esa-sceva/satcom-chunk-collection huggingface dataset:
# ICENET: An Information Centric Protocol for Big Data Wireless Sensor Networks
## 4 ICENET--Information Centric Protocol for sEnsor NETworks
### Trickle Algorithm for Interests  
The Trickle Algorithm is a scalable, powerful, and simple mechanism that can be applied to a wide range of protocol design problems [49]. ICENET adopts Trickle principles to efficiently refresh the interests stored in the node Icache. This is accomplished by defining specific consistency and inconsistency events. Consistent messages, or events, do not change the perception of the node about the network topology or channel conditions, and therefore reflect stability. ICENET nodes consider that to receive a data message from one of the child nodes represents a consistency, while not receiving any data message from any of the child nodes represents an inconsistency.  
The Trickle Algorithm presents three configuration parameters: Minimum interval size (\(I_{\text{min}}\)), maximum interval size (\(I_{\text{max}}\)), and a positive integer constant (\(k\)). In addition to these parameters, there are three variables: Current interval size (\(I\)), a random time point (\(t\)) in the interval \([1/2,\;I]\), and a consistency counter (\(c\)). If a consistent message/event is received or detected, \(c\) is incremented. At time \(t\), ICENET nodes broadcast the stored interest if \(c<k\). When \(I\) expires, the time interval is doubled, in order to slow the communication rate exponentially. If an inconsistency is detected, Trickle resets the interval length \(I\) to the initial value (\(I=I_{\text{min}}\)), in order to increase the sending rate, so that the nodes quickly adapt to changes in network conditions. Whenever \(I\) is reset, Trickle sets \(t\) to the interval \([I/2,\;I]\). Table 2 shows the main operations of the Trickle Algorithm.  
Figure 5: Interest message processing and forwarding in ICENET.  
ICENET sets the parameter \(I_{\text{min}}=\text{SP}\) (sample period) of the interest. The intuition is that, after sending an interest message, the node would start receiving data packets after a time approximately equal to the sample period. This assumption assumes ideal channel conditions, which means error-free links and no losses in the MAC layer. However, in real environments, the interest message may not be received by all child nodes. For this reason, ICENET does not require all child nodes to answer an interest. This can be parametrized by the constant \(k\), which represents the number of child nodes that should answer the interests. This is a very important feature, as we can tune the protocol behavior depending on the application requirements.
## 4 The Results and Discussion of Experiment  
In this section, 23 classical benchmark functions are used to evaluate the performance of the proposed algorithm. Furthermore, nine optimization algorithms are selected for comparison, namely the Gorilla Troops Optimization (GTO) [41], Arithmetic Optimization Algorithm (AOA) [43], Salp Swarm Algorithm (SSA) [2], Whale Optimization Algorithm (WOA) [44], Grey Wolf Optimizer (GWO) [6], Particle Swarm Optimization (PSO) [20], Random-Opposition-Based Learning Grey Wolf Optimizer (ROLGWO) [45], Dynamic Sine-Cosine Algorithm (DSCA) [46] and Hybridizing Sine-Cosine Algorithm with Harmony Search (HSCAHS) [47]. For the sake of fairness, the maximum iteration and population size of all algorithms are set to 500 and 30, respectively.  
The 23 classical benchmark functions can be divided into three categories: unimodal functions (UM), multimodal functions (MM) and composite functions (CM). The unimodal functions (F1-F7) have only one optimal solution that is fluently used to evaluate the exploration ability of the algorithm. The multimodal functions (F8-F13) are characterized by multiple optimal solutions. These functions can be utilized to evaluate the ability of jumping out from the local optimal solution in complex situations. The composite functions (F14-F23) are usually adopted to evaluate the stability of algorithms [48].  
In addition, the benchmark functions (F1-F30) provided in CEC2014 and the benchmark functions (F1-F10) provided in CEC2020 are used in the other experiments [49]. These benchmarks are applied in many papers to evaluate the performance for the ability of solving problems. The standard to evaluate the performance of the optimization algorithm is whether it can keep the balance between exploration and development and avoid the local optimum.
# Quarantining Malicious IoT Devices in Intelligent Sliced Mobile Networks
## 3 Proposed Solution
### Dynamic Reassignment of Devices to Slices  
The dynamic reassignment of UEs to slices can be carried out in a transparent and seamless way both to the devices and to the mobile network elements by extending our previous work for transparent session and service continuity in dynamic Multi-Access Edge Computing (MEC) [36] to Network Slicing. This solution is valid both for 4G and 5G networks and consists in replicating the state of the user-plane functions to the different slices through an SDN application and then configuring the flow rules in the SDN switch to forward the traffic of each UE to the desired slice. The original solution described in [36] is intended for dynamic MEC, i.e., the traffic is originally forwarded to the core network and at some point, user-plane resources are dynamically deployed at the edge and the user-plane context is replicated from the core to the edge, before the traffic of the UE is diverted to the edge resources. In this paper, we generalize that solution to cover the case of Network Slicing, where both slices are deployed at the core network and the state replication solution is used for continuously maintaining state consistency between the two slices, and per-user traffic redirection is leveraged for transparently moving IoT devices from the IoT NS to the Quarantine NS and vice versa as needed.
## 3 Verification of the Analytical Solution  
### Compared with the Numerical Model  
The 1D numerical model is established to verify the proposed analytical solution in this section. Darcy's Law and Transport of Diluted Species in Porous Media modulus are used to simulate the contaminant transport through CGCW and aquifer system. The thicknesses of the SBCW, GMB, and aquifer are assumed to be 0.6 m, 0.0015 m and 10 m [12; 20]. The extra fine physics-controlled meshing sequences are invoked and 166 computational units are generated. Methyl tertiary butyl ether (MTBE) is considered as the contaminant in the source [33]. The parameters used in the calculation are all listed in Table 1. It should be noted that the leakage through the GMB is calculated to be 2.7 \(\times\) 10\({}^{-10}\) m/s in this section. It can be concluded from Figure 2 that the results obtained from the analytical solution are in agreement with those from the numerical simulations. The differences between the results predicted by the proposed analytical solution and the numerical model for \(t=25\) and 50 years are only 0.1% and 0.12%, respectively. This indicates that the analytical solution can accurately predict contaminants transport through the CGCW and aquifer system.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **SBCW** & **GMB** & **Aquifer** \\ \hline Porosity & 0.42 [31] & - & 0.47 [31] \\ \hline Hydraulic conductivity (m/s) & \(1.0\times 10^{-9}\)[34] & \(1.0\times 10^{-14}\)[34] & \(1.0\times 10^{-5}\)[34] \\ Thickness (m) & 0.6 [12] & 0.0015 [20] & 10 [12] \\ Cross-sectional area (m\({}^{2}\)) & 1 & 1 & 1 \\ Partition & MTBE & - & 0.6 [35] & - \\ coefficient & TOL & - & 100 [35] & - \\ Retardation & MTBE & 1.5 [20] & - & 1 [20] \\ factor & TOL & 2.5 [20] & - & 1 [20] \\ Diffusion & & & & \\ coefficient & MTBE & \(3.5\times 10^{-10}\)[21] & \(7.7\times 10^{-13}\)[21] & \(3.3\times 10^{-10}\)[21] \\ (m\({}^{2}\)/s) & TOL & \(3.8\times 10^{-10}\)[36] & \(3.8\times 10^{-Additionally, the CGCW can effectively block the water flow and decrease the contaminant concentration in the groundwater. For example, the contaminant concentrations at \(x\) = 0.6 and 0.6015 are 0.73 and 0.31 when \(t\) = 25 years, respectively. The main reason for this phenomenon is that the diffusion coefficients (7.7 \(\times\) 10\({}^{-13}\) m\({}^{2}\)/s) and partition coefficients (0.6) in GMB for MTBE are small. GMB can effectively impede the transport of MTBE. This will lead to a huge contaminant concentration mutation between two sides of GMB [30].
## Materials and Methods
### Inoculated Milk Analyses
#### 2.4.3 Determination of Free Amino Acids with Liquid Chromatography-Mass Spectrometry (LCMS)  
A 40-\(\mu\)L sample of supernatant of inoculated milk was placed in a 1.5 mL polypropylene microcentrifuge tube, mixed with 40 \(\mu\)L of methanol containing 10 mg/L of d4-alanine as internal standard-spiked methanol. LC-MS (Agilent 1260 Infinity Quaternary LC System, Santa Clara, CA, USA) equipped with Kinetex Evo C18 (150 mm \(\times\) 2.1 mm \(\times\) 1.7 \(\mu\)m. Phenomenex, Torrance, CA, USA) column was used. Preparation and analysis methods used by the same research group, [26] were followed. Each of the inoculated milk sample was analysed three times.  
4.4 Determination of Volatile Compounds with Solid Phase Microextraction--Gas Chromatography-Mass Spectrometry (SPME-GC-MS)  
Volatile components of the inoculated milk samples were analysed according to the methods used by the same research group [27] with slight modifications.  
A sample of approximately 1 g of inoculated milk sample was quickly introduced to 10 mL headspace vial, followed by addition of 4 \(\mu\)L of internal standard (2-chloro-phenol). It was then sealed with a polytetrafluoroethylene silicon septum. The vials were then incubated at 50 \({}^{\circ}\)C in a thermal block for 15 min with agitation. The Solid Phase Microextraction (SPME) fibre of 50/30 \(\mu\)m Divinylbenzene/Carboxen/Polydimethylsiloxane (DVB/CAR/PDMS), StableFlex fiber 24 ga, with length of 10 mm (Supelco, Bellefonte, PA, USA) was exposed to the sample headspace for 5 min. The volatile compounds were separated by Agilent DB-FATWAX UI column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\mu\)m, Santa Clara, CA, USA) with helium as a carrier gas at a constant flow rate of 1.1 mL/min, using Gas Chromatography-Mass Spectrometry (GC-MS) (Agilent Technologies 7890B GC-System & 5977B MSD, Santa Clara, CA, USA).  
The mode of injection was splitless, and the inlet temperature for the injection port was set to 250 \({}^{\circ}\)C with 45 mL/min split flow and 2 min splitless time. Chromatographic conditions were as follows: the oven was held at 40 \({}^{\circ}\)C for 2 min, then raised to 240 \({}^{\circ}\)C at a rate of 8 \({}^{\circ}\)C/min and held for 10 min. The total run time was 37 min. The MS was operated in the electron impact mode (EI) with a source temperature of 230 \({}^{\circ}\)C, a quadrupoletemperature of 150 \({}^{\circ}\)C, an ionising voltage of 70 eV, and transfer line temperature of 250 \({}^{\circ}\)C. The mass spectrometer scanned masses from 38 to 450 m/z.  
Samples were analysed in at least triplicate. The concentration of volatile compounds in the inoculated milk samples were calculated using the following formular:  
\[[\mathrm{P}]=(\mathrm{AP}/\mathrm{AIS})\times[\mathrm{IS}] \tag{3}\]  
where: P (\(\mathrm{\mu g}/\mathrm{L}\)) = the concentration of the product; IS (\(\mathrm{\mu g}/\mathrm{L}\)) = the concentration of internal standard; AP = the area of the product; AIS = the area of the internal standard
# Instance Segmentation and Number Counting of Grape Berry Images Based on Deep Learning
## 3 Results and Discussion
### Evaluation Indicators  
1. Mean Intersection over Union (_mloU_): _mloU_ is the average of the union-intersection ratio for each category in the dataset, which is used to represent the segmentation accuracy, and the calculation formula is shown in (3).  
\[mIoU=\frac{|A\cap B|}{|A\cup B|}=\frac{1}{k+1}\Sigma_{i=0}^{k}\frac{p_{ii}}{ \Sigma_{i=0}^{k}p_{ii}+\Sigma_{i=0}^{k}p_{ii}-p_{ii}} \tag{3}\]  
Figure 10: Model of Soft-MRBS (grape berry segmentation based on Mask R-CNN with Soft-NMS) for grape berry instance segmentation.  
where:  
\(k\): the total number of categories output by the model;  
\(k+1\): includes the background;  
\(p_{ii}\): number of correctly classified pixels;  
\(p_{ij}\): represents the number of pixels that belong to class \(i\) but are misclassified as class \(j\);  
\(p_{ji}\): represents the number of pixels that belong to class \(j\) but are misclassified as class \(i\).  
2. Average Precision (_AP_):  
Taking the recall rate as the abscissa and the precision as the ordinate, we draw the PR curve, and the result of integrating the PR curve is the average precision \(AP\), which is defined as shown in Formula (4).  
\[AP=\int_{0}^{1}PdR \tag{4}\]  
To accurately analyze the model detection performance, _AP_ with _IOU_ thresholds be-tween 0.50 and 0.95 were calculated with an interval of 0.05, and the _APs_ under all thresholds were averaged to obtain the \(mAP\).  
3. Coefficient of determination (\(R^{2}\)):  
\(R\)-squared is a convenient and intuitive statistical measurement that indicates the closeness of predicted values and actual values, known as the coefficient of determination.  
\[R^{2}=\frac{SSR}{SSt}=\frac{\sum\left(\hat{y}_{i}-\bar{y}\right)^{2}}{\sum \left(y_{i}-\bar{y}\right)^{2}} \tag{5}\]  
where:  
\(\hat{y}_{i}\): predictive value;  
\(\bar{y}\): Average of actual values;  
\(y_{i}\): actual value.
## 2 Principle  
This section introduces the theoretical background of MIMO equalizers, including LMS, RLS, and the space-time block-coding (STBC) algorithm for SDM optical transmission. Moreover, we also cover the aspects of polarization-related dynamic equalization, orthogonal frequency-division multiplexing (OFDM) FDE requirements, and the MIMO hardware requirements in this section. Although hypothetically any modal cross-talk can be electronically managed by DSP, the algorithmic complexity of the MDM coherent receiver remains one of the main limiting factors to the MIMO equalization scheme. In general, the total complexity of the DSP algorithm can be measured by the number of complex multiplications per symbol per mode, for the reason that the complex multipliers are usually the most resource-consuming arithmetic logics in terms of power consumption, area, and cost in application-specific integrated circuit (ASIC) design [21].  
Figure 2: The recent records of spatial multiplicity vs. the transmission distance in the state-of-the-art SDM-WDM experiments [19].
# Speech Enhancement for Hearing Aids with Deep Learning on Environmental Noises
###### Abstract  
Hearing aids are small electronic devices designed to improve hearing for persons with impaired hearing, using sophisticated audio signal processing algorithms and technologies. In general, the speech enhancement algorithms in hearing aids remove the environmental noise and enhance speech while still giving consideration to hearing characteristics and the environmental surroundings. In this study, a speech enhancement algorithm was proposed to improve speech quality in a hearing aid environment by applying noise reduction algorithms with deep neural network learning based on noise classification. In order to evaluate the speech enhancement in an actual hearing aid environment, ten types of noise were self-recorded and classified using convolutional neural networks. In addition, noise reduction for speech enhancement in the hearing aid were applied by deep neural networks based on the noise classification. As a result, the speech quality based on the speech enhancements removed using the deep neural networks--and associated environmental noise classification--exhibited a significant improvement over that of the conventional hearing aid algorithm. The improved speech quality was also evaluated by objective measure through the perceptual evaluation of speech quality score, the short-time objective intelligibility score, the overall quality composite measure, and the log likelihood ratio score.  
h _Article_  
Speech Enhancement for Hearing Aids with Deep Learning on Environmental Noises
# An FPGA Implementation of a Convolutional Auto-Encoder
## 4 Conclusions  
An implementation of CAE based on FPGA is presented in this paper, which newly introduces a periodic layer-multiplexing framework. The encoder part of the proposed CAE framework, which is similar to the decoder, contains five modules, zero padding, a channel distributor, convolutional encoding, a channel arbitrator, and an output controller. In order to enhance the compatibility of the framework and further enhance the speed of convolution calculation, a parallel channels method caching input data and a parallel convolution method based on shift registers are developed, respectively. To evaluate the performance of the CAE framework, an example of image compression is carried out. The experimental results of image compression show that the framework works well and takes up fewer hardware resources, especially as the resource utilization of LUT is just 9.26%. In terms of the compression speed, it consumes 15.73 ms running on FPGA and 115.29 ms running on CPU. In terms of the computing performance per watt, it is 26.49 GOPS/W running on FPGA, which is far beyond that of CPU and GPU. In future work, more multiplexing techniques, like encoder multiplexing, can be adopted to enhance the CAE performance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Resource** & **Utilization** & **Available** & **Utilization\%** \\ \hline LUT & 22,441 & 242,400 & 9.26 \\ FF & 27,100 & 484,800 & 5.59 \\ BRAM & 55.50 & 600 & 9.25 \\ IO & 120 & 520 & 23.08 \\ BUFG & 8 & 480 & 1.67 \\ MMCM & 2 & 10 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Device utilization summary.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Performances** & **FPGA** & **CPU** & **GPU** \\ \hline Computing time (ms) & 15.73 & 115.29 & 13.65 \\ Power consumption (W) & 2.762 & 32 & 44 \\ Computing performance (GOPS) & 73.18 & 3.25 & 76.38 \\ Computing performance per Watt (GOPS/W) & 26.49 & 0.101 & 1.73 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of performance when running on the FPGA, CPU, and GPU.  
**Author Contributions:** [PERSON], [PERSON], and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed analysis tools; [PERSON], [PERSON], and [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Conclusions  
Predefined free-space semantic segmentation information where dynamic objects are not included is crucial for autonomous driving systems. In real-world roads, it is challenging to obtain a pair dynamic and static segmentation map in which supervised learning is unavailable for removing the dynamic objects. For this reason, unsupervised adversarial training plays an important part. Many studies have been conducted on using adversarial training for segmentation maps, but there are limitations to probability maps to apply existing adversarial training directly. In this work, we proposed an unsupervised method for semantic segmentation inpainting with a novel preprocessing method and cross-entropy-based total variation loss function to stabilize the unsupervised adversarial learning for segmentation maps. Our network is based on the GAN, including a U-Net-based encoder-decoder generator trained with one adversarial loss function, two stabilizing loss terms, and semantic regularization as the discriminator. The proposed model successfully synthesized semantically and visually plausible content to remove dynamic objects and naturally replace the objects with static objects. We compared the qualitative and quantitative experiments on both the MICC-SRI and Cityscapes datasets, with three adversarial training methods for the segmentation map. We demonstrated that our model completion results exhibit a high perceptual quality and are flexible for various situations in unsupervised semantic segmentation inpainting.  
Conceptualization, W.-J.-A. and D.-W.K.; methodology, D.-S.P. and M.-T.L.; software, W.-J.A.; validation, T.-K.K. and W.-J.A.; formal analysis, D.-W.K.; investigation, D.-W.K. and W.-J.A.; resources, D.-S.P.; data curation, T.-K.K.; writing--original draft preparation, W.-J.A.; writing--review and editing, D.-W.K. and W.-J.A.; visualization, D.-S.P.; supervision, M.-T.L.; project administration, M.-T.L.; funding acquisition, M.-T.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Basic Science Research Program through National Research Foundation of Korea (NRF) (Grant No. 2022R1F1A1073543).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Maximum Value \(\tau\)** & **N(m,\(\sigma\))** & **Pixel Accuracy** \\ \hline
1 & 0.01 & 67.32 \\
0.99 & 0.01 & 72.28 \\
0.97 & 0.01 & **75.59** \\
0.95 & 0.01 & 71.27 \\
0.90 & 0.01 & 68.78 \\ \hline
0.97 & 0 & 73.62 \\
0.97 & 0.010 & **75.59** \\
0.97 & 0.025 & 74.35 \\
0.97 & 0.035 & 73.14 \\
0.97 & 0.050 & 73.47 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Result on various hyperparameters.  
Data Availability Statement: The MICC-SRI and Cityscapes datasets presented in this study are openly available in the MILC-SRI paper [9] and Cityscapes paper [10], respectively.
## 4 JTAG Security Solutions
### Exploitation Using Statistical Machine Learning
The study tries to solve this classification problem by using a supervised \begin{table}
\begin{tabular}{l l} \hline \hline
**Steps for JTAG Attacking Strategies** & **Sub-Steps** \\ \hline Identification of JTAG ports on PCB & **1.** Identification of JTAG port is performed either manually or \\  & third-party tools such as JTAGulator [7], ftjrev [34], etc. \\ \hline Exploration of Data Register(DR) & **2.** Recollect details about length of Instruction register (IR). \\  & **3.** Recollect details about length of data register (DR). \\  & **4.** Get details about opcodes and their association with DR register. \\  & **5.** Check configurability status of DR register. \\  & **6.** Identify internal scan chains. \\ \hline Get Details of undocumented functions & **7.** Differentiate functions and opcodes which we extracted \\  & during Sub-Step 4. \\  & **8.** Understanding of functioning of each DR register. \\  & **9.** Get all details of opcodes and their functioning. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Attacking strategies. learning method known as SVM. Input sample behavior data (training data) is fed into SVM for classification. Here, the SVM decides the boundary between attacker behavior and normal behavior. In addition, delayed labeling method is used to avoid false positive and/or false negative results. The authors used the OpenSPARC T2 platform for this experiment. The dataset included 767 authentic programs which contained 125,017 opcode sequences and 1092 attacking programs which contained 154,980 opcode sequences. The study also demonstrates two different sets of results following two different sets of strategies. In the first set, [PERSON] et al. made divisions based on the component under target. They came up with eight different IC components targeted by attacks. They used SVM with neural network (hidden layer = 1, hidden neuron = 10), \(k\)-nearest-neighbor (\(k\)-NN, \(k=3\)) and decision tree models. In the second set, the authors made nine divisions based on attacking strategies used for exploitation. The whole experiment showed them 94% overall accuracy while detecting unseen attacks. This article shows the results based on a two-layered learning technique, which is significant, but this scheme has more area overhead as well as more latency. This scheme is well suited where hardware complexity is higher and is not battery powered. The accuracy is dependent on the datasets of previous attacks to a great extent. The results of this experiment are significant, e.g., physical security is enhanced up to 94% with machine learning techniques, and improved by 50% on average compared to previous work.