esa-sceva/satcom-chunk-collection huggingface dataset:
# PCCAU-Net: A Novel Road Extraction Method Based on Coord Convolution and a DCA Module
## 3 Experiments and Results
### Computational Efficiency  
In the field of deep learning, the number of parameters and floating-point operations per second (FLOPs) serve as crucial measures for evaluating the complexity and computational demands of a model. A higher number of parameters might indicate a model's ability to learn more intricate patterns, but it could also lead to overfitting and extended training time. A comparison of the computational efficiency of different methods is shown in Table 5. Our model, the PCCAU-Net, demonstrated balanced attributes with respect to both parameter quantity and FLOPs. Therefore, while significantly improving segmentation performance, the PCCAU-Net did not introduce an excessive number of parameters or  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Scheme**} & \multicolumn{2}{c}{**Massachusetts Roads Dataset**} & \multicolumn{2}{c}{**DeepGlobe Road Dataset**} \\ \cline{2-6}  & **Precision (\%)** & **ReCall (\%)** & **IoU (\%)** & **Precision (\%)** & **ReCall (\%)** & **IoU (\%)** \\ \hline a & 78.823 & 83.764 & 77.672 & 83.436 & 84.454 & 75.365 \\ \hline b & 79.182 & 84.035 & 77.768 & 84.273 & 85.145 & 76.092 \\ \hline c & 80.357 & 83.913 & 77.937 & 84.224 & 86.047 & 77.642 \\ \hline d & 81.136 & 83.849 & 78.217 & 85.423 & 86.864 & 78.652 \\ \hline e & 83.395 & 84.076 & 78.7 noticeably increase the training time. This demonstrates that our model not only prioritizes performance but also focuses on computational efficiency and model generalizability.
## 2 Preliminaries  
The Jacobi polynomial of degree \(i\) on \([0,1]\) is given by [28]  
\[\sigma_{i}(t)=\sum_{k=0}^{i}(-1)^{i-k}\frac{\Gamma(i+b+1)\Gamma(i+k+a+b+1)}{ \Gamma(k+b+1)\Gamma(i+a+b+1)(i-k)!k!}t^{k} \tag{4}\]  
The orthogonal property of the Jacobi polynomials with respect to the weight function \(w^{(a,b)}(t)=(1-t)^{a}t^{b}\) is given by  
\[\int_{0}^{1}\sigma_{n}(t)\sigma_{m}(t)w^{(a,b)}(t)dt=v_{n}^{a,b}\delta_{mn} \tag{5}\]  
where \(\delta_{mn}\) is Kronecker delta function and  
\[v_{n}^{a,b}=\frac{\Gamma(n+a+1)\Gamma(n+b+1)}{(2n+a+b+1)n!\Gamma(n+a+b+1)} \tag{6}\]  
A function \(f\in L^{2}[0,\ 1]\), with \(\left|f^{\
u}\left(t\right)\right|\leq Q\), can be expanded as follows:  
\[f(t)=\lim_{n\rightarrow\infty}\sum_{i=0}^{n}c_{i}\sigma_{i}(t), \tag{7}\]  
where \(c_{i}=\frac{1}{v_{i}^{a,b}}\int_{0}^{1}\sigma_{i}(t)f(t)w^{(a,b)}(t)dt\).  
Equation (7), for finite dimensional approximation, is written in the following form:  
\[f\cong\sum_{i=0}^{m}c_{i}\sigma_{i}(t)=C^{T}q_{m}(t), \tag{8}\]  
where \(C\) and \(q_{m}(t)\) are \((m+1)\times 1\) matrices given by \(C=\left[c_{0},c_{1},\ldots,c_{m}\right]^{T}\) and \(q_{m}(t)=\left[\sigma_{0},\sigma_{1},\ldots,\sigma_{m}\right]^{T}\).  
**Theorem 1**.: _If \(q_{n}(t)=\left[\sigma_{0},\sigma_{1},\ldots,\sigma_{n}\right]^{T}\) denotes the shifted Jacobi vector and if \(v>0\), then \(\Gamma^{v}\sigma_{i}(t)=I^{(v)}q_{n}(t)\), where \(I^{(v)}=(u(i,j))\), is the \((n+1)\times(n+1)\) operational matrix of fractional integral of order \(v\), and its \((i,j)\)th entry is given by_  
\[u(i,j)=\sum_{k=0}^{i}\sum_{l=0}^{i^{\prime}}(-1)^{i+j+l\cdot}\frac{\Gamma(a+1 )\Gamma(i+b+1)\Gamma(i+k+a+b+1)\Gamma(j+l+a+b+1)\Gamma(j+k+l+a+b+1)(2j+a+b+1)!}{(i-k)!(j-l)!(l)!\ \Gamma(k+b+1)\Gamma(i+a+b+1)\Gamma(j+a+1)\Gamma(l+b+1) \Gamma(k+l+v+a+b+1)} \tag{9}\]  
Proof.: Please see [28].
## 4 Experiment
### Illustration of an Example  
When applying the CAM-NAS neural network architecture search algorithm, a SOTA model is first selected as the teacher model, e.g., ResNet-152. N images are randomly selected from the dataset and fed into the teacher model, and then (3) is used to obtain \(A_{Target}\). At the same time, CAM is used to extract the saliency maps of the last layer of the teacher model, which will show the distribution of the teacher model's attention on different images. Next, CAM-NAS is used to obtain a new submodel. For the search submodel, we input the same N images into the search submodel and use the same CAM technique to extract the saliency maps of the last layer of the submodel, these saliency maps will show the distribution of attention of the submodel on the same images. Then (4) is used again to obtain \(A_{Search}\). Finally, (5) is used to compute the similarity score. If the saliency map of the submodel has a high similarity with the saliency map of the teacher model on the same image, it means that the submodel is able to capture similar important features as the teacher model and may have a better performance. Conversely, if the similarity is low, it indicates that the submodel may have performance shortcomings. By comparing the  
Figure 4: The time needed for different zero-shot NAS methods to judge a single submodel.  
similarity scores of different submodels on key features, better-performing submodels can be filtered out and used in further neural network architecture searches and optimization processes. This approach can efficiently find excellent network architectures without the need to fully train all submodels, saving time and computational resources.
# A Survey on Variable Neighborhood Search for Sustainable Logistics
## 5 Research Trends and Gaps
The proliferation of VNS variants and terminologies necessitates a more structured classification. Additionally, some researchers label their methods as VNS when applying neighborhood-based searches, even when they primarily use local search strategies. A systematic clarification of these methodologies would enhance understanding and consistency in the field. * Broader integration of sustainability metrics. While environmental sustainability remains a central focus, optimization models often lack comprehensive metrics. Factors such as resource efficiency, waste minimization, and community impact should be incorporated into future models to provide holistic solutions. * Need for comprehensive benchmarks and standardization. A major challenge in applying VNS to sustainable logistics is the absence of standardized benchmarks and performance metrics. Standardizing problem definitions, datasets, and evaluation metrics would improve the comparability of results across studies and facilitate a clearer understanding of the strengths and limitations of VNS approaches. * Emerging areas of application. New application domains for VNS in sustainable logistics are emerging, including renewable energy logistics, last-mile delivery using drones, and sustainable tourism optimization. Exploring and adapting VNS techniques to these novel challenges will expand their applicability and foster innovation in logistics research.
## 4 Numerical Results
### Estimation Results Based on Simulation Data  
To compare RU-EPD and C-EPD, we collected the simulation data generated by a self-developed simulation tool. It generated the measurement points of a 3D ToF sensor mounted in front of a robot on a pipe inner surface. The reference sensor model was the Helios2 3D ToF camera [17] with a resolution of 640 \(\times\) 480 and a FoV of 69\({}^{\circ}\)\(\times\) 51\({}^{\circ}\). The pipe thickness was 12 mm. For each simulation, 100 random poses of the sensor were generated where the ranges were as follows: roll angle range, \([-180,180]\); pitch and yaw angle range, \([-5^{\circ},5^{\circ}]\); and Y- and Z-displacement range, \([-50,50]\) mm. Data points within 6 m were used for estimation.
# An Overview of CRISPR-Based Technologies in Wine Yeasts to Improve Wine Flavor and Safety
## 3 CRISPR in the Vineyard  
There are several methods for giving grapevine more favorable properties. The commonly used and widely accepted form of human control is conventional plant breeding by crossing two plants. Sexual propagation combines the DNA and the properties of the plant. In vineyards, it has occurred naturally for many millenniums or by the action of humans in recent centuries [39]. To avoid losing already present favorable characteristics in the wine grape due to the cross, like adaptations to climate changes [40], grapevines are crossed back with the original plant. After a long process of selection (for the right properties), backcrossing, selection, backcrossing, and so on, eventually, a grapevine is produced that is like the original plant but with the additional required property. Genetic modification by cutting and pasting DNA from one plant to another, using vectors such as _Agrobacteriumum tumefaciens_ and plasmids, is another well-developed technique. A new technique is gene editing using the CRISPR/Cas9, a method that allows adjusting the plant's DNA. By back-crossing, it is possible to remove the plant-foreign DNA [41].  
Grapes are vulnerable to pests and environmental stresses due to a lack of genetic diversity [42]. One example is the crisis created by the phylloxera bug in the late nineteenth century that devastated vineyards across Europe. Therefore, scientists are trying to keep the flavors and aromas of grapes while protecting them against environmental stresses. Researchers are experimenting with CRISPR, and one of the first works was performed by [PERSON] and coworkers [41] in the grape variety Chardonnay. The former authors transformed \"Chardonnay\" embryyogenic cell masses (_calli_) to gain point mutations in the L-idonate dehydrogenase gene. They were able to regenerate plants with altered production of tartaric acid and vitamin C. Moreover, using two sgRNAs targeting distinct sites in the gene, they obtained a 100% mutation frequency in the transgenic cell mass.  
In another study, [PERSON] et al. [43] utilized the CRISPR/Cas9 system in the cultivar \"Thompson Seedless\" to show that the transcription factor gene VvWRKY52 is necessary for vulnerability to _Botrytis cinerea_. Recently, [PERSON] and coworkers [44] used the CRISPR/Cas9 system to study the role of grapevine PR4 in the resistance to downy mildew and demonstrated that the CRISPR/Cas9 system can be a high-efficiency tool for generating targeted mutations in grapevine and is an excellent technology for gene functional analyses in grapevine.  
However, what about in wine yeasts?
## 4 Results and Discussion
### Washing Water
To verify the feasibility of using microfiltered water as mixing water, the last requirement to be respected concerns the compressive strength. The latter, as determined after \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**Demineralized Water Samples**} & \multicolumn{2}{c}{**Microfiltered Water Samples**} \\ \cline{2-3}  & **(min)** & **(min)** \\ \hline Initial setting time & 125 & 145 (+ 16\%) \\ Final setting time & 280 & 295 (+ 5.3\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Cement paste sample setting times. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Parameter**} & **U.M. ** & **Microfiltered Water** & **Limit of UNI EN 1008** \\ \hline Oil and fats & - & compliant & Not more than visible traces \\ Detergents & - & compliant & Any foam should disappear \\ Color & - & compliant & within 2 min \\ Suspended matter & - & compliant & Pale yellow or paler. \\  & & & Estimated in water with \\  & & & & a density \textgreater{} 1.01 kg/L \\  & & & No smell, except the odour allowed for \\ Odor & - & compliant & potable water and a slight smell \\  & & & of cement \\ Acids & - & 12.8 & pH \(\geq\) 4 \\  & & & The colour shall be assessed \\  & & & qualitatively as yellowish brown or \\  & & & & paler, after addition of NaOH. \\  & & & 500 \\ Chlorides & & & mg/L & 18 & 1000 \\  & & & & \\  & & & & \\  & & & & \\  & & & mg/L & 4500 \\  & & mg/L & \textless{}10 & 2000 \\  & & mg/L & \textless{}20 & 100 \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & &  
7 days on mortar specimens prepared with the microfiltered water, must be at least 90% of the mean compressive strength of specimens prepared with demineralized water. The results shown in Table 7 highlight that the average compressive strength of samples prepared with microfiltered water is 98% of the compressive strength provided by samples produced with control water. This demonstrates that the microfiltered water produced by the new treatment plant complies with all the performance requirements imposed by UNI EN 1008 and, therefore, can be used as mixing water for new concrete production.
## Materials and Methods
### Model Selection  
The modelling was approached in two stages: First, least squares linear regression was used to investigate the strength and form of the image-specific relationship between NDVI and ANPP. This was initially done for all habitat types and then for just grassland habitats, as these were expected to give the strongest relationships. The image dates that gave a coefficient of determination, R\({}^{2}\), greater than 0.5 were selected for further analysis. Second, a linear mixed model, including a random effect to account for the clustering of the plots within the survey squares, was used to determine the relationship between ANPP and NDVI for each image. This model was derived using 80% of the available data, with the remaining 20% being reserved for validation and uncertainty estimation. The number of field plots/pixels used to derive the relationships varied depending on the image, as shown in the Results Section. The derived relationships were applied to the NDVI imagery to produce maps of ANPP for Wales. Land Cover Map 2007 [6] was used to mask all arable, inland water, urban and coastal areas from the resulting map.  
Figure 2: Methodological framework for producing high-resolution regional scale ANPP maps from ground-based measurements and EO data. GMEP is the Glastir Monitoring and Evaluation Program and cLDMC is cover-weighted Leaf Dry Matter Content.
# Integration of LTE 230 and LTE 1800 in Power Wireless Private Networks
## 2 High-Level Design of the PWPN
### LTE 1800  
The TD-LTE 1800 system has a 1785-1805 MHz operation frequency [5]. In addition to the public network frequency, the Chinese government has designated two 20 MHz broadband frequency bands, 1447-1467 MHz and 1785-1805 MHz, for the construction of TD-LTE private networks for the government and important industries, that is, the 1.4 G TD-LTE system and the 1.8 G TD-LTE system. Specifically, in the \"Notice on the Frequency Use of the Broadband Digital Trunking Private Network System in the 1447-1467 MHz Frequency Band (No. 59)\" issued by the Ministry of Industry and Information Technology, it is proposed that that the 20 MHz in the range 1447-1467 MHz are planned to be used in government private networks, such as government networks, public safety networks, and emergency communication networks, using the time-division duplex (TDD) broadband digital trunking system. In the \"Notice on the Reissue of the Frequency Use of Wireless Access Systems in the 1785-1805 MHz Frequency Band (No. 65)\" issued by the Ministry of Industry and Information Technology, it is proposed that the 20 MHz in the range 1785-1805 MHz are planned to be used in the industries of transportation (urban rail transit, etc.), electric power, petroleum, and others, as well as public communication networks, using the TDD wireless access system. The industry chain of these two systems from chip, to system equipment, to testing, to devices is exactly the same as the  
Figure 1: Illustration of the power wireless private network architecture.  
LTE system industry chain of the operator. Its main supporting manufacturers include Huawei, ZTE, Dingqiao, and other domestic mainstream telecom equipment manufacturers.
###### Abstract  
Protein identification in complex biological samples using the shotgun mode of LC-MS/MS is typically enhanced by employing longer LC columns and extended gradient times. However, improved identification rates can also be achieved by optimizing MS acquisition frequencies and employing advanced software, without increasing analysis time, thus maintaining the throughput of the method. To date, we found only one study in the literature examining the influence of MS acquisition frequency on protein identification, specifically using two ion trap mass spectrometer models. This study aims to address the gap by analyzing the impact of MS acquisition tuning of the QTOF instrument on the analysis of complex samples. Our findings indicate that increasing acquisition frequency generally improves protein identification, although the extent of improvement depends on the sample type. For CHO cell lysates, protein identifications increased by over 10%, while _E. coli_ and albumin-depleted plasma samples demonstrated gains of 3.6% and 2.6%, respectively. Higher contributions to protein identification were also achieved with extended LC gradients, resulting in improvements of 21.6% for CHO, 18.2% for _E. coli_, and 10.3% for plasma. Moreover, enabling PEAKS' deep learning feature significantly boosted identifications, with increases of 22.9% for CHO, 23.2% for _E. coli_, and 9.2% for plasma.  
Keywords:collision-induced dissociation (CID); data-dependent acquisition or shotgun (DDA); deep learning (DL); false discovery rate (FDR); full width at half maximum (FWHM); liquid chromatography-tandem mass-spectrometry LC-MS/MS; mascot generic format files (.mgf files); machine learning (ML); peak capacity; peptide spectrum match (PSM) +
Footnote †: journal: Applied sciences
## 7 Conclusions  
In conclusion, the integration of drones, computer vision, path-planning optimization, Python programming, and machine learning within an educational surrogate project framework represents a significant shift toward innovative educational methodologies in engineering and technology. This study not only serves as a surrogate for the challenges of wind turbine blade inspection problems, but also serves as a foundational educational tool that simulates real-world scenarios in a classroom setting. Through the implementation of a scalable, low-cost autonomous drone system that utilizes a real-time feed of the camera instead of GPS data, we demonstrated a viable approach to teaching complex engineering concepts:  
* Educational surrogate project: Utilizing PFB to represent wind turbines, our surrogate project enables students to explore the nuances of drone-based inspections in a controlled environment. This approach allows for hands-on learning and experimentation with autonomous navigation systems, computer vision-based path planning, and collision avoidance strategies, all tailored to mimic real-world applications in a safe and cost-effective manner.
* Deep learning for fault detection: The comparative analysis of various deep learning architectures, such as DenseNet and ViT, highlights their efficacy in classifying faults in WTBs. By integrating these models into the path-planning experiments, students can engage with advanced data analysis techniques, contributing to their understanding of AI applications in real-world scenarios.
* Benefits for education: The project provides a less expensive alternative to GPS-based systems and is particularly useful in scenarios where GPS data may be unreliable or inaccurate. This makes the drone system an attractive educational tool, offer ing a practical demonstration of technology that students can build upon in future engineering roles.
* Impact on future engineering education: By reducing the need for human intervention and enhancing the accuracy and safety of inspections through autonomous systems, this study not only advances the field of wind turbine maintenance but also equips students with the skills and knowledge necessary to contribute to sustainable energy technologies.  
The findings and methodologies presented in this study underscore the potential of using low-cost, intelligent drone systems, not just for industrial applications, but as powerful educational tools that prepare students for technological challenges in renewable energy and beyond.
## 8 TF-SPME with Thermal and Solvent Desorption  
Desorption of analytes from the TF-SPME device can be carried out at high temperatures or in an organic solvent. Thermal desorption requires the sorptive phase and its support to be highly thermally stable. Due to the larger size of the TF-SPME device than the SPME fiber, performing thermal desorption of analytes from the TF-SPME sorbent involves the use of a gas chromatograph column dispenser adapter. The adapter, a thermal desorption unit (TDU), is an accessory for the GC gas chromatograph. During thermal desorption, the sorptive phase is heated with hot gas to release all volatile analytes adsorbed on it. The increase in temperature of the sorption phase increases the partition coefficient of the analytes and consequently promotes their release from the sorbent solid phase into the gas phase. Thermal desorption in the TDU of the gas chromatograph allows direct introduction of all analytes, except non-volatile substances, into the chromatographic column. The non-volatile substances remain on the TF-SPME device [9].  
Liquid desorption is commonly used in conjunction with liquid chromatography. It uses a small amount of organic solvent (or a mixture of water and multiple organic solvents) to re-extract all compounds from the solid phase of the TF-SPME device to the liquid phase. After desorption, the analytically enriched organic solvent is injected into the liquid chromatograph. Desorption in the solvent is most commonly used while analyzing non-volatile, thermally labile compounds and biomolecules. Diffusion of analytes in the liquid occurs more slowly than in the gas phase, and consequently, desorption in organic solvents is a longer process than thermal desorption [8].
## 1 Introduction
### Related Studies
Most of the best practices and approaches described in this study have been applied to this project. The paper \"Experience with Safe Dynamic Reconfigurations in Component-Based Embedded Systems\" [6] discusses ensuring dynamic reconfiguration based on secure and efficient updates without service interruption. The authors propose a component-based framework and a domain-specific language (DSL), FScript, for effectively managing reconfigurations. Their approach, implemented on the \"Cognichip\" platform, offers solutionsto achieve flexibility, security, and simplicity in reconfigurations, applicable to interpreted languages. In this context, functional units of the execution environment can be treated as separate components, with the virtual machine instance remaining active during microcontroller operation.
# Recursive Least Squares for Near-Lossless Hyperspectral Data Compression
## 3 Experimental Results  
The proposed scheme, named near-lossless recursive least squares (NLRLS), has two parameters, namely, the radius of the context window \(r\) and the prediction length \(k\). Since these two parameters critically impact the compression results, the selection of optimal values is explained in the first subsection. Furthermore, the compression performance of the proposal is compared with several state-of-the-art schemes, including compression results in the distortion metrics of the reconstructed image. This section highlights the corresponding results and analysis.  
The platform for testing is a personal computer powered by a single Intel Core i7-700K central processing unit (CPU) at 4.2 GHz with 16 GB random access memory (RAM). We adopted the standard hyperspectral image test data recommended by the International Consultative Committee for Space Data Systems, which includes Atmospheric Infrared Sounder (AIRS) and Airborne Visible/Infrared Imaging Spectrometer Calibrated and Uncalibrated (AC and AU) [29]. Each of them has a bit depth of 16 bit-per-pixel (BPP). Table 1 details the dataset used in the tests, including sensor abbreviations, scene names, and dimensions.