esa-sceva/satcom-chunk-collection huggingface dataset:
# All About Audio Equalization: Solutions and Frontiers
## 4 Graphic Equalizers
### FIR Graphic Equalizers
#### 4.4.1 Graphic Equalization Using Fast Convolution  
The _Fast Fourier Transform_ (FFT) algorithm provides a way to reduce the computational cost of a high order FIR filter in computers using sequential processing. This is called _fast convolution_ and was first introduced by [PERSON] [82]. In fast convolution, FIR filtering is implemented as a complex multiplication of the discrete Fourier transforms of the signal and the filter, and then using the inverse FFT algorithm to obtain the output signal in the time domain. The FFT of the FIR filter can be computed in advance and stored, but the input signal must be transformed in frames (buffers) using the FFT. For long filters, the complex multiplication of spectra and the inverse FFT require much less operations than a direct convolution. [PERSON] and [PERSON] have proposed to create a graphic equalizer using this approach, allowing the user to control the gain at 512 linearly spaced frequency points when the FFT-length is 1024 [83]. [PERSON] _et al._ implemented a 20-band equalizer [84], and [PERSON] and [PERSON] implemented a 31-band equalizer [85] using the fast convolution method, using 512-point and 32768-point FFTs, respectively. A disadvantage of this method is the processing latency, which is generally twice the impulse response length.  
[PERSON] [86] proposed to divide the impulse response of the equalizer into several segments, which are processed using the FFT technique. This reduces the latency considerably, but can still be quite efficient, as only one inverse FFT is needed, when all segments have the same length. It is also possible to process the first segment of the impulse response using the direct convolution to avoid the delay completely [87]. This approach has become popular in artificial reverberation, where very long FIR filters are used [88; 89; 90; 91].
## 6 Conclusions  
Under the effect of the LVRT and virtual impedance control of a grid-forming PV inverter, the fault characteristics of both terminals of the transmission line exhibit significant differences, which deteriorates the performance of the traditional protections. To this regard, a pilot protection scheme for transmission lines of grid-forming PV systems based on the Jensen-Shannon distance is proposed in this paper. Qualitative analysis, quantitative analysis, and extensive simulations demonstrate that the proposed protection efficiently adapts to various conditions: different fault types, different fault locations, different fault resistances, different PV outputs, and different noise intensities.  
Theoretical analysis and simulation verification confirm that the proposed protection operates correctly under fault conditions where traditional current differential protection experiences reduced sensitivity or even fails to operate, exhibiting high reliability and sensitivity. Compared to other existing solutions, the proposed protection demonstrates stronger synchronization error immunity and improved reliability. In the future, a prototype relay based on the proposed protection will be developed, and a closed-loop test platform utilizing real time digital simulator will be established to evaluate the protection performance.  
However, some limitations of the proposed protection may exist and are required to be further investigated. In the context of extremely long transmission lines, the non-negligible distributed capacitance introduces significant high-frequency components into fault currents during the initial stage of faults. These components adversely impact waveform matching results. Consequently, the proposed protection must incorporate filtering or frequency-band selection techniques to address the requirements of extremely long transmission line applications. Furthermore, the proposed protection demands stringent real-time communication and precise synchronization. Given that communication equipment in extremely long transmission lines may be susceptible to environmental or human-induced interference, the reliability and accuracy of protection devices could be compromised, and the overall protection performance may be degraded.  
Conceptualization, K.L. and R.F.; methodology, K.L.; software, Q.H.; validation, R.F., K.L. and R.F.; formal analysis, R.F.; investigation, K.L.; writing--original draft preparation, R.F.; writing--review and editing, K.L.; visualization, Q.H.; supervision, K.L.; project administration, K.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Independent Research and Development Project of State Grid Shandong Electric Power Research Institute, grant number 2024A-004.  
Not applicable.  
Data Availability Statement: Data will be made available on request.  
Conflicts of Interest: [PERSON] was employed by the State Grid Shandong Electric Power Company. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# A Blockchain-Based Cooperative Authentication Mechanism for Smart Grid
## 3 Preliminaries
### System Model  
This section details the overall architecture of our proposed blockchain-based collaborative authentication mechanism between multi-domain smart grid devices, as shown in Figure 1.  
The architecture consists of multiple smart point grid domains, each with roughly the same structure, including smart meters, a domain manager, and blockchain agent nodes. At the top of each domain is a blockchain. The type of blockchain we have chosen in this paper is a federated chain. Each domain sends a blockchain agent node to jointly maintain the federated chain. The notation is described in Table 1 and each module of the architecture is described in detail below.  
Smart devices: Examples of smart devices are smart meters or smart switches. One of the main uses of smart meters is to monitor, record, and transmit electrical energy data. Unlike conventional meters, smart meters are able to monitor the electrical load and electricity usage in real time and transmit the data to a smart grid centre or relevant applications via a built-in communication module. Smart switches play a very important role in the smart grid, ensuring the safe and stable operation of the power system, and improving its reliability and self-healing capability.  
Domain manager: The domain manager is the administrator for each domain and is unique within each domain with powerful storage and computing capabilities. The domain manager is responsible for managing smart devices such as smart meters in the domain, including the joining, updating, and exiting of devices. The domain manager performs system initialization at the start and generates the necessary parameters for the system. The domain manager is secure and trusted within the domain.  
Blockchain agent nodes: Blockchain agent nodes can interact with domain managers and devices in the smart grid, with domain managers in each domain working together to maintain the federated blockchain. The blockchain we use is the federated chain, which is a permission blockchain where nodes trust each other. The identity information of the devices, including device certificates, is stored in the blockchain. As the blockchain is tamper-proof, this ensures that devices without certificates cannot forge them, ensuring that authentication is secure.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Description** \\ \hline \(D_{i}\) & The ith device in domain A \\ \(D_{j}\) & The jth device in domain B \\ \(M_{A}\) and \(M_{B}\) & Administrator of domain A and B \\ \(P_{A}\) and \(P_{B}\) & Blockchain proxy nodes of domain A and B \\ \hline \hline \end{tabular}
\end{table}
Table 1: Symbol description.  
Figure 1: System architecture.
## 3 Results and Discussion
### Lipophilicity of ZnPcBCH\({}_{3}\) and ZnPc  
Lipophilicity was determined by measuring the absorbance of the two sensitizers in DMSO, DMEM, and DMEM + 10% FBS. The higher absorbance value for ZnPc in DMSO confirmed that the molar extinction coefficient of this dye is higher than for ZnPcBCH\({}_{3}\), Table 1 and Figure 6. On the other hand, the higher absorption between 600-700 nm for ZnPcBCH\({}_{3}\) in DMEM + 10% FBS solution shows the higher interaction of this dye in FBS media compared to the ZnPc. The major protein in FBS is bovine serum albumin, which is known to contain hydrophobic pockets for binding lipids, fatty acids, and other non-polar components [25; 26]. The higher absorbance observed for ZnPcBCH\({}_{3}\) compared to ZnPc in DMEM + 10% FBS (Figure 6) indicates the higher interaction of ZnPcBCH\({}_{3}\) with lipids and proteins in this cell culture media at the 2 uM level. In support of the observed lipophilicity of ZnPcBCH\({}_{3}\) is the ability of ZnPcBCH\({}_{3}\) to dissolve in nonpolar organic solvents such as dichloromethane, while ZnPc does not.  
To increase the solubility of ZnPc in water or organic solvents, four benzyloxy groups with a small alkyl group were added to prepare ZnPcBCH\({}_{3}\). This derivative dye exhibited high solubility in organic solvents such as THF, dichloromethane, and moderate solubility in acetone and acetonitrile. This suggests that increasing solubility in organic solvents results from increasing the lipophilicity of the parent dye. Bulky alkyl groups were not chosen since they would reduce rather than increase water solubility [27].
## 2 Methods  
This section describes our proposed methodology for developing a pollutant proxy. Figure 1 presents a generic data-driven-based methodology for a pollutant proxy development and deployment. The concept is mainly based on two key approaches. The first is called a _mutual information_ (MI) approach that is used to select the most relevant proxy inputs. The second is a probabilistic _machine learning_ (ML) method that is used to model an air pollutant.  
Having a large amount of field measurement data, the first step is to explore the available database. For example, data analysis is typically performed to understand data statistical properties, such as mean, standard deviation, minimum and maximum values of each variable, the number of missing data, the linear correlation, etc. From data exploration, it is known how one measured variable is related to other measured variables. Standard correlation methods, such as Pearson correlation coefficient (PCC), is typically applied. However, as air pollution is a complex process, the relationship between the measured variables may not always be linear. Therefore, we complement the correlation analysis by an information theory-based technique, named mutual information (MI), to search relevant measured variables correlated linearly and nonlinearly to an air pollutant to be modelled.  
From the data exploration, it is known that there is a substantial amount of missing data. To solve the problem in practice, an additional guidance method is required for inputs selection. When generating a proxy, we are often limited to the measurement period during which data of all variables exist (i.e., the data with same time index). This leads to fewer data points available as the inputs for training phase of the pollutant proxy. The missing data scenario results in significant data loss for the training purposes. Furthermore, the use of a large number of inputs typically increases the model complexity leading to poor proxy performance. Limiting the number of inputs also allows the proxy to be used flexibly without relying on many other measurements in practice. Therefore, it is vital to consider these effects when determining the number of inputs involved in modelling, shown as the box of performance and data loss monitoring in Figure 1. The selected input data is then used for ML training and validation data. Finally the results are validated and analysed. This whole process can be called pollutant proxy development. The development results in the pollutant proxy where it can then be deployed for filling the missing data in the database and/or act as a virtual sensor.  
Figure 1: The block diagram of general methodology for pollutant proxy development.
## 4 Discussion
compared the efficacy in their study of both autogenous and allogeneic blocks in hard tissue augmentation, and consequently reported 12.5% and 14.4% resorption, respectively, after 12 months of follow up [26]. [PERSON] et al. [27] compared the efficacy of GBR and autogenous block graft for bone reconstruction in maxillary regions with insufficient thickness. They observed an average of 12.48% bone resorption at the end of 6-7 months of the healing period where GBR procedure using particulate autogenous bone, xenograft, and collagen membrane was applied (control group), whereas only 7.20% bone resorption was observed at the end of 4 months where autogenous block was used in the procedure (test group). Since the results of the studies mentioned earlier are somewhat similar to those of the present study, CS protocol is suggested as a successful alternative to autogenous block grafts. There are few studies in the literature concerned with allogeneic block grafts application in horizontal augmentation with the help of the shell technique. Our study is the only one in the literature where the success of the allogeneic block graft was evaluated volumetrically. [PERSON] et al. [28] used autogenous and allogeneic blocks for horizontal bone augmentation using the shell technique and only particulate autogenous bone graft was applied between the block and the host bone. They observed a 5% loss in the autogenous block group and a 2% loss in the allogeneic block group in the linear measurements carried out during the implantation phase after 5 months of augmentation recovery. The reasons for the lower amount of resorption in the [PERSON] study, compared to the present study, may be attributed to the relatively shorter waiting time of that study, in addition to the different radiological measurement techniques used in both of them. One of the limitations presented in this study is the lack of histological and histomorphometric samples harvested from the augmentation areas for detailed evaluation. Only the three-dimensional volumetric change in the newly formed bone was examined after a healing period of 7 months, with no information available on parameters such as status of newly forming bone, remaining graft, or connective tissue in the augmentation area. In this study, the efficacy of block allogeneic and GBR techniques were compared for horizontal bone augmentation in maxillary regions where insufficient thickness was found. Compared to the mandible, vascularization and nutrition were higher in the maxilla; thus, similar studies on the mandible are needed to fully determine the effectiveness of the augmentation materials. Moreover, the relatively short follow-up period is considered another limitation of this study. After 7 months of healing, volumetric changes were examined by comparing CBCT images taken before implantation with images taken immediately after bone augmentation. Long-term radiological evaluation is needed to assess possible continuous resorption that might continue in the augmented zone.
# An Equilibrium Strategy-Based Routing Optimization Algorithm for Wireless Sensor Networks
###### Abstract  
In energy-constrained wireless sensor networks (WSNs), the design of an energy-efficient smart strategy is a key to extend the network lifetime, but the unbalance of energy consumption and node load severely restrict the long-term operation of the network. To address these issues, a novel routing algorithm which considers both energy saving and load balancing is proposed in this paper. First of all, the transmission energy consumption, node residual energy and path hops are considered to create the link cost, and then a minimum routing graph is generated based on the link cost. Finally, in order to ensure the balance of traffic and residual energy of each node in the network, an \"edge-cutting\" strategy is proposed to optimize the minimum routing graph and turn it into a minimum routing tree. The simulation results show that, the proposed algorithm not only can balance the network load and prolong the lifetime of network, but meet the needs of delay and packet loss rate.  
wireless sensor networks; \"edge-cutting\" strategy; load balancing; energy saving; routing
# Personal Protective Equipment as a Potential Source of
## 4 Discussion
### Phthalate Concentrations in Disposable Plastic Gloves  
The glove samples analyzed in this study included 16 brands from different countries of origin, including Malaysia, China, and Thailand. All glove samples were disposable and made of different plastic materials, namely, latex (three brands), nitrile (three brands), polyethylene two brands), and polyvinyl chloride (eight brands). In general, PVC gloves contained higher total phthalate contents (1.66-141,714 ug/g) than the other types of gloves, i.e., latex (<LOQ-5.12 ug/g), nitrile (0.93-1.68 ug/g), and polyethylene (1.59-1.96 ug/g). This finding was not surprising because phthalates are common plasticizers for PVC polymers [19]. High percentages of phthalates were present in PVC plastic products, including gloves that may contain levels higher than 30% of the glove's weight [18; 21; 45; 46; 47]. In disposable plastic gloves, extremely variable contents of plasticizers were reported depending on the glove manufacturing materials. Plasticizers were found to be predominant in vinyl gloves at high levels ranging from 30% to 44% (_w/w_), while gloves made from other materials, such as latex, nitrile, and neoprene, contained a much lower total plasticizer content of less than 0.2% (_w/w_) [46].  
High-molecular-weight phthalates, such as DEHP and DINP, are common additives in PVC polymers. DEHP and DINP were reported to comprise up to 4.10% (_w/w_) and 35.8% (_w/w_) of disposable vinyl gloves, respectively [46]. These levels are higher than the levels found in PVC gloves analyzed in this study, which reached a maximum of about 14% (_w/w_). However, this might be due to the use of phthalate-alternative plasticizers such as DOIP, which was identified in most PVC gloves. High-molecular-weight phthalates can migrate from plasticized PVC gloves to the surrounding environment, therefore, increasing human dermal exposure to these phthalates through direct skin contact [46]. In addition, when gloves plasticized with phthalates are used during food production, shipment, and storage, they can increase the exposure levels to phthalates through diet intake [32]. Furthermore, the massive amounts of disposable plastic gloves released into the environment during the COVID-19 pandemic can adversely affect humans and wildlife through the direct ingestion of plastic materials and through the ingestion of organic pollutants, such as phthalates, released into the environment [18].  
Although most of the analyzed glove samples in this study contained relatively low levels of the target phthalates, other plasticizers, including a phthalate, were present at relatively high concentrations, as discussed below.
# A Blockchain and IoT-Enabled Framework for Ethical and Secure Coffee Supply Chains
## 4 Results and Discussion
### Discussion and Observations  
The average transaction fee across 1000 operations is ETH 0.00034, with a standard deviation of ETH 0.00040. Similarly, the mean transaction time is 65.74 s, with a standard deviation of 34.24 s. These values reflect a system in which the majority of interactions occur at very low gas costs, demonstrating cost-efficiency in smart contract execution. The low mean fee and right-skewed distribution suggest that only a small number of operations (e.g., initial deployment) incur higher costs, while routine transactions remain lightweight. On the other hand, the transaction time shows greater variability, with a standard deviation exceeding 50% of the mean. This indicates that while many transactions are processed quickly, others experience delays, likely caused by external testnet factors such as congestion or wallet response latency. Overall, these behavioral patterns align with expected outcomes in a simulated Ethereum environment and reinforce the practicality of using blockchain for decentralized supply chain interactions.  
The above performance evaluations confirm that the smart contract system performs reliably and cost-effectively in the Sepolia testnet, which closely mimics the behavior of the actual Ethereum mainnet. Most transactions incur low and consistent gas fees, indicating that routine operations can be executed without significance resource demands. Even administrative functions such as _transferOwnership0_ demonstrate minimal overhead, supporting the efficiency of control-level processes within the smart contract.  
While transaction times vary, the majority fall within acceptable bounds for supply chain scenarios that are event-driven but do not critically require real-time execution. The observed inconsistency points to limitations in the testing infrastructure rather than to flaws in the smart contract logic, suggesting that performance can be improved under optimized deployment conditions.  
These outcomes align with the concerns raised in the literature review in Section 2.1. Existing studies emphasize the importance of ensuring transparency and data immutability in agricultural supply chains. The current system supports this by providing time-stamped and tamper-resistant records. Section 2.2 discusses the technical challenges of integrating IoT and blockchain systems; the dual-layer architecture used in this work demonstrates a structured way of modeling sensor input in a blockchain-friendly format. Section 2.3 highlights the lack of traceability and trust in conventional supply chains. By making all transactional data visible and verifiable on-chain, the proposed system improves traceability and builds stakeholder confidence. Finally, Section 2.4 notes the absence of practical full-stack models. Our work fills that gap by offering a complete design, from data simulation to blockchain integration. These connections reinforce the value of the proposed framework and its relevance to the existing body of research.
## 7 Does the DnaA-Initiation Hyperstructure Contain SeqA?  
_E. coli_ avoids multiple reinitiations of chromosome replication by a sequestration mechanism that depends on the SeqA protein binding preferentially to newly replicated, hemi-methylated GATC sites, many of which are clustered in _oriC_. This sequestration, which involves the membrane, occurs only when _oriC_ is hemi-methylated [54] and when SeqA, which has an affinity for the membrane, is present [55; 56]; the result is an inhibition of initiation [57]. SeqA forms multimers and SeqA-DNA complexes can cover 100 kb of DNA and are close or integral to the replication hyperstructure(s), and have a bidirectional movement that differs from that of the origins (which goes to the poles) [58; 59]. GATC sites are clustered not only in the _oriC_ region but also in many genes involved in the replicationand repair of DNA (such as _dnaA_, _dnaC_, _dnaE_, _gyrA_, _topA_, _hepA_, _Ihr_, _parE_, _mukb_, _recB_, _recD_, and _uvrA_), as well as genes involved in the synthesis of the precursors of DNA (such as _nrAA_, _purA_, _purF_, _purL_, _pyrD_ and _pyrI_), consistent, perhaps, with the presence of these genes and their products in a replication hyperstructure [60]. One related question is whether the DnaA-initiation hyperstructure in its earliest form contains SeqA and, reciprocally, a second question is whether the replication hyperstructure contains DnaA?