esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Primary Market Design
### Analyzing Buyers' Behaviors
#### 4.2.1 Simulating Static Markets with Naive DA Mechanism  
Before investigating online markets, we analyze buyers' utility in static markets using the naive mechanism composed of a greedy allocation policy and a k-DA pricing policy. In the static markets, every agent \(k\) enters and leaves the market simultaneously as \(a_{k}^{p}=24(p-1),d_{k}^{p}=24(p+1)\).  
Figure 2a-c show buyers' surplus, resale profit and total utility, respectively. The x-axes of the graphs represent 15 buyers in the markets. A buyer with a larger number bids a lower value than its truthful valuation (i.e., \(\phi_{j}^{t}=v_{j}-2(j-1)\)). The graphs in each figure show the seven results obtained in distinctive market conditions.  
In the simulations, all the bids can be matched within the trading periods, because (1) sellers discount their asks monotonously to zero; and (2) quantities of supply and demand are always balanced in the markets. Thus, buyers' resale profits are constant, as shown in Figure 2b. Furthermore, buyers' surpluses are increased by buyers' strategic bidding, as shown in Figure 2a, because low-valued bids match discounted asks and yield large surpluses from their true valuation. Therefore, as shown in Figure 2c, we find that in static markets with a naive DA mechanism, buyers can obtain larger utility by misreporting their value than by bidding truthfully, especially in markets with high risk of trade failures (i.e., markets with a small \(n\) value).
## 3 Methods and Materials
### Model Architecture
#### 3.1.3 Extracting, Linking, and Mapping  
This phase consists of three main parts: topic modeling, similarity measures, and extracting expertise. In terms of topic modelling, this involved the clustering of authors and the extraction of the main area for each of them, which determined the field of expertise. This is followed by similarity measures based on three factors: keywords, publications statistics, and the research topic. The sections below will elaborate on these processes in detail. Figure 4: Process of collecting the academic publications. In the academic community, publications are used to express scientific progress. Additionally, most academic papers adopt common formats and structures; consequently, research projects have similar keywords that can be defined using a uniform topic-event structure. In order to find the similarities between authors whose writings engage with the same area, topic modeling was applied with the aim of clustering in order to identify relatedness among these individuals. Subsequently, in order to determine the author's expertise, the topic has been extracted from each paper and the mean value for each topic has been calculated to determine the most relevant topic for each author based on their publications. However, as keywords are considered to be short texts, this may induce a sparsity problem; thus, the non-negative matrix factorization (NMF) model is the most suitable and popular method for overcoming this problem, as it offers an unsupervised means of reducing the dimensionality of non-negative matrices [41]. Furthermore, in order to represent the matrix, it uses inverse document frequency (IDF) as a statistical measure of the terms present within a dataset, giving more weight to the words or terms that appear less frequently across the whole dataset or documents and assigning less relevance to more frequent terms. On the other hand, term frequency (TF) offers more weight to terms appearing in a single document, that is, the number of times a term appears in a document. The TF-IDF is the product of these two statistical measures. The TF is calculated as follows:  
\[\text{TF = f}_{\text{t}\text{d}}/(\text{number of words in d}) \tag{1}\]  
where d is the document under consideration. The IDF is calculated in the following manner [42]:  
\[\text{idf}(\text{t}_{\text{i}})=\log\frac{\text{N}}{\text{n}_{\text{i}}} \tag{2}\]  
where n\({}_{\text{i}}\) is the frequency of term t\({}_{\text{i}}\) in the total N documents. Additionally, NMF will view each paper as a mixture of various topics that are represented by a vector of weights over topics. Nevertheless, we only considered the topic with the highest weight as the main topic for that publication, which indicates the degree of the topic's relatedness to the author's publication. Figure 5 denotes the hierarchy of topic layers [43].
## 4 Discussion
### Performance Metrics for Evaluating the Platform's Effectiveness
#### 4.3.3 API Performance Metrics  
Table 2 presents the average response times for key backend operations within the SPARK-IT platform. These include user authentication, profile management, business project and proposal handling, and interactions with decentralized storage. The recorded times demonstrate that most API endpoints perform efficiently, with the majority of requests completed in under 200 ms, ensuring a responsive user experience even under typical operational loads.
## 3 System Design and Implementation
### Software Design
#### 3.3.3 Protocol Conversion Design  
For the conversion from Modbus AUDP to Modbus RTU, after the W5500 Ethernet controller receives Modbus AUDP data, it stores the data into the data reception buffer RX_Buffer in the STM32 via SPI communication. For UDP communication based on the W5500 Ethernet controller, the 8-byte header Figure 14 must be skipped before protocol conversion to enable data decapsulation and encapsulation. Upon entering the formal conversion process, the first step is to ensure the received data length is at least 18 bytes (8 bytes UDP header + 7 bytes MBAP header + at least 1 byte PDU + 2 bytes CRC). If the length requirement is met, retain the CRC from the received Modbus UDP frame, perform CRC validation on the remaining data, and compare the result with the retained CRC. If they match, proceed to the next step. Subsequently, extract the MBAP header (bytes 9 to 15), record the Transaction ID from the MBAP header, verify whether the protocol identifier (bytes 11-12) is 0, extract the Unit ID (byte 15) as the Modbus RTU address code, copy the remaining bytes after the Unit ID, and finally append a CRC to complete the conversion from Modbus AUDP to Modbus RTU. The converted data is then transmitted via serial port to the downstream device.  
For the conversion from Modbus RTU to Modbus AUDP, after the gateway receives the RTU response frame via the serial port, it performs CRC validation to verify data integrity. The RTU response frame is then decomposed into Slave Address + PDU + CRC Code. The CRC is removed, retaining the Slave ID and PDU. The Slave Address and the previously recorded Transaction ID are used to construct the MBAP header of the Modbus UDP. The PDU portion of the RTU frame is appended to the MBAP header, followed by a CRC calculation on the combined MBAP+PDU data. The resulting CRC is appended to form the complete Modbus UDP frame. This completes the conversion from Modbus RTU to Modbus AUDP, and the data is transmitted back to the upper-level device via the RJ45  
Figure 14: UDP data received by W5500.  
Figure 13: Schematic diagram of the Modbus AUDP reliability mechanism.
## 5 Analysis of HWMP Security Protocols  
### Ccmp and Bip  
Assuming that HWMP frames are treated as normal management frames, they can be protected according to the 802.11w confidentiality and integrity protocols. All of the HWMP frames (including the MF and NMF) will be treated as a frame body, and they will be encapsulated with a MAC HDR before transmission. Detailed information on CCMP and BIP can be found in the standards [8,19].  
Figure 11 explains how frames can be protected using CCMP. Only the frame body and the message integrity code (MIC) will be encrypted. CCMP HDR is the header that contains the information required for decryption and integrity checks, such as the packet number (PN), Initialization Vector (IV), and key ID. MAC HDR contains information such as the transmitter and receiver MAC addresses.  
BIP introduces the Integrity GTK (IGTK) and the integrity packet number (IPN), which are solely used in BIP. The use of IGTK and IPN is similar to the use of PTK and PN in CCMP. Figure 12 shows  
Figure 12: The working principle of BIP.  
Figure 11: The working principle of CCMP.  
how the frames can be protected using BIP. The management MIC Integrity Element (MMIE) contains the information required for the integrity check, including the IPN and the key ID.  
Table 10 lists the security services provided by CCMP and BIP. The only difference between them is confidentiality; BIP does not provide encryption. BIP is intended for broadcast frames, while CCMP is intended for unicast frames. However, the use of the protocols in this security context is interchangeable. We generalize the security services provided by both security protocols in Table 10, and we highlight the security services that are not addressed.
## 2 Materials and Methods
### Testbed Design  
The testbed shown in Figure 8 was integrated to measure the transmission power and frequency values and also to check the operation of the RABE prototype. The hardware devices and software used for the testbed implementation are: Keysigth(r) N9030A Signal  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layer** & **Type** & **Material** & **Thickness (mm)** & **Thickness (mil)** \\ \hline \multirow{3}{*}{Top} & Surface & Air & & \\  & Conductor plane & Copper & 0.0514 & 2.025 \\  & Dielectric & FR-4 & 0.2032 & 8 \\ L1\_PWR & Conductor plane & Copper & 0.0342 & 1.35 \\  & Dielectric & BT-epoxy & 0.1524 & 6 \\ L2\_SIG & Conductor plane & Copper & 0.0342 & 1.35 \\  & Dielectric & FR-4 & 0.2032 & 8 \\ L3\_SIG & Conductor plane & Copper & 0.0342 & 1.35 \\  & Dielectric & BT-epoxy & 0.1524 & 6 \\ L4\_PWR & Conductor plane & Copper & 0.0342 & 1.35 \\  & Dielectric & FR-4 & 0.2032 & 8 \\ Bottom & Conductor plane & Copper & 0.0514 & 2.025 \\  & Surface & Air & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: RABE PCB layers.  
Figure 7: RABE-printed circuit board. A 3D model of the RABE ((**a**) top layer and (**b**) bottom layer).  
Analyzer, personal desktop computer, Digilent/Xilinx(r) brand Zedboard test card, RF cables with SMA connector, ethernet cable, RABE prototype, control and programming software for AD9364 IIO Oscilloscope, and IP address monitoring software.  
The steps for running the testbed are as follows:  
1. Download and install the IIO Oscilloscope software on your PC.
2. Insert the RABE to the Zedboard through the FMC connector.
3. Connect the RF cable from the RABE SMA connector to the N9030A.
4. Connect the ethernet and USB cable between the PC and the Zedboard.
5. Run the IIO Oscilloscope software on your PC.
6. Use the IIO Oscilloscope to configure the transmission frequency, bandwidth, attenuation, and other parameters.
7. Configure the N9030A to perform a scan on parameter values close to those set in the previous step.
8. Save the data to the N9030A.
# Modelling and Assessment of a New Triple-Frequency IF1213 PPP with BDS/GPS
## 2 Materials and Methods
### Relationships in the IF1213 PPP Models  
Both the TF-C and TF-F models are derived on the basis of the IF1213 observation model. The TF-CF model is derived from the TF-C and TF-F models. The TF-C model is applied to GNSS with the time-varying part of the phase hardware bias of the satellite, but the TF-F model is not. The TF-C model parameterizes the part of the receiver and the satellite named PIFCB, as well as the code hardware delays of the receiver and the satellite named CIFCB, which contains the components of IFB. The TF-F model is applied to GNSS without the satellite PIFCB and ignores the effect of the receiver PIFCB. The TF-CF model is applied to a dual system, the first with satellite PIFCB and the second without it. The TF-FP model is applied to GPS, which corrects the \(pifc_{r,IF13}^{s,Q}\) in the TF-FP model by means of the IGMAS product. After the correction of the \(pifc_{r,IF13}^{s,Q}\), it is consistent with the TF-F model.  
The three IF1213 PPP models have different parameters to estimate. The TF-C model needs to estimate the IFCB parameters, the TF-F model needs to estimate the IFB parameters, and the TF-CF model needs to estimate both of them. In addition, the three models do not have exactly the same ambiguity parameters to be estimated. The TF-C model does not require the DCB product and IFCB product to correct the corresponding deviation terms. However, an increase in the number of parameters to be estimated may increase the convergence time.
## 3 Results
### Pearson Correlation Coefficient Analysis  
This section standardized the raw data into Z-scores for lower limb joint mobility (including hip joint, knee joint, and ankle joint), lower limb explosiveness (including jump time, ground reaction force, rate of force development, hang time, and jump height), and advanced layup success rates (including left-hand and right-hand crossover layouts, left spin and right spin layups, and left-hand and right-hand straight-line layups). A Pearson correlation coefficient analysis was conducted to explore their relationships, with the results being presented in Figure 2. The analysis revealed that, except for jump time, which showed a low correlation with advanced layup success rates, all other parameters of lower limb joint mobility and lower limb explosiveness demonstrated a moderate to high positive correlation with advanced layup success rates. When performing a layup, the lower the angle of the lower limb joints, the higher the resulting rate of force development, thereby enhancing the success rate of advanced layup techniques. These results demonstrated that lower limb joint mobility and lower limb explosiveness significantly influenced the improvement of advanced layup success rates. However, their impact was not absolute and might be further modulated by other factors, such as technical stability and neuromuscular coordination. The findings of this study validated the significant correlations between lower limb joint mobility, lower limb explosiveness, and advanced layup success rates. Furthermore, the results provided empirical support for the application of glyometric training in improving the technical performance of basketball players.
## 4 Discussion
### Relevance of the Study  
The findings of this study demonstrate an unprecedented opportunity for deriving reliable near-real-time information pertaining to parameters describing the water content in grasslands, presented by the interacting influence of environmental variables in concert with Sentinel-2 MSI data and the RF algorithm in the GEE. Such information is crucial for rangeland managers in understanding GWC variations across different seasonsas well as different ecological gradients. Since the grasslands in Vulindlela are utilized specifically for livestock farming, the findings of this study are a step towards generating spatially explicit information that could be used in understanding the spatial variability of rangelands through the two seasons so as to inform sustainable grazing management strategies. Furthermore, the seasonal estimations obtained in this study could also offer water resource managers information and insight pertaining to catchment variation in hydrological elements such as CSC that are directly linked to the hydrological system of these rangelands. This may be useful in understanding the hydrological cycle, particularly surface water resource systems critical for management planning, while contributing towards the achievement of United Nations' Sustainable Development Goals 1, 2, 6, and 12, which advocate for the provision of clean water, responsible consumption, and production, addressing hunger, and preventing climate change through carbon sequestration.  
In conducting this study, the winter data collection was performed before the driest month. This might have had an impact on depicting GWC variability across the two seasons. The grass species in the study area are mixed, although the dominant species is _Aristida junciformis_. The canopy geometry of these species is a critical component that could have affected the spectral reflectance of the GWC variables. This could probably explain the mismatch between LAI spatial distribution and that of CWC and EWT obtained in this study. Additionally, the varying pixel size from the different data sources used might have had an impact on the accuracy estimates obtained in this study. However, all GWC variables were estimated with acceptable accuracies regardless of the influence of field conditions such as differences in canopy structure from the grass species in the study area.
## 6 Performance Comparison of IEEE 802.11p and LTE-V2V
\begin{table}
\begin{tabular}{l l} \hline \hline
**Common Parameters (Symbol)** & **Value** \\ \hline Beacon frequency & 10 Hz \\ Beacon size & 300 bytes \\ Channel bandwidth & 10 MHz \\ Equivalent radiated power & 23 dBm \\ Antenna gain at the receiver & 3 dB \\ Path loss at 1 m & 47.86 dB \\ Loss exponent & 2.20 \\ \hline
**IEEE 802.11p** & **Value** \\ \hline MCS & 1, 3 and 8 \\ Carrier sensing sensitivity & \(-\)85 dBm \\ Noise power & \(-\)95 dBm \\ \hline
**LTE-V2V** & **Value** \\ \hline Noise power over an RB & \(-\)110 dBm \\ MCS & 4, 7, 12 and 20 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The main simulation parameters and settings. Figure 4: Packet reception ratio (PRR) vs. transmitter–receiver distance: comparison between IEEE 802.11p and LTE-V2V for different MCSs.
## 2 Study Area and Data  
### Study Areas  
Four study areas, including two sites in the United States and two sites in South Korea, were explored in this study (Figure 1). The two U.S. sites are located near Lamont, Oklahoma (Site 1: 97.89\({}^{\circ}\)W, 36.27\({}^{\circ}\)N) and Twitchell Island, California (Site 2: 121.65\({}^{\circ}\)W, 37.58\({}^{\circ}\)N), and feature two distinct climate conditions based on the Koppen-Geiger climate classification map [31]. Site 1 experiences a humid subtropical climate (Koppen climate map classified as Cfa) with hot, wet summers and cold, dry winters. The average temperature is around 16.3 \({}^{\circ}\)C and annual precipitation is around 880 mm. Twitchell Island is within the Sacramento-San Joaquin Delta approximately 100 km inland from the Pacific Ocean and a Mediterranean climate zone, with hot, dry summers, and cool, wet winters (Koppen climate map classified as Csb). The mean air temperature is 15.1 \({}^{\circ}\)C and the mean annual precipitation is 335 mm.  
Two sites in South Korea are Seolmacheon in Paju (Site 3: 126.95\({}^{\circ}\)E, 37.93\({}^{\circ}\)N) and Cheongmicheon in Yeoju (Site 4: 126.65\({}^{\circ}\)E, 37.15\({}^{\circ}\)N). The climatic characteristics of the two sites are similar. Both are of humid climate and hot in summer while cold in winter. Annual mean temperature of the sites is about 11.5 \({}^{\circ}\)C and annual precipitation is about 1150 mm. While Seolmacheon is located in a mixed forest, Cheongmicheon is located in rice paddy fields.
## 2 Background and Theory
### Feature Extraction Techniques  
This study utilizes seven distinct feature extraction techniques: namely, Katz, Petrosian, Higuchi, box-counting, MFDFA, DFA, and correlation dimension. This section provides an in-depth explanation of each method.
## 1 Introduction
Moreover, a path following control policy is designed by applying a single-parameter neural network, a finite-time auxiliary dynamic system, a higher-order tracking differentiator and a finite-time disturbance observer. The main characteristics of the proposed control scheme are summarized as follows:  
1. Considering the practical application requirements of path following control, some practical problems encountered in the process of vessel motion are taken into consideration, For instance, these problems include unknown external time-varying disturbances, deviation of vehicle model parameters and actuator saturation. The above problems are solved by disturbance observers, neural networks and auxiliary dynamic systems, respectively. 2. For the sake of reducing the complexity of the control policy, the following two measures are taken in this paper. (1) The higher-order tracking differentiator (TDS) is introduced into the backstepping controller, which reduces the number of derivations in the backstepping controller and overcomes the issue of complex calculation of the controller. (2) The single-parameter approximation strategy is used to approximate the vehicle model. Because the single-parameter neural network only needs to adjust one parameter online, the complexity and amount of calculation of the controller are effectively reduced compared with Paper [16]. 3. The rapidity of the control system is taken into account. (1) FCDO is devised to approach the external time-varying disturbance, which effectively speeds up the convergence speed of the proposed scheme. (2) A finite-time auxiliary dynamic system is adopted for the actuator saturation issue to further accelerate the convergence speed of the control system compared with Paper [17]. The rest of this paper is organized as follows: Problem formulation is put forward in Section 2. In Section 3, IALOS guidance law is utilized. In Section 4, the path following policy is presented. In Section 5, analysis of system stability is formulated. In Section 6, simulation results are presented to testify of the excellence of the control strategy. The full text is concluded in Section 7.