esa-sceva/satcom-chunk-collection huggingface dataset:
# Co-Processing Parallel Computation for Distributed Optical Fiber Vibration Sensing
## 1 Introduction
In this paper, without changing the amount of data in the system, we reduce the overall running time of the system by optimizing data acquisition and data demodulation methods. A novel data co-processing parallel computation method is demonstrated, which combines the field programmable gate array (FPGA) technology with CPU and GPU processing. Specifically, the generation of an external modulation pulse signal and the acquisition of RBS light signals are realized simultaneously by using the FPGA technology with parallel operation characteristics in the hardware. The differential accumulation algorithm and fast Fourier transform (FFT) of RBS curves are realized by using the GPU technology alongside the parallel computing advantages of the software. The contrast diagram of the internal structures of the FPGA, CPU and GPU is shown in Figure 1. As illustrated in Figure 1a, FPGA technology is programmable and flexible, meaning it can can synchronously implement multiple tasks, such as pulse modulation, data acquisition and transmission [27; 28]. In Figure 1b, the CPU has many complex control units and a lot of cache space. In Figure 1c, the GPU has a macroscopic parallel structure containing two graphics processing clusters (GPC). Each GPC includes a raster engine and multiple polymorph engines, texture processing clusters (TPC) and a stream processor (SM). Each SM has 128 arithmetic logical units (ALU) which are used to realize parallel computation. Therefore, the CPU is an expert in logical control and computing different types of data, while the GPU is suitable for large-scale data parallel computing [29; 30; 31]. In summary, the advantage of this combination is the improvement of the real-time performance of the sensing system, achieving the high-speed detection and location of the vibration signal in the practical long-distance application situations. However, it also has a lot of room for improvement. For example, the current common problem of internal signal communication overhead can be solved by optimizing the shared memory communication mechanism between execution units [32].
###### Abstract  
Modern telecom operators will have to change their business approach, organization, products development and services development, customer approach and many other important issues if they want to be competitive on the telecom markets in the following years and decades. They will have key roles in this period dubbed the Industry 4.0 era. There are many different models of analysis for telecom operators, but they all have a partial approach to analysis and none of them gives a complete picture of the analysis of telecoms. The Comprehensive Techno-Economic (CTE) model for the analysis of telecom operator potentials is a new and original model for analysis and will significantly help in their transformation processes. This model will enable a quick and easy analysis of the potential of the telecom operator, but also of individual parts of it, regardless of other parts of the company. Despite the fact that the model is easy to define the input data and that it is fast in applying the analysis, it gives precise and mathematically defined results from which one can see the assessment of the potential of telecom or independent assessment of its parts. The main advantages of this model are simplicity, speed of telecom analysis, accuracy of results and its modularity, i.e., independent evaluation of individual parts. Such a model is necessary for telecom operators to achieve fast and reliable potential assessment, analysis, modeling and the easier adaptation of new products and services. This is a unique model with a scientific background and theoretical settings, and it provides practical application in the telecommunication market.  
new techno-economic model; Industry 4.0; telecommunication; telecom operator; potential 1
# An Efficient Digital Channelized Receiver for Low SNR and Wideband Chirp Signals Detection
## 3 Results and Discussion  
This section verifies the performance of the proposed receiver architecture. In Section 3.1, the simulation experiments are conducted using MATLAB, including the filter bank structure, the channel arbitration logic, and the frequency estimation algorithm [32]. The performance of the IFM algorithm is also evaluated in Section 3.2. Section 3.3 gives the computational complexity analysis. Finally, a discussion is shown in Section 3.4.
# FuGeF: A Resource Bound Secure Forwarding Protocol for Wireless Sensor Networks
## 2 Related Works
The modification includes propagating the message beyond the region of attacker influence to abates the Sybil attack and verification of node propagation location to subdue the Sinkhole attack. The verification is done by temporarily storing each node message along with its location and ensuring the attacker does not claim to provide the highest advances towards the destination. These modifications improved the protocol's security performance but it, however, consumed more resources (memory and energy) and exhibited MFRs drawbacks. Furthermore, strategic placements of malicious nodes may hinder the protocol's performance as the MFR selection of next hop node may be easily predicted. SIGF [2] was proposed to mitigate Blackhole and Sybil attacks. It employs both MFR and RP forwarding strategies in SIGF-priority and SIGF-random respectively, for node selection. The protocol's semantics was modified by introducing a sampling process which lasted for a fixed window of time. The fixed window period allowed the selection of a non-malicious node from gathered response obtained from next hop nodes (forwarding candidates) thus abating the attacks. The protocol (SIGF-priority) achieves efficient resource utilization but it, however, maintains a consistent routine that is highly predictable and vulnerable to malicious nodes placed strategically along its routinely followed path. SIGF-random erratic behavior achieves minimal security at a heightened cost of resource utilization. DWSIGF [3] builds on SIGF [2]. It improves on the sampling process introduced in SIGF by proposing a dynamic window period to create a possible time shift in the protocol's semantics, thus, causing a dynamic routine or uncertainty during the selection process. An attacker in this instance is unable to respond timely for sampling within the window period. This improvement enabled DWSIGF to outperform SIGF. However, DWSIGF also employs the MFR and RP forwarding strategy for node selection (DWSIGF-P and DWSIGF-R, respectively). The strategies, despite the improved routing semantics, still suffer from SIGF drawbacks. In this paper, our approach builds on the DWSIGF protocol. We leverage the use of three parameters which are progressive distance, remaining energy, and connectivity cost, as well as a FLS for node selection. These parameters describe features external to a node, as well as influence distribution of traffic load among the nodes. The FLS is used to evaluate the chance value of each next hop node based on its three criteria values. The values obtained are used to determine the most appropriate forwarding node for the routing process. Additionally, dynamism introduced in DWSIGF was maintained with a slight modification such that a shift in the proposed protocol semantics is also achieved. The main aim of the proposed FuGeF is to achieve efficient security while maintaining an acceptable level of base performance.
# Valorization of Cassava By-Products: Cyanide Content and Quality Characteristics of Leaves and Peel
## 3 Results and Discussion
### Changes in Composition, Fiber, and Cyanide Content of Cassava Tuber Peel after Enzymatic Treatment
Firstly, this study specifically focuses on the use of Viscozyme(r) L, which has not been previously employed for this purpose. In a previous study, we successfully identified its role in the mechanical peeling of cassava tubers. Building upon that, this study further explains its ability to reduce cyanide content and hydrolyze insoluble fibers. Additionally, this study highlights the possibility of reusing the same Viscozyme(r) L solution in multiple cycles, which has not been reported before. This demonstrates the potential for reusing the enzyme solution and optimizing its efficiency in cyanide reduction. Figure 4: Dry matter, organic matter, ash, neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), crude fiber (CF), and cyanide content of fresh cassava tuber peel (FP), control peel (CP) treated with hot water without enzyme solution, and enzyme-treated peel (ETP1-3) obtained after three separate runs with the same enzyme solution. Different letters for the same parameters indicate significant differences (\(p<0.05\)).
## Appendix A
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Machine Learning Model**} & \multicolumn{2}{c}{**Linear Regression**} & \multicolumn{2}{c}{**Linear Regression**} \\  & \multicolumn{2}{c}{_AUC vs. GIL_} & \multicolumn{2}{c}{_MCC vs. GIL_} \\ \hline Logistic Regression & yAUC = 0.77886 \(-\) 0.1914 \(\times\) x\({}_{\text{GIL}}\) & yMC = 0.54762 \(-\) 0.3393 \(\times\) x\({}_{\text{GIL}}\) \\ \hline Decision Tree Classifier & yAUC = 0.9182 \(-\) 0.03648 \(\times\) x\({}_{\text{GIL}}\) & yMC = 0.84385 \(-\) 0.1185 \(\times\) x\({}_{\text{GIL}}\) \\ \hline KNeighborsClassifier & yAUC = 0.78136 \(-\) 0.04867 \(\times\) x\({}_{\text{GIL}}\) & yMC = 0.56195 \(-\) 0.0921 \(\times\) x\({}_{\text{GIL}}\) \\ \hline GaussianNB & yAUC = 0.659 \(-\) 0.2924 \(\times\) x\({}_{\text{GIL}}\) & yMC = 0.41258 \(-\) 0.6357 \(\times\) x\({}_{\text{GIL}}\) \\ \hline SVC & yAUC = 0.72082 \(-\) 0.1865 \(\times\) x\({}_{\text{GIL}}\) & yMC = 0.44355 \(-\) 0.26539528 \(\times\) x\({}_{\text{GIL}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The parameters of the linear models fitted to model the relationship between AUC and GIL, as well as MCC and GIL, for each machine learning model used in the context of this paper. \begin{table}
\begin{tabular}{l l c c c c} \hline  & \multicolumn{2}{c}{**DecisionTreeClassifier**} & \multicolumn{1}{c}{**GaussianNB**} & \multicolumn{1}{c}{**KNN**} & \multicolumn{1}{c}{**LogisticRegression**} & \multicolumn{1}{c}{**SVC**} \\ \hline Decision Tree & Mean difference & - & 0.348 *** & 0.156 *** & 0.1995 *** & 0.2629 *** \\ classifier & \(p\)-value & - & \textless{}0.001 & \textless{}0.001 & \textless{}0.001 & \textless{}0.001 \\ \hline Gaussian NB & Mean difference & - & \textless{}0.001 & \textless{}0.001 & \textless{}0.001 \\ \hline KNN & Mean difference & - & \textless{}0.001 & \textless{}0.001 & \textless{}0.001 \\ \hline Logistic Regression & Mean difference & - & 0.0431 *** & 0.1065 *** \\  & \(p\)-value & & - & \textless{}0.0001 & \textless{}0.001 \\ \hline Logistic Regression & Mean difference & & - & 0.0633 *** \\  & \(p\)-value & & - & \textless{}0.001 \\ \hline SVC & Mean difference & & & - \\  & \(p\)-value & & & - \\ \hline \end{tabular} Note. *** \(p\) < 0.001.  
\end{table} 
Table 6: The results of the Games–Howell pos hoc test on train AUC.
## Abbreviations  
* DE-XRT Dual-energy X-ray Transmission ECS Eddy Current Aeration ECT Eddy Current Test XRT X-ray Transmission XRF X-ray Fluorescence LIBS Laser-induced Breakdown Spectroscopy EMS Electromagnetic Sensor SQP Sequential Quadratic Programming MCNP Monte Carlo N-Particle SS Stainless Steel NM-SS Non-magnetic Stainless steel Al Al Aluminum Alc Wrought Aluminum Alloy Alw Cast Aluminum Alloy Mg Magnesium Cu Copper Ti Titanium PCA-SVM Principal Component Analysis-Support Vector Machines 3D Three-dimensional CT Computed Tomography SD Standard Deviation SNR Signal to Noise Ratio CR Contrast Ratio IPC Industrial Personal Computer MCIO Motion Control and Input/Output PCI Peripheral Component Interconnect GigE Gigabit Ethernet PC Personal Computer OS Operating System HMI Human\(-\)machine Interface LM Light Metal HM Heavy Metal OvEx Materials of Overex UnEx Materials of Underexposure EP Effective Points PAM Projection Area of Material RMSE Root Mean Square Error
## 3 Signal Processing  
Decoding brain states is problematic since they have a poor signal-to-noise ratio, variability between trials (in different sessions or even on the same session), high dimensionality data, highly location-dependent data, etc. [50]. Thus, for correct decoding, the usage of brain signals requires several steps, starting from signal acquisition (e.g., EEG and ECoG recordings), feature extraction, pattern recognition and, finally, translation into control signals (Figure 1).
###### Abstract  
In order to provide better navigation service for a wide range of applications, modernized global navigation satellite systems (GNSS) employs increasingly advanced and complicated techniques in modulation and multiplexing of signals. This trend correspondingly increases the complexity of signal despreading at the receiver when matched receiving is used. Considering the numerous low-end receiver who can hardly afford such receiving complexity, it is feasible to apply some receiving strategies, which uses simplified forms of local despreading signals, which is termed unmatched despreading. However, the mismatch between local signal and received signal causes performance loss in code tracking, which is necessary to be considered in the theoretical evaluation methods of signals. In this context, we generalize the theoretical signal evaluation model for unmatched receiving. Then, a series of evaluation criteria are proposed, which are decoupled from unrelated influencing factors and concentrates on the key factors related to the signal and its receiving, thus better revealing the inherent performance of signals. The proposed evaluation criteria are used to study two GNSS signals, from which constructive guidance are derived for receivers and signal designer.  
GNSS; signal evaluation; unmatched receiving; anti-interference; decouple Article
## 4 Characteristics of Different Time Distribution Systems
### White Rabbit  
The White Rabbit (WR) Project is a collaborative project conceived and developed by the European Organization for Nuclear Research (CERN), as well as parties from academiaand industry. Its main purpose is to develop a method capable of fully deterministic, sub-nanosecond clock synchronization within distributed, Ethernet-based computer networks [87].  
CERN, home to the Large Hadron Collider (LHC), which accelerates particles around a 27 km ring, relies on a large Ethernet network for time synchronization, with performance requirements at the subnanosecond level. To meet these stringent requirements, CERN developed a PTP Profile known as White Rabbit [80].  
White Rabbit introduces several innovative techniques, including enhancements for utilizing Ethernet hardware clocks and calibrating asymmetry in fiber-optic cabling. These innovations have been incorporated into a new PTP Profile, known as the High-Accuracy Delay Request-Response Default PTP Profile, specified in the IEEE 1588-2019 standard. By formalizing White Rabbit within the IEEE 1588 framework, this profile extends best-in-class performance to a variety of applications, including nonscientific fields such as electric power grid measurement and control [80].
###### Abstract  
_Sambucus nigra_ (SN) and _Sambucus ebulus_ (SE) are widely used in folk medicine, primarily as antiviral agents for colds and influenza. In the current study, the antiviral activity of extracts of SN and SE fruits, flowers, and leaves were tested against herpes simplex virus type-2 (HSV-2). The HPLC analysis of the investigated extracts revealed the presence of phenolic acids, flavonoids, and anthocyanins. Rutin and chlorogenic acid were the main polyphenol constituents in flower and leaf extracts, whereas anthocyanins were predominant in fruit extracts. The flower extract of SN was characterized by the highest content of rutin and chlorogenic acid--14,232.1 mg/100 g dry weight (DW) and 7086.7 mg/100 g DW, respectively. SN fruit extract revealed the highest antioxidant activity measured using ORAC and HORAC methods--11,443.1 umol TE/g and 8198.9 umol GAE/g, respectively. To evaluate cytotoxicity, antiviral, and virucidal activities against HSV-2, the MTT assay and method of Reed and Muench were used. The least toxic extracts were PSNFrE and PSEFrE. The maximum tolerable concentration (MTC) of PSNFrE was 2000 ug/mL and the calculated CC\({}_{50}\) value for that extract was 3570 ug/mL. The inhibitory activist against virus replication was established for three of the extracts--PSNFIE, PSNLE, and PSNFrE. PSEFrE showed neither activity against virus replication, nor virucidal activity. The data suggest a significant inactivation of more than 98% after 60 min of contact of HSV-2 virions with the PSNFrE applied in MTC. The current study provides evidence that _Sambucus nigra_ reveals anti-HSV-2 activity; however, the most active parts of the species were fruits. Therefore, SN fruits and their extracts can be used as an attendant therapy for HSV-2 viral infections.  
black elder (_Sambucus nigra_); dwarf elder (_Sambucus ebulus_); herpes simplex type-2; antioxidant activity; anthocyanins; antiviral agents; antiherpetic activity; phytochemical composition
# Routing Protocols for UAV-Aided Wireless Sensor Networks
## 4 Routing Protocols for UAWSNs
### Protocol Operation-Based Routing
#### 4.2.2 Multi-Path Routing  
Frame selection-based routing protocol (FSRP): The FSRP is a routing algorithm for UAWSNs along with a data-acquisition framework, as shown in Figure 8. Efficient data gathering is a challenging task in WSNs.  
In FSRP [54], a data-acquisition framework is introduced to increase the efficiency of data gathering in a WSN using UAVs. To increase the network throughput, redundant data transmission between static sensor nodes and UAVs is restricted. This is done by using a priority-based frame-selection scheme. Nodes within the coverage of UAVs are classified into different frames based on their locations. The contention window value in IEEE 802.11 MAC is also adjusted for this purpose. A lower contention window range is assigned to the frame with high priority in an urgent area, and a higher contention window range is assigned to frames with low priority in unimportant areas. Based on this framework, FSRP aims to reduce the distance between senders and receivers to obtain better channel quality. At least one CH is responsible for data communication with a UAV. This decreases the distance between sensors. With a shorter distance, the channel quality becomes better, thus saving energy.
## 3 Three-Stage Development of BCI-Based Language Decoding  
The evolution of BCI-based language decoding exhibits a clear three-stage developmental trajectory. The basic stage focuses on signal interpretation, where neural signals are processed through fundamental computational architectures and static pattern recognition algorithms. This initial stage established essential methodologies for neural signal analysis and classification. The advanced stage marked significant progress in BCI systems, characterized by dynamic communication frameworks that integrate multimodal fusion techniques and real-time processing, enabling more sophisticated applications, such as  
Figure 4: **(a)** Sequential processing stages of EEGNet (adapted from [63]), showing temporal filtering (Conv2D), frequency-specific spatial filtering (DepthwiseConv2D), and feature map integration (SeparableConv2D), followed by classification. **(b)** An integrated deep learning pipeline for neural signal classification (adapted from [67]), demonstrating the sequential stages of data augmentation, feature selection, feature extraction, and classification.  
continuous speech decoding. The innovative stage, driven by emerging AI technologies, introduces comprehensive platforms that synergize biological and AI. To conceptualize this evolutionary progression, we propose the ICI architecture (Figure 1). This section examines the core architecture, distinctive features, and principal applications that characterize each developmental stage, demonstrating how BCI-based language decoding has evolved from basic signal processing to sophisticated neural-artificial interfaces.  
A comprehensive analysis of this evolutionary trajectory necessitates an examination of the fundamental signal transformation process that underlies all BCI-based language decoding systems. Table 1 presents an overview of the brain signal transformation process, illustrating the technological sophistication in BCI-based language decoding systems, particularly those utilizing EEG and electrocorticography (ECoG) signals. This process encompasses five interconnected stages that form the foundation for dynamic communication platforms. Beginning with neural signal acquisition through EEG/ECoG sensors, the pipeline progresses through sophisticated preprocessing and feature extraction phases, leveraging advanced signal processing techniques, such as independent component analysis (ICA) and wavelet transform. The subsequent decoding stage employs state-of-the-art machine learning algorithms and neural networks to interpret these processed signals, culminating in the output generation stage, where language models and speech synthesizers transform neural patterns into meaningful linguistic outputs. This comprehensive signal transformation framework underpins the enhanced real-time processing capabilities and multimodal fusion approaches that characterize advanced BCI systems, enabling more sophisticated and responsive communication platforms. The following subsections analyze how each developmental stage has advanced and optimized these fundamental transformation processes.
# Novel Highly Dispersed Additive for Proton-Conducting Composites
## 1 Introduction
Earlier, it was demonstrated that the modification of CsH\({}_{2}\)PO\({}_{4}\) with such highly dispersed complex oxides, such as SrZrO\({}_{3}\)[48] or NdOPO\({}_{4}\)[52], resulted in the formation of composite solid electrolytes with a high proton conductivity. Similarly, highly dispersed zinc stannate, ZnSnO\({}_{3}\), may also be regarded as a promising additive to create proton-conducting composite electrolytes. The present study is focused on studying the decomposition products of the zinc hexahydroostannate precursor, which are suitable for use as a matrix for the creation of CsH\({}_{2}\)PO\({}_{4}\)-ZnSnO\({}_{3}\) composite proton solid electrolytes, as well as investigating their transport properties. The synthesis of nanosized particles of ZnSnO\({}_{3}\) was carried out by the thermal decomposition of the zinc hexahydroostannate precursor ZnSn(OH)\({}_{6}\). The obtained highly dispersed ZnSnO\({}_{3}\) was used as a disperse additive for the preparation of the CsH\({}_{2}\)PO\({}_{4}\)-ZnSnO\({}_{3}\) composites, and the transport properties of these proton solid electrolytes were investigated as a function of composition and temperature.