esa-sceva/satcom-chunk-collection huggingface dataset:
# A Decision Support System for Crop Recommendation Using Machine Learning Classification Algorithms
## 3 Materials and Methods
### Evaluation Metrics Applied to the Different Algorithms  
We shall apply the performance evaluation metrics, i.e., sensitivity, specificity, precision, F-measure, AUC, training time, and testing time, on different machine learning algorithms used for crop classification. They help to analyze the performance of each method and improve the performance of our model by tuning.  
We can derive the confusion matrix from the multi-class model. The predictions in the confusion matrix are \(P\) (positive), \(N\) (negative), \(T\) (true), \(F\) (false), \(TP\) (true positive), \(TN\) (true negative), \(FP\) (false positive), and \(FN\) (false negative). Then, the common measures we can observe from the matrix are as follows:  
Accuracy: The accuracy rate is the ratio of correct predictions and total predictions, and it is used for identifying the performance of a model.  
\[\text{The Accuracy rate}=(TP+TN)/(TP+TN+FP+FN)\]  
Precision: The measuring of the ability of a model by finding the ratio between true positive instances and all positive instances.  
\[\text{The precision}=TP/(TP+FP)\]  
Recall: It evaluates the performance of classification models, particularly when the fast negative is costly. This means that the recall should minimize the number of false negatives.  
\[\text{The recall is}=TP/(TP+FN)\]  
F-measure/F1-score or F-score: We have considered the F-measure as a parameter here, as it is more useful than accuracy. Even though there are uneven classes of distribution, we can use the F-measure to measure performance.  
\[\text{F-Measure}=2*\frac{(Precision*Recall)}{(Precision+Recall)}\]  
AUC: We apply the AUC-ROC (Area Under the Receiver Operating Characteristic Curve) to visualize the performance of the classification models. We have obtained the AUC range to see the percentage of right predictions and wrong predictions in terms of a curve.  
Sensitivity, true positive rate, or recall: We have identified the fraction of correct prediction using the recall measure or sensitivity.  
\[\text{Sensitivity}=TP/(TP+FN)\]  
Specificity (or True Negative Rate): This measures the fraction of negative samples correctly identified by the model. It is defined as: specificity = \(TN/(TN+FP)\).  
Where TN is the number of true negative and FP is the false positives.
## 3 Proposed Methodology
### Multipath Link Routing Protocol (MLRP)  
MLRP comprises five various stages, namely, detecting neighbors, constructing topology, distributing pairwise keys, forming a cluster and transmitting data. Every process is described in detail below.  
Detection of neighbors and construction of topology: It is considered that every node holds an ID(ID\({}_{x}\)), certificate {CERT\({}_{x}\)}, public key {Kbs} and unique shared key {Kbs}. To identify the neighbors, node broadcasts and receives NBR DET packet containing its ID as well as its CERT as in Equation (1).  
\[x\rightarrow\ *:NBR\_DET|ID_{x}|CERT_{x} \tag{1}\]  
Figure 1: Proposed architecture.  
The node receiving the NBR_DET packet initially authenticates the ID of the node by verifying \(\text{CERT}_{\text{x}}\). When authenticated, the ID is added to the neighbor list by the receiver, or else the packet is dropped such that the unauthenticated node does not participate in this process of detecting neighbors. Once broadcasting is complete, neighbor information is forwarded to BS as in Equations (2) and (3).  
\[x\to BS:NBR\_INFO|ID_{x}|CERT_{x}|E(k_{xbs},NBR_{x})| \tag{2}\]  
\[MAC(k_{xbs},NBR\_INFO|ID_{x}|CERT_{x}|E(k_{xbs},NBR_{x})) \tag{3}\]  
An intermediate node that is receiving the NBR_INFO packet performs few functions such as:  
1. The authentication of the SN is verified by its certificate.
2. When the ID of the node is authorized, the packet is again broadcasted by the receiver node.
3. When the receiver receives a similar packet having a similar ID again, the packet is simply dropped.  
Hence, a table is maintained by every node termed as a receiver packet table. Thus, the network traffic is reduced and node energy is saved to some extent. Once the NBR INFO packet is reached at BS, BS verifies MAC for authenticity and integrity; then neighbor information is authenticated using \(\text{K}_{\text{xbs}}\) between SN and BS. MAC, obtained from the data and by encrypting \(\text{K}_{\text{xbs}}\), is used such that the intruder cannot either alter or spoof neighbor information.
## 2 Materials and Methods
### Physical Characterization  
Powder X-ray diffraction (XRD) spectra were registered between 4\({}^{\circ}\) and 70\({}^{\circ}\) on a Bruker-AXS, Germany using CuK\(\alpha\) (filtered) radiation (\(\lambda\)=0.154 nm) at 40 kV and 30 mA. Thermogravimetric analysis (TGA) carried out in TA instruments incorporated high-resolution thermogravimetric analyzer (series Q500) under N2 atmosphere to a maximum temperature of 800 \({}^{\circ}\)C. Differential scanning calorimetry (DSC) analysis was carried out using TA series Q 600 (USA) under N\({}_{2}\) atmosphere, with a heating rate of 10 \({}^{\circ}\)C min\({}^{-1}\). FT-IR spectra (KBr disc method) were recorded on a Perkin Elmer Spectrum 400. Transmission electron microscopy (TEM) was carried out at room temperature using JEM 2100F with 200 kV acceleration voltage. Energy-dispersive X-ray spectrometry (EDS) measurements were carried out with an Electron Probe Micro analyzer JED 2300. Magnetic measurements were performed at room temperature using microsense EV9 model vibrating sample magnetometer (VSM) with a maximum applied field of \(\pm\)2 T.
## 5 Simulations and Performance Evaluations
### Comparison with Other Optimization Algorithms  
As shown in Figure 11, we compare our algorithm with two other ones, including the Particle Swarm Optimization (PSO) algorithm and the Simulated Annealing (SA) algorithm. The convergence speeds of the 3 algorithms are measured to find the solution of our optimization problem with \(M=70\), \(R=6\), \(K=5\), \(\phi_{k}=2.4\times 10^{6}\) MBPS, \(s_{k}^{\text{C2 NBS}}=80\) MB/s, \(s_{k}^{\text{NBS2C}}=30\) MB/s, \(s_{k}^{\text{NBS2 NBS}}=s_{k}^{\text{NBS2B}}=30\) MB/s, \(\forall k\in\mathcal{K}\), while other settings are listed in Table 1. The simulation results show that although all of the 3 algorithm can converge after multiple iterations, our Genetic algorithm has the fastest convergence speed (our Genetic algorithm is converged at iteration 252, PSO algorithm is converged at iteration 281, and SA algorithm is converged at iteration 297). It demonstrates that our algorithm is more suitable for our optimization problem.
# An MMIC LNA for Millimeter-Wave Radar and 5G Applications with GaN-on-SiC Technology
## 2 Analysis and Design of MMIC LNA
### Device Characteristics
(R_{\text{d}}+R_{i}+\frac{1}{jw\text{L}_ {\text{gs}}}+jw\text{L}_{\text{s}})}\\ \hskip 28.452756 pt+\frac{(w\text{L}_{\text{S}})^{2}}{R_{\text{ds}}+R_{i}+\frac{ jw\text{L}_{\text{gs}}}{\text{L}_{\text{gs}}}+jw\text{L}_{s}}+\frac{g_{m}\text{L}_{ \text{S}}\frac{g_{m}\text{L}_{\text{gs}}}{\text{L}_{\text{gs}}}}}{\text{C}_{gs }. (R_{\text{ds}}+R_{i}+\frac{1}{jw\text{L}_{\text{gs}}}+jw\text{L}_When designing the IMN for LNA, it is necessary to try to make the S11 curve intersect the curve of Voltage Standing Wave Ratio (VSWR) = 2.0 curve on the Smith chart. As shown in Figure 5, introducing Ls causes the reflection coefficient to move towards the real axis and approach to the circle of VSWR = 2.0. By implementing the above measures, the values of the components used in the input matching circuit can be reduced, thereby minimizing the frequency offset caused by device errors. Figure 4: Simulated noise of GaN HEMT cell based on 50-ohm. Figure 3: A 2 \(\times\) 50 \(\upmu\)m CPW transistor characteristics versus source L\({}_{\text{S}}\). Figure 6 depicts the values of forward gain S21 and reverse gain S12 for a GaN HEMT cell without MNs design. It can be observed that within the operating frequency range, the forward gain exhibits minor attenuation, while the performance of reverse gain has been significantly improved. These results serve as a foundation for further refinement and optimization of the design, allowing engineers to make informed decisions and tradeoffs to achieve the desired performance objectives. Figure 5: Simulated reflection coefficient of GaN HEMT cell based on 50-ohm. Figure 6: Simulated S-parameters of GaN HEMT cell based on 50-ohm.
# Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements
## 2 Methodology
### Iterative Standard Deviation Filter  
The static standard deviation filter has low computational requirements; thus, it may be applied with multiple parametrisation at the same time. In contrast, the iterative standard deviation approach from [PERSON] (19), adapted by [PERSON] (2008) has higher computational costs due to a two looped application.  
The standard deviation within a point-wise moving temporal interval is calculated. A measurement point is considered to be an outlier if the value exceeds the range of more than 3.5 standard deviations within the interval. The point is replaced by a linear interpolation. Outliers will not be replenished if four or more consecutive values are detected. This procedure is repeated until no outliers can be found. With each iteration the standard deviation factor will be increased by 0.1.  
Appling both types of standard deviation filters imply the assumption of a Gaussian distributed filtering signal.  
Figure 1: Example of a staring mode LiDAR measurement in the \(u_{r}\)-\(\alpha\) diagram for a duration of 30 min in distances in the range of 361 m to 2911 m. (**a**) Blue points represent single measurements points, the red horizontal line indicates the lower CNR-threshold of \(-\)24 dB. (**b**) Visualisation of data density of measurement point distribution. Colours indicate different values of frequency distribution.
# Railway Axle and Wheel Assembly Press-Fitting Force Characteristics and Holding Torque Capacity
## 4 Results and Discussion  
### Comparison between the FEA and Theoretical Results  
To verify the effectiveness of the numeric model, the finite element results were compared with the calculated theoretical results. The contact pressure between the wheel and axle wheel seat at different sections was determined from Equation (1), and the average contact pressure was calculated by Equation (2). The contact pressure of the axle reached its maximum at the end of the contact edge. The distribution of the contact pressure and average contact pressure along the contact length of the wheelset is shown in Figure 8. The finite element analyses led to higher average contact pressure than the theoretical model because the volume of the 3D model used in the finite element simulation was greater than that of the theoretical model: the finite element results were 3% greater than the theoretical ones. At the start of the wheelset assembly, the two parts did not contact each other because of the taper of the axle. Therefore, the press-fitting force and contact pressure were zero until the end of the taper was reached, and then they suddenly increased. The theory showed that the contact pressure was directly proportional to the outer radius or profile of the wheel and increased in the centre of the contact region. The contact pressure of the axle increased sharply at the start of the contact and decreased towards the centre of the contact length and maximum at the rear end of the edge. The press-fitting force was calculated from the contact length using Equation (4). Figure 9 compares the finite element and theoretical results. The maximum press-fitting force at the end of the assembly was 877 kN from the finite element analysis and 822 kN from the theory, when the interference was 240 \(\upmu\)m. The 7% difference between the results was considered acceptable.  
Figure 8: Contact pressure distribution along the contact length.
## 2 Data  
### Data Collection  
The dataset utilized for this investigation comprises two distinct segments. The initial segment is formed by publicly disclosed reports from the Macau Environmental Protection Bureau, which furnish daily average concentration data for PM2.5 and PM10 spanning the years 2015 to 2023. The subsequent segment originates from the Macau Meteorological Bureau, which supplies hourly pollutant concentration and meteorological data for the period from 2020 to 2022. This dataset includes hourly records of PM2.5, PM10, sea-level air pressure (PSEA), temperature (TEMP), relative humidity (HUMI), wind direction (WDIR), wind speed (WSPD), gusts (WGUS), precipitation (PREC), and sunshine duration (INSO). The comprehensive nature of these records ensures the reliability and accuracy of the data.
## 1 Introduction
Other details of the communication protocol are abstracted as a typical digital modulation scheme and noisy communication channels; however, for complete system description, we also provide the decoding algorithm, based on modified message-passing flow. The detailed particular contributions of this paper can be listed as follows. 1. With regard to implementation, we show how the QC-RL-LDPC encoding can be efficiently implemented in a CPU device, by decomposing the encoding into simple cyclic shifts and GF(2) addition operations, in direct correspondence to the CPU program instructions. 2. We verify the designed CPU implementation of the encoder, with several QC-RL-LDPC matrix representations, measuring the encoding time and providing measurement results. Figure 1: Considered model of the IoT uplink communication with incremental redundancy ECC scheme. We provide a concise procedure for a QC-RL-LDPC binary code design, which is based on the PEG construction of base graph [16] of the HR code; an IR part optimization with reciprocal channel approximation, as in [13]; and then combined lifting of protographs with a proprietary computer search algorithm. 4. For the receiver side of the system, we provide an effective decoding scheme, based on LDPC belief propagation (BP) decoding, with incremental redundancy. 5. We perform a number of experiments of the entire system operation, providing the results in the form of simulated transmission goodput, which is defined as the ratio of the number of information bits transmitted to the total number of modulation symbols utilized in the IR, required for successful decoding. Several different designed QC-RL-LDPC codes with different code lengths are experimentally tested. Moreover, we also simulate rate-adaptive coding based on state-of-the-art 5G codes [17; 18] and compare them with our designed codes. 6. Taking into account the timing results and system simulation goodput results, we assess the energy consumption of the encoder plus radio transmitter in a typical IoT wireless transmission scenario, and provide the results in the form of energy consumption normalized per transmitted information bit, which allows for an energy efficiency comparison with a fixed-rate coding system, as well as with a 5G coding system. The experimental results show the energy efficiency gain that can be achieved in comparison to fixed-rate coding.
## 2 Materials and Methods  
### Materials  
CEM I 42.5 R ordinary Portland cement (OPC) was used to prepare the mixture specimens. The chemical compositions and physical characteristics of OPC are presented in Table 1. In this study, 0-5 mm river sand was used as fine aggregate. Crushed stone aggregate in the size range of 5-15 mm was used as coarse aggregate. The specific gravity of fine and coarse aggregates was determined as 2.64 and 2.60, and the water absorption values were determined as 1.10 and 0.92, respectively.  
To improve the workability of concrete mixtures, the third-generation polycarboxylic ether-based, highly water-reducing BASF Master Glenium 123 superplasticizer with a density of 1.13 g/cm\({}^{3}\) and a pH of 5.5 \(\pm\) 1 was used. In addition, 3 types of fibers produced from different geometries and materials were used in this study. The properties and images of the fibers are presented in Table 2 and Figure 1.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline
**Chemical Compositions** & **SiO\({}_{2}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **Fe\({}_{2}\)O\({}_{3}\)** & **CaO** & **MgO** & **K\({}_{2}\)O** & **SO\({}_{3}\)** & **LOI (\%)** \\ \hline \multirow{3}{*}{Physical properties} & 17.73 & 4.56 & 3.07 & 62.80 & 2.07 & 0.62 & 2.90 & 2.05 \\  & Specific gravity & \multicolumn{2}{c}{Insoluble residue (\%)} & \multicolumn{2}{c}{Fineness (cm\({}^{2}\)/g)} & & \\ \cline{1-1} \cline{2-6}  & 3.15 & \multicolumn{2}{c}{0.66} & \multicolumn{2}{c}{3450} & & \\ \hline \end{tabular}
\end{table}
Table 1: Chemical compositions and physical properties of cement.  
\begin{table}
\begin{tabular}{l c c} \hline  & **Steel Fiber (SF)** & **Basalt Fiber (BF)** & **Polypropylene Fiber (PPF)** \\ \hline Length (mm) & 35 & 24 & 12 \\ Diameter (mm) & 0.55 & 0.009–0.023 & 0.018–0.020 \\ Density (g/cm\({}^{3}\)) & 7.85 & 2.60–2.80 & 0.91 \\ Tensile strength (MPa) & 1345 & 4840 & 350 \\ Modulus of elasticity (GPa) & 210 & 89 & 3.50 \\ \hline \end{tabular}
\end{table}
Table 2: Properties of fibers.
## 3 Results and Discussion
### Oxidative Stability of Fried Peanuts Packed in the Developed Films  
#### 3.3.1 Hexanal Content in Peanut Samples  
The oxidative stability of packaged peanuts (P_ML and P_ML/LDPE_CAR) was determined by the measurement of hexanal content. This compound is related to the oxidation of linoleic acid and used as a rancidity indicator [4]. Figure 4 shows that the hexanal content increased during storage in P_ML peanuts. The higher hexanal relative area values were reached after 2 months of storage in ML bags. In P_ML/LDPE_CAR, the hexanal level was constant over the three evaluated months.  
As the oxidation process in peanuts took place in a larger extension when they were packaged into ML bags compared to ML/LDPE_CAR bags, the oxidation of ML/LDPE_CAR bags was analysed. \(\beta\)-carotene degradation leads to a bleaching of the colour and, thus, colour changes in films can also be used as a method to follow oxidation reactions. Then, the colour of the ML/LDPE_CAR bag was measured after 3 months of thermal exposition to evaluate film oxidation. The colour parameter \(\Delta\)E did not significantly change in comparison with the initial one, obtaining a value of 13.6 \(\pm\) 1.3. Therefore, as caroten films loss their strong colour when they are oxidised [34], we can expect that the shelf-life of developed films is longer than 3 months.  
In a similar work, [PERSON] et al. [37] packaged walnuts in LDPE films with atmospheric air at 21 \({}^{\circ}\)C. Hexanal content did not increase significantly until 5 months. Hexanal content in walnuts, which were in packages with an oxygen absorber, remained without major changes.  
A similar behaviour was also reported by [PERSON] et al. [38] for the neat poly(\(\epsilon\)-caprolactone) (PCL) films and materials containing almond skin extract as antioxidant, in which neat PCL showed higher hexanal content at the end of the study, which can be related to a higher rancidity of packaged food. Based on the obtained results, the addition of \(\beta\)-carotene into the packaging material promotes a lower extent of oxidation in fried peanuts compared to food samples packaged in control films, suggesting the potential of ML/LDPE_CAR films as sustainable and antioxidant food packaging systems to offer protection against lipid oxidation in foods.
## 4 Experiment and Results
### Networking Measures
#### 4.2.4 Trust  
The last metric that was calculated was trust, and it is given in Figure 15. The results show that trust reached a value of 10 for NSGA-II, which is higher than the around 8 for NSGA-III and around 6 for SC-MOPSO. However, considering that we selected a solution from the Pareto front, it is normal for one objective to be dominant over other objectives.
# Mobile-AI-Based Docent System: Navigation and Localization for Visually Impaired Gallery Visitors
## 2 Background of Analysis
### Guide Service  
The manual entry of numbers represents a common and straightforward method of accessing the information of the artwork, whereby visitors input the numerical codes displayed on the adjacent exhibit labels to retrieve complete details of the corresponding pieces [27]. For information delivery in cultural spaces, multiple approaches have been developed. QR code-based methods have gained popularity in various fields, with applications scanning these codes to display collection information [28]. These marker-based QR code technologies offer significant benefits, including low implementation cost, information-rich content delivery, and widespread smartphone compatibility [12; 28]. However, they present substantial limitations for visually impaired users, including visual obtrusiveness in gallery settings, institutional policy restrictions regarding their placement, and the interaction burden they impose on users who must first locate and then accurately scan these codes.  
Wireless communication technologies, particularly BLE beacons, provide alternative approaches for visually impaired individuals who cannot easily access visual information, such as artwork numbers or QR code locations. These systems deliver proximity-based information passively and can offer personalized content [12; 29]. For example, users can obtain artwork information via beacon advertising messages [29] or receive increasingly detailed information based on proximity to exhibits [6]. Despite these advantages, beacon systems face technical challenges including RSSI (Received Signal Strength Indicator) variability affecting accuracy, requirements for extensive infrastructure deployment, and the necessity for device-specific calibration [12]. Additionally, a significant limitation exists: information about artworks remains available even after users have left their vicinity, with no processing method to address this issue--creating potentially uncomfortable situations for visually impaired users receiving unnecessary audio information. Current assistive technologies for visually impaired individuals in cultural spaces predominantly offer fragmented functionalities--isolated route guidance, basic obstacle detection, or rudimentary audio descriptions--lacking comprehensive integration for meaningful engagement with artistic content.  
To address these limitations, we propose a new decent system offering comprehensive and in-depth guide services. This system utilizes real-time information about the user's location and direction to provide personalized audio guidance, simultaneously detectingobstacles along the route and dynamically modulating audio content delivery according to the user's circumstances. By integrating location awareness, obstacle detection, and contextual content delivery within a unified framework, our proposed system aims to transcend the fragmented functionality of existing solutions, thereby facilitating more meaningful and autonomous engagement with cultural spaces for visually impaired individuals.