esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Proposed Model  
In this study, we proposed a BA-ELM model and applied it to estimate battery SOH for the first time. The process of SOH estimation based on the BA-ELM model is shown in Figure 11. The pseudo code of BA-ELM is shown in Table 5. The main steps are as follows:  
S1: Battery charge and discharge test.  
The lithium-ion battery was charged and discharged at room temperature. The specific test process is described in Section 3.1.  
S2: Obtain the battery data and analyze the correlation.  
1. The data of the eight variables in the test were sorted. The sample data were normalized to the same dimension range: 70% of the data were randomly selected for training, and the remaining 30% was tested.
2. Through Pearson and Spearman correlation analysis, \(T_{L}\), \(T_{V}\), \(T_{total\_discharge}\), \(\Delta V\)_charge_, \(\Delta Templ_{discharge}\), and \(\Delta_{Vdischarge}\) were determined as the input variables of the model, and \(C_{aged}\) was determined as the output variable.  
S3: The global exploring ability of the BA was used to optimize the output connection weight, input connection weight, and bias b of the hidden layer of the ELM.  
1. Initialize the parameter of BA.
2. While \(t\) < the max number of iterations; calculate the frequency \(Q_{i}\), location \(S_{i}\), speed \(V_{i}\), and _fitness_ of each bat.
3. If \(rand>r_{i}\). Obtain an optimal solution \(BestS\) and calculate the local solutions around the optimal solution.
4. End if. Produce new solutions by change randomly.
5. If \(rand<A_{i}\) and \(Fitness\) (\(x_{i}\)) < \(Fitness\) (\(x_{*}\)), accept the new solutions and decrease the impulse loudness \(A_{i}\) and increase the impulse emission rate \(r_{i}\).
6. End if. Sort all bats and obtain the optimal solution \(BestS\) in this iteration. Update \(\beta\) and \(Fitness\).
7. End while. Accept the optimal output connection weight \(\beta\).  
S4: Determine the input connection weight \(\omega\).  
S5: Determine the bias \(b\) of the hidden layer. Determine the network structure of BA-ELM.  
S6: The test data set was tested in the BA-ELM to estimate the actual battery capacity.  
In the parameter initialization of BA, \(n\) = 10, \(A_{0}\) = 0.9, \(r_{0}\) = 0.9, \(Q_{\max}\) = 1, \(Q_{\min}\) = 0, \(\alpha\) = 0.9, \(\gamma\) = 0.9, the max number of iterations \(N\) was 5000. The fitness function of the bat is the objective function, expressed by the root MSE (RMSE) of Equation (17), where \(T\) is the output of the training set, \(G\) is the sample size, and \(Y\) can be calculated using Equation (18):  
\[Fitness=\sqrt{\frac{1}{G}\sum_{i=1}^{G}\left(T-Y\right)^{2}} \tag{17}\]  
\[Y=\left[H^{T}\beta\right]^{T} \tag{18}\]Figure 11: Algorithmic process of BA-ELM.
# Biodegradation of Picolinic Acid by _Rhodococcus_ sp. PA18
## 1 Introduction  
Picolinic acid (PA), a pyridine derivative [1], has emerged as an important intermediate from the industrial syntheses of agricultural chemicals, drugs, dyestuffs, dyes, textiles, and mining [1; 2; 3; 4]. It is also a dead-end product of L-tryptophan biosynthesis in living organisms [5; 6; 7]. In many common biological processes, PA is produced from the biodegradation of nitrobenzene, catechol, and anthranilic acid [8; 9]. Owing to its hydrophilic nature (water solubility of 887 g/L at 20 \({}^{\circ}\)C), PA is easily transported to the aquatic environment and to soil [1; 10]. Reports show that the pyridine concentration in wastewaters ranges from 20 to 300 mg/L [4]. Nevertheless, the toxicity or disadvantages of the use of PA have also been found. Owing to their relationship with environmental health, the removal of PA from contaminated ecosystems is considered essential for controlling environmental damage. Both physical and chemical methods are too expensive and ineffective in waste disposal management. Biological methods are efficient and cost effective, and hence, play an important role in the clean-up of toxic and hazardous wastes in the contaminated environment. Studies show that PA can be degraded efficiently by microorganisms, and many PA-utilizing bacterial strains, such as _Achromobacter_ sp. JS18 [11], _Aclaligenes faecalis_ JQ135 [12], _Arthrobacter picolinophilus_ DSM 20665 [13], _Burkholderia_ sp. ZD1 [3], and _Streptomyces_ sp. Z2 [14], have been isolated and characterized.  
An oxidative attack on the \(N\)-heterocyclic aromatic ring of PA is the main step initiating its bacterial degradation [1; 3]. However, all of the above strains are Gram-negative bacteria,and whether Gram-positive bacteria can also degrade PA is not known. In this study, a Gram-positive PA-assimilating strain, _Rhodococcus_ sp. PA18, was isolated, identified, and characterized. The aerobic degradation of PA by strain PA18 and the formation of 6-hydroxypicolinic acid (6 HPA), the initial product formed after the oxidative attack on the \(N\)-heterocyclic aromatic ring, have been discussed.  
To the best of our knowledge, this is the first report on the degradation of PA by a Gram-positive bacterium, _Rhodococcus_ sp. PA18. This may be developed as a potentially low cost and environmentally-friendly approach to restore the environments contaminated by PA.
## 3 The Establishment of the Photovoltaic Array Fault Diagnosis Model
### The Establishment of the Initial LSSVM Multi-Classifiers Model
#### 3.1.2 The Initial LSSVM Multi-Classifiers Model  
The basic idea of the kernel function \(K\left(x_{i},\ x_{j}\right)\) is for mapping the random vectors in \(n\)-dimensional space to the high-dimensional feature space by nonlinear function, which can reduce the dimensionality.  
In practice, there are three most common kernel functions: the polynomial kernel function, the Radial Basis Function (RBF), and the Sigmoid kernel function. [PERSON] et al., testified that the polynomial kernel function is the special form of the RBF kernel function [23]. In [24], the Sigmoid kernel function is similar to the RBF kernel function in some cases, so, in this work, the RBF kernel function is used as the kernel function of the photovoltaic array fault diagnosis model.  
\[K\big{(}x_{i},x_{j}\big{)}=\exp\Bigl{(}-\|x_{i}-x_{j}\|^{2}/\sigma^{2}\Bigr{)} \tag{10}\]  
where \(\sigma\) is the kernel parameter of \(K\left(x_{i},y_{i}\right)\).  
For the sake of processing, the objective function of the LSSVM optimization problem is divided by \(C\), and \(1/C\) is replaced by \(\theta\), which is defined as regularization parameter.  
First, we set the initial values of \(\theta\) and \(\sigma^{2}\) arbitrarily, and establish the initial model \(H_{0}\) to train the training set.  
The SVM and LSSVM were presented for secondary classification initially, but the photovoltaic array fault diagnosis model is a multi-classification problem; thus, the classification algorithm can be used to convert the multi-classifiers into several two-classifiers. The common classification algorithms are \"One vs. One\", \"One vs. Rest\", and \"Divide-and-Conquer approach\"; the algorithm of \"One vs. One\" is better than the \"One vs. Rest\" in diagnosis efficiency, and the algorithm of \"Divide-and-Conquer approach\" can lead to accumulations of errors--that is, if the classification of root is wrong, then the error will accumulate [25], so we use the \"One vs. One\" classification algorithm in the LSSVM multi-classifiers.  
In this paper, the LSSVM multi-classifiers were converted into six two-classifiers by the classification algorithm of \"One vs. One\", which are \"the normal vs. the short-circuits\", \"the normal vs. the open-circuits\", \"the normal vs. the abnormal aging\", \"the short-circuits vs. the open-circuits\", \"the short-circuits vs. the abnormal aging\", and \"the open-circuits vs. the abnormal aging\".
## 2 Efficiency Estimation Methods
### Efficiency Estimation from IM Sound Analysis
The load (% of output power in relation to nominal power) versus efficiency curve should be previously recorded in the embedded system firmware. Therefore, the value of the online efficiency can be obtained by interpolation from the \(\hat{\text{P}}_{\text{out}}\) value. Analysis of the temperature behavior of the induction motor under the efficiency estimation was already carried out by previous works. The major part of the effect of the temperature change in three-phase induction motors is due to the Joule effect in the armature resistance of the motor. According to [PERSON] et al. [27], the stator resistance can vary up to 70% of its nominal value, which would impact in about 4.5% of the final efficiency value for the case of the motor used in the experiments calculated using Equations (1)-(3). The proposed method does not perform temperature compensation since it would increase the invasiveness level and the loss of the portability of the technique, as there is a need to install at least one remote sensor.
# Evaluation of Rural Visual Landscape Quality Based on Multi-Source Affective Computing
## 2 Materials and Methods
### Experimental Elements
#### 2.2.3 Experimental Equipment and Questionnaire  
The experimental setup comprised an Eyeso Glasses head-mounted eye tracker (Braincraft, Beijing, China) and a Waveguard(tm) 8-lead electrode cap (ANT Neuro, Berlin, Germany). The eye movement sampling rate was 380 fps, and the EEG was recorded via a 500 Hz micro-EEG amplifier. Supporting equipment included a 24-inch display (resolution 1920 \(\times\) 1080), a laptop (running Windows 10), a dongle, and an adapter for convenient mobile portability. The laptop also served as the medium for displaying the landscape images (as shown in Figure 4). The experimental questionnaires were categorized into two sections: (1) The Beauty Assessment Scale and (2) The SAM Scale. (1) The Beauty Assessment Scale is a subjective tool for quantifying individuals' visual experiences of landscapes. It integrates the SBE (Scenic Beauty Estimation) scale with the SD (Semantic Differential) method to create a streamlined evaluation framework, enabling participants to intuitively rate landscape aesthetics--higher scores correspond to greater perceived beauty. The SBE method quantifies the landscape's aesthetic appeal based on evaluators' personal standards [49], while the SD method employs a verbal scale to conduct psychological measurements of individuals' intuitive responses [51], providing quantitative data for landscape assessment. Based on the works of [PERSON] et al. [59] and [PERSON] et al. [60], and considering the specific visual characteristics of rural landscapes in Dalian, eight semantic variables were chosen as evaluation criteria: naturalness (N), diversity (D), harmony (H), singularity (S), orderliness (O), vividness (V), culture (C), and agreeableness (A). (2) The SAM Scale (as shown in Figure 5) [61] is a versatile tool designed to track emotional responses to stimuli across various settings, enabling quick assessment of emotional reactions. During the experiment, participants' emotional states were recorded through the SAM questionnaire, capturing both emotional valence (positive or negative) and arousal (high or low). Both questionnaires were rated using a 5-point Likert scale, with scores ranging from \(-2\) (lowest) to \(+2\) (highest) [62]. By synthesizing subjective impressions with objective measurements, the evaluation of landscape aesthetics can more fully elucidate visual allure and underpin a rigorous scientific basis for the sustainable design of rural landscapes.  
Figure 4: Laboratory equipment drawing. It includes devices such as eye movements, EEG, signal amplifiers, display screens, and laptopps.
# Wireless Caching: Making Radio Access Networks More than Bit-Pipelines
## 3 Request Time Prediction: Beyond Content Popularity  
Request time prediction is potentially highly beneficial in proactive caching. Unfortunately, conventional popularity based models, either static or time-varying, are content-specific. They mainly focus on the content popularity distribution among users. For instance, the number of future requests for Facebook video clips can be forecast based on real data. More recently, the prediction accuracy keeps increasing owing to the advances of machine learning and time series analysis. Cross-domain factors such as social connections, recommendation systems, and user activities are also considered in cutting-edge research. Content-specific prediction enables a BS to forecast the distribution of the number of requests arriving in a certain period, based on which the edge caching and multicast pushing can be optimized.  
Due to preference skewness and asynchronous requests, content-centric prediction is insufficient for caching at user devices. In particular, content-specific popularity models are incapable of coping with request time prediction. By contrast, a beyond-bit-pipeline RAN not only predicts what will be asked for, but also foresees when it will be requested to unlock the full potential of caching. To this end, time-varying popularity prediction and user-specific prediction play a key role in capturing dynamic popularity and asynchronous requests. We shall discuss how to foresee a user's request time for a certain content item in this section.
## 3 Materials and Methods
As it concerns the mold used for the active ultrasonic measurements, it consists of a circular opening on the backside for the ideal fit of the longitudinal piezoelectric contact transducer. To avoid the leakage at the opening, a thin plastic film was placed around the hole. The transducer's peak frequency was at 0.20 MHz while its distance from the thin glass was 50 mm. To prevent the influence of the glass to harmonic oscillations amplitudes, a hole was opened, and a 1 mm-thick special glass was placed on the back side of the mold. In addition, the mold was positioned on a vice and was clamped using a torque wrench for achieving perfect contact between the transducer and the fresh cement mixture. For generating nonlinear ultrasonic oscillations an absolute linear pulser, RPR-4000-VHP High Power Pulser Receiver system by Ritec was used. This way, it was ensured that the received nonlinear signals were due to the material's nonlinear behavior and not due to the measuring device itself. In addition, it was placed a thermocouple for monitoring the maturity process. The maturity method is a reliable technique determining the real-time strength values during the hardening of concrete, based on monitoring the concrete temperature over time. It is according to the ASTM C1074 standard and represents the progression of concrete hardening. The ConReg-700 device by Germann Instruments was used for recording maturity values in this study. For controlling the outside temperature, the whole experimental setup was placed into an environmental chamber (VC3 4018 by [PERSON]). At the beginning of the experiment, the wave propagation parameters such as frequency, high-power voltage and tone burst signal cycles were determined using the LABVIEW software, which communicated with the RITEC instrument with a custom-built routine. Data were collected via PXIe-1071 chassis by National Instruments, consisting of a PXIe-8360 card (PXI Remote Control Module) which was connected with a central computer, while a PXI-8430/2 card (PXI Serial Interface Module) was connected with the Ritec-4000. For the data and waveforms recording, an 8-channel 12-Bit 60 MS/s PXI-5105 (PXI Oscilloscope) was used. Figure 1 depicts the experimental setup. ConReg-700 was controlled via the central computer. \begin{table}
\begin{tabular}{c c} \hline
**Property** & **Value** \\ \hline Length & 5–15 μm \\ \hline Main range of diameter & 20–40 nm \\ \hline Purity & \(\geq\)97\% \\ \hline Ash content & \(\leq\)0.2 wt.\% \\ \hline Special surface area & 40–300 m\({}^{3}\)/g \\ \hline Amorphous carbon content & \(\leq\)3\% \\ \hline \end{tabular}
\end{table} 
Table 1: Properties of multiwall carbon nanotubes.
## 4 Results and Discussions
### Preliminary Tests of Chlorophyll a Measurements  
With the propose of evaluation, validation and output of the electronic features and measuring principles presented in this paper, a simple and portable device was fabricated, using excitation at 450 nm and detection at 680 nm. The fabricated device has only one excitation wavelength (450 nm) and one fluorescence wavelength (680 nm). The analysis was performed in a 1 cm\({}^{3}\) volume. This device was used to measure chlorophyll a content of cells.
## 3 Results and Analysis
### Calibration and Validation of Sweetness Estimation Models
#### 3.4.1. Sweetness Estimation Based on Hyperspectral Features from SPA  
In general, the accuracy of models based on SNV and MSC methods surpassed that of the model employing the SG method(Tables 4 and 5; Figure 5). During the T1 period, after spectral preprocessing, the R\({}^{2}\) increased from 0.20 to 0.52, and the RMSE decreased from 1.55 to 1.09. In the T2 period, the R\({}^{2}\) increased from 0.17 to 0.33, and the RMSE decreased from 1.57 to 1.40. During the T3 period, the R\({}^{2}\) increased from 0.2  
From a modeling perspective, the LSSVM method outperformed the others in the whole harvest period (T4). The accuracy of each estimation model followed the order of LSSVM, BP, MLR, and PLS. The optimal estimation model was given by the LSSVM using MSC processing in combination with features extracted by the SPA algorithm, with the R\({}^{2}\) of the validation dataset being 0.79 and RMSE being 1.32. The scatter distribution between measured and predicted sweetness values was close to the 1:1 line (Figure 6).  
For the other harvest stages, both the PLS and MLR models had higher accuracy than the LSSVM and BP models. The accuracy of the models in the T3 period was higher than that in the T1 period, and the T2 period was the lowest. For the T3 period, the best model was MSC-SPA-MLR, with an R\({}^{2}\) of 0.59 and RMSE of 1.04 respectively. For the T1 period, the R\({}^{2}\) was concentrated in the range of 0.20\(\sim\)0.52, and the RMSE was in the range of 1.09\(\sim\)1.55. The best model was MSC-SPA-MLR with an R\({}^{2}\) of 0.52. The models in the T2 period were all less accurate, with R\({}^{2}\) between 0.17 and 0.33 and RMSE between 1.40 and 1.64.
## 3 Results
### User Satisfaction Results  
After development was completed, a prototype was delivered for a hands-on trial in a controlled environment by 14 project managers and bridge inspectors from the Thailand Department of Highways. After the trial, satisfaction surveys and in-depth interviews were conducted to elicit and measure feedback. Four domains were assessed: functionality, user interface design, user experience, and overall performance. User satisfaction in each domain was scored from 0 (unsatisfactory) to 5, indicating highly satisfied.  
The average satisfaction result of each domain was 4.00, 3.714, 4.00, and 4.142, respectively, and the total score was 4.024 (80.48%). (Table 3) This means that the implemented features in this application that are required for behavioral intention to use, were built and tested by another group of users who were highly satisfied. However, the lowest score of user interface design (Table 4) can be improved in the future. According to the concept of stage-gate process, the improvement will be performed after collecting and analyzing more user validation feedback.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Score (Max. 5.00)** & **Percentage** \\ \hline Functionality & 4.00 & 80\% \\ User interface design & 3.714 & 74.28\% \\ User Experience & 4.00 & 80\% \\ Overall Performance & 4.142 & 82.84\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: User satisfaction for bridge inspection mobile application with 14 trial users.  
Figure 9: System overview of the application.
## 2 Wireless Corrosion Monitoring Sensor and Network
### Wireless Corrosion-Monitoring Network  
For the corrosion-monitoring sensor more platform, another challenge is how to collect the corrosion data from all the motes with as little human intervention as possible. Using the TinyOS operating system for MicaZ and the Xserve communication framework, we propose a many-to-one routing protocol, to transport data from all sensor motes to a central mote (also called sink). The sink, storing all the corrosion data, can display it visually, and domain scientists can then well understand and predict the progress of the corrosion process. In the proposed routing algorithm, the sensor motes are organized into a multi-hop network which can work in the following two ways: (1) the sink broadcasts a query message, and the motes receiving this message send data back to the sink, and (2) all the motes report their data to the sink regularly, e.g., once per day. In our experiments, we use the latter one. To guarantee the reliability of the data transmission, two schemes are adopted in our work. First, as shown in Figure 3a, we employ the Acknowledgement (ACK) scheme of the IEEE 802.15.4 protocol [29] to improve the link quality with multiple transmissions. Second, a mote always chooses the mote with maximal link quality as its forwarder (the link quality is generally measured by the reception ratio of packets over the link). For example, the mote \(B\) in Figure 3b has two possible forwarders, \(D\) and \(E\), but it will select \(D\) as the next hop if the link to \(D\) has higher quality. Based on the CC2420 radio chip used by MicaZ, the link quality can be calculated at the hardware level as the bit error rate (BER) value and consequently, needs no extra codes. Even though the link quality is obtained at the receiver, it can be piggy-backed by the ACK messages from the receiver to the transmitter. By doing so, the transmitter has information about the qualities of links to all the potential forwarders. Figure 3c presents the detailed control logics of our routing algorithm. Note that these control programs can be updated over the air, even if they have been deployed in motes. Such a reprogramming method without recycling motes facilitates the deployment of the new monitoring tasks. The experiments results show that only two packets are lost due to link failure within two hours in our routing scheme.
###### Abstract
focused on the energy constraints on satellite data transmission, and modeled the satellite network topology as a transmission graph that varies according to the power level of ISLs. An efficient data offloading algorithm was proposed based on the transmission graph in their research. Nevertheless, the majority of these studies are principally concerned with offloading data for remote sensing observations and cannot be adapted to LEO satellite network broadband communication due to a variety of constraints, including latency, priority, bandwidth, etc. In this paper, we present the Cooperative Data Downloading (CDD) routing scheme to overcome the problem that a high quantity of user data must be downloaded and feeder link resources are not balanced in a large-scale broadband LEO satellite. Following is the primary creative contribution of this paper:  
1. As far as we know, we are the first to use ISLs to facilitate data download in the large-scale broadband LEO satellite network; In order to increase system throughput, traffic is sent through ISLs to satellites with substantial feeder link resources in our scheme;
2. Secondly, we model the topology of LEO satellites network and analyze the data transmission requirements between visible satellites;
3. Finally, we present a maximum-flow minimum-cost routing algorithm to calculate routes for traffic demands, and implemented our algorithm on satellite router. The remainder of the paper is structured as follows: In Section 2, the system model is presented along with the optimization target of maximizing feeder link bandwidth usage. In Section 3, we describe the ISLs-based collaborative data download scheme. In Section 4, we conduct a simulation experiment and analyze the numerical results. Finally, the paper is summarized in Section 5.