esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Input Variable Intervals  
The selected key parameters of the LPBF process in this research were as follows:  
* Laser power, which represents the energy output of the laser source during the melting process and affects the ability to effectively fuse powder particles [25].  
Scanning speed, which determines the speed at which the laser beam moves across the powder bed, affecting melting efficiency [26].
* Track overlapping, which indicates the degree of overlap between neighbouring laser scan tracks and affects the uniformity of the melt pool and the structural integrity of the part [27].
* Hatch spacing, the distance between parallel scan lines in the powder bed, which affects the energy distribution and consolidation of the material during layer formation [28].
* Layer thickness, the height of each powder layer deposited during the LPBF process, which affects the resolution, build time and thermal behaviour of the part to be produced [29].  
The selected inputs are shown in Table 1, while the values are listed in Table 1 for better readability. The parameter intervals were determined regarding the machine capability and work conducted by researchers [3,8].  
Certain process parameters such as hatch spacing and track overlapping were kept constant to ensure consistency and minimise the complexity of the experimental design. Overlapping, which was set at 30%, is a calculated parameter that depends on the size of the laser spot and the hatch distance and significantly affects the stability of the melt pool and the density of the parts. Accordingly, the hatch spacing was set to 0.035 mm to ensure uniform energy distribution and reduce discrepancies in the thermal behaviour of the individual layers. By adjusting these parameters, attention could be focussed on the main variables--laser power, scanning speed and layer thickness--allowing a more direct assessment of their different effects on the final quality of the part. This method increased the reliability and clarity of the results by reducing the number of interacting elements.
# Peptide Mediated Antimicrobial Dental Adhesive System
## 3 Results and Discussion
### Bacterial Viability on Treated Discs
As a positive control for antimicrobial activity, chlorhexidine gluconate was added at a multiple of its measured MIC value. 12.5\(\times\) or greater concentration of CHX was sufficient for bacterial inhibition with or without \(\varepsilon\)-polyllysine. **(b)**_S. mutans_ UA159 growth/viability based on AlamarBlue assay on dental adhesive discs soaked with either an antimicrobial peptide or a chlorhexidine aqueous solution as a multiple of minimum inhibitory concentration. agent is active against the introduced bacteria. The non-specific adsorption of the peptide to the commercial analogue resin appears to be more stable than the potential interactions taking place between either the peptide and the \(\varepsilon\)-polyllysine disc or the CHX and both resin discs. The stability of this non-specific adsorption for antimicrobial peptides on polymeric materials are considered as a result of the similarities between polymers and bacterial membranes [92]. Non-specific interactions of antimicrobial peptides with polymers that mimic bacteria membranes limit their availability for antibacterial efficacy. Thus, there is a challenge to overcome these non-specific interactions. Our approach is to provide an adequate orientation of the peptide away from the resin through coupling the antimicrobial peptide to the resin with a spacer domain in between the peptide and the resin. The spacer domain is always anchored to the resin. Anchoring the peptides to the resin minimizes the interactions between peptides. Several studies including ours have shown that spacer domains provides flexibility for peptides to keep their active conformation while they interact with the surfaces, which improves their biological function [43; 54; 93]. For example, a rigid spacer was shown to improve the antibacterial activity of a peptide compared to a flexible spacer of the same length [43]. In this work it was noted that the addition of a spacer sequence, flexible or rigid, allowed greater functional activity of the peptides coupled to the resin, when compared to respective peptides without a spacer [93]. Overall spacer design approach which was inspired by the domain linker sequences observed in multi-domain proteins, provided an active orientation for the antimicrobial peptide when coupled to the adhesive formulation. [94; 95].
## 3 Numerical Results
### Multi-Object and Rough Sea Surface  
In the last example, a complex electromagnetic scattering scene of multiple objects above a rough sea surface is calculated and shown in Figure 12. Obviously, the electromagnetic scattering problem of multiple objects is more complex for than a single object. Furthermore, as a result of multiple coupling between the three multi-scale models as well as a rough surface, these will increase the computational complexity and difficulty of the hybrid algorithm.  
\[S(k)=\frac{\alpha}{4|k|^{3}}\exp\left(-\frac{\beta g_{c}^{2}}{k^{2}U_{19.5}^{ 4}}\right) \tag{21}\]  
where \(\alpha\) and \(\beta\) are the dimensionless empirical constants, \(\alpha=8.10\times 10^{-3}\) and \(\beta=0.74\); \(g_{c}\) is the acceleration of gravity; \(g_{c}=9.81\)m/\(s^{2}\); and \(U_{19.5}\) is the wind speed at 19.5 m above sea level [38].  
As shown in the Figure 13a,b, the RCS of multiple objects at 0.5 GHz and 1 GHz were calculated via the DGTD method, and the NRMSD values were 0.0328 and 0.0619 respectively. These results demonstrate that DGTD method can also be used to calculate the multi-object problem.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **DGTDPO** & **TDPO** & **TDSBR** \\ \hline NRMSD (1.5 GHz) & 0.0823 & 0.1530 & 0.1431 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The NRMSD of DGTDPO, TDPO and TDSBR for multi scale-plate composite objects at 1.5 GHz.  
Figure 12: The multi-object-rough sea surface composite object.  
As shown in the Figure 14, the transient scattered electric field at theta = 0\({}^{\circ}\), phi = 0\({}^{\circ}\) was calculated, and the NRMSD was 0.0312. This illustrates that the DGTDPO method calculates results for a multi-object-rough sea surface composite well in the time domain. Besides, Figure 15 shows the RCS of the multi-object and rough sea surface composite object at 1 GHz and 1.5 GHz calculated by DGTD method. These results are almost consistent with those of MoM. This shows that the DGTD method can also be used to calculate the electromagnetic scattering problem of the multi-object and rough sea surface composite.  
Table 6 shows that DGTDPO was more accurate than TDSBR and TDPO for the multi-object-rough sea surface composite.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **DGTDPO** & **TDPO** & **TDSBR** \\ \hline NRMSD & 0.0971 & 0.2744 & 0.2003 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The NRMSD of DGTD, TDPO and TDSBR at 1.5 GHz for multi-object-rough sea surface composite objects.  
Figure 14: Transient scattered electric field of the multi-object-rough sea surface composite computed at theta = 0\({}^{\circ}\), phi = 0\({}^{\circ}\).  
Figure 13: The RCS of multiple objects computed at **(a)** 1 GHz and **(b)** 1.5 GHz from the solutions of the DGTD method and MoM.  
As shown in Table 7, the complex multi-scale structure and rough sea surface bring about higher CPU time and memory requirements. Nevertheless, compared with DGTD, the calculation time and memory consumption of the DGTDPO method were decreased by 99.65% and 75.34% respectively.
## 3 Proposed Methodology for Out-of-Stock Prediction Model Using BCHO-TCN LightGBM
### ARFIMA Features  
The ARFIMA attribute is applied to the dataset in this research, which stands for features derived from Autoregressive Fractionally Integrated Moving-Average models. This involves long-range dependencies and memory effects, making the model more accurate, especially for complicated tasks where deep learning is involved.  
[PERSON], [PERSON], and [PERSON] suggested the ARFIMA \((q,e,r)\) model as a way to study the long-memory property by combining fractional differenced noise and the autoregressive moving average. The formula for the ARFIMA \((q,e,r)\) model for the time series \(z_{t}\) is as follows:  
\[\varphi(Z)(1-Z)^{e}z_{t}=\Theta(Z)e_{t},\qquad t=1,2,.......U \tag{11}\]  
The time series \(z_{t}\) consists of the autoregressive and moving-average polynomials, \(\varphi(Z)=1-\varphi_{1}Z-\varphi_{2}Z^{2}-\ldots-\varphi_{q}Z^{q}\) and \(\Theta(Z)=1-\theta_{1}Z-\theta_{2}Z^{2}-\ldots-\theta_{q}Z^{r}\), respectively, where \(\varepsilon_{t}\approx(0,\sigma^{2})\) represents the white noise process with a mean of zero and a variance of \(\sigma^{2}\). The backward shift operator \(Z\) is used to define the fractional differencing operator \((1-Z)^{e}\) through a binomial expansion.  
\[(1-Z)^{e}=\sum_{k=0}^{\infty}\left(\begin{array}{c}e\\ k\end{array}\right)(-1)^{k}Z^{k} \tag{12}\]\[\left(\begin{array}{c}e\\ k\end{array}\right)(-1)^{k}=\frac{\Gamma(e+1)(-1)^{k}}{\Gamma(e-k+1)\Gamma(k+1)}= \frac{\Gamma(-e+k)}{\Gamma(-e)\Gamma(k+1)} \tag{13}\]  
The gamma function is denoted by \(\Gamma(*)\) and \(e\) represents the number of differences needed for a series to become stationary in an ARFIMA \((q,e,r)\) process. If \(e\in(0,0.5)\) is the number of differences required for the process to be stationary, then the process exhibits long-memory behavior. The concatenated statistical and _ARFIMA_ features are denoted as \(T=[SF,ARFIMA]\), and form the input for the LightGBM-enabled deep temporal convolutional neural network model.
## 4 Experiment Setup  
The purpose of the tests was to investigate the slip resistance properties of synthetic rubber track grooves under the tilt angle of the PV panel. Three factors, namely contact area (roughness), shore hardness, and outside pattern, have been identified as influential factors affecting the slip resistance of synthetic rubber track grooves. Each experimental test run follows the procedure outlined below:  
1. Attach numerous synthetic rubber track grooves with a specific shore hardness, outside pattern, or contact area to the bottom side of the weight-bearing frame;
2. Position the weight-bearing frame on the PV panel and mount it to the electronic loadcell through the strut bar;
3. Gradually decrease the tilt angle of the PV panel until it reaches the minimum value;
4. The theoretical tilt angle is calibrated using an inclinometer;
5. Adjust the clamp position of the electronic load cell to provide the median tensile force, then zero-return the electronic load cell;
6. Supply water with a specific flow rate to simulate the wet working condition, or;
7. Clean water on the PV panel to facilitate the dry working condition;
8. Increase the tilt angle with the increment of 1\({}^{\circ}\) (approximately 10 mm of the caster) until slipping occurs;
9. A slip occurs when the electronic loadcell indicates an abruptly large change;
10. Record the tilt angle and the tensile force at the slipping occurrence;
11. Compute the COSF through the two-step procedure.  
It is important to note that the tensile force indicated by the electronic load cell is influenced not only by the \(x\)-component of the force of gravity and the compression force of the synthetic rubber track groove exerted by the displacement mass but also by the water force and a certain level of elongation in the rubber thickness. Therefore, fluctuations in the indicated value of the electronic load cell may occur during the experimental process.  
Figure 6: Coefficient of static friction measurement.  
However, these fluctuations are considered insignificant compared to those observed during slipping events.  
The practical slip resistance test apparatus is depicted in Figure 7. The slip specimens used in the tests to determine the COSF are constructed from synthetic rubber, the same material utilized for the tank track groove of the PV cleaning robot. The slip specimens are securely attached to the underside of the weight-bearing frame during the tests, ensuring that they do not cause any scratches on the PV panel. Each specimen has dimensions of 30 mm in length, 30 mm in width, and 10 mm in thickness, resulting in a contact area of 900 mm\({}^{-2}\) between the slip specimen and the PV panel. The shore hardness of each slip specimen is determined using a durometer mounted on a sliding guide frame, as depicted in Figure 8. This setup ensures the consistency of the trials. The evaluation of the geometric and hardness parameters of the specimens is carried out under both dry and wet working conditions, as outlined in Table 2.
## 4 Impacts on the Range Delay
### Topospheric Delay  
The Regional Reference Frame Sub-Commission for Europe (EUREF) operates a permanent network of reference stations with precisely known coordinates using global navigation satellite systems (GNSS). For the current study, the measured zenith path delay (\(ZPD_{station}\)) is used which is provided by EUREF [21]. The data tracked by each EUREF station can be downloaded via ftp from the EUREF website. The measured delay is converted into a height corrected zenith path delay (\(ZPD_{height\:corrected}\)) by using the station altitude (\(h_{station}\)) and Equation (5) with a reference height (\(h_{reference}\)) of 8000 m:  
\[ZPD_{height\:corrected}\:=\:ZPD_{station}\cdot e^{-h_{station}/h_{reference}}. \tag{5}\]  
Figure 5 shows the total delay from height corrected data for two EUREF stations close (<120 km) to the DLR calibration field (OBE4: Oberpfaffenhofen, Germany, PFA2: Bregenz, Austria). The left sub-figure focuses on S-1A acquisitions and covers an observation period of one month during the late summer 2015 where a ZPD of about 2.45 m is found on average. The right sub-figure depicts the period of S-1B acquisitions early 2017 with a mean ZPD of about 2.4 m. Although seasonal and daily variations of ZPD values in the order of a few 10 cm exists, the spatial variation is much slower. Both EUREF stations, with a distance of 150 km in between, show a similar trend with low ZPD differences below 1 cm. For the current study, a specific ZPD value is derived for each satellite overpass by using the best matching ZPD value in time for the GNSS station OBE4 (nearest neighbor).  
The additional range offset due to tropospheric effects (\(\delta\:r_{troposphere}\)) is then calculated with Equation (6) using the altitude at the corner reflector location (\(h_{corner\:reflector}\)) and the incidence angle (\(\theta\)) of the SAR satellite for this position. As the temporal resolution of the delivered ZPD values is one hour and the distance from the reference station to the related corner reflector is between 40 and 110 km, an uncertainty in the order 1 cm is expected for the tropospheric delay contribution. The tropospheric delay, which is to be considered within the evaluation, can be calculated by multiplying the related path length with half the speed of light:  
\[\delta\:r_{troposphere}\:=\:ZPD_{height\:corrected}\:\cdot\:e^{-h_{corner\:reflector }/h_{reference}}\:\cdot\:\frac{1}{cos\:\theta}. \tag{6}\]
## 1 Introduction  
In today's highly competitive business environment, to achieve competitive advantage or to survive, many companies have formed strategic linkages with others [1]. The necessity to serve firms with truly global supply chain requirements and distributive infrastructure has helped stimulate the formation of alliances within the global air cargo industry [2; 3; 4]. This phenomenon is becoming more prevalent as the growing adoption of supply chain management strategies are resulting in greater integration and cooperation between actors participating in air cargo supply chains [5]. Like airline passenger services, air cargo carriers have started to cooperate through common product/service options, sales and compatible information systems and to develop global networks. Such alliances are partly intended to combat the challenges posed by regulation in the industry, as well as the rapid growth of the integrated carriers, such as FedEx and United Parcel Service (UPS) [6]. These alliances enable the combination of shipment volumes and distribution channel requirements and the ability for the partners to offer consistent time scheduled flights to satisfy customer requirements. The alliances also facilitate joint profit optimization [7]. Moreover, as no single airline could cover all air cargo customers' requirements of its own accord, cooperation and partnerships, therefore, become critical [8].  
Despite their growing significance, there are few published papers that have examined strategic joint ventures in the international air cargo industry. The objective of this paper is to examine the development and the progress to date of the strategic joint venture all-cargo airline--AeroLogic--between Lufthansa Cargo A.G. and DHL Express, the global air cargo industry's largest and most intensive operating collaboration and partnership between an airline and a leading international express and logistics provider [9]. Additional aims of the paper are to examine how the joint venture airline AeroLogic has enhanced DHL Express and Lufthansa Cargo AG competitive positions in the world air cargo industry, to examine the selection of the AeroLogic freighter aircraft type and the airline's principal operational base, as well as to examine how the AeroLogic route network has evolved over time.
## 6 Performance Evaluation  
For performance evaluation of the SLS, the coverage rate change was investigated according to the SLS application under identical environmental conditions and the same number of sensor nodes. Figure 6 shows the observation results for the coverage rate over time, where the identical number of sensor nodes is deployed under four different environments. In the case of 100 sensor nodes, the topology where the SLS was displayed showed relatively higher coverage from the tenth day. Similarly, the coverage showed a large differences between SLS and non-SLS over time in the case of 200 sensor nodes, 300 sensor nodes, and 400 sensor nodes, even though no large difference was observed at the beginning. Despite the use of identical protocols, the topology coverage rate for big data turned out to be higher with the application of SLS.
## 5 Materials, Methods, and Experiment Setup
### Objective Edge-Oriented Metrics  
We omitted commonly used metrics such as MSE, SNR, PSNR, and median absolute error (MAE), which, as argued in [20], have been proven to be insufficient for assessing performance in scenarios where edge preservation is paramount. Instead, we followed the authors' recommendation to employ metrics that specifically measure edge detection capabilities.  
Therefore, we considered the following edge-focused metrics: the Edge-Based Structural Similarity (ESSIM) [21], which compares the information edges of distorted image blocks to the corresponding of the original image and uses this result for computing the Structural Similarity Measure (SSIM); an ESSIM evaluation leads to results similar to the Mean Opinion Score (MOS); the Multi-Scale Structural Similarity (MS-SSIM) [22], this approach replaced the Sobel filter involved in the process by a dyadic wavelet transform, and thus, the assessment spawns several decomposition levels of the wavelet transform. The Gradient Conduction Mean Square Error (GCMSE) can be thought of as an edge-aware version of the Mean Square Error (MSE). This algorithm computes the average of the weighted sum of gradients for four directions for computing a MSE-based estimate of the GCMSE [23] for this experiment.
## 2 Related Work  
### Content-Based Image Retrieval  
There are different approaches to Content-Based Histopathological/Medical Image Retrieval techniques in histopathology [24], which we briefly review here. Ref. [25] proposed a framework for size-scalable query regions of interest (ROIs), including epithelial breast tumors in the Motic data set. This work reached 96% precision in retrieving the top 20 similar images. In [25], the authors applied the proposed technique on the Motic database (Motic (Xiamen) Medical Diagnostic Systems Co. Ltd., Xiamen, China) with the original color of 145 stained WSIs. The authors of [26] applied a supervised kernel hashing technique on several thousand histopathological images from breast microscopic  
Figure 1: Image examples from five hospitals that collaborated to collect images into a single data set (CAM17).  
tissue. The precision result for the top 30 retrieved images was reported to be 77.0%. In this study, they considered the gradient of the pixels around the detected regions to make the model robust to subtle changes in the color of patches. A combination of unsupervised feature learning (UFL) with the classical bag of features (BOF) was introduced in [27]. This proposed method was evaluated in particular histopathological images to show that the learned representation has a positive impact on the retrieval performance. CBHIR based on multi-scale, multichannel decoded local ternary pattern features and VLAD coding was presented in [28]. The authors evaluated their method on the KIMIA Path960 data set [29] by retrieving the top 10 matching images from the data set. The authors of [15] presented a modified convolutional auto encoder (CAE) to extract features of patches from SICAPv2 as the largest pixel-annotated prostate data set [30]. The experimental results of this paper demonstrated 85% and 78% accuracy for the top 7 and top 5, respectively. However, one of the challenges that the authors had with SICAPv2 was the color variation of the images in the data set. To deal with this variation, they applied a simple histogram equalization to the patches, but still, this issue affected the results.  
Among the previously mentioned studies, only refs. [15; 26] applied CN techniques to tackle color variation, while the rest did not address this issue. Another challenge in CBHIR is the lack of annotated images, which the papers mentioned above struggled with.
## 4 Discussion
### The Meaning of the Equations
Therefore, although P salts are usually used in wastewater treatment, the P-Se ratio may be indicative of an agricultural origin of both elements. Predictive elements K, S, Ca, Cl, Al, Bal (i.e., organic matter), Mn, and Ba generally appeared as a second or third term in the equations. Therefore, they exercised a lower level of influence. These elements are major (K, S, Ca, Al) or common (Mn, Ba) constituents of the soil/plant system and phenomena, such as complexation, graphic characteristics, redox conditions in the soil or during composting processes, and the treatment process of wastewater in the case of sludge, could be the causes of the relationships. For volatile elements, such as Hg and Se, atmospheric transport and deposition would also be involved. To explain their effects on the target elements, additional studies would be required. After taking into account the small number of cases (5) used, the relationships found in the case of organic fertilizers should be taken with caution. For compost and sludge, it should be noted that several equations included any of the elements of anthropogenic origin as the first predictor term (Equations (6), (10), and (12)), and in some other equations, any of the elements of edaphic or geogenic significance was added as a second term. (Equations (3), (4), (7), (9), (15), and (16)). Taking into account that the (standardized) coefficients of the first terms were always higher than those of the following terms, this indicates the importance of anthropogenic contamination in these matrices (compost and sewage sludge) and the common origin for a significant fraction of the elements included in these equations (Cd, Hg, Se, As, Zn, Pb). However,the crustal elements (Zr, Mn, S, K, Al) that represent the contribution of the edaphic or geological environment, although to a lesser extent, had a significant effect on the concentration of the objective elements (Cd, Hg, Se).