{-\infty}^{x_{i}}e^{\frac{-(t-y)^{2}}{ 2\sigma^{2}}}dt\] (8)
2. Use linear transformation to map y to the range {1, 2, \(\ldots\), x} as shown in Equation (9), where R is the rounding function and c is the number of categories. \[Z_{j}^{c}=R(c\cdot y_{j}+0.5)\] (9)
3. Calculate the embedding vector \(Z_{i}^{(m,c)}\) as shown in Equation (10). \(m\) is the embedding dimension, and \(d\) is the time delay, where \(i=1,\ 2,\ \...,N-(m-1)d\). \[Z_{i}^{(m,c)}=\left\{Z_{i}^{c},Z_{i+d}^{c},\ldots,Z_{i+(m-1)d}^{c}\right\}\] (10)
4. Calculate the probability of each dispersion pattern, as shown in Equation (11), where Number(\(\pi_{v0,\ v1,\ldots,vm-1}\)) represents the number of embedding vectors mapped to the dispersion pattern. \[p(\pi_{v_{0,\ v_{1},\ldots,\ v_{m}}})=\frac{Number(\pi_{v_{0,v_{1},\ldots,\ v_{m}}})}{N-(m-1)d}\] (11)
5. According to the definition of Shannon entropy, the dispersion entropy value of the original signal x is defined as shown in Equation (12): \[DE(x,m,c,d)=-\sum_{\pi=1}^{c^{m}}p(\pi_{v_{0,v_{1},\ldots,\ v_{m-1}}})\cdot \ln(p(\pi_{v_{0,v_{1},\ldots,\ v_{m-1}}}))\] (12)  
The larger the dispersion entropy value, the higher the complexity of the time series; conversely, a smaller value indicates lower complexity. If the dispersion entropy value of the IMF components of the strain data is large, it proves that the complexity of the IMF components is higher and contains more noise signals. Conversely, it proves that the IMF component contains useful information from the original time series. Therefore, when the dispersion entropy values of adjacent IMFs change abruptly, it indicates a sharp increase or decrease in the noise content between the two IMF components. In this paper, the threshold point is denoted as M. The dispersion entropy values and the ratio of the dispersion entropy values of adjacent IMFs are calculated for each IMF component obtained from the EEMD decomposition.
# Low-Carbon Certification Systems in Agriculture: A Review
## 3 Existing Low-Carbon Certification Systems
### EU Countries
#### 3.1.3 LEAF Marque Standard (UK)  
The LEAF Marque Standard is a certification program developed in 2015 by LEAF (Linking Environment and Farming), a non-profit organization committed to promoting sustainable agriculture [53,54]. The standard provides a framework for assessing and recognizing environmentally responsible and socially sustainable farming practices. The LEAF Marque Standard sets out a checklist of criteria and requirements that farmers must comply with to obtain the certification [55]. These criteria include biodiversity conservation, soil and water management, energy efficiency, waste management, wildlife habitats, and social aspects such as worker welfare and community engagement (Figure 5).  
One of the key features of the LEAF Marque Standard is the implementation of strategies to reduce GHG emissions and enhance carbon sequestration in soils and vegetation.  
Figure 5: The Leaf Marque Standard framework based on the integrated farm standard principles.  
These may include practices such as agroforestry, cover cropping, and conservation tillage. By increasing the organic matter content in soils and promoting vegetation growth, farms can actively sequester carbon from the atmosphere, contributing to the mitigation of climate change. The LEAF Marque Standard also places a strong emphasis on soil health. Farmers are required to implement practices that maintain and enhance soil quality, such as minimizing soil erosion, promoting organic matter content, and managing nutrient inputs effectively. Soil health is crucial for long-term agricultural sustainability, as it directly impacts crop productivity, water retention, and carbon sequestration.  
Water management is another important aspect addressed by the LEAF Marque Standard. Farms seeking certification must implement strategies to minimize water usage, reduce water pollution, and manage water resources responsibly. This includes measures such as implementing efficient irrigation systems, monitoring water quality, and saving water through proper land management practices.  
Social responsibility is another integral component of the LEAF Marque Standard. Farms seeking certification are required to demonstrate fair treatment of workers, comply with relevant labor laws, and contribute positively to local communities. This includes promoting good working conditions, providing training and development opportunities for staff, and engaging with local stakeholders.  
The latest version, 16, of the LEAF Marque Standard that was issued on 1 October 2022 and became effective on 1 April 2023 incorporates advancements and updates to ensure that certified farms continue to meet the highest sustainability standards [54]. The standard, which is updated biannually, incorporates the latest research and technological advancements to ensure that certified farms are at the forefront of sustainable agriculture. Furthermore, v. 16 places increased emphasis on holistic farm management and the integration of sustainable practices across the entire farm system.
# A Novel Real-Time Match-Moving Method with HoloLens
## 4 Experimental Results
### Match-Moving with Cross Pattern  
The videos recorded in both scenarios were synthesized with a virtual scene as explained in the previous section. Furthermore, in order to find the match rate, we found the difference of the image frames extracted from the RGB-camera and the virtual camera. For this purpose, we extracted four frames randomly at the points marked in Figure 8. The frames extracted from the real scene are considered as ground truth and the virtual-real synthesis of the images obtained from both methods are compared, and the difference between the ground truth and synthesized images is calculated. For evaluation purposes, we used the MATLAB software (2018b, MathWorks, MA, USA).  
The feature point detection process can obtain stable results from images with edges and corners with high frequency changes. We present the results of match-moving (virtual-real synthesis images) between Boujou and the proposed method under given circumstances for the feature point detection process, as shown in Figure 10. In addition, to observe the match rate, we calculated the image difference between the ground truth and virtual-real synthesized images. As most parts of the compared images are identical, a binary mask was created to separate the region of interest from the background. For our purposes, the region of interest was only the cube in the recorded image frames with a resolution 200 \(\times\) 300 pixels. The mismatched pixels between the ground truth and virtual-real synthesized images are illustrated in white and indicated in Table 2.  
Figure 9: Accuracy evaluation of the camera trajectory extracted from HoloLens (**a**,**c**) and Boujou (**b**,**d**) method against ground truth (Oculus Rift).  
The lowest match rate at different frames for the first scenario was 96.77% and 94.38% for the feature point method and proposed method (PM), respectively. If we consider the uncertainty in size and distance between real and virtual cubes from the camera rig, the calculated difference between the ground truth (GT) and the synthesized images can be neglected. Therefore, in the first scenario, both the feature point (BM) and proposed method showed similar performance.
## 6 Case Study
### Workcell Performance Measures  
By definition the makespan is the total amount of time required to complete a group of jobs. In our case study we have five batch orders with an overall of 76 pump unit. The overall makespan of all the batch orders can be easily seen in Figure 12 which equals to 80 min. Table 1 is analyzing the different measurements which can be implied from Figure 12. The availability measure can be seen at the first row of Table 1. The availability is measuring the actual time which the operational resource is available to operate at the workcell with respect to the makespan. As it can be seen from the table, worker-1 and robot-1 were available all over the makespan. While worker-2 and robot-2 were available only 82.5%, and 71.9% of the makespan respectively.  
The idle period is the time when an operational resource is available but waiting for a task assignment. Apparently worker-2 was the least idle operational resource, while robot-1 was the most idle. The productivity of an operational resource is the number of units an operational resource produced with respect to the allover number of units produced by the workcell. It can be seen that worker-1 has higher productivity than worker-2, and robot-1 has higher productivity than robot-2. The final measuring factor is the average production rate. This measuring factor expresses how many units the operational resource produces per minute. As it has been mentioned before, this factor has been assumed to be constant for the robot however it is variable for the worker. It can be seen that worker-1 is much faster than worker-2, as the production rate of worker-1 can be rounded to 2.5 unit per minute while it is around 1.0 unit per minute for worker-2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Items** & **Worker-1** & **Worker-2** & **Robot-1** & **Robot-2** \\ \hline Availability & \(\frac{80}{80}=100\%\) & \(\frac{(80-14)}{80}=82.5\%\) & \(\frac{80}{80}=100\%\) & \(\frac{(80-22.5)}{80}=71.9\%\) \\ Idle Period & \(\frac{80}{80}=25\%\) & \(\frac{80}{80}=32.5\%\) & \(\frac{85}{80}=73.1\%\) & \(\frac{80}{80}=41.9\%\) \\ Productivity & \(\frac{80}{80}=65.8\%\) & \(\frac{80}{80}=34.2\%\) & \(\frac{85}{76}=56.6\%\) & \(\frac{83}{76}=43.4\%\) \\ Average & \(\frac{20}{23}+\frac{17}{23}+\frac{13}{8}=\frac{10}{24}+\frac{16}{32}=\) & \(\frac{10}{20}+\frac{16}{32}=\) & \(2.0\ U./\ Min\) & \(2.0\ U./\ Min\) \\ Production Rate & \(2.48\ U./\ Min\) & \(0.92\ U./\ Min\) & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Operational Resources Performance.  
Figure 12: Case-study Gantt Chart.
## 4 Performance Evaluation
### Experiments  
We implemented _HeaLow_ on off-the-shelf devices named _Samsung Galaxy Nexus_ with Android OS. Using the devices, we conducted experiments to analyze effectiveness of techniques used for _HeaLow_.