esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results and Analysis
### Monte Carlo Simulations
Figure 9: Detection probabilities of the ML bit synchronization algorithm under the CN0s of 20–40 dB-Hz and different frequency deviations. Finally, the performances of two algorithms are compared with different frequency deviations. Figure 11 is the detection probability without frequency deviation. The conventional maximum likelihood method outperforms the proposed bit synchronization algorithm which has gain attenuation. Figure 12 is the detection probability with a frequency deviation of 25 Hz and the performances of two algorithms are about the same. Then, Figure 13 is the detection probability with a frequency deviation of 50 Hz. It is clear that the proposed bit synchronization algorithm outperforms the conventional maximum likelihood method and the detection probability of the proposed bit synchronization algorithm is always higher than the conventional maximum likelihood method. Figure 11: Detection probabilities of ML and DCML bit synchronization algorithms under the CN0s of 20–40 dB-Hz with a frequency deviation of 0 Hz. Figure 10: Detection probabilities of the DCML bit synchronization algorithm under the CN0s of 20–40 dB-Hz and different frequency deviations. The results above show that the detection probability of the traditional maximum likelihood method attenuates seriously with the increase of frequency deviation and the ML method is even nullified when the frequency deviation is 50 Hz. By contrast, the detection probability of the proposed bit synchronization algorithm does not attenuate very seriously with the increase of frequency deviation. The detection probability of the proposed bit synchronization algorithm is always higher than the detection probability of the traditional maximum likelihood method. Figure 12: Detection probabilities of ML and DCML bit synchronization algorithms under the CN0s of 20–40 dB-Hz with a frequency deviation of 25 Hz. Figure 13: Detection probabilities of ML and DCML bit synchronization algorithms under the CN0s of 20–40 dB-Hz with a frequency deviation of 50 Hz. than the conventional maximum likelihood method when the frequency deviation is 50 Hz. Therefore, the proposed bit synchronization algorithm outperforms the conventional maximum likelihood method with large frequency deviation which is consistent with the theory analysis in Section 3.2.
# Efficient Layered Parallel Architecture and Application for Large Matrix LDPC Decoder
## 4 Results
### BER Performance Test  
After punching the last 1.5 columns of the parity-check matrix, the decoding performance of the 6-bit quantized decoder is tested by FPGA, the BER/FER curve shown in Figure 6 is obtained, and Table 4 is the conversion of the input BER with the corresponding \(\sigma\) and SNR. From Figure 6, it can be seen that at an input BER of \(2.3\times 10^{-2}\), \(11\) iterations were able to achieve an output BER lower than \(1\times 10^{-12}\) with a coding gain of about 10.4 dB, which is sufficient to meet the demand of optical communication for output error rate.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Design FPGA** & [19] & [20] & **PBP** \\  & **Virtex-7 VC707** & **XCVU125** & **Virtex-7 VC707** \\ \hline code length & (1280 1536 2048) & 17,664 & 17,664 \\ code rate & 1/2 2/3 4/5 & 57/69 & 57/69 \\ algorithm & NMS & NMS & NMS \\ FF & 13,274 & 675,854 & 76,012 \\ LUT & 32,560 & 537,504 & 144,086 \\ iteration cycle num & 73/109/181 & N/A & 96 \\ sub-matrix num & 60/96/156 & 275 & 275 \\ max iteration num & 6 & 12 & 12 \\ \(f\) (MHz) & 180 & 75 & 100 \\ throughput (Gbps) & 0.320 & 1.200 & 1.235 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Decoder FPGA Implementation Comparison.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Design Process** & [19] & [21] & **PBP** \\  & **TSMC 65 nm** & **TSMC 65 nm** & **TSMC 65 nm** \\ \hline code length & (1280 1536 2048) & 8176 & 17,664 \\ code rate & 1/2 2/3 4/5 & 7/8 & 57/69 \\ algorithm & NMS & NMS & NMS \\ area (mm\({}^{2}\)) & 0.819 & 2.3 & 2.610 \\ \(f\) (MHz) & 500 & 380 & 800 \\ throughput (Gbps) & 0.887 & 4.1 & 9.864 \\ TAR (Gbps/mm\({}^{2}\)) & 1.083 & 1.783 & 3.779 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Synthesis Result Comparison.
The main products of this type of analysis are maps of mean displacement rates that show the rate at which the specific Radar Targets (RTs) move. In addition, the entire considered time series, showing the cumulative displacement for every single RT, are obtained and shown in charts for more straightforward interpretation. Thanks to its morphology and moderate urbanization, providing many surfaces that generate RTs, we obtained a great density of RTs in the area of San Marco dei Cavoti. In the context of this study, the COSMO-SkyMed mission plays a pivotal role by providing the necessary SAR data for DInSAR analysis to monitor and assess surface deformations with great detail and accuracy. Additionally, an analysis based on the decomposition of the data obtained along the Line of Sight (LoS) in two components (horizontal and vertical) has been performed using methods already presented in the literature [30], which have shown good performance in the past [31; 32; 33]. Combining the results of these analyses made it possible to obtain valuable insights into the observed deformations, thus contributing to identifying areas that may require further investigation or intervention measures.
# Weight-Based PA-GPSR Protocol Improvement Method in VANET
## 5 Simulation and Performance Analysis
### Simulation Parameter Setting  
All simulations are based on NS-3.27. Using the Manhattan grid, as shown in Figure 8, the simulation area of \(1100\times 1100\) m\({}^{2}\) consists of 9 intersections and 12 two-way streets. We imported this data into SUMO to generate realistic movement trajectories. The initial position of the node obeys a random distribution, the movement of the node on the street is limited by the Krauss model, and the maximum speed of the node is 15 m/s. We generated several motion tracking files that were input into our NS3 module. Trace files were used in NS3 to generate different VANET communication scenarios. All vehicles had the same physical configurations. We implemented this approach using IEEE 802.11p MAC with a channel data rate of 3 Mb/s. The MAC layer did not have any QoS. The MAC layer used the TwoRayGround propagation model to measure the wireless signal fading characteristics.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Greedy Forwarding** & **Perimeter Forwarding** \\ \hline GPSR & O(n\({}^{2}\)) & O(n\({}^{3}\)) \\ MMGPSR & O(n\({}^{2}\)) & O(n\({}^{3}\)) \\ PA-GPSR & O(n\({}^{2}\)) & O(n\({}^{3}\)) \\ W-PAGPSR & O(n\({}^{2}\)) & O(n\({}^{3}\)) \\ \hline \hline \end{tabular}
\end{table}
Table 4: A time complexity comparison table.  
The number of vehicles varies between 30 and 110. For communication, it is assumed that each vehicle is equipped with wireless transceivers that comply with the IEEE 802.11p standard, which is applicable to multi hop propagation schemes. The maximum number of packets allowed to be buffered via the routing protocol is 64, and the maximum time allowed for buffering via the routing protocol is 30 s. The maximum communication radius of the node is set to 250 m. The location of the node can be obtained through accurate location services, and there is no wrong location information. At the beginning of each simulation, S and D are randomly generated and remain unchanged until the end of the simulation. At the beginning of each simulation process, nodes S and D are randomly determined. Node S uses UDP, and each connection generates a 512 byte packet every 0.2 s as a constant bit rate (CBR). Considering the impact of data traffic, we set the number of CBR connections to vary from 5 to 20 pairs to simulate VANET scenarios under different data traffic conditions [40]. The simulation of each scenario was run for 200 s, and all data results were averaged 30 times. According to CRITIC calculation results, during simulation, we set the greedy forwarding weight parameter to \(w_{1}=0.4\), \(w_{2}=0.3\), \(w_{3}=0.2\), and \(w_{4}=0.1\), and we set the perimeter forwarding weight parameter to \(\beta_{1}=0.6\) and \(\beta_{2}=0.4\). Other simulation parameters are shown in Table 5.
## 4 Sensitivity Analysis
### Effect of Electricity Cost  
Sensitivity analysis for the electricity cost is conducted because the energy cost occupies a large percentage of the total cost (Table 6), and the electricity cost varies widely within a given range. Figure 14 shows the effect of changes in the electricity cost on the product cost. The electricity cost varies between 0.08 and 2 $/kWh, and this range is referred from the previous studies [45; 61]. As shown in Figure 15, the effect of the electricity cost on the product cost is to be significant. The SMR is the most affected with a 24.1% difference, followed by dual nitrogen expansion (23.9%) and propane pre-cooled self-refrigeration (21.7%).
# AI Algorithms in the Agrifood Industry: Application Potential in the Spanish Agrifood Context
## 3 Materials and Methods
### Data Analysis Tools  
Given the extensive scale of the dataset under examination, the bibliometric study used a comprehensive suite of advanced techniques associated with big data and machine learning, meticulously adapted for the efficient management and analysis of large-scale datasets. Central to the methodology was the construction of a binary matrix based on author keywords, systematically categorizing terms into algorithm-related and application-related keywords. Prominent algorithms, including neural networks, deep learning, random forest, and support vector machines, were focal points of the analysis and were scrutinized in relation to specific applications such as crop yield prediction, soil management, food quality, and digital agriculture.  
Natural language processing (NLP) techniques were applied to the abstracts to identify and elucidate relationships between the algorithms and their respective application areas, utilizing regular expressions and NLP libraries for precise keyword mapping. Additionally, machine learning models, specifically random forest and deep learning, were used to detect patterns and predict outcomes within the dataset. Latent Dirichlet Allocation (LDA) facilitated topic modeling to uncover underlying thematic structures, while sentiment analysis using TextBlob 0.17.1 assessed the emotional tone of the abstracts, providing insights into the prevailing sentiments within the research community.  
The study also used hierarchical agglomerative clustering for bibliographic coupling similarity analysis, revealing distinct clusters of related research topics based on shared references. Co-occurrence matrices were generated to explore the relationships between specific algorithms and their practical implementations in the agrifood sector. To manage and visualize the complex data, several Python libraries--including Pandas 2.2.3 for data manipulation, Matplotlib 3.10.0 for visualization, and NetworkX 3.4.2 for constructing knowledge graphs--were used.  
These methodologies enabled a structured and scalable approach to extracting meaningful insights from a dataset composed of records with 47 attributes, including complex textual fields such as abstracts. The integration of knowledge graphs visualized international collaborations, while the combination of LDA and sentiment analysis provided a comprehensive understanding of both thematic and emotional dimensions of the research corpus.  
In summary, the study utilized a combination of ETL processes, bibliometric analysis, NLP, and machine learning models, topic modeling, sentiment analysis, hierarchical clustering, and co-occurrence matrices, supported by Python-based tools. This multifaceted analytical framework facilitated an in-depth and nuanced evaluation of the application of AI algorithms within the Spanish agrifood sector, highlighting trends, collaborations, and emerging technological strategies.
## 3 Results and Discussion
### Commercial Starter Culture Recognition  
In order to evaluate the ability of the commercial starter culture to dominate over the thermoduric indigenous milk LAB and to drive the fermentation process in presence of GPP, a total 101 isolates, collected from inoculated milk to final cheeses, were identified using a polyphasic approach based on microscopic inspection and RAPD-PCR analysis. The results of microscopic inspection confirmed that all isolates were cocci with cells occurring in long chains. The direct comparison of the polymorphic profiles of LAB isolated from pasteurized milk with those of _L. lactis_ strains originating from commercial starter LAB, allowed the assessment of the evolution of the added LAB strains (results not shown). This approach confirmed the dominance of the added _L. lactis_ strains, originating from commercial LAB starter culture, over thermoduric indigenous milk LAB. Moreover, these data confirmed those obtained by plate counts that showed the ability of commercial starter LAB to drive the fermentation process in presence of GPP.  
Figure 3: Dendrogram obtained with combined RAPD-PCR patterns generated with three primers for thermoduric indigenous milk LAB strains isolated from pasteurized milk. Abbreviations: _E., Enterococcus_; _L.c., Lacticaseibacillus_.
# An Approach to Data Analysis in 5G Networks
## 3 SELFNET Analyzer Module Design
### Analyzer Module Architecture
In addition, it is important to bear in mind that the easier implementation of the inference engine considers basic implication elimination rules (i.e., modus ponens rules) driven by propositional logic [56]. They can be adapted to different representations of uncertainty, such as fuzzy logic [57], rough sets [58] or Bayesian networks [26]. But in order to facilitate the understanding of this proposal, the current specification of rules on the SELFNET Analyzer applies only basic propositional logic rules (as described in Section 5), hence postponing for future works a most complex but generic definition. * _Memory:_ stores all the known facts Fa concerning with the use cases (ex. Temperature = 3\({}^{\circ}\), Latency >200 ms, etc.) considering those predicted/inferred \((Fa(PR),Fa(AT_{h}),Fa(Fit))\) and those provided by the SELFNET Monitoring/Aggregation stages \((Fa(T_{h}),Fa(KPI),Fa(Ev))\). Metadata related with qualitative additional information about the nature of the discovered facts is also stored. * _User Interface_: configures Patter Recognition PR for each use case and allows updating the knowledge-base by inserting, modifying or deleting data associated with every use case, such as objects O, rules Ru operations Op or prediction metrics Ft. The information is preprocessed aiming to ensure compatibility and coherence [59]. The latter is particularly important, as it tries to avoid contradictions and ambiguity between rules, prior to their incorporation into the SELFNET intelligence. * _Uncertainly Estimation_: complements the inference engine and facilitates the study of the conclusions bearing in mind their uncertainty. Its outputs are the acquired conclusions as potential symptoms of relevant incidences, their uncertainty and the information associated with their inference (facts, triggering rules, etc.). This is the only optional element of the architecture, since its use is only required when the SELFNET Diagnostic task [60] need to disambiguate conclusions, filter those of greater uncertainty or convert the logic on the Analyzer to data specified for upper layers of SELFNET. For example, when the inference engine operates on fuzzy logic rules, the element of Uncertainly Estimation generates a quantifiable result use-friendly for Diagnosis as crisp logic, given fuzzy sets and the corresponding membership degrees (i.e., defuzzification) [61].
## 4 Methodology and Algorithm Explanation
### Data Preprocessing Phase
#### 4.2.1 Dataset Splitting  
The process of dividing data into groups for independent model training and evaluation is called data splitting, or train:test splitting. The procedure comprises dividing the dataset into test and train sets, with equal percentages of development, testing, and cross-validation sets, and the biggest training set [27]. While the training dataset is used to build the model, the testing dataset assesses its predictive potential. Nevertheless, the train:test split ratios and dataset sizes might have a significant influence on the model's output and classification performance. Therefore, dividing data is a common technique used in deep learning [28]. The dataset may be split using a number of ratios, such as 10:90, 20:80, 30:70, 40:60, 50:50, 60:40, 70:30, 80:20, and 90:10 train:test splits. The testing dataset is used to evaluate the model's predictive capacity, whereas the training dataset is used to develop the model [29]. The Sleep-EDF dataset is split into two subsets for this work: the training dataset contains 70% of the total observations in the original dataset, and the test dataset contains 30% of the total observations.
# A Tunable Microstrip-to-Waveguide Transition for Emergency Satellite Communication Systems
## 4 Discussion  
The comparison between the previous literature and this work is shown in Table 2. Compared to the previous transitions, the proposed microstrip-to-waveguide transition provides significantly greater out-of-band suppression. Meanwhile, we have introduced the tuning screw to demonstrate the effectiveness of tuning capability in a transition. Though the tuning screw is widely used in filters, antennas, and shifters [32; 33; 34], it is not common in transitions. This advantage helps prevent amplified noise signals around the receiving frequency band from entering the receiving terminal, and thus eliminates the need for additional waveguide filters. As the amplified noise signals are uncertainly located within the receiving frequency band, we need to enhance suppression through tuning. Meanwhile, as the amplified noise signal is usually narrowband, there is no need to increase noise suppression within the entire receiving frequency band.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Refs.**} & **Operating Band** & \(\mid\)**S11\(\mid\)** & **Out-of-Band** & \multirow{2}{*}{**Tunability**} \\  & **(GHz)** & **(dB)** & **Suppression** & \\ \hline
[19] & 11.7–18.7 & \(<\)–10 & \(\sim\)20 dB @11.5 GHz & No \\
[20] & 8–14.5 & \(<\)–10 & \(\sim\)29 dB @7 GHz & No \\
[21] & 8.75–12.4 & \(<\)–13 & Non & No \\
[23] & 8.58–12.01 & \(<\)–15 & \(\sim\)8 dB @8 GHz & No \\
[26] & 12–18 & \(<\)–15 & Non & No \\
[30] & 33–36.7 & \(<\)–15 & \(\sim\)20 dB @32 GHz & No \\ This work & 14–14.5 & \(<\)–10 & \(\sim\)48 dB @12.55 GHz & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison between current transitions and this work.  
Figure 5: Measured and simulated S parameters.  
Figure 6: Simulated S parameters for different \(m3\) values.
# Radiator Enablers for Wireless Communication Evolution
## 3 Radiators' Novel Technologies, Design, and Implementation Issues
### Terahertz Antennas for 6G Applications
#### 3.7.2 System Requirements  
While THz antennas are envisioned to provide unparalleled capacity gains, especially for fixed-type communications, a key concern is present. Presently, the technology seems not to be mature enough to provide the necessary THz power sources needed for the wireless links. Indeed, in order to achieve the necessary improvements, we need to thoroughly manipulate already existing technologies that can give us the desired results.  
As observed by the authors of (ref. [132]), semiconductors, such as gallium arsenide (GaAs) and indium phosphide (InP) present numerous benefits over conventional silicon-based technologies, including higher electron mobility, enhanced breakdown voltages, and superior high-temperature performance. In particular, InP-based high-electron-mobility transistors (HEMTs) and heterojunction bipolar transistors (HBTs) are pivotal in THz circuit design, achieving maximum oscillation frequencies exceeding 1 THz. The use of the above semiconductors in combination with resonant tunnelling diodes, traveling wave tubes, and photonic techniques such as quantum cascade lasers are promising directions concerning THz applications, which are still plagued by cost, fabrication complexity, integration, and scalability problems. In fact, ongoing research is still conducted to improve on those issues.
# MQC-MB: Multiphoton Quantum Communication Using Multiple-Beam Concept in Free Space Optical Channel
## 4 The Implementation of MQC-MB
### Initial Phase
#### 4.1.1 Step 1: Preparation of Quantum Signals  
Alice converts the information \(X\) to a string of bits:  
\[Information\ X\Longrightarrow bits\in\{0,1\}^{n} \tag{16}\]  
\[\{0,1\}^{n}\Longrightarrow\left\{\begin{array}{ll}|0\rangle,&S_{out}=M_{HWP }(0^{\circ}).&S_{in}\\ |1\rangle,&S_{out}=M_{HWP}(90^{\circ}).&S_{in}\\ \end{array}\right. \tag{17}\]  
Figure 3: A system model of MQC-MB.  
where \(S_{in}\) is the Stokes parameters of the input light, which are illustrated as:  
\[S_{in}=\left[\begin{array}{c}S_{0}\\ S_{1}\\ S_{2}\\ S_{3}\end{array}\right]=\left[\begin{array}{c}1\\ 1\\ 0\\ 0\end{array}\right] \tag{18}\]  
where \(S_{0}\) defines the total intensity of the optical beam, \(S_{1}\) defines the linear horizontal or vertical polarized light, \(S_{2}\) defines +45\({}^{\circ}\) or \(-45^{\circ}\) polarized light, and \(S_{3}\) defines right circular or left circular polarized light [37; 38]. The simple rotation operator will only affect \(S_{1}\) and \(S_{2}\), while \(S_{3}\) remains unchanged. \(S_{out}\) is the stokes parameters of the output light:  
\[S_{out}=\left[\begin{array}{c}S^{\prime}_{0}\\ S^{\prime}_{1}\\ S^{\prime}_{2}\\ S^{\prime}_{3}\end{array}\right] \tag{19}\]  
Then, [PERSON] converts the string of bits as qubit through photon polarization. The procedure starts with [PERSON] transmitting the laser beam through a beam splitter that splits the beam into two paths, which are \(0^{\circ}\) and \(90^{\circ}\) polarizers. The light polarization is based on the parameter Stokes with linear polarization encoding, where \(0^{\circ}\) polarization indicates \(|0\rangle\) and \(90^{\circ}\) polarization indicates \(|1\rangle\).
## 4 Classification of Routing Protocols
### QoS-Aware Routing Protocols
#### 4.1.5 Energy-Aware Peering Routing (EPR)  
In [37] the authors presented an Energy-aware Peering Routing protocol aimed at reducing the network traffic and the energy consumption, based on both centralized and distributed approaches. It is designed to display the patients' real-time data inside a hospital. In this scheme, they have used three types of communication devices: Type 1--Nursing Station Coordinator (NSC), Type 2--Medical Display Coordinator (MDC), and Type 3--Body Area Network Coordinator (BANC). NCS is a centralized device with continuous power supply, which keeps the peering and type of communication information of all BANCs. MDCs are display devices with replaceable power supplies while the BANCs with limited energy, are responsible for collecting the data from the tiny bio-medical sensor nodes and forward it towards the corresponding MDC(s) after required processing. Initially, the BANC will try to access the NSC to get the peering and communication type (p-p or p-mp) information of MDC(s). After getting the required information, the BANC will discover the corresponding MDC(s) and display the data, as shown in Figure 7, redrawn from [37]. The energy efficiency is achieved by controlling the broadcasting mechanism of the hello packets. At the same time the selection of next hop node is based on the aforementioned device types, geographic information and residual energy of the neighbor.