esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Uncertainty Modeling Techniques
### Possibilistic Methods  
Possibilistic methods offer a framework for dealing with uncertainty where classical probabilistic methods may be inadequate or inapplicable. Unlike probability theory, which quantifies uncertainty in terms of likelihoods, possibilistic approaches focus on the degrees of possibility, typically representing uncertainty without requiring precise statistical data. These methods are particularly useful in handling incomplete, imprecise, or non-statistical data.
## 3 Quality of Service
### Space Diversity
#### Acknowledgement Management  
Interferences or obstacles can also lead to packet loss. To ensure transmission reliability, _acknowledgements_ (ACK) at _Data Link Layer_ (DLL) are supported by all the selected standards, as well as end-to-end acknowledgements at _Transport Layer_ (TL) for the WirelessHART and ISA100.11a stan  
\begin{table}
\begin{tabular}{c|c|c|c|c|c}  & WISA & WirelessHART & ISA100.11a & ZigBee & 802.15.4e MAC \\ \hline \hline Mesh topology & & x & x & x & \\ \hline Channel agility & & & & x\({}^{5}\) &?6 \\ \hline Channel hopping & x & x & x & &?6 \\ \hline Channel blacklisting & & x & x & &?6 \\ \hline DDL acknowledgements & x & x & x & x & x \\ \hline TL acknowledgements & & x & x & & \\ \hline Automatic repeat request & x & x & x & x &?6 \\ \hline \end{tabular}
\end{table}
Table 4: Features relevant for robust/reliable features.  
dards. Each standard however manages its ACK mechanisms according to its transmission scheme. For example, the WISA specification transmits each ACK in the downlink channel, whereas each ACK is transmitted during the same timeslot as the received data in the WirelessHART standard. Once an ACK is missing, all standards7 support the automatic retransmission of the data.  
Footnote 7: Except for the 802.15.4e FA MAC standard that does not address this issue in the current proposal.  
The comparison of the aforementioned mechanisms summarized in Table 4 shows that:  
* The technologies targeting _process automation_ applications provide a good protection against potential obstacles and node failures, as mesh topology is supported.
* The WirelessHART and ISA100.11a standards offer the most complete set of mechanisms with channel blacklisting and frequency hopping to avoid perturbations caused by interference. While channel blacklisting allows saving time and energy by avoiding scanning channels previously identified as faulty, the obtained benefits are minor in comparison with the frequency hopping capability that is fundamental. Even if the ZigBee PRO version shows some improvements, the frequency diversity proposed by the ZigBee standards is insufficient to fulfill the strong reliability requirements of industrial applications.
* All selected technologies use acknowledgements at data link layer and automatic repeat request to ensure reliable transmissions and identify packet losses. At transport layer level, only the WirelessHART and ISA100.11a standards support acknowledgements.
# DDL R-CNN: Dynamic Direction Learning R-CNN for Rotated Object Detection
###### Abstract  
Current remote sensing (RS) detectors often rely on predefined anchor boxes with fixed angles to handle the multi-directional variations of targets. This approach makes it challenging to accurately select regions of interest and extract features that align with the direction of the targets. Most existing regression methods also adopt angle regression to match the attributes of remote sensing detectors. Due to the inconsistent regression direction and massive anchor boxes with a high aspect ratio, the extracted target features change greatly, the loss function changes drastically, and the training is unstable. However, existing RS detectors and regression techniques have not been able to effectively balance the precision of directional feature extraction with the complexity of the models. To address these challenges, this paper introduces a novel approach known as Dynamic Direction Learning R-CNN (DDL R-CNN), which comprises a dynamic direction learning (DDL) module and a boundary center region offset generation network (BC-ROPN). The DDL module pre-extracts the directional features of targets to provide a coarse estimation of their angles and the corresponding weights. This information is used to generate rotationally aligned anchor boxes that better model the directional features of the targets. BC-ROPN represents an innovative method for anchor box regression. It utilizes the central features of the maximum bounding rectangle's width and height, along with the coarse angle estimation and weights derived from DDL module, to refine the orientation of the anchor box. Our method has been proven to surpass existing rotating detection networks in extensive testing across two widely used remote sensing detection datasets, namely UCAS-AOD and HRSC2016.  
remote sensing detection; anchor box regression; directional feature extraction; high-aspect-ratio detection +
Footnote †: journal:  
e
# Zero-Shot Detection of Visual Food Safety Hazards via Knowledge-Enhanced Feature Synthesis
## 3 Methodology
### Food Safety Knowledge Graph
#### 3.2.1 FSKG Construction
To populate the FSKG, we extract information from three primary sources: (1) food safety databases such as the FDA's Bacteriological Analytical Manual [43] and the USDA's Microbiology Laboratory Guidebook [44], (2) the scientific literature on food safety and quality assessments, and (3) expert knowledge from food safety professionals. We organize visual safety attributes into four main categories based on their phenomenological characteristics. Appearance Attributes encompass basic visual features that indicate potential safety issues, including discoloration, abnormal texture patterns, and visible foreign materials (such as plastic fragments or metal shavings). These attributes often provide the most immediate visual cues for safety assessments. Decomposition Attributes capture visual indicators of food spoilage or decay processes, including mold growth, rotting tissues, and fermentation-related changes like abnormal bubble formation. These attributes specifically target biological degradation processes. Decomposition Attributes capture visible indicators of microbial activity or decay processes, including mold growth, bacterial colonies that have formed visible biofilms, rotting tissues, and fermentation-related changes. However, it is important to note that many dangerous foodborne pathogens do not produce visible changes in food, and our vision-based system cannot detect invisible contamination. These attributes specifically target visible biological degradation processes rather than the pathogens themselves. Contamination Attributes represent visual signatures of external agents compromising food safety, including visible bacterial colonies, chemical residues, and evidence of pest activity such as insect parts or rodent hairs. These attributes focus on exogenous contaminants that may render food unsafe. Structural Attributes identify abnormalities in the physical integrity of food items, such as irregular cracks, punctures, or surface deformations that may indicate internal contamination or improper processing. These attributes often signal issues that might not be immediately visible on the surface. In total, our FSKG includes 26 food categories and 48 visual safety attributes, with 1248 food-attribute relations, 325 food-food relations, and 574 attribute-attribute relations.
## 4 Applications of Microextraction Techniques to the Determination of Organic Micropolultants
### Perfluorinated Compounds  
Perfluorinated compounds (PFCs) comprise a class of artificial, fully fluorinated organic compounds and may exhibit both hydro- and lipophilic characteristics. These anthropogenic compounds have numerous applications as surfactants, fire-fighting foams, textiles, _etc._; their entry into the medium may be attributed to industrial discharge, the degradation of precursor compounds and the use of articles containing them. These bioaccumulative substances are abundant in the aquatic environment, where they might adversely affect humans and animals [76]. Although these compounds were first produced in the 1950s, the wide distribution of PFCs in the environment was not apparent until 2000. Among these compounds, perfluorooctane sulfonate (PFOS) and perfluorooctanoate (PFOA) have received the most attention in recent years. PFCs are detected in both waste- and surface waters at \(\mathrm{ng\cdot L^{-1}\!-\!\mu g\cdot L^{-1}\! levels}\)[77], as well as in open ocean waters [78].  
PFCs are determined using LC-MS [79] after extraction and pre-concentration through methods such as SPE. Few studies have used the microextraction techniques described in this review, as shown in Table 2.  
In this context, three different microextraction methods have been developed. IT-SPME [80] and SPME [81] were used to analyse PFOS and PFOA. The results were very similar in both cases because these techniques have the same theoretical basis. SPME fibres were prepared by the chemical bonding of a sol-gel precursor to anodised Ti, whereas IT-SPME utilised a CP-Pora PLOT amine capillary column. It is possible that IT-SPME (RSD below 3.7%) was slightly more accurate than SPME (RSD below 5.2%), but the recoveries with both methods were above 81%, and the LODs were approximately a few \(\mathrm{ng\cdot L^{-1}}\) in both cases.  
LLME with a mild emulsification procedure, specifically vortex mixing, was used by [PERSON] _et al._[82] to determine the PFOS concentration in aqueous environmental matrices with an octanol-like acceptor phase. The recoveries under the optimal conditions ranged from 90.8% to 105.1%, and the LOD was satisfactory (1.6 \(\mathrm{ng\cdot L^{-1}}\)).
## 5 Results
### User Spacecraft Orbit Determination Results
#### 5.2.2 Case #2
To calculate the TDRS orbit, an unconstrained approach was adopted, whereby the a priori value of the measurement bias was specified as zero, and the covariance was deemed infinite. For instance, the LANDSAT-8 spacecraft's maximum deviation in velocity from the operational solution was about 1.6 mm/s, resulting in a maximum Doppler shift of approximately 12 mHz in the S-band. Notably, this value is negligible compared to the mean and standard deviation of the LANDSAT-8 one-way Doppler residuals. Thus, TT&C-only measurements did not significantly alter the user spacecraft velocity with respect to the TDRS satellites enough to affect the accuracy of the LOF offset estimation and yielded an extremely close agreement with the operational values.
# Applying CRISPR Technologies for the Treatment of Human Herpesavirus Infections: A Scoping Review
## 4 Discussion
### Treatment Window  
CRISPRR systems can be used either as a preventive or a treatment approach. Many studies are conducted in a preventive approach, transfecting or transducing the cells with the CRISPR system before herpesvirus infection. However, considering the ubiquitous spread and asymptomatic transmission of herpesvirus infection, delivering the CRISPR system post-herpesvirus infection in a treatment approach is clinically more interesting. Moreover, a preventive approach implies the persistent presence of the CRISPR system intracellularly, which entails prolonged Cas expression with associated biosafety risks. Additionally, a clinically reasonable time window for treatment should be established. In most studies, the antiviral effects only lasted a few days post-infection, suggesting a small time window for treatment. Given the high specificity of the CRISPR system, targeting sequences specific for a certain type of herpesvirusirus, the relevant system can only be administered after diagnosing the exact type of herpesvirus. Since a significant delay between the initial infection and the specific diagnosis is highly possible, the time window for treatment of acute lytic infection or reactivation should be realistically large enough for clinical implementation. However, this should again be weighed against the biosafety concerns associated with prolonged Cas expression. Alternatively, the virus could be targeted outside the acute phase, specifically the latent state, of which three approaches were described earlier.
# Low-Power, High Data-Rate Digital Capsule Endoscopy Using Human Body Communication
###### Abstract  
A technology for low-power high data-rate digital capsule endoscopy with human body communication (CEHBC) is presented in this paper. To transfer the image data stably with low power consumption, the proposed system uses three major schemes: Frequency selective digital transmission (FSDT) modulation with HBC, the use of an algorithm to select electrode pairs, and the LineSync algorithm. The FSDT modulation supports high-data rate transmission and prevents the signal attenuation effect. The selection algorithm of the electrode pair finds the best receiving channel. The LineSync algorithm synchronizes the data and compensates for data polarity during the long data transmission section between the capsule endoscope and the receiver. Because all the major functional blocks of the CEHBC transmitter can be implemented as digital logics, they can be easily fabricated using the field programmable gate array (FPGA). Moreover, this CEHBC transmitter can achieve low power-consumption and can support a relatively high data rate in spite of using its clock a few tens of MHz slower. The proposed CEHBC-TXD is the digital portion of the CEHBC transmitter that provides low-power (3.7 mW) and high data-rate (6 Mbps) performance while it supports a high-resolution image (480 \(\times\) 480 byte) at 3.13 fps.  
capsule endoscopy; human body communication; frequency selective digital communication; selection algorithm of electrode pair; synchronization +
Footnote †: journal: Applied Sciences
# A New Life Prediction Scheme for Mechanical System with Considering the Mission Profile Switching
## 4 Results and Discussion
### The Prediction Accuracy of the Proposed Method
#### 4.3.1 The Prediction Accuracy of Single Sample  
On the basis of the prediction in Section 4.2, the predicted life under different lengths of the sample to be predicted is calculated according to the above steps. The selected lifecycle data are divided into two parts: The sample to be predicted with different lengths and the remaining data, the operating conditions of which are used as the mission profile in the prediction. The predicted results are shown in Figure 9, where the upper and lower limit of the confidence interval is 20% of the actual RUL.  
As shown in Figure 9, the predicted RUL is basically within the confidence interval. Besides, in most cases, the predicted RULs are close to the actual RULs. Therefore, for selected life-cycle data, the proposed method can obtain good prediction results under the mission profile experienced by the remaining data.
## 2 Modeling and Computational Approach
### Flow Considered  
Figure 1 shows a schematic diagram of the experimental configuration and the computational domain for the axisymmetric transonic bump considered along with the applied boundary conditions. This case pertains to shock-triggered boundary layer separation induced by an axially symmetric bump mounted on a slim spherical cylinder, which extends 61 cm upstream. The case reflects the upper surface of a transonic wing. It is characterized by a Mach number (\(M_{\infty}\)) of 0.875 and a Reynolds number (\(Re\)) of 2.763 M relative to the airfoil's chord length \(c\). Main flow features are also shown in Figure 2.  
The experiment [33] was conducted using a closed return tunnel with a variable density and featured a spherical cylindrical body with a diameter of 0.152 m. The tunnel had upper and lower walls, both made up of porous-slotted material, which allowed approximately 21% of open area. The incident boundary layer on the bump measured approximately 1 cm in thickness, and the estimated momentum-thickness Reynolds number at a point of 1.5 chord lengths was around \(Re_{\theta}\approx 6600\). The chord length of the bump was fixed at 0.2032 m. In the experiment, a shockwave formed downstream of the bump, which extended from the cylindrical surface. This shockwave, along with an adverse pressure gradient, led to unsteady flow separation from the surface with reattachment further downstream. The reattachment point is largely influenced by the turbulent stresses generated in the detached shear layer.  
While the Bachalo-Johnson [33] experiment has provided detailed data on mean velocity profiles, the Reynolds shear stress, and surface pressure, it is important to note that measurements of skin friction coefficients were omitted. The latter were provided by a recent experiment of [PERSON] et al. [44].  
Figure 1: Axisymmetric transonic bump geometry: Experimental and computational configuration [41; 42].  
Figure 2: Main flow features [43].
## 4 Conclusions  
This paper investigates the intelligent identification of grape diseases in their natural state based on an improved YOLOXS. A grape disease dataset in a natural environment was initially constructed. The YOLOXS algorithm was then improved through adding the FOCUS module to the backbone network, introducing CBAM, double residual edges in the prediction head. Finally, a grape disease identification model with a mAP of 99.10% (GFCR-YOLOXS) was obtained through training. The rich experimental and comparative results show that the model in this paper is superior and practical compared to the classical model and the models in the relevant recent authoritative literature, and has important reference values for the control of grape diseases.  
In the future, we will investigate the following aspects: (1) maintenance and update of the dataset. The initial dataset for this work was small and did not include photos from complex environments, such as rain and snow, or the lack of breakdown of deficiency diseases. In the future, we will collect more experimental data, further refine the severity of grape diseases, and include non-invasive disease species of grapes to improve the comprehensiveness and reliability of the dataset. (2) Multimodal data fusion. In practical applications, multiple data sources may be involved, such as infrared images, hyperspectral images, etc. Therefore, the fusion of various data can improve the recognition accuracy and robustness of the model to further improve the diagnosis and treatment. (3) Identification of other crop diseases. We will study the disease characteristics of different crops and  
Figure 16: Effect of grape disease identification.  
improve the existing models to apply them to disease identification in other crops. This approach will help farmers to detect and treat crop diseases promptly, and enhance the efficiency of agricultural production and the quality of the farm output. In conclusion, future research directions will further explore the characteristics of crop diseases and their detection techniques in depth, and continuously improve the models' accuracy, robustness, and practicality to promote the sustainable development of agricultural production.  
Methodology, Y.W. and C.W.; Dataset Acquisition, C.M. and G.M.; Writing--original draft, Y.W.; Writing--review and editing, G.B.; Supervision, C.W. and C.M.; Formal analysis: C.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (No. 62072363) and the Natural Science Foundation of Shaanxi Province (No. 2019 JM-167).  
Not applicable.  
The data set for this article are available from Y.W. on request.  
The authors declare no conflict of interest.
## 3 Implementation and Experimental Results
### Wafer Map Generation for Inspection Visualization  
The final result of our proposed method is a multi-class wafer map, the classes of which can be manually defined by users. In this paper, four classes are applied: background, alignment mark, and normal and abnormal patterns. Let the original SAT image be the input; thereafter,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Extractor**} & \multicolumn{3}{c}{**Time (Unit: ms)**} & \multicolumn{2}{c}{**Number of Extractor**} & \multicolumn{1}{c}{**Total Number of**} \\ \cline{2-5}  & **Min.** & **Max.** & **Avg.** & **Parameters** & **Model Parameters** \\ \hline VGG-16 & 30.25 & 35.63 & 31.02 & 14,714,688 & 39,904,192 \\ VGG-19 & 36.75 & 39.63 & 37.19 & 20,024,384 & 45,213,888 \\ InceptionV3 & 33.38 & 45.88 & 35.02 & 21,802,784 & 73,104,288 \\ MobileNet & 24 & 30.63 & 25.03 & 3,228,864 & 53,506,368 \\ ResNet-50 & 31 & 42 & 32.58 & 23,587,712 & 25,737,216 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of different models for pattern classification.  
Figure 16: Training accuracy for different CNN-based networks.  
Figure 17: Validation accuracy for different CNN-based networks.  
automatic template extraction, pattern detection, and prediction steps, followed by pattern classification, are conducted. All patterns are found, and the information of each pattern, including the location, width, height, and its class is also obtained. Figure 18 shows the final results corresponding to images _img01_, _img02_, and _img03_. The patterns belonging to the background, alignment mark, and normal and abnormal pattern classes are plotted in gray, white, green, and red, respectively. The analyzed wafer maps are useful for visualizing defects and finding potential fabrication issues.
## 5 Conclusions  
This paper proposes a day-ahead planning and scheduling model for wind/storage systems that integrates multi-scenario generation with CVaR constraints. Wind power output scenarios are generated using non-parametric modeling combined with Copula functions. The model also incorporates initial \(SOC\) optimization and a peak-valley arbitrage mechanism, effectively addressing the scheduling challenges posed by wind power uncertainty.  
Simulation results show that the proposed model significantly enhances both economic performance and risk control. Compared with the benchmark without CVaR and arbitrage design, the expected revenue increases by CNY 340.67, the CVaR improves by CNY 420.74, and the minimum scenario revenue rises by CNY 563.95. When the initial \(SOC\) is optimized to 0.1, the expected revenue is CNY 189.08 and CNY 310.66 higher than those with \(SOC\) values of 0.5 and 0.9, respectively. Under different confidence levels, raising the level from 0.8 to 0.95 leads to a CVaR increase of CNY 739.96 and a minimum revenue gain of CNY 1599.73, at the cost of CNY 681.42 in expected revenue, reflecting a clear trade-off between risk and return. In addition, increasing the number of clusters from 5 to 10 improves both CVaR and expected revenue, while further increasing to 15 results in only a slight revenue gain (CNY 145.89) but a decline in CVaR by CNY 132.49, indicating diminishing returns and potential overfitting.  
Although the proposed model improves the economic efficiency and robustness of the wind/storage system, it primarily focuses on day-ahead scheduling and does not fully capture the dynamic response capabilities required during real-time operation. This limitation may affect its adaptability under highly volatile conditions. Future work will aim to incorporate real-time dispatch mechanisms to enhance the responsiveness and practical applicability of the proposed approach.