esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Equalization Technologies
### Summary of Recent Work
As is shown in Table 4, the SVM an NN have a higher complexity than conventional equalization, although the performance can be significantly improved. However, the computation cost is getting lower as the continuous development of the integrated circuit. So, from the long-term point of view, the equalization based on machine learning algorithms will play an increasingly important role for short-reach PAM4 optical links. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Equalization Technologies** & **FFE/DFE** & **DD-FTN** & \
\begin{tabular}{c} **ID-FFE/** \\ **ID-DFE** \\ \end{tabular} & **CR-FFE** & **VNLE** &
\begin{tabular}{c} **Sparse** \\ **VNLE** \\ \end{tabular} & **SVM** & **NN** \\ \hline Computational Complexity & low & fair & low & low & high & fair & high & high & high \\ Performance Improvement & low & fair & fair & fair & high & high & high & high \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the computational complexity and performance improvement for different equalization technologies. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Equalization** & **Distortion** & \begin{tabular}{c} **Rate** \\ **(Gbps)** \\ \end{tabular}  & \begin{tabular}{c} **Reach** \\ **(km)** \\ \end{tabular}  & \begin{tabular}{c} **Wavelength** \\ **(nm)** \\ \end{tabular}  & **FEC** & **Tx** & **Rx** & **Ref. ** \\ \hline \multirow{2}{*}{FFE/DFE} & bandwidth & \multirow{2}{*}{56} & \multirow{2}{*}{60} & \multirow{2}{*}{1295.13} & \multirow{2}{*}{\(3.8\times 10^{-3}\)} & \multirow{2}{*}{EML} & \ \begin{tabular}{c} APD + TIA \\ \& PD + TIA \\ \end{tabular}  & [26] \\  & limitation \& CD & & & & & & & \\ \hline \multirow{2}{*}{FFE/DFE} & bandwidth & \multirow{2}{*}{106} & \multirow{2}{*}{40} & \multirow{2}{*}{1309.49} & \multirow{2}{*}{\(2\times 10^{-4}\)} & \multirow{2}{*}{EML} & \multirow{2}{*}{APD} & \multirow{2}{*}{[27]} \\  & bandwidth & & & & & & & \\  & limitation \& CD & & & & & & & \\ \hline \multirow{2}{*}{FFE/DFE} & bandwidth & \multirow{2}{*}{112} & \multirow{2}{*}{40} & \multirow{2}{*}{1314} & \multirow{2}{*}{\(1\times 10^{-3}\)} & \multirow{2}{*}{EML} & \multirow{2}{*}{PD + TIA} & [28] \\  & limitation \& CD & & & & & & & \\  & bandwidth & & & & & & & \\  & limitation \& \& 100 & 40 & 1300 & \(4\times 10^{-3}\) \& \multirow{2}{*}{EML} & \multirow{2}{*}{PD + TIA} & [72] \\  & power fading & & & & & & & & \\ \hline \multirow{2}{*}{DD-FTN} & enhanced noise & \multirow{2}{*}{140} & \multirow{2}{*}{20} & \multirow{2}{*}{1296.2} & \multirow{2}{*}{\(3.8\times 10^{-3}\)} & \multirow{2}{*}{EML} & \multirow{2}{*}{PD + TIA} & [35] \\  & chirp of & & & & & & & & \\  & modulator & & & & & & & & \\ \hline \multirow{2}{*}{CR-FFE} & \begin{tabular}{c} clock offset with \\ CD \\ \end{tabular}  & \multirow{2}{*}{50} & \multirow{2}{*}{40} & \multirow{2}{*}{1549.39} & \multirow{2}{*}{\(2\times 10^{-2}\)} & \multirow{2}{*}{DML} & \multirow{2}{*}{OA + PD} & [39] \\  & nonlinearity & & & & & & & \\  & nonlinearity & & & & & & & \\  & nonlinearity & & & & & & & \\
## 4 Electric Vehicles
### Energy Storage and Transfer Systems Advancements  
With the recent advancements in electrochemical lithium extraction techniques involving several materials for working electrodes [73], lithium-ion rechargeable batteries have become one widespread option in EV energy storage systems.  
Using an analog-to-digital measurement and monitoring circuit, namely the BMS (Battery Management System), the EV control unit takes measurements regularly, and based on a predictive algorithm, it is possible to determine the remaining travel range or other battery pack parameters [74; 75]. Different battery types became available for the energy storage system [13], with their specifications shown below in Figure 16.  
Because the former battery type covers a greater range of benefits than the latter, efficiency improvement and lithium-ion battery packs became an option in a significant percentage of modern EVs. Nevertheless, researchers have achieved lower consumption-related battery pack losses for a new type of rechargeable battery described in [76].  
Additional charging circuits could be implemented for EVs, such as including solar panels over certain sides of the chassis. During the most favorable climate conditions, this charging system can deliver power of up to 50 watts per hour, allowing the battery pack to recharge further [77]. The diagram describing this charging system is shown below in Figure 17.  
The provided charging energy is negligible compared to the total battery pack storage capacity. Charging the EV only by this means would require a very long time to finish, increasing the vehicle complexity with an additional maximum power point detection and an energy conversion circuit.  
Figure 16: Comparison of lithium-ion (Li-ion) batteries to lead-acid.  
However, as a possible replacement for the previous energy transfer system, the V2G (Vehicle-To-Grid) topology was developed as an alternate way of storing and releasing electric energy, with the block diagram shown below in Figure 18.  
Depending on the design, for derived V2G circuits, during the day, the vehicle can charge its battery pack using a solar panel inverter, while during the night, the battery pack could convert DC to AC to power the grid and all consumers connected to it [78].
## 3 Digital Implementation
### Network-On-Chip (NoC) Architecture
```
loop ifposedge clkthen \(\Delta x1\)=XC-1; \(\Delta x2\)= WN-XC; //WN: Width of the NoC, XC: xcurrent \(\Delta y1\)=YC-1; \(\Delta y2\)= HN-YC; // HN: Height of the NoC, YC: ycurrent \(\Delta xsign\) <-((xdest-XC) > 0)?0:1 \(\Delta ysign\) <- ((ydest-YC) > 0)?0:1 if\(\Delta xsign=0\)then\(\Delta x\)<- (xdest-XC); \(\Delta xreverse\)<- (WN -xdest) + XC; if\(\Delta x\leq\Delta xreverse\)thenRoute the packet EAST; elseRoute the packet WEST; endif else{\(\Delta xsign\)!= 0}; \(\Delta x\)<- (XC-xdest); \(\Delta xreverse\)<- (WN -XC) + xdest; if\(\Delta x\leq\Delta xreverse\)thenRoute the packet WEST; elseRoute the packet EAST; endif endif if\(\Delta ysign=0\)then\(\Delta y\)<- (ydest-YC); \(\Delta yreverse\) <- (HN -ydest) + YC; if\(\Delta y\leq\Delta yreverse\)thenRoute the packet SOUTH; elseRoute the packet NORTH; endif else{\(\Delta ysign\)!= 0}; \(\Delta y\)<- (YC-ydest); //\(\Delta yreverse\) <- (HN -YC) + ydest; if\(\Delta y\leq\Delta yreverse\)thenRoutee the packet NORTH; elseRoute the packet SOUTH; endif endif ifXC=1 or XC=WN or YC=1 or YC= HN thenRoute the packet UP; // The situation that the current node is the edge node elseif\(\Delta x\)>=\(\Delta x2\)thenRoute the packet West; elseRoute the packet East; endif endif endloop
```  
**Algorithm 1**The HIP router for routing the packets in torus-based NoC
## 1 Introduction
Researchers have been able to use a wide range of new technologies, such as wireless sensor networks (WSNs), optical fiber sensors (OFS), electromagnetic sensors, and scanners, because the number of new technologies has grown so quickly in the last few decades. On the other hand, the Internet of Things has made it much easier to communicate, check, and collect data remotely and automatically. Approaches based on artificial intelligence are a key part of every step of monitoring the health of a structure, from gathering data to preparing it and analyzing it. These methods, which include planning and optimizing data collection tools like sensors, preparing data in the data center, processing and analyzing data, and planning and optimizing maintenance tools like 3D printers, can make bridge structure health monitoring systems more effective [23; 24; 25; 26]. The goal of this research is to explore the possible benefits, problems, current methods, and recent advances in SHM systems for bridges that use AI and new technologies. One further objective of this investigation is to provide researchers with resources that will enable them to get a more in-depth understanding of the monitoring systems that are present on bridges. In particular, as part of the SHM system evaluation process for bridges, AI, drones and 3D printing technology will be looked at and discussed. It should also be mentioned that there are sections of AI, drone technology, 3D printers, and SHM systems that need further investigation, as well as elements of these techniques that have not been looked into as extensively, and where the need for more extensive research is felt. Even though SHM has received a lot of attention and its literature has been looked at many times, this study tries to give a broad and complete view of AI and recent technologies in the SHM of bridges, as well as recent advances and trends in this field. Figure 1 shows a graphical illustration of the structure of the study. According to the intended structure, the rest of this study is as follows: bridge structural health monitoring systems and conceptualizations are presented in Section 2. Section 3 describes AI, drone technology and the 3D printer. Discussion of cutting-edge AI and recent technologies in bridge structural health monitoring (SHM) is included in Section 4. There are concluding remarks to be found in Section 5.
# Adaptive Software Defined Equalization Techniques for Indoor Visible Light Communication
## 6 Measurements
### Software  
As mentioned before, the software side was implemented in LabVIEW 2018. This platform offered a high level of modularity and adaptability, as the software side, which was the backbone of the whole prototype, communicated directly with the software defined radios. Therefore, any modifications of the software side could be instantaneously tested during the real measurements.  
The front panel of the prototype consisted of four parts. The first part was used to set the proper signal parameters (carrier, bandwidth, USRP gain, sample width), modulation parameters (mainly QAM order), IP addresses of USRPs, and the path of the output file with the measured parameters. The second part focused on equalizations, offering variable configurable parameters, like the regularization factor, leakage, or forgetting factor. The third part or block was mainly used for measurement, offering various readable parameters (BER, MER, EVM, etc.). Blocks 4 and 5 showed the waveform graphs on the transmitting and receiving side.
# An Alternative Series Solution for Free Vibration Analysis of Asymmetric L-Shaped Membranes
## 4 Discussion
### Accuracy and Performance  
The slight differences between the eigenvalue squares obtained from the 3-SDD and 2-SDD strategies are minimal, typically with a 1% or less relative error. This indicates that both methods are highly accurate.  
The 3-SDD strategy, while slightly more complex due to the additional segment, does not show significant advantages over the 2-SDD strategy in terms of accuracy for the first four eigenvalues. However, the additional segment in the 3-SDD strategy could offer more flexibility for more complex geometries or higher-order modes.
# Multi-Subject Image Retrieval by Fusing Object and Scene-Level Feature Embeddings
## 5 Conclusions  
In this study, we proposed a MSIR method by fusing image embeddings from object-level and scene-level features. These were generated by applying BoVW to local features from DETR and random patches, respectively. Class embedding and weighted histograms were used to better represent object-level features. Thereafter, the fusion strategy for the object- and scene-level features, was introduced in three stages. Experiments were performed on single-subject and multi-subject datasets to characterize existing image retrieval methods and to evaluate the performance of the proposed method. Among the fusion methods, the histogram-level fusion method showed the best results, owing to the generation of more representative codebooks. In the ablation study, class embedding and object-level embedding using the COCO dataset yielded more effective results. The proposed image retrieval method performed better in MSIR because the proposed image embedding method manages scene and object information simultaneously.  
Conceptualization, Y.H.; Methodology, C.-G.B.; Software, C.-G.B. and D.P.; Data curation, D.P. and R.-Y.J.; Writing--original draft, Y.H.; Supervision, M.-S.C.; Project administration, R.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a Research and Development project, Building a DataAI-based Problem-solving System of Korea Institute of Science and Technology (KISTI), South Korea, under Grant K-22-L04-C05-S01.  
Not applicable.  
We have used open datasets that are available in public.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{3}{c}{**Multi-Subject Codebook**} & \multicolumn{3}{c}{**COCO Codebook**} \\ \cline{2-7}  & **Feature** & **Codebook** & **Histogram** & **Feature** & **Codebook** & **Histogram** \\ \hline C+A & 36.24 & 35.37 & 40.97 & 37.16 & 40.24 & 43.54 \\ C+P & 47.08 & 45.81 & 50.83 & 50.08 & 53.16 & 55.42 \\ C+P+O & 43.95 & 43.66 & 44.98 & 43.79 & 47.22 & 48.49 \\ C+P+O+A & 29.37 & 29.68 & 38.23 & 30.32 & 31.31 & 39.55 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The ablation study of codebooks generated using Mutli-subject and COCO datasets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Class Embedding**} & **Weighted Histogram** & **Accuracy** & **Dataset** \\ \hline  & & & 53.25 & COCO \\ ✓ & & & 81.34 & COCO \\ ✓ & ✓ & & **85.35** & COCO \\ \hline ✓ & & & **18.12** & Paris \\ ✓ & ✓ & & 14.11 & Paris \\ \hline ✓ & & & **45.59** & COCO+Paris \\ ✓ & ✓ & & 37.43 & COCO+Paris \\ \hline \hline \end{tabular}
\end{table}
Table 3: The ablation study of class embedding and weighted histogram. The bold means the best accuracy for each dataset.
## Results and Discussion
### Identified Species
_cremoris_, which commonly cause the spoilage of food packaged in a modified atmosphere and stored in cold conditions and were detected in abundance in a study carried out on sausages [25; 33; 34]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Identified Species** & **\%**_vs_**Isolated Bacteria** & **\%**_vs_**Isolated Microorganisms** \\ \hline \
\begin{tabular}{c} Molds and yeasts \\ Bacillus spp.1 \\ _Pseudomonas_ spp.2 \\ _Mannheimia haemolytica_ \\ _Rhizobium radiolobacter_ \\ _Staphylococcus_ spp. \\ _Aeronomas_ spp. \\ _Leifsonia aquatica_ \\ _Serratia spp._ \\ _Enterobacter asburiae_ \\ _Proteins vulgaris_ \\ _Corrupnebacterium spp._ \\ _Helococccus kunzii_ \\ _Aerococcus urinae_ \\ _Gardnerella vaginalis_ \\ _Stenotoprohomonas malophilia_ \\ _Ewingella americana_ \\ _Ralstonia pickettii_ \\ _Vibrio_ spp. \\ _Ochroductrum antlropi_ \\ **Total** \\ **Gram stain** & \% \\ \hline
\begin{tabular}{c} 1 Blue indicates that the bacteria are Gram-positive. 2 Orange indicates that the bacteria are Gram-negative. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Percentage of identified genera and species from Plate Count Agar (PCA) and its corresponding Gram stain (Gram positive or negative bacteria). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Identified Species** & **Total** & **\%** \\ \hline _Leuconostoc mesenteroides_ ssp. _cremoris_ & 23.0 & 51.1 \\ _Lactobacillus delbrueckii_ ssp. _delbrueckii_ & 8.0 & 17.8 \\ _Lactococcus lactis_ ssp. _lactis_ & 6.0 & 13.3 \\ _Leuconostoc mesenteroides_ ssp. _mesenteroides_ & 4.0 & 8.9 \\ _Lactobacillus acidophilus_ & 1.0 & 2.2 \\ _Pediococcus danniosus_ & 1.0 & 2.2 \\ Non-identified & 2.0 & 4.4 \\
**Total** & **45.0** & **100.0** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Identified lactic acid bacteria from Man, Rogosa and Sharpe agar (MRS), in number of isolates and respective percentage from the total of lactic acid bacteria. In this study, of the close to 30% of yeasts and molds, _Candida_ spp. was the most abundant yeast followed by _Cryptococcus_ spp. (Table 4). These results also coincide with other studies [24; 35]. However, few studies that characterize the surface microbiota mention the isolated species of yeasts and molds, like [PERSON] et al. and [PERSON] et al. [36; 37].
# Explaining Deep Learning Models for Tabular Data Using Layer-Wise Relevance Propagation
###### Abstract  
Trust and credibility in machine learning models are bolstered by the ability of a model to explain its decisions. While explainability of deep learning models is a well-known challenge, a further challenge is clarity of the explanation itself for relevant stakeholders of the model. Layer-wise Relevance Propagation (LRP), an established explainability technique developed for deep models in computer vision, provides intuitive human-readable heat maps of input images. We present the novel application of LRP with tabular datasets containing mixed data (categorical and numerical) using a deep neural network (1D-CNN), for Credit Card Fraud detection and Telecom Customer Chum prediction use cases. We show how LRP is more effective than traditional explainability concepts of Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP) for explainability. This effectiveness is both local to a sample level and holistic over the whole testing set. We also discuss the significant computational time advantage of LRP (1-2 s) over LIME (22 s) and SHAP (108 s) on the same laptop, and thus its potential for real time application scenarios. In addition, our validation of LRP has highlighted features for enhancing model performance, thus opening up a new area of research of using XAI as an approach for feature subset selection.  
explainability; 1D-CNN; structured data; layer-wise relevance propagation; deep learning; transparency; SHAP; LIME 2021
# A 2.4-GHz Fully-Integrated GaAs pHEMT Front-End Receiver for WLAN and Bluetooth Applications
## 1 Introduction
It is worth noting that the Bluetooth mode certainly needs gain as the WLAN mode does, but the receiving gain, NF and linearity performance for WLAN system are much higher than in the Bluetooth mode, so an extra WLAN front-end module consisting of a transmitting PA and a receiving bypass LNA with switch is required. The PA and LNA functions for Bluetooth are all incorporated in the subsequent CMOS RF transceiver module. In this work, we focus on the frond-end receiver that integrating the LNA and SP3T switch. A trade-off between power handling and isolation is discussed in the asymmetrical SP3T switch architecture. Additionally, a cascode LNA topology is chosen due to its high output impedance and remarkable NF performance [11]. To optimize the gain and noise performance, the effect of impedance and voltage-gain behavior on the given LNA are explored. Figure 1: Block diagram of proposed front-end receiver for wireless local area network (WLAN) and Bluetooth applications.
# Microbial Growth Dynamics in Minced Meat Enriched with Plant Powders
## 4 Discussion
The strong antibacterial effect of rhubarb roots has also been shown by [PERSON] et al. [26] and [PERSON] et al. [27]. In the current study, rhubarb petioles were selected because petioles are used as food and have good organoleptic properties in different food matrices. The second lowest growth potential of _L. monocytogenes_ (\(\delta\) = 3.63 log cfu/g) was found in samples enriched with Apple+On+BCber. Additionally, blackcurrant berries, which are well-known for containing phenolic acids, anthocyanins, and other flavonoids, have shown good antioxidant and antimicrobial effects in both in vitro and _in producto_ studies [8; 27; 28]. The growth potential value of 3.74 log cfu/g was found for cooked minced meat samples enriched with Apple+Tom+Rubub. Minced meat with Apple+Ga+Tom had a growth potential of 5.35 log cfu/g and in all other samples was higher than 7.00 log cfu/g. It is important to mention that the only additives used in the meat samples were plant powders and the samples were stored in sterile screw-cap jars, which means that no modification of the packaging atmosphere was used. Our results indicate that plant powders alone cannot sufficiently inhibit the growth of _L. monocytogenes_ in RTE minced meat samples. The combination with other food additives (e.g., preservatives) and/or modified packaging is often necessary to achieve the criterion of 0.5 log cfu/g. Comparing the results of the control and the treatment samples demonstrates that the plant powders do have some effect on _L. monocytogenes_ growth, especially the blends of apple, rhubarb, tomato, onion, garlic, and blackcurrant. Other studies have also demonstrated that other plant materials including berries are effective against pathogenic microorganisms. Using electron microscopy, [PERSON] et al. [29] showed that cranberry concentrate damaged the cell wall and cell membrane and induced cell lysis of _L. monocytogenes_. It was shown that plant extracts with high flavonoid contents can effectively inhibit the growth of Gram-positive bacteria by damaging the integrity of the cell wall and cell membrane, inhibiting the activity of the intracellular enzymes, changing the expression of associated genes, and inducing bacterial apoptosis [30]. Furthermore, [PERSON] et al. [31] showed that naturally occurring phenolic compounds in food matrices have antibacterial activity against _L. monocytogenes_ and other pathogenic microorganisms. Despite exceeding the _L. monocytogenes_ growth potential criterion, the aerobic plate count levels in the specified storage period were very low, indicating that meat products with plant powders can have good microbiological quality and a sufficiently long shelf-life.
# Short-Term Traffic Flow Prediction Based on CNN-BILSTM with Multicomponent Information
## 4 Experiment
### Data Collection  
To test and verify the effect of the model advanced in this study, we employed the dataset provided by PEMS, which is a public highway database in California, USA, for training evaluation. The database contains real-time and historical data of a total of 47,000 detection points in California. Among which, 15 detection points in the I10-E7 area of the highway in Los Angeles, California were chosen as the research targets, and the corresponding positions are presented in Figure 4. In particular, the data from May 1 to July 31, 2021 were regarded as the experimental data. The data were recorded in intervals of 5 min, producing a total of 288 \(\times\) 92 \(\times\) 15 \(\times\) 2 pieces of sample data. The flow data of the last 1000 samples are displayed on the left-hand side of Figure 5, and the traffic flow chart from June 7 to June 11, 2021 is presented on the right-hand side. As observed, the characteristics of the traffic flow were strongly periodic, and the total traffic flow data described a similar trend.  
Figure 4: Detection point location map.  
Figure 5: Traffic flow map.
# Transparent CoAP Services to IoT Endpoints through ICN Operator Networks +
## 3 Scenario and Problem Statement
### CoAP Group Communication  
SensiHome may periodically prompt many sensors, actuators, and controllers, creating bursts of traffic. CoAP group communication can alleviate this phenomenon. Nevertheless, this requires IP multicast support with DNS mapping group names (included in the CoAP Uniform Resource Identifier (URI) requests) onto the appropriate IP multicast address. With this approach, the CoAP servers should implement IP multicast and, for each group, a specific IP multicast address should be assigned (and configured in the CoAP endpoints). Therefore, there is a need for supporting resolution systems that translate resource URIs into IP addresses. However, it is difficult for constrained devices to allocate more resources for a DNS client implementation.  
In our approach, CoAP server does not need to implement IP multicast. In addition, any modification to the DNS is not required. Group names are transparent to CoAP server since the is responsible to manage the group names. Furthermore, there is no necessity of mapping a group name a priori to a lower layer network address. Therefore, the resource-constrained IoT devices do need to allocate more resources for a DNS client implementation and IP multicast.