esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
### The Base Station Resources Are Optimized Separately  
In the existing research work, most of the documents are based on the mobile edge of single base station to compute resource management, including single user and multi user problems in single base station environment. Specifically, in Reference [13], the authors mainly consider the computing offloading problem of multi terminal users in single base station environment, which are not aimed at multi base stations environment. In Reference [8], the problem of single user computing offloading is considered, in which MEC resources are not limited. In Reference [15], a MEC system composed of mobile devices and heterogeneous edge servers supporting various wireless access technologies is studied. In Reference [16], the problem of partial offload scheduling and resource allocation in multi task MEC system is studied, and the problem of partial offload scheduling and power allocation in single user MEC system is proposed. In Reference [17; 18], although the authors take into account the computing offloading of mobile users in multiple cells,the main work is focused on the allocation of wireless and computing resources within one base station, without consideration of resource sharing and assistance between the base stations.  
The authors of Reference [19] studied the single terminal user scheduling its computing task into a number of smart devices in a number of adjacent environments, and the execution of the task depends on the coordination of multiple devices. The above research work does not focus on the mobile communication network environment, the proposed cooperation concept is limited to the end user centric collaboration, and there is no resource sharing and joint allocation among the intelligent devices.  
The architecture design of the future mobile communication system covers the cooperative function between the base stations. Through close collaboration, the base stations in the network can be virtualized as a resource whole and serve each end user flexibly in distributed computing and distributed storage. Therefore, the cooperation between base stations needs to be studied in depth. The technology of resource management improves the utilization ratio of the whole network resources and improves the service quality of users.
## 5 Experimental Results
### Simulation Setup  
The validation of the proposed model is executed in network simulator tool version 3.26. The hardware and software requirements for the purpose of simulation are provided in Table 4. The static sensor nodes, mobile sensor nodes and base station are deployed for the purpose of experimentation of the proposed model. The simulation of the proposed coverage hole detection and recovery is performed in 1000 \(\times\) 1000 environment. The parameters required for the simulation are illustrated in Table 5. The sensing range of the nodes is set to 35 m and the data acquired bytes are taken as 1 KB. The length of bits is considered as 450 bits. The Rmax refers to maximum sensing range and Pmax refers to power at maximum sensing range, respectively. The size of the coverage hole is taken as 80 m\({}^{2}\). The initial energy is of the sensor nodes is 1 J.
## 4 Classification of MAC Protocols Based on Channel
### Virtual Token Based  
This approach allocates different time slots to vehicles, thereby minimizing collision and providing efficient channel access. The protocol also provides minimized propagation delay with high throughput.
# Global Formulation of the Synthetic Storm Technique Oriented to Satellite Link-Budget Design
## 2 The Full SST and the Global SST
### The Global SST  
Let us recall the first version of the global SST. In [37], we found that the \(P_{SST}(A)\) generated by the SST can be approximately reproduced by calculating rain attenuation as  
\[A=\left[C_{o}k_{A}R^{\alpha_{A}}+(1-C_{o})k_{B}(3.134\times R)^{\alpha_{B}} \right]L^{m} \tag{2}\]  
where \(k\) and \(\alpha\) are the constants that give the specific rain attenuation in the two layers from the rain rate \(R\) measured at the ground, calculated at 20 \({}^{\circ}\)C in layer A and at 0 \({}^{\circ}\)C in layer B [1]; values obtained from [43]. The constant \(C_{o}\) is given by  
\[C_{o}=\frac{L_{A}}{L} \tag{3}\]  
For example, at Spino d'Adda \(C_{o}=0.877\) at \(\theta=20^{\circ}\), therefore, a fraction 0.877 of the attenuation is given by layer A and a fraction \(1-C_{o}=0.123\) by layer B. Finally, notice that \(L\) is _not_ an equivalent path length because it does not depend on radio electrical parameters but it is calculated from Equation (1). The parameter \(m\) is referred to as the average exponent of the global SST.  
Now, by knowing the annual probability exceeded by \(R\), \(P(R)\), from (2) we can calculate the rain attenuation exceeded with the same probability and, therefore, \(P(A)\), because of the simple transformation of the variable.  
Of course, there are significant differences between \(P_{SST}(A)\), obtained with the full SST, and \(P_{SST,glo}(A)\), obtained from Equation (2), as shown in the previous version of the global SST [37]. In particular, the value \(P_{SST,glo}(A>0)=P(R>0)\), is smaller than that calculated with the full SST, i.e., \(P_{SST}(A>0)>P_{SST,glo}(A>0)\), because of the physical relationship described by Equation (18) of [1].  
The aim of this paper is to estimate more reliable and global values of the exponent \(m\) as a function of the elevation angle (in 5\({}^{\circ}\) steps) and frequency (in 5 GHz steps). Our actual estimates refer to the sites listed in Table 2, for which we have calculated the average value of \(m\) and modelled it as reported in the next section.
## 5 Comparative Analysis of Traditional and Modern Methods
### LLM-Based Methods  
#### 5.3.1 Fake News Classification Using LLMs  
In [69], [PERSON], et al. fine-tuned the ChatGPT and Google Gemini models using the LIAR benchmark dataset. Google Gemini exhibited slightly better results compared to ChatGPT, as evidenced by higher accuracy, F1-score and superior performance on the AUC-ROC curve.  
The authors in [70] used a pre-trained BERT model and BiLSTM to detect fake content on the ArCOV19-Rumors dataset from Twitter and on the AraNews dataset and achieved an accuracy of 93.0% and 88.1%, respectively. They also applied the Att-BiLSTM model, which boosted the accuracy to 96.9% and 90.5%, respectively, in the two classification tasks.  
In [71] the BERT model was trained on different pre-processed data of the CMU-MisCOV19 dataset. The first case involved using the raw tweet data with an accuracy of 84.1% and the second using tweets without emojis with an accuracy of 87%. The third case uses tweets with emojis and achieves an increased accuracy of 87.5%. The authors also combined the BERT model with different socio-contextual information and normalization of numerical data in association with cleaned textual tweets, achieving an accuracy above 92%. The results demonstrate that incorporating socio-contextual information, including user and tweet information, enhances the model's performance.  
In the case of multi-modal content, the vision-enhanced framework of FakeNews-GPT4 [72] outperformed all the methods it compared against, such as MFND and HAMMER, showing a 7.7% increase in AUC when tested across different subsets, in the single-domain. In multi-domain the performance of FakeNewsGPT4 remains higher than PandaGPT and HAMMER. The authors employed the DGM benchmark dataset for their experiments.
## 5 Experiments and Results
### Audio Dataset and Computing Platform  
The audio dataset consisted of 982 CD-quality audio clips; each clip had a sampling rate of 44,100 samples per second, had one channel, and was 5 s long. All of clips were musical, from different music styles ranging from classical to big band and including Latin pop and Caribbean rhythms; no music style classification was explicit (The dataset is fully available in [27]). All experiments were carried out using the Matlab R2014b software (Mathworks, Natick, MA, USA) running on a workstation with an Intel Xeon processor at 3.2 GHz and 32 GB of RAM.
# Haystack Fires in Australia: Causes and Considerations for Preventative Management
## 5 Monitoring of Agricultural Parameters
### Microwave Sensing  
A microwave is electromagnetic radiation with an approximate wavelength of 1 mm to 1000 mm and a frequency in the range of 0.3 gigahertz (GHz) to 100 GHz [75]. Moisture measurements in the microwave region of the electromagnetic spectrum can occur in several ways, including transmission/absorption and reflectance [76]. Some microwave sensors can operate at a distance from an object of interest, while other types can be mechanically joined with it [77]. The potential for a material to absorb energy from certain frequencies increases proportionately with the dielectric constant. This allows the determination of water content relative to solids because the dielectric constant of fibrous solids is around 4, while that of water is around 80 [78; 79].  
Microwave sensing was shown to be very accurate (R\({}^{2}\) = 0.867) on a large square baler with maize material with up to 28.9% moisture content when comparing measured vs. actual moisture in the maize bales [80]. The dielectric constant relates field intensity in a space under vacuum to a time when the material is present. It is important to note that this constant varies with temperature, density and moisture content [79]. The antenna can then detect the waves that have reflected from the material or those which passed through to identify a change in frequency. Thus, the microwave system provides a non-intrusive measurement of the cross section of the bale. Though a surrounding metal structure will produce reflective interference, it has been found to be a constant linear relationship, allowing the error to be removed through calibration [81].  
[PERSON] [69] investigated a method utilising microwave reflectance with promising results for lucerne square bales but concluded that there was not enough data to verify the accuracy of this method. Microwave transmission and absorption is another type of microwave moisture measurement where two different types of antennas are engaged. Firstly, a transmitting antenna sends a microwave signal, while on the other hand, the receiver antenna determines the extent of microwave signal energy transmitted through. At the same time, absorption can be calculated and correlated with the moisture content of the given material.  
Factors such as the bulk density of the given material and the depth of the material through which the signal is transmitted may affect the sensor's accuracy. [PERSON] [69] built a microwave transmission system for square bales. While there was limited success, it was discovered that their design was not accurate or precise for lucerne, in one test the sensor showed no sensitivity to moisture when the sample's actual moisture ranged between 11% and 22% moisture.
###### Abstract
Although LDs have a larger modulation bandwidth than LEDs, LEDs are more suitable for medium-bit-rate applications due to their lower cost, higher power efficiency, and longer lifetime. LEDs were first employed for underwater illumination in the 2000s and are currently the industry standard. They are now found in a wide range of applications, from indicator lights to solid-state illumination, and are available in various shapes and sizes. Owing to their high optical power density, they have been used in underwater white light illumination to extend the underwater lighting distance. LEDs have gained considerable attention in recent years for data transmission applications, where LED luminaires are used to provide both general-purpose lighting and Mb/s or Gb/s optical wireless connectivity. If this technology is successfully implemented, every light bulb can be used to transmit wireless data, similar to a Wi-Fi hotspot, and significantly contribute to a greener, more cost-effective, and brighter future. In this work, the feasibility of LED-based UWOC, considering the Red Sea environment, was investigated. The Red Sea's most obvious characteristic that should be investigated is its high salinity, which can affect underwater communication. In light of this, an experimental investigation was conducted to determine how received signal intensity changed as water salinity increased. Moreover, as turbidity is the most important parameter and likely to completely block an LED-based communication system with a minor increase, its effect was also examined. The results presented here can serve as the starting point for further investigation into the use of UOWC in the Red Sea environment. The rest of the paper is organized as follows: Section 2 presents a literature review; Section 3 provides the characteristics of the Red Sea; and Sections 4 and 5 discuss the experimental setup and the obtained results, respectively. The study is concluded in Section 6, which also highlights future research directions.
# The Full-Field Path Tracking of Agricultural Machinery Based on PSO-Enhanced Fuzzy Standley Model
###### Abstract  
The unmanned operation of agriculture machinery in the full field of farmland is an important part of unmanned farm and smart agriculture. Although the autonomous navigation for agriculture robot has been widely studied in literature, research on the full-field path tracking problem of agriculture machinery is rare. In this paper, in order to enhance the adaptivity of path tracking algorithm, an improved fuzzy Stanley model (SM) is proposed based on particle swarm optimization (PSO), where the control gain is modified adaptively according to the tracking error, velocity and steering actuator saturation. The PSO-enhanced fuzzy SM (PSO-FSM) is verified by experiments on numerical simulation and self-driving of mobile vehicle. Simulation results indicate that the PSO-FSM achieves a better result than SM and FSM, where PSO-FSM changes the control gain adaptively under different velocities and actuator saturation conditions, and the maximum lateral errors of SM and PSO-FSM for mobile vehicle autonomous turning are 0. 32 m and 0.03 m, respectively. When the location of the mobile vehicle deviates from the expected path at 4 m in a lateral direction, the distance of the guided trajectory for the mobile vehicle to reach the expected path is no more than 5 m. A preliminary experiment is also carried out for a wheeled combine harvester working on slippery soil, and the result indicates that the maximum lateral tracking error of PSO-FSM is 0.63 m, which is acceptable for the path tracking of a combine harvester with a large operation width.  
agriculture machinery; autonomous navigation; adaptive path tracking; Stanley model +
Footnote †: journal: Applied  
1
Footnote 1: School of Agriculture Engineering, Jiangsu University, Zhenjiang 212013, China; [EMAIL_ADDRESS] (Y.S); [EMAIL_ADDRESS] (F.J.); [EMAIL_ADDRESS] (X.W.); [EMAIL_ADDRESS] (Y.Z.)  
X; [PERSON] The Full-Field Path Tracking of Agricultural Machinery Based on PSO-Enhanced Fuzzy Standley Model. _Appl. Sci._**2022**, _12_, 7683. [[https://doi.org/10.3390/appl2157683](https://doi.org/10.3390/appl2157683)]([https://doi.org/10.3390/appl2157683](https://doi.org/10.3390/appl2157683))
## 4 Case Studies: Using DSM to Address GPM Limitations in DPD
### Modeling a Process with DSM (PROVE)  
By comparison, an equivalent PROVE model is shown in Figure 12, relying on the analysis of Table 2. A detailed description of PROVE's ontology and modeling process is given in [24; 25] and is beyond the scope of this paper. We briefly note that in PROVE,  
Figure 11: Process description modeled in OPM using PROVE’s Development Process Design (DPD) ontology.  
Figure 10: PROVE composite-state construct represented using OPM.  
process descriptions are represented by rectangles; and objects in specific states are denoted by directed arrows from source process to target process, conveying the object and attribute using an \"object::attribute\" label (unspecified states/attributes are designated as \"0\"). Objects are uniquely identified by their name, and so are the states/attributes. The PROVE model includes 11 representational elements. The four processes that are identified in Table 2 are modeled by four rectangles. The objects that are identified in Table 2, in their specific states, appear as seven object-in-state flows (based on Input/Output designation): (1) ComponentA specification in Unspecified state flows is input to Manufacturing; (2) ComponentA in Produced state flows from Manufacturing (Output) to Functional Testing (Input); (3) ComponentA in Produced state also flows from Manufacturing (Output) to Environmental testing (Input); (4) ComponentA in Verified functional state flows from Functional testing (Output) to System integration (Input); (5) ComponentA in Verified environmental flows from Environmental testing (Output) to System integration (Input); note that ComponentA is the same object that is input to System integration with two different states obtained in parallel; in one process it was verified functionally and in another verified environmentally; (6) Software in Unspecified state is input to System integration; and (7) ComponentA in Software integrated state is the result (Output) of System integration. In total, this modeling requires 11 modeling steps (i.e., creating the entities and attaching them); significantly reducing the modeling effort (to less than a third, compared with OPM). Furthermore, since the number of representational elements is an indication of the diagrammatic complexity, the PROVE representation is preferable to the OPM representation in concisely communicating the process description.
# AI Optimization-Based Heterogeneous Approach for Green Next-Generation Communication Systems
## 3 Proposed SDR-Based LTE-LAA Architecture and Its Resource Management Scheme
### Energy Model  
The user's power consumption comprises two types: static and dynamic. The static power \(p_{j}^{st}\) is consumed by the user's smartphone to remain on and running without data exchange, and it is assumed to be constant. The dynamic power \(p_{j}^{dy}\) is consumed by the user's smartphone while communicating with the BS. This power depends on the number of used carriers and the distance of the user \(j\) from the BS \(i\). Hence, the user's total power consumption (in Watts) can be formulated as follows:  
\[p_{j}=p_{j}^{st}+\psi_{j}p_{j}^{dy} \tag{15}\]  
where \(\psi_{j}\) is a linear coefficient which represents the relationship between the data traffic load and the dynamic power.  
In this paper, our main focus is to reduce the dynamic power, which is dependent on the user and BS association. In line with the Friis transmission equation, the user's transmission power can be calculated (in Watts) as follows:  
\[P_{ij}=K\cdot\mu_{ij}\cdot x_{ij} \tag{16}\]  
where \(K\) is a constant and can be expressed by  
\[K=\frac{16\pi^{2}\cdot p_{r}}{C^{2}} \tag{17}\]  
where the speed of light is C and \(p_{r}\) denotes the minimal detectable power which is recognizable by the BS. However, \(\mu_{ij}\) is a dynamic term (with units of Hz\(\cdot\) m\({}^{2}\)) and depends on the assigned subcarrier frequencies and the measured distance between the user \(j\) and the BS \(i\). Accordingly, \(\mu_{ij}\) is formulated as follows:  
\[\mu_{ij}=\frac{d_{ij}^{2}}{G_{j}\cdot G_{i}}\cdot\sum_{z=1}^{t}f_{z}^{i} \tag{18}\]  
where \(d_{ij}\) is the measured distance, \(G_{j}\) and \(G_{i}\) are the antenna gain for the user and BS, respectively, t represents the total number of assignable subcarriers, and \(f_{z}^{i}\) represents the frequency of the assigned subcarrier \(z\) of a BS \(i\).
## 5 Experimental Validation and Parameters Identification  
In this section, experiments of passive and active dynamics are carried out for the identification of all key parameters, and the detailed procedure is shown in the Table 1. Moreover, the influences of boundary conditions on dynamic experiments were explored, and the distinct features that could be used for parameter identification were analyzed based on the test results. Thus, a simulation of active dynamics was carried out to validate the correctness of the identified parameters. The dynamics and corresponding distinct features according to the analysis of passive and active dynamics are summarized in Table 1.
## 3 Results
### Influence of Model Selection Criteria on Predictions and Complexity of Selected Models  
This section compares the prediction accuracy and the average number of variables selected by various variable selection approaches, each tuned using CV, AIC, and BIC criteria.  
Figure 4 shows the prediction performance for small (upper panel) and large (lower panel) sample sizes under low-correlation settings across various values of SNR.  
In small sample sizes, models tuned with BIC generally showed worse prediction accuracy than those tuned with AIC and CV across methods. An exception was observed with the BE and FS methods, where BIC and CV performed similarly. Overall, AIC and CV showed comparable prediction performance across most methods, with CV slightly outperforming AIC in RLASSO at low SNR, while AIC had a slight edge in the BE and FS methods.  
In large sample sizes, interesting patterns emerged. When the SNR was low-to-moderate (SNR < 1)  
Regarding variable selection, BIC selected simpler models than AIC and CV, which selected a similar number of variables (see Figure A7 in Appendix A).  
Figure A8 (Appendix A) shows the prediction performance under high-correlation settings. Again, AIC and CV showed comparable prediction accuracy in penalized methods, except in RLASSO, where CV outperformed AIC. In classical methods, AIC and CV produced similar predictions in small sample sizes, but AIC had a slight advantage in large sample sizes. Models tuned with BIC generally showed worse prediction accuracy compared to AIC and CV, particularly in large sample sizes (lower panel).
## 3 Application Community
### Application Community Detection Method
#### 3.2.1 Network Topological Partitioning Based on Communication Relationship
In order to verify this idea, here we use the NetFlow log collected on an enterprise boundary route, with <IP, Port> as the node, to calculate the \(|P_{i}|\) and \(|Q_{i}|\) values of each node. According to the well-known service port (20, 21, 22, 53, 80, 123, 443, 1080, 8080), we label the client and service nodes. The results are shown in Table 2, indicating that 94% of the server nodes satisfy \(|P_{i}|>|Q_{i}|\) and 98% of the client nodes satisfy \(|P_{i}|\leq|Q_{i}|\). \begin{table}
\begin{tabular}{c c c} \hline \hline \(i\) & \(P_{i}\) & \(Q_{i}\) \\ \hline
1 & \([2,3,4,5,6,7]\) & \([8]\) \\ \hline
2 & \([1,8]\) & \([3,4,5,6,7]\) \\ \hline
3 & \([1,8]\) & \([2,4,5,6,7]\) \\ \hline
4 & \([1]\) & \([2,3,5,6,7]\) \\ \hline
5 & \([1]\) & \([2,3,4,6,7]\) \\ \hline
6 & \([1]\) & \([2,3,4,5,7]\) \\ \hline
7 & \([1]\) & \([2,3,4,5,6]\) \\ \hline
8 & \([2,3]\) & \([1]\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: \(P_{i}\) and \(Q_{i}\) for each \(i\). Based on the above idea, the communication entities in a network (nodes represented by <IP, Port>) can be divided into different subgraphs. The specific process is shown in Algorithm 1, where the variable \(c\) is the subgraph number to be assigned. The vector \(C\) records the subgraph number, which each node belongs to. It skips if a node has been assigned a subgraph number. If the value of \(|P_{i}|+|Q_{i}|\) is very small (not larger than 2), the partitions formed by node \(i\) and its neighbors are small enough to constitute an influential subgraph. ```
Input: list of nodes Output: list of partition numbers to which the node belongs  Initialize all element values in \(C\) to \(-1\), \(c=0\)  For \(i\) in nodes:  If \(C[i]\)! = \(-1\):  continue  End If  calculate \(P_{i}\) and \(Q_{i}\)  If \(|P_{i}|+|Q_{i}|>2\) and \(|P_{i}|>|Q_{i}|\) : \(C[i]=c\)  For each \(j\) in \(P_{i}\): \(C[j]=c\)  End For \(c\) += 1  End If  End For return C
```  
**Algorithm 1** Network Relationship Division