esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Optical Metro Node Architecture Based on the WDM Cross-Connect  
The optical metro node architecture exploiting the WDM cross-connects is reported in Figure 2. The optical node architecture include both optical and electrical (interfaces) switching. It consists of multiple interfaces (PON, LTE/5G, enterprise, metro network) that use multi-protocol, multilink interfaces, and hybrid electronic/optical switches to interface with access network segments along with the ability to mix and match protocols (i.e., CPRI, IP, Ethernet, etc.) over the same physical link. Moreover, the optical node includes also a number of servers organized in racks and interconnected via an electronic Top-of-the-Rack (TOR) switch. Each interface and TOR switch is equipped with a number of WDM bi-directional optical links. As shown in Figure 2, the interfaces and TORs are interconnected by a number of WDM cross-connect in a spine and leaf architecture. The main difference with the CORD approach is that the interconnect networks is built on a WDM cross-connect instead of electronic switches as in CORD. This allows to transparently switch the data traffic between the interfaces and the TORs without expensive O/E/O converters, greatly reducing the power consumption and costs. Moreover, the ability of the WDM cross-connect to switch data signals not only in wavelength and space, but also in time (at the nanosecond time scale), allows to fully exploit the statistical multiplexing switching at the optical data plane level. The number of interconnected interfaces and TORs and the capacity depends on the radix of the WDM cross-connect and the amount of parallel WDM cross-connect. For the PON, LTE/5G, and enterprise, the interface consists of the access interface and network interface, while for TOR it consists of a server interface and the network interface. Part of the traffic that belongs to the same access (the same PON or same server pool in the same rack) is then directly exchanged by the interfaces (or TORs). The other part of the traffic is directed to the network interface via multiple WDM transceivers, eventually with variable data rate and modulation formats. The reason for employing multiple WDM transceivers is that WDM allows scaling the communication bandwidth between the interfaces/TORs and the WDM cross-connect network employing multiple wavelengths to generate a high capacity channel. The control plane is, therefore, in charge of (re-)configuring the transceivers (data format and data rate) and, accordingly, the WDM cross-connects. As the main topic of this work is not the control plane and the implementation of the interfaces, the rest of the paper will discuss in detail and assess the design, photonic integration, and validation of the SOA-based WDM cross-connects.
## 2 Methodology
### Arrhenius-Based Bi-Exponential Model  
The primary operational factors contributing to PEMFC stack degradation include temperature, humidity, and voltage [5; 6; 10]. In this study, temperatures were selected as the primary acceleration factor for ADT. The following Arrhenius equation provides a basis for understanding the relationship between reaction rates and temperature [26]:  
\[\tau=A\exp\left(-\frac{E_{a}}{k_{B}T}\right) \tag{2}\]  
where \(\tau\) is the rate constant, \(E_{a}\) is the activation energy of the reaction (typically expressed in electron-volts (eV)), \(k_{B}\) is Boltzmann's constant (\(8.617\times 10^{-5}\) electron-volts per Kelvin (eV/K)), \(T\) is the absolute Kelvin temperature, and \(A\) is a constant that depends on the product characteristics and testing method employed. Using Equation (2), the acceleration factor (\(AF\)) between a normal operating temperature (\(T_{0}\)) and an accelerated temperature (\(T\)) can be expressed using the Arrhenius equation as follows:  
\[AF=A\exp\left(-\frac{E_{a}}{k_{B}T}\right)/A\exp\left(-\frac{E_{a}}{k_{B}T_{0}} \right)=\exp\left(\frac{E_{a}}{k_{B}}\left(\frac{1}{T_{0}}-\frac{1}{T}\right)\right) \tag{3}\]  
Given a normal operating temperature of 60 \({}^{\circ}\)C for the PEMFC stack, \(T_{0}\) is 333.15 K (\(60+273.15\)). By incorporating the acceleration factor from Equation (3) into the \(t\) of Equation (1), the accelerated degradation model for the fuel cell can be expressed as follows:  
\[y(t,T)=\phi_{1}e^{-\gamma_{1}\left(t\cdot\exp\left(\frac{E_{a1}}{k_{B}}\left( \frac{1}{k33.15}-\frac{1}{T}\right)\right)\right)}+\phi_{2}e^{-\gamma_{2} \left(t\cdot\exp\left(\frac{E_{a2}}{k_{B}}\left(\frac{1}{33.15}-\frac{1}{T} \right)\right)\right)},\quad t\geq 0 \tag{4}\]
# Transmission Tower Re-Identification Algorithm Based on Machine Vision
## 2 The Proposed Method
### The Transmission Tower Detection Based on the Improved YOLO  
The YOLO series, as a classic single-stage detection model, has spawned numerous object detection algorithms [40]. To date, the YOLO series has evolved up to YOLOv8. [PERSON] et al. [22] indicated that YOLOv7, when applied to anti-vibration hammer corrosion detection, not only offered rapid detection speed but also a higher accuracy, even surpassing two-stage detection models like Faster R-CNN. In their study on insulator defect detection tasks, [PERSON] et al. [10] revealed that YOLOv51 achieves a [EMAIL_ADDRESS] detection accuracy surpassing that of both YOLOv6l and YOLOv7. Currently, YOLOv8's detection accuracy on the COCO dataset surpasses that of YOLOv7 and the YOLOv5 [17]. Given the characteristics of the YOLO network and its applications in the transmission tower defect detection, this study utilizes the YOLOv8 network in establishing the transmission tower identity matching template library. To further accelerate the inference speed of the transmission tower object detection, the improved YOLOv5n-conv head network is employed during the identity matching stage.  
Figure 3: The key technical points in this paper.
# Windowed Joint Detection and Decoding with IR-HARQ for Asynchronous SCMA Systems
### Paper Organization  
The rest of the paper is structured as follows. Section 2 briefly reviews the RC-LDPC codes, based on Kite codes, and introduces the proposed SCMA system model with the IR-HARQ scheme. Section 3 introduces the asynchronous IR-HARQ scheme, and then the RC-LDPC code-based IR-HARQ schemes for SCMA systems are detailed. The simulation results are presented in Section 4, and the conclusion is summarized in Section 5.
## 3 Results and Discussion
### The Protease Activities of ML Silage
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \multirow{2}{*}{**Item**} & \multirow{2}{*}{**Treatments**} & \multicolumn{4}{c}{**Ensiling Days**} & \multirow{2}{*}{**SEM**} & \multicolumn{4}{c}{\(p\)**-Value**} \\ \cline{3-4}  & & **3** & **7** & **14** & **30** & & **D** & **T** & **D\(\times\)T** \\ \hline \multirow{3}{*}{Carboxypeptidase (units/h/DM)} & CK & 70.9 \({}^{\text{\,A,b}}\) & 66.9 \({}^{\text{\,A,b}}\) & 80.8 \({}^{\text{\,A,a}}\) & 83.1 \({}^{\text{\,A,a}}\) & & & \\  & CA1 & 69.2 \({}^{\text{\,A,a}}\) & 74.8 \({}^{\text{\,B,a}}\) & 76.5 \({}^{\text{\,A,a}}\) & 80.8 \({}^{\text{\,A,a}}\) & & & \\  & CA2 & 66.9 \({}^{\text{\,A,a}}\) & 66.9 \({}^{\text{\,A,a}}\) & 66.2 \({}^{\text{\,B,a}}\) & 65.9 \({}^{\text{\,B,a}}\) & & & \\  & CK & 86.5 \({}^{\text{\,A,a}}\) & 83.4 \({}^{\text{\,A,a}}\) & 23.9 \({}^{\text{\,A,b}}\) & 17.4 \({}^{\text{\,A,b}}\) & & & \\  & CA1 & 59.6 \({}^{\text{\,B,a}}\) & 65.0 \({}^{\text{\,A,a}}\) & 18.8 \({}^{\text{\,A,B,b}}\) & 14.7 \({}^{\text{\,A,b}}\) & 28.2 & 0.000 & 0.000 & 0.000 \\  & CA2 & 23.6 \({}^{\text{\,A,a}}\) & 17.7 \({}^{\text{\,B,a}}\) & 14.75 \({}^{\text{\,C,b}}\) & 13.5 \({}^{\text{\,A,b}}\) & & & \\  & CK & 51.3 \({}^{\text{\,A,a}}\) & 47.1 \({}^{\text{\,B,a}}\) & 50.5 \({}^{\text{\,A,a}}\) & 36.6 \({}^{\text{\,A,b}}\) & & & \\  & CA1 & 55.2 \({}^{\text{\,A,a}}\) & 53.9 \({}^{\text{\,A,a}}\) & 53.5 \({}^{\text{\,A,a}}\) & 41.3 \({}^{\text{\,A,b}}\) & 6.76 & 0.000 & 0.018 & 0.360 \\  & CA2 & 49.7 \({}^{\text{\,A,b}}\) & 55.2 \({}^{\text{\,A,a}}\) & 54.4 \({}^{\text{\,A,b}}\) & 45.8 \({}^{\text{\,A,b}}\) & & & \\ \hline \end{tabular}  
* D, ensling days; T, treatments; D\(\times\)T, the interaction of ensling days and treatments; DM, dry matter; CK, the control; CA1, 1% citric acid; CA2, 2% citric acid. Different lowercase letters \({}^{\text{\,(A,b)}}\) in the same row indicate significant differences at \(p<0.05\). Different uppercase letters \({}^{\text{\,(A,b)}}\) in the same column indicate significant differences at \(p<0.05\).
* (\(\times\)C) in the same column indicate significant differences at \(p<0.05\).  
\end{table} 
Table 3: The protease activities of mulberry leaf silages. which might partly explain the proteolysis in ML silage. The results indicated that CA inhibited (\(p<0.01\)) carboxypeptidase activity could effectively inhibit extensive proteolysis in ML silage during the ensiling process, with 2% CA showing the best outcome.
# A Review of Vertical Shaft Technology and Application in Soft Soil for Urban Underground Space
## 4 Technological Advancements in Mechanized Systems
### Blind Shaft Boring
#### 4.2.1 Raise Boring Machine (RBM)
The RBM ZFY5.0/600 constructed a 5 m diameter ventilation shaft in a coal mine and drilled numerous 3.5 m diameter deep shafts in hard basalt at the Baihetan hydropower station [42]. In shaft construction, raise boring offers several advantages, minimizing rock mass damage, preserving inherent strength, and providing smooth-finished shafts without the need for lining, which is particularly significant for ventilation shafts. However, raise boring also presents several drawbacks. Its remote excavation face restricts immediate ground support in unfavorable conditions, unlike conventionally sunk shafts, where support is integrated early in the cycle. Additionally, access for remedial support installation is limited due to the vertical nature of raise-bored shafts, in contrast to tunnel boring, which allows for access behind the advancing face for support purposes. Figure 6: Overview of blind boring machines; (**a**) SBR and (**b**) SBC modified after [26], (**c**) blind shaft boring rig after [41], (**d**) RBM modified after [45]. Lastly, raise boring operations lack hands-on access to the working face, necessitating precise rock mass conditions to ensure compatibility with specific diameters and accurate evaluation of rock conditions.
## 2 Experimental Work
### Sample Preparation  
It can be observed from the Scanning Electron Microscopy (SEM) images of CNTs given in the Figure 1 that these tubes are intertwined with each other. This means that higher efforts are required to properly disperse CNTs in bitumen. The most difficult process in bitumen modification with CNTs is its homogeneous dispersion because of their aggregation, which leads to unsatisfactory results. There are two ways to disperse CNTs in bitumen: (i) dry mixing; and (ii) wet mixing. In dry mixing technique, CNTs are directly added into bitumen and dispersed with mixers at high frequencies. In wet mixing technique, CNTs are first dissolved in a solvent. This solution is then dispersed in bitumen with mixers. Different nanomaterials require different mixing conditions for their dispersion in bitumen. For some nanomaterials, dry mixing and mechanical stirrer is sufficient to disperse them in bitumen, while some require high shear mixing and wet mixing for homogeneous dispersion. Thus, a thorough study was required on uniform dispersion of CNTs in asphalt binder.  
In this study, sample preparation involved three major steps:  
1. Mixer selection
2. Solvent selection
3. Technique (dry/wet) selection
# Brain-Inspired Data Transmission in Dense Wireless Network
## 3 Ultra-Dense Wireless Networks and Their Biological Counterparts  
In our work, we consider a highly dense network consisting of \(N\) wireless nodes deployed randomly over a certain square area of size \(D\), as illustrated in Figure 1. Each wireless network node may act as a transmitter (source), a receiver (sink), or as a relay of the message. In other words, the network of all wireless nodes may be treated as some sort of transport network. These wireless nodes operate in the same frequency band (central frequency \(f_{c}\) and bandwidth \(B\)); thus, when transmitting at the same time, they may cause mutual interference. Moreover, we assume that any two wireless nodes which are close enough to receive the signal with the power above some threshold create the wireless link.  
The illustrative situation is shown in Figure 1, where the light-green circle represents the coverage area of the transmitting node and the common part of two such areas is marked by orange and represents the interference area. All nodes within the interference area are susceptible to harmful interference and degradation of the signal-to-interference-plus-noise metric. Without loss of generality, in our further analysis, we select one of the nodes as transmitter and the other distant node as the destination node for this reception of this message; the other nodes laying between them are treated as relays. As the network is dense, we follow our prior inspirations from the human nervous system (HNS), which we recap briefly below.
## 3 Materials and Methods
### Proposed Forward Error Correction Framework
By contrast, our proposed FEC mechanism has a computational complexity of \(O\left(n\right)\) for encoding and \(O\left(n+r\right)\) for decoding, where \(n\) is the number of encoded bits and r is the number of redundant bits. This linear complexity makes our framework more efficient and straightforward to implement, without requiring extensive training data or complex model training. A comparative analysis of the computational complexities of these techniques is given in Table 1. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Technique** & **Encoding Complexity** & **Decoding Complexity** & **Remarks** \\ \hline \multicolumn{4}{c}{Longer codes increase computational complexity; higher decoding complexity; iterative decoding overhead; matrix sparsity vs. performance trade-off; non-deterministic execution time.} \\ \end{tabular}
\end{table} 
Table 1: Comparative analysis of our proposed method with these existing techniques.
# A Review of the Challenges in Deep Learning for Skeletal and Smooth Muscle Ultrasound Images
## 3 Results and Discussion
### Limitation  
There are two limitations in this study. The first limitation is that the muscle segmentation and classification studies use data augmentation or transferred learning because of the inherent muscle ultrasound image problems; however, both solutions currently still have disadvantages and need further investigation [69]. Future work will discover additional solutions for muscle ultrasound image problems, such 4D ultrasound, on-the-fly data augmentation for 3D images, and active learning as an alternative for augmentation or transferred learning to overcome such limitations [70, 71]. The second limitation is that this study is limited by two types of muscles: skeletal muscles and smooth muscles, without examining cardiac muscles. The difficulty with cardiac muscle ultrasound imaging is the sequential nature of muscle data. A deep learning model which can read sequential data is needed, such as a long short-term memory (LSTM) model [72, 73].
## 3 MAC Protocols for CRBANs
### Asynchronous MAC Protocol for Spectrum Agility
In C-RICER, power adaption methods are used prior to consider channel adaption because channel inferences can be unpredictable and frequent channel switching costs a considerable amount of energy. Furthermore, sensing energy is additionally reduced by sensing the interference level only at the current channel instead of in the entire frequency band. In addition, the C-RICER protocol with cognitive functions eludes interference and enhances network performance. C-RICER adjusts both channel frequency and transmission power to reduce interference and energy consumption. By dynamically scanning and switching channels, the WBANs can avoid coexisting interference, save energy and reduce packet drop and packet delay. In a high-interference environment involving high energy consumption, the C-RICER protocol can outperform RICER3b.
# The Oral Microbiome in Children with Black Stained Tooth
## 3 Results
### Diversity of the Oral Microbiome  
A total of 10 samples were sequenced, and 60,371 reads in total were obtained. Of all the reads, 39,013 reads (64.6%) passed quality filtering (low-quality sequence reads and a minimum length of 300 bp) and remained. Both the BS and control groups contained 28,142 and 10,871 reads with an average length of 472 and 465 bp, respectively (Supplementary Table S1). The microbial abundance based on the read number was significantly higher in the BS group than the control group (Figure 1A, \(p<0.05\)). OTUs were identified by pairwise alignment to the EzTaxon database at a 3% distance. Totals of 15 phyla, 28 classes, 45 orders, 80 families, 145 genera and 423 OTUs (species) were identified in these samples. The BS group contained 348 OTUs in total (without duplication), whereas the control group had 293 OTUs. The alpha diversity of the microbiome was estimated by Simpson and Shannon indices, indicating that the diversity in the BS group was significantly higher than in the control group (Figure 1B,C, \(p<0.05\)).
# A Comparative Study of Effective Domain Adaptation Approaches for Arabic Sentiment Classification
## 3 Materials and Methods
### Evaluation Metrics  
In a domain adaptation setting, source data form the labeled training set, where target data is separated into two sets. Commonly, 80% of the target data form an unlabeled training set, and the remaining 20% are used for testing (unseen during training). In line with most existing studies, adaptation performance was evaluated as the sentiment classification accuracy on the target. Testing accuracy was reported for each source-target pair, after which the accuracies were compared with the baselines (no adaptation). The accuracies of all source-target pairs were then averaged to attain a summarized accuracy score for each tested method.  
In addition, to assess the performance of a method on MSA compared with DA, as well as its obtained results on Arabic compared with its reported results on English, we calculated the average transfer loss. A transfer loss using two metrics was proposed in a prior study [21]. The first was a transfer error e(S,T), which refers to the classification errors of a classifier trained on a source S and tested on a target T. The second was a baseline in-domain error e\({}_{\text{b}}\)(T,T), which refers to the errors of a classifier trained and tested on T. In turn, the transfer loss between S and T was computed as follows:  
\[\text{t(S,T)}=\text{e(S,T)}-\text{e}_{\text{b}}\text{(T,T)}. \tag{1}\]
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 3 Current Gridded Forest Biomass Maps
### AGB Maps Derived from a Combination of Field Biomass, LiDAR Data and Optical and/or Radar Data
#### 3.4.3 The First AGB Map of Tropical Africa's Forest  
[PERSON] et al. [89] provided the first map of tropical Africa's forest AGB on the basis of extensive field measurements, 2003 GLAS data, and 2000-2003 MODIS observations using regression tree models. Cross-validation results showed that the model explained 82% of the variance in AGB, with a root mean square error of 50.5 Mg/ha. [PERSON] et al. [90] suggested that this map tended to underestimate in woodlands and overestimate in grasslands and savannas due to the usage of inappropriate field data or an inappropriate model.
## 3 Materials and Methods
### Antifungal Testing  
Antifungal testing was performed in accordance with the guidelines (document M38-A) [57] with modifications described by [PERSON] et al. [58]. In this study the most important mycotoxin-producing species [59] were chosen as tested fungi. _Aspergillus flavus_ (NRRL 3251), _Aspergillus ochraceus_ (CBS 589.68), _Fusarium garminearum_ (CBS 110.250), _Fusarium verticillioides_ (119.825), and _Penicillium expansum_ (CBS 164.59) were used as producers of regulated mycotoxins (aflatoxins, ochratoxin A, deoxynivalenol, T-2 and HT-2 toxins, fumonisins, zearalenon, patulin, and citrinin). In addition, several species that are important producers of emerging mycotoxins were also used, such as _Alternaria alternata_ (wild type) and _Rhizophus_ spp. (wild type). For the inoculum preparation, the fungal spores were harvested from 7 days old potato dextrose agar (PDA) slants grown at 25 \({}^{\circ}\)C in the dark (except for _Fusarium_ spp. where the mung bean agar (MBA) was used instead of PDA as a better alternative for sporulation). The number of the spores for inoculation was adjusted to 10\({}^{6}\) CFU/mL by using Haemocytometer. For the antifungal susceptibility testing the RPMI 1640 medium buffered with 0.164 M MOPS (3-(\(N\)-morpholino)propanesulfonic acid) at pH 7.0 was sterilized by filtration through 0.22 \(\upmu\)m filter.  
The ground, freeze-dried sample of _C. bursa_ L. (1 g; FD-CB) was used for ultrasound-assisted extraction with water (H\({}_{2}\)O) and dimethyl sulfoxide (DMSO) as a solvent using an ultrasound bath with temperature control at 37 kHz and the ultrasonic power of 50 W (Elma, Elmasonic P 70 H, Elma Schmidbauer GmbH, Gottlieb-Daimler, Singer, Germany). The obtained extracts (500 mg/10 mL) were sterilized by filtration and diluted to tested concentrations (5000, 500, 50, and 5 \(\upmu\)g/mL) in sterile buffered RPMI 1640 media. The plates were incubated at 35 \({}^{\circ}\)C in the dark during 72 h (to ensure stable growth of all tested fungi). After growth, the plates were read on a microplate reader (Azure Ao Absorbance microplate reader, Azure biosystems, Dublin CA, USA) at 405 nm. The Minimal inhibitory concentration for 50% cells (MIC\({}_{50}\)) was defined as the lowest concentration reducing the optical density by 50% at 405 nm compared to growth control.  
During the experiment growth, increasement was also noted in some cases compared to control and, therefore, growth inducing concentration (GIC\({}_{50}\)) for at least 50% of the cells was defined as the tested extract concentration where optical density was at least 50% higher at 405 nm compared to growth control.
# Band Gap Properties in Metamaterial Beam with Spatially Varying Interval Uncertainties
## 3 Results and Discussion
### Influences of the Number of Terms in KL Expansion on the Safe Band Gap  
Figure 6 displays the convergence of the ending frequency of the safe band gap with the number of terms \(N\) in KL expansion under three different cases. \(N\) takes the first 15 orders in MCM and IPFEM. However, in the vertex method, the computation time increases dramatically as \(N\) increases. When \(N\) is greater than 11, the computational efficiency is extremely low. Thus, only the first 11 orders of \(N\) are taken in the vertex method. It can be seen that a larger \(N\) denotes a more accurate interval density field simulation; thus, the ending frequency of the safe band gap is also more accurate. But it also means more computing time. In these three figures, the ending frequencies obtained by the three methods all changed rapidly when \(N<5\) and became stable until \(N\) was near 10. Considering the calculation accuracy and efficiency comprehensively, the final value of \(N\) was determined to be 10. Another interesting phenomenon is that in all the numerical examples presented in these three figures, as \(N\) increased, the ending frequencies obtained  
Figure 5: IVTA of MB with interval density field (\(C_{B}=0.05,I_{B}=0.8L_{\text{beam}}\)).  
by the MCM and VM gradually coincided. However, the ending frequency obtained by the IPFEM was always higher than the calculation results of the MCM and VM.