esa-sceva/satcom-chunk-collection huggingface dataset:
# A Phase Angle-Modulated Bat Algorithm with Application to Antenna Topology Optimization
## 4 Experimental Results and Discussions
### Zero-One Knapsack Problems
** & **Best** & **Worst** & **Mean** & **std** & **Time** & **D/Opt** \\ \hline  & BPSO & **295.00** & **295.00** & **295.00** & **0.00** & **0.0105** & \\  & BBA & **295.00** & **295.00** & **295.00** & **0.00** & 0.0520 & D = 10 \\ \(k_{1}\) & AMBA & **295.00** & **295.00** & **295.00** & **0.00** & 0.0534 & Opt = 295 \\  & A-AMBA & **295.00** & **295.00** & **295.00** & **0.00** & 0.0553 & Opt = 295 \\  & P-AMBA & **295.00** & **295.00** & **295.00** & **0.00** & 0.0579 & \\ \hline  & BPSO & **1024.00** & 860.00 & 949.15 & 42.48 & **0.0149** & \\  & BBA & **1024.00** & **1013.00** & 1021.45 & **3.72** & 0.0756 & D = 20 \\ \(k_{2}\) & AMBA & **1024.00** & 1010.00 & 1021.70 & 3.96 & 0.0654 & Opt = 1024 \\  & A-AMBA & **1024.00** & 995.00 & 1019.30 & 7.66 & 0.0672 & Opt = 1024 \\  & P-AMBA & **1024.00** & 1004.00 & **1022.05** & 5.60 & 0.0706 & \\ \hline  & BPSO & 2854.00 & 2494.00 & 2699.60 & 92.71 & **0.0335** & \\  & BBA & 2976.00 & 2303.00 & 2827.60 & 158.28 & 0.1510 & D = 50 \\ \(k_{3}\) & AMBA & 2956.00 & 2840.00 & 2884.13 & **  
The optimal, worst, and mean values in Table 4 show that the higher the dimensionality of the optimization problem, the more prominent the optimization ability of P-AMBA. By comparing the optimization time of the five algorithms, although the BPSO algorithm took less time, it did not perform as well as the other algorithms on high-dimensional problems. With the introduction of angle modulation technology, the time consumption of the bat algorithms was greatly reduced. In a high-dimensional environment, the time consumption of the P-AMBA algorithm was only half that of the traditional binary bat algorithm. The comparison of the convergence curves in Figure 5 obviously shows that P-AMBA has a strong ability to optimize high-dimensional problems. After 500 iterations, a better solution set of optimal values was found by P-AMBA in comparison to the other optimization algorithms. Different from AMBA and A-AMBA, in P-AMBA, in addition to the effect of the amplitude amplifying the vertical displacement coefficient, new parameters were also introduced to adjust the phase of the cosine wave, thereby further affecting the disturbance of the function and adding more 0/1 combination possibilities.
## 3 Results and Discussion  
### Design and Realization of the TPU/GE Porous Structures  
TPU/GE composite powder was used in the SLS printing process in order to build three porous structures by using Schwarz, Diamond, and Gyroid unit cells. Moreover, an accurate study to understand the correlation between the offset factor (C) present in the Equations (1)-(3), and the structure porosity of the structure is reported in Figure 1. The porosity linearly decreases by increasing the offset value, and this enables design of structures in a range of porosities (from 40% to 80%).  
Porous structures, consisting of 3 \(\times\) 3 \(\times\) 3 unit cells, were SLS printed by using the TPU/GE composite powder with three different porosity values. Figure 2 clearly shows that the three different unit cells give rise to a different distribution of pores within the resulting 3D structure. In particular, the Schwarz unit cells bring about a structure with bigger pores, and so the trabeculae between pores (i.e., struts in the foams) are bigger. The structures generated by G and D unit cells present more pores with smaller dimensions, and consequently the trabeculae have a smaller size.  
The designed architectural features are preserved and porosity is almost unaffected by the fabrication process, as shown in Figure 2. These results clearly show the suitability of the TPU/GE powder to print porous structures with narrow pore size distributions and high pore interconnectivity by SLS manufacturing.  
Figure 1: Correlation between percentage of porosity and C value for Diamond, Gyroid and Schwarz-based unit cells architectures.
## 4 Discussion  
### Evaluation on Edge-YOLO  
The verification of the proposed lightweight object detection model Edge-YOLO is taken on a set of surveillance images captured by surveillance videos in the real airport apron environment. There are 20,885 marked frames of outdoor airport apron surveillance video, in different lighting conditions and weather conditions. There are 10 categories in this set, such as aircraft, people, and each kind of vehicles in the apron. The training set is made up of 80% frames of the set, while the test set made up of the rest 20%. Figure 11 shows some frames of the set as examples.  
The experiments to test the accuracy and inference speed to analyze the size and calculation amount of the model is taken on the cloud server and the edge device(Raspberry Pi 4B) respectively. Raspberry Pi is a series of small single-board computers (SBCs) developed in the United Kingdom by the Raspberry Pi Foundation in association with Broadcom. We can check its detailed introduction by [[https://en.wikipedia.org/wiki/Raspberry_Pi](https://en.wikipedia.org/wiki/Raspberry_Pi)]([https://en.wikipedia.org/wiki/Raspberry_Pi](https://en.wikipedia.org/wiki/Raspberry_Pi)) (accessed on 7 September 2022). The details of the experiment platform are shown in Table 3.  
Figure 11: Some sample pictures from the dataset.  
Figure 10: The number of frames detected by the cloud in different situations (one T frame segment).  
The related experimental results are shown in Tables 4 and 5. Compared with the baseline model YOLOv5s, the proposed lightweight model Edge-YOLO has achieved considerable improvements in detection speed, model size, and computational complexity. The accuracy loss of Edge-YOLO is no more than 5% and the loss will be further reduced by using Edge-YOLO-CA which is added the coord attention [27] module. Compared with the large-scale network SSD [19], the accuracy and detection speed of our proposed model have obvious advantages in the airport apron scene. Compared with the lighter models MobileNet-SSD [20] and YOLOv4-tiny [23], our proposed model achieves a large improvement in detection speed with close accuracy. Subsequently, we also proposed some strategies to further improve the detection speed of the model for airport apron operation videos and make up for the loss of detection accuracy.
## 2 Problem Description and Modeling  
In this section, we introduce a system model for task offloading in SEC. The detailed description of the SEC network, task, communication, and computation models is presented as follows. Table 1 provides explanations for the notations used in this paper.  
\begin{table}
\begin{tabular}{l l} \hline \hline  & \multicolumn{2}{c}{**Problem Descriptions**} \\ \hline \(n\) & Total number of satellites. \\ \hline \(v\) & Total number of applications. \\ \hline \(\mathbf{S}\) & Set of satellites. \\ \hline \(\mathbf{U}\) & Set of edge servers. \\ \hline \(l_{i,j}^{a}\) & Intersatellite link distance between satellite \(s_{i}\) and \(s_{j}\) at time \(a\). \\ \hline \(L_{a}\) & Matrix representing satellite communication topology at time \(a\). \\ \hline \(G_{i,j}^{a}\) & Route between satellites \(s_{i}\) and \(s_{j}\) at time \(a\). \\ \hline \(SG_{i,j}^{a}\) & Shortest route between \(s_{i}\) and \(s_{j}\) at time \(a\). \\ \hline \(\partial_{i,j}^{a}(d)\) & Communication delay for transmitting data of size \(d\) from satellites \(s_{i}\) to \(s_{j}\) at time \(a\). \\ \hline \(\mathbf{W}\) & Applications, each of which can be decomposed into several tasks with dependencies. \\ \hline \(w\) & Application, \(w\in\mathbf{W}\). \\ \hline \(\mathcal{G}\ =\ (\mathcal{V},\ \mathcal{E})\) & Directed acyclic graph representing data-dependent constraints. \\ \hline \(m\) & Task. \\ \hline \(T_{p}^{com}\) & Computation time for task \(m_{p}\). \\ \hline \(T_{p}^{vri}\) & The time that assigned edge server is available for executing \(m_{p}\). \\ \hline \(T_{p}^{ready}\) & The time that assigned edge server received all predecessors’ results of \(m_{p}\). \\ \hline \(T_{p}^{start}\) & The time when edge server starts processing \(m_{p}\). \\ \hline \(T_{p}^{finish}\) & Finish time of \(m_{p}\). \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations.
# An Efficient Adaptive Data-Link-Layer Architecture for LoRa Networks
## 3 LoRa Parameter Background  
To develop an efficient LoRa data-link-layer protocol, it is necessary to control SF, Bw, and other main LoRa parameters. This is performed by LoRaWAN to implement useful functionalities, such as ADR, which manages the TxP and SF according to the received packet SNR.  
LoRaWAN allows the selection of bandwidths in the range between 125 and 500 kHz for uplink packets. On the other hand, the range of available bandwidths for LoRa networks is larger, with values that can go as low as 7.8 kHz. The TxP impacts the link budget directly, and its value depends on the hardware implementation and the regional limitations, where its maximum value may range from 14 to 20 dBm. The spreading factor (SF), which can vary from 7 to 12, defines the number of chips per symbol, and each step up doubles the symbol period, as shown by Equation (1), where Bw is in Hz [19].  
\[Symbol\ period=\frac{2^{SF}}{Bw}\quad(s) \tag{1}\]  
For a LoRa node to receive a packet, it must perform CAD using the same SF and Bw of the sending node. This operation takes a time expressed in the transceiver datasheet [20] as a multiple of the LoRa symbol period.  
These parameters have impact on communication throughput, range, and energy consumption. As shown by Equation (1), using a higher Bw decreases the symbol period (and, consequently, increases the data rate), which allows us to increase the network throughput. A higher data rate decreases the time on air (ToA) for the packets, which means that less energy is spent to transmit them. On the other hand, the communication range decreases when Bw increases.  
A higher TxP provides a longer communication range but increases energy consumption. Therefore, this parameter should be controlled to adequately balance communication range and energy consumption.  
Increasing the SF increases the symbol period (Equation (1)), so the data rate is reduced. Equation (2), derived from [19], shows that a higher SF increases the processing gain of the receiver, and thus communication range is also increased. However, as seen in Equation (3), each step up of SF divides the bit rate approximately by half and, consequently, the ToA of each packet is roughly doubled. This means that more energy is required to send packets with higher spreading factors. Therefore, the SF is another LoRa parameter that can be used to balance the range and the energy consumption.  
\[Processing\ gain=10\times log_{10}\left(\frac{2^{SF}}{SF}\right)\quad(dB) \tag{2}\]  
\[Bit\ Rate=SF\times\frac{BW}{2^{SF}}\times CR\quad(pps) \tag{3}\]  
Other relevant LoRa parameters are the coding rate (CR) and header mode, which is largely application dependent. The CR improves communication robustness by performing forward error correction (FEC); its possible values in Equation (3) range from 4/8 (higher robustness) to 4/5 (lower robustness). The header mode can be implicit, implying a fixed payload length, or explicit, giving the freedom of arbitrary payload sizes.
## 3 Results and Discussion
### Headspace vs. Nosespace  
To perform an in-depth comparison between headspace and nosespace, a more thorough analysis of the two datasets was carried out. The headspace mass peaks used in this part of the analysis were a total of 105. These corresponded to mass peaks having a concentration higher than 1 ppbV and tentatively linked to a known coffee volatile compound (Table S3, Supplementary Material). These were sorted according to chemical class (e.g., pyrroles, carbonyls, sulfur compounds, etc.) and relative abundance. The same procedure was carried out on the 35 nosespace mass peaks. In this case, to calculate relative abundance, the area parameter was employed. The number of remaining NS mass peaks was in total 25, 28, and 27 for coffees A, B, and E, respectively. Coffee E contained _m/z_ 82.065 and 99.081 in addition to coffee A; coffee B also contained _m/z_ 98.064 in addition to coffees A and E.  
The comparison between the HS and NS datasets is represented by means of three circular graphs, one for each coffee, as shown in Figure 3. In each graph, the inner circle represents the NS data; mass peaks are labeled by the respective exact mass values, ranked first by chemical class and then by relative abundance within each class, with values increasing in an anti-clockwise direction. Similarly, the outer circles depict the HS data;  
Figure 2: Graphical output of the Principal Least Square Discriminant Analysis of the espresso coffee nosespace analysis. In the score, plot letters depict nosespace sessions, and highly influential variables (VIP scores higher than 1.5) are represented by means of the corresponding rounded-up masses. each peak is represented by a dot, and whenever the HS mass peak is present in the NS as well, the dot is filled, and a line joins the two data points. Empty dots represent instead HS mass peaks that were not detected in NS. Chemical classes are represented by means of the different coloring of the two circles. Figure 3 shows that chemical groups that are abundant in HS remain prevalent in NS as well. For example, Figure 3 shows that carbonyls are the chemical class having the highest number of entries in both the HS and the NS. Carbonyls were also the most abundant in terms of overall concentration, with more than 50% of the total peak area in both cases (Table S3). The relative abundances of the chemical groups in the HS followed the same order for coffee A and E (carbonyls, furans/pyrans, esters/acids, alcohols,  
Figure 3: Circular plots. For each coffee, the composition of the headspace and the nosespace are represented by the outer and inner circles, respectively. Masses are sorted according to chemical class and relative abundance. When the same masses are detected in both experimental modes, these are linked by a continuous line.
# A Secure and Efficient Multi-Factor Authentication Algorithm for Mobile Money Applications
## 3 Proposed Algorithm  
This section describes the proposed secure and efficient multi-factor authentication algorithm for mobile money applications where users are authenticated using a PIN, OTP, and biometric fingerprint. It also uses a biometric fingerprint and a secure QR code to confirm money withdrawal. Besides, authentication factors (identifiers) such as PIN and OTP are protected using SHA-256, biometric fingerprint by FIDO, which uses the public-key cryptography technique (RSA encryption), and Fernet encryption to secure a QR code and the records in the databases.  
The proposed algorithm for mobile money applications has three main phases, i.e., enrolment, authentication, and transaction. The symbols used in the algorithm are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbols** & **Meaning** \\ \hline \(U_{i}\) & User \\ \(FN_{i}\) & User’s first name \\ \(LN_{i}\) & User’s last name \\ \(SIM_{m}\) & SIM serial number \\ \(SID_{i}\) & Subscriber ID \\ \(PN_{i}\) & User’s phone number \\ \(PIN_{i}\) & User’s PIN \\ \(PIN_{j}\) & Re-entered PIN \\ \(OTP_{i}\) & User’s OTP \\ \(BF_{i}\) & User’s biometric fingerprint \\ \hline \hline \end{tabular}
\end{table}
Table 1: The symbols used in the algorithm.
# Helping CNAs Generate CVSS Scores Faster and More Confidently Using XAI
## 5 Experimental Design and Results
### Hyperparameter Selection  
While numerous hyperparameters are available for fine-tuning, only the ones that had an impact on the modeling results are provided here. The model parameters we experimented with are as follows:  
* Number of estimators: This determines the number of decision trees in the XGBoost model.
* Learning rate: This controls the step size during each iteration of model training. It influences the magnitude of the updates made to the model based on the estimated error.
* Preprocessing technique: This refers to the specific method used to manipulate the raw dataset. In our case, it is one of the three methods mentioned in the previous section.
* Max features selected: This defines the maximum number of features (in our case, words) to be included in the final dataset. The selection of features is based on the scores calculated by the chosen preprocessing technique.
* N-gram: This refers to a contiguous sequence of n items from a given sample of text or speech. The most common n-grams are unigrams (n = 1), bigrams (n = 2), and trigrams (n = 3), but n-grams of larger sizes can also be used. The goal of fine-tuning this parameter is to see whether word groups can help the model's decision making better than single words, as some words change meaning depending on context.  
Our approach involves executing numerous trial runs, each employing distinct combinations of these parameters, to pinpoint the most optimal configuration. This sequence of trials is commonly known as an ablation study, wherein the significance of specific components within a system or model is assessed by selectively removing or \"ablating\" them and evaluating the ensuing impact on performance.
## 2 Materials and Methods
### Extraction of RNA and Evaluation of _tps1_ Gene Expression  
The expression of the _tps1_ genes was examined using real-time quantitative PCR (qPCR) to observe potential changes in a _tps1_-deleted or -overexpressed mutant. _T. atroviride_ HB20111, \(\Delta\)_tps1_, and _Otps1_ were grown in PDA for 4 days. Subsequently, the mycelia was washed, ground in liquid nitrogen, and the RNA was extracted using the HiPure Fungal RNA Mini Kit (Magen Biotechnology (Guangzhou) Co., Ltd., Guangzhou, China). The DNase On Column Kit (Magen) was used to remove any residual DNA. This extracted RNA was reverse-transcribed using a 5\(\times\) Evo M-MLV RT Master Mix Kit (Accurate Biology, Changsha, China). The products were used as templates for a qPCR analysis with actin [30] used as a control reference gene. All the primers utilized for qPCR in this study are listed in Table S1. Real-time reactions were conducted with the BIO-RADcfx90 Thermal Cycler (Bio-Rad Laboratories, Hercules, CA, USA). Each 20 \(\upmu\)L reaction contained cDNA from 100 ng total RNA and 10 \(\upmu\)L SYBRGreen qPCR Master Mix (Accurate Biology, Changsha, China) and 200 \(\upmu\)M of each primer. Three replicates of each reaction were performed, and melting curves were generated to confirm the amplification results. The relative difference in expression between samples was determined by calculating the \(2^{-(\Delta\Delta\text{CI})}\) ratio between the CT value of the reference gene and that of the target gene.
###### Abstract  
The aim of the present study was to evaluate the possibility to extract, by supercritical fluids, nutraceuticaluels as polyphenic compounds, able in the prevention and in the treatment of a series of chronic-degenerative diseases, from plant matrices like the cactus pear. Supercritical fluid technology is an innovative method to extract nutraceuticaluls from natural matrices. This method offers numerous advantages that include the use of moderate temperatures, solvents with good transport properties (high diffusivity and low viscosity), and cheap and nontoxic fluids. Fresh cladodes from two different wild cotypes of _Opuntia focus-indica_ (L.) Mill. were extracted both with methanol and with SFE-CO\({}_{2}\) using different samples preparations, to maximize the % yields and the selectivity of extraction of polyphenols. The biggest contents of phenolics, evaluated by _Folin-Ciccalue_ assay, has been observed with the sample dehydrated of \(O\). _focus-indica_ cultivar that shows, as well, the best yield % (m/m) of extraction with both methanol and SFE-CO\({}_{2}\). Better results were obtained with the samples of \(O\). _focus-indica_ cult. (_OFI cult._), in spite of the \(O\). _focus-indica_ s.l. (_OFI s.l._); the two different cotypes of _OFI_ showed dissimilar phytochemicals profile. We noticed that the reduction of both quantity and quality of polyphenols was drastic with the increase of pressure at 250 bar; this shows that high pressures result in a loss of bioactive principles, like polyphenols. By changing the variables of extraction processes with SFE-CO\({}_{2}\) and by varying the preventive treatments of the natural matrices, it was possible to increase the selectivity and the purity of the products. Thus, the optimization of this useful and green technique allowed us to increase the value of the _Opuntia_ cladodes, a by-product very diffused in Calabria, which is an extraordinary source of nutraceuticaluls. These extracts could be used directly as functional foods or as starting material in the pharmaceutical, nutraceuticalul or cosmetic companies; they are safe and without any solvents traces and it is possible to obtain it in a few hours respect to the conventional extraction that requires longer extraction time.  
cladodes; _Opuntia_; nutraceuticaluls; antioxidants; polyphenols; SFE-CO\({}_{2}\); rutin; iso-quercitrin; nictoiflorin; narcissin
# MAS-YOLO: A Lightweight Detection Algorithm for PCB Defect Detection Based on Improved YOLOv12
## 3 Experiment and Analysis  
### Datasets  
In this study, two publicly available PCB defect detection datasets were selected: the Kaggle PCB Defects dataset and the Peking University Intelligent Robotics Open Laboratory public PCB defect dataset. These images were captured in industrial production environments and are annotated with six common defect types (as shown in Figure 3), including Missing hole, Mouse bite, Open circuit, Short, Spur, and Spurious copper, closely aligning with real-world PCB defect detection scenarios.
## 4 Numerical Results
Figure 2: Dimensionless frequency versus radial ratio for different Winkler coefficients. Figure 4: Dimensionless frequency versus thickness ratio for different variations in external electric potential. Figure 5: Dimensionless frequency versus thickness ratio for different variations in external magnetic potential. Another parameter that can be changed to control the stability of a system, however, is represented by moisture. As plotted in Figure 7, the frequency features a monotone decrease for an increasing \(r_{i}/r_{o}\) ratio under the same assumption for \(\Delta C\), whereas an increase in moisture can provide a slight reduction in the frequency response under the same assumption for \(r_{i}/r_{o}\). A more pronounced sensitivity of the frequency response is noticed in temperature. As plotted in Figure 8, an increased temperature variation seems to reduce the frequency response of the structure with a fixed geometry due to an overall reduction in its stiffness. The highest sensitivity, in any case, is observed for different structural constraints, as depicted comparatively in Figure 9 for clamped-clamped, clamped-simply and simply-simply supports, as well as different radial ratios, while keeping \(g~{}=~{}0\) and \((h_{c}/h_{t})~{}=~{}2.5\) fixed. Note that the use of clamped supports makes the structure stiffer and more stable such that it could support higher loads than simply supported structures. Figure 6: Dimensionless frequency versus power law index for different variations in radial ratio. Figure 7: Dimensionless frequency versus radial ratio for different variations in moisture.