esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
The data collected using the portable multispectral fibre-optics reflectometer were divided into two independent groups in the analysis, one representing white eggshells and the other representing brown eggshells. Within each of these groups, there were two subgroups: one consisted of eggshells from healthy hens and one from diseased hens. The data from each group were processed separately. In the first step, the data were randomly divided into training data and validation data at a ratio of 7:3. The training data were normalised to unity, and the normalisation parameters were recorded. In the second step, the SOTA network was trained. After completing the training, the third step involved normalising the test data using the normalisation parameters calculated from the training data. The final stage was prediction, and the results were recorded in the output dataset. This processing path is illustrated by the diagram Figure 2. All these steps were repeated one hundred times to mitigate the influence of random data arrangement. The final result was calculated as the average for this set.  
The machine learning algorithm's performance was evaluated based on F-score, Precision, and Recall metrics. These metrics are based on the values of TP (true positives), TN (true negatives), FP (false positives), and FN (false negatives) [32].  
The Precision is calculated as Precision = TP/(TP + FP) and indicates how well the algorithm correctly classifies instances relative to all the data identified as correct.  
The Recall is calculated similarly as Recall = TP/(TP + FN), but it refers to all the elements that should have been identified as correct.  
F-score is calculated as F-score = 2 \(\times\) (Precision \(\times\) Recall)/(Precision + Recall). It represents their harmonic mean. This metric helps identify whether either Precision or Recall are too low. It is used in statistics and machine learning when it is desirous to  
Figure 2: The data processing method used for the evaluation of the SOTA algorithm.  
have both high Precision and high Recall at the same time. When one or both of these parameters are low, then the F-score calculated for them is low. Only when both are high is the F-score high.  
The results of the eggshell origin classifications quality for white and brown eggs obtained using the portable multispectral fibre-optic reflectometer and the SOTA algorithm are shown in Table 3.  
The use of the SOTA algorithm proved to be justified in the case of the analysed data. Compared to the SVM algorithm, the average Precision increased by 0.08 across all groups, with the maximum increase observed in the case of diseased white eggs at 0.18. Regarding Recall, the increases were 0.08 and 0.17, respectively, with the maximum difference visible in the case of healthy white eggs. For the F-score parameter, the overall result also improved by an average of 0.08. The maximum improvement was observed for both diseased and healthy white eggs cases, and it was equal to 0.13.
# Determination of Intrinsic Sodium in Grass-Fed Bonsmara (_Bos taurus indiacus_) Meat
## 1 Introduction
### Rationale of the Study  
This study aims to investigate the intrinsic sodium and trace minerals in grass-fed Bonsmara (_B. indicus_) meat cuts, commonly used for cooking and in processed products. The cattle were fed grass pea (_Lathyrus sativus_) for a period of 19-22 months as part of the feeding regime. Bonsmara cattle are a native South African crossbreed with medium-sized meat cuts and good beef quality traits that were innovated in South Africa in the mid-1900s [28]. It is a breed that yields prime beef cuts due to its coarseness, muscle marbling, and lean subcutaneous fat that is suitable for use as fresh meat (retail cuts) and for use in processed beef products [29]. Intrinsic sodium and trace elements with saline characteristics ((potassium (K), magnesium (Mg), and calcium (Ca)) have been touted as driving factors for beef meat salinity [16; 30]. Copper (Cu) is the only microelement with saline tributes that was tested in all respective meat cuts. Although copper is a microelement, it has a higher salinity influence than raw beef [20; 31].  
There is a dilemma of an unknown concentration level of intrinsic sodium in raw beef [32]. This study hypothesizes that the intrinsic sodium in grass-fed meat cuts is higher in grass-fed beef than was previously estimated. Additionally, we suggest that the intrinsic sodium content in different cuts varies and that the saline minerals inherent in grass-fed beef are present at a lower concentration compared to the intrinsic sodium.
###### Abstract  
To improve the interfacial stability at high temperatures, \(n\)-type skutterudite (SKD) thermoelectric joints with sandwich structures of Ti/Mo/Yb\({}_{0.3}\)Co\({}_{4}\)Sb\({}_{12}\) were successfully designed and fabricated. In this structure, Mo and Ti were introduced as the barrier layer with the goal of suppressing the interfacial diffusion and the buffer layer with the goal of enhancing the bonding strength, respectively. To evaluate the high temperature interfacial behavior of the Ti/Mo/Yb\({}_{0.3}\)Co\({}_{4}\)Sb\({}_{12}\) joints, thermal shocking between 0 \({}^{\circ}\)C and 600 \({}^{\circ}\)C and isothermal aging at a temperature range of 550 \({}^{\circ}\)C to 650 \({}^{\circ}\)C were carried out in vacuum. During the isothermal aging process, Ti penetrates across the Mo layer, and finally diffuses into the Yb\({}_{0.3}\)Co\({}_{4}\)Sb\({}_{12}\) matrix. By increasing the isothermal aging time, Ti continuously diffuses and reacts with the elements of Sb and Co in the matrix, consequently forming the multilayer-structured intermetallic compounds of Ti\({}_{3}\)Sb/Ti\({}_{2}\)Sb/TiCoSb. Diffusion kinetics was investigated and it was found that the interfacial evolution of the Ti/Mo/Yb\({}_{0.3}\)Co\({}_{4}\)Sb\({}_{12}\) joints was a diffusion-controlling process. During the diffusion process, the formed Mo-Ti buffer layer acts as a damper, which greatly decelerates the diffusion of Ti towards the Yb\({}_{0.3}\)Co\({}_{4}\)Sb\({}_{12}\) matrix at high temperatures. Meanwhile, it was found that the increase in the contact resistivity of the joints mainly derives from the inter-diffusion between Ti and Yb\({}_{0.3}\)Co4 Sb\({}_{12}\). As a result, the Ti/Mo/Yb\({}_{0.3}\)Co\({}_{4}\)Sb\({}_{12}\) joint demonstrates the excellent stability of the interfacial contact resistivity. Service life prediction was made based on the stability of the contact resistivity, and it was found that the Ti/Mo/Yb\({}_{0.3}\)Co\({}_{4}\)Sb\({}_{12}\) joint is qualified for practical applications at 550 \({}^{\circ}\)C.  
Keywords:thermoelectric joint; skutterudite; diffusion kinetics; contact resistivity; service life prediction +
Footnote †: journal: Applied Physics Letters
# An Energy-Efficient Approach to Enhance Virtual Sensors Provisioning in Sensor Clouds Environments
## 5 Performance Evaluation
### Performance Metrics
#### 5.1.2 Energy Consumption  
For the purpose of consumption estimation, the energy model addressed in [59; 51] is used. The transmission rate of 0.26 u bit/s is considered, the electric current flowing through the node to receive a package is considered as 7.0 mA while that needed to transmit is considered as 21.5 mA. Thus, the following model is defined:  
* \(Q_{transmission}=3*21.5\;mA\;*\;(0.26*10^{-6}\;bit/s\;*\;Data\_Length)\)
* \(Q_{Receive}=3*7.0\;mA*(0.26*10^{-6}\;bit/s*Data\_Length)\)  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Deployment** & **Nodes** & **\# Data** & **Sensing Interval** & **Type** \\ \hline Intel Lab & 54 & 5000 & 31 segs. & indoor \\ Green Orbs & 271 & 248 & 10 min. & outdoor \\ \hline \hline \end{tabular}
\end{table}
Table 2: Deployment summary.  
\(Q_{Listen}=3\,*\,7.0\,mA*(0.26\,*\,10^{-6}\,bit/s\,*\,104\,bits)=0.00056784\,m \big{/}/\textit{message}\)  
where  
* \(\textit{DissipatedEnergy}(Q)=\textit{Voltage}*\textit{ElectricCurrent}*\textit{Time}\)
* \(\textit{Time}=\textit{TransmissionRate}*\textit{Data\_Length}\).  
We have considered a data frame format as defined in 802.15.4 MAC Layer [60] (Figure 9). We also have considered that a sensor node in listening mode only needs to receive the data frame header (104 bits). The energy spent in transmission or receiving mode is dependent on the data frame length. Since ACxSIMv2 choose which sensor device each sensor node will use, the length of the packets may be different. In our scenarios, all physical sensors nodes monitor four different variables (temperature, humidity, light and its energy level). Therefore, an active sensor node could transmit a data frame up to four variables of 4 bytes, yielding data frames with 152, 184, 216, or 248 bits. The sensors nodes start with energy level of 2 J.
# Provenance and Paleo-Environment of the Late Carboniferous Bauxite Formations in Southern Shanxi
## 2 Geological Background  
As one of the oldest cratons in the world, the NCC is bordered on the south by the Qinling Orogenic belt, on the north by the Central Asian Orogenic Belt, and on the east by the Pacific Plate [1,2]. The craton's basement predominantly comprises Archean to Paleoproterozoic metamorphic rocks, which are covered by Mesoproterozoic to Neoproterozoic unmetamorphosed volcanic-sedimentary rocks, as well as Phanerozoic Cambrian-Ordovician marine carbonates, Carboniferous-Permian iron-aluminum strata and coal-bearing strata, and Mesozoic Triassic-Cretaceous clastic strata [10,27]. The region features two major ancient weathering crust deposit-type bauxite metallogenic areas: the Shanxi fault-uplift aluminum district and the southern margin of the North China Block aluminum district (Figure 2a).  
The Pinglu bauxite mining area is situated in Pinglu County, Yuncheng City, southern Shanxi Province, China. In the geological history, the area of Pinglu County has experienced the ancient rock metamorphism and tectonic movement, forming a unique geological structural characteristics and rich mineral resources. It lies on the southern edge of the NCC, at the intersection of the western Henan uplift and Zhongtiao Mountain fault arch. The regional stratigraphy, from oldest to youngest, includes Lower Proterozoic Zhongtiao Group metamorphic rocks, Mesoproterozoic Ruyang Group carbonate clastic rocks,  
Figure 1: The workflow diagram.  
Figure 2: **(a)** Distribution map of bauxite deposits in the NCC (modified according to [PERSON] et al., 2023 [19]); **(b)** the geological map of the Pinglu area (modified according to the geological map of Shanxi Province).  
Cambrian-Ordovician carbonates, Carboniferous-Permian clastic coal-bearing strata, and Cenozoic Paleogene, Neogene, and Quaternary formations (Figure 2b).  
The study area features intricate tectonics, with principal structural lines extending northeast and predominantly featuring high-angle normal faults. The Pinglu bauxite reserves are primarily found in the Benxi Formation of the Late Paleozoic Carboniferous system, with the underlying bedrock consisting of Ordovician Majiagou Formation carbonates. The bauxite-bearing strata, from bottom to top, primarily include ferruginous claystone, bauxite, and claystone, interspersed with occasional coal seams or thin mudstone beds. The overlying strata primarily consist of mudstone and sandstone.  
The morphology of the ore bodies within the region varies significantly owing to changes in the deposition basement topography. In areas with expansive, relatively flat dissolution basin or depression, the ore bodies typically exhibit a lamellar or quasi-lamellar with good continuity. In contrast, where there are notable height differences in the basement paleotopography, the ore bodies tend to be lenticular with poor continuity. In cases where the basement paleotopography resembles a dissolution funnel, the ore bodies take on a funnel-like shape.
Experimental Investigations on Temperature Generation and Release of Ultra-High Performance Concrete during Fatigue Tests  
[PERSON]  
1 Institute of Concrete Structures, Technische Universitat Dresden, 01069 Dresden, Germany;
There are different recommendations for the determination of fatigue strength and Wohler curves, e.g., [11] in Germany. To create Wohler curves for concrete, pressure-swell fatigue tests are generally carried out. High frequencies are used so that the tests allow many load changes in a short time. In comparison to normal strength concrete, however, in the case of UHPC and high performance concrete (HPC) the temperature of the material increases. The fact that the material can heat up when fatigue tests are performed on concrete has already been observed repeatedly, for example as one of the first by [PERSON] et al. (1959) [12]. Especially for HPC, UHPC, or high strength, fine-grained mortars, heating was observed and partly investigated more closely [13; 14; 15; 16; 17; 18; 19; 20]. In order to increase the state of knowledge concerning this phenomenon, it was systematically investigated mainly for UHPC in our own research project to find the main influencing parameters. The aim of the project is to understand the heating mechanisms in such a way that the negative effect on the number of load cycles can be taken into account retrospectively and thus, in a future standardization of the material, UHPC will not be underestimated in terms of fatigue strength. The first results have already been presented, e.g., in [21; 22; 23; 24]. This article deals with the heating and the release of temperature during pressure-swell fatigue tests. The basis of this approach was the results presented in [24], which could not all be sufficiently explained at that time. The new results should help to find out how the grade of damage influences the temperature development. For this purpose:  
* tests on temperature release on cylindrical specimens with and without pre-damage were carried out,
* the heating under fatigue loading on samples with and without pre-damage was studied,
* and thermally insulated cylinders were tested to compare the results with tests under normal conditions to find out whether the maximum absolute temperature or the gradient within the cross section is decisive.  
Findings on the cooling-off behaviour, the heating behaviour, and their influence on the reachable number of load cycles are presented.
## 3 Results
### Structure of HA Scaffolds
#### 3.3.3 Wall Thickness  
It has been reported that a thicker wall between the pores of the artificial scaffolds yields better mechanical properties, including the compressive strength [38; 39]. Additionally, the wall thickness and porosity were known to be inversely related [40]. However, in this study, there is no significant difference in the wall thickness among HA0 to HA75 at the before sintering state (Figure 8a). This phenomenon was also observed after the sintering process (Figure 8b). The difference in the wall thickness between the before sintering and after sintering states tended to increase as the amount of PMMA addition increased. As shown in Table 4, the average wall thickness of HA0 exhibited no significant difference between before and after sintering, whereas at HA75, the average wall thickness increased about 5 \(\upmu\)m after sintering. Moreover, as the amount of PMMA increased, the range of the wall thickness became broader.  
Figure 8: Wall thickness distribution of the scaffolds with respect to the PMMA addition amount, before sintering and after sintering.
## 2 Data and Methods  
### Data  
In this study, remote sensing data for the years 2000, 2005, 2010 and 2018 were selected, in which land use data, population (POP), gross domestic product (GDP), elevation (DEM) and slope (Slope) data were obtained from [[https://www.resdc.cn/](https://www.resdc.cn/)]([https://www.resdc.cn/](https://www.resdc.cn/)) (accessed on 19 February 2022) The spatial resolution of the land use data was 1 km and included 19 land use types (Figure 1). In this study, 19 land use types were reclassified into seven categories, including cultivated land (CL), forestland (WO), grassland (GL), water area (WA), construction land (CO), unused land (UL) and wetland (WL). In this paper, unused land is defined as sandy land, Gobi, saline-alkali land, swampy land, bare land, bare rock texture and other unused land, including alpine desert, tundra, etc. Spatialization of the POP and GDP data was achieved based on a multifactor weight assignment method and multiple factors closely related to population with a resolution of 1 km. The elevation and slope data were based on the latest SRTM V4.1 (National Aeronautics and Space Administration, Washington, DC, USA) data that were resampled to produce a 1 km resolution dataset. The spatial resolution of the gross primary productivity (GPP) of the vegetation was 0.05\({}^{\circ}\) ([[http://www.geodata.cn/](http://www.geodata.cn/)]([http://www.geodata.cn/](http://www.geodata.cn/))) (accessed on 27 December 2021) [58]. Precipitation (Pre) and temperature (Temp) data were datasets obtained by [PERSON], et al. [59] based on a bilinear interpolation method with a spatial resolution of 0.5\({}^{\prime}\). PM2.5 (PM) data were obtained from [[https://sites.wustl.edu/acag/](https://sites.wustl.edu/acag/)]([https://sites.wustl.edu/acag/](https://sites.wustl.edu/acag/)) (accessed on 19 February 2022) with a spatial resolution of 0.01\({}^{\circ}\). The driving factors used in this paper are all averaged in counties, and then calculated and analyzed.
# Storm Surge Prediction Based on Long Short-Term Memory Neural Network in the East China Sea
## 2 Methods
The nonlinear activation function \(\sigma\) is the sigmoid function which enables values between 0 and 1, and it is expressed as:  
\[\sigma(x)=\frac{1}{1+e^{-x}}, \tag{9}\]  
From the flow chart of LSTM neural network in Figure 1b, the computational process is as follows: firstly, the external state \(h_{t-1}\) of the previous time step and the input \(x_{t}\) of the current time step are used to calculate the three gates \(f_{t}\), \(i_{t}\), \(o_{t}\) and the candidate state \(\widetilde{c}_{t}\); secondly, the forget gate \(f_{t}\), and the input gate \(i_{t}\) are integrated to update the internal state \(c_{t}\); finally, combined with the output gate \(o_{t}\), information about the internal state \(c_{t}\) is passed to the external state \(h_{t}\). In addition to the LSTM neural network method, several other ML methods, i.e., Bayesian Ridge Regression (BRR), Gradient Boosted Decision Tree (GBDT), Linear Regression (LR) and Support Vector Regression (SVR), are used in this research as a comparison group. BRR, based on Bayesian knowledge, is aimed to solve the problem of multicollinearity in linear regression, and to serve the purpose of estimating regression coefficients and selecting variables [94]. GBDT is a suitable method for classification and regression problems, which uses decision stumps or regression tress as weak classifiers [95]. LR is the most basic and widely used model in ML and statistics, which is a regression analysis to model the relationship between independent variables and dependent variables. SVR is also widely used as another type of ML approach, which finds a line or a hyperplane in a higher dimension to fit the data [96]. The ML models were constructed based on scikit-learn [25] that is a simple and efficient tool for ML, readers can refer to it for more detailed information of the four ML methods used in this study.
## 2 Related Work
### Optimization Algorithm for Scheduling
#### 2.2.1 Artificial Intelligence (AI) Methods, Local Search Methods, and Metaheuristic Methods  
Recent advances in AI have enabled intelligent solutions to handle scheduling problems in real-time [24]. However, the AI methods require a distinct cyclical nature that demands constant iteration, tuning, and improvement to find the best solution. The local search method is a greedy methodology used to solve a big combinatorial optimization problem within a reasonable time [25]. As the local search method is very dependent on time, it can achieve optimization if there is sufficient time given. Metaheuristic methods use a set of intelligent strategies to explore and exploit the search base to find the opti  
Figure 3: Types of scheduling algorithms. Adapted with permission from Ref. [20]. Copyright 2019 [PERSON].  
mal solution [26]. Many metaheuristic methods focus on reducing the makespan in flow shops [27]. The metaheuristic method can solve large and more complex problems than the local search method. Other than that, [PERSON] et al. [28] applied clustering methods to optimize storage location planning and consolidated picking strategy for 98 orders with a preplanned storage space. This indicates that the time required for local search methods or metaheuristic methods can be saved with the help of domain knowledge.  
In summary, AI methods are suitable for dynamic job shops to deal with random job arrivals with unexpected machine breakdowns. Local search methods are more efficient to solve large combinatorial optimization problems, while metaheuristic methods are more efficient to solve larger and more complex optimization problems in a reasonable time frame. These algorithms are usually iteratively learnt and modified to reach the desired result. This may not be suitable to implement in our case study that sorts orders based on their urgency and product customization.