esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### The Effects of Prevailing Torque  
Based on the phenomenon and analysis above, the G-S24# connection was proven to be the optimal configuration with both efficient shear resistance and economy. As a result, the G-S24# connection only was selected to examine the influence of the prevailing torque on the slip factor of slip-critical connections. The prevailing torques used in the tests were 5 N\(\cdot\)m, 6 N\(\cdot\)m, 7 N\(\cdot\)m, 8 N\(\cdot\)m, 9 N\(\cdot\)m, and 10 N\(\cdot\)m. Each set of tests was performed with two identical specimens. The slip load and slip factor are listed in Table 9 and the load-displacement curves are shown in Figure 10. The load-displacement curves of the G-S24# connection with different prevailing torques in Figure 10 can be described by the (a) type curve from Figure 3. These curves are similar to the load-displacement curve of the G-S24# connection from Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Specimen** & \begin{tabular}{c} **Prevailing** \\ **Torque** \(T\) **(N\(\cdot\)m)** \\ \end{tabular} & \begin{tabular}{c} **Slip Load** \\ **F (kN)** \\ \end{tabular} & \begin{tabular}{c} **Average** \\ **Value** \\ \end{tabular} & \begin{tabular}{c} **Growth Rate** \\ **Compared with the** \\ **Former (\%)** \\ \end{tabular} & \begin{tabular}{c} **Slip** \\ **Factor \(\mu\)** \\ \end{tabular} & \
\begin{tabular}{c} **Average** \\ **Value** \\ \end{tabular} &
\begin{tabular}{c} **Growth Rate** \\ **Compared with the** \\ **Former (\%)** \\ \end{tabular} \\ \hline T-S-1 & 5 & 4.90 & 4.92 & / & 0.44 & 0.44 & / \\ T-S-2 & & 4.94 & & / & 0.44 & 0.45 & 2.27 \\ \hline T-6-1 & 6 & 5.93 & 5.89 & 19.72 & 0.45 & 0.45 & 2.27 \\ T-6-2 & & 5.85 & & 0.44 & & 0.41 & -8.89 \\ \hline T-7-1 & 7 & 6.37 & 6.40 & 8.66 & 0.41 & 0.41 & 0.41 & -8.89 \\ T-7-2 & & 6.78 & & 5.94 & 0.39 & & \\ \hline T-8-1 & 8 & 6.78 & 6.78 & 5.94 & 0.39 & & \\ T-8-2 & & 6.77 & & 0.36 & & 0.36 & -7.69 \\ \hline T-9-1 & 9 & 7.05 & 7.09 & 4.57 & & 0.35 & 0.36 & -7.69 \\ \hline T-10-1 & 10 & 7.39 & 7.41 & 4.51 & & 0.33 & & \\ T-10-2 & & 7.43 & & 4.51 & & 0.33 & 0.33 & -8.33 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The slip load and factor values of tested connections with different prevailing torques.  
Figure 9: Plate surfaces of G60#-S24# connection after shear test. (**a**) GFRP connecting plate surface after shear test; (**b**) stainless-steel cover plate surface after shear test.  
From Table 8, it was clear that as the prevailing torque was increased, the slip load of the connections increased gradually, and the slip factor slightly decreased. Each time the prevailing torque was increased, the slip load also increased, however, the magnitude of the increment was lessened each time. [PERSON] et al. [12] demonstrated that the slip factor decreased with an increase in contact pressure in steel-steel connections. They explained that the surface asperities were flattened by the applied contact pressure, which decreased the deformation component of the friction force.
## 2 Materials and Methods
### PCR Array  
In order to evaluate the effect of BiodentineTM on the odonto/osteogenic signaling pathway, PCR array was performed after 3 weeks of treatment.  
RNA was extracted from hDPSCs using the TRIzol-hybrid method (Qiagen, Germantown, MD, USA) from all three groups (treated with BiodentineTM, positive and negative). A total of 0.5 \(\upmu\)g extracted RNA was converted to cDNA by using the RT2 First Strand Kit (Qiagen, Germantown, MD, USA). Then, cDNA samples were diluted and amplified with the RT2 SYBR(r) green master mix of (PAHS-058Z, RT2 ProfilerTM PCR Array Human osteogenesis pathway, Qiagen, Germantown, MD, USA) according to the manufacturer's instructions, and loaded onto the 96-well array. The amplification conditions were as follows: 95 \({}^{\circ}\)C for 10 min, then 40 cycles of 95 \({}^{\circ}\)C for 15 s and 60 \({}^{\circ}\)C for one min. Samples were run on the CFX 96 C1000 system (Biorad, Hercules, CA, USA). Data were analyzed automatically, according to the SABiosciences company (Qiagen, Germantown, MD, USA) web portal, www.SABiosciences.com/pcrarraydataanalysis.php, by using the \(\Delta\)C\({}_{t}\) method, according to the following equations:  
\[\Delta\text{C}_{t}=\text{C}_{t\text{ GOI}}-\text{C}_{t\text{ HKG}}\]  
GOI: Gene of interest  
HKG: Housekeeping gene  
\[\text{Fold regulation}=2^{-\Delta\text{C}_{t}}\]  
The expression levels of the genes were normalized to the following housekeeping genes: beta-2-microglobulin (B2M), hypoxanthine phosphoribosyl transferase 1 (HPRT1), and actin beta (ACTB). For data analysis, the differential expression level of the odonto/osteoblastic genes was identified, using Student's \(t\)-test (two-tailed, unpaired). A cut-off point of 2 was used as a threshold to determine the statistical significance of the upregulated or downregulated genes (\(p<0.05\)). Day 1 of each biological replicate was used as a reference sample to compare gene fold regulation for all treatment groups, of the same biological replicate.
# Spectra-Based Selective Searching for Hyperspectral Anomaly Detection
## 2 Materials and Methods  
In this section, the common measurement of spectral similarity is firstly introduced. Then the detailed description of the proposed method is presented. Figure 1 shows a schematic of the proposed spectra-based selective searching algorithm for anomaly detection in hyperspectral images. This algorithm contains two main phases: oversegmentation and region merging. First, a graph-based image segmentation technique was applied to the HSI to divide the image into a series of initial regions as the processing unit. The similarities between all neighboring regions were then calculated and recorded. Subsequently, the greedy algorithm was applied to iteratively merge the regions until the separation of the anomalies from the background was achieved. The widely-used criteria to evaluate the anomaly detection performance and two common datasets are introduced briefly.
# Investigation of Pellet Properties Produced from a Mix of Straw and Paper Sludge
## 2 Materials and Methods
### Plant Biomass and Paper Sludge  
The primary raw material for the production of experimental samples in the form of pellets was wheat straw. The paper sludge was used as an additive in various proportions in order to increase the melting temperature. The average measured moisture content of the sludge was 25% as a result of its storage in the open air. Therefore, it had to be dried in a laboratory oven MEMMERT UFP 500 at 100 \({}^{\circ}\)C for 12 h. The resulting average moisture content of the paper sludge after drying was about  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Predicted Effects** & **Additives** \\ \hline \multirow{4}{*}{**1.**} & & kaolin, halloysite, cat litter, emathlite, clay minerals, clay sludge, illite, detergent zeolites, ammonia sulfate, aluminum sulfate, iron sulfate, limestone, lime, marble sludge, ammonia phosphate, phosphoric acid, sewage sludge, paper sludge, bear ash, coal fly ash, dolomite, bauxite, quartz, titanium oxide \\ \cline{2-3}  & Physical adsorption and eliminating troublesome ash species from combustion facilities & kaolin, zeolite, halloysite, clay sludge, sewage sludge, paper sludge, clay minerals, lime, limestone, dolomite, calcined dolomite, bauxite, gibbsite \\ \hline \multirow{2}{*}{**3.**} & Increasing the ash melting temperature by adding inert elements & bauxite, lime, limestone, silicon oxide, marble sludge \\ \cline{2-3}  & Restraining ash sintering by diluting and powdering effects & lime, limestone \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overview of additives application [10].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value (\%)** \\ \hline Na\({}_{2}\)O & 0.031 \\ K\({}_{2}\)O & 0.053 \\ CaO & 54.990 \\ MgO & 1.020 \\ Al\({}_{2}\)O\({}_{3}\) & 3.292 \\ SiO\({}_{2}\) & 4.488 \\ TiO\({}_{2}\) & 0.094 \\ Fe\({}_{2}\)O\({}_{3}\) & 0.205 \\ MnO & 0.012 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Chemical analysis of the paper sludge.  
5%. An average measured moisture of straw was around 4%, so drying was not necessary in this case. The pelleting process was carried out on a pellet press KAHL 33-390/500 with a vertical die and the input material was crushed before compaction. The three samples as the mixtures of wheat straw with paper sludge in the following proportion were produced: 70% straw-30% paper sludge (SPS-70/30), 80% straw-20% sludge (SPS-80/20), 90% straw-10% sludge (SPS-90/10), (see Figure 1). In a previous study, the influence of paper sludge on melting temperature was investigated and the increased melting point of produced samples was already reached at 50% of sludge content [26]. In order to reduce the ash from combustion and elevate the melting point at the same time, the range of paper sludge for the production of wheat straw pellets was analyzed from 10% to 30%. The reference pellets from 100% of wheat straw (S100) and 100% of paper sludge (PS100) were produced as well for comparison.
## 3 Experimental Results and Discussions
### Dataset Description and Preprocessing  
The UCM, AID, and NWPU datasets used in this study are publicly available general remote sensing image datasets. The UCM dataset originates from the National Map Urban Area Imagery series of the United States Geological Survey, offering labeled examples of diverse categories within typical urban remote sensing scenes. The AID, unveiled by the Huazhong University of Science and Technology and Wuhan University, constitutes an extensive aerial image dataset compiled from samples extracted from Google Maps imagery. The NWPU dataset, released by Northwestern Polytechnical University, stands as an openly accessible dataset showcasing notable variations across scene samples concerning translation, spatial resolution, and other factors. Table 1 provides the specific details for each of them.  
In our experiments, each remote sensing scene in the datasets was divided into three sets. Specifically, in the UCM dataset, we randomly selected 11 categories as the training set, 5 categories as the validation set, and 5 categories as the test set. Similarly, in the AID dataset, we randomly selected 16, 7, and 7 categories, and in the NWPU dataset, we randomly selected 23, 11, and 11 categories for training, validation, and testing, respectively. Our model was trained on two of these subsets and evaluated on the remaining one in a cross-validation fashion. For each testing task, we randomly sampled five scenes from the test set to simulate five new remote sensing scenes as encountered in the real world. Each scene was assigned only one or a few labeled samples for the scene classification task.
## 2 Materials and Methods
### Mathematical Model of System Coupling
#### 2.3.2 Structure Matrix of the System  
The structure matrix is determined based on a decomposable structural diagram of the system (Figure 3) and includes all the couplings in the system. This matrix, together with the decomposable structural diagram, describes the organization and operation of flexible manufacturing systems for bearing ring manufacturing  
\begin{table}
\begin{tabular}{c} \hline \hline
**Subsystem Type** & **Symbol** \\ \hline Input subsystem (S\({}_{\text{i}}\)) & \(\begin{array}{c}\text{X}_{\text{pi}}\\ \text{S}_{\text{i}}\end{array}\) \\ Output subsystem (S\({}_{\text{o}}\)) & \(\begin{array}{c}\text{Y}_{\text{qj}}\\ \text{S}_{\text{o}}\end{array}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Symbols for subsystems and flows.  
The structure matrix (K\({}_{\text{STR}}\)) is the matrix for which the elements are the coupling matrices (Kci) of all component subsystems in the lower levels and is a zero-diagonal dimension matrix (N \(\times\) N).  
\[\begin{array}{c|cccccccc}1&0&K_{21}&K_{31}&K_{41}&K_{51}&K_{61}&K_{71}\\ 2&K_{12}&0&K_{32}&0&0&0&0\\ 3&K_{13}&K_{23}&0&K_{43}&0&K_{63}&K_{73}\\ 4&K_{14}&0&K_{34}&0&K_{54}&K_{64}&0\\ 5&K_{15}&0&0&K_{45}&0&0&0\\ 6&K_{16}&0&K_{36}&K_{46}&0&0&0\\ 7&K_{17}&K_{27}&K_{37}&0&0&0&0\end{array} \tag{6}\]  
The elements of the structure matrix exist if \(e_{q}\to i_{p}\Rightarrow K_{qp}\).  
For example, \(e_{1}\to i_{2}\Rightarrow K_{12},e_{1}\to i_{5}\Rightarrow K_{15};e_{2}\to i_{6} \Rightarrow K_{26}\)  
For the analyzed FMS (Figure 3), the following dimension structure matrix (N \(\times\) N) resulted, where N = 7. The expressions of the coupling matrices corresponding to the decomposed structural diagram of the FMS were elaborated by the authors in previous papers [49].  
Based on the decomposed structural diagram (Figure 3), it was possible to develop the model to be used for the simulation program, a program based on sets of rules for each subsystem (workstations, robots, conveyors, and storage). We have found that, of all the system components, those having the most couplings are the most complex components. From the decomposed structural diagram, it results that one of the most complex components subsystems is the industrial robot.
## 2 Materials and Methods
### Field Site and Measurements
#### 2.1.3 Destructive Sampling and Post-Processing  
Destructive sampling--covering the range of N applications--was carried out at five of the wheat plots (Figure 1) on four dates (17 April, 25 May, 20 June and 4 July) during the 2018 season. Destructive sample analysis included direct leaf area measurements and the analysis of the leaf C:N ratio, used to derive linear N dilution functions that were incorporated into DALEC-Crop (see details in Section 2.2.2).  
The destructive sampling routine entailed randomly placing a 0.25 m\({}^{2}\) quadrat within the sampled plots and removing all the enclosed above-ground vegetation. The leaves were separated from the sampled vegetation and leaf area was measured using a Li-3100C leaf area meter (Li-Cor, Lincoln, NE, USA). The destructively measured LAI was then used to correct biases in the non-destructive LAI estimates (i.e., SunScan data) as detailed in [PERSON] et al. [11]. The leaf N concentration was estimated by, first, drying and milling the samples. The samples were then weighed and analysed for percentage C and N content using a Flash 2000 elemental analyzer (Thermo Fisher Scientific, Waltham, MA, USA).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Ground Measurement Date** & **Growth Stage Description** \\ \hline
20 April 2017 & Stem elongation—early (GS31) \\
16 May 2017 & Stem elongation—early (GS32) \\
30 May 2017 & Ear emergence (GS50) \\
12 June 2017 & Ear emergence (GS59) \\
26 June 2017 & Flowering (GS71) \\
10 July 2017 & Milk development (GS78) \\ \hline
17 April 2018 * & Tillering—late (GS30) \\
08 May 2018 & Stem elongation—early (GS31) \\
25 May 2018 * & Stem elongation—late (GS38) \\
05 June 2018 & Ear emergence (GS54) \\
20 June 2018 * & Flowering (GS68) \\
04 July 2018 * & Milk development (GS79) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dates and corresponding average growth stages (GS; [PERSON] et al. [38]) when non-destructive ground measurements were made at the winter wheat trial plots for two seasons: 2016–2017 and 2017–2018. Note: asterisk (*) indicates additional destructive sample analysis dates.
# Intelligent Reflective Surfaces for Wireless Networks:
## 3 IRS-Assisted Wireless Networks
### Channel Estimation
#### 3.2.5 Sub-6 GHz Narrow Band Systems  
In [49] the authors consider the downlink of a multiuser IRS-assisted multi-user MISO system with NLOS conditions and develop two methods based on the PARAllel FACtor (PARAFAC) modelling to unfold the BS-IRS-user cascaded channel model. The proposed method ([49], Algorithm 1) includes an alternating least squares procedure to iteratively estimate both the channel between the BS and the IRS, as well as the channels between the IRS and the users. The results are presented in terms of the NMSE and the algorithms are compared between them. The main limitation of these algorithms is the high computational complexity which makes them not suitable for practical implementations.  
In [44], another LS approach is considered to estimate the uplink cascade channels in a point-to-point MISO system with a multi-antenna BS and an energy harvesting (EH) user. The results are presented in terms of the energy harvested by the user and comparing the system performance with the proposed method versus that obtained with perfect CSI, showing a small gap between them. The cascade channel usually has a large size and this method may incur a considerable amount of training overhead by neglecting the sparse structure of the mmWave wireless channels.
## 4 Implementation of Proposed Architecture
### Distributed MEC/Cloud Orchestrator
#### 4.3.2 Sequence Implementation  
The basic sequence flow triggered by the end user is the same as in Section 4.2.2. Here, the supplementary information in the distributed-type sequence is explained with reference to Figure 9.  
Figure 8: Illustration of the Distributed Type of an MEC/Cloud Orchestrator.  
In Figure 9a, regarding the sequence of regularly collecting data on the cloud and MEC, the cloud side needs to obtain the physical/logical data via the interface officially published by the Cloud Owner. On the other hand, because the resource area on the MEC side is inside the network, the resource controller considers information such as RAN. MEC then becomes available because the main function of the MEC/Cloud Orchestrator is under the Private/Local Telecom Operator. Thanks to the above information, a roadmap for future hardware procurement, etc., could be planned. The Private/Local Telecom Operator can easily decide on the purchase order supported by the MEC/Cloud Orchestrator. Regarding subscription registration, when resource usage is about to exceed the capacity pre-reserved on the cloud, the MEC/Cloud Orchestrator can request the cloud owner or end user to acquire additional resources.  
Next, the application deployment sequence in Figure 9b is described below. The application deployment sequence is almost the same as in the centralized type. The difference is the deployment method, which uses the localized cached contents method. In the localized cached contents method, the subscription of registered users is based on the analysis result of the usage logs of the ranking information in which many applications are used, and the stored end user information is based on the history information of the  
Figure 9: Sequence of MEC/Cloud Orchestrator of distributed type: (**a**) subscription registration process and (**b**) deployment process.  
application used by the Private/Local Telecom Operator. In the localized cache contents method, the cached application is deployed based on the analysis result of application ranking and trend provided by the end user. This kind of application deployment method can be classified as localized information-based digital twins.
## 4 Multiple Metrics Implementation
### Implementation Overview and Description
#### 4.1.1 Application  
The application refers to an SG application traffic type that will use the functions provided by the multiple metrics framework. This is because an SG application may have different data types; each with unique QoS (reliability or delay) requirements. Examples of this application traffic types are: Demand Respond (DR), Electronic Vehicle (EV) charging, power quality and meter reading. Each of these application traffic types can be assigned an RLMT and Relative Link Metric Type Weight (RLMTW), which can be used to find the best route to the destination.
## 3 Object Detection Performance Evaluation Results
### Object Detection Performance Degradation Effects of Multiple Droplet Contamination  
In this section, we quantitatively evaluate the effects of multiple droplet contamination on object detection performance. To increase the reliability of the evaluation, we collected 600 images based on car driving data provided by the Korea Automotive Parts Research Institute's Civilian Technology Research Center (now known as the Korea Automotive Technology Institute) and CCTV data provided by the Korean National Police Agency. The object detection performance was analyzed for seven object classes (person, car, truck, bus, bicycle, motorcycle, and traffic light) that appeared most frequently in the images.  
The multiple droplet contamination method and level are the same as in Section 2. Figure 12 shows images from the evaluation dataset captured when the camera sensor was contaminated by multiple droplets. On the hydrophobic surface, the droplets caused light refraction and scattering, altering the overall color of the image. In contrast, on the hydrophilic surface, the sprayed droplets resulted in localized blurring in parts of the image.  
Figure 11: Object detection performance evaluation results with five different object sizes and droplet volume on hydrophobic and hydrophilic cover glass: (**a**) mAP for person class; and (**b**) mAPloss, representing the difference between the mAPclean and mAPcontamination.  
Figure 13 shows the evaluation results of object detection performance using the four algorithms (RTMDet, YOLOv8x, DetectoRS, and DINO). The performance degradation due to droplet contamination is represented by the mAP\({}_{\text{loss}}\) calculated by Equation (7). All algorithms showed a decrease in the mAP due to multiple droplet contamination, with the most significant degradation observed under severe contamination. Comparing surface properties, the mAP reduction was more pronounced on the hydrophilic surface than on the hydrophobic surface. These results demonstrate that droplet contamination degrades the performance of object detection algorithms, with the impact varying based on the wettability of the camera lens surface.  
Figure 12: Images for evaluating object detection performance degradation on camera sensors resulting from multiple droplet contamination on hydrophobic and hydrophilic surfaces, with contamination levels as follows: (**a**) clean; (**b**) slight; (**c**) moderate; and (**d**) severe.  
Figure 13: The mAP\({}_{\text{loss}}\) caused by multiple droplet contamination for various object detection algorithms: (**a**) RTMDet; (**b**) YOLOv8x; (**c**) DetectoRS; and (**d**) DINO.