esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Lithium-Ion Battery Fault Diagnostic Algorithms: Current Progress and Future Challenges
## 2 Types of Fault in the Li-Ion Battery System
### External Battery Faults
#### 2.2.1 Sensor Fault  
It is crucial to have a reliable sensor fault diagnostic scheme to ensure battery safety and performance. It also helps to prevent internal faults, such as overcharge, overdischarge, overheat, external and internal short circuit, and, most importantly, thermal runaway. Sensor faults include failure of temperature, voltage, and current sensors. Sensor faults are caused by vibration, collision electrolyte leakage, and other physical factors [35]. They can also be attributed to the loose battery terminals or corrosion around the battery sensor. A sensor fault can accelerate the degradation process of a battery, hinder the BMS functions due to incorrect state estimation, and cause other internal battery faults [10].  
The temperature sensor is a key component in the Li-ion battery system, as it helps provide critical temperature data for the BMS to manage the battery operation effectively. A temperature sensor fault can cause it to send incorrect measurements to the BMS, which can cause further problems due to ineffective thermal management [36]. Inaccuracy in the BMS thermal management function can result in a significant decrease in battery life. Temperature sensor fault can also lead to short-circuiting, overheating, aging under high temperatures, capacity fade due to high temperature, and ultimately thermal runaway [33].  
The voltage sensor is used to monitor the voltage of cells in the battery pack. A voltage sensor fault can cause the cell or the entire pack to exceed the upper and lower voltage limits that are specified by the manufacturers, which can result in overcharge and overdischarge [13]. A fault in the voltage sensor also leads to inaccurate SOC and SOH estimation, which can result in an internal fault as the battery suffers from overcharge and overdischarge [37].  
The current sensor monitors the current that enters and exits the battery and sends the data to the BMS. It is important to detect a faulty current sensor as it can lead to further problems. The current can bypass the sensor, and the readings will be inaccurate [37]. A current sensor fault also leads to inaccurate estimation of SOC and other parameters, which impacts the control actions in the BMS, and causes the cell to overcharge, overdischarge, or overheat.
## 3 Results and Discussion  
### Effects of STL Additions on the Sensory Qualities of BT  
Aroma and taste have an important impact on BT quality and its market value as they are the key attributes in tea sensory evaluation [22; 23]. The different methods of STL addition and the different STL concentrations tested on the tea leaves were found to have a substantial impact on the taste and aroma of BT but little impact on the appearance, liquor color, and infused leaves (Table 2). STL leaves were found to have a strong glutinous flavor and a sweet and thick taste, which is in accordance with previous studies [11; 12], and the addition of STL significantly influenced BT flavor. The comparison among the different methods of STL addition during tea leaf processing showed that the sensory quality of the BoS samples obtained using treatment #2 (i.e., adding STL to rolled tea leaves) was the best, resulting in a glutinous sweet aroma and a sweet mellow taste. In contrast, adding STL to whitened tea leaves resulted in an excessively heavy glutinous flavor that concealed the characteristic aroma of the BT. Adding STL to fermented tea leaves rendered the BT aroma a little grassy, and although the taste was sweet and mellow, STL addition did not improve the sensory qualities of the BT.  
The comparisons among the different concentrations of STL tested revealed that the sensory quality of BoS samples prepared using treatment #2 (i.e., 20:1500 \(w/w\)) was the highest, with a strong and lasting glutinous sweet aroma and a sweet, mellow, and thick taste. In contrast, STL concentrations exceeding 20:1500 (\(w/w\)) concealed the characteristic flavor of BT, which did not benefit BT quality. When the concentration was too low, the aroma and taste of the STL had no positive effect on the BT flavor. To clarify the influence of STL on the properties of BT aroma, BT, BoS (as per treatment #2), and STL samples were selected for further study to detect and analyze their VMs.
## 4 Proposed HE-DEPSO Algorithm
### Adaptive Hybrid Mutation Operator  
In order to achieve a better balance between exploration and exploitation, the mutation operator of the HE-DEPSO algorithm adopts a dual mechanism in which it adaptively selects between two mutation strategies. First, a new mutation strategy called \"DE/current-to-eHE/1\" is presented, which is oriented to improve the balance between exploration and exploitation abilities within the early stages of the evolutionary process. On the other hand, to improve the exploitation capacity within the more advanced stages of the evolutionary process, the mutation scheme of the PSO algorithm (Equation (32)) is incorporated in a similar way to the self-adaptive mutation strategy of the DEPSO algorithm (Equation (18)).  
Unlike the \"DE/e-rand/1\" scheme used within the self-adaptive mutation strategy of the DEPSO algorithm, in which only the information of the individuals of the current iteration is used, the \"DE/current-to-eHE/1\" strategy uses the historical information of the elite and obsolete individuals of the evolutionary process to improve the exploration capability of the algorithm. This strategy is described below.
# Polar Code Parameter Recognition Algorithm Based on Dual Space
###### Abstract
[16] introduced the likelihood difference to deduce the standard polar code parameter recognition and obtained improved performance, but the algorithm failed in terms of unstructured polar code recognition. To solve the problems encountered in the practical process of polar code parameter recognition and further improve the performance of the algorithm, in this paper we study a polar code parameter recognition algorithm based on dual space. Since the additive white gaussian noise (AWGN) channel model is the first and most important stage of 5G channel coding simulation, we study the problem of blind recognition of polar code parameters under the AWGN channel. The rest of this paper is organized as follows. The basic knowledge of polar codes is reviewed in Section 2. In Section 3, we describe the principle of polar code parameter recognition, the model system, and the calculation details of the decision threshold, and propose the parameter recognition algorithm. Section 4 provides the simulation results of algorithm effectiveness and influence of different parameters, followed by conclusions in Section 5. The main contributions of this paper are the following. 1. The two basic principles of polar code parameter recognition are given and proven. 2. The polar code parameter recognition problem is transformed into a hypothesis testing problem, and a complete hypothesis-testing process is provided. The average log-likelihood ratio (LLR) is introduced as a statistic, and the corresponding threshold is derived in detail. 3. An index of sub-channels transmitting information bits (also called information-bit position) is directly constructed using a Gaussian approximation (GA) method, which helps avoid errors involving correct information bit numbers but incorrect information bit positions.
# Capacity Analysis of Incentive Schemes in Opportunistic Networks
## 2 Mathematical Model for Incentive Schemes
### Utility Formula  
The utility formula describes the incentive degree of a node with entanglement \(\lambda_{m}\), reward function \(r(q_{m})\), cost function \(c(q_{m})\), and constant cost \(c^{\prime}\), as follows:  
\[\hat{\mathbf{5}}_{m}=\lambda_{m}q_{m}-r(q_{m})-c(q_{m})-c^{\prime}\,, \tag{3}\]  
where \(r(q_{m})\) is represented as the incentive scheme. According to the introduction of incentive schemes in Section 2.1, an incentive scheme consists of a reward function \(R(q_{m})\) and a price function \(P(q_{m})\). The incentive degree is more dependent on their relation than each individual calculation, and the calculation makes the incentive process more rational and balanced. Therefore, in the utility formula, only the relationship between \(R(q_{m})\) and \(P(q_{m})\) is considered, as follows:  
\[r(q_{m})=q_{m}\frac{R(q_{m})}{P(q_{m})}\,. \tag{4}\]  
We show the correction with simulations in Section 3. Entanglement \(\lambda_{m}\) means the cooperation degree determined by the scenario, such as selfish degrees, social groups, movement paths, etc., which can be avoided by fixing the entanglement in the model during analyses.  
The cost constant \(c^{\prime}\) is for each transmission, such as the fixed energy consumption [16]. The cost function \(c(q_{m})\) describes what relay nodes have to pay for when transmissionssucceed, which is made up of an energy cost \(E(q_{m})\), buffer cost \(B(q_{m})\), and security cost \(S(q_{m})\), as follows:  
\[c(q_{m})=E(q_{m})+B(q_{m})+S(q_{m})\,. \tag{5}\]  
The security cost is simple:  
\[S(q_{m})=sq_{m}^{2}\,, \tag{6}\]  
where \(s\) is the willingness to forward messages, determined by reasons such as security. Unlike entanglement \(\lambda\), \(s\) is a node's own attribute and is not affected by other nodes.  
The incentive degree does not continue to increase with larger buffer size and energy capacity, as it is limited by the cooperation between nodes. For instance, a node can only forward messages to parts of others, even if it has infinite buffer and energy. Therefore, they should have asymptotic values. Furthermore, the slope change for the buffer is slower than that for energy, as the energy capacity grants a node the basic ability to participate in forwarding, instead of enough resources, such as buffer and the cooperation degree. To sum up, they are defined as follows:  
\[E(q_{m})=\left(\frac{E_{c}}{E_{s}}\right)^{5}r(q_{m})\text{ and }B(q_{m})= \left(\frac{B_{c}}{B_{s}}\right)^{3}r(q_{m})\,, \tag{7}\]  
where \(E_{c}\) is the energy cost per message, \(E_{s}\) is the energy capacity, \(B_{c}\) is the buffer cost per message, and \(B_{s}\) is the buffer size.
# DNCL: Hybrid DOA Estimation and NMDS Cooperative Multi-Target Localization for RFID
## 5 Experiment and Result Analysis  
### Experimental Setup  
#### 5.1.1 Implementation  
In order to validate our proposed cooperative localization approach, a DNCL localization system was implemented. Its hardware system consists mainly of commercial RFID components, among which there is a COTS RFID reader Impinj Speedway R420. The reader hardware has not been changed, follows EPCglobal Gen2 protocol standard, the transmission power is 32.5 dBm, and the operating frequency is set to 924.375 MHz. It is connected through an Ethernet cable to a laptop with an AMD Ryzen 5 4600H processor, 3.0 GHz, and 16G RAM. A Laird S9028 PCL UHF-RFID antenna is also used, which is a circularly oriented antenna with a 9 dBic gain and a 70\({}^{\circ}\) azimuth beamwidth, and a group of UHF Passive RFID tags. At the same time, the AMC4030 localization system was used as a platform for moving the antenna with millimeter accuracy. The AMC4030 controller is connected to the PC controller via a serial port.
## 3 4-by-4 DLTSA Array Design  
We designed a 4-by-4 DLTSA array to further analyze the RCS characteristics of the proposed antenna. The 1-by-4 power divider is designed for a single feeding network as shown in Figure 6a [20,21]. The primary objective of the array design in this work is to analyze the RCS characteristics according to the presence of the absorbers, rather than to achieve beam steering capabilities. Therefore, a simple structure with single array feeding is adopted. The power divider is designed with a line width of 0.65 mm to maintain 50 \(\Omega\) impedance at the operating frequency, and the same TLY-5 substrate of the DLTSA is used. The ports spacing between the divider is 7.9 mm, ensuring that it remains below 1\(\lambda\) at the operating frequency. The performance of the power divider is presented in Figure 6b, demonstrating a power division of about 7.5 dB at each port. A discrepancy compared to the identical 6 dB power division is due to the lossy characteristics of the substrate and divider lines. Nevertheless, as observed from the current distribution in Figure 6c, the power divider exhibits a uniform and acceptable power dividing performance at 35 GHz.  
Finally, the geometry of the 4-by-4 proposed array antenna prototype with two 1-by-4 power dividers is designed as shown in Figure 7a. The identical two power dividers are designed on the same TLY-5 substrates to demonstrate uniform power division at 35 GHz. The spacing between the antennas where the EM absorbers are placed is set to 7.9 mm, equal to \(W\) of the proposed antenna. The modeling of a 4-by-4 array connected to the  
Figure 6: Simulated results of the 1-by-4 power divider. (**a**) Power divider design. (**b**) Simulated S-parameter results. (**c**) Surface current distribution at 35 GHz.  
designed power divider is shown in Figure 7a,b, with and without absorbers. The reflection coefficient simulation in Figure 7c shows that the presence of the absorber does not degrade the matching characteristics in the 4-by-4 array, and the array modeling with the absorber is matched at 35 GHz with \(S_{11}<-10\) dB 32.86-39.2 GHz. There is a slight decrease in bandwidth in the absence of the absorber, which is confirmed to be due to the reduced loss due to the absence of the absorber. As shown in the radiation pattern in Figure 7d, the difference is insignificant at 15 dBi and 14.7 dBi with and without absorber, respectively. While the 4-by-4 array is not the actual number of arrays in the missile seeker, it can be built in the lab, and its size is large enough to prove the proposed design concept by measuring the RCS.
## 2 Materials and Methodology
### Process for the Determination of Micro- and Macroplastics in Water  
After passing the material through a set of sieves [18; 26; 87; 88; 89; 90; 91]. The solids were transferred to a pre-weighed clean beaker and dried at 90 \({}^{\circ}\)C for 24 h in a desiccator [92; 93; 94; 22]. After a lapse of time, the mass of the laboratory beaker with dried solids was weighed to obtain the total mass of MPs and natural materials, and 20 mL of iron (II) sulfate catalyst and 20 mL of 30% hydrogen peroxide [95; 96] were added to the chemical beaker with the solids. During the removal of organic matter in the prepared solution of iron (II) sulphate, 3 mL of concentrated H\({}_{2}\)SO\({}_{4}\) (pH 4) was added. The mixture was left at room temperature for 5 min and then heated to 75 \({}^{\circ}\)C while being stirred by a magnetic  
Figure 2: Sampling locations in lake zones of Lake Markakol and inflowing rivers.  
stirrer [89; 97]. After oxidation, 6 g of sodium chloride [30; 79; 98] was added for every 20 mL of sample to increase the density of the solution and remove inorganic matter through foam separation, undergoing density separation through flotation using a separator. The solution was transferred to a density separator and left overnight. Then, the precipitate was visually inspected for MP particles, and the residue was passed through a 0.315 mm sieve. The microplastics collected on the sieve were dried and weighed to determine their concentration. The isolated MP particles were visually inspected using a microscope with a 40\(\times\) magnification [76; 84], which allowed the visual identification of plastic particles. The main criteria were luster, brightness and unusual color, which could indicate the presence of plastic, especially if the particles exhibited typical plastic material properties [26; 79], as well as elasticity and hardness, which could be determined using tweezers [99]. MP particles larger than 1.0 mm could be visually separated using the following rules [74; 79; 100; 101; 102]: (1) absence of a cellular structure and other organic forms, which helps exclude organic materials, (2) uniform coloration and fiber thickness and (3) a clean and uniform color (i.e., uniform color without inclusions or stains). Color is an important parameter in the visual identification of plastic particles in both marine and freshwater environments because it can provide information about the type and condition of the plastic, as well as its history and environmental impacts [26; 103; 102]. The visual identification of colors was carried out both by eye and by examination under a microscope.  
The described steps provide a systematic and comprehensive analysis of samples for the presence of MPs, from the extraction of particulate matter from a sample to its visual and quantitative analysis [84].
## 3 The Proposed Tracker
### Structural Correlation Filter  
In visual tracking tasks, partial occlusion is one of the major challenges limiting performance of tracker. The correlation filter based on a single holistic appearance model is sensitive to partial occlusion, which may reduce the overall performance and even lead to failure in the tracking challenge. To address this defect, we combine the correlation filters of holistic target and local parts to preserve the inner spatial structure of the target. We employ effective spatial distributions to divide the target into two local parts, one for the horizontally and one for the vertically aligned object based on the ratio of the height and width of the target. The scale and positions of the local parts are strictly confined. As illustrated in Figure 4.  
In most situations, the relative movement between the holistic target and local parts is limited, so we can combine all confidence maps to obtain a robust correlation filter. It is critical to note how to combine confidence maps of each part. If we simply sum confidence maps with the same weight, the false tracked parts maybe unexpectedly highlighted. Intuitively speaking, the response of reliable parts should be given larger weight, while the failure detection part will have less weight. Fortunately, the Peak to Sidelobe Ratio (PSR) is a suitable indicator to quantify the reliability of filter. PSR is used to measure the sharpness of the correlation peak, the higher PSR score means more confident detection. Thence, we adopt PSR to define the weight of every part.  
The PSR is defined as:  
\[\rho_{i}=\frac{max(f_{i}(\boldsymbol{z}))-\mu_{i}}{\sigma_{i}} \tag{10}\]  
where \(\mu_{i}\) and \(\sigma_{i}\) are the mean and the standard deviation of \(i\)-_th_ confidence map, respectively.  
The final confidence map can be defined as:  
\[f(\boldsymbol{z})=\sum_{i=1}^{3}c_{i}\cdot\rho_{i}\cdot f_{i}(\boldsymbol{z}) \tag{11}\]  
where \(c_{1}=1\) denotes the holistic part, \(c_{2,3}=0.5\) denote local parts.  
Figure 4: Two spatial distributions based on the ratio of the height and width of the target. The red rectangle represents holistic part, the green and blue rectangles represent two local parts. (**a**) horizontally aligned object from car4; (**b**) vertically aligned object from single1.
# Isoprene Emissions, Oxidation Chemistry and Environmental Impacts
## 1 Introduction  
Terrestrial vegetation accounts for around 90% of the total atmospheric non-methane volatile organic compounds (VOCs) emission. Hence, this source dominates biogenic VOCs (BVOCs) emissions into the atmosphere [1]. Among BVOCs, isoprene (C\({}_{5}\)H\({}_{8}\)) has the largest emission flux, contributing approximately half of the total BVOCs (about 1000 TgC yr\({}^{-1}\)) [2], and has been the focus of a significant amount of research in terms of the controlling factors of its emission, atmospheric oxidation chemistry, and subsequent environmental impacts.  
A review of literature concerning isoprene emissions and its measurements, recent updates to isoprene oxidation chemistry and its environmental impacts, the impact of deforestation and reforestation on isoprene emissions, and its oxidation products are presented.  
As isoprene is emitted mainly from terrestrial vegetation, its emissions are directly dependent on the land cover (e.g., tree, shrub, grass, crop) and on the plant species within those land covers [2,3]. Isoprene emissions from plants is directly linked to photosynthesis, as the formation of the C\({}_{5}\) compound is non-trivial, in fact requiring a significant amount of energy [4,5]. The production of isoprene occurs within plants during photosynthetic metabolism through the precursors dimethylallyl pyrophosphate (DMAPP) and isopentyl pyrophosphate (IPP), with the former being the substrate for the isoprene synthase [6,7]. The isoprene synthase converts DMAPP to isoprene through cleavage of pyrophosphate, and emission of isoprene can subsequently occur, although it does so at significant cost to the plant in terms of carbon and adenosine triphosphate (ATP). Plants can release isoprene through other metabolic pathways when drought inhibits photosynthesis [8]. Algae, which are photosynthetic organisms, can produce isoprene in darkness [9, 10, 11]. Various bacteria can emit isoprene through enzymatic mechanisms [12, 13, 14]. Different root-associated fungi can emit isoprene, but its production mechanism is still undetermined [15]. The ocean is a source of isoprene to the atmosphere, but global isoprene emissions from the ocean are small (0.32-11.6 TgC yr\({}^{-1}\)) [16, 17, 18, 19, 20] compared with the terrestrial sources. Isoprene is also emitted from vehicular exhaust [21, 22, 23] and human exhalation [24, 25]. In addition to biogenic emissions, other emissions are found to contribute only approximately 15% (\(\sim\)100 TgC yr\({}^{-1}\)) [26] of the total global isoprene emissions. In this review we will focus mainly on the biogenic emissions of isoprene and therefore discuss the biological controlling factors of its emission.