esa-sceva/satcom-chunk-collection huggingface dataset:
# Algorithms for Plant Monitoring Applications: A Comprehensive Review
## 5 Challenges and Future Directions  
Precision agriculture, enabled by the integration of intelligent sensors, next-generation wireless networks, and data analysis, has made significant advances for the enhancement of farming practices. However, today's path of transformation presents several challenges and constraints. A relevant limitation at the moment lies in the acquisition of data, especially those that can be detected with proximal sensors, where elements such as calibration, maintenance, sensor durability, and digital divide influence the reliability of the collected data. Among these, digital divide is particularly critical, as it affects primarily the countryside, particularly the uninhabited areas, where the application of smart sensors would be more profitable. Therefore, research efforts and investments should be dedicated to favoring the adoption of new energy-efficient, cost-effective, license-free Internet of Things (IoT) wireless standards.  
On the contrary, satellite data are available from various sources, with the main limitation being represented by the resolution of images. The use of UAVs can offer much higher resolution, unfortunately with a significant increase in costs. On the contrary, algorithm techniques have significantly improved during the past 10 years, thanks to machine learning in the beginning and then deep learning. Because of the listed difficulties,  
Figure 21: Heat map showing the number of papers using a specific deep learning algorithm applied to a specific agronomic aim.  
many studies have been applied to existing datasets, and consequently transfer learning techniques have been increasingly introduced. The explosion of transformer use in recent months forecasts an imminent adoption of artificial intelligence techniques in the research fields investigated in this paper.  
One of the challenges is represented by the lack of standardized image datasets. Plant monitoring is frequently conducted by means of image processing, where deep learning architectures play a crucial role. While many studies demonstrate the possibility to achieve high scores, the lack of standard image datasets reduces the possibility to compare or benchmark different models. Moreover, the adoption of these technologies outside research remains an issue, often because public datasets poorly generalize realistic use cases. The problem is partially overcome by the application of transfer learning, even if it does not eliminate the need of recognized public datasets captured in the field.
# All-Optical Non-Inverted Parity Generator and Checker Based on Semiconductor Optical Amplifiers
## 2 Principle  
The all-optical non-inverted parity generator and checker based on SOAs is shown in Figure 1. From the principle of the optical generator and checker circuit, we found that it can be composed of XOR [14; 15; 16] and XNOR [17; 18] gate, the dotted line section, and the entire working in Figure 1 correspond to the parity generator and checker, respectively.  
Figure 1: Principle of the all-optical non-inverted parity generator and checker based on semiconductor optical amplifiers (SOAs) (\(\oplus\) and \(\odot\) are the symbolic representation of exclusive OR (XOR) and exclusive NOR (XNOR), respectively).  
To verify whether there are errors or noise interference during the binary data transmitted from the sending end to the receiving end, the parity checker is applied to the sending end and receiving end of the data transmission to detect whether an error occurs (0 changed 1, 1 changed 0). On the one hand, the parity bit (0 or 1) is added to the data bit at the sending end so that total number of \"1\" codes is odd or even in the added data bit. On the other hand, the receiving end counts the number of \"1\" codes in the received data. If the total number of \"1\" codes is odd, it is called odd parity. On the contrary, it is called even parity. In odd or even parity, if the number of \"1\" codes are even or odd in the received data, it means that error has occurred.
## 5 Simulation of a 40-Channel WDM-PON
### Protection Scheme for ROA\({}_{2}\)  
The simulation setup and parameters for the protection state where ROA\({}_{3}\) is used to backup ROA\({}_{2}\) are similar to those shown in Figure 10 with additional SSMF between RN and the optical amplifier unit. The de-multiplexer and optical receivers are also included to characterize the system performance. Based on Section 5 results, the EDFA in ROA\({}_{3}\) has a 7.5 m long EDF and 50 mW pumping power. In the simulation scenario, the 40-channel signals are amplified by ROA\({}_{3}\) and bypass the failed EDFA in ROA\({}_{2}\). The upstream data rate is 10 Gb/s, the same as the downstream signals. The transmitted optical power of each channel is \(-3\) dBm.  
Figure 18 shows the output optical spectrum after the optical amplifier unit. The gain difference \(\Delta\)G is 7.36 dB, which can be improved by adding a GFF. The BER curves of CH1, CH20, and CH40 are shown in Figures 19-21, respectively. Because of the pre-amplification, these channels' Rx sensitivities are remarkable in the B2B condition for a BER of \(10^{-9}\). CH20 has the highest gain, and so the greatest Rx sensitivity of \(-38.65\) dBm. After the signal transmission by the SMF, fiber dispersion has the most significant effect on CH1; the longest transmitted distance of CH1 is 80 km, whereas fiber dispersion has the least influence on CH40, resulting in the longest transmitted distance of up to 100 km. The minimum required ROP for a BER of \(10^{-9}\) at CH1, CH20, and CH40 are \(-26.52\) dBm, \(-32\) dBm, and \(-33\) dBm, respectively, after signal transmission via the 80 km SMF.  
To verify our protection scheme's performance for ROA\({}_{1}\) and ROA\({}_{2}\) in the full band (CH1 to CH40), we adopted the same channels to demonstrate and analyze upstream and downstream unidirectional transmission, separately. Noticeably, the Rayleigh scattering and reflection issues of the same wavelength band used for both downstream and upstream can significantly degrade the signals' quality. We can mitigate this degradation by splitting the full band into two sub-bands or the odd and even channel groups, one for each direction.
## 4 Hardware Security
### Physical Unclonable Functions (PUFs)
#### 4.1.5 Integration of FPGAs-Based PUFs with Edge AI
Deployment of the OpenVINO toolkit and the Intel FPGA AI Suite in the development of DL-enhanced embedded systems on multiple FPGA-accelerated servers is shown in Figure 20. The OpenVINO toolkit comprises tools and libraries that utilize techniques like pruning, quantization, etc., for the optimization of neural networks. The basic workflow of Intel Distribution of the OpenVINO toolkit is as follows:  
* **Model Optimizer** converts models from various frameworks like Caffe, TensorFlow, Open Neural Network Exchange (ONNX), and Kaldi to an intermediate representation format for faster inference. * **Inference Engine** reads the IR format and supports heterogeneous execution across different hardware architectures such as CPU, GPU, Integrated GPU, etc. * **Model Zoo** is a common interface for heterogeneous hardware that contains examples to get started with OpenVINO quickly. Vitis AI 3.0 (Xilinx Inc., San Jose, CA, USA), is a unified software platform that includes optimized IP, tooling, and libraries to grant users access to AI inference acceleration through adaptable hardware. It consists of a rich set of AI models, optimized deep learning processor unit (DPU) cores, tools, libraries, and example designs for AI at the edge and in the data center. It provides a unified programming model for accelerating Edge, Cloud, and Hybrid computing applications. Vitis AI integrated development environment is presented in Figure 21, with the target platform, i.e., FPGAs, as the base layer. The Xilinx runtime library in the second layer controls the data movement across domains. Also, compilers are used in the layer for mapping the AI model's optimal instruction set and dataflow model as well as carrying out optimization tasks. There are more than 400 optimized and open-source applications across eight Vitis libraries that are defined in the third layer and offer out-of-the-box acceleration with minimal to zero code changes to your existing applications [279]. Figure 20: Edge-ready AI toolkits for Intel FPGAs [272].
# Evaluation of 6 LoWPAN Generic Header Compression in the Context of a RPL Network
## 4 Generic Header Compression  
One of the main problems of 6 LoWPAN-NHC is that it does not define any compression for headers other than UDP or IPv6 extension headers. 6 LoWPAN-GHC, defined in RFC 7400 [4], tries to solve this issue. It defines a compression mechanism that can compress any header or header-like payload. 6 LoWPAN-GHC uses the same principles as the LZ77 compression format [5]. It has a dictionary, which can be referenced to achieve compression.  
The RFC 7400 document defines three additional 6 LoWPAN-NHC dispatch values. These values are used to indicate that UDP, ICMP and IPv6 extension headers are compressed using the 6 LoWPAN-GHC format. For the UDP, the header is compressed using the 6 LoWPAN-NHC compression format and only the UDP payload is compressed using 6 LoWPAN-GHC.
## 4 Alteration of Gut-Brain Axis and Neurodegeneration
### Gut Microbiota in Alzheimer's Disease: Role of \(\beta\)-Amyloid  
Increased permeability of the gut and BBB can contribute to the pathogenesis of Alzheimer's disease (AD) [68]. AD is characterized by an accumulation of aggregate amyloid fibrils such as \(\beta\)-amyloid (A\(\beta\)) protein and neurofibrillary tangles. Amyloid precursor protein (APP) from which A\(\beta\) derives is expressed by enteric neurons and glia, thus suggesting a role of the ENS in the pathogenesis of AD [69]. The continuous deposition of the A\(\beta\) peptide in the extracellular space provokes an inflammatory response that culminates with the constant release of pro-inflammatory cytokines, worsening the cognitive functions and dementia observed in AD. Furthermore, a vicious cycle between A\(\beta\) deposition and microglia activation in the brain is generated. An analysis of the gut microbiota composition of AD patients demonstrated a predominance of Firmicutes and Bacteroidetes phyla. An in vitro study showed that valeric acid and butyric acid produced by anti-inflammatory bacteria are able to inhibit the conversion of A\(\beta\)40 monomers to pathological A\(\beta\) fibrils, thereby preventing their accumulation in the brain [70]. Misfolding of proteins is a hallmark of AD, and modification of the neuronal autophagy flux through the gut microbiota or metabolites could contribute to the balance between production and clearance of proteins in the brain.  
Interestingly, the gut microbiota produces high levels of amyloids to form biofilms with resistance to infections. However, the chronic accumulation of A\(\beta\) protein may trigger an immune response, enhancing the release of inflammatory cytokines and the transport of amyloid deposits to the brain [71]. Finally, high levels of LPS were observed in the hippocampal and temporal lobe of AD patients. The abundance of LPS in the human GI tract could affect the immune system, contributing to AD neurodegeneration [72].
## 2 Materials and Methods
### QSM Reconstruction  
QSM was reconstructed with a susceptibility tensor imaging software suite (Version 2.2, Brain Imaging & Analysis Center, Durham, NC, USA). The process consisted of 3 steps. In the first step, the acquired phase map was unwrapped to a continuous phase image with HARmonic (background) Phase Removal using the Laplacian operator (HARPERELLA) [18]. Because magnetic sources other than brain tissue (e.g., bone) can affect the obtained field, the second step involved removing the background field, originating from outside the tissue of interest [19]. These two steps were performed simultaneously by estimating the phase originating from the background and applying fast Fourier transform-based inverse Laplacian. In the third step, the magnitude image and tissue field from the first step were combined to reconstruct the QSM image. Because the inversion from k-space to field map is ill-conditioned due to the insufficiency of k-space values, magnetic susceptibility obtained from k-space includes streaking artifacts. Thus, we used the least-squares algorithm-based method to identify the artifacts and exclude them from obtained susceptibility maps.
# A Comprehensive Survey of the Harmony Search Algorithm in Clustering Applications
## 1 Introduction
The HSA can make trade-offs between convergent and divergent regions, and this is the main cause for its energy, success, and reputation. In the HSA rule, exploitation is chiefly controlled by pitch adjustment rate (PAR) and bandwidth (BW) [32,34], and exploration is controlled by the harmony memory considering rate (HMCR) [35,36]. Optimization algorithms can be tested by searching for minima or maxima; authors will note their approach. Figure 1 shows the quantity of issued papers (i.e., conferences, book chapters, and journal articles) concerning the HSA as of February 2020, sorted by publisher (IEEE, Wiley, Elsevier, Springer, Taylor & Francis, and others). Figure 2 shows the number of published papers on each of the main variants of the HSA, and Figure 3 shows the number of publications on applications of the HSA to clustering. Figure 1: Number of publications on the Harmony Search Algorithm (HSA) since it was proposed until the first half of 2020, sorted by publisher and type. Many different variants of HSA have been developed since the basic algorithm was introduced: enhanced, hybrid, modified, and multi-objective HSAs [37; 38; 39; 40; 41]. These algorithms were proposed to deal with different problems having a complex dependence on variables and associated fitness functions [42; 43]. Moreover, researchers have elaborated on the basic HSA by producing novel procedures, including specific operators, chaotic maps, hybridization, and exploratory search [44]. Some of these proposed enhancements lead to more efficient HSAs in terms of computational values and solutions. The existence of these different variants and improvements encourages researchers to address difficulties in real-world problems. The sections of this survey paper are organized as follows. In Section 2, the main procedures of the HSA are presented. Section 3 shows the variants of the HSA that have been used in solving clustering problems. Such applications to clustering are discussed further in Section 4. Discussion and theoretical analyses are given in Section 5. Finally, the outcomes of the survey and prospects for further investigation are presented in Section 6. Figure 3: Number of publications on clustering applications of the HSA since it was proposed until the first half of 2020. WSN = Wireless Sensor Networks. Figure 2: Number of publications on variants of the Harmony Search Algorithm since it was proposed until the first half of 2020.
# Traffic Noise Prediction Applying Multivariate Bi-Directional Recurrent Neural Network
## 3 Results and Discussion
### Final Model Evaluation  
The final model has been validated with testing data. A certain period of the prediction on each day can be observed in Figure 19.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Value** \\ \hline Number of layers & 3 (one bidirectional GRU layer, two dense layers) \\ Number of neurons & \(50,100,1\) \\ Learning rate & 0.0001 \\ Batch size & 256 \\ Optimization algorithm & Adam \\ Dropout rate & 0.4 \\ Sequence length (n\_steps) & 30 \\ Number of Epochs & 143 (controlled by Keras Early Stopping) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Final GRU model’s hyperparameters.  
It can be seen that the proposed model catches the tendency of the dynamic change of the real traffic noise level in a short time interval. When the sound level increases or decreases drastically, it is relatively difficult for the model to precisely estimate the noise peak values. This phenomenon is explainable with the statistical rule of regression toward the mean, which describes the model aims to predict the expected value, closer to the mean on the measurements [48].  
Figure 20 illustrates the predicted SPL against the real SPL of the testing data (Figure 20a) and the distribution of the corresponding residuals (Figure 20b). In Figure 20a, it is clear to see that the predicted SPL values and the actual SPL values have a good fit around the red dashed reference line indicating the ideal case, where the predictions equal the ground truth. A mean of \(-0.509\) dB and a standard deviation of \(2.309\) dB with a bell shape can be observed in the distribution of testing data residuals in Figure 20b.  
Figure 19: Illustrations of captured and estimated SPL data over a selected time on each day (data from Mic 8): (**a**) model prediction over a selected time on the first day; (**b**) model prediction over a selected time on the second day.  
Figure 18: Model learning curve showing the reduction in training data error and validation data error over the GRU model training process.  
The overall results after leave-one-subject-out cross-validation with GRU, many-to-many architecture, can be found in Table 7.  
As shown in Table 7, given 95% confidence interval, the model achieves an RMSE of 2.448 \(\pm\) 0.67 dB and a MAE of 1.979 \(\pm\) 0.51 dB.  
The results of each iteration might slightly vary after multiple running, due to the random initialization of weights in neural networks. Besides, the used batch for updating the weights during one iteration through gradient descent is generated with random sampling techniques, which could also lead to a slight variation in the final result.
## 3 Methodology
### Spatial Layer Construction  
The spatial layer of GLMI manages two-dimensional spatial partitions through the Trie tree. Each leaf node of the Trie tree represents the Geohash-Hilbert encoding of a partition. By combining Geohash-Hilbert coding and Trie tree, the spatial layer of GLMI can efficiently encode and index two-dimensional spatial information and better ensure spatial proximity.
## 4 Results and Discussion
### Research Trends and Key Findings
#### 4.4.1 Publication Trends  
Figure 8 reveals a fluctuating trend in patent retrieval research publications from 2013 to 2023, with the highest number of annual publications reaching 14 in 2015. This period of peak activity between 2013 and 2015 is, however, followed by a notable decrease in 2016 and 2017. The number of publications saw a resurgence in 2018 and 2019, only to decline again in 2020. Despite this inconsistency, a slight increase in 2021 and 2022 indicates a renewed interest, particularly in leveraging NLP techniques. As the search was initially conducted in July 2023, the publication data for the year 2023 constitutes only partial data, as it takes up to 6 months for a publication to be properly indexed in databases. Overall, this observation suggests a growing interest among researchers in exploring advanced NLP applications in patent retrieval, hinting at a potential rise in publications in the near future, as further discussed in the subsequent section.  
Figure 9 illustrates that a significant majority of literature in this field, approximately 66% of the overall articles, is published in conference proceedings, with the remaining 34% appearing in journals. This distribution suggests a preference among researchers for conference presentations due to the immediate feedback, networking, and visibility they offer. Conferences also facilitate multi-disciplinary interactions, providing a dynamic platform for researchers to share and refine their ideas, a factor that could explain the trend toward conference publications in the realm of patent retrieval research.  
Table 12 shows that out of the top 10 publications that received the highest quality score, seven (7) are journal publications while only three are conference publications. This result reiterates the already known fact that the quality of journal papers is generally considered superior to that of conference papers. Moreover, these studies are associated with various well-known publishers including Elsevier, IEEE, Taylor & Francis, ACM, Springer, and Emerald. Similarly, Table 13 shows that out of the top 10 most cited papers, five are journal publications, with another five being conference papers. This observation indicates that citations on papers are not merely dependent on the type of publication,  
Figure 8: Number of published papers per year.  
Figure 11 summarizes the trend in the number of journal and conference publications per publisher. It is evident that IEEE, ACM, and Springer are favored venues for conference publications, likely due to their specialization in fields related to information retrieval, data mining, and artificial intelligence. This specialization is a key factor contributing to their high number of conference papers. In contrast, Elsevier and Taylor & Francis are noted for only journal publications, focusing on a wide array of disciplines, which explains their prominence in journal outputs.
## 3 Results
### Incubation Time Required for the Evaluation of Degradation  
Figure 1 shows time-dependent changes of pharmaceutical residual ratios in humic andosol slurry (Ome) under aerobic condition (a) and anaerobic condition (b). Higher degradation ratios were observed in aerobic condition compared with those in anaerobic condition. Figure 1 also shows that 14 days of incubation were long enough to observe aerobic degradation of the target compounds in soil slurry, while longer incubation time may be needed to assess the degradability in anaerobic degradation. The residual ratio of DCF at day 1 in the aerobic condition was less than 10%, suggesting  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **CA** & **IBP** & **FEP** & **NPX** & **KEP** & **DCF** & **IDM** \\ \hline Kawagoe Sum & 98 & 92 & 73 & 74 & 93 & 74 & 49 \\ Kawagoe Win & 104 & 91 & 88 & 84 & 134 & 84 & 40 \\ Shoubuen surface & 113 & 99 & 87 & 84 & 90 & 63 & 70 \\ Shoubuen underlayer & 124 & 113 & 96 & 91 & 96 & 66 & 77 \\ Niigata & 95 & 77 & 99 & 98 & 102 & 74 & 102 \\ Ome & 113 & 99 & 85 & 77 & 83 & 55 & 23 \\ Average (n = 6) & 107 & 95 & 88 & 85 & 99 & 69 & 60 \\ Standard Deviation & 11 & 12 & 9 & 9 & 18 & 10 & 28 \\ \hline \end{tabular}
\end{table}
Table 1: The residual ratios (%) of pharmaceuticals in sterile slurry with the breathable sponge caps.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **CA** & **IBP** & **FEP** & **NPX** & **KEP** & **DCF** & **IDM** \\ \hline Kawagoe Sum & 113 & 95 & 117 & 119 & 118 & 72 & 74 \\ Kawagoe Win & 105 & 91 & 99 & 96 & 105 & 58 & 37 \\ Shoubuen surface & 100 & 90 & 118 & 74 & 80 & 56 & 77 \\ Shoubuen underlayer & 111 & 103 & 101 & 99 & 105 & 69 & 88 \\ Niigata & 93 & 76 & 83 & 76 & 86 & 48 & 38 \\ Ome & 95 & 86 & 72 & 69 & 72 & 51 & 30 \\ Average (n = 6) & 103 & 90 & 98 & 89 & 94 & 59 & 57 \\ Standard Deviation & 8 & 9 & 18 & 19 & 17 & 9 & 25 \\ \hline \end{tabular}
\end{table}
Table 2: The residual ratios (%) of pharmaceuticals in sterile slurry with the sealed caps.  
the necessity of the sampling between day 0 and day 1 to obtain exact half-life times of this compound. The results also showed almost no anaerobic degradation of CA, IBP, FEP, and KEP. The persistence of DCF was also observed under anaerobic condition judging from the residual concentration after 14 days of incubation, although the measured residual ratios of DCF in anaerobic condition were even increased during incubation. The inconsistency in the measured concentrations of DCF might be due to the low recovery of DCF from soil, as mentioned in the earlier section, although other chemical or biological factors may have influenced the concentration. On the other hand, more than 95% of the initial concentrations of IBP, FEP, NPX, KEP, and DCF were degraded in aerobic condition during 14 days of incubation. It is noted that more than 60% aerobic degradation was observed for CA by this slurry, although the rate of degradation was the slowest among the target pharmaceuticals.