esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
To enhance the understanding of the hydrometeorological conditions in semi-humid area of northern China, six rainfall events that occurred in two medium-scale catchments were sorted with different homogeneousness in space and time. The spatiotemporal characteristics of rainfall and several key water cycle elements (e.g., soil moisture, evapotranspiration and generated surface runoff) were investigated by the stand-alone WRF model and the fully coupled WRF/WRF-Hydro modeling system. We proved that the precipitation events in the fully coupled system were slightly improved than those in WRF-only, and the simulation performances were related with the temporal and spatial homogeneousness of storm events. For both WRF-only and the fully coupled WRF/WRF-Hydro,the events with inhomogeneous spatiotemporal distributions of rainfall could lead to somewhat worse simulations than those with homogeneous rainfall distributions in space or time and both the two modeling simulations showed to be more realistic in space than in time. The fully coupled system could improve the spatial distribution, especially the advantage of a lower mean bias error and the enhanced prediction of rainstorm center than WRF-only. In the fully coupled system, the fluctuation of precipitation was corresponded to the initial soil moisture, and for the temporal inhomogeneous precipitation, the system might produce a large false alarm than WRF-only. Compared with WRF-only, the simulations of the crucial water cycle elements by the fully coupled system differed with relatively small amounts in the average of two catchments and generally increased soil moisture and decreased surface runoff. The spatial distributions in the fully coupled system were redistributed and the generated runoff and the soil moisture was generally consistent with that of the cumulative rainfall, while the evapotranspiration did not always show the same trend due to more complicated influencing mechanisms.  
For semi-humid areas, how to exactly capture the rainfall process with inhomogeneous distributions in space and time or with precedent dry soil conditions is the key point to be solved in the following study.  
Conceptualization, W.W. and J.L.; methodology, W.W. and F.Y.; software, W.W. and Y.L.; validation, W.W. and Y.L.; formal analysis, W.W. and J.L.; investigation, W.W. and F.Y.; writing--original draft preparation, W.W. and J.L.; writing--review and editing, W.W., J.L. and C.L.; visualization, W.W. and E.Y.; funding acquisition, J.L. and C.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Major Science and Technology Program for Water Pollution Control and Treatment (2018 ZX07110001), the National Natural Science Foundation of China (51822906), the National Key Research and Development Project (2017 YFC1502405), and the IWHR Research & Development Support Program (WR0145B732017). The authors declare no conflict of interest.
# Contrast-Invariant Edge Detection: A Methodological Advance in Medical Image Analysis
## 4 Experimental Results and Analysis
### Evaluation Metrics  
In the task of edge detection, in addition to intuitive visual assessment, it is crucial to assess edge detection methods' performance in an objective and comprehensive manner. To evaluate edge detection algorithms from different perspectives, we employ evaluation metrics such as accuracy, precision, recall, and F1-score, which collectively provide a comprehensive assessment of the performance of edge detection algorithms from various dimensions [39].  
Accuracy reflects the ratio of pixels that are correctly classified, specifically the percentage of pixels correctly identified as either edge or non-edge. The formula is defined as Equation (19). True Positives (\(TP\)) refer to pixels correctly identified as edges. True Negatives (\(TN\)) indicate pixels correctly recognized as non-edges. False Positives (\(FP\)) represent pixels incorrectly labeled as edges. False Negatives(\(FN\)) denote pixels that are actually edges but were missed. However, in edge detection tasks, general edge pixelsusually occupy a small proportion relative to non-edge pixels, and relying only on accuracy may lead to less comprehensive results.  
\[\text{Accuracy }=\frac{TP+TN}{TP+TN+FP+FN} \tag{19}\]  
Precision is used to measure how many of the edges detected by the algorithm are true edge pixels. It is calculated by Equation (20). High precision means that the edge detection algorithm produces fewer false edges and the detected edges are usually accurate.  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{20}\]  
Recall is employed to measure the ability to recognize edges that are actually present, and the formula is shown in Equation (21). High recall means that most of the actual edges are recognized and fewer edges are missed.  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{21}\]  
The F1-score combines the roles of precision and recall, and it is used as a composite evaluation when both are equally important. Thus, it can provide a balanced view between the accuracy and completeness of the detection results. The formula of the F1-score is shown in Equation (22).  
\[\text{F1-score}=\frac{2\cdot\text{Precision}\cdot\text{Recall}}{\text{ Precision}+\text{Recall}} \tag{22}\]  
By using these four evaluation metrics, the performance of edge detection methods can be evaluated from different perspectives in medical image application scenarios.
# Multimode Physics in the Mode Locking of Semiconductor Quantum Dot Lasers
## 3 Towards Transform-Limited Performance  
### Experiment versus Transform Limit  
Here, we give an example of the insight gained into experimental device performance by comparing experiment and theory. The device is a single-section InAs QD laser still epitaxially grown on silicon, with device parameters available in Refs. [12; 22; 27]. In Figure 6, the red curves are from measurements and the black curves are from numerically solving Equations (6), (8) and (9). The operating current for the experimental spectrum (red curve) and the calculated spectrum (black curve) is 14 times and 10 times the threshold, respectively. The calculated RF linewidth is 143 kHz, compared to the 100 kHz measured value (Figure 6a). There is also good agreement on the measure pulse shape (Figure 6b), where fitting with a sech pulse shape gives approximately 500 fs of pulse duration [28].  
Reproducing the measured results with the multimode mode-locking model allows us to understand the experimental laser performance. In Figure 7a, the intensity of each cavity mode is shown by the solid gray curve. The solid black curve from theory indicates only partial locking of the beat-notes in the experimental laser, with the flat portion of the black curve covering only 30% of the cavity modes that are calculated to be above the lasing threshold. Additionally, we found that the carrier-induced refractive index change leads to a nonlinear dispersion across the lasing modes (dashed blue curve). This results in appreciable phase variation, contributing to considerable deviation from the transform limit (dashed blue curve in Figure 6b). In Figure 7a, the difference between the solid black curve and dotted red curve indicates the improvement made with the present experimental device.  
Figure 6: (**a**) RF spectrum and (**b**) mode-locked pulse from theory (solid black curves) and experiment (solid red curves). The frequency and time are referenced to the peak values. The dashed blue curve in (**b**) shows the transform-limited pulse based on the lasing bandwidth.  
Figure 7: (**a**) Effects of mode locking on beat-notes. The solid black curve is the beat-notes spread over the lasing spectrum (thin black curve). The red dotted curve is the variation in beat-note in the absence of mode locking. The solid gray curve is the mode intensities. (**b**) The red dotted curve is the total variation in beat-notes (same as in **a**). The contributions to it are from the GaAs waveguide (solid black curve) and InAs QD carrier-induced refractive index change (blue dashed curve).  
At a minimum, there are contributions from the passive waveguide material and from the carrier-induced refractive index change in the active region. Figure 7b shows these contributions for the example of an InAs QD active region embedded in a GaAs waveguide. In order for the beat-notes to lock, some optical nonlinearity is necessary to overcome the dispersion. This nonlinearity may be from saturable absorption incorporated inside the optical cavity or from four-wave mixing within the gain medium itself.
## 4 Proposed Methodology
Embed each block of the binary watermark W into each block of the cover image I using the following formula. \(\alpha\) is a scaling factor that is obtained using several options WI (Watermarked Image) = W + \(\alpha\times\) I
6. First level of inversion applied to embedded image coefficients to obtain watermarked image WI

OUTPUT: Binary image embedded watermarked image WI
```  
**Algorithm 2** Block-based DWT Embedding Algorithm  
In Algorithm 3, the flexible scaling factor is applied to find the optimal \(\alpha\) value to reach a robust, secure and resistant watermark methodology. ```
1. If a cover image is a color image, convert it into the grey scale image
2. Apply 4 \(\times\) 4 (or another size) of decomposition to the cover image and binary image. Each block size of the cover image and size of the watermark should be the same
3. Transform each block of the cover image into the first level decomposition of the discrete Wavelet Transformation
4. Apply flexible scaling factor algorithms to each block. Every block embeds a watermark into the high frequencies using optimal scaling factor values
5. Embed each block of the binary watermark W into each block of the cover image I using the following formula WI (Watermarked Image) = W + \(\alpha\times\) I
6. First level of inversion applied to embedded image coefficients to obtain watermarked image WI
```  
**OUTPUT: Binary image embedded watermarked image WI**  
**Algorithm 3** Block-DWT Flexible Scaling Factor Embedding Algorithm  
The flexible scaling factor in this work is defined as follows [43]:  
\[FSF=\sum_{i=1}^{3}\mid A(i)\mid\frac{\sum_{i=1}^{3}A(i)}{mxn} \tag{12}\]  
where \(A(i)\) is the selected neighbor coefficients in block-based DWT, \(FSF\) is the flexible scaling factor and \(mxn\) is the total number of pixels in the image block. There are several metrics that can be used in watermarking. Peak signal to noise ratio (PSNR), Mean Square Error (_MSE_) and Structural Similarity Index (_SSIM_) are commonly used metrics to measure the quality of the watermarked images or distortions on the images after some processes or attacks. In this work, the similarity ratio (_SR_) is calculated for each extracted watermark to evaluate the quality of extraction and level of distortion after attacks. \[MSE=\frac{1}{AxB}\sum_{a=0}^{A}\sum_{b=0}^{B}[n(a,b)-m(a,b)]^{2} \tag{13}\]where \(A\) and \(B\) demonstrate the number of pixels in the watermarked image and original image. \[PSNR=20x\log_{10}\frac{MaximumImageA}{\sqrt{MeanSquareError}} \tag{14}\]  
where \(A\) is the watermark or distorted image. \[SSIM=\left[\frac{2a_{1}a_{2}}{a_{1}^{2}+a_{2}^{2}}\right]x\left[\frac{2b_{12}} {b_{1}^{2}+b_{2}^{2}}\right] \tag{15}\]  
\[SIM=[Luminace(a,b)]^{i}x=[ Contrast(a,b)^{j}x=[Correlation(a,b)]^{z} \tag{16}\]  
where \(a\) is the mean and \(b\) is the variance of images. The similarity ratio is the ratio between a number of pixels with the same value and a total number of pixels in the watermark.
# Edible Packaging: A Technological Update for the Sustainable Future of the Food Industry
## 3 Results
### Gelatin Films
#### 3.5.3 Lipid-Based Packaging Materials  
Lipids are naturally hydrophobic polymers that come from plants, animals, or insects. Natural waves, acetylated monoglycerides, and resins are the most frequently utilized edible packaging lipids. Lipids cannot form independent and cohesive films. Therefore, they can be used for packaging as composite films. Lipids can make films and coatings that are excellent at keeping out moisture [38]. Coatings of wax are considerably more impervious to the migration of water when compared with other lipid-based or non-lipid edible films. These can be used by themselves or with other ingredients [26]. Table 1 groups edible packaging materials based on different lipids.  
Waxes are made of esters of a long-chain alcohol and a long-chain fatty acid, and they have a larger molecular weight. Waxes have animal and vegetal origins and serve as protective covering tissues. Due to their strong hydrophobicity, waxes made from esters aid in minimizing moisture permeability, which is why they are utilized in edible packaging [32; 81]. Both natural and synthetic waxes have been employed as protective coatings either alone or in combination with other materials. Natural waxes include carnauba wax [82], candelilla wax [83], rice bran wax [84], and beeswax [85], all of which can used to prevent fungal growth and extend the shelf life of fruits [86]; synthetic waxes include paraffin wax [87] and petroleum wax [88].  
Essential oils have good antimicrobial and antioxidant properties and can reduce lipid oxidation, improving the shelf life of foods [89]. Chitosan films supplemented with active agents have potential applications in meat preservation because of their antimicrobial properties. [PERSON] et al. [90] developed a chitosan film with Chrysanthemum morifolium essential oil to enhance the storage period of fresh raw beef and chicken. The developed active chitosan film seemingly inhibited the activity of S. aureus, and the authors of this study recommended using the film as a substitute for plastic bags.
# Mode Choice Effects on Bike Sharing Systems
## 5 Results
: arterial) & & & & & & & & & \\  & side street & \(-\)0.013 & 0.146 & \(-\)0.089 & \(-\)0.030 & 0.243 & \(-\)0.124 & 0.071 & 0.187 & 0.380 \\  & cycleway & 0.195 & 0.145 & 1.339 & 0.337 & 0.243 & 1.387 & 0.102 & 0.185 & 0.552 \\  & surface type (Ref. : macadam) & & & & & & & & & \\  & cobblestones & 0.004 & 0.143 & 0.028 & 0.322 & 0.231 & 1.395 & \(-\)0.142 & 0.187 & \(-\)0.757 \\  & asphalt & 0.239 & 0.146 & 1.634 & 0.377 & 0.234 & 1.610 & 0.208 & 0.192 & 1.087 \\ \hline \multirow{8}{*}{**Test**} & ASC & 0.669 & 0.632 & 1.059 & \(-\)1.677 & 1.207 & \(-\)1.389 & 0.980 & 0.795 & 1.233 \\  & travel time [min] & \(-\)0.116 & 0.014 & \(-\)8.299 & \(-\)0.085 & 0.028 & \(-\)3.059 & \(-\)0.111 & 0.017 & \(-\)6.607 \\  & fuel costs [EUR] & \(-\)0.474 & 0.129 & \(-\)3.684 & \(-\)0.325 & 0.244 & \(-\)1.336 & \(-\)0.530 & 0.158 & \(-\)3.346 \\  & parking costs [EUR] & \(-\)0.532 & 0.048 & \(-\)11.037 & \(-\)0.717 & 0.094 & \(-\)7.587 & \(-\)0.489 & 0.058 & \(-\)8.380 \\  & age & \(-\)0.065 & 0.023 & \(-\)2.829 & \(-\)0.002 & 0.049 & \(-\)0.042 & \(-\)0.085 & 0.029 & \(-\)2.941 \\  & age squared & 0.001 & 0.000 & 3.722 & 0.000 & 0.001 & \(-\)0.034 & 0.001 & 0.000 & 3.739 \\  & female [Ref. : male) & 0.745 & 0.150 & 4.976 & 1.541 & 0.297 & 5.192 & 0.510 & 0.187 & 2.724 \\  & BS user (Ref. : non-user) & \(-\)0.606 & 0.190 & \(-\)3.194 & \(-\)0.743 & 0.487 & \(-\)1.524 & \(-\)0.807 & 0.224 & \(-\)3.608 \\  & car available always & & & & & & & & & \\  & (Ref. : sometimes/never) & & & & & & & & & & \\  & PT season ticket (Ref. : none) & \(-\)0.494 & 0.171 & \(-\)2.897 & \(-\)0.108 & 0.337 & \(-\)0.321 & \(-\)0.644 & 0.215 & \(-\)2.998 \\  & winter (Ref. : autumn) & 0.360 & 0.162 & 2.230 & 1.209 & 0.348 & 3.480 & 0.215 & 0.188 & 1.144 \\  & home municipality (Ref. : small) & & & & & & & & & \\  & larger town & \(-\)1.318 & 0.263 & \(-\)5.010 & \(-\)2.222 & 0.562 & \(-\)3.956 & \(-\)1.111 & 0.310 & \(-\)3.586 \\  & city & \(-\)1.062 & 0.240 & \(-\)4.429 & \(-\)1.561 & 0.525 & \(-\)2.972 & \(-\)0.866 & 0.282 & \(-\)3.066 \\  & trip mandatory (Ref. : leisure) & \(-\)0.608 & 0.163 & \(-\)3.739 & & & & & & \\ \hline \multirow{8}{*}{**Test**} & ASC & 0.756 & 0.487 & 1.552 & 1.080 & 0.791 & 1.365 & 0.413 & 0.652 & 0.634 \\  & travel time [min] & \(-\)0.101 & 0.012 & \(-\)8.302 & \(-\)0.136 & 0.021 & \(-\)6.410 & \(-\)0.093 & 0.015 & \(-\)6.088 \\  & access and egress [min] & \(-\)0.208 & 0.017 & \(-\)12.04 & \(-\)0.254 & 0.027 & \(-\)9.572 & \(-\)0.174 & 0.024 & \(-\)7.184 \\  & travel costs [EUR] & \(-\)0.779 & 0.049 & \(-\)15.885 & \(-\)0.900 & 0.079 & \(-\)11.369 & \(-\)0.737 & 0.065 & \(-\)11.391 \\  & capacity (Ref. : middle) & & & & & & & & & \\  & high & \(-\)0.498 & 0.141 & \(-\)3.536 & \(-\)0.501 & 0.226 & \(-\)2.214 & \(-\)0.509 & 0.185 & \(-\)2.752 \\  & overloaded & \(-\)1.147 & 0.147 & \(-\)7.775 & \(-\)1.115 & 0.226 & \(-\)4.936 & \(-\)1.221 & 0.201 & \(-\)6.068 \\  & age & \(-\)0.056 & 0.021 & \(-\)2.694 & \(-\)0.0
# Packaging Design Image Segmentation Based on Improved Full Convolutional Networks
## 5 Discussion  
The results of this study demonstrate the effectiveness of the improved FCN model for ISS in packaging design. By integrating superpixel technology, multi-branch networks, dual-attention mechanisms, and edge knowledge distillation, we were able to significantly enhance the accuracy of small-object segmentation and optimize computational efficiency. This is particularly relevant in packaging design, where precise segmentation of small details such as logos, text, and patterns is critical for ensuring high-quality visual representation.  
One notable aspect of this work is the incorporation of techniques inspired by recent advances in NPL. In NLP, attention mechanisms and contextual learning have been instrumental in improving the accuracy of tasks such as language translation and sentiment analysis. By applying similar attention mechanisms to image data, our model is able to focus on key areas within an image, improving segmentation accuracy, especially for small or intricate details. This mirrors the way attention mechanisms in NLP allow models to focus on important parts of a sentence or text, enhancing overall understanding and output.  
Moreover, the use of edge knowledge distillation in this study reflects an analogous approach in NLP, where knowledge distillation is often used to compress large language models while retaining their accuracy. This approach allows the model to be more lightweight and efficient, reducing memory usage and computation time without sacrificing performance. The ability to draw on innovations from NLP to enhance image processing models represents an exciting convergence of machine learning techniques across different domains.  
The results of our experiments showed that this interdisciplinary approach yielded superior performance compared to traditional methods, particularly in tasks requiring precision and speed. However, one limitation of this study is the lack of a specialized packaging design dataset. Although public datasets such as CamVid and Cityscapes were employed for model validation, a domain-specific dataset could further improve the model's performance in real-world packaging design applications. Future research should focus on the creation of such datasets to facilitate more targeted model training and evaluation. In addition, it is also worth noting that despite the model's excellent performance on publicly available datasets, future work will focus on working with industry to validate using real-world packaging design cases, testing the model through real project scenarios to assess its performance in real-world applications, and further optimizing the model's performance and efficiency to ensure its effectiveness and scalability in packaging design.
# Error Detection for Arabic Text Using Neural Sequence Labeling
## 2 Background and Related Work
### Arabic Language and Grammar  
Arabic is a Semitic language that is rich in its morphology (internal word structure) and syntax (arrangement of words to make sentences and phrases). Although it has many variations (dialects) around the Arab World, Modern Standard Arabic (MSA) is the official language. MSA is a simple form of Classical Arabic (CA), the language of the Qur'an (Islam's Holy Book). However, MSA has much more modern vocabulary [13]. It is the main language used in media and education. Additionally, MSA is primarily written, not spoken [13].  
Arabic words are generally classified into nouns, verbs, and particles [3]:  
1. Noun: A noun in Arabic is a name of a person, thing, or idea. It is divided into derivatives and primitives. These could be further classified by number, gender, definition, and case.
2. Verb: The verb is any word that conveys an action. It is divided according to tense, structure, mood, and voice. These could be further classified by number, person and gender.
3. Particle: The particle is a word that can precede a noun, a verb, or both. Particles include prepositions, conjunctions, interrogative particles, exceptions, and interjections.  
Moreover, an Arabic sentence can be one of two forms [3]:  
1. Nominal: A nominal sentence contains two structures: inchoative () and enunciative (). Enunciative can be verbal/nominal sentence. Additionally, a nominal sentence can start with Inna/Kan and its sisters, which change its case ending.
2. Verbal: A verbal sentence contains two structures: verb and subject. A transitive verb needs to have one or more object. In its passive voice it includes a verb and a proagent ().  
Arabic grammar rules are very complex and might even be confusing to Arabic natives. The difficulty is due to several reasons--for example: (1) the sentence length and complex grammar; (2) the relatively free word order in Arabic [3]. Therefore, automatically checking Arabic grammar for errors could help improve the quality of written Arabic text.
# Performance Analysis of the Demand-Based Ventilation in a Nordic Apartment Building
## 3 Results
### Performance of Standard Demand-Based Ventilation System
The acceptable level of negative pressure in residential buildings was assumed to be \(-\)10 Pa, and \(-\)30 Pa was set as a warning level [12]. The four-room apartments had an acceptable pressure level at any airtightness due to their larger floor area. Two- and three-room apartments had an unacceptable pressure level at an airtightnesses of 0.5 1/h and better, and studios exceeded the acceptable pressure level in new buildings and had a warning trend in future buildings with a pressure difference of around 40 Pa.