esa-sceva/satcom-chunk-collection huggingface dataset:
# Brewer's Spent Grains: Possibilities of Vaporization, a Review
## 3 Possible Uses of BSG
### Source of Phenolic Compounds
Very important roles in the case of these fermentations lie with humidity and water activity, which show us the water available for microbial growth, which affects the development of biomass, metabolic reactions, and mass transfer processes [59]. According to studies carried out by [PERSON] et al. (2013), up to 20 mg GAE/g BSG can be extracted [51]. The most important factor in the extraction of phenolic compounds is the solvent used. [PERSON] et al. (2013) reported a total phenolic compound content of \(9.90\pm 0.41\) mg GAE/g dry matter, using 60% acetone (\(v\)/\(v\)) and 2.14 mg GAE/g BSG when ethyl acetate was used, with an increase in extraction yield of 4.6 times [16]. When acetone was used as a solvent, the flavonoid content was between 0.51-2.12 mg QE/g BSG. [PERSON] and [PERSON] (2017) reported results of \(1.26\pm 0.10\) and \(4.53\pm 0.16\) mg GAE/g dry matter for pale BSG and dark BSG (obtained by drying at very high temperatures), using acidified methanol as a solvent (HCl/methanol/water 1:80:10 \(v\)/\(v\)) [17], while [PERSON] et al. (2013) reported contents of \(19.5\pm 0.6\) mg GAE/g dry matter for BSG light and \(16.2\pm 0.6\) mg GAE/g dry matter for BSG dark [51]. The type of malt used and the malting process (burning regime and roasting temperatures) influence the content of phenolic compounds, BSG light having a higher content than BSG dark [51]. According to studies by [PERSON] et al. (2014), the total content of polyphenols in BSG was 167.07 mg GAE/l following a first extraction with 80% ethanol and 66.75 mg GAE/l following a second extraction with the same solvent [60]. When the extract is used in food, the solvents used are very important and are regulated from the perspective of food safety (according to good manufacturing practices are the mixture acetones:water or ethanol:water). Methanol is a very effective extraction solvent, but its toxic nature limits the use of the extract in the food industry or in the pharmaceutical industry [16]. An alternative and ecological method to conventional extractions with organic solvents is the method of supercritical CO\({}_{2}\) extract (SCE-CO\({}_{2}\)), which uses CO\({}_{2}\) in a supercritical condition when both temperature and pressure are equal to or exceed the critical point of 31 \({}^{\circ}\)C and 73 atm, providing the ideal conditions for extracting compounds with a high degree of recovery in a short time [61]. BSG has a lower content of phenolic compounds than berries, which have a content between 28-51 mg GAE/g, but its phenolic content is much higher than many vegetables (e.g., onions 2.5 mg GAE/g, potato peel 4.3 mg GAE/g, tomatoes 2.0 mg GAE/g), making it an interesting by-product to evaluate [16]. An interesting application is an increase in the content of bioactive phenolic compounds in food, which have a big impact lately due to the desire of consumers to improve their health through food [59].
## 4 Simulation Analysis
### Simulation
#### 4.2.2 Control Effect with Method 2  
If the prediction of forward slip and backward slip is accurate, high-precision tension control results can be achieved using method 2. Using the same rolling process as described above, the tension control effect with method 2 is shown in Figure 12. As can be seen, the maximum forward tension error is about \(-0.26\) kN, and the maximum backward tension error is about \(-0.2\) kN. The reason is that by using the feedforward speed, the roll rotation can be accurately tracked based on the speed of the tension cylinder, and thus the servovalve achieves equivalent tension closed-loop control under conditions where the rolls are static. The response speed of the servovalve is very high, so it achieves satisfactory tension closed-loop control accuracy.  
Figure 11: The tension control effect and the flow through the proportional relief valves with method 1 when \(S_{\rm h}=0.04\) and \(S_{\rm H}=0.4\). (**a**) The control effect of forward tension; (**b**) the flow through the proportional relief valve on the exit side; (**c**) the control effect of backward tension; (**d**) the flow through the proportional relief valve on the entry side.  
However, the forward and backward slip are related to the material, reduction rate, friction coefficient, and roller diameter, among many other factors. This makes accurate prediction difficult, especially when variable gauge rolling is carried out. Assuming that the forward slip prediction value is 0.01 and the actual value is 0.04, and the backward slip prediction value is 0.1 and the actual value is 0.4, the control effect using method 2 is shown in Figure 13. It can be seen that the maximum error of the forward tension control is about \(-0.32\) kN, and the maximum error of backward tension control is about \(-0.78\) kN. The control accuracy is significantly reduced. The reason for this is that when there is a large error in the prediction of forward or backward slip, the tension cylinder cannot accurately track the roll rotation. There is high load stiffness in the tension closed loop, so the values of its controller parameters are low, which does not allow complete elimination of deviations between the tension cylinders and the roll rotation, resulting in large tension control errors.
## 5 Experimental Setup
### Evaluation Criteria and Performance  
The performance of each method has been measured by using accuracy, precision and recall. Accuracy: \(\frac{T_{p}+T_{n}}{T_{p}+T_{n}+T_{p}+T_{n}}\); precision: \(\frac{T_{p}}{T_{p}+T_{p}}\); recall: \(\frac{T_{p}}{T_{p}+T_{n}}\); where \(T_{p}\), \(T_{n}\). are true positive and true negative; these are the correct positive and correct negative predictions, respectively. \(F_{p}\), \(F_{n}\) are false positive and false negative and are the wrongly positive and wrongly negative predictions, respectively. A 10-fold cross-validation approach has been used in order to evaluate the performance for every classifier.  
The above flowchart (Figure 2) depicts the formation process of each dataset and the experimental evaluation procedure. As shown in the image, the original corpus is treated at the beginning with some basic preprocessing steps, while the idea of applying the English translation to the original set is depicted in the second row of the chart. Based on this augmentation, the mixed dataset is created. Furthermore, since we need to evaluate the proposed methodology in real-world scenarios, where it is very common that one class dominates the other in distribution ratios, we have proceeded to the generation of two, highly imbalanced sets per each class label. Finally, for all described datasets, the PCA dimensionality reduction and the FS technique were optionally applied, to assist the traditional classifiers in recovering from data sparsity.
## 4 Discussion
Users were more accepting of the lighting environment under LED lights with a CRI \(\geq\) 80 at 6500 K than under LED lights with a CRI \(<\) 80 at 6500 K. LED lights with a CRI \(\geq\) 80 at 6500 K had a lower value for the IES Rg than LED lights with a CRI \(<\) 80 at 6500 K. LED lights with a CRI \(\geq\) 80 at 6500 K had a positive D\({}_{uv}\) value, whereas LED lights with a CRI \(<\) 80 at 6500 K had a negative D\({}_{uv}\) value. Experiment 2 had the same results as experiment 1 except that the LED lights with a CRI \(<\) 80 at 3000 K and the LED lights with a CRI \(\geq\) 80 at 3000 K had negative D\({}_{uv}\) values. The CCT and CRI parameters were not enough to describe the color properties of LED lighting. This study makes significant additions to several other studies. First, it contributes to the study of user acceptance by showing that the CRI and CCT are important determinants of user acceptance for HCL in offices. Previous studies on the effects of lights on user acceptance have focused on the effects of either the CRI or CCT. However, this study attempted to represent the interaction effects between CRI and CCT. The results revealed that interaction between the CRI and the CCT of LED lights has an impact on user acceptance. Future studies could explore user acceptance under various thresholds for the CRI and CCT, such as LED lights at 4000 K with a CRI \(<\) 80 or \(\geq\) 80 or LED lights at 5000 K with a CRI \(<\) 80 or \(\geq\) 80 [16]. In addition, this study adds to the literature on the appropriate brightness level for HCL in offices. Lights of an appropriate brightness level are necessary for users to comfortably perform office tasks. Previous studies showed the effects of either the CRI or CCT on the appropriate brightness level. This study provides evidence that manipulating the CRI and CCT of LED lights can create the appropriate brightness level for HCL in offices. Further studies could investigate the appropriate brightness levels under various thresholds for the CRI and CCT for HCL in offices. One of the limitations of this study is that our experiments were performed in a room with a single light. In real offices, several rows of LED lights are typically installed. A blackout curtain was used to block the effects of daylight in our test room, but we cannot be sure whether daylight would increase or decrease user acceptance [50]. Since daylight is generally present in offices, more studies are needed on the effects of multiple LED lights or LED lights with daylight on user acceptance. Finally, participants were asked to undertake our reading task within a relatively short time. Therefore, the experimental results might be different from the results obtained in a real office environment, in which the working duration is longer. Another suggestion for future studies is to investigate user acceptance in real office environments.
## 6 Scaling Phase
### Technological and Economical Sustainability  
With this new consortium, EHAS has obtained the support of the American Development Bank (CAF), which is financing the deployment of 3G services in 15 communities in what has been called the NAPO Project. For CAF, this model was particularly interesting because it combines the interests of operators with the interests of developing, isolated rural areas. The involvement of a bank such as CAF strengthens the chances of scaling up this initiative, since it can offer loans to large operators, SROs, or governments that want to replicate this initiative.  
In this new consortium, Mayu assumes responsibility for the maintenance of the telecommunications network. In this way, in exchange for being able to take advantage of the infrastructure already deployed in previous projects (and thus reduce their CAPEX and OPEX costs), Mayu offers connectivity for telemedicine services to EHAS, PUCP, and PANGO. This reduction in CAPEX and OPEX allows Mayu to assume the cost and maintenance tasks of the network, which helps to guarantee the sustainability and availability of the telemedicine services that operate over it.  
As this is a very isolated region, the only possible terrestrial gateway to operators' core networks is in Iquitos city and it is a wireless backbone with no capacity available. Therefore, satellite communications are still needed to offer quality communications. Very powerful enterprises such as Google or SpaceX are working on LEO (Low Earth Orbit) satellite constellations that would be very interesting when available in the near future. Important organizations such as Geeks Without Frontiers, which share our interests and goals, are already taking positions in this sense. However, the best solution currently available is a GEO satellite. Hence, in the case of the Napo River, the deployed network connects through one of Hispasat's GEO satellite with Telefonica's core network, waiting for better terrestrial or spatial solutions to be available.
## 2 Problem Description and Model Formulation
### Model Formulation
#### 2.2.5 Symbol and Variable Definitions  
\(N\) is the set of all nodes in the distribution network, including the distribution center and all customer points;  
\(N^{\prime}=N\backslash\{0\}\) is the set of all customer points;  
\(K\) is the collection of vehicle serial numbers;  
\(H\) is the set of all time periods;  
\(D_{ij}\) denotes the distance between nodes \(i,j\);  
\(\varphi\) is the vehicle unit distance driving cost;  
\(\delta\) is the fixed use cost of the vehicle;  
\(T_{ik}\) is the time when the vehicle \(K\) arrives at the customer \(i\);  
\(T_{is}\) refers to the time when the vehicle starts to serve customer \(i\) and the time when the customer receives fresh products;  
\(P_{c}(T_{ik})\) is the penalty cost function of the time window;  
\(E_{ijk}^{ik}\) is the carbon emission (kg) of vehicle \(k\) on the road section \((i,j)\) in the period \(h\);  
\(\mu\) represents unit carbon emission cost (yuan /kg);  
\(\xi_{0j}\) indicates freshness loss coefficient;  
\(p\) is the price of fresh products (yuan/kg);  
\(q_{j}\) represents the demand (kg) of customer \(j\);  
\(Q\) represents the demand of all customers in the distribution network;  
\(t_{ijk}^{h}\) represents the time for vehicle \(k\) to travel on section \((i,j)\) within the period \(h\);  
\(d_{ij}^{h}\) represents the distance traveled by vehicle \(k\) on the road section \((i,j)\) within the time period \(h\); \(s_{i}\) indicates the service duration for customer \(i\).  
\(t_{ijk}\) represents the total time for vehicle \(k\) to travel on section \((i,j)\);  
\(L_{ik}\) is the point in time when vehicle \(k\) leaves customer \(i\);  
\(X_{ijk}\) is a variable of 0-1. \(X_{ijk}=1\) when the vehicle \(k\) travels on the section \((i,j)\), otherwise \(X_{ijk}=0\);  
\(x_{ijk}^{h}\) is a variable of 0-1. \(x_{ijk}^{h}=1\) when the vehicle \(k\) travels on the road section \((i,j)\) within the time period \(h\), otherwise \(x_{ijk}^{h}=0\);  
\(y_{ik}\) is a variable of 0-1. When vehicle \(k\) visits customer \(i\), \(y_{ik}=1\); otherwise, \(y_{ik}=0\);  
\(z_{k}\) is a 0-1 variable, if vehicle \(k\) is used \(z_{k}=1\), otherwise \(z_{k}=0\).
## 8 Conclusions
### Future Research Directions  
(1) Integration of KANs with Physical Models  
Construct hybrid models by combining KANs with explicit physical equations to enhance robustness. For tool wear processes, compute predictions using physical equations first, then employ KANs to compensate for discrepancies between the calculated results and the actual wear measurements. Develop a white-box model, integrating physical knowledge with KAN inference capabilities.  
(2) KAN-Based Monitoring, Prediction, and Parameter Feedback  
Leverage KANs' lightweight architecture with significantly fewer parameters than traditional neural networks to develop embedded systems for real-time monitoring and predictive analysis of tool wear states. Dynamically feed predictive outputs back into CNC systems to optimize cutting parameter configurations.  
(3) Multi-Material and Multi-Tool Wear PredictionInvestigate the accuracy and generalization capability of KANs in wear modeling across dissimilar materials (e.g., titanium alloys and composites) and tool geometries (e.g., coated tools and indexable inserts).  
Conceptualization, Z.C.; Methodology, Z.C.; Software, Z.C.; Validation, J.W.; Formal analysis, Z.C., C.P. and J.W.; Investigation, R.Z. and C.W.; Data curation, J.W., R.Z., C.W. and X.S.; Writing--original draft, Z.C.; Writing--review & editing, C.P., R.Z. and X.S.; Visualization, C.W.; Supervision, C.P.; Project administration, C.P.; Funding acquisition, C.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Jiangxi Province Key Laboratory of High-end CNC Machine Tools and the National Key Scientific Instrument and Equipment Development Projects of China (62227812).  
Not applicable.  
All data used in this study are publicly available. The \"Flank Wear of CNC Lathe Tool Insert Dataset\" can be accessed through Kaggle at: [[https://www.kaggle.com/datasets/drganeshkumars/flank-wear-of-cnc-lathe-tool-insert-dataset](https://www.kaggle.com/datasets/drganeshkumars/flank-wear-of-cnc-lathe-tool-insert-dataset)]([https://www.kaggle.com/datasets/drganeshkumars/flank-wear-of-cnc-lathe-tool-insert-dataset](https://www.kaggle.com/datasets/drganeshkumars/flank-wear-of-cnc-lathe-tool-insert-dataset)) (accessed on 7 January 2025). Moreover, the preprocessed data are directly accessible via GitHub at: [[https://github.com/567](https://github.com/567) ZYC/Data-Processing-and-Explanation-for-Flank-Wear-of-CNC-Lathe-Tool-Insert-Dataset-]([https://github.com/567](https://github.com/567) ZYC/Data-Processing-and-Explanation-for-Flank-Wear-of-CNC-Lathe-Tool-Insert-Dataset-) (accessed on 10 May 2025).  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# Model-Driven Approach to Cloud-Portability Issue
## 7 Implementation and Verification
#### Verification and Validation  
Implementation requires model verification. In our case, we transformed descriptions from/into the generic model. For syntactic control of transformed descriptions, we used real implementations of CC systems that guarantee correctness of syntactic transformation. As a reference topology, we used one virtual instance of the server in the network behind the logical router, which is also shown in Figure 1.  
Figure 4: Source file containing mapping rules.  
As in the OpenStack platform, we used our own OpenStack deployment, Pike release, which we run for our own purposes, mainly as a research and teaching platform. For purposes of validation of our generic model, we set up the Microsoft Azure development kit (ASDK), which is a private set up of Microsoft Azure. It is restricted to a single physical machine, just for evaluation processes. ASDK has the same Application Programming Interface (API) and control as the public cloud version from Microsoft. For testing of transformations to and from Amazon AWS, we asked friends in the company that have access to AWS consoles. Because of the fact that every running instance is paid immediately, we just tried to import transformed script into the environment. This was just enough to test the correctness of the syntactical transformation.  
We transformed the scripts from all of the three chosen Cloud Computing platforms into the generic model and then from the generic model back to the particular platform. The first step was to create a test topology script. In our case, we used OpenStack as the first transformation system because we have the most experience with this cloud platform. After syntax verification of the script in OpenStack was completed, the second step was to transform this script into a generic model. The third step was to transform the script in the general format back into the OpenStack format. This file was then successfully syntactically validated in OpenStack, which confirmed the correctness and functionality of our generic model implementation.  
Using the same three-step method, we also verified syntactic translation accuracy for Amazon AWS and Microsoft Azure. In both cases, for AWS and Azure, we used a real implementation of CC environments to verify the accuracy of the script's translation. As mentioned above, we transformed the same test topology that consisted of a single machine server located in the virtual network behind a logical router. As the real implementations of these cloud platforms were used for the testing process, we can guarantee the syntactical correctness and accuracy of the transformed scripts.  
On the basis of the syntactic accuracy tests on real implementations of CC IaaS systems, we can state that our implementation of the generic model as well as the transformation rules are functional and ready to use. However, for production use, future generalization of the generic model will require the addition of additional entities.
# Adapted Diffusion for Energy-Efficient Routing in Wireless Sensor Networks
## 5 Discussion  
The results are achieved by setting up energy levels for the nodes and the amount of energy required for sending an interest and the energy required for retrieving the event to the sink. After a run, the average energy is recorded for the network. The source is removed and a new source created, then the process is repeated. Running the model led to generating the results presented in Figure 5.  
Two energy parameters were selected for running the model and to generate the data in Figure 5 as follows:  
1. Energy to search 2 units for interest movement and 4 units for event packet, energy to return source data;
2. Energy to search 6 units for interest movement and 12 units for event packets, energy to return source data.  
Higher energy is usually required for transmitting a bigger packet of data from the source, hence there is a higher value for returning data. A smaller package of data is used for searching the source.  
An extract of the data from Figure 5 is presented in Table 1 for clarity.  
The results clearly indicate that AD leads to significantly less energy usage and extended life of the network. At an energy usage of 2 for searching for the source and 4 for returning data to the sink, for DD after 10 repeat searches the average energy was 81 units, compared to 97 units for AD.  
Figure 5 and Table 1 indicate that at an energy usage of 6 for searching for the source and 12 for returning data to the sink, for DD after nine repeat searches the system failed and no more transmission was possible; however, more than double the number of searches was possible with adapted diffusion.
# The Size Effect of Shear Bands in Dense Sands--A Discrete Element Analysis
## 2 The DEM Biaxial Experimental Model
### Simulation of the Biaxial Tests  
The biaxial tests consisted of two stages. The first stage was to add an isotropic confining stress, and the second was to add an axial loading of compression. During the test, all the specimens were set to be saturated and drained.  
For the first stage, a fast way to add isotropic confining stress is to take four rigid frictionless boundaries (see the left in Figure 1), with isotropic confining stress applied by setting the velocity on the left and the right boundaries:  
\[\dot{u}_{n}=\frac{\alpha A}{k_{n}N_{c}\Delta t}(\sigma-\sigma_{\mathrm{t}}) \tag{1}\]  
where \(\dot{u}_{n}\) is the applied normal velocity, \(\sigma\) is the measured stress, \(\sigma_{\mathrm{t}}\) is the target isotropic confining stress, \(k_{n}\) is the normal stiffness, \(\alpha\) is a stress release factor controlling the stability of loading, \(N_{c}\) is the number of contacts between wall and particles, and \(\Delta t\) is the timestep. Such a method can reach equilibrium in a relatively quick manner.  
Figure 1: Traditional method vs. proposed method (take the traditional method first, then change to flexible boundaries to eliminate the influence of boundary conditions. Arrows represent the confining stress to be applied).  
However, rigid boundaries can restrict the particle displacements, and thereby the formation of shear bands. For this reason, a simple modification is proposed below.  
We first used the traditional method to apply confining stress, then replaced the rigid boundaries with flexible boundaries with equivalent forces suggested in [32].  
\[F_{x}=0.5(l_{12}\cos\theta+l_{23}\cos\beta)\sigma_{\mathrm{t}} \tag{2}\]  
\[F_{y}=0.5(l_{12}\sin\theta+l_{23}\sin\beta)\sigma_{\mathrm{t}} \tag{3}\]  
where \(F_{x}\) and \(F_{y}\) are equivalent forces to be applied, and \(l_{12}\) and \(l_{23}\) are distances between two nearby balls, as shown in Figure 2. In this way, the isotropic confining stress can be applied efficiently and without introducing redundant boundary constraints. Here, it should be mentioned that there are also other methods to eliminate the influence of boundary conditions.  
After the confining procedure, the axial displacement loading was loaded step by step by controlling the normal velocity of the top wall and the bottom wall. Since the left and right boundaries are flexible, the generation of shear bands is allowed.