esa-sceva/satcom-chunk-collection huggingface dataset:
# DARAL: A Dynamic and Adaptive Routing Algorithm for Wireless Sensor Networks
## 3 Dynamic and Adaptive Routing Algorithm (DARAL)  
The novelty of this paper is the development of a centralized non-beaconing routing algorithm based on a dynamical clustering, which is based on the link quality between nodes. _DARAL_ is a routing algorithm, that works on top of the _IEEE 802.15.4_ standard, based on the idea of DARP [31]. DARP is the first approach to the DARAL proposal, and it has been developed to efficiently cluster nodes in sub-networks. Based on these ideas, DARAL has been designed for low-/medium-density scenarios, like Smart Cities, focusing on infrastructure support. The clusters are configured like virtual sub-networks, as in DARP, but they can now work autonomously and in parallel, due to the parallel execution of the algorithm in every node. Therefore, every node, except for the network coordinator, make use of the _Dynamical Role Selection Process_ or _DRSP_, an algorithm that is also part of _DARAL_ and it is used as cluster creation and link selection mechanism.  
Before entering in a fine grain description, it is necessary to define some general concepts about _DARAL_, like the sub-network concept and the different types of roles. The sub-network concept comes from the clustering techniques and the idea of Smart City organization. In Smart City approaches, nodes usually communicate between them in the neighbourhood. Thus, if clustering is applied, the traffic can be contained in a determined area, allowing the reduction of interferences with other nodes and also minimizing the necessity of looking for a node among the complete network topology. In order to improve the clustering concept, the virtual sub-network concept extends this idea, applying it in routing tasks. Each virtual sub-network is identified with a virtual identification (vID), which is used to route messages among the network tree. Figure 1 shows a global Personal Area Network (PAN) with its own identification, but internally sub-divided in three different sub-networks. The first one is where the network root is located, while the other two group different nodes.  
Another general concept about \(DARAL\) is the node role, considering that every node should be similar in terms of hardware. \(DARAL\) runs DRSP at the start-up of every node, which allows selecting the role to be played by a node in the network. Figure 1 shows the two available roles in \(DARAL\), end node (EN) and virtual coordinator (VC). ENs are nodes that only communicate with the VC of its sub-network. VCs have the same functionality as ENs but also manage virtual sub-network and store routing tables.
## 2 Principles of RISs
The spacing should be chosen to achieve the desired interference pattern and minimize the interference between adjacent elements. The element spacing should be smaller than the wavelength of the incident waves, in order to ensure sub-wavelength diffraction and provide the desired phase shift range [31]. The size of the RIS's elements should be optimized for the frequency range of operation and the desired phase shift range. The size should be small enough to ensure sub-wavelength diffraction, but not too small to cause excessive losses and reduced efficiency [11]. The power consumption of the RIS should be minimized to reduce the overall system cost and improve efficiency. The RIS should be designed to operate with low power consumption by using low-loss materials, efficient electronics, and optimized control algorithms [32]. The number of elements in the RIS array should be optimized for the desired interference pattern and the frequency range of operation. The more elements in the array, the finer the control over the interference pattern, and the higher the complexity and cost [33]. The RIS's control algorithms should be optimized so as to achieve the desired interference pattern and minimize power consumption. The algorithms should take into account the location of the transmitter and receiver, the desired signal quality, and the interference from other sources [34]. The fabrication process for the RIS should be optimized to achieve the desired element size, spacing, and shape. The fabrication process should be scalable and cost-effective, with high repeatability and low variability [35].
# Green Strategies for Molecularly Imprinted Polymer Development
## Green Strategies in MIP Development
### Supercritical Fluid Technology
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Template** & **Strategy** & **Monomer** & **Crosslinker** & \begin{tabular}{c} **Particle Size** \\ **Diameter** \\ \end{tabular} & **Year** & **Ref.** \\ \hline Salicylic acid and & Non-covalent & DEGDMA & - & 1.7 μm & 2006 & [22] \\ \hline Propranolol & Non-covalent & MAA & DVB & 100 nm & 2006 & [23] \\ \hline Boc-l-tryptophan & Non-covalent & NIPAAm & EGDMA & micron-sized & 2010 & [24] \\ \hline Flufenamic acid & Non-covalent & \begin{tabular}{c} MAA \\ NIPAAm \\ \end{tabular} & EGDMA & micron-sized & 2011 & [25] \\ \hline Ibuprofen & Non-covalent & DMAEMA & EGDMA & \(\sim\)1 μm & 2011 & [26] \\ \hline Bisphenol A & Non-covalent & \begin{tabular}{c} MAA, \\ FMMA \\ \end{tabular} & EGDMA & micron-sized & 2012, 2018 & [18; 46] \\ \hline Bisphenol A & Semi-covalent & BPADM & EGDMA & micron-sized & 2012 & [18] \\ \hline Aspirin and & \begin{tabular}{c} MAA \\ Acetaminophen \\ \end{tabular} & Non-covalent & \begin{tabular}{c} MAA \\ 4-Vpy \\ MMA \\ \end{tabular} & EGDMA & 250–300 nm & 2013 & [30] \\ \hline Bisphenol A and & \begin{tabular}{c} N-covalent \\ acid \\ \end{tabular} & Non-covalent & MAA & EGDMA & 300 nm & 2013 & [34] \\ \hline Carbamazepine & Non-covalent & MAA & EGDMA & 200 nm & 2013 & [35] \\ \hline Labdanolic acid & Non-covalent & DMAEMA & EGDMA & micron-sized & 2014 & [38] \\ \hline DBTSO\({}_{2}\) & Non-covalent & MAA & EGDMA & micron-sized & 2014 & [40] \\ \hline Gallic acid & Non-covalent & \
\begin{tabular}{c} MAA \\ MMA \\ \end{tabular} & EGDMA & 150–200 nm & 2016 & [41] \\ \hline Acetamide & Non-covalent &
\begin{tabular}{c} MAA and \\ MAM, \\ ITA and \\ HEMA \\ \end{tabular} & EGDMA & 3.4–5.3 μm & 2017 & [43; 47] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Molecularly imprinted polymer (MIP) synthesis in supercritical carbon dioxide (scCO\({}_{2}\)).
## 5 Comparative Evaluations
### Evaluation Results for Blind-Detectability  
Blind-detectability was evaluated by carrying out the frame synchronization test and bit detection test of the blind detection process with the frame synchronization process when the embedding-strength level was varied. In this paper, we regard blind-detectability to be achieved if the following two conditions are satisfied: (a) the BER is less than or equal to 10% and (b) the accuracy of the frame synchronization is greater than or equal to 80%. The magenta marker shows the result when the embedding-strength level \(L_{\text{SSL}}\) is \(-\)15 dB.  
Figure 13 shows the results of blind-detectability, where the horizontal axis represents the accuracy of frame synchronization and the vertical axis represents BER. The gray areas indicate that two conditions can be satisfied simultaneously. We can see here that the BER  
Figure 11: Inaudibility results of four compared methods (Proposed, MC, DSS, and LSB): (**a**) LSD and (**b**) PESQ.  
Figure 12: Robustness results of four compared methods (Proposed, MC, DSS, and LSB) in terms of BER in ten cases: (**a**) normal, (**b**) resample-12, (**c**) resample-24, (**d**) requant-8, (**e**) requant-24, (**f**) Ogg, (**g**) G711, (**h**) G723.1, (**i**) G726, and (**j**) MP4.  
decreases as the accuracy of the frame synchronization increases. The results at 4 and 8 bps show that the accuracy of the frame synchronization is greater than 80%, and the BER of less than 10% is satisfied at the embedding-strength level \(L_{\text{SSL}}\) of \(-15\) dB.
## 4 Experiments and Results
### Data Pro-Processing and Training  
LSTM pre-processes the historical time series to mask the uninteresting features, as mentioned in Section 3.1.The relevant embedding vectors are given the position information by the sin-cos function, which selects the output vector sequence. The following parameters were first chosen for the transformer model's encoder construction in order to guarantee the model's top performance in light of various examples of temporal prediction using the transformer model:  
\[\textit{feather\_size}=8,\]  
\[\textit{num\_layers}=2,\]  
\[\textit{dropout}=0.5;\]  
The following parameters are chosen to aid in the smooth operation of the model for the decoder component of the transformer:  
\[\textit{feather\_size}=8,\]  
\[\textit{num\_layers}=3,\]  
\[\textit{dropout}=0.5;\]  
During the training process, the learning rate is set to 0.0001, and the computational loss and optimization effect are observed with the random descent of the MSEloss loss function. In the transformer model training process, the choice of batch_size determines the number of times that the trained samples are into the computational method. The decline and oscillation magnitude of MSEloss are observed when trying batch_size of 4,6,8,16, respectively (Figure 6). It is  
One epoch signifies the conclusion of the forwarding and backward training of the entire training set once during the training of a neural network. The complete training set must typically be run through the same neural network multiple times because one epoch is typically insufficient to process the entire training set in a neural network. Overfitting results from using too many epochs, whereas using too few epochs results in suboptimal training parameters. In this study, the batch size was set to 16, and we tested the Transformer training at epochs of 50, 100, 200 and 300 to obtain the MSEloss function decrease graph shown in Figure 7. It is clear that the convergence effect is satisfactory at epochs of 200 or 300.
# Reviews on Corrugated Diaphrags in Miniature Fiber-Optic Pressure Sensors
## 2 The Principles of Operation of Miniature Fiber-Optic Pressure Sensors
### Demodulation Methods of EFPI Sensors  
The demodulation method is an important part of the fiber-optic sensing system, whose function is to measure external physical quantities by detecting changes in the reflection spectrum or the reflection intensity. The demodulation method determines the accuracy and speed of the demodulation results and should be selected according to the needs and sensor probe structure due to their own advantages and disadvantages. The intensity method and phase method are usually adopted to demodulate the EFPI sensors.
## 8 Results and Discussion of Reviewed Papers
### Veri-Wild  
The VERI-Wild dataset has been extensively utilized as a benchmark by several researchers in the development of innovative methods for vehicle re-identification (ReID) in real-world scenarios. [PERSON] et al. [102] introduced the Parsing-based View-aware Embedding Network (PVEN) and applied it to the VERI-Wild dataset, achieving significant improvements in mean average precision (mAP) across small, medium, and large test datasets, with increases of 47.4%, 47.2%, and 46.9%, respectively. PVEN's ability to perform view-aware feature alignment allowed it to consistently outperform state-of-the-art models, particularly in cumulative match characteristic (CMC) metrics, where it showed a 32.7% improvement over FDA-Net at rank 1.  
Similarly, [PERSON] et al. [103] evaluated the FastReID toolbox on the VERI-Wild dataset, highlighting its effectiveness in accurately identifying vehicles across various conditions. FastReID achieved state-of-the-art performance, particularly in Rank-1 accuracy (R1-Accuracy) and mAP, showcasing its robustness in handling the complexities of vehicle ReID tasks in surveillance and traffic monitoring environments.  
[PERSON] et al. [105] applied the GiT method on the VeRi-Wild dataset, securing top performance across all testing subsets, including Test3000 (T3000), Test5000 (T5000), and Test1000 (T1000). The GiT method outperformed the second-place method, PCRNet, by 0.41% in Rank-1 identification rate and 0.45% in mAP on the Test1000 subset. The study emphasized the importance of leveraging both global and local features, as GiT demonstrated superior generalization across different datasets and conditions. In a separate study, [PERSON] et al. [107] developed the Hybrid Pyramidal Graph Network (HPGN) approach, which achieved the highest Rank-1 identification rate among the evaluated methods on the VERI-Wild dataset, thus making more contributions to the advancing field of vehicle ReID. The findings highlighted the resilience of HPGN, especially in difficult circumstances such as fluctuating day and night situations, where alternative approaches exhibited a decrease in effectiveness.  
Lastly, [PERSON] et al. [109] presented a residual generation model that improved mAP by 2.0% and CMC1 by 1.0% compared to baseline models. The model's reliance on residual information, as indicated by a high alpha value (\(\alpha\) = 0.94), proved crucial in extracting robust features from the dataset. This self-supervised method further proved its adaptability and usefulness in vehicle ReID tasks by showcasing its efficacy on the VERI-Wild dataset.
# Smartwatch-Based Monitoring and Alert System for Factory Operators Using Public Cloud Services
## 3 Materials and Methods
### The Implementation of the Proposed Solution
#### 3.2.6 Implementing the Web Application  
The web application integrated into this system is designed to fulfill three main objectives: allowing administrators to configure and manage employee accounts, offering supervisors a platform to track the real-time activities and performance of their team members, and serving as the authentication mechanism for each smartwatch user. To accomplish these goals, the application's functionalities were modularized. Leveraging React and Next.js, each page of the application was developed using a combination of prebuilt components from the Material-UI (MUI) library--an open-source collection of React components adhering to Material Design principles--and custom-built components tailored to specific design needs. This approach enabled the creation of a seamless, intuitive interface while incorporating specialized components to meet the unique requirements of the project.
# The Influence of Copper Oxide Particle Size on the Properties of Epoxy Resin
## 2 Materials and Methods  
### Materials  
The test samples were made using LG206 epoxy resin and HG359 hardener (GRM Systems, Bielsko-Biala, Poland), and copper oxide particles of various sizes were used as reinforcements, that is, in the ranges of 1-10 \(\upmu\)m, 40-60 nm, and 10-30 nm, (Suzhou Canfuo Nanotechnology Co., Ltd., Suzhou, China). The resin-to-hardener weight ratio was 100:34. The reinforcement content corresponded to 10% of the weight. The sample production process included accurately weighing the proportions of resin and hardener on a Radwag 22 W laboratory scale (Radom, Poland), the ingredients were then mixed using a SH-II-7C series mechanical digital mixer up to 40 L, with a power of 80 watts (Chemland, Stargard, Poland). Mixing was carried out at a speed of 500 rpm by gradually introducing copper oxide powder. In order to obtain a homogeneous suspension, a dispersion disk stirrer with a diameter of 5 cm was used. The mixing time lasted approximately 5 min after the introduction of the reinforcement. After mixing the ingredients, the mixture was placed in a vacuum chamber. During the production of the samples, a VC3028 AG vacuum chamber (VacuumChambers, Bialystok, Poland) was used to minimize porosity resulting from, among other factors, mixing the ingredients. The mixture was de-aerated at a pressure of 10 mbar (abs). The hardening process was carried out at room temperature. The setting time was 24 h, and the test samples were demolded 7 days after their production. Samples for strength tests were made in accordance with the standard for plastics PN-EN ISO 3167 [25], and type A shapes were selected at dimensions of 120 mm \(\times\) 10 mm \(\times\) 4 mm, whereas samples for the thermal conductivity tests had the shape of a plate with the following dimensions: 150 mm \(\times\) 24 mm.
###### Abstract
Moreover, there were vibrations lasting over five minutes in the Kanto Plain, where the holder was located, despite being over 300 km from the epicenter off the Pacific coast of the Tohoku earthquake in 2011 [16]; this has been indicated as a cause for the liquefaction of the ground in coastal areas, which contain large amounts of fine particles [17]. There are also concerns regarding long-duration vibrations from the Nankai Trough megathrust earthquake, which is expected to occur in the future. Therefore, there is also a need to determine the effects of long-duration earthquakes on holders. In fact, studies on long-duration earthquakes have already begun in some industrial facilities [18]. For example, there were severe damages to oil storage tanks due to liquid sloshing during the 2003 Tokachi-oki earthquake, which was caused by long-period and long-duration seismic waves [19]. Additionally, the importance of considering the detailed structures of the soft near-surface sediments was recognized [20]. In this paper, a three-dimensional soil-water coupled finite deformation analysis was conducted for the holder of the previous study [13] using long-duration strong ground motions, and a seismic performance evaluation was performed. The input earthquake motion used in the analysis is the trench-type earthquake motion specified in the JGA guidelines. In order to compare the results with the results of previous studies (i.e., analysis results of short duration strong ground motion) [13; 14], the amplitude of the acceleration response spectrum was adjusted to be the same as that of the strong ground motion set in the previous study. In addition, it was discussed that the effects of input seismic motion characteristics such as duration and maximum acceleration on the pile's damage extent and support functions from the perspectives of non-linear interactions between an alternating sand-clay layer ground. The analysis code used in the present study was the same as that in a previous study [11], which allows the use of the SYS Cam-clay model [12] to conduct evaluations from the sand to the intermediate soil and clay in the ground as well as handle the mechanical response of the coupled holder-pile-ground system during and after the earthquake. This study aims to contribute to one of the crucial Sustainable Development Goals (SDGs) adopted in the UN agenda, \"11: Make cities and human settlements inclusive, safe, resilient, and sustainable\". The holders are essential for the stable supply of city gas. By utilizing the seismic verification carried out in this study, the holders can continue to function even after a large-scale natural disaster, thereby minimizing the damage to the economy and society without causing catastrophic damage, as is the case with earthquake countermeasures for industrial lifeline and equipment [21].
## 4 Optimization Scheduling Model  
### Objective Function  
The optimization target for the proposed industrial park IES is to minimize the total cost, which includes procurement cost, operation and maintenance cost, wind curtailment cost, and carbon cost. The objective function is expressed in the following manner.  
\[\text{min}F=\left(C_{\text{buy}}+C_{\text{costt}}+C_{\text{W}}+C_{\text{CO}_ {2}}\right) \tag{14}\]  
where \(F\) represents the total system cost; \(C_{buy}\) represents the cost of purchased energy; \(C_{\text{cost}}\) represents the operational cost; \(C_{W}\) represents the cost of wind power curtailment; \(C_{\text{CO}_{2}}\) represents the carbon cost. The specific expressions for each component are as follows:Procurement Cost  
The procurement cost is composed of the costs associated with purchasing electricity, gas, and gray hydrogen for the system. It is expressed as:  
\[C_{\text{buy}}=\sum_{t=1}^{T}c_{t}^{buy}P_{buy}(t)+c_{\text{g}}^{buy}\sum_{t=1}^{ T}G_{buy}(t)+c_{\text{q}}^{buy}\sum_{t=1}^{T}Q_{buy}(t) \tag{15}\]  
where \(C_{\text{bp}}\), \(C_{\text{bg}}\) and \(C_{\text{bq}}\) represent the costs of purchased electricity, purchased gas, and purchased gray hydrogen, respectively; \(c_{\text{t}}^{bp}\) represents the electricity price at time t; \(P_{buy}(t)\) represents the purchased electricity power at time \(t\); \(c_{\text{g}}^{buy}\) and \(c_{\text{q}}^{buy}\) represent the unit prices of gas and gray hydrogen, respectively; \(G_{buy}(t)\) and \(Q_{buy}(t)\) represent the gas power and the mass of purchased gray hydrogen at time \(t\), respectively.  
The operation and maintenance cost encompasses the expenses associated with the operation and maintenance of all energy-consuming and energy-storage equipment within the integrated energy system. It is expressed as:  
\[C_{\text{cost}}=\sum_{t=1}^{T}\left[C_{t}P_{t}(t)+C_{\text{soc}}S_{1}^{d}(t)+C _{\text{soc}}S_{1}^{c}(t)\right] \tag{16}\]  
where \(i\) represents various functional devices; \(C_{i}\) represents the unit operational cost of energy-using devices; \(P_{i}(t)\) represents the power usage of energy-using devices; \(C_{\text{soc}}\) represents the unit operational cost of energy storage devices; \(S_{1}^{d}(t)\) and \(S_{1}^{c}(t)\) represent the discharging and charging power of the energy storage device at time \(t\), respectively.  
Wind Curtailment Cost  
\[C_{\text{w}}=c_{\text{w}}\sum_{t=1}^{T}P^{\text{w}}(t) \tag{17}\]  
where \(c_{\text{w}}\) represents the penalty for wind power curtailment per unit power; \(P^{\text{w}}(t)\) represents the wind power curtailment at time \(t\).  
The carbon cost incorporates the expenses related to carbon emissions and management within the integrated energy system. It is the sum of carbon trading costs and carbon sequestration costs and is expressed as:  
\[C_{\text{CO}_{2}}=\sum_{t=1}^{T}C_{\text{p}}^{\text{CO}_{2}}(t)+C_{\text{f}}^ {\text{CO}_{2}} \tag{18}\]