esa-sceva/satcom-chunk-collection huggingface dataset:
# A Design of a Parcel Delivery System for Point to Point Delivery with IoT Technology
## 7 Conclusions  
In this paper, we proposed an improved parcel delivery system, which reduces the total delivery distance with IoT technology and improves economic feasibility. It showed the development process and implementation of experimental IoT platform. Previously, the central cloud had the most control processing and calculations. However, processing can be divided into IoT devices using Bluetooth and Wi-Fi functions. Later, we will supplement NFC and RFID. Since then, various functions of IoT devices have allowed the data to be processed without going through servers. In addition, the corresponding module can be deemed to be highly useful, such as by changing it to the delivery system or the tracking system. That is, these devices can help implement a more efficient delivery system. It will also handle internal operations of IoT devices to increase efficiency in data processing. By using location information generated from these systems, it can be used as an active test data set in hub-related papers to prove the efficiency of the Hub and spoke method currently used and could check other additional logistics delivery methods [6; 7]. The generated data can be changed in real time using AI and are used to predict the shortest path considering intermediate-fall. It can also combine blockchain, IoT platform and IoT device functionality to devise and utilize methods for the security and integrity of the logistics system [30; 31; 32; 33]. In particular, Blockchain can solve a security problem at packet communication and is easy to track based on strict validity. More features can be considered; we can also identify the weight, type, size, and delivery area of the product entered prior to delivery and add features that show how to increase space efficiency and delivery speed while loading the parcels. It will be able to develop its ability to sensitively detect changes in IoT and respond in line with the upcoming IoT era in a modern era where the overall development and change of the industry with which IoT is applied are very active. Being able to adapt to these rapid industrial changes will make them more competitive.  
Conceptualization, H.Y.S.; methodology, H.H.; software, H.H.; validation, H.H.; investigation, H.H.; resources, H.Y.S.; data curation, H.H.; writing--original draft preparation, H.H.; writing--review and editing, H.Y.S.; visualization, H.H.; supervision, H.Y.S.; project administration, H.Y.S.; funding acquisition, H.Y.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (NRF-2019R1F1A1056123).  
The authors declare no conflict of interest.
## 5 Results and Discussion
### Feature-Importance Results
#### 5.2.1 Tree-Based Feature-Importance Results
Each of the four feature clusters identified through PCA and K-means demonstrates a distinct strategy for leveraging available information. Cluster 0's primary driver is terrestrial radiation instant, indicating that these trees focus heavily on that parameter to achieve good predictive performance. Cluster 1, on the other hand, emphasizes shortwave and direct radiation instants. Meanwhile, Cluster 2 incorporates broader features, such as direct normal irradiance instant, direct normal irradiance, diffuse radiation instant, and smaller contributions from global tilted irradiance and terrestrial radiation instants. Finally, Cluster 3 centers its predictions on the global tilted irradiance instant. Across all clusters, the trees have a maximum depth of seven levels, and their splitting thresholds are fine-tuned to optimize the Gini criterion, reflecting unique ways of capturing the most relevant aspects of the data. Figure 13 presents a snapshot of the first two levels (depths) of a single decision tree within the random forest. This visualization clarifies how specific parameters, such as different types of radiation, are used in early splits to predict active power. While a random forest aggregates the predictions of all its trees, focusing on one tree at a time can help us understand how certain features drive splits and influence outcomes. Interpreting ML models involves more than just reviewing their internal splits. It also requires domain knowledge about PV power generation and environmental factors. By matching model decisions (e.g., \"if direct_normal_irradiance > threshold, then...\") with real-world context (e.g., \"High direct normal irradiance usually leads to increased power output\"), we can form more meaningful insights. This combination of data-driven modeling and subject-matter expertise is essential for explaining why the model behaves as it does and for identifying potential improvements or inconsistencies in the model's reasoning. Figure 12: Average features’ importance in clusters of the random forest estimators.
## 3 Results and Discussion  
### Microplastic Analysis (FTIR Spectroscopy)  
The results of counting the microplastic particles via FTIR spectrometry of the six different plastic bowls, as well as the bowl made of ABS (ABSI) with the salt treatment, are shown in Figure 1. The IR microscope recognized some particles within the first blank sample, but none of them matched with the references found in the database, which means they were not MP. These particles were probably lime from the faucet. In the second blank sample, only one particle was found, which matched the database. The one particle had a 60% correlation to polyurethane. Due to only one plastic particle in the reference samples for the glass bowl and the used water, which does not correspond to the plastic types used, no normalization of the values in Figure 1 was performed. For the sake of comparison, the samples treated with only water show abraded particles in descending order from  
Figure 1: FTIR spectrometer microplastic analysis of the different plastic bowls listed according to the type of material. Red bar = use of water and salt. Blue bar = use of water only.  
Melamine with 898 particles > PS with 827 > PP with 662 > ABS 591 > SAN with 567 > PE with only 331 particles. The effect of the salt test on the plastic ABS shows an increase from 591 particles (ABS II) of about 320% to 1890 particles (ABS I).
# Retro-Reflective-Marker-Aided Target Pose Estimation in a Safety-Critical Environment
## 3 Results  
In this section, we evaluate the performance of the proposed system and, when possible, of its individual components, on synthetic and real data, and establish a comparison between the single and stereo estimation methods. In all the upcoming experiments, the threshold parameters for marker detection (as defined in Section 2.3) have the following values: initial proposal of thresholding parameters s and T are 1.18 and 0.03, respectively. These values are set based on the lighting conditions and should be refined for each specific setup. They can also be tuned by the operator if the search for the markers is unsuccessful. The neighborhood size for adaptive thresholding is 11 \(\times\) 11 pixels. The structural element for the morphological closing operation is a disk with radius of three pixels. The flood fill operation and connected component labeling use a pixel connectivity of 4. For the elimination of false blobs, the acceptable ranges for area and circularity are [20, 400] pixels and [1, 1.5], respectively. The maximum allowed value for eccentricity is 0.9. These values are set based on the relative sizes of the markers and the image for the specified operating distances.
## 2 Statement of the Problem for Heterogeneous Media  
A heterogeneous medium occupying a volume \(\Omega\in\Re^{3}\) with a boundary \(\partial\Omega=\partial_{1}\Omega\cup\partial_{2}\Omega\) is considered as a periodic two-phase elastic FRC (inclusion/matrix) at the Cartesian coordinate system \(\mathbf{x}=(x_{1},x_{2},x_{3})\) (see Figure 1). In particular, an elastic FRC with transversely isotropic CNT inclusions embedded into an isotropic matrix is studied. The CNT's inclusions have a periodic distribution along the axes \(Ox_{1}\), \(Ox_{2}\) and \(Ox_{3}\) without overlapping within the homogeneous matrix. The periodic unit cell \(S\) is taken as a cubic matrix with a centered CNT inclusion in the form of discs, spheres, ellipsoids and hollow cylinders at the Cartesian coordinate system \(\mathbf{y}=(y_{1},y_{2},y_{3})\). The regions occupied by the matrix \(S_{1}\) with volume \(V_{1}\) and the CNT inclusions \(S_{2}\) with volume \(V_{2}\) satisfy that \(S=S_{1}\cup S_{2}\) with \(S_{1}\cap S_{2}=\emptyset\), and the unit cell volume is \(V=V_{1}+V_{2}\). Perfect contact conditions between the matrix and the inclusions are assumed; that is, mechanical displacements and stresses are continuous across the interface, denoted by \(\Gamma\) (see, for instance, [23]). The values associated with the matrix and the inclusion are indicated by superscripts in parentheses \((2)\) and \((1)\), respectively.  
Figure 1: Representative section of a two-phase composite material reinforced by aligned (left) and random (right) inclusions. Three-dimensional periodic cell (middle) with different inclusion shapes: discs (**a**), spheres (**b**), ellipsoids (**c**) and hollow cylinders (**d**).  
The static governing equations for a heterogeneous multi-phase elastic composite associated with the theory of linear elasticity [42; 43] are defined by  
\[\frac{\partial\sigma_{ij}}{\partial x_{j}}=0,\quad\text{in}\quad\Omega, \tag{1}\]  
together with the boundary conditions  
\[u_{i}=0,\quad\text{on}\quad\partial_{1}\Omega, \tag{2}\] \[\sigma_{ij}n_{j}=0,\quad\text{on}\quad\partial_{2}\Omega,\]  
where \(i,j=1,2,3\) and \(\sigma_{ij}\) and \(u_{i}\) represent the components of the second-order stress tensor and the displacement vector, respectively. In addition, \(n_{j}\) is the normal unit vector to \(\partial_{2}\Omega\).  
Furthermore, the constitutive equations are defined by [PERSON]'s law:  
\[\sigma_{ij}=C_{ijkl}(\mathbf{x})\varepsilon_{kl}, \tag{3}\]  
where \(C_{ijkl}\)\((k,l=1,2,3)\) is the elasticity modulus and \(\varepsilon_{kl}\) represents the strain components, such as  
\[\varepsilon_{kl}=\frac{1}{2}\Big{(}\frac{\partial u_{k}}{\partial x_{l}}+ \frac{\partial u_{l}}{\partial x_{k}}\Big{)}. \tag{4}\]  
Besides, the perfect contact conditions between the matrix and inclusion phases are assumed:  
\[u_{i}^{(1)}\mid_{\Gamma}=u_{i}^{(2)}\mid_{\Gamma},\quad\sigma_{ij}^{(1)}n_{j} ^{(1)}\mid_{\Gamma}=\ \sigma_{ij}^{(2)}n_{j}^{(2)}\mid_{\Gamma}. \tag{5}\]
## Materials and Methods
### Experimental Program
#### 2.2.2 Autogenous Shrinkage, Internal Relative Humidity, and Internal Temperature  
The current ASTM C1698 standard can only measure the autogenous shrinkage of concrete, but it cannot simultaneously measure the internal relative humidity and internal temperature. For cast-in-place concrete in actual engineering, engineers are interested not only in the autogenous shrinkage of concrete but also in its internal relative humidity and internal temperature. Therefore, an experimental machine that can simultaneously measure the three indicators of autogenous shrinkage, internal relative humidity, and internal temperature is urgently needed. Based on the above features, such an experimental device was invented in the present study. Figure 1a shows the overall schematic diagram of the internal relative humidity, internal temperature, and deformation measurement set-up. A corrugated tube with concrete is placed on the steel support, and the magnet absorbs one end of the corrugated tube. The linear variable differential transducer (LVDT) measures the deformation throughout the experiments. To ensure the internal relative humidity and temperature reflect the real values inside the samples, a sensor bar with a measurement accuracy of \(\pm\)0.8% RH and \(\pm\)0.1 \({}^{\circ}\)C is embedded into the specimens. A PVC tube with an inner diameter 15 mm is used to hold the sensor. Two rectangular holes are made on the surface of the PVC tube to keep the sensor bar at the same humidity as the surrounding materials. Figure 1b is the physical object of the measurement set-up. The whole set-up was placed in a constant temperature chamber at 20 \(\pm\) 1 \({}^{\circ}\)C for 7 days. The most obvious advantage of the new measuring instrument is that it can simultaneously measure the autogenous shrinkage, internal relative humidity, and internal temperature.  
Figure 1: The internal relative humidity, internal temperature, and deformation measurement set-up. (**a**) Design drawing; (**b**) physical object.
# A Deep Learning Approach for Maximum Activity Links in D2D Communications
## 5 Deep Learning-Based Link Scheduling
### Deep Belief Network
#### 5.3.3 Training Set  
Once the input and output layers are settled, the training set consisting of pairs of D2D location information and a D2D links activation state must be constructed. The training set is usually obtained from historical data or an off-line solution to sample problems. In this paper, we randomly generated D2D user location information and simulated the transmission state of the channel. A conventional optimization algorithm was exploited to solve the D2D link schedule problem and produce labeled training samples containing link information and a binary link state value. We present the training process in Algorithm 1.  
```
1. Generate a random location set of D2D nodes and cellular node;
2. Formulate the D2D link schedule problem;
3. initialization: \(n=0\);
4. while not at end of this document do
5. Solve the optimization problem by conventional method;
6. Add labeled solution to the training set;
7. \(i=i+1\);
8. end while
```  
**Algorithm 1**Procession of Training Set Generation
## 4 Conclusions  
We have demonstrated the construction of a highly sensitive ECL genosensor for detecting sequence-specific DNA based on TRA, RCA and hemin/G-quadruplex. The signal amplification relies on \"Y\" junction formation followed by TRA and RCA on the sensing surface and the generation of massive hemin/G-quadruplex. The proposed ECL sensor was compared with other DNA genosensors reported in literatures for the detection of DNA, as listed in Table 3. The sensors based on RCA showed lower detection limit at least one order of magnitude than most of the others, and our ECL sensor combined TRA, RCA and hemin/G-quadruplex exhibited comparable (2.6-18.7 fold-improvement) detection limit against the other two RCA-based ECL sensors [40,41].With the effective signal amplification routes, our protocol provides aM (3.8 aM) detection of target DNA. In addition, this DNA detection approach has also excellent selectivity, which is evidenced by its detection capability of single base-mismatched DNA. In this paper, we only show the capability of the proposed strategy for detecting a _S. aureus_ specific DNA sequence; however, our method can be expanded to detect a wide range of DNA sequences if corresponding assistant, capture and padlock probes are specifically chosen and designed.  
This study was kindly supported by National Natural Science Foundation of China (NSFC: 81371898). The authors wish to thank other members of our laboratory for their support.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Detection Technique** & **Type of Assay** & **Detection Limit** & **Signal Amplification Strategy** & **Reference** \\ \hline ECL & Sandwich & 15.0 fM & Ru(phen)\({}^{3^{\text{\textminus}}}\) intercalated into HCR procuct & [PERSON] _et al._ (2012) [40] \\ \hline ECL & Indirect & 10.0 aM & Hyperbranching RCA & [PERSON] _et al._ (2011) [41] \\ \hline ECL & Sandwich & 1.2 fM & Magnetic streptavidin-coated nanobeads & [PERSON] _et al._ (2012) [42] \\ \hline ECL & Direct & 5.0 pM & Molecular beacon modified with ferrocene & [PERSON] _et al._ (2010) [43] \\ \hline ECL & Sandwich & 3.0 fM & Acriidinium NHS ester (AE NHS) labels attached to DNA & [PERSON] _et al._ (2011) [44] \\ \hline ECL & Sandwich & 8.5 aM & Calcium carbonate/carboxymethyl chitosan (CaCO3/CMC) hybrid microspheres _@_ luminescent silver nanoparticles (AgNPs) composites & [PERSON] _et al._ (2014) [45] \\ \hline ECL & Direct & 100.0 fM & Nano-gold enhancement and ferrocene quenching & [PERSON] _et al._ (2013) [46] \\ \hline Chemiluminescenceence & Indirect & 71.0 aM & RCA & [PERSON] _et al._ (2010) [47] \\ \hline ECL & Indirect & 3.8 aM & TRA and RCA & This work \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of the proposed electrochemimuminescent(ECL) sensor with some reported genosensors.
# Influence of Synthesis Parameters in Obtaining KIT-6 Mesoporous Material
## 3 Results and Discussions
In general, it can be observed that all samples had between two or more mass losses, according the Table 2.  
It can be observed that the standard KIT-6 showed three well-defined mass losses. The first loss corresponds to the removal of physisorbed water in the interior of the materials; the second loss is related to the degradation of the organic template and the removal of chemisorbed water; the third loss corresponds to the condensation of the remaining silanol groups in the surface and in the pores of the materials [33; 34]. In general, the other samples showed similar losses, with some cases of specific changes in the temperature intervals of the mass losses and the quantity of losses being also observed. One of these cases was the KDT4 sample, in which four mass losses were observed. This higher difficulty on the removal of the organic template can be directly connected to the disorder found in its structure, which corroborates with the XRD results. Another pertinent discussion is in regard to the samples that formed hexagonal structures with unidirectional channels of the SBA-15 type, according to the XRD technique. All samples showed the second mass loss event at lower initial temperatures compared to the other samples with KIT-6 structure (see Figure S2 in the Supplementary Material). This fact can demonstrate a greater facility on the removal of the organic template due to the unidirectional system of its channels. Figure 4C presents the FTIR spectra of the standard KIT-6 and the KDT4 and KDT2 representative samples for the ordered 3D cubic and unidirectional hexagonal structures. In general, bands related to the stretches of the inorganic functional groups, in both KIT-6 and SBA-15 materials, can be observed. In all samples were identified the absorption bands around 3443, 1637, 1227, 1075, 959, 806 and 455 cm\({}^{-1}\). The band around 3443 cm\({}^{-1}\) is attributed to the stretching vibrations of the hydroxyl groups, related to the O-H of the Si-OH groups found in the samples [35]. The band around 1637 cm\({}^{-1}\) is attributed to the interaction of water with the support surface [36]. Bands around 1075 cm\({}^{-1}\) and the identified shoulder at 1227 cm\({}^{-1}\) are typical bands referring to asymmetric stretches of the Si-O-Si bonds. The band an 806 cm\({}^{-1}\) are attributed to symmetrical stretches of the Si-O-Si bonds, and the bands near 959 and 455 cm\({}^{-1}\) refer respectively to the symmetrical and asymmetric stretches of the Si-O bonds of the Si-OH groups [37]. The structural profile mentioned above was observed for both samples with 3D cubic structure and 2D hexagonal structure. This fact occurs because the materials have in their composition only bonds between Si and O atoms, including the formation of silanols (Si-OH) groups. Figure 5 shows the results of adsorption and desorption analysis of N\({}_{2}\) at 77 K, performed on samples that presented ordered structures.
# A Fuzzy Inference System for Unsupervised Deblurring of Motion Blur in Electron Beam Calibration
## 2 Modeling and Implementation  
As mentioned, when there is similarity between \(L_{f}\) and \(L_{h}\) it is difficult to discriminate between their null frequencies just by looking at \(G\). This introduces an uncertainty and makes it hard to decide which null frequency belongs to the probe (\(H\)) because null frequencies can belong to either beam (\(F\)) or probe (\(H\)). To address the uncertainty of unsupervised \(L_{h}\) detection, all the null frequencies in \(G\) are identified and only the first two nulls with lowest frequencies are extracted while avoiding the harmonics. This implies that a maximum of two null frequencies (\(\omega_{i=1,2}\)) are to be extracted from \(G\). There are three possibilities based on the extracted number of null frequencies: (a) If no null frequency is detected due to \(L_{h}\ll L_{f}\), then motion blur effect is negligible and deconvolution is not necessary; (b) if a single null frequency is detected as a result of \(L_{h}\gg L_{f}\), then the deconvolution can progress without involving the fuzzy system as the null frequency belongs to \(L_{h}\); (c) in case two null frequencies are extracted (\(\omega_{1},\ \omega_{2}\)), two deconvolutions are performed where each of the deconvolutions are performed by adjusting their corresponding \(\hat{L}_{i=1,2}\) (\(\hat{L}_{i=1,2}\ \propto\ 1/\omega_{i=1,2}\)). This is done because both \(\omega_{1}\) and \(\omega_{2}\) could be belonging to \(h\) of different sizes.  
The FIS is defined with three merits to grade the deblurrings. Deblurrings are performed by two individual Weiner filters that use \(\hat{L}_{1}\) and \(\hat{L}_{2}\) resulting in \(\hat{f}_{1}\) and \(\hat{f}_{2}\) respectively. The fuzzy system produces a single crisp output deconvolution grade (\(E_{i=1,2}\)) for each restoration. The restoration process that produces a higher \(E_{i}\) is then chosen as the correct process with its corresponding \(\hat{L}_{i}\) being the correct probe size (\(L_{h}\leftarrow\hat{L}_{i}\)). A single layer (non-hierarchal) fuzzy inference system of three inputs and a single output is designed to evaluate the overall deblurring process. These inputs are: PSF length deviation, null frequency magnitude, and residue, and the deconvolution grade is the only output. These inputs and the output are explained in detail as follows.
## 2 Related Works
### Flower Quality Grading  
Image processing and object detection algorithms have been employed for grading the quality of flowers. Bubble sequencing and cluster segmentation were used to measure the crown spathe width and spathe number of potted Anthuriums, respectively, and a minimum closed rectangle was used to measure crown width and spathe. Based on these features and the existing grading standard of potted Anthurium, a grading accuracy of 85.86% was achieved [11]. YOLO-V3 was utilized to detect objects' position on phalaenopsis, including red flowers, white flowers, stems, red buds, white buds, and inappropriate deciduous buds on stems, so as to determine the three grades of phalaenopsis, with an accuracy of 82% [12]. A CNN was employed to grade Gloxinia quality based on images, with its grading capability enhanced by determining whether samples containing buds belonged to medium grade, achieving an accuracy of 89.6% [13]. However, the above models merely output grades based on images, and the importance of each feature to the grading result was not clear.
## 5 Sensitivity Analysis
### Global Horizontal Irradiation  
To sensitivity analysis, the simulation is set by GHI ranging from 3.4 to 5.0 kW/m\({}^{2}\)/day with the interval of 0.2 kW/m\({}^{2}\)/day. When the GHI increases, RF increases and grid purchases decrease in Figure 12, and the NPC decreases accordingly in Figure 13. When the expenditure of cash flow decreases, the project benefit will increase, and the LCOE and discounted payback will both decrease as shown in Figures 13 and 14. According to GHI of 5.0 kW/m\({}^{2}\)/day, it results that RF rises to 34.6%, NPC drops to \(-\$48.679\times 10^{6}\), project benefit increases to \(\$6.134\times 10^{6}\), LCOE drops to \(\$83.49\times 10^{-3}\)/kWh, and discounted payback is reduced to 13.25 years. The longer sunshine time leads to higher investment benefits.  
Figure 12: Effect of varying on the RF and grid purchases.  
Figure 13: Effect of varying GHI on the NPC and project benefit.