esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Study
### Load Discretization  
The initial load of the building was obtained through DeST based on the building parameters. Thereafter, MATLAB was used to disperse the initial load to obtain the discrete load. Figures 7 and 8 show the delayed load and the typical daily load of the discrete load with \(\theta\) from \(-\)0.05 to 0.1. It was found that discretization changes the fluctuation of the annual load under the premise of the same total load. In addition, 100 sets of discrete loads with \(\theta\) from \(-\)0.06 to 0.11 were obtained to avoid errors in the optimization results caused by the small sample size.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Time** & **Price (Yuan-(kWh)\({}^{-1}\))** \\ \hline Peak period & 8:00–11:00 & 1.074 \\  & 6:00–8:00 & \\ Intermediate period & 11:00–18:00 & 0.671 \\  & 21:00–22:00 & \\ Valley period & 0:00–6:00 & 0.316 \\  & 22:00–24:00 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Electricity prices in different periods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Equipment** & **Equipment Cost** & **Maintenance Cost** & **Efficiency** & **Life (Year)** \\  & **(Yuan-(kWh)\({}^{-1}\))** & **(Yuan-(kWh)\({}^{-1}\))** & **Efficiency** & **Life (Year)** \\ \hline GT & 6500 & 0.0472 & \(\eta_{gt,E}\)\({}^{*}\) & 30 \\ HRSG & 800 & 0.0022 & 0.85 & 20 \\ GB & 900 & 0.0022 & 0.90 & 20 \\ ER & 900 & 0.0087 & 4.5 & 20 \\ ARU & 1228 & 0.008 & 1.2 & 20 \\ HE & 100 & 0 & 0.95 & 20 \\ \hline \hline \end{tabular} \({}^{*}\) The values of \(\sigma_{1},\sigma_{2},\sigma_{3}\) and \(\sigma_{4}\) are \(-1.8\times 10^{-8}\), \(4.97\times 10^{-6}\), \(4.99\times 10^{-5}\), 0.022 and 0.017, respectively [35]. \({}^{**}\) The values of \(\tau_{1},\tau_{2},\tau_{3}\
# Radio Resource Dimensioning for Low Delay Access in Licensed OFDMA IoT Networks +
## 4 Dimensioning Tools: Interference and Fading Characterization
### Single-User: Case of Single Antenna Receiver
#### 4.1.1 Interference Laplace Transform  
Considering a SISO antenna configuration, the Laplace transform of the interference is,\[\mathcal{L}_{I}(s)=\mathbb{E}\left[e^{-sI}\right]=\mathbb{E}\left[\prod_{x_{i}\in \Phi_{I}}e^{-sP_{\text{RR}}\;\alpha\;|x_{i}|^{-\beta}A_{J,i}}\right], \tag{30}\]  
in which \(\Phi_{I}\) is the set of the interfering nodes that transmit on the same RR as shown in (3). The set of interfering nodes \(\Phi_{I}\) cannot be considered as homogeneous PPP and its exact statistical distribution is generally a difficult problem to model. For this, we use the same approximation proposed in [7] that is shown to almost capture the statistical behavior of the interference field. Let \(d_{i}\) be the distance between the interferer and the intended collector. The probability of finding at least one collector in the ball with radius \(d_{i}\) centered at the interferer is \((1-e^{-\lambda_{b}\pi d_{i}^{2}})\). In this case, a node at distance \(d_{i}\) from the intended collector is considered as interfering with probability of \((1-e^{-\lambda_{b}\pi d_{i}^{2}})\). It was shown in [7] that the effective interference observed at the tagged collector can be approximately modeled as a nonhomogeneous PPP with intensity \(\lambda_{b}(1-e^{-\lambda_{b}\pi d_{i}^{2}})\). Considering exponential power fading attenuation coefficients, the interference Laplace transform from (30) for the SISO case is,  
\[\mathcal{L}_{I}(s)=\mathbb{E}\left[\prod_{x_{i}\in\Phi_{I}}\frac{1}{1+sP_{ \text{RR}}\;\alpha\;|x_{i}|^{-\beta}}\right]. \tag{31}\]  
Using the Probability Generating Functional (PGFL) property, the Laplace transform is then,  
\[\mathcal{L}_{I}^{\text{SISO}}(s)\approx\mathcal{L}_{I}(s)\triangleq\exp\left(- 2\pi\lambda_{b}\int_{0}^{\infty}\left[\frac{sP_{\text{RR}}\;\alpha\;u^{-\beta }}{1+sP_{\text{RR}}\;\alpha\;u^{-\beta}}\right](1-e^{-\lambda_{b}\pi u^{2}}) udu\right). \tag{32}\]
# Recent Strategies for Environmental Remediation of Organochlorine Pesticides
## 5 Photocatalysts for Pesticides Degradation
### g-C\({}_{3}\)N\({}_{4}\)-Based Photocatalysts for Pesticide Degradation
[PERSON] et al. [115] fabricated AgI/g-C\({}_{3}\)N\({}_{4}\)/Ag\({}_{3}\)PO\({}_{4}\) which was a z-scheme, and the catalyst achieved a better pesticides degradation than when only ordinary graphitic carbon nitride, silver iodide or silver phosphate was used as the catalyst. Recalcitrant polychlorinated biphenyl (PCB) was removed from water sample by using velvet-like nanocomposite of graphitic carbon nitride [116]. The nanocomposite showed good reusability potential, which made it even more interesting. Seven different insecticides have been degraded by nanocomposites prepared by co-doping of oxygen and sulfur with graphitic carbon nitride [117]. Over 90% degradation rate of some of these insecticides were achieved under 30 min. [PERSON] et al. [118] degraded ortho-dichlorobenzene by using photocatalyst composed of vanadium pentoxide and graphitic carbon nitride. About 62.4% of the organochlorine was degraded within 8 h when just 2% of the nanocomposite was used. In addition, the removal efficiency was found to be 2.18- and 2.77-fold better than when pure vanadium pentoxide and pure graphitic carbon nitride were used for the degradation. [PERSON] et al. [73] also prepared TiO\({}_{2}\)/g-C\({}_{3}\)N\({}_{4}\) nanotubes and nanopores which were used to separately degrade pesticides. The nanotube-containing nanocomposite was reported to successfully degrade 90.8% of the pesticide under 4 h reaction time. Other reported studies involved the use of activated carbon supported graphitic carbon nitride to activate peroxymonosulfate, which was used for pollutant degradation [119]. Trihalomethane and haloacetonitrile have been degraded under visible light by using g-C\({}_{3}\)N\({}_{4}\) as photocatalyst [120]. g-C\({}_{3}\)N  
Figure 8: Imidalcoprid degradation route with graphitic carbon nitride as the photocatalyst. Reproduced with permission from [71]. Copyright (2015) American Chemical Society. modified with \(\beta\)-cyclodextrinrin has recently been used for pollutants degradation [121]. [PERSON] et al. [112] degraded persistence organic pollutants under visible light irradiation by using graphitic carbon nitride. Graphitic carbon nitride was also used to degrade fluazaindolizine under simulated irradiation. The pathway for fluazaindolizine was comprehensively discussed [113]. Bi2 WO6 and g-C3N4 nanocomposite has been used to photocatalytically degrade 2,4-dichlorophenol pesticide under visible light irradiation [122]. Nanohybrid of NiO/WO3 decorated on graphitic carbon nitride has been reported for the degradation of some organic pollutants [123]. Platinum nanoparticles deposited on carbon nanotube of g-C3N4 were used for destroying p-chlorophenol under visible light irradiation [124]. Both Z-scheme nanotube and nanoparticles of g-C3N4/TiO2 have been used to decompose isoniazid by [PERSON] et al. [73]. The degradation pathway for isoniazid degradation was also proposed.
# Low-Cost Dual-Frequency GNSS Receivers and Antennas for Surveying in Urban Areas
## 3 Results and Discussion  
This section presents the results from the previous section. The quality test results of observations from low-cost GNSS devices are shown in Section 3.1, while the results of positioning performance of the same equipment in urban areas are presented in Section 3.2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **RTKLIB** \\ \hline Observations & Phase \\ Duration & 1 h, 30 min, 20 min, and 15 min \\ Constellations & GPS, GLONASS, Galileo \\ Ambiguity & Continuous \\ Elevation mask & 15\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Used parameters in the data processing.
# Image Watermarking Scheme Using LSB and Image Gradient
### Our Contribution  
In this paper, we introuduce a watermarking strategy for protecting the watermark signal, in which an original image is divided into 16 \(\times\) 16 blocks, and a gradient is applied to calculate the magnitude and direction of each block. The gradient magnitude denotes image changes, while the gradient angle denotes the direction of changes. The capacity of the watermark signal is embedded according to the gradient of each block using the least significant bit technique. In image watermarking, the researcher tries to embed maximum payload without affecting the image visual quality. Greater embedding ensures more robustness; thus, spatial-domain techniques, e.g., LSB, provide a higher payload compared to frequency-domain techniques. The main concern is to balance the performance in terms of undetectability, robustness, and algorithmic complexity. LSB is computationally efficient and has high perceptual quality. To increase security, the watermark to be embedded is scrambled using a chaotic substitution box. This method shows high robustness and imperceptibility when the middle coefficient is used for embedding.
# Structural Optimization of Slotting Nozzle Used to Improve Coal-Seam Permeability
###### Abstract  
Slotting directional hydraulic fracturing is a new method for improving permeability of a coal seam in underground coal mines that can solve problems related to non-uniform permeability enhancement in the seam. A slotting nozzle is the key to this technology: its performance determines the length and stability of the slotted hole. In this study, computational fluid dynamics was used to study the effects of stable segment length \(L\), convergent angle \(\theta\), and straight segment length \(l\) on the performance of slotting nozzles. The results showed that the order of parameters affecting nozzle performance is: \(\theta>L>l\). When the total length of the slotting nozzle was fixed, the dynamic pressure gradually decreased with an increase of \(L\) and the rate of decrease slowed down. With an increase of \(\theta\), dynamic pressure increased quadratically and the rate of change gradually decreased. With an increase of \(l\), the dynamic pressure decreased quadratically and the rate of change gradually increased. A slotting nozzle (\(L=0\) mm, \(\theta=30^{\circ}\), \(l=9\) mm) was manufactured and measurements of flow coefficient, water jet morphology, and a slotting experiment were carried out. The experimental results showed that the flow coefficient, water jet convergence, and slotting depth of the optimized slotting nozzle were obviously higher than those of the original design, which proved the validity of using this numerical method to optimize the slotting nozzle structure.  
coal seam; permeability; slotting nozzle; structural optimization Article
# Image Classification of Parcel Boxes under the Underground Logistics System Using CNN MobileNet
###### Abstract  
Despite various economic crisis situations around the world, the courier and delivery service market continues to be revitalized. The parcel shipping volume in Korea is currently 3.37 billion parcels, achieving a growth rate of about 140% compared to 2012, and 70% of parcels are from metropolitan areas. Given the above statistics, this paper focused on the development of an underground logistics system (ULS), in order to conduct a study to handle the freight volume in a more eco-friendly manner in the center of metropolitan areas. In this paper we first analyzed the points at which parcel boxes were damaged, based on a ULS. After collecting image data of the parcel boxes, the damaged parcel boxes were detected and classified using computerized methods, in particular, a convolutional neural network (CNN), MobileNet. For image classification, Google Colaboratory notebook was used and 4882 images were collected for the experiment. Based on the collected dataset, when conducting the experiment, the accuracy, recall, and specificity of classification for the testing set were 84.6%, 82% and 88.54%, respectively,. To validate the usefulness of the MobileNet algorithm, additional experiments were performed under the same conditions using other algorithms, VGG16 and ResNet50. The results show that MobileNet is superior to other image classification models when comparing test time. Thus, in the future, MobileNet has the potential to be used for identifying damaged boxes, and could be used to ensure the reliability and safety of parcel boxes based on a ULS.  
urban logistics system; convolutional neural network; parcel box; MobileNet; object classification algorithm +
Footnote †: journal: Applied
## 2 Analysis of Limitations of Existing Representative Methods  
### Analysis of Security Vulnerabilities in the Chaudhry Protocol  
In 2022, [PERSON] et al. [26] proposed a lightweight, authenticated, two-party key-agreement protocol for Smart-Grid Infrastructure. The key security of this protocol is ensured by temporary random numbers that are generated by each party, thereby realizing secure two-party authentication under the premise of eight scalar multiplication operations. However, upon analysis, we found that the above protocol cannot withstand attacks based  
Figure 1: The schematic diagram of the key chapter structure.  
on known session-specific temporal information. A symbolic definition of the Chaudhry protocol is provided in [26]. The security analysis of the Chaudhry protocol is as follows:  
If the attacker obtains the temporary session information \(r_{sm}\) and \(r_{nan}\) during the execution of the Chaudhry protocol, the session key \(SK\) of the session can be calculated through the following steps:  
1. Attacker \(C\) obtains the message by listening to the public channel: \(R_{sm}\), \(R_{nan}\), \(T_{sm}\), \(T_{nan}\);
2. Leveraging the publicly available \(Q_{nan}\), \(Q_{sm}\) and the obtained \(r_{sm}\), \(r_{nan}\), the adversary can calculate \(K_{sm}\) through the scalar-point-multiplication operation \(K_{sm}=r_{sm}Q_{nan}\);
3. Attacker \(C\) obtains the \(K_{nan}\) by calculating \(r_{sm}Q_{nan}+K_{sm}\) because \(K_{sm}=d_{nan}R_{sm}\) ;
4. Attacker \(C\) obtains the \(SK\) by calculating \(SK=H_{1}(K_{sm}||K_{nan}||R_{sm}||R_{nan}||T_{sn}||T_{nan})\).  
To summarize, since the temporary information \(r_{sm},r_{nan}\) of the specific session is leaked, the attacker can calculate and obtain the session key of the current session through the above steps.
# Drug-Food Interactions with a Focus on Mediterranean Diet
## 3 Mediterranean Food Products and Potential DFIs
### Drug-Med Diet Interactions
#### 3.2.4 Milk Dairy Products, White and Red Meat  
Milk and dairy products (yoghurt, cheese) are part of the traditional domestic livestock practices around the Mediterranean basin and are part of the historical heritage of the dietary habits for the region. In medicine, milk and dairy products are an old case of potential DFIs due to their content in Ca\({}^{2+}\) and tyramine. The presence of Ca\({}^{2+}\) in milk and dairy products can create unabsorbed chelate ligands with antibiotic classes of tetracyclines and quinolones resulting in reduced bioavailability (PK-DFIs) [62]. Tyramine is a precursor of catecholamines and the inhibition of their metabolism from MAOIs can lead to increased catecholamine levels which can cause hypertension. Tyramine is known to interact with mono-amino oxidase inhibitors (MAOIs) resulting in an effect known as tyramine pressor response with high blood pressure and risk of cerebral hemorrhage which can be fatal [167, 42]. Finally, another important PK-DFI is the reduced bioavailability of ferrous from cow milk. Caseins in cow milk bind Fe\({}^{2+}\) by clusters of phosphoserine, keeping it soluble in GI's alkaline pH, preventing its free form from being available for absorption, thus decreasing its bioavailability in cases of ferrous supplement co-administration [168]. In addition, co-administration of mercaptopurine and cow milk in patients with chronic myelogenous leukemias reduces the bioavailability of the drug due to milk's high content of xanthine oxidase, thus this co-administration should be avoided [169, 170].  
Meat products within Med-D, although consumed to a limited extent, are a valuable source of nutrients for a healthy and balanced diet. Their dietary value lies in their high protein content with essential amino acids, ferrous from red meat, vitamin B12 and other vitamins of B-complex, zinc, selenium, and phosphorus. Fat content is dependent on meat species, feeding system, as well as the meat part that is used in food [171]. As stated earlier, high-fat content may lead to raised salt and increase the solubilization of lipophilic drugs [38].
## 3 Materials and Methods
### Research Methods
#### 3.1.6 Spatial Autocorrelation Analysis  
* Moran index \(I\) is an important indicator to measure spatial correlation, and is generally used to express the overall trend of spatial correlation of various variables in the entire study area [28; 29]. The calculation formula is:  
\[I=n\frac{\sum\limits_{i=1}^{n}{\sum\limits_{j=1}^{n}{{{w_{i}}(x_{i}-\overline{ x})}(x_{j}-\overline{x})}}}{\sum\limits_{i=1}^{n}{{{\sum\limits_{j=1}^{n}{{{w_{ij}} \sum\limits_{i=1}^{n}{{{(x_{i}-\overline{x})}^{2}}}}}}}}} \tag{8}\]  
Among them, \(x_{i}\) and \(x_{j}\) are the mean number of sample points in area \(i\) and \(j\); \(\omega_{ij}\) is the space vector matrix; \(n\) is the total number of samples. The Moran index I is distributed in the interval [\(-\)1, 1]. When \(I>0\), it indicates that the sample points have a positive spatial correlation. The larger its value is, the more significant the spatial correlation is. When \(I<0\), it indicates that the sample points have a positive spatial correlation. For spatial negative correlation, the smaller the value, the greater the spatial difference; when \(I=0\), it indicates that the sample points are spatially random.
* Getis-Ord index  
Moran index I can measure spatial correlation, but it cannot fully reflect the specific characteristics of correlation. The Getis-Ord index \(G\) is generally used to analyze the degree of aggregation of local spatial observations, and can also observe the spatial distribution of hot and cold spots [30; 31]. The calculation formula is:  
\[G=\frac{\sum\limits_{i=1}^{n}{\sum\limits_{j=1}^{n}{{{w_{ij}}(d)x_{j}x_{j}}}}} {\sum\limits_{i=1}^{n}{\sum\limits_{j=1}^{n}{{{x_{ij}}}}}}(i\
eq j) \tag{9}\]  
When the result is positive, it means that the values around area \(i\) are relatively high, which is a high-value spatial agglomeration; when the result is negative, it means that the values around area \(i\) are relatively low, which is a low-value spatial aggregation.
## 3 Results and Discussion
### Instance-Level Feature Importance Analysis
#### 3.2.2 Global Interpretation  
Global interpretation is conducive to analyzing the prediction of DL further. Multiple OCs/instances are selected at random to be interpreted with SHAP. Figure 6 intuitively presents the results of 20 OCs with the top 20 most influential features.  
Among all features, \(P_{G7}\), \(P_{G8}\), \(P_{AC29}\), \(P_{AC42}\), \(Q_{AC27}\), \(P_{G4}\), \(Q_{AC42}\), \(P_{AC43}\), \(P_{AC44}\), and \(P_{G5}\) are the top features that are the most important to the prediction of the trained DL model for the specific fault. In the expert's experience, the devices corresponding to the features are more related to the fault on \(AC29\) because they are close to the fault electrically. Therefore, the interpretation results are matched with the real physical rules.  
For each feature, there are also other exciting results from the Shapley value. Taking \(P_{G7}\) as an example, the Shapley value of \(P_{G7}\) is roughly negatively correlative with the  
Figure 6: Global interpretation for 20 OCs.  
value of itself, indicating that the lower the value of \(P_{G7}\) is, the higher the probability of predicting the OC as secure is. By contrast, as for \(P_{\rm{C4}}\), its Shapley value is positively correlative with the value of itself. Although some values of features are not wholly monotonic, the grasp of the overall interpretation of the model will not be affected. The findings of the interpretation are beneficial to further the SC application, as discussed in the following section.
# Validating the Use of Smart Glasses in Industrial Quality Control: A Case Study
## 3 Methodology
### Model Training  
In the initial stage, as illustrated in Figure 2, we outline the proposed process for model training.  
The process for training a custom image dataset comprises five main steps. Firstly, data collection and preprocessing are essential. In the context of quality control in industrial manufacturing, especially in the automotive industry, a custom image dataset is curated directly from real-world field environments. This industry's intricate tasks necessitate a dataset reflecting the complexity of actual operations. Utilizing images from natural field settings ensures that the dataset becomes a potent tool for training models to accurately detect defects, anomalies, and quality variations. This approach aligns with the unique challenges of the automotive quality control sector, resulting in robust and industry-relevant solutions.  
Images should ideally encompass both faulty and non-faulty examples for each product and component, collected under various lighting conditions and angles and featuring different parts and defect types. Preprocessing involves resizing, normalizing, and augmenting the data to enhance the model's ability to generalize and perform well on real-world data. Augmentation simulates different scenarios by applying transformations like rotation, scaling, and cropping.  
Next is the crucial step of image annotation, where each image is meticulously reviewed, and relevant objects are marked or labeled with details about the part and nature of the defect. While manual and time-consuming, this annotation process is paramount for creating a reliable training dataset, providing the model with ground truth data points to learn.  
With the dataset ready, the appropriate models are selected. A convolutional neural network (CNN) is suitable for part identification due to its ability to learn the spatial hierarchies of image features. In the case of object detection, the framework leverages YOLOv8, a state-of-the-art object detection model known for its accuracy and speed in detecting and localizing multiple objects in a single pass, as indicated in the previous section.  
The subsequent step involves model training as an iterative and multi-step process. The dataset is split into training and validation sets, hyperparameters are fine-tuned, and various optimization algorithms are tested. Post-training, models are evaluated on a separate test set, refining them based on the results. Once trained, considered, and refined, the models are deployed to the server, ready for the second stage, which is real-time processing.