esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Descriptive Analysis and TCATA Sensory Panel
#### 3.4.2 Formal Evaluations  
Formal evaluations were conducted over five sessions in individual sensory cabinets at the J. Lohr Sensory Lab of the Wine & Viticulture department at California Polytechnic State University, San Luis Obispo, in September 2022. All replicates were evaluated three times. Descriptive analysis and TCATA were assessed in separate sessions. For descriptive analysis testing, 30 mL aliquots of samples were served. All samples regardless of the sensory method were served in clear, ISO glasses with four-digit random codes, and an aluminum foil top at room temperature. Wines were served monadically either under red lighting (Luna 3 AO, 18:18W, Zaniboni Lighting, Clearwater, FL, USA) or for color assessment under daylight setting (Luna 3, 26:26W, Zaniboni Lighting, Clearwater, FL, USA). Order of serving was randomized according to [PERSON]'s Latin Square Design. The assessment used a 15 cm line scale anchored at 1 cm and 14 cm with the terms \"low\" and \"high\" respectively.  
To lower the bias of panelists when assessing the different modalities of the wine, color, and aroma were assessed separately. This resulted in six separate tests with 9 wines per test. The run order of the wines was randomized per test per session. Panelists assessed wines following the evaluation procedures described during training. Following each sample there was a forced palate cleansing of 15 s. A 10 min break was given halfway through each testing session.  
TCATA formal evaluations were conducted over three sessions separate from DA and evaluated in individual sensory cabinets. All replicates were evaluated three times. 15 mL aliquots of sample were served at laboratory temperature (18 \({}^{\circ}\)C) in ISO glasses with four-digit random codes, and an aluminum foil top. Wines were served monadically under red lighting (Luna 3 AO, 18:18W, Zaniboni Lighting, Clearwater, FL, USA). Order of serving was randomized according to [PERSON]'s Latin Square Design. Panelists assessed eighteen wines per session, following the evaluation procedure described during training, with a 5 min break after nine samples. Following each sample there was a required palate cleansing of 15 s. Unsalted crackers (Nabisco unsalted tops, premium salting crackers, East Hanover, NJ, USA) and water (Evian natural spring water, Evian, France) were provided for palate cleansing. All TCATA data collection was done in RedJade Sensory Software (RedJade, Silicon Valley, CA, USA). Panel performance was analyzed using the panel analysis feature for descriptive analysis and the TCATA function in the sensory package of XL STAT 2022.1.2 (Addinsoft, Paris, France) was utilized for agreement and repeatability during retronasal and mouthfeel assessment.
# Surface Defect Detection Model for Aero-Engine Components Based on Improved YOLOv5
###### Abstract  
Aiming at the problems of low efficiency and poor accuracy in conventional surface defect detection methods for aero-engine components, a surface defect detection model based on an improved YOLOv5 object detection algorithm is proposed in this paper. First, a k-means clustering algorithm was used to recalculate the parameters of the preset anchors to make them match the samples better. Then, an ECA-Net attention mechanism was added at the end of the backbone network to make the model pay more attention to feature extraction from defect areas. Finally, the PANet structure of the neck network was improved through its replacement with BiFPN modules to fully integrate the features of all scales. The results showed that the mAP of the YOLOv5s-KEB model was 98.3%, which was 1.0% higher than the original YOLOv5s model, and the average inference time for a single image was 2.6 ms, which was 10.3% lower than the original model. Moreover, compared with the Faster R-CNN, YOLOv3, YOLOv4 and YOLOv4-tiny object detection algorithms, the YOLOv5s-KEB model has the highest accuracy and the smallest size, which make it very efficient and convenient for practical applications.  
aero engine; surface defect detection; YOLOv5; attention mechanism +
Footnote †: journal: Applied
# 3D Point Cloud Generation Based on Multi-Sensor Fusion
## 6 Conclusions  
In areas where there is no GNSS signal, such as in the tunnels, relying solely on IMU calculation results in positioning and attitude errors rapidly accumulating beyond the limit. We proposed TMLS, a mobile laser scanning measurement system with a high-precision laser scanning sensor as the main body and integrated multi-type sensors mounted on rail cars. The method mainly integrated sensors such as a Z + F 5016 3D laser scanner, FSINS3X inertial navigation system, inclinometer, rail car and laptop computer in the TMLS, and developed related processing programs to realize the original point cloud. The decoding of the file generated Las data, the solution generated trajectory data, and the three-dimensional point cloud coordinates were solved. A 3D point cloud generation method based on multi-sensor fusion was designed. The method used the target as the control point, applied the RTS smoothing algorithm to correct the attitude angle and odometer speed of the IMU, calculated the trajectory data, used a high-precision inclinometer to correct the IMU roll angle, and used a recursive average filtering algorithm to optimize the three-axis attitude angle and suppress high-frequency jitter noise. Finally, 3D point cloud data were generated.  
Finally, the hardware system and the algorithm were experimentally verified in a test experiment. The 3D point cloud generation method based on multi-sensor fusion was verified in the track scene. The experimental data was scanned at a test site of about 260 m in Beijing, using a Z + F 5016 three-dimensional laser scanner and FSINS3X inertial navigation system. In the experiment, the car with the GNSS and IMU integrated navigation system installed was used to calculate and compare the trajectory and trajectory deviation before and after filter correction. The accuracy of the 3D point cloud generation method based on multi-sensor fusion was experimentally verified. Controlled at 0.037 m, the elevation error was controlled at 0.04 m.  
Our proposed method in this study can process the tunnel point cloud data more accurately and quickly and can effectively avoid the deformation of the tunnel and the sharp increase of the error.  
Conceptualization, Y.H. and H.S.; methodology, Y.H., H.S. and Y.L.; software, Y.H.; validation, Y.H., C.J. and H.S.; formal analysis, Y.H. and Y.L.; investigation, Y.H.; resources, S.X. and R.Z.; data curation, Y.H.; writing--original draft preparation, Y.H.; writing--review and editing, H.S., Y.L. and S.X.; visualization, Y.L.; supervision, H.S. and S.X.; project administration, R.Z.; funding acquisition, H.S. and R.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under grant number 42101444 and 42071444, in part by the General scientific research projects of Beijing Municipal Commission of Education under grant number KM202010028012, and Open fund of State Key Laboratory of Rail Transit Engineering Informalization (SKLK22-09).  
Not report any data.  
The authors declare no conflict of interest.
# A Comparative Evaluation of Transformers and Deep Learning Models for Arabic Meter Classification
## 4 Results  
The dataset used in this study is the MetRec data, comprising 55,440 verses and a total of 110,880 half-verses. There are no studies based on half-verses and transformer models. The data are split into training, validation, and testing sets in a 60:20:20 ratio. The class labels are encoded numerically from 0 to 13.
## 1 Introduction
In these circuits, the common role of the fundamental resonators was to provide a dual-band performance and a compact size; however, the design approach was complex. In [45], the stepped-impedance stubs were coupled together to create a single and a dual-band BPF, resonating at 2.5 GHz and 3.5 GHz. These topologies suppressed the unwanted bands up to the 3 rd harmonics; however, the attenuation in the stopbands was suboptimal. In a different design technique, artificial-intelligence-based approaches, such as particle swarm optimization, neural networks and ant colony, have recently been used to design microstrip filters as well as other microwave and electromagnetic components [46; 47; 48; 49; 50; 51; 52; 53]. A simple topology of a dual-band BPF using a hybrid structure was developed in [54], where the passbands could separately be shifted at a price of increased passband insertion loss. In [27], a wideband balun BPF was designed using microstrip-to-slot line transition structures and some stub-loaded resonators. To realize a tunable dual-band BPF in [55], a loop structure coupled with bended stubs was loaded by square and T-shaped stubs. It could also be used in LPFs for extending the stopband [56; 57; 58; 59]. Various design approaches for dual-band BPFs have been reported in the open literature. Most of them, however, focused on one or some critical properties at the price of other critical aspects. In this paper, a straightforward yet systematic design approach which can sufficiently address all (or most of the) critical properties required for narrowband dual-band BPFs is presented. The proposed dual-band BPF provides properties of very good inter-band isolation level, strong attenuation in the stopband and low fabrication cost in a compact size. It can also provide fully tunable passbands, which can be independently adjusted by only one parameter, reducing the complexity of re-tuning the proposed filter for other possible applications.
# Adaptive Parameters for LoRa-Based Networks Physical-Layer
## 2 Related Work
The authors explore the orthogonal characteristics of different frequency carriers to elaborate an algorithm that periodically measures the SNR signals from the 433 MHz and 915 MHz frequency bands. After evaluating the measurements, a primary node decides between changing the frequency band of all network nodes for the best one or _staying_ on the current frequency, according to the SNR values of those channels. However, the obtained results did not show a significant change, as the average improvement on SNR was 4.68% with an elevated _number_ of carrier changes. The high frequency of change is a consequence of the decision-making strategy based on the instant measurements of SNR on both bands. During a measuring time, an impulsive noise (i.e., caused by another transceiver) could bring a low distorted SNR value compared to the historical average. In this scenario, the proposed algorithm could make a bad decision due to the impulsive noise on the communication medium. [PERSON] and [PERSON] enhance LoRa networks through a practical approach for signal carrier changes. Contrasting the methodology presented in our previous work [PERSON] and [PERSON] [14], this article proposes the utilization of an SNR measurements history and a decision maker to decisions based on the average SNR sample instead considering only the last one. Thus, there is a probability of lowering the error rate while deciding due to the filtering effect of the sliding average. Our proposal is simple and presents a linear computational complexity in terms of processing and storage, allowing it to be implemented in resource-constrained IoT nodes. The validation experiments complement the state-of-the-art once a real environment is deployed. Table 1 summarizes related works based on their optimization goals, and if they (or do not) consider characteristics of energy consumption, application requirements, and targeting LoRa modulation parameters (Spreading Factor--SF, Transmission Power--TP, BandWidth--BW, and Coding Rate--CR). The optimization goals addressed in these studies contribute to improving system performance in terms of increasing of the QoS level by reducing collisions. From the analysis of the state-of-the-art, we identified that there is a lack of studies that exploit the reduction of inter-symbolic interference in communication channels in LoRa networks.
# Inter-Annual Variability of Peatland Vegetation Captured Using Phenocam- and UAV Imagery
## 1 Introduction
### Phenological Monitoring
#### 1.3.2 Limitations of Current Remote Sensing Approaches  
Phenology metrics derived using remote sensing are sensitive to the resolution of data employed [45; 46; 47; 48], which is an inherent problem [49; 50]. At the ground level, phenology is strongly heterogeneous, both spatially and temporally. Field measurements in a deciduous forest, for example, found that the onset of greening varied by as much as 2 weeks overdistances <500 m, despite stands having similar composition, age and structural characteristics [51]. Such fine-scale heterogeneity (e.g., differences due to micro-topography or species type) is not captured by the relative coarseness of satellite data pixels, which commonly employ spatial resolutions > 30 m. This 'coarse' spatial resolution can be problematic as it masks a large amount of heterogeneity and pixels contain mixed spectral signals [47]. As such, satellite data are often not comparable with the plot scale (0.1-1 m\({}^{2}\)) at which many flux measurements are conducted, which makes it difficult to directly compare datasets. The temporal coverage of phenology datasets is also important. For example, differences in the temporal resolution of satellite data products (e.g., comparing the MODIS 8 day and twice daily vegetation index timeseries) were found to cause a discrepancy of over 30 days in the timing of inferred phenological stages in a deciduous forest [52].  
Phenocam data, although advantageous in their high temporal resolution, are spatially limited. The majority of peatland phenocam studies examine greenness averaged across a single region of interest (ROI), spanning multiple species with varying phenology; whereas few have examined phenology at the community or plot level, and related this to photosynthetic uptake of CO\({}_{2}\)[53; 54]. Furthermore, phenocams are consumer-grade cameras, and hence the acquired images are neither spatially referenced nor spectrally calibrated. As such, phenocam-derived phenology metrics are hard to scale and compare, for example with satellite data.
# A Review on Anaerobic Digestion of Lignocellulosic Wastes: Pretreatments and Operational Conditions
## 4 AD Stability or Inhibition
### Sulfide  
In the anaerobic process, sulfate is reduced to sulfide by sulfate-reducing bacteria [156]. The produced sulfide could inhibit the process, either by toxicity from the competition for a common organic matter substrate that cause stress for methanogenesis, or through the toxicity of sulfide to various bacteria population [127; 157; 158].
## 4 Results and Discussion
### Future Work  
Based on these results, we plan to continue our experiments in several different directions. In the future, we plan to either further validate our work, either with the C/C++ datasets [31; 32] discussed in the survey literature or other frameworks--after all, the underlying concept of our methodology does not depend on the specific programming language and framework--or fine-tune it to explore further areas of research, such as navigation, as a result of which we might at some point even enable a new kind of dynamic, artificial-intelligence-based visual description language for code bases where this has so far proved impossible.  
Another interesting area could be the testing of alternative sensitivity and protection scales. As the current scales have been highly subjective and focused on the usability of the methodology through logical reasoning and construction, we plan to test more domain-specific definitions in the future, for example, tailored to our own healthcare applications.  
Since it has been confirmed that GPT is capable of detecting weaknesses and vulnerabilities that previously required human intervention due to contextual interpretation, it is now worthwhile to attempt to automatically detect many other vulnerabilities in the CWE database using large language models along similar lines.  
As GPT APIs become more lenient with token limits, concerns about potential overruns diminish. It should be remembered that high token counts also increase costs. Also, if a research group might want to test our methodology on smaller LLMs that can be run locally and trained further, a significantly lower token limit should be expected. Thus, although the use of large prompts has been shown to be necessary to achieve the results presented, further research may include minimizing the size of the prompts.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **GPT-4 Original** & **GPT-4 Strict** \\ \hline Detected Vulnerability & 40 & 79 \\ False Vulnerability & 0 & 0 \\ Undetected Vulnerability & 49 & 10 \\ Undetected High- & 10 & 0 \\ Sensitivity Vulnerability & & \\ Undetected & & \\ Medium-Sensitivity & 31 & 3 \\ Vulnerability & & \\ Undetected & & \\ Low-Sensitivity Vulnerability & 8 & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of the original and more strict vulnerability detection rules.
###### Abstract  
Unconditionally secure quantum bit commitment (QBC) was widely believed to be impossible for more than two decades, but recently, based on an anomalous behavior found in quantum steering, we proposed a QBC protocol which can be unconditionally secure in principle. The protocol requires the use of infinite-dimensional systems, so it may seem less feasible in practice. Here, we propose a quantum optical method based on the Mach-Zehnder interferometer, which gives a very good approximation to such infinite-dimensional systems. Thus, it enables a proof-of-principle experimental implementation of our protocol, which can also serve as a practically secure QBC scheme. Other multi-party cryptographic protocols such as quantum coin tossing can be built upon it too. Our approach also reveals a relationship between infinity and non-locality, which may have an impact on the research of fundamental theories.  
secure multi-party computation; bit commitment; coin tossing: Mach-Zehnder interferometer; quantum steering
# A Technological Framework to Support Asthma Patient Adherence Using Pictograms
## 2 Materials and Methods
### SIMAP: System for Automatic Association of Medical Indications with Pictograms
#### 2.2.2 Pictogram Validation Process  
In response to the challenges presented by the COVID-19 pandemic, we implemented a two-stage pictogram validation process.  
_First validation process:_ In this stage, we employed a paper-based selection and evaluation tool in the form of a booklet. This booklet included the pictograms, their respective alternatives, and a comment section to facilitate the process. The booklet was applied during meetings with clinicians and patients conducted at the Family Health Centers. To ensure a thorough qualitative assessment, during this meeting, we also conducted a focus group session upon completing the pictogram selection and validation process. This session enabled us to collect insights from both individual and collective perspectives on the pictograms, facilitating any necessary adjustments or revisions for a comprehensive evaluation.  
_Second validation process:_ In this stage, we devised an instrument comprising 18 pictograms associated with medical indications with the aim of testing the correspondence between medical indications and pictograms. This instrument adhered to ISO 9186 guidelines and consisted of two sections [25; 26; 27]. The initial section collected information about the sociodemographic and occupational background of the experts, covering aspects such as age, gender, health center, profession, working hours in ARU (Asthma Respiratory Unit), and specialization courses in the field.  
The subsequent section aimed to ascertain whether the presented pictogram corresponded to the medical indication, providing two response choices: (a) It matches indicating that the pictogram accurately represented the indication, or (b) it does not fit signifying that the pictogram did not accurately represent the indication. Each question was followed by an open-response box to allow for suggestions and comments regarding the pictogram. This instrument was completed by professionals from the ARUs, including kinesologists, physicians, nurses, nursing technicians, and pharmaceutical chemists. Each expert was tasked with determining whether the pictogram accurately represented the linked medical indication.  
The assessment of the pictograms was expressed in terms of the percentage of experts who deemed the pictogram to adequately represent the medical indication.
## 5 Precoder and Combiner Design Based on Reinforcement Learning
### Reward Function  
The focus of whether RL can better solve the optimization problem lies in the reward function. In the precoding optimization problem of the downlink MIMO system, the joint optimization problem of the precoding matrix and combining matrix can be reduced to a minimization MSE problem. The optimization problem is shown in (26).  
Figure 2: The framework of MADDPG-based joint precoding and combining algorithm.  
Due to the existence of constraints, the reward function designed in this paper is divided into two parts: one is the desired optimization objective, and another one is the penalty term for not satisfying the constraints. The first part of the reward function is  
\[R_{1}=\text{tr}\Big{\{}\mathbf{F}^{T}\text{diag}\Big{(}\text{diag}\Big{(}g \mathbf{W}\mathbf{W}^{T}g^{T}+n_{b}^{2}+n_{b}\Big{)}\Big{)}\mathbf{F}-2\mathbf{ F}^{T}\mathbf{G}\mathbf{W}\Big{\}}. \tag{27}\]  
The second part of the reward function is the penalty term, which is set to \(-\beta\) for any constraint that is not satisfied. Since the MSE value should be positive, consider increasing the penalty when \(MSE<0\). This penalty term is set to \(-10*\beta\), which means that  
\[penalty_{1}=\left\{\begin{array}{c}-\beta,Constraint1 not\text{ }\\ 0,Constraint1 met\end{array}\right. \tag{28}\]  
\[penalty_{2}=\left\{\begin{array}{c}-\beta,Constraint2 not\text{ }\\ 0,Constraint2 met\end{array}\right. \tag{29}\]  
\[penalty_{mse}=\left\{\begin{array}{c}-10*\beta,ConstraintMSEnot\text{ }\\ 0,ConstraintMSEmet\end{array}\right.. \tag{30}\]  
The second part of the reward function can be expressed as  
\[R_{2}=penalty_{1}+penalty_{2}+penalty_{mse}. \tag{31}\]  
In summary, the ultimate reward of MDP can be represented as the linear combination of optimization objective and the penalty term.  
\[Reward=R_{1}+R_{2}. \tag{32}\]
# Enhancing the SRAM PUF with an XOR Gate
## 4 Results
### PUF Metrics
#### 4.3.1 Entropy  
To assess the entropy of our PUF design, we employed the Shannon entropy formula, which can be referenced as Equation (1) as is used in [34]. This formula allows us to quantify the correlation among the bits in the responses obtained from our PUF design. In the given equation, the variable \(P(x)\) represents the probability of a random variable X having the value x. The logarithm, typically taken to the base-2 (\(\log_{2}\)), is used to quantify entropy in terms of bits.  
The Shannon entropy has a minimum value of zero, indicating that the input lacks randomness or variability, while its maximum value is \(\log_{2}(n)\), where \(n\) represents the number of possible inputs. In this case, since the input is a binary sequence (with two possible inputs, 0 and 1), \(n\) equals 2, and thus, the upper bound of the entropy per bit is 1. This signifies that the input demonstrates maximum randomness or unpredictability.  
\[H(X)=-\sum_{i=1}^{n}p_{i}\log_{2}(p_{i}) \tag{1}\]  
The client device generated 1000 keys consisting of 500 bits at different temperatures. Subsequently, the entropy of each key was determined and averaged together. Specifically, at temperatures of 0 \({}^{\circ}\)C, 23 \({}^{\circ}\)C, and 80 \({}^{\circ}\)C, the entropy was found to be 0.9999 per bit.  
These results indicate that the entropy remains consistently high across different temperatures. Therefore, the temperature variations have minimal influence on the entropy.