esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of Data-Driven Soil Temperature Forecast--The First Model in Bangladesh
## 5 Results and Discussion
### Forecasting Soil Temperature at 10 cm Depth in Bangladesh
#### 5.1.2 Three-Day Lead-Time Forecast  
Table 3 shows the evaluation of the five tree-based models' performance under various input scenarios and time windows on the testing datasets. For DT forecasts, the MEANMAX-MIN input scenario under a five-day time window achieved good accuracy. When the size of the time window decreased from five days to one day, the forecast accuracy increased an average RMSE from 1.849 \({}^{\circ}\)C to 1.885 \({}^{\circ}\)C and decreased the average R value from 0.918 to 0.915. For RF forecasts, the sixth input scenario performed better than other input scenarios, except for the one-day time window; in particular, it achieved the lowest average RMSE (1.759 \({}^{\circ}\)C) and the highest average R (0.926) under the five-day time  
Figure 3: Comparison of observed and optimal forecasted ST10 at four stations. window. The GBT forecasts showed improved performance when larger time windows were applied. The eighth input scenario consistently achieved the highest accuracy under all time windows, with the lowest average RMSE (1.755 \({}^{\circ}\)C) and the highest average R (0.926) demonstrating strong predictive capability. For the hybrid DT-GBT forecast, the sixth input scenario yielded the lowest average RMSE and the highest average R values under one/two/three-day time windows, whereas the eighth input scenario yielded the best forecast under a four-day time window. Under a five-day time window, the eighth input scenario yielded the lowest average RMSE and the highest average R value. The hybrid RF-GBT achieved the lowest average RMSE of 1.752 \({}^{\circ}\)C and the highest average R value of 0.927 under a five-day time window using all input factors. **Best combination for three-day lead-time forecast:** The hybrid RF-GBT model utilizing all input factors under a five-day time window excelled among all combinations of model, input, and time widows. It achieved the lowest average RMSE (1.752 \({}^{\circ}\)C) and the highest average R value (0.927). In Figure 4, the soil temperature forecast by the hybrid RF-GBT model closely matched the observed data at the Dinaipur, Barisal, Mymensingh, and Bogra stations, especially most of the lowest soil temperature data were well captured using the hybrid RF-GBT. The observed extremely high soil temperature at the Mymensingh station was clearly bought by abnormal measuring instruments. Figure 4: Comparison of observed and optimal forecasted ST10 at four stations.
## 4 Proposed Architecture
### Our Control Plane: FRR  
The control plane is responsible for signaling and managing routing tables [5; 52; 53]. FRR is an open-source software stack on Berkeley Software Distribution (BSD)-based systems [18]. It is the control plane of many systems, such as virtual routers and white box switches. The FRR project is the continuation of Quagga (as an open-source project which implements routing stack) [54]. FRR itself, as a control plane, implements various kinds of routing algorithms on Linux Kernel as the data plane, and it is also inter-operable with other vendors' systems such as Cisco, Juniper, and Huawei.  
In traditional systems, the routing engine is made as one monolithic process which provides all of the functionalities required for routing [53]. But instead, FRR takes a quite different approach to implementation. As can be seen in Figure 11, each routing protocol in FRR (simply FRR) has a daemon. FRR consists of a set of daemons that collectively build the ultimate routing table and the forwarding table for the data plane. The interface between daemons and the data plane is another daemon called Zebra [18].  
Daemons in FRR can be maintained by a single user interface called vtysh. vtysh connects to every daemon using the UNIX domain socket and acts as a proxy for user input.
## 2 Materials and Methods
### Apparatus and Experimental Steps  
#### 2.2.1 Single Briquetting Unit  
Cyanobacteria densification is conducted in a single briquetting unit (PC-15), shown in Figure 2. The unit consists of a support, a stainless steel cylinder, a plunger and a turning disc. The stainless steel cylinder works as the cyanobacteria sample model, and a plunger provides the compressive load on the cyanobacteria sample in the cylinder. Cyanobacteria powder of 0.5 g on a dried basis is loaded into the stainless steel cylinder model. The compressive load is then provided by the plunger, and the compressive load can be adjusted by rotating the turning disc connected to an Instron tester. When the setting compressive load is reached, the densification process should be maintained for enough time (1 min), and the relaxation process should also be conducted slowly. The briquette cyanobacteria are ejected from the lower position of the stainless steel cylinder.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Climate Analysis/(wt.\%)** & **Proximate Analysis/(wt.\%)** \\ \hline C\({}_{\text{ad}}\) & 40.74 & M\({}_{\text{ar}}\) & 84.73 \\ H\({}_{\text{ad}}\) & 13.02 & V\({}_{\text{d}}\) & 85.32 \\ O\({}_{\text{ad}}\) & 34.44 & FC\({}_{\text{d}}\) & 7.55 \\ N\({}_{\text{ad}}\) & 11.41 & A\({}_{\text{d}}\) & 7.13 \\ S\({}_{\text{ad}}\) & 0.39 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ultimate analysis, proximate analysis and chemical components of cyanobacteria powder.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Components** & **Fe\({}_{2}\)O\({}_{3}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **SiO\({}_{2}\)** & **TiO\({}_{2}\)** & **P\({}_{2}\)O\({}_{5}\)** & **CaO** & **K\({}_{2}\)O** & **Others** \\ \hline Content (wt.\%) & 83.21 & 5.37 & 7.06 & 0.08 & 0.38 & 0.23 & 0.03 & 3.64 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Chemical analysis of the hematite oxygen carrier.  
Figure 2: Sketch of single briquetting unit (PC-15).
## 3 Modeling and Formulation for the Optimization Problem
### Objective Function and Constraints for Optimal Scheduling  
In order to adapt the optimization method to the described problem, the objective function and constraints are determined as described in this section. Here, the control devices are the BESS, BESS-interfaced inverter, PV-inverter and tap control transformers.  
**Objective function:**  
The objective function for conventional scheduling can be represented as follows:  
\[min:F_{con}(P_{LBinv},Q_{LBinv},Q_{i},T_{k})=\sum_{t=1}^{24}\sum_{i=1}^{N_{ node}}P_{Loss,i}(t) \tag{3}\]  
The objective function \(F_{con}\) is used to minimize the distribution losses for a period of one day. \(P_{LBinv}\), \(Q_{LBinv}\), \(Q_{PVinv}\), \(T_{k}\) are determined as control variables. \(N_{node}\) means the number of distribution nodes. This function is used to find the optimal reference value of cooperative operation. In the proposed incentive approach, the objective function is updated as follows:  
\[min:F_{inc}(P_{LBinv},Q_{LBinv},Q_{PVinv},T_{k})=\sum_{t=1}^{24}\sum_{i=1}^{N_{ node}}(P_{Loss,i}(t)-v_{i}(t)) \tag{4}\]
## 3 Materials and Methods
### Prediction Module
#### 3.3.2 DioU-Nms  
In order to filter the projected bounding boxes, this study applies nonmaximum suppression operation (NMS). The CV technique known as NMS is used for many tasks. It is a sort of algorithm that chooses one entity from among numerous overlapping instances. A set of criteria is chosen to produce the intended effects. In NMS, which suppresses redundant boxes by taking into consideration both the overlap area and the separation distance between two center points in two bounding boxes, DIoU can be utilized as a criterion. This makes NMS more resistant to occlusion conditions. The typical NMS approach is utilized the most frequently out of all the target detection methods. Our model employs DIoU instead of the computed portion of the IoU and is based on the DIoU/CIoU loss approach. The DIoU loss remains insensitive to the size of the regression problem. If the bounding box does not overlap with the target box, such as GIoU loss, DIoU loss might offer movement directions. It converges significantly more quickly than GIoU loss because DIoU loss may directly decrease the distance between two boxes. As a result, DIoU serves as NMS's judgmental foundation. DIoU-updated NMS's formula considers both the area of overlap and the center space distance between the two boxes. \(Si\) can be formally defined as:  
\[S_{i}=\left\{\begin{aligned} S_{i},IoU-R_{DIoU}(B_{M},B_{i})< \varepsilon\\ 0,IoU-R_{DIoU}(B_{M},B_{i})\geq\varepsilon\end{aligned}\right. \tag{11}\]  
If the center lengths of the IoU and the two boxes are also taken into account, the \(Bi\) can freely eliminate. \(Si\) means the categorization score, while NMS denotes the NMS threshold. Two boxes, with a central point located apart from each other, can be placed on a large number of objects according to the DIoU-NMS technique. The main difference between DIoU-NMS and NMS is not to delete the two boxes mentioned earlier. At each threshold, the tests in the program [52] show that DIoU-NMS outperforms ordinary NMS. Consequently, the final prediction box in Auto-T-YOLO is screened using the DIoU-NMS approach.
## 2 B. _thuringiensis_  
### B. _thuringiensis_  
_B. thuringiensis_ is a Gram-positive, aerobic, spore-forming, entomopathogenic soil bacterium found in various ecological habitats [16]. \(Bt\) is well known for expressing an extensive repertoire of insecticidal toxins. Given this beneficial property, \(Bt\)-derived toxins have been genetically engineered into crops and used in disease prevention [17]. The most ubiquitously employed of these insecticidal toxins are the three-domain crystal (Cry) and cytolytic proteins (Cyt) [18; 19]. The diversity and specificity of Cry and Cyt proteins have allowed them to serve as effective, environmentally friendly agents against pest species of _Lepidoptera_ (butterflies and moths), _Diptera_ (mosquitoes and other flies), _Coleoptera_ (beetles), as well as other harmful arthropods [18; 19; 20]. These attributes have also made Cry and Cyt proteins useful in targeting vector populations that transmit arbovirus diseases such as chikungunya, dengue, and Zika virus [18; 19]. Some Cry proteins also function in a binary toxin-like manner. Notably, this includes the Cry23 Aa and Cry37 Aa proteins expressed by \(Bt\) that work in concert to elicit toxicity towards _Cylas puncticollis_ (the sweet potato weeevil) and _Anthonomus grandis_ (the cotton bolt weeevil) [21; 22]. In addition to Cry and Cyt, \(Bt\) produces several secreted insecticidal proteins (Sips) and vegetative insecticidal proteins (Vips) upon germination [16]. In particular, Vip proteins have emerged as useful alternatives in generating pest-resistant crops and are considered the second-generation of \(Bt\)-derived insecticidal proteins [16]. These proteins share little sequence and structural homology and bind to different receptors than Cry and Cyt proteins [16; 19]. As such, they are used increasingly with the Cry and Cyt proteins expressed in rice, corn, and cotton to enhance protection of these crops from insect damage [16; 19].
# Absorption-Translocation of Veterinary Antibiotics in Rice Plants Introduced with Irrigation Water
###### Abstract  
Veterinary antibiotics (VAs) have been indiscriminately used in animal feed for the past five decades to increase and ensure profits with negligible environmental considerations. The VAs amoxicillin (AMX), chlorotetracycline (CTC), and oxytetracycline (OTC), which can be unintentionally introduced by irrigation water during rice cultivation, were evaluated for their phytotoxic effects, absorption-translocation into plants, and soil residues using a randomized complete block design. It was found that exposure to VAs can severely affect the photosynthetic pathway of rice plants. The uptake and translocation of VAs by rice plants varied significantly. CTC and OTC translocated more easily than AMX, a member of the \(\beta\)-lactam class, which accumulated at the lowest concentration compared to CTC and OTC across all treatments. Rice yield was about 4.3-5.7% lower in the experimental plots that received fifty-fold the background levels of VAs compared to the control. The findings indicate that these widely used veterinary antibiotics can hamper crop production, leave residues in the soil, and constitute a risk to human health if introduced into the agro-ecosystem unintentionally.  
veterinary antibiotics; translocation; phytotoxicity; bioconcentration factor; rice paddy +
Footnote †: journal: Medical  
Korea. Among these, three antibiotics, amoxicillinillin (AMX, 0.1 mg L\({}^{-1}\)), chlortetracycline (CTC, 0.3 mg L\({}^{-1}\)), and oxytetracycline (OTC, 0.05 mg L\({}^{-1}\)), have been found to be persistent in soil, with frequent detection in aquatic systems and high absorption by plants. Recently, several studies have evaluated plant physiological responses to VAs (photosynthesis, respiration, nitrogen metabolism, oxidative metabolism, germination, and plant establishment) and the soil environment (soil enzyme activity, soil microbial communities, and soil functional redundancy) [3; 6; 7; 9; 10; 14; 15]. Arable land serves as the primary reservoir of residual antibiotics. Antibiotics used in livestock enter the environment through the direct application of veterinary antibiotic-laden manure to arable land [3]. In other words, VAs contained in manure applied to agricultural fields are unintentionally introduced into the agricultural ecosystem through rainfall-runoff events.  
However, all of these studies were conducted in hydroponic or greenhouse environments which did not reflect plant uptake and the accumulation of antibiotics under open field conditions. Therefore, this study aimed to assess the absorption-translocation by rice plants of AMX, CTC, and OTC unintentionally introduced with irrigation water during cultivation and the impact of these VAs on plant growth and crop yield.
# Preassembly Cryogenic Drilling in Carbon Fiber Sandwich Sheets with Inner Foam Core
## 3 Results
### Optimized Cutting Conditions and Predictions  
The optimization process provides ideal cutting condition values, as shown in Table 4. For an FT of 4 mm, Fz is 16.6 N and \(\Delta\)Di and \(\Delta\)Do are 0 \(\upmu\)m. For an FT of 10 mm, Fz takes a value of 19.3 N and \(\Delta\)Di and \(\Delta\)Do are 0 \(\upmu\)m. Figure 5 shows the estimated values for the range of defined cutting conditions and for the two core thicknesses. Figure 5 describes the behavior of Equations (1)-(3). The results are very different for FT = 4 and FT = 10 for  
Figure 5: Estimated response surface: (**a**) Fz for FT = 4 mm; (**b**) Fz for FT = 10 mm; (**c**) \(\Delta\)Di for FT = 4 mm; (**d**) \(\Delta\)Di for FT = 10 mm; (**e**) \(\Delta\)Do for FT = 4 mm; (**f**) \(\Delta\)Do for FT = 10 mm.  
\(\Delta\)Do, probably due to the influence of their significant interactions (FT \(\times\) F\({}^{2}\) and FT \(\times\) F). Note that the scale for \(\Delta\)Di (Figure 5c,d) and \(\Delta\)Do (Figure 5e,f) starts at \(-8\) um and \(-3\) um, and \(-2\) um, respectively, because possible diameter contraction can occur after drilling. However, the goal for optimization is to maintain diameter differences at zero because excessive contraction can lead to the need for additional operations before assembly. In all cases, a range around the optimum can be used to achieve the expected outcomes.  
Experimental tests corroborate these outcomes with a slight deviation of 0.05%, 3.21%, and 0.04% with respect to the values of Fz, \(\Delta\)Di, and \(\Delta\)Do, which, in general, are consistent with the values obtained for R\({}^{2}\) regarding the predictive ability.  
In addition, to validate the models, predictions are provided, as shown in Figure 6. The outcomes are acceptable for FT = 4 mm and FT = 10 mm. In particular, in Figure 6b, the phenomenon of outlet diameter contraction is observed in the predictions obtained (see test 4 and test 11). The mean deviations between the experimental and predicted values were 0.05%, 5.46%, and 0.03 for Fz, \(\Delta\)Di, and \(\Delta\)Do, respectively. Obviously, the predictive models of Fz and \(\Delta\)Do are consistent, as has been shown with the optimal value. Although the mean deviation for \(\Delta\)Di is higher, this deviation could be reduced if a rotation speed of 6000 rpm was avoided in 4 mm thick laminates.  
Thus, in a range of rotation speeds of approximately 2100-3100 rpm and feed rates of approximately 200-320 mm/min, suitable values for Fz, \(\Delta\)Di, and \(\Delta\)Do can be found.
## 3 Experimental Results and Discussion
### Energy Dissipation Behavior  
\[\mathrm{E}=\frac{S_{1}}{S_{\Delta\Delta OB+S_{\Delta COD}}}\]  
\(S_{1}\) is the area of the maximum hysteresis loop.  
The energy dissipation coefficient of each test specimen can be calculated by the hysteresis curve. Figure 12 is the schematic diagram of energy dissipation coefficient E. Table 6 shows the energy dissipation coefficient of each test specimen.  
(1)The energy dissipation coefficient of each test specimen is relatively large, with the minimum energy dissipation coefficient being 1.59 of rsl-6 and the maximum being 1.96 of rsl-1, which indicated that the abdominal board-type replaceable energy dissipation link is able to dissipate the energy well in earthquake and it has good seismic performance.
2. By comparing rsl-4, rsl-7 with rsl-8, it can be seen that when the link length, stiffening rib spacing, and yield type are the same, and the energy dissipation coefficient of the link with a large section size is relatively low.
3. By comparing rsl-1, rsl-4 with rsl-6, the energy dissipation coefficient decreases when the length of the energy dissipation link increases, and it can be seen that the energy dissipation capacity of shear link is better than that of bending link.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Number** & **RSL-1** & **RSL-2** & **RSL-3** & **RSL-4** & **RSL-5** & **RSL-6** & **RSL-7** & **RSL-8** \\ \hline E & 1.96 & 1.63 & 1.59 & 1.70 & 1.72 & 1.65 & 1.66 & 1.62 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Energy dissipation coefficient.  
Figure 12: Calculation diagram of energy dissipation coefficient.  
By comparing rsl-4 to rsl-5 in shear yield, when the stiffened ribs spacing between rsl-4 and rsl-5 decreases by 52.47%, the energy dissipation coefficient decreases by 0.74%. By comparison of rsl-5 and rsl-6 in the bending yield, rsl-5 decreases by 52.47% as compared with rsl-6 stiffening rib spacing, but the energy dissipation coefficient decreases by 2.45%. The energy dissipation coefficient of stiffening ribs decreases when the space between them is reduced.
5. By comparing the energy dissipation coefficients of rsl-1, rsl-4, rsl-5, rsl-7, and rsl-8, the energy dissipation coefficients of the link decrease with the increase of \(\zeta\), and the smaller the cross section of the link is, the higher the energy dissipation coefficient of the link.
# Review and New Aspects in Combining Multipoint Moulding and Additive Manufacturing
## 1 Introduction  
In recent years, additive manufacturing has become a very important manufacturing method. The components are built up in layers of a pre-material. Therefore, additive manufacturing allows function integration within a component. This function integration enables components with better mechanical properties and lower weight to be produced. In addition, time-consuming joining operations can be avoided. The currently used additive manufacturing methods have the disadvantage that increasing component sizes lead to massively increasing construction times.  
On the other side, there may be the possibility to mould larger parts. In the production of individual parts or small batches, the fabrication of moulds is a main cost factor. It can be avoided by using vacuum-assisted multipoint moulding (VAMM). The possibility of dispensing mould making not only leads to a reduction in costs, but also to an improved environmental balance sheet, as the disposal of moulds that are little or only used once is no longer necessary. On the other hand, vacuum-assisted multipoint moulding is limited regarding the resolution and the possible part shapes.  
A possible solution is the combination of additive manufacturing with vacuum assisted multipoint moulding. Thereby, the whole process could gain from the speed and flexibility of vacuum-assisted multipoint moulding with an increased resolution and higher shape flexibility offered by the additive manufactured attachments.  
Possibilities of this combination for a new process called enhanced vacuum-assisted multipoint moulding with additive attachments (EMMA) and resulting advantages will be examined in this paper. Therefore, the literature review in the next sections will discuss the actual state of the art of multipoint moulding and additive manufacturing. For this purpose, the article will review the different additive manufacturing technologies and analyse if they accomplish for the combined process and what challenges remain. In the next step, this paper reviews the actual research topics which could be related to these challenges andpossibly provide evidence to solve them. The main objective of this article is therefore to provide an overview of the existing evidence and the further research needed to implement the enhanced vacuum assisted multipoint moulding with additive attachments process.  
The next section will discuss the actual state of technology in vacuum-assisted multipoint moulding and show the possibilities and restrictions of this technology.
# Qualitative Analysis of Sleeper Supporting Condition for Railway Ballasted Tracks Using Modal Test
## 3 Natural Frequency Analysis of Ballasted Track Sleepers
### Numerical Analysis Results  
#### 3.2.1 Natural Frequency of Sleepers in Normal Gravel Ballast Conditions  
Finite element analysis was employed to verify the FRFs at the same location as the modal test, using the field measurement results from normal gravel ballast conditions. The results are shown in Figure 10.  
The numerical analysis results indicate that setting the spring stiffness of the gravel ballast to 200 kN/mm resulted in a first natural frequency of approximately 247.73 Hz. This indicated a deviation of approximately 0.91% from the field-measured natural frequency of approximately 250 Hz for sleepers in good condition, demonstrating that the analysis model adequately reflects actual track conditions.  
Additionally, the mode shapes of the gravel-ballasted sleepers were examined. The results for the first, second, and third bending modes under normal conditions are presented in Figure 11.  
Analysis of the bending mode shapes of the gravel-ballasted sleepers under normal conditions indicated that the largest bending deformation occurred in the center of the sleeve at the first natural frequency of 247.73 Hz, as shown in Figure 11a. In the first mode, deformation was initiated directly beneath the rails on both sides. In contrast, the largest deformation at the second natural frequency of 319.71 Hz occurred on the outer part of the  
Figure 10: Numerical analysis results of frequency response function.  
Figure 9: Loading conditions of numerical analysis.  
sleeper, as shown in Figure 11b. Additionally, at the third natural frequency of 450.3 Hz, significant deformations occurred between the center of the sleeper and directly beneath the rails on both sides, as depicted in Figure 11c.