esa-sceva/satcom-chunk-collection huggingface dataset:
# Technologies and Fabrication of Intelligent Packaging for Perishable Products
## 2 Intelligent Packaging Techniques
### Identification Techniques
For communication, the tags use the power derived from the magnetic field and generated by the reader. They then transfer the stored information back to the reader. **RFID Reader**: _The reader sends and receives radio frequency (RF) data to and from the tag using one antenna or several antennas. The reader is composed of a transmitter, receiver, and microprocessor._  
**Antennas**: _The antennas are usually connected to the RFID reader. They broadcast RF signals in the air and receive information from the RFID tags in the read range._  
**Host Computer System**: _The host computer system is a backend system. It acquires data from the RFID readers and routes the data to the correct application for processing into useful information._  
The application of RFID has many benefits in intelligent packaging as it gives packaging the functionality of tracing and tracking, while preventing counterfeiting. For example, [PERSON] et al. proposed a RFID web-based info-tracking system for artisanal Italian cheese to obtain quality traceability. The system integrates the RFID technique with a non-destructive quality analysis system to evaluate the final quality based on the quality of a typical Italian cheese [14]. [PERSON] et al. applied RFID to trace a dairy product. All item movements are automatically recorded during the production phase, handling in the maturing room and warehouse, delivery, packaging, and selling phases [15]. [PERSON] et al. designed a supply-chain pedigree interactive dynamic explore (SPIDER) for food safety control based on an item level RFID traceability system [16]. Identification techniques play an important role in intelligent packaging for tracing and tracking; however, a lack of sensing functionality makes it difficult for the status of perishable products or their surrounding environment to be monitored. Hence, the sensing technique is another important part of intelligent packaging. In the next section, a brief introduction to sensing techniques in intelligent packaging is presented.
# Calculation of Nonlimit Active Earth Pressure against Rigid Retaining Wall Rotating about Base
## 4 Derivation of the Analytical Solution for Active Earth Pressure
### Definition of Small Principal Stress Traces Parameters
#### 4.2.1 Definition of Deflection Angle of Principal Stress
As with Equations (8) and (9), Equations (13) and (14) can be obtained in the same way:  
\[N=\frac{\sigma_{1}^{b}}{\sigma_{3}^{b}}=\tan^{2}(45^{\circ}+\frac{\varphi}{2}) \tag{13}\]  
\[\tau_{2m}=\sigma_{2m}\tan\varphi_{m} \tag{14}\]  
where \(N\) is the ratio of maximum principal stress to minimum principal stress, \(\varphi\) is the soil friction angle, and \(\varphi_{m}\) is the developed value of soil friction angle. By solving Equations (11)-(14), Equation (15) can be obtained:  
\[\theta_{b}=\alpha_{s}+\arctan\left[\frac{\left(N-1\right)+\sqrt{\left(N-1 \right)^{2}-4N\tan^{2}\varphi_{m}}}{2N\tan\varphi_{m}}\right] \tag{15}\]  
where \(\theta_{b}\) is the included angle between the direction of small principal stress and the vertical direction at the potential slip plane, and \(\varphi_{m}\) is the developed value of soil's friction. Equations (10) and (15) suggest that \(\theta_{a}\) and \(\theta_{b}\) are exclusively connected to parameters \(N,\delta,\varphi\) and are unrelated to the depth. However, the principal stress deflection angle \(\theta\) in the soil is not a constant along the principal stress trace, as shown in Figure 2a.
###### Abstract  
Remote sensing scene classification aims to automatically assign proper labels to remote sensing images. Most of the existing deep learning based methods usually consider the interclass and intraclass relationships of the image content for classification. However, these methods rarely consider the hierarchical information of scene labels, as a scene label may belong to hierarchically multi-grained levels. For example, multi-grained level labels may indicate that a remote sensing scene image may belong to the coarse-grained label \"transportation land\" while also belonging to the fine-grained label \"airport\". In this paper, to exploit hierarchical label information, we propose an attention-embedding multi-task multi-grained network (AEMMN) for remote sensing scene classification. In the proposed AEMMN, we add a coarse-grained classifier as the first level and a fine-grained classifier as the second level to perform multi-task learning tasks. Additionally, a gradient control module is utilized to control the gradient propagation of two classifiers to suppress the negative transfer caused by the irrelevant features between tasks. In the feature extraction portion, the model uses an ECA module embedding Resnet50 to extract effective features with cross-channel interaction information. Furthermore, an external attention module is exploited to improve the discrimination of fine-grained and coarse-grained features. Experiments were conducted on the NWPU-RESISC45 and the Aerial Image Data Set (AID), and the overall accuracy of the proposed AEMMN is 92.07% on the NWPU-RESISC45 dataset and reached 94.96% on the AID. The results indicate that hierarchical label information can effectively improve the performance of scene classification tasks when categorizing remote sensing imagery.  
remote sensing imagery; scene classification; multi-task learning; attention mechanism +
Footnote †: journal: Applied  
Acticle  
Exploiting Hierarchical Label Information in an Attention-Embedding, Multi-Task, Multi-Grained, Network for Scene Classification of Remote Sensing Imagery  
1 Hunan Institute of Land and Resources Planning, Changsha 410007, China 1  
2 Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China 2  
3 School of Computer, Central China Normal University, Wuhan 430079, China 3
## 5 Experiments and Results
### Evaluation Metrics  
To evaluate our approach, we used the metrics of recall, precision, and F1-score which are shown in Equations (10)-(12), respectively. We supposed that spam tweets are positive while non-spam tweets are negative. Then, we constructed the confusion matrix accordingly as shown in Table 4, where TP (true-positive) refers to all spam tweets that are predicted correctly as spam tweets, FN (false-negative) denoted as all spam tweets which are predicted wrongly as non-spam tweets, TN (true-negative) denoted as all non-spam tweets which are predicted correctly as non-spam tweets, and FP (false-positive), which refers to all non-spam tweets predicted wrongly as spam tweets.  
\[\text{Recall}=\frac{\text{TP}}{(\text{TP}+\text{FN})} \tag{10}\]  
\[\text{Precision}=\frac{\text{TP}}{(\text{TP}+\text{FP})} \tag{11}\]  
\[F1-\text{score}=\frac{2*\text{Precision }*\text{Recall}}{\text{Precision}+ \text{Recall}} \tag{12}\]
## 4 Experiments
### Baseline  
In order to verify the effectiveness of SE-AIPT, we adopt three basic algorithms for comparative experiments. The fixed hyperparameters of the three methods are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Hyperparameters** & **DQL** & **Tabular Q-Learning** & **Random** \\ \hline Batch Size & 32 & * & * \\ Learning Rate & 0.01 & 0.01 & * \\ Epsilon & 0.9 & 0.9 & * \\ Discount Factor & 0.015 & 0.015 & * \\ Replay Memory Size & 10,000 & * & * \\ Target network update frequency & 10 & * & * \\ \hline \hline \end{tabular}  
* indicate that there are no values.  
\end{table}
Table 2: Fixed hyperparameters of the three algorithms.  
* **Random:** The selection of agent actions is not relevant to the agent state. An agent could randomly select actions to interact with the environment; the feedback and reward of the environment have no influence on the agent's choice of the next action.
* **Tabular Q-learning:** Tabular Q-learning is a value-based algorithm of RL algorithms. The main idea is to construct a table of states and actions to store Q values and then select the actions that can obtain the maximum benefits according to the Q values. However, for the RL tasks of high-dimensional state space and action space, the limited space of the table cannot store all the states and actions, which limits the performance of the algorithm.
* **DQL:** DQL combines deep learning and reinforcement learning to effectively solve the large state-action storage space. DQL uses a neural network to replace the Q-value table in Tabular Q-learning. Thus, the original convergence problem of the action value function is transformed into the function-fitting problem of a neural network, which is a representative work in the field of DRL.
# Clustered Distributed Learning Exploiting Node Centrality and Residual Energy (CINE) in WSNs
###### Abstract  
With the explosion of big data, the implementation of distributed machine learning mechanisms in wireless sensor networks (WSNs) is becoming required for reducing the number of data traveling throughout the network and for identifying anomalies promptly and reliably. In WSNs, the above need has to be considered along with the limited energy and processing resources available at the nodes. In this paper, we tackle the resulting complex problem by designing a multi-criteria protocol CINE that stands for \"Clustered distributed learnIng exploiting Node centrality and residual Energy\" for distributed learning in WSNs. More specifically, considering the energy and processing capabilities of nodes, we design a scheme that assumes that nodes are partitioned in clusters and selects a _central_ node in each cluster, called _cluster head_ (CH), that executes the training of the machine learning (ML) model for all the other nodes in the cluster, called _cluster members_ (CMs). In fact, CMs are responsible for executing the inference only. Since the CH role requires the consumption of more resources, the proposed scheme rotates the CH role among all nodes in the cluster. The protocol has been simulated and tested using real environmental data sets.  
distributed learning; WSNs; clustering; degree centrality; energy efficiency; resource optimization +
Footnote †: journal:  
00 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 5 Future Direction: AI-Based State Consistency Architecture
### Reinforcement Learning (RL) Challenges
#### 5.2.3 Continuous Control Problems  
Based on experiences demonstrated by [45] using a variety of RL approaches to solving the specified Markov Decision Process (MDP), several non-trivial issues are highlighted, deriving primarily from the two following characteristics.  
First, if the problem of MDP is a \"discrete control\", which means that the system's actions or inputs are limited to a finite set of options, this will pose a barrier to employing several widely recognized and mature actor-critic techniques [60], founded on the policy gradient theorem [61]. For instance, the state of the art is DeepMind's work on the Deep Deterministic Policy Gradient (DDPG) agent [62], designed to handle \"continuous control\" problems effectively where actions can vary across a continuous range.  
Second, of the challenges faced in employing AI-based SDN, one is whether the state-action space in the network environment of the defined MDP is large. There are up to 2N different acts that a state could perform, specifically. Therefore, the action space size grows exponentially when there is more information in the network. A large action space has been demonstrated to be extremely challenging to explore and generalize from. In fact, traditional RL approaches and their modifications, effective in situations with comparatively small discrete action spaces, are inadequate to address this issue [45]. It has been traditional to save experiences in tabular form. However, due to the insufficiency of generalization for a large state-action space, this method is unfeasible for many RL problems.
## 5 Prospect of Colloidal Silica as a Grouting Material
### Advantages of CS as a Grouting Material  
#### 5.2.1 Low Disturbance  
Among ground improvement techniques, dynamic compaction, vibro-compaction and explosive compaction have been widely adopted to densify soil layers. However, all of these compaction methods produce great disturbances to the surrounding areas and are not applicable to solely the constrained or developed area. Except for the compaction methods, the grouting method is another common practice to improve ground liquefaction resistance. In all grouting materials, a cement-based material is mostly used. Nevertheless, the particle size of cement averages at 20 microns, which greatly restricts its permeation properties. Cement particles can only transport through voids three to five times larger than their own particle size [15]. Besides, cement has a relatively high initial viscosity, so cement is usually injected under pressure or is adopted to form grout columns, rather than distribution through the entire ground. In addition, grouting cement has a remarkable impact to the surrounding environment. Since conventional methods show great disturbance, grouting CS is an effective method and presents significant advantages over cement. CS has a very low initial viscosity which is very close to pure water once mixed with an electrolyte. The low initial viscosity provides excellent flowability to the grouting materials. Additionally, the particle size of CS ranges from 7-20 nm in general, which is much finer than cement particles. Thus, CS can achieve transport through the entire targeted scope in the porous media under a low pressure. Therefore, grouting CS for ground improvement is feasible for both undeveloped and constrained areas, as it only has low levels of disturbance to the surrounding area.
## 3 Results
### Evaluation of AM_A, AM_B, and Forging_C Mechanical Properties
#### 3.1.4 Microstructural Analysis of AM_A, AM_B, and Forging_C  
A field-emission scanning electron microscopy (FESEM) Gemini 300 (Carl Zeiss NTS Ltd., Jena, Germany) was used to analyze the metallographic properties of the forged and AM materials. Figure 20 shows the metallurgical structures of the commercial forged alloy tool steel D2 and Forging_C after heat treatment. Precipitated phases were irregularly precipitated in the matrix of the heat-treated forged materials. Additionally, the precipitated phases of Forging_C were smaller and more finely distributed than those of the D2 material. Figure 21 shows the microstructures of the heat-treated and non-heat-treated AM_A specimens. No precipitation phases were observed in the matrix structure of the non-heat-treated AM_A specimen. By contrast, it was observed that a number of precipitation phases were finely precipitated in the matrix structure of the heat-treated AM_A specimen because of the quenching effect.  
Figure 22 shows the microstructures of the heat-treated and non-heat-treated specimens of AM_B. The precipitation phases in the non-heat-treated AM_B specimen were precipitated along the grain boundaries in the matrix structure. Furthermore, the precipitation phases that appeared in the matrix structure of the non-heat-treated AM_B specimen were reduced by the heat treatment. This is considered to be characteristic to each material based on the application of the heat treatment process. Additionally, according to the microstructural comparison between the forged and AM materials, the precipitated phases in the forged material were precipitated in irregular shapes and sizes in the matrix structure upon the application of heat treatment. By contrast, the precipitated phases in the AM materials were precipitated in a specific pattern along the grain boundaries in the matrix structure. This is attributed to the difference in the alloy composition and the manufacturing processes of the forged and AM materials. It is anticipated that these microstructural and mechanical properties will affect the lifetime of each material.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Specimen Manufacturing Conditions** & \
\begin{tabular}{c} **Shock Absorption Energy** \\ **(0)** \\ \end{tabular} &
\begin{tabular}{c} **Hardness** \\ **(HRC)** \\ \end{tabular} \\ \hline Forging\_C/heat treatment applied & 104.0 & 59.8 \\ AM\_A/no heat treatment & 22.0 & 59.3 \\ AM\_A/heat treatment: Q\_1030 \({}^{\circ}\)C, T\_150 \({}^{\circ}\)C & 11.0 & 60.3 \\ AM\_A/heat treatment: Q\_1030 \({}^{\circ}\)C, T\_520 \({}^{\circ}\)C & 6.3 & 57.8 \\ AM\_B/no heat treatment & 88.5 & 60.2 \\ AM\_B/heat treatment: Q\_1030 \({}^{\circ}\)C, T\_150 \({}^{\circ}\)C & 85.5 & 60.0 \\ AM\_B/heat treatment: Q\_1030 \({}^{\circ}\)C, T\_550 \({}^{\circ}\)C & 53.3 & 57.1 \\ \hline \hline \end{tabular}
\end{table}
Table 16: Impact test results for AM_A, AM_B, and Forging_C.  
Figure 21: FESEM photographs of metallurgical structure of AM_A.  
Figure 20: FESEM photographs of metallurgical structures of commercial tool-steel forgings.
# Development of Compact and Robust Physical System for Strontium Optical Lattice Clock
Specific improvements include a robust and low-power atomic oven, a short Zeeman slower, a miniature magneto-optical trapping (MOT) vacuum chamber equipped with a magnetic shield, integrated optical setups, and a laser frequency locking system. To realize the stable operation of the physical system outside the laboratory, strengthening structural design was applied. The system had dimensions of 465 mm \(\times\) 588 mm \(\times\) 415 mm and a mass of 66.6 kg, making it one of the smallest optical clocks reported to date. Compared with complex laboratory optical clocks, the physical system described herein offers further practical applications. To verify the physical system's adaptability to mechanical vibrations and temperature fluctuations, we conducted mechanical vibration tests and high-low temperature cycle tests. The results showed that the impact of mechanical vibrations and temperature fluctuations on the physical system is relatively limited, laying a robust foundation for conducting experiments in more challenging environments. Moreover, to ensure the steady operation of the physical system in environments with periodic fluctuations in the peripheral magnetic field, laboratory simulations were performed to analyze the impact of magnetic field variations on lattice loading. Consequently, a magnetic shield box, effectively attenuating substantial magnetic field fluctuations, was developed and installed to cover the MOT chamber. Additionally, to enable the automatic calibration of the attached lasers, a wavemeter was integrated into the system to monitor the wavelengths of all the lasers and facilitate automatic calibration.
## 5 HUVEC Model Applications
### Other applications
#### 5.3.2 Stent Use  
Stents are prostheses with antithrombotic and antiproliferative properties, that can be inserted in narrowed coronary arteries promoting the regeneration of healthy endothelium. They are used as an alternative treatment for coronary artery obstruction. However, the two main complications of stent implantation are restenosis and thrombosis [171; 172; 110]. HUVEC models have been used to explore ways to prevent these complications.  
Tirofiban was found to improve EC migration and proliferation in bioabsorbable stents using a HUVEC endothelial model [172]. Several studies in the HUVEC model have shown that coating stents with other polymers can reduce the risk of complications [173; 174; 175]. [PERSON] et al., reported that HUVEC incubated with titanium oxynitride (TiOxNy) coated discs showed lower metabolic activity after 48 hrs as compared to HUVEC treated with uncoated discs. They also reported that stents covered with TiOxNy have a lower rate of deposition of salts and albumin; stating that this compound is highly recommended for that purpose [174]. Finally, [PERSON] et al., proposed that organoselenium compounds generate NO in a HUVEC model. Coating stents with this bioactive compound provided a microenvironment capable of mimicking the endothelium and promoting endothelial recovery of the luminal surface of the stent [175].
# SVM-Based Multiple Instance Classification via DC Optimization
## 3 Solving DC-MIL using a Nonsmooth DC Algorithm
```
1:Solve \(\mathcal{Q}P(I)\) and obtain \((\bar{d},\sigma)\)\(\triangleright\) Find the search-direction and the predicted-reduction
2:if\(|\sigma|\leq\theta\)then\(\triangleright\) Stopping test
3: set \(z^{*}=z\) and exit\(\triangleright\) Return the approximate critical point \(z^{*}\)
4:endif
5:Set \(t=1\)\(\triangleright\) Start the line-search
6:if\(f(z+t\bar{d})-f(z)\leq mt\sigma\)then\(\triangleright\) Descent test
7: set \(z:=z+t\bar{d}\)\(\triangleright\) Make a serious step
8: calculate \(g_{+}^{(1)}\in\partial f_{1}(z)\) and \(g_{+}^{(2)}\in\partial f_{2}(z)\)\(\triangleright\)
9: update \(\alpha_{i}^{(1)}\) for all \(i\in I\) and \(\alpha_{l}^{(2)}\) for all \(l\in L\)\(\triangleright\)
10: set \(\mathcal{B}_{1}=\mathcal{B}_{1}\setminus\{(g_{i}^{(1)},\alpha_{i}^{(1)}): \alpha_{i}^{(1)}>\epsilon,\,i\in I\}\cup\{(g_{+}^{(1)},0)\}\)\(\triangleright\)
11: set \(\mathcal{B}_{2}=\mathcal{B}_{2}\cup\{(g_{+}^{(2)},0)\}\)\(\triangleright\)
12: update appropriately \(I\) and \(L\), and go to 1
13:else if\(t\|d\|>\eta\)then\(\triangleright\) Closeness test
14: set \(t=\sigma t\) and go to 6\(\triangleright\) Reduce the step-size and iterate the line-search
15:endif
16: Calculate \(g_{+}^{(1)}\in\partial f_{1}(z+t\bar{d})\)\(\triangleright\) Make a null step
17: calculate \(\alpha_{+}^{(1)}=f_{1}(z)-f_{1}(z+t\bar{d})+tg_{+}^{(1)\top}\bar{d}\)\(\triangleright\)
18: set \(\mathcal{B}_{1}=\mathcal{B}_{1}\cup\{(g_{+}^{(1)},\alpha_{+}^{(1)})\}\), update appropriately \(I\), and go to 1 \(\triangleright\)
```  
**Algorithm 1** DCPCA Main Iteration  
We remark that the stopping condition \(\bar{\sigma}\geq-\theta\), checked at Step 2 of the DCPCA, is an approximate \(\theta\)-criticality condition for \(z^{*}\). Indeed, taking into account Equation (12), the stopping condition ensures that  
\[f_{1}(y^{*})-f_{2}(y^{*})\leq f_{1}(y)-f_{2}(y),\ \ \forall y\in\mathcal{N}. \tag{5}\]8  
which in turn implies that \(g_{*}^{(1)}\in\partial_{\theta}f_{1}(z^{*})\) and \(g_{*}^{(2)}\in\partial f_{2}(z^{*})\) such that  
\[f_{1}(y^{*})-f_{2}(y^{*})\leq f_{1}(y)-f_{2}(y),\ \ \forall y\in\mathcal{N}. \tag{5}\]9  
namely, that  
\[\partial_{cl}f(y)\subseteq\partial f_{1}(y)-\partial f_{2}(y), \tag{6}\]0  
an approximate \(\theta\)-criticality condition for \(z^{*}\), see Equation (9).
# Multiple-Intelligent Reflective Surfaces (Multi-IRSs)-Based NOMA System
## 5 Simulation and Numerical Results
### Numerical Results of a Single-Reflection Surface Scenario
Figure 4: The required transmission power by IRS-NOMA and IRS-OMA for different destination locations to achieve a target rate of 50 Mbit/s. Figure 3: EE trends against various numbers of reflective elements. Figure 6 displays the EE-SE trade-off for IRS-NOMA against IRS-OMA. This figure shows that the EE-SE relationship is a convex function of SE, and it also illustrates that IRS-NOMA considerably outperforms IRS-OMA in terms of the achievable EE for different SE levels.