esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Challenges and Limitations  
#### 4.4.1 Data Availability  
One of the primary challenges observed in agricultural commodity price forecasting is the reliance on limited or region-specific datasets, which can block the generalizability of the models [71]. Models trained from datasets belonging to certain countries or markets perform poorly when applied to others with different economic conditions. This limitation can lower the credibility of forecasting models, particularly for long-term predictions. Additionally, incomplete and low-quality data, such as missing export/import statistics or meteorological conditions, reduce the effectiveness of models.
## 3 Results  
### Study Selection  
The initial screening of electronic databases yielded a total of 2024 articles and 11 publications identified through a manual search. Duplicated studies were eliminated, leaving 2013 articles for further examination. After reviewing the titles and abstracts of the remaining articles, 66 were chosen for full-text evaluation. Of these, 45 articles were excluded for failure to report clear periodontal outcomes, had insufficient sample sizes of less than 10 patients, and/or involved duplicate patient samples from other studies (Supplemental Table S1). Finally, 21 studies met the inclusion/exclusion criteria and were assessed in this systematic review, as shown in the PRISMA flowchart in Figure 1.  
The significant heterogeneity among the included investigations prevented the conduction of any quantitative synthesis. Hence, a descriptive but detailed analysis of the reported outcomes was performed. The inter-reviewer reliability in the screening and inclusion process, assessed with [PERSON]'s \(\kappa\), was 0.93 and 0.88 for the assessment of titles as well as abstracts and full-text evaluation, respectively.  
Figure 1: PRISMA flowchart of the screening process in the different databases.
## 4 Characterization of Damage Under Blast Loading
### Analysis of Damage Parameters and Main Influencing Factors
#### 4.3.2 Effect of Location on Damage  
Locations 1, 2, and 3 correspond to the influence of only single partitions, transverse and longitudinal partitions, and no partitions, respectively. From Figures 11 and 12 and Table 7, it can be seen that the maximum fracture area of the top plate is 1.1532 m\({}^{2}\), 0.7904 m\({}^{2}\), and 2.3805 m\({}^{2}\) for a TNT equivalent of 200 kg, respectively, and the fracture-restraining effect of the transverse and longitudinal partitions on the top plate is obvious. The maximum crack area of the base plate is 0.0168 m\({}^{2}\), 0.0144 m\({}^{2}\), and 0.0272 m\({}^{2}\). It can be seen that, under the same TNT equivalent, the explosion-resistant performance enhancement of the partition plates on the steel box girder bridge is obvious; the crack area at location 2 is the smallest, followed by location 1, and at location 3 it is the largest.  
As shown in Figure 13, the base plates at positions 1 and 2 have two cracks, appearing at the left and right; due to the randomness of the fragmentation, the location of the cracks is not symmetrical, and the cracks are not of the same size. At location 3, there is only one crack, but the crack area is larger than at locations 1 and 2. At locations 1 and 2, due to the stiffening ribs in the longitudinal partitions reflecting the shock wave, the base plate's fracture is offset, and with the increase in the explosion equivalent, there is a farther offset distance; due to the consumption of shock waves by the partitions, preventing them from reaching the base of the plate, at location 2, the shock wave is smaller, resulting in a smaller fracture. Location 3 is not blocked by the partitions, so the shock wave is transmitted directly to the base plate, causing serious damage to the base plate.  
The stiffening ribs in the longitudinal partitions reflect the shock waves, deflecting them from the base plate cracks, and with the increase in TNT equivalent, the offset distance is farther; at locations 1 and 2, the offset distances are 1.06 m and 1.27 m, and 0.54 m and 0.79 m. At location 3, due to the stiffening ribs in the longitudinal partitions, there is less influence, so the base plate's maximum fissure center is almost in the center of the projected explosion point. The maximum offset of the crack center of the base plate is shown in Figure 14 below.  
Figure 14: Relationship between maximum crack center offset of base plate and TNT equivalent.  
Figure 13: Damage patterns of steel box girder base plate (200 kg).
# Comparison of NeQuick G and Klobuchar Model Performances at Single-Frequency User Level +
## 4 Discussion  
### Network-Wise Performance Comparison  
This study explores the benefits of using multiple networks of stations around the world to improve ionospheric models for SF users. NeQuick G and Klobuchar models are  
Figure 4: Monthly means of the TEC availability for the NeQuick G and Klobuchar models. The top panels show results for high latitudes, the middle panels for middle latitudes divided into the northern and southern hemispheres, and the bottom panel for low latitudes.  
compared across a wide geographic range and under different ionospheric conditions to evaluate their performance. Data collection from multiple networks provides a more comprehensive evaluation of the models' ability to mitigate ionospheric effects. SF positioning accuracy is considered for each network of stations, but results may vary due to differences in ionospheric characteristics and data quality. Variations in the number and distribution of ground-based stations, data quality, and local ionospheric conditions could lead to differing results. Overall, this study highlights the benefits of using multiple networks to improve ionospheric models for SF users.
## 5 Conclusions  
Although English offensive language detection has been extensively studied, Chinese offensive language detection needs to be more studied, especially considering the complexity of Chinese text. Our study fills the Chinese research gap in offensive language detection. Our approach adopts multi-level information fusion strategies, including RoBERTa's sentence-level and word-level embedding, a bidirectional GRU model, and a multi-head self-attention mechanism, which enables the model to consider different levels of semantic information simultaneously. It helps to understand the complexity of Chinese text better. In addition, by concatenating the output results of multi-head attention with RoBERTa's sentence embedding, the efficient fusion of local and global information was realized, and the representation ability of the model was improved, which helps to better deal with the complex context in Chinese text. Our study further expands the scope of detection of offensive language, focusing on specific categories of offensive language, including race, gender, and region. This fine-grained exploration provides deeper insights to help us better understand and address the complexity of Chinese offensive language.  
The experimental results show that our proposed method effectively detects Chinese offensive language. In addition, the ablation experiment also confirmed that the performance of our model depends on the synergistic effect of each module, and each module is indispensable. We also developed a series of strategies to continue to improve. First, we plan to build a dictionary of implicit insult and offensive words to introduce implicit offensive word features, thereby improving the performance of the model. Secondly, we will actively collect more Chinese offensive language data to ensure better generalization performance of the model. In addition, our future work will focus on promoting research in the field of mixed-language offensive language detection to adapt to diverse language environments and application scenarios. Finally, we plan to delve deeper into the joint analysis of multimodal information to further improve the performance and applicability of our method.  
Methodology, M.X.; data curation, M.X.; writing--original draft preparation, M.X.; writing--review and editing, M.X. and S.L.; supervision, S.L.; project administration, S.L.; funding acquisition, S.L. 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 (Grant No.: 61762085) and the Natural Science Foundation of Xinjiang Uygur Autonomous Region from Xinjiang, China (Grant No.: 2019D01C081).  
Not applicable.  
Data Availability Statement: Dataset derived from [[https://github.com/thu-coai/COLDataset](https://github.com/thu-coai/COLDataset)]([https://github.com/thu-coai/COLDataset](https://github.com/thu-coai/COLDataset)) (accessed on 26 August 2023).
## 3 Experiments and Results Analysis
### Experimental Results and Comparison  
In this paper, the PCCSTSR network is compared with several advanced lightweight single-image super-resolution networks at scales of \(\times\)3 and \(\times\)4, including CARN [28], CRAFT [29], EDSR [4], ELAN [30], IMDN [31], LAPAR [32], LatticeNet [33], MAFFSRN [34], and SwinIR [11]. Table 1 presents the test results obtained in the same experimental environment, with the best results highlighted in bold. From the test results, it can be observed that despite having a smaller model, the proposed network achieves good performance with respect to the evaluation metrics, striking a good balance between model performance and computational cost. Compared to the classical SWINIR, the proposed PCCSTSR has fewer parameters and yields better results. For example, at the \(\times\)4 scale, on the Urban100 and Manga109 datasets, it improves by 0.16 dB and 0.4 dB, respectively. As shown in Figure 5, a visual comparison of various SISR methods on the Urban100 dataset at the \(\times\)4 scale is provided. Also shown in Figure 6, a comparison of reconstruction results among original high-resolution image, low-resolution image, and PCCSTSR on the Urban100 dataset at the \(\times\)4 scale is provided. It can be observed that the proposed PCCSTSR restores more pixel details, while the other methods suffer from issues such as insufficient details, blurred edges, and artifacts. The five images in Figure 5 clearly demonstrate that the proposed method is capable of recovering more accurate details and textures. This is attributed to the inclusion of PCCSTB in the model, which complements the shifted window attention mechanism and expands the receptive field. As seen in the high-resolution reconstruction, the proposed model exhibits better recovery performance. By leveraging the strengths of both the CNN and Swin Transformer in a parallel architecture, the model extracts image details through a progressively expanding receptive field and integrates both local and global features. The experimental results validate the superiority of the proposed method. Additionally, network inference time is an important metric for lightweight SR algorithms. To further validate that the proposed PCCSTSR network is a lightweight model, a comparison experiment was conducted on the reconstruction speed at a \(\times\)4 scale on the Urban100 dataset using mainstream networks. The experiment was run on the following hardware: CPU Intel 13700 KF(Intel Corporation; Intel Shanghai Production; Shanghai; China); GPU NVIDIA Geforce RTX 4070 ti Super 16 GB (NVIDIA Corporation; TSMC Production; China); Memory DDR5 5600 MHz 32 GB\(\times\)4(KingBank; Shenzhen; China); SSD PCIe Gen 4.0 2 TB. (Yangtze Memory Technologies Corporation; Wuhan; China). The test image size was 64\(\times\)64 pixels, and the inference time for the first image, which may include network loading time, was not included in the calculation. The average inference time was obtained after running the test 10 times, as shown in Table 2.
## 3 Problem Formulation, Assumptions, and Notations
### Buffer Allocation Problem  
Let \(TP\) denote the throughput of the above line, i.e., \(TP\) is the amount of material produced per unit of time by the last machine \(M_{K}\) in the long run. When machine parameters are specified, \(TP\) is a function of the buffer capacities \(b_{1},\ldots,b_{K-1}\). We write \(TP=f(b_{1},\ldots,b_{K-1})\), or \(f(\mathbf{b})\) for short, where \(\mathbf{b}\) is the vector \((b_{1},\ldots,b_{K-1})\).  
We consider the following throughput maximization problem:  
\[\max f(\mathbf{b}) \tag{1}\]  
subject to  
\[\sum_{i=1}^{K-1}b_{i}=N \tag{2}\]  
\[b_{i}\]  
non-negative integers, \[i=1,\ldots,K-1\] (3)  
where \(N\) is the given total buffer capacity to be allocated.  
Clearly, the above problem is a combinatorial optimization problem. As \(N\) or \(K\) increases, it can be shown that the number of feasible solutions increases exponentially. For large production lines, complete enumeration is impractical, so efficient solution methods need to be developed.
# Powdered Foods: Structure, Processing, and Challenges: A Review
## 6 Improvements and Challenges on Powdered Foods
### Microstructure  
The microstructure of powdered materials plays an important role regarding product stability. Among the existing forms, powdered foods can be crystalline, amorphous, or mixed, depending on the type of processing used to obtain the powder and the intrinsic characteristics of the food, with the crystalline structures exhibiting greater molecular organization and the amorphous structures displaying less organization. Examples of food with a crystalline structure include salts and sugars, while the majority of foods have an amorphous structure due to their complex matrix, as seen in powdered dairy foods, fruit juices, and hydrolyzed protein powder [4].  
Powdered foods in crystalline form tend to have little or no hygroscopicity, are stable, and flow easily, whereas foods with an amorphous structure have greater porosity and high hygroscopicity, besides being thermodynamically unstable. On the other hand, foods with an amorphous structure are easier to dissolve compared to powders with a crystalline structure due to differences in molecular organization [4]. Furthermore, [PERSON] et al. [49] reported that particle size is directly proportional to the degree of rehydration. [PERSON] et al. [50] emphasize the importance of particle shape in achieving desirable rehydration characteristics. Spherical particles, commonly formed via the spray-drying process, have less interaction with water due to their smaller contact surface angle compared to irregular particles, generally created from the freeze-drying process.
## Materials and Methods
### Characterizations  
Surface textural characteristics of the adsorbents were acquired from the adsorption-desorption isotherms of nitrogen gas, measured at -196 \({}^{\circ}\)C, which was conducted using an accelerated surface area and porometry (ASAP) apparatus--Micromeritics ASAP 2020 (Norcross, GA, USA). Before the measurements, the samples were degassed at 350 \({}^{\circ}\)C for 24 h. The equilibrium interval for each adsorption point was set as 45 s. The specific surface area of each sample was determined using the Brunauer-Emmett-Teller (BET) method, which was denoted as S\({}_{\mathrm{BET}}\). The total pore volume (V\({}_{\mathrm{t}}\)) of the adsorbent was obtained from the adsorption amount of nitrogen gas at P/P\({}_{\mathrm{o}}\) \(\sim\)0.99. The micropore volume (<2 nm, V\({}_{\mathrm{mi}}\)), micropore area (S\({}_{\mathrm{mi}}\)), ultramicropore volume (<0.7 nm, V\({}_{\mathrm{ultra}}\)) and mesopore volume (2 \(\sim\) 50 nm, V\({}_{\mathrm{me}}\)) were calculated from the density functional theory (DFT) method. Field emission scanning electron microscopy (FESEM) was used to probe the morphology of the filaments using a microscope (Hitachi S-4800, Hitachi, Krefeld, Germany). To understand the surface chemistry of the adsorbents, X-ray photoelectron spectroscopy (XPS) was applied and a spectrophotometer (PHI 5000 VersaProbe II, ULVAC-PHI, Kanagawa, Japan) was used to measure the XPS spectra on the surface of the adsorbent. A scanning X-ray monochromator of Al source was used. The kinetic energy and electrons emitted from the top 1-10 nm of the sample surface were collected. C\({}_{1\mathrm{s}}\) electron binding energy corresponding to graphitic carbon was set at 284.6 eV for calibration purposes. The XPSPEAK software (version 4.1, The Chinese University of Hong Kong, Hong Kong, China) was utilized for deconvolution of the XPS spectra, which was a nonlinear least squares curve-fitting program. Prior to quantification, a Shirley-type background was selected to be subtracted. After the baseline was subtracted, curve fitting was performed using a Gaussian function fitting program under an optimized peak shape. The thermogravimetric analysis (TGA) was applied to evaluate the oxidation resistance of the adsorbents. A thermogravimetric analyzer (Q500, TA Instrument, New Castle, DE, USA) was selected to measure how the weight of the sample varied with the temperature in air, and the dependence of the rate of weight loss on the temperature.
## 5 Discussion
### Limitations and Challenges  
While our system offers numerous benefits, it is important to acknowledge its limitations and potential challenges:  
* Range limitations: Ultrasound signals attenuate relatively quickly in air, potentially requiring a higher density of beacons for larger areas. However, for the applications we envision--installations with foot traffic and dynamic environments--the current range of 6 to 10 m should cover all cases.
* Acoustic interference: Environments with significant ambient noise or other ultrasonic sources may experience interference. However, the ultrasound band is relatively quiet, and the narrow frequency of our emitted signal allows for easy filtering, reducing potential ambient interference.
* Update rate: The system's update rate may be lower compared to some other technologies. For the target application with robots that move at walking pace this is less of a concern.
* Obstacles: Solid obstacles between beacons and receivers could potentially block signals. However, ultrasound can pass around obstacles with some power loss, and our system has sufficient gain to detect very soft signals. Additionally, placing the receiver high on the robot will mitigate most potential signal blockages.  
It is worth noting that these limitations are less significant in our target application of indoor environments with walking-speed robots. In such settings, the update rate is sufficient, range limitations are manageable, and acoustic interference can often be minimized through environmental design. The installation of a solution is also part of the system, and any coverage limitations should be addressed in this phase.
## 2 Description of the Systems Under Investigation and Experimental Setups
### Random Vibration Testing  
Next, random vibration testing was performed on the individual systems. Random vibration testing allows for a qualitative analysis of the dynamics of these systems. As stated previously, a pseudorandom experimental test is considered since this method is more sensitive to nonlinearities due to the constant amplitude and periodicity in the same testing interval. Additional advantages of pseudorandom testing include that the sample is always periodic in the sample interval, the test is relatively fast, and it takes fewer averages than random testing [35]. The setup for random vibration testing is shown in Figure 6. Input excitation levels of \(1\times 10^{-7}\), \(1\times 10^{-6}\), \(1\times 10^{-5}\), and \(1\times 10^{-4}\) V\({}^{2}\)/Hz were considered for this experimental test.
## 4 Limitations of Using Plant Products as Bioherbicides
### Effect of Nepetalactone and Phloretin on Non-Target Organisms
#### 4.3.3 Effects on Crops
Treatment with a ferulic acid water solution at a concentration of \(10^{-2}\) M (1.94 mg mL\({}^{-1}\)) results in 95% and 98% inhibition of the seed germination of _Rumex crispus_ and _Amaranthus retroflexus_, respectively [323]. NL (2 and 4%) in the atmosphere of Petri dishes had no effect on the germination of _R. crispus_[41], while 0.05 and 0.1% NL in a water emulsion caused 95 and 100% inhibition of _A. retroflexus_, respectively [38; 41]. Juglone at a concentration of \(10^{-3}\) M (0.174 mg mL\({}^{-1}\)) caused 100% inhibition of germination of _Lepidium sativum_[324], while 4% NL in the atmosphere of Petri dishes caused 65% inhibition [38]. It would be valuable to investigate the potential synergistic effect of volatile NL and soluble phloretin to potentially reduce the required amount of bioactive compounds, making use of these substances more efficiently and cost-effectively. Based on our previous research, _A. thaliana_ would be the next target plant we would investigate as we have demonstrated the inhibitory effect of both NL and phloretin on it. In our future work, we also plan to investigate the combined effect of NL and phloretin on other weed species, as well as on non-target organisms. However, the scientific results summarized in this review represent just the first step to introducing repetalactone and phloretin as potential bioherbicides. Registering a natural product, such as refined EO from _N. cataria_[325] or phloretin, as a biopesticle requires navigating through intricate regulatory frameworks. These frameworks include a detailed characterization of the active ingredient, focusing on its physicochemical properties, followed by a thorough assessment of human health risks. Key evaluations include toxicity, the potential for eye and skin irritation, genotoxicity, inhalation risks, dose-response analysis, and the possibility of contaminating drinking water. Additionally, a complete cumulative effect assessment is required. Equally important is the ecological evaluation, which involves studying the environmental behavior of the product, the risk of groundwater contamination, and potential impacts on non-target organisms, including endangered and protected species. Given the complexity of these requirements, further research is essential to refine repetalactone and phloretin in bioherbicide formulations and application methods. Such research will ensure that bioherbicides can be effectively integrated into sustainable agricultural systems while minimizing risks to human health, the environment, and crop productivity.
# Design Model of Flow Lines to Include Switch-Off Policies Reducing Energy Consumption
## 3 Switch off Policy  
As described in the literature [23], switching off policies based on buffer level information can lead to significant energy savings without reducing productivity. The proposed policies are upstream (UP), downstream (DP), and upstream and downstream (UDP). In the upstream policy, the machine switches off when the upstream buffer is empty and switches on when the upstream buffer level is \(N^{U}_{on}\). The level of downstream buffer controls the state in the downstream policy. The machine switches off when the threshold \(N^{D}_{off}\) is reached and turns on when the number of pieces in the buffer is equal to \(N^{D}_{on}\). According to Reference [15], the state of machine \(s_{j}\) is defined as follows (Equation (12)):  
Figure 2: Framework for flow line designs.  
\[s_{j}=\left\{\begin{array}{ll}1&if\;\;out-of-service\\ 2&if\;\;idle\\ 3&if\;\;in\;start-up\\ 4&if\;\;working\\ \end{array}\right. \tag{12}\]  
The states 1, 2 and 3 are unproductive, i.e., no pieces are being processed when a machine is in one of these states. The states 1 and 2 are called inactive states. According to Reference [23], the upstream and downstream combines the UP and DP policies as follows (Equation (13)):  
\[\left\{\begin{array}{ll}Switch-Off&if\;\;s_{j}=2\;\;AND\;\;(n_{j}=0\;\;OR\; n_{j+1}\geq N_{off}^{D})\\ Switch-On&if\;\;s_{j}=1\;\;AND\;\;(n_{j}=N_{on}^{U}\;\;AND\;\;n_{j+1}\leq N_{on }^{D})\\ \end{array}\right. \tag{13}\]  
Figure 3 summarizes the states of the generic machine and the transition from one state to another.