esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Firstly, using the finite element analysis (FEA) method to conduct simulations on the model, the magnetic field distribution of three models under no-load conditions was analyzed, and the characteristics of the proposed model combined with Halbach array magnetization method and magnetic focusing effect were verified. Subsequently, performance analysis and comparison were conducted on the basic model and the proposed model under load conditions. Afterwards, the rotor topology parameters of proposed model two were analyzed using uniform Latin hypercube sampling and sensitivity analysis [24; 25]. The piOPT multi-objective global autonomous optimization algorithm was used to perform multi-objective optimization on the topology parameters with high correlation in proposed model two [26; 27; 28] and to compare their torque characteristics before and after optimization. Eventually, the demagnetization performance of the optimized model's permanent magnet under overcurrent conditions was verified [29]. Targeting the characteristic of low average torque in magnetic pole-biased PMA-SynRM and based on the above research, this article proposes two PMA-SynRM models that combine Halbach array permanent magnet placement methods. The article has the following chapter arrangement: In Section 2, the motor topology structure of the basic model and the proposed models are introduced, and the performance of the three models in both no-load and load operating modes are compared, attesting to the torque improvement of proposed model two. In Section 3, maximization of the average torque as the objective of moment optimization under the condition of torque ripple being less than 30% and sensitivity analysis and multi-objective optimization are carried out on the rotor topology size parameters of proposed model two. In Section 4, by comparing and analyzing the performance of the optimized model with proposed model two, it was found that the average torque of the optimized model significantly increased, and the torque ripple slightly decreased. Anti-demagnetization analysis was conducted on the optimized model, proving that the optimized model has positive anti-demagnetization performance. In Section 5, a brief summary is provided for this article.
## 3 Selection of Methods for Methodology  
### A Framework of the Methodology Implementation  
The usual process of evaluating the quality of segmentation involves calculating individual internal, external, or relative metrics without considering several criteria simultaneously. The proposed methodology can be used to determine the most effective segmentation algorithm for specific use cases. The proposed methodology for the selection of image segmentation algorithms includes six essential steps:  
1. Determination of a set of basic visual characteristics (properties) of satellite image content.
2. Selection of a set of segmentation algorithms.
3. Evaluation of the influence of the visual properties of satellite images on the result of the respective segmentation algorithms. The images of different content (with the selected visual properties varying within the min-max range) were used to evaluate the algorithms.
4. Evaluation of satellite image content according to a set of visual characteristics.
5. Application of the PROMETHEE-IFS method for optimal decision-making using selected criteria weights.  
Ranking of segmentation algorithms according to their suitability for the respective type of satellite image.  
After obtaining the ranks, additional steps could involve verifying the results, for example, through a subjective quality evaluation.  
The suggested approach is represented in Figure 1. It is versatile and can be applied with any segmentation method, distinct visual image features, or dataset. The method for criteria weights can also be selected according to preference.
# Non-Ionizing Radiation Measurements for Trajectory Radars
## 4 Results and Discussion
### Analysis and Discussion
#### 4.3.1 Measurements and Predictions  
Comparing the predicted and measured power density values for point P01, the power density measured value is considerably lower. We advocate the coherence of these results because the measurement was taken in a point (P01) misaligned to the antenna axis and the predicted value represents the maximum possible power density level that occurs at the axis of the antenna beam.  
In contrast, the predicted and measured values are very close for points P02 and P04. This result is also reasonable since they are in a LoS condition scenario, with no additional meaningful attenuation other than free space, a consequence of a static link configured by a directional receiving antenna and a very narrow beam transmitting antenna.  
Otherwise, for the P03 point, the measured value is 96.44% far from the predicted value. This result is explained by the shadowing caused by vegetation located on the signal path, configuring a Non-Line of Sight (NLoS) scenario condition, as shown in Figure 5.
## 4 Results and Discussion
### Experimental Results
These differences caused a divergence in the cumulative impulse curves beyond the primary peak decay time and in the peak cumulative impulse magnitude at gauge A. The secondary peak pressure was sensitive to charge conditions such as shape and location. In general, values were found to have a larger range within an experimental scale test series compared to other gauges. Furthermore, the angle of the charge relative to the corner wall will change the behaviour of the reflected wave (particularly the time of arrival and magnitude of pressure as the reflective wave reaches gauge A). That said, the predicted initial and overall (positive phase duration and impulse) parameters of gauge A were reasonable. Further work is recommended to understand what influences the secondary peak overpressure. Gauges B1 and B2: There was a 2 to 5% variation in the parameters found for gauges B1 and B2. Additionally, the curves for the predicted 100% were similar to the actual 100% curves (see Figure 7). In general, these gauges showed good agreement between the two scales. The predicted time of arrival from the mean 75% value was slightly later (by 4% to 5%) than the measured 100% experimental mean averages. Gauge B3: Similar to gauges B1 and B2, the time of arrival for the predicted 100% was slightly later than expected (by 2%). The incident overpressure of the predicted 100% was within the actual 100% range, with a difference of 4% between the mean values, showing good agreement for this parameter. The predicted positive phase duration of 0.79 ms was just outside the experimental upper bound of 0.78 ms, with a difference of 5% from the mean values. Despite the similar values found for these three parameters, the impulse was under-predicted by 11% at gauge B3. A noticeable decrease in pressure for the predicted 100% curve occurred at 2 ms in Figure 7. Considering the blast wave travelled through the internal structure of the building (further insight is found in the numerical results), the  
Figure 7: Overpressure–time histories and cumulative specific impulse at each gauge for the 100% (from Test 1—100%) and predicted 75% to 100% (from Test 4—75%) scale tests. interaction between the corner wall propagating wave and the internal structure may have influenced the measurement at gauge B3. As the same square hollow sections were used to construct the corner structure in both tests (i.e., it was not scaled), the effect would not be scalable.
## 4 Discussion  
### Spatial Pattern of Dominant Factors Controlling Variations in ET and GPP  
According to the climatic divisions of the China Natural Geography Atlas, our study area has four sub-regions. Figure 11 shows the spatial distributions of the dominant factors controlling variations in ET and GPP over China from 1999 to 2018. Among the four sub-regions, most areas of TMon and TP have arid and semi-arid climate characteristics, and precipitation is the predominant source of water and dominates the surface ET change in these regions, which corresponds to previous findings [55; 56]. Because precipitation also has an important influence on vegetation growth in semi-arid areas, it controls the increase in GPP in eastern TCon. TP mainly covers the Qinghai-Tibet Plateau, where the temperature has significantly increased and limited vegetation growth [57]. The LAIH and temperature warming together contribute significantly to regional GPP variations, while precipitation and temperature warming contribute more to regional ET variations.  
Humid and semi-arid climates are distributed in the north and south of TMon, respectively. ET and GPP in northern TMon significantly decrease due to a significant decrease in SR. Southwestern TMon, namely the Loess Plateau, is the core district for the implementation of the Grain for Green Program (GFGP). Intensive vegetation restoration significantly controls the increases in LAIH, ET, and GPP in this region [10; 58; 59].  
Figure 10: Spatial patterns of actual contributions of influencing factors to GPP variations. Prec is precipitation, Temp is temperature, WS is wind speed, SH is specific humidity, SR is solar radiation, LAIH is LAI influenced by human activities.  
The SubT region, conversely, is located in Southern China, which is characterized by high temperatures and sufficient precipitation. Figure 11 shows that SR significantly influences the ET and GPP variations in this region [60,61]. The decreasing SR in the middle of TMon induces decreases in ET and GPP. Vegetation dominates the increases in ET and GPP in southwestern TMon (the Yunnan-Guizhou Plateau) due to the extensive implementation of Rocky Desertification Integrated Management. Previous researchers determined that the influence of SW on ET changes decreases from arid to humid regions [62, 63, 64]. The results shown in Figure 11 also support this observation.
# Real-Time Text-to-Cypher Query Generation with Large Language Models for Graph Databases
## 5 Implementation  
The NLI built as part of this project was designed on the basis of modular architecture. This approach allows for easier testability, maintainability of the individual components and, above all, expandability for additional databases or language models. Figure 2 shows the schematic architecture of the chatbot. The entire chatbot was implemented in the Python programming language. The whole system is hosted in docker [64] containers, and consists of three main pipelines, namely the _Agent_, _Chat_from_History_, and _QA_, which are responsible for processing the user's request and generating a suitable response. In the following, the implementation of the individual modules and the processing of user questions in them will be described.  
Figure 2: Schematic architecture of the chatbot.  
At the start of the chatbot, the user interface, database descriptors, and language models are initialized first. For the creation of the user interface (UI), the open source python library gradio [65] was used, which provides a variety of components to build a UI for a chatbot quickly and easily.  
The database descriptors Listing 1 contain the name of the available databases, as well as a brief description of the information they contain. This is necessary in order to be able to select the appropriate database for the user's question later in the agent pipeline. The database adapter contains the connection information and a client to the respective database. The required large language models are also initialized at the same time. Three independent LLMs, i.e., _Json LLM_, _Query LLM_ and _Chat LLM_, are required for the chatbot. Due to hardware limitations, it was decided to use the latest version of Open AI's GPT-4-Turbo for each of the models. Table 1 shows an overview of the models and their parameters. Regarding the temperature parameter, it is particularly important for the first two models to be set to 0.0 so that the output is as focused and deterministic as possible.  
The reason behind this is that when making decisions or generating CQL statements, it is fundamentally important that the model only uses the nodes and parameters that are given to it in the prompt, and does not invent its own values here, as these would then lead to an incorrect decision/cipher query. For the _Chat LLM_, this parameter was set slightly higher in order to prevent the model to include its internal knowledge into the answer. In the latest GPT-4-Turbo models, a so-called response_format can also be defined. This allows the JSON mode [66] to be activated for a model, which is used in the _Json LLM_. Its functionality will be explained in more detail in the Agent Pipeline section. For the other two models, the default parameter of the response_format were applied.
# Policy Compression for Intelligent Continuous Control on Low-Power Edge Devices
## 4 Methodology
### Distilling the Mean Action  
The first loss is the simplest, serving as a baseline that is mostly useful in combination with deterministic teachers in the teacher-driven scenario or in case \(\sigma\) was not learned by the model. It consists of only distilling the mean action that the student needs to follow, resulting in a fully deterministic policy, similar to what is proposed by [PERSON] et al. (2018). The original policy distillation loss (Equation (1)) is based on the KL-divergence between two probability vectors, which cannot be used for learning a single mean value for each action ([PERSON] et al., 2015). Instead, we propose to use the Huber loss between the mean of the student (\(\mu^{S}\)) and the teacher (\(\mu^{T}\)) for each action (\(a\)):  
\[L(D,\theta_{S})=\sum_{i=1}^{|D|}\sum_{a\in A}\text{Huber}(\mu^{S}_{i,a},\mu^{T }_{i,a},1) \tag{2}\]  
\[Huber(a,b,\delta)=\begin{cases}\frac{1}{2}(a-b)^{2}&\text{for }|a-b|\leq\delta,\\ \delta\cdot\Big{(}|a-b|-\frac{1}{2}\delta\Big{)},&\text{otherwise}.\end{cases} \tag{3}\]  
We chose to make use of the Huber loss for this baseline instead of the MSE loss used by [PERSON] et al. (2018) since it is less sensitive to outliers and has a smoother slope for larger values, resulting in it outperforming the MSE loss in our initial experiments.
## 3 Results and Discussion
### Chemical Composition of the Obtained Distillates
In turn when assessing the mash preparation methods, the highest levels of this compound were found in the samples of distillates obtained from mashes prepared by the PLS-SHF method. The application of the PLS-SSF, NSH-N/A and NSH-A methods resulted in lower concentrations of aldehydes in the distillates. In accordance with the aforementioned regulation [55], the acidity of agricultural distillates, expressed in grams of acetic acid per litre of alcohol 100% \(v/v\), should not exceed 80 mg/L for rye- and potato-based spirits, and 0.2 g/L for the so-called'mixed spirits'. The obtained bread-based spirit distillates were characterised with acidity levels exceeding the recommended limit. Both the type of bread and the method of mash preparation were identified as influencing factors in this regard. The highest acidity (0.940 \(\pm\) 0.04 g acetic acid/L alcohol 100% \(v/v\)) was observed in the distillate obtained from wheat bread processed by the PLS-SHF method. Additionally, the distillate from wheat-rye bread processed by the NSH-A method exhibited relatively high acidity, with a value of 0.850 \(\pm\) 0.02 g acetic acid/L alcohol 100% \(v/v\). The lowest acidity was observed in samples of both wheat and wheat-rye bread processed by the PLS-SSF method, with values ranging from 0.583 \(\pm\) 0.04 to 0.620 \(\pm\) 0.01 g acetic acid/L alcohol 100% \(v/v\) (\(p\leq\)0.05). Acetic acid is formed during the Maillard reaction as the result of the degradation of Amadori products [56]. Those processes occur during the baking of bread. Furthermore, the presence of acetic acid in the distillates may also be attributed to the fermentation process, resulting from the metabolic activity of yeast and other microorganisms [51]. Depending on the intended use of the obtained spirit distillate, there may be a need to eliminate volatile congeners. In the production of a purified spirit, a multiple distillation process utilising a multi-column continuous system is typically employed in order to achieve the desired concentration of alcohol and the level of congeners. Following the primary distillation, which in industrial conditions consists of a stripping and pre-rectifying (strengthening) column, the spirit may undergo processing through an additional multicolumn rectifying system. This system typically comprises three to five columns, including a heads column for the elimination of congeners such as aldehydes and acids, a main rectification column for purified spirit recovery, a demethylising column and a fused oil recovery column [57; 58]. The main product, namely purified/rectified spirit, may be used for many biotechnological processes and for the production of spirit beverages. By-products may be utilised in the preparation of cost-effective solvents [59]. Moreover, fused oils can serve as a source of supply energy for processing plants and as additives in petroleum-based fuels [60].
# Multi-Region OpenFOAM Solver Development for Compact Toroid Transport in Drift Tube
###### Abstract  
Compact toroid (CT) injection, with its characteristics of high plasma density and extremely high injection velocity, is considered a highly promising method for core fueling in fusion reactors. Previous studies have lacked investigation into the transport process of CT within drift tubes. To investigate the dynamic processes of CT in drift tubes, this study developed a compressible magnetohydrodynamics (MHD) solver and a magnetic diffusion solver based on the OpenFOAM platform. They were integrated into a multi-region coupling framework to create a multi-region coupled MHD solver, mhdMRF, for simulating the dynamic behavior of CT in drift tubes and its interaction with finite-resistivity walls. The solver demonstrated excellent performance in simulations of the Orszag-Tang MHD vortex problem, the Brio-Wu shock tube problem, analytical verification of magnetic diffusion, and validation of internal coupling boundary conditions. Additionally, this work innovatively explored the effects of the geometric structure at the end of the inner electrode and finite-resistivity walls on the transport processes of CT. The results indicate that optimizing the geometric structure at the end of the inner electrode can significantly enhance the confinement performance and stability of CT transport. The resistivity of the wall profoundly influences the magnetic field structure and density distribution of CT.  
MHD; compact toroid; multi-region; OpenFOAM; CFD; finite-resistivity wall Article
## 2 Materials and Methods
### The Sites, Measurements, and Soil Properties
The monthly averaged data for the air temperature (\(T_{air}\)) and precipitation (Prec) were collected from the Complex Background Monitoring Station, which is situated nearby to the coniferous-deciduous forest site (blue; bottom, Figure 2). All the data were quality checked and averaged on a monthly base to be fitted into the models. The monthly averaged temperature (\(T\)) and precipitation (\(P\)) data have also been used for the separation of the years of the measurements into \"wet\", \"dry\", and \"normal\" (top, Figure 2) by an application of the following indexes to the data:  
* Selyaninov hydrothermal coefficient--HTC = \(\sum P/\sum T/10\), when \(T>10\)\({}^{\circ}\)C (HTC\({}_{6-8}\)--summer period, June to August months) [53];
* Wetness Indexes--WI = \(lg(\sum P/\sum T)\) (WI\({}_{5-8}\) and WI\({}_{5-9}\) for May to August and May to September periods, respectively) [53]. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **Entic Podzol** & **Haplic Luvisol** \\ \hline \multirow{3}{*}{Forest} & Forest zone & coniferous-deciduous & Deciduous \\ \cline{2-4}  & & mature mixed with pine, linden, aspen, birch, and oak, the age of & secondary deciduous with aspen, linden, and maple of an average \\  & & which reaches 90–120 years \({}^{2}\) & tree age of 50–70 years \({}^{2}\) \\ \hline \multirow{3}{*}{Soil} & Texture & sandy-loamy \({}^{3}\) & loamy \({}^{3}\) \\ \cline{2-4}  & granulometry (sand-silt:clay) & 11.6:1:0.1:3 \({}^{1}\) & 4:4:2 \({}^{2}\) \\ \cline{2-4}  & & pH\({}_{\text{KCl}}\) & 3.67 \({}^{1}\) & 5.56 \({}^{2}\) \\ \cline{2-4}  & C/N & 15.3 \({}^{1}\) & 12.8 \({}^{2}\) \\ \cline{2-4}  & SOC storage [kg C/m\({}^{2}\)] & 1.23 (0–20 cm) \({}^{4}\) & 5.02 (0–20 cm) \({}^{4}\) \\ \cline{2-4}  & Water-holding capacity [\%] & 40.5 \({}^{2}\) & 57.5 \({}^{2}\) \\ \hline \hline \end{tabular}  
* \({}^{1}\)[20]; \({}^{2}\)[40]; \({}^{3}\)[52]; \({}^{4}\)[53].  
\end{table} 
Table 1: Site description and soil properties of the forest ecosystems. If the values of any of these indexes differ (higher or lower) more than a standard deviation (STD) from the averages for the chosen measurement period, the year was placed into the \"wet\" or \"dry\" datasets, respectively. The following 5 years were classified as \"wet\"--1999, 2003, 2006, 2008, 2020; the following 9 years were \"dry\"--2002, 2007, 2010, 2011, 2014, 2015, 2018, 2021, 2022; and the remaining 11 years stayed as \"normal\"--1998, 2000, 2001, 2004, 2005, 2009, 2012, 2013, 2016, 2017, 2019. For the initialization of the empirical models dependent on _SOC_, the estimations of the _SOC_ storage in 20-cm layers of Entic Podzol and Haplic Luvisol were used (Table 1).