esa-sceva/satcom-chunk-collection huggingface dataset:
# A _uv_-Cellular Network: Design, Implementation and Experiments
## 2 Related Work
The contributions of [27] include the initial steps towards an indoor testbed, an outdoor testbed, and an open-access software suite. Ref. [28] proposes a new low-cost distributed networked localization and time synchronization framework for underwater acoustic sensor networks. This scheme is implemented and tested in a testbed based on Teledyne Benthos Telesonar SM-975 underwater modems. Ref. [29] is UAN testbed which can provide the reference to ocean going autonomous vehicles, static sensor and communication nodes, and underlying software tool chain. Ref. [30] aims to provide a community testbed for underwater wireless sensor networks. Comparing to the terrestrial wireless networks, there are more difficulties in selecting and managing the optimal path for transmitting data from the source node and to the destination node in the large-scale UWSN application scenarios. The issues routing protocol design should address includes the movement of nodes, high and inefficient propagation delay, energy constraints, location estimation and 3D deployment [31; 32; 33; 34]. Depending on the application scenarios, the underwater routing protocol may have different core issues to address and can be divided into three categories: energy-based protocols, data-based protocols, and geographic information-based protocols [35]. Other than that, combining the above information to make routing decisions is another type of routing protocol. Our approach in this article falls into this category. In [36], an asymmetric link-based reverse routing (AREP) is proposed. In AREP, each node maintains a neighbor table in which items are used to analyze the link state. This method determines whether there is a symmetrical link or an asymmetrical link between two nodes that are neighbors of each other by determining whether they can communicate with each other successfully. LAFR [37] also proposed a link detection mechanism. In order to get the link state information (symmetrical link or asymmetric link), nodes need to send Link Detection messages periodically. However, due to the nature of the hydroacoustic channel, it is not enough to estimate the communication link state by whether the communication was successful or not. In addition, link detection packets usually contain no communication information, which not only wastes energy but also occupies the channel, potentially preventing successful transmission of the data packet. Compared with other works, the main contribution of this paper focuses on the realization of a large-scale UAN system that can operate stably in a real environment by solving the data forwarding path planning problem and the large number of node channels sharing problem in a mobile topology. The design methodology and experimental results of this paper will be described in detail as follow.
# Blockchain Applications in Agriculture: A Scoping Review
## 4 Discussion
### Summary of Evidence
#### 4.1.1 Blockchain Frameworks  
One of our primary findings was the variations in the specific blockchain framework used (RQ5). The result reflects the situation that prevails in the overall ecosystem of the blockchain. Most solutions use Ethereum and Hyperledger Fabric. Beyond that, a large percentage do not mention the blockchain technology they use. The papers that do not mention technology are mainly conceptual (67%, 22 out of 33 papers). Apart from the above in the use of blockchain in the agricultural sector, other technologies have been used less frequently, such as Hyperledger Sawtooth (3%), IOTA (1%), NEO (1%), Corda (1%), and Multichain (1%). Two assistive blockchain-based technologies, BigchainDB (4%) and Polkadot (1%), also appear. Finally, seven research papers in this field combine more than one technology. All these papers use Ethereum combined with another technology [53; 54; 55; 56; 57; 58], except one [59]. In one of them [58], Polkadot is used for two-chain communication, which allows cross-blockchain transfers. Correspondingly, we can say that according to the purpose of each application, the appropriate blockchain technology has been chosen. Each blockchain framework has a different ecosystem and philosophy that is reflected through its blockchain type (RQ6). The only blockchain we have come across in our study that can be adapted and used in more than one blockchain type is Ethereum which can be either public permissionless, public permissioned, or private permissioned. Finally, we concluded that most solutions opt for private permissioned blockchains, possibly trying to represent some existing systems.
## 3 Results and Discussion
### Evaluating the Performance of the Optimal Model  
When evaluating the model performance in terms of the F1 score, the model with a linear SVM as the classifier, no class weighting of the training data, and fine-tuning of the feature extraction layer (NW-FT-SVM-L) exhibited the highest performance for the validation data. The model with fine-tuning of the feature extraction layer that used two FCs as the classifier and class weighting of the training data (W-FT-FC-N) exhibited the best performance in terms of recall. Because the optimal model calibration differed depending on the index used for evaluation, the model configuration should be considered based on the purpose of the model.  
The performances of the two models for the test data are depicted in Figure 8. The results were generally similar to those of the validation data, confirming that there was minimal variation in the performance of the input data. Although the two models had different estimation trends, the NW-FT-SVM-L model with the highest F1 score exhibited the best overall performance in asbestos detection. Conversely, the W-FT-FC-N model exhibited performance on the safe side, with fewer missed asbestos fibers. When selecting a model, it is advisable to use one of these models for different purposes. Examples of the asbestos detection results are shown in Figure S8. The model successfully detected asbestos fibers, although a few overlooked and erroneous detections occurred.
## 4 Image Semantic Segmentation Algorithm
### Evaluation Metrics  
The evaluation of the model in this paper is conducted using several metrics, including Accuracy, mean Accuracy (mAcc), Intersection over Union (IoU), mean Intersection over Union (mIoU), and mean Pixel Accuracy (mPA). Additionally, performance metrics such as parameter count (Params), floating-point operations (FLOPs), and frames per second (FPS) are recorded. The formulas for calculating accuracy, precision, and IoU are shown in Equations (9)-(13):  
\[Acc=\frac{TP+TN}{TP+FP+TN+FN} \tag{9}\]  
\[mAcc=\frac{\sum\limits_{i=1}^{K}Acc_{i}}{K+1} \tag{10}\]  
\[IoU=\left|\frac{S_{Seg}\cap S_{GT}}{S_{Seg}\cup S_{GT}}\right|=\frac{TP}{TP+ FP+FN} \tag{11}\]  
\[mIoU=\frac{\sum\limits_{i=1}^{K}IoU_{i}}{K+1} \tag{12}\]  
\[mPA=\frac{1}{K+1}\sum\limits_{i=0}^{k}\frac{p_{ii}}{p_{ij}} \tag{13}\]where TP denotes the number of samples correctly identified as coal, FN denotes the number of coal samples misclassified as gangue, FP denotes the number of gangue samples misclassified as coal, and TN denotes the number of gangue samples correctly classified as gangue. \(K+1\) refers to the total number of segmentation categories, including a background class, while \(I\) represents the true class and \(j\) the predicted class. The variable \(p_{\bar{u}}\) refers to the number of pixels correctly predicted as class \(i\), while \(p_{\bar{y}}\) refers to the number of pixels belonging to class \(i\) but predicted as class \(j\).
## 4 Case Study and Discussion
### Cooling Load Distribution and Searching Range of Design Cooling Capacity  
In order to generate a reasonable distribution of cooling load, before conducting the Monte Carlo simulations, it is necessary to quantify the uncertainties of input parameters. In this case study,  
Figure 7: Trials of simulation to select optimum chiller plant design.  
the uncertainties of key inputs for cooling load calculation (including outdoor temperature, relative humidity, number of occupants, fresh air rate, lighting, and equipment load) are considered and their distributions are referred to in Table 1. Based on the quantification results of uncertainties from MATLAB, the cooling load scenarios are computed by the adoption of the TRNSYS building energy model. According to Reference [9], the sampling number of Monte Carlo simulation is set to be 780. Figure 8 shows the profiles of two types of cooling load distribution (i.e., the distribution of reference case, and distribution with considering uncertainties). In this paper, the distribution of reference case is a typical cooling load distribution of specified design year without considering uncertainties.  
The next step is to find out the searching range of the total cooling capacity for a chiller plant. Various cooling capacities corresponding to different unmet hours are shown in Figure 9. The cooling capacities in the profile of \"mean\" are obviously larger than those in the profile of \"reference\" when the unmet hours are less than 10 hours, while such capacity differences become smaller gradually with the increase of the unmet hours. As defined, the maximum cooling load (i.e., 5600 kW) of a typical design year is higher than the minimum cooling capacity (i.e., around 5100 kW). When all the cooling load scenarios are fulfilled, the maximum cooling load is much smaller than the maximum cooling capacity (i.e., 6600 kW). The total cooling capacity searching range is between 5100 kW and 6600 kW and the interval is set to be 100 kW.  
Figure 8: Distribution of cooling load with and without considering uncertainties.  
Figure 9: Cooling capacities vs. number of annual unmet hours.
# Explainable Security Requirements Classification Through Transformer Models
## 5 Discussion
#### Limitations  
The quantitative findings and the explainability study show the shortcomings of current methods for classifying security requirements, emphasizing significant issues with model performance, data augmentation, and interpretability. First, managing the language variety brought forth by data augmentation presents a challenge. Although augmentation methods such as contextual word embeddings, back-translation, and paraphrasing might improve model robustness and boost data diversity, they can also contribute irrelevant information or noise. For example, paraphrasing and back-translation frequently drastically change sentence structure, which may weaken the meaning of crucial terms or diminish language about security. This may result in models misinterpreting important terms on supplemented datasets, showing up as somewhat poorer recall and F1 score. As seen by DistilBERT's performance on the paraphrased dataset, this heterogeneity makes it difficult for the models to maintain high accuracy across various language expressions, particularly when paraphrasing. Besides, even training these models over a small dataset requires selecting hyperparameters carefully, and the time for training them is significantly high. SetFit optimization, thanks to its efficiency, addresses these challenges of model fitting quite well. However, some issues remain particularly related to overfitting on small training sets or overfitting on augmentation. Models trained over a small dataset tend to memorize the training data too well and not generalize well to new unseen data, not in the training set. This poses severe threats in the case of security requirement classification, where the model is expected to perform well on various iterations in language structure and terminologies.
## 4 Micro-Vibration Suppression and Image Stabilization Principle  
The mid-high disturbances and low-frequency LOS jitter can be suppressed by combining the first-order vibration suppression by passive BVILs and the second-order vibration suppression controlled by the FSM, respectively. As shown in Figure 7, the offset amplitude of the image points decreases obviously after being suppressed by the two-stage image stabilization system.  
Figure 6: LOS jitter angle around the (**a**) X-axis and the (**b**) Y-axis.
# Graph-Based Resource Allocation for Integrated Space and Terrestrial Communications
## 5 Graph-Based RA in D2D Networks  
D2D networks have created a promising paradigm for the integration of distributed close-range communications within the range of other networks (mostly cellular networks). These applications include vehicular communications, IoT-based services, and cellular offloading (users that request the same content can have it shared by others in their vicinity, rather than downloading it separately through the BS) [6]. Generally, the D2D nodes are considered in pairs (a transmitter and receiver), while the cellular users are considered individually. Even though D2 Ds operate in both inband (in the cellular band) and outband (in unlicensed bands) modes, graph-based RA has been utilized mostly in the former mode of deployment, via underlay spectrum access (the D2 Ds utilize the spectrum opportunistically, so that they may not impede the cellular communications). This is because RA in the outband mode may lead to overly complex graph topology due to the inclusion of an additional set of frequency channels, and the difficulty in overhead exchange between the D2 Ds and the BS. The reviewed methods aim to minimize both interference from the UL communications of cellular UEs (CUEs)/DL transmissions of the BS to the D2D pairs, as well as that resulting from their own exchange towards other D2 Ds, the CUEs, and the BS. Most of these works use a system model comprised of a macro-cell BS with \(N_{CUE}\) CUEs and \(N_{DUE}\) D2D pairs (DUEs) operating in its range. The scenario of D2D networks is illustrated in Figure 5. A generalized illustration of the most common graph models for RA problems (usually used independently from each other), and a summary of their subcategories comprises this Section. A tabular summary of the main design characteristics of the algorithms reviewed in this Section is given in Table 7.
# Exploiting Lunar Navigation Constellation for GNC Enhancement in Landing Missions
## 4 Results  
After presenting in Section 4.1 the adopted LNS constellation architecture, the considered GNC strategy is applied to a specific scenario, described in the following Section 4.2. The results obtained in the nominal landing scenario are reported in Section 4.3. Finally, the sensitivity of the approach is tested through a Monte Carlo analysis in Section 4.4.
# Automated Modal Analysis Using Stochastic Subspace Identification and Field Monitoring Data
## 4 Examples of Field Applications
### Description of the Second Dataset  
The second dataset was collected from a four-story RC frame at Ming-Li Elementary School in Hualien, Taiwan. The frame is approximately 97 m, 10 m, and 14 m in length, width, and height, respectively. It was built in 1980s and was also impacted by the Chi-Chi earthquake. Similarly, this RC frame is fitted with a SHM system, and the accelerometers measured all the structural responses before and after the strong excitation. A total of 30 channels of acceleration was used to collect the structural responses during a series of earthquake excitations. The accelerometers were distributed on the basement, first, second, and fourth floors, measuring responses in different directions. Figure 4b shows the dimensions of the four-story RC frame, as well as the locations of all sensors, marked with individual channel numbers. The other ten-year dataset, ranging from 1994 to 2004, was collected, and 20 sets of measurements were identified using the proposed procedure.  
It is noted that, besides the destructive earthquake in 1999, several strong seismic events and some aftershocks resulting from the Chi-Chi earthquake were recorded during the service time. Due to the structural damage generated by this seismic event, two stages of retrofitting were applied to these structures. One took place just after the destructive earthquake because of the considerable damage caused to the frames. Thanks to the nationwide school building enhancement program, the other took place from January 2010. Unfortunately, both datasets only include measurements from the first stages of retrofitting.
## 3 Theoretical Model  
### Bearing Performance Analysis  
When operating the hydrostatic bearing in a steady state, the pressure of the bearing land part is calculated using the Reynolds equation, which can be expressed as shown in Equation (1).  
\[\frac{\partial}{\partial\theta}\bigg{(}H^{3}\frac{\partial p}{\partial\theta} \bigg{)}+\frac{\partial}{\partial Z}\bigg{(}H^{3}\frac{\partial P}{\partial \theta}\bigg{)}=\Lambda\frac{\partial H}{\partial\theta} \tag{1}\]  
The dimensionless variables used in Equation (1) are defined in Equation (2)  
\[\theta=\frac{x}{R},Z=\frac{z}{R},P=\frac{p}{p_{a}},H=\frac{h}{C},\ \Lambda=\frac{6\mu \omega}{p_{a}}\bigg{(}\frac{R}{C}\bigg{)}^{2} \tag{2}\]  
where \(C\) is the bearing clearance, \(R\) is the bearing radius, \(h\) is the film thickness, \(\mu\) is the viscosity of the lubricant, and \(\omega\) is the rotating speed of the shaft.  
The boundary conditions of the Reynolds equation above are as follows  
\[\begin{split}& P(\theta,\ 0)=P(\theta,\ L)=P_{e}\\ & P(\theta,\ z)=P(2\pi+\theta,\ z)\\ & P=P_{r}\ at\ recess  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Properties** & **Symbol** & **Unit** & **Value** \\ \hline Diameter & \(D\) & mm & 70 \\ Length & \(L\) & mm & 70 \\ Radial clearance & \(C\) & mm & 0.04 \\ Rotating speed & \(\omega\) & rpm & 21,000 \\ Hole location & \(l\) & mm & 17.5 \\ Supply pressure & \(p_{s}\) & bar & 70 \\ Supply temperature & \(T_{s}\) & deg & 20 \\ Discharge pressure & \(p_{e}\) & bar & 60 \\ Orifice hole diameter & \(d\) & mm & 1.8 \\ Discharge coefficient & \(C_{d}\) & - & 0.8 \\ Rotor weight & - & kg & 17.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Design parameters for hydrostatic radial bearing and rotor.  
Figure 2: Illustration of hydrostatic radial bearing.  
where \(P_{e}\) is the dimensionless pressure outside of the bearing and \(P_{r}\) is the dimensionless recess pressure. The dimensionless recess pressure \(P_{r}\) is calculated using the flow continuity relationship between the dimensionless supply pressure \(P_{s}\) and the orifice restrictor, as follows  
\[\oint_{L_{i}}\left(\Lambda Hi-H^{3}\cdot\
abla P\right)\cdot dl=\Gamma_{t}[2(P_{ s}-P_{r})]^{1/2} \tag{4}\]  
The dimensionless flow coefficient is defined as follows  
\[\Gamma_{t}=\frac{12\mu}{C^{2}p_{a}}C_{d}A_{o}\sqrt{\frac{p_{a}}{\rho}} \tag{5}\]  
Bearing performance analysis was conducted based on the governing equation above, and the bearing stiffness and damping were calculated using the governing equation derived, using the perturbation method from Equation (1) [21; 22]. The finite element method was used to conduct a numerical analysis, and \(120\times 60\) grids were used in the circumferential and axial directions.
###### Abstract  
This study investigated the growing environmental concern of particulate matter (PM)-induced pulmonary injury and explored novel preventive strategies. In particular, it evaluated the protective effects of _Attractylodes japonica_ Koidz. ex. Kitam root extract (AJ), which is known for its anti-inflammatory and antioxidant properties, against PM\({}_{2.5}\)-induced subacute pulmonary injuries in Balb/c mice. The experimental design involved administering AJ at a concentration from 400 to 100 mg/kg over a ten-day period, with comparisons made to the mucolytic agent amboxol hydrochloride (AX). The results revealed that AJ significantly alleviated PM\({}_{2.5}\)-induced pulmonary injuries, mucus overproduction, and respiratory acidosis in a dose-dependent manner. Notably, body surface redness was reduced by up to 55% at a concentration of 100 mg/kg compared to the control. These effects were evidenced by reduced mRNA expression of the mucus-associated genes MUC5B and MUC5 AC and increased concentrations of substance P (up to 475%) and acetylcholine (up to 355%) in the lungs at 400 mg/kg, compared to the intact vehicle control. Particularly, the 400 mg/kg dose of AJ demonstrated comparable effectiveness to AX, highlighting its potent mucolytic and expectorant activities. The results of this study highlight the fact that AJ could act as a promising alternative for respiratory protection, with potential applications as a functional food ingredient. This study substantiates AJ's role in enhancing respiratory health, emphasizing its capacity as a candidate for further development into therapeutic agents against toxic environmental exposure.  
antioxidant activity; anti-inflammatory; _Attractylodes japonica_; herbal medicine; mucolytic expectorant; respiratory protection
# Lithium Battery SoC Estimation Based on Improved Iterated Extended Kalman Filter
## 2 Construction of Battery Model
### Equivalent Circuit Model Parameter Identification  
In order to obtain accurate model parameters, it is necessary to identify the parameters OCV, \(R_{0}\), \(R_{1}\), \(C_{1}\), \(R_{2}\), and \(C_{2}\) in the model at different SoCs. In this paper, the INR 18650-20R lithium-ion battery is used, which has a nominal voltage of 3.6 V, a charging cutoff voltage of 4.2 V, a discharging cutoff voltage of 2.5 V, a single-unit rated capacity of 2000 mAh, and a maximum discharging multiplication rate of 11 C (at 25 \({}^{\circ}\)C).  
The hybrid power pulse characterization test, or the HPPC test [26], is performed on the battery not only to obtain the relationship between the OCV of the battery and the SoC but also to identify the parameters in the equivalent circuit model of the battery. The open-circuit voltage of the battery at this time is recorded once every second during the process of identification, and the temperature is maintained at a constant of 25 \({}^{\circ}\)C during the experimental process. The specific procedure of the test is as follows:  
1. Fill the battery with a constant current and leave it for 2 h to restore the battery to equilibrium. This process brings the battery to a steady state, which is a prerequisite for HPPC testing.
2. Discharge the battery using a current of 0.5 C for 12 min to reduce the SoC by 10%. The goal of stopping the discharge as soon as the discharge time is up is to accurately obtain the rebound characteristics of the voltage-to-SoC curve at every 10% of the charge.
3. Let the battery stand for two hours. This process also brings the battery to a steady state so that the rebound characteristics of the voltage-to-SoC curve can be tested at a later date when other SoCs can be further tested.
4. Repeat steps 2 and 3 until the SoC becomes zero. The open-circuit voltage of the battery is measured immediately after the end of each step 2, and these data will provide data support for the subsequent fitting of the OCV-SoC curve.