esa-sceva/satcom-chunk-collection huggingface dataset:
# A Message Passing-Assisted Iterative Noise Cancellation Method for Clipped OTFS-BFDM Systems
## 5 Simulation Results
### BER Comparison between the MP-AIC and MP-A Schemes
In addition, with the increase of \(\gamma\), the BER of MP-AIC also decreases, because the lager \(\gamma\) is, the less distortion of the signal. When \(\gamma=1\) and 3 dB, the BER of the MP-AIC with two iterations are \(1.62\times 10^{-3}\) and \(9.194\times 10^{-4}\), respectively, at \(SNR=16\) dB, while those of MP-A are, respectively, \(3.472\times 10^{-3}\) and \(2.701\times 10^{-3}\). Then, the BER of MP-AIC is 53% lower than that of MP-A at \(\gamma=1\) dB. Similarly, in Figure 7b, when \(\gamma=1\) and 3 dB, the BER of MP-AIC with two iterations are  
Figure 5: (a) BER comparison of the non-clipped OTFS-BFDM system with rectangular, RRC (\(\alpha=0\)), and Drichlet PWFs; (b) BER comparison of the non-clipped OTFS-RRC system with \(\alpha=0.1\), \(\alpha=0.5\), and \(\alpha=0.9\). The channels in both scenarios contain the maximum Doppler shifts of 979.74 Hz, 1451 Hz, and 1935.48 Hz. Figure 6: (a) BER performance of the MP-A [26] method in the clipped OTFS-Rect system on the channel with a maximum Doppler shift of 1935.48 Hz; (b) BER performance of the MP-A [26] method in the clipped OTFS-RRC system on the channel with a maximum Doppler shift of 1935.48 Hz (\(\alpha=0.1\)). \(2.828\times 10^{-3}\) and \(1.817\times 10^{-3}\), respectively, at \(SNR\) of 16 dB, while those of MP-A are, respectively, \(8.007\times 10^{-3}\) and \(3.476\times 10^{-3}\). Then, the BER of MP-AIC is about 47.7% lower than that of MP-A at \(\gamma=1\) dB. To sum up, the BER performance of MP-AIC is much better than that of MP-A in canceling clipping noise interference. Figure 8 shows the BER convergence diagram of the clipped OTFS-RRC system under different iterations of the MP-AIC method, where \(\alpha=0.1\). It can be seen that with the increase of SNR, the MP-AIC method improves the BER performance more and more significantly. Furthermore, in the first iteration, the BER decreases the most, while after two iterations, the BER tends to converge. Referring to (25), most of the clipping noise is reconstructed and canceled in the first iteration and only the remaining residual clipping noise terms are gradually reduced in the second and more iterations. Therefore, two iterations can ensure that the BER performance of system is close to optimal. Similarly, for clipped OTFS-BFDM signals with different PWFs, the MP-AIC method also has a consistent convergence effect on BER. Figure 8: BER convergence of a clipped OTFS-BFDM system with RRC PWFs under different iterations (\(\alpha=0.1\)). Figure 7: (**a**) BER comparison of the proposed MP-AIC method and MP-A [26] method in the clipped OTFS-Rect system on the channel with a maximum Doppler shift of 1935.48 Hz; (**b**) BER comparison of the proposed MP-AIC method and MP-A [26] method in the clipped OTFS-RRC system on the channel with a maximum Doppler shift of 1935.48 Hz (\(\alpha=0.1\)).
# Modeling the Trip Distributions of Tourists Based on Trip Chain and Entropy-Maximizing Theory
## 4 The Trip Distribution Forecasting Model Based on Trip Chains
### Trip Distribution Forecasting Model
#### 4.2.3 Model Solving  
This section provides the solution to calculate the value of \(\alpha\), \(\beta\), and \(\gamma\) of Equation (8), and further calculate the parameter \(\gamma\), \(R_{m}^{\lambda+1}\), and \(T_{n}^{\lambda+1}\) (see Equation (14) below) according to Equation (10). In this solution, the number of produced trips of origin zone \(m\) (i.e., \(O_{m}\)) and the number of trip chains of destination zone \(n\) within the tourism cities of the study area, as well as the total trip distance (i.e., \(d\)) obtained according to the traffic network of the tourism city, are already given. The proposed model solving steps of this solution are described in Figure 4.  
Detailed steps and explanations of this solution are listed as follows:  
* step 1: initialize parameter values, let \(\gamma=1\), \(\lambda=1\), \(T_{n}^{0}=1\), and the value of random residual \(\varepsilon=0.05\);
* step 2: calculate \(R_{m}^{\lambda+1}\) and \(T_{n}^{\lambda+1}\) according to Equation (14): \[\left\{\begin{array}{l}R_{m}^{\lambda+1}=\left[\underset{H\in H^{\mu}}{\sum} \bigg{(}\underset{n=1}{\overset{\varphi}{\prod}}T_{n}^{\lambda+1}D_{n}\bigg{)} P_{mH}\exp(-\gamma d_{mH})\right]^{-1}\\ T_{n}^{\lambda+1}=\left[\underset{j=1}{\overset{\lambda}{\sum}}\underset{m=1 }{\overset{M}{\sum}}\underset{H\in H^{\mu}|n=n_{j}}{\overset{\varphi}{\prod}} R_{m}^{\lambda+1}O_{m}(\underset{\prod}{\overset{\varphi}{\prod}}T_{n^{*}}D_{n^{*}})P_{mH} \exp(-\gamma d_{mH})\right]^{-1}\end{array}\right.\] (14) where \(m=\{1,\,2,\,3,\ldots,M\}\), \(n=\{1,\,2,\,3,\ldots,N\}\).
* step 3: judge the two conditions \(\left|R_{m}^{\lambda+1}-R_{m}^{\lambda}\right|/R_{m}^{\lambda+1}<\varepsilon\) and \(\left|T_{n}^{\lambda+1}-T_{n}^{\lambda}\right|/T_{n}^{\lambda}<\varepsilon\) iteratively, if they satisfy, then go to step 4, otherwise let \(\lambda+1\) and back to step 2;  
Figure 4: Model solving steps.  
step 4: let \(x^{k}=\gamma^{k}\) and calculate \(\gamma^{k}\) iteratively using [PERSON]'s method, as shown in Equation (15): \[\left\{\begin{array}{l}x^{k+1}=x^{k}-\frac{f(x^{k})}{f^{\prime}(x^{k})}\\ f(x)=\sum\limits_{m=1}^{M}\sum\limits_{H\in H^{n}}R_{m}O_{m}(\prod\limits_{n= 1}^{\varphi}T_{n}D_{n})P_{mH}\exp(-\gamma d_{mH})d_{mH}-d\\ f^{\prime}(x)=-\sum\limits_{m=1}^{M}\sum\limits_{H\in H^{n}}R_{m}O_{m}(\prod \limits_{n=1}^{\varphi}T_{n}D_{n})P_{mH}\exp(-\gamma d_{mH})d_{mH}^{2}\end{array}\right.\] (15) if the iterative calculation satisfies the condition \(\left|x^{k+1}-x^{k}\right|/x^{k}<\varepsilon\), then end the iteration, otherwise let \(\gamma^{k+1}=x^{k+1}\).
* step 5: let \(\lambda=\lambda+1\), then go back to step 2.
# A Novel Method for Wheat Spike Phenotyping Based on Instance Segmentation and Classification
###### Abstract
[17] processed the side-view spike images and pre-annotated the spikelets through an unsupervised method based on the watershed algorithm, and a Faster RCNN model was retrained to detect and count the spikelets in each spike. [PERSON] et al. [18] used proposed Mask R-CNN models to segment the side-view wheat spikelets, and designed an automatic extraction algorithm for spike phenotypic parameters, including length, width, and the number of grains. [PERSON] et al. [19; 20] added an attention mechanism to the segmentation network, realizing a mean squared error (MSE) of 3.13 and a coefficient of determination (R\({}^{2}\)) of 0.93. However, the spike grain counting strategies of the above research were all based on side-view spikelet segmentation. Simply adding the number of spikelets on both sides of the spike may lead to errors, for a spikelet may contain more than two grains. Most recent research has focused on the segmentation of wheat spikes from individual or group wheat, but ignored wheat spikes and more detailed structures where plenty of phenotyping information about wheat grain was contained. Due to the irregular and dense distribution of wheat spikelets and grains, the research illustrates more demands on the quality of the phenotyping method. Meanwhile, the researchers do not explore different varieties and growth periods of wheat and mostly rely on a destructive approach in a laboratory environment. Above all, this study proposed an accurate and fast method for wheat spike phenotyping in field environments, which considered different varieties and growth periods of wheat to strengthen practicality and generalization. The specific objectives of this study are as follows: (1) to conduct instance segmentation of the frontal wheat spikelets using a deep learning model; (2) to adopt a machine learning algorithm based on the image features of the wheat spikelets to classify and count the grains of wheat spikes; (3) to validate the performance of the model with different wheat varieties and growth periods.
## 2 Proposed Calculation Method  
### Principle of Compensated Level of Deflection  
Compensation grouting is mainly used for lifting surface buildings or tunnels. Most of the current research in this field focus on analysis of the vertical lifting effect caused by grouting, with only a few studies conducted on the effect of injection-induced control in the horizontal direction. In recent years, in Shanghai, China, more and more existing tunnels have experienced horizontal deformation due to the unloading effect of surrounding projects. The sleeve valve pipe grouting technology has been used to rectify the horizontal displacement of existing tunnels [11]. The principle of horizontal corrective action of grouting is shown in Figure 1.  
According to the research results of [PERSON] et al. (2012) and [PERSON] et al. (2013), grouting is usually divided into two processes: compaction grouting and split grouting. In practice, these two processes often happen simultaneously. Not only are the mechanism and grouting efficiency of compaction grouting different from those of compensation grouting, but the numerical methods used for simulating these two grouting techniques are also different. Nevertheless, from an analysis of the effect of grouting, we inferred that compaction grouting and compensation grouting can cause the soil around the grouting area to expand. [PERSON] et al. (2012) proposed a model of volume expansion of injected soil. The model assumes that there is a spherical area with a volume of \(V_{0}\) around the grouting point and that the volume of the spherical area increases by \(\Delta V\) after grouting. If compact grouting occurs, \(\Delta V\) is the sum of the bubble volumes; if split grouting occurs, \(\Delta V\) equals the sum of the volumes of splitting slurry in this area.  
To sum up, grouting causes the volume of the soil in the grouting area to expand, and volume expansion of the soil applies additional stress on the surrounding soil and the existing tunnel. As the existing tunnel has already experienced some displacement before grouting, the grouting effect can correct its deformation. In the rectification of tunnel displacement, controlling the size of the correction is very important. If the corrective effect of grouting is too small, displacement of the tunnel will not be corrected. If grouting has a profound corrective effect, the existing tunnel may experience a displacement in the opposite direction. The magnitude of the grouting corrective effect is related to design variables, such as grouting volume, grouting distance, grouting section depth, and grouting section length. In the following sections, a theoretical calculation method for evaluating the corrective effect of grouting on the horizontal displacement of an existing tunnel is proposed.
# Fault Detection in Active Magnetic Bearings Using Digital Twin Technology
###### Abstract  
Active magnetic bearings (AMBs) are widely used in different industries to offer non-contact and high-velocity rotational support. The AMB is prone to failures, which may result in system instability and decreased performance. The efficacy and reliability of magnetic bearings can be significantly affected by failures in the sensor and control systems, leading to system imbalance and possible damage. A digital twin is an advanced technology that has been increasingly used in different industrial fields. It allows for the creation and real-time monitoring of virtual replicas of physical systems. This paper proposes a novel method for fault detection of Active Magnetic Bearings (AMBs) using digital twin technology and a neural network. The digital twin model serves as a virtual representation that accurately replicates the actual AMB system's efficiency and features, allowing continuous real-time monitoring and detection of faults. The conventional neural network (CNN) is used as the primary tool for identifying faults in the Active Magnetic Bearing (AMB) within a digital twin model. Experiments proved the effectiveness and robustness of the suggested approach method to fault detection in the AMB.  
digital twin; fault detection; magnetic bearing; neural network; rotary machine +
Footnote †: journal: Applied
## 4 Discussion  
We hypothesized that applying LoRA to ProtBERT would improve the model efficiency at the cost of decreasing the model performance. However, the in silico training results presented in Section 3.1 proved otherwise, as all the performance measures increased as compared with those of normal fine-tuning. We presume that this is because of the difference in the number of trainable parameters and how the LoRA works. Normal fine-tuned ProtBERT has too many parameters to train as compared to that for ProtBERT with LoRA, which has approximately 400 times fewer parameters. With fewer parameters to train, the likelihood of catastrophic forgetting reduces. Additionally, LoRA trains only newly added layers and does not include the original pre-trained ProtBERT weights. Therefore, the pre-trained weights of the ProtBERT model are already capable of extracting useful contextual data.  
Figure 3: Assessing fungal toxicity in human cell lines through in vitro analysis. Effects of selected fungal species on the viability of MRC5 and Hela cells. For all assays, heat-inactivated fungal samples were added to cell lines pre-seeded in 96-well plates after 24 h of incubation. (**A**) The cytotoxicity threshold was determined by conducting with various concentrations of _C. cladosporoides_ for establishing a standard concentration of fungal samples. (**B**) Cell viability rates of 14 fungi are shown as bars and heat maps. Results are represented as cell viability as a percentage of cells incubated without fungi. Experiments were reiterated twice, evaluating each condition in triplicate. Data are shown as the mean \(\pm\) SD. **** \(p<0.0001\), as determined by two-way analysis of variance. CFU; colony forming unit.  
The indoor airborne fungal species predicted by our algorithm to harbor potential toxic and/or virulent proteins includes _Alternaria alternata, Aspergillus niger, Chaetonium globosum, Fusarium equiseti, Fusarium proliferatum, Neurospora tetrasperna, Penicillin brasiliam, Penicillium chrysogenum, Penicillium oxalicum, Phanerochaete sordida, Schizophrenia commune, Trichoderma harzianum_, and they exhibited toxic activity in the two human cell lines used in this study. Known as plant pathogens, they are subjects of ongoing research in various areas, including basic research or biotechnology related to enzymes and genomics. While the fungal spores that they produce, like those of most fungi, can induce allergic reactions in humans, their pathogenic potential in humans remains to be further explored. Our in vitro cellular viability results closely align with in silico predictions, suggesting a potential methodology for evaluating the in vitro cytotoxicity of fungi present in indoor air, combining in silico prediction with experimental assays. Despite limited data on fungal protein sequences for training transformer models, this pilot study successfully developed an in silico prediction module, running in parallel with in vitro cytotoxicity evaluation. These efforts contribute to the advancement of technology development for a swift understanding of unidentified fungi floating indoors, which might pose threats or exacerbate human health.
# Real-Time Farm Surveillance Using IoT and YOLOv8 for Animal Intrusion Detection
## 1 Introduction
### Literature Review
A systematic analysis using Cochrane reporting rules evaluated 588 research publications from 2015 to 2021, identifying major technologies and solutions within smart farming [17]. A full discussion on the usage of wireless sensors and IoT devices in agriculture was also offered, stressing their applications in soil management, crop monitoring, irrigation, and pest identification, along with the role of unmanned aerial vehicles (UAVs) in maximizing agricultural yields [18]. In light of the obstacles faced by traditional agricultural techniques in India, such as antiquated equipment and scarce skilled personnel, the emergence of drone technology provides potential alternatives for precision agriculture [19]. The cited article reviews current developments in drone capabilities for crop monitoring and pesticide application. The notion of smart agriculture, or \"Agriculture 4.0\", is studied in the context of IoT and big data, while it is noted that these developments come with security concerns that require attention. These are crucial, as India ranks as the second-largest producer of food globally.
## 4 Results
### Results of the Proposed Model
#### 4.2.2 The Dispatch Results of the Multiple Resources with CSP and EH  
The multiple generation system is composed of CSP, PV, wind power and traditional thermal units. EH is added to the CSP station, whose capacity is set as 50% of the CSP capacity.  
The power dispatch results among the multiple resources are shown in Figure 5. Figure 5a illustrates the expected power generation of different generators. Figure 5b depicts the dispatch strategy of the TES system. The employment of TES turns CSP into a partially dispatched resource and the dispatchability of CSP is reflected in the following aspects. (1) At noon, when the illumination is rich and the power output of PV station is relatively high, the grid-connected CSP could decrease its output to around 0 (e.g., results from 12:00-16:00). In this time period, TES system is operated in the charging state to store the excess solar energy and EH is employed to absorb the surplus power generation from the gird for later use. (2) Correspondingly, when the power output of renewables is insufficient to meet the late peak period, CSP station could convert to the discharging state to maintain the power supply of CSP without light (e.g., results from 18:00-24:00). CSP could make up for the power vacancy and keep the power balance. (3) The fast ramp of traditional thermal units resulting from the fluctuation of load and renewables could be partially offset by the fast ramping rate of CSP. (e.g., results from 6:00-9:00 and 18:00-20:00).  
Figure 4: Point forecast and scenario-based probabilistic forecast containing: (**a**) wind power forecast; (**b**) PV forecast; (**c**) CSP solar thermal power forecast; (**d**) load demand.  
Figure 5: _Cont._Apart from the dispatchability of the CSP station, the additional functions of TES and EH could be demonstrated in two aspects which are clearly shown in Figure 5b. (1) TES could shift solar energy to periods of peak demand. Through adjusting the state of TES, the TES system could absorb solar energy from \(SF\) in the daytime, instead of generating electricity. The stored energy could be released when needed. The control strategy of the TES system guarantees the SOC of TES at the end of the day which supports the solar energy to be shifted between different days. The EH turns out to be the additional option for the better use of large-capacity of TES. (2) TES provides ancillary services like reserve services to cope with the uncertainty of renewables. From the storage level of TES system, it is recognized that the reserve capacity is not only determined by the reserve requirement but also the charging and discharging limits of TES, the up and down storage limits of TES and the ramping rate of the PB system.
## 2 Results and Discussion
### N\({}_{2}\) Adsorption-Desorption  
The porous structure of TP2 is displayed by N\({}_{2}\) adsorption-desorption measurement as presented. It was found that the TP2 sample in Figure 4 exhibited the type IV nitrogen isotherm according to the International Union of Pure and Applied Chemists (IUPAC) classification; thus, this indicated that the TP2 sample is a mesoporous structure. According to [PERSON] et al. [47], a large surface area with a mesoporous structure is favorable to obtain a high photocatalytic activity, as it promotes adsorption, desorption and diffusion of reactants and products. The BET surface area of TP2 was enhanced to 88.3 m\({}^{2}\cdot\)g\({}^{-1}\) as compared to pristine TiO\({}_{2}\), which was circa 50 m\({}^{2}\cdot\)g\({}^{-1}\), where a 43.4% increment occurred due to the increased porosity and number of pores. By comparing TP0 with TP2, it is clear that the BET surface area for TP2 was increased up to 44% as compared to TP0, which was circa 49.25 m\({}^{2}\cdot\)g\({}^{-1}\). The results of TP2 surface area measurements are consistent with the results of photocatalytic activity where the TP2 sample gave a higher photocatalytic degradation rate of 0.087 min\({}^{-1}\) as compared to pristine TiO\({}_{2}\), which is 0.048 and 0.054 min\({}^{-1}\) for TP0, respectively. A higher BET surface area is vital for the adsorption and desorption of dyes and catalysts, since it encourages higher photocatalytic performance. In addition, PEG had successfully enhanced the possibility of the pollutant being trapped within the pores and showed better catalytic activity by providing additional surface active groups [48].  
Figure 3: Scanning electron micrograph of surface morphologies for the (**a**) TP0 and (**b**) TP2 samples. EHT, extra high tension.
# Performance Evaluation of CF-MMIMO Wireless Systems Using Dynamic Mode Decomposition
### Contributions  
The contributions of this work are the following:  
1. Evaluating the application of DMD for Channel Estimation in CF-MMIMO wireless systems.
2. Analyzing and valuating the behavior of DMD in a system that considers the correlation of the channel in terms of delay and Doppler.
3. Evaluating the enhancement in overall system capacity achieved through DMD-based Channel Estimation.
## 2 Related Work
### Session Key of Diffie-Hellman Method  
Diffie-Hellman method's session key [30; 31] solves the key delivery problem and is a highly stable. Thus, this dynamic encryption key method is frequently used. This method solves the key delivery problem by using discrete logarithm to synchronize the same dynamic encryption key at both ends. The method of Diffie-Hellman is carried out in the manner shown in Figure 2 below. A uses the private key a to generate g\({}^{\text{a}}\) mod P (asymmetric key of a). [PERSON] also uses the private key b to generate g\({}^{\text{b}}\) mod p (asymmetric key of b). A sends g\({}^{\text{a}}\) mod P to B, and [PERSON] sends g\({}^{\text{b}}\) mod p to A. Finally, [PERSON] uses its own private key a to generate g\({}^{\text{ab}}\) mod p, and B uses its own private key to generate g\({}^{\text{ab}}\) mod p. A and B use the newly generated key as the same symmetric key (secret key). The attacker cannot generate the same symmetric key even if the attacker were to obtain the key generation information.  
Diffie-Hellman method uses a same key in a session and generates a new key through safe random number algorithm for the next session. However, Diffie-Hellman method requires at least two steps of synchronous communication in order to generate a new key. Moreover, it uses a discrete logarithm-based asymmetric key method. therefore, the availability decreases due to large computational cost and time consumption. When the encryption key synchronization fails due to packet loss, two or more steps of synchronous communication are required. For generation of a new session key, a safe random number generation algorithm is required to generate a seed value of the key. However, if the random number generation algorithm used is exposed or identified, the encryption key can be predicted.