esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Experimental Results and Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Pruning Method\textbackslash{}Pruning Rate** & **Baseline** & **60\%** & **70\%** & **80\%** & **90\%** & **95\%** \\ \hline ASFP\_LLE & 94.47 & 90.77 & 90.24 & 83.98 & 63.54 & 50.93 \\ ASFP & 94.47 & 90.23 & 89.82 & 82.19 & 61.57 & 48.66 \\ PFEC\_LLE & 94.47 & 91.74 & 91.03 & 85.91 & 65.34 & 49.76 \\ PFEC & 94.47 & 91.74 & 91.05 & 90.14 & 84.85 & 48.12 \\ LWCE\_LLE & 94.4 and 2.71% higher than that of the post-cutting model by using the PFEC pruning method, respectively. When cutting the model at 60% to 95% by using the LWCE_LLE pruning method, the accuracy of the model was 0.54%, 1.38%, 1.12%, 2.38%, and 2.64% higher than that of the cropped model by using the LWCE pruning method, respectively. When cutting the model at 60% to 95% by using the FPGM_LLE pruning method, the accuracy of the model is 0.69%, 1.16%, 1.44%, 1.71%, and 2.28% higher than that of the cropped model by using the FPGM pruning method, respectively. Under the same pruning ratio, the ViT model is pruned using the baseline method and the comparison method, respectively, and the experimental results indicate that the test results of the four comparison methods combined with the LLE method are better than those of the baseline method without the LLE method, which proves that the LLE algorithm plays a positive role in reducing the loss of model performance in the pruning process. The experimental results presented in Table 2 demonstrate that the conclusions align with those in Table 1. When using the ASFP_LLE, PFEC_LLE, LWCE_LLE, and FPGM_LLE pruning methods on the ViT model, these methods achieved higher model accuracy on the dataset compared to their respective benchmark pruning methods. For instance, at a pruning rate of 95%, the accuracies were 2.27%, 1.64%, 2.61%, and 1.88% higher than those of the benchmark methods, respectively. These results indicate that combining the LLE algorithm with the loss function during model pre-pruning training achieves the desired model compression rate while effectively minimizing the loss of model accuracy. Assuming that in the experiment, the experimental parameters of each set of baseline methods and their respective comparison methods are the same, the experimental results of 60% to 95% of the large-scale pruning of the model indicate that when the pruning ratio is the same, the comparison method combined with the LLE method can complete the pruning work of the model and improve the accuracy of the model after pruning compared with the original pruning method without the LLE method. Therefore, the experimental results can prove the validity of the hypothesis proposed in Section 2.3 that \"using the LLE algorithm as a loss function for model training before pruning plays a positive role in reducing the loss of model performance during the pruning process\".
# Bench Test and Analysis of Cleaning Parameter Optimization of 4 L-2.5 Wheat Combine Harvester
## 3 Results
### Impurity Rate  
#### 3.2.1 Establishment of the Regression Model of Impurity Rate and Significance Test  
According to the experiment results shown in Table 4, variance analysis was carried out for the impurity rate; the results are shown in Table 7. The regression equation for the impurity rate was as follows:  
\[\begin{split} Z=2.15+1.15\text{A}+(9.375\text{E}-003)\text{B}-0.2 4\text{C}-0.38\text{D}-0.067\text{E}+(2.500\text{E}-003)\text{AB}-0.015\text{AC} -0.30\text{AD}+0.038\text{AE}+\\ (7.500\text{E}-003)\text{BC}-(7.500\text{E}-003)\text{BD}+0.000\text{BE}+ (1.000\text{E}-002)\text{CD}+(2.500\text{E}-003)\text{CE}-(2.500\text{E}-003) \text{DE}-\\ 0.27\text{A}^{2}-0.056\text{B}^{2}+0.028\text{C}^{2}+0.093\text{D}^ {2}-0.043\text{E}^{2}\end{split} \tag{8}\]  
From Table 7, it can be seen that the \(p\)-value of the impurity rate model was less than 0.01, indicating that the established regression model was extremely significant. Furthermore, the decision coefficient R\({}^{2}\) of the model was 0.9783, which implies that the model can reflect a response value change of 97.83%, indicating that the obtained linear regression equation fitting effect was very good. In the regression model, the \(p\)-values of A, C, D, AD, and A\({}^{2}\) were all less than 0.01, indicating that the influence on themod was very significant. The \(p\)-values of the other items were greater than 0.05, indicating insignificant influence.  
Figure 5: Response surface analysis of the effect of each parameter on cleaning loss rate.
## 3 Results
### Production-Related Greenhouse Gas Emissions  
The global warming potentials of the binder fractions of 1 m\({}^{3}\) of HVFA and CEM II/B-V 42.5 N mortar can vary considerably with the way impact was assigned to the fly ash (Figure 3). More consideration of the basic treatment of the fly ash (drying + storage) barely increases the GWP value (HVFA: +2 kg CO\({}_{2}\) eq; CEM II/B-V 42.5 N: +1 kg CO\({}_{2}\) eq) relative to the no-impact approach (HVFA: 202 kg CO\({}_{2}\) eq; CEM II/B-V 42.5 N: 335 kg CO\({}_{2}\) eq). Additional inclusion of a partial impact of coal-fired electricity production by means of economic allocation affects the GWP values obtained more substantially.  
Figure 2: CO\({}_{2}\) sequestration potential CO\({}_{2}\)SP (kg/m\({}^{3}\)) for the binder fraction of a generic 1 m\({}^{3}\) and a 100-year timeframe, dimensions specific 2.24 \(\times\) 2.24 \(\times\) 0.20 m\({}^{3}\) = 1 m\({}^{3}\) volume of the HVFA mortar (**a**), CEM II/B-V 42.5 N mortar (**b**) and CEM I 42.5 N mortar (**c**), as obtained from natural (0.04% CO\({}_{2}\)), slightly accelerated (1% CO\({}_{2}\)) and highly accelerated carbonation (10% CO\({}_{2}\)). Error bars correspond with the standard deviation on the mean (\(n=3\)).  
As a German fly ash was used for this research, it is evident that the Tr(DE) + Ce(DE) value is seen as reference case for the economic allocation approach (C\({}_{\rm e}\): 0.50% cf. Table 3). It adds 43 kg CO\({}_{2}\) eq and 20 kg CO\({}_{2}\) eq to the carbon footprints of the binder fractions of the HVFA mortar and the CEM II/B-V 42.5 N mortar, respectively. It is logical that this increase is lower for the latter, given its lower fly ash content (23% as opposed to 50%). The GWP values drop further when the lower economic allocation coefficients representative for China (C\({}_{\rm e}\): 0.25% cf. Table 3) and UK (C\({}_{\rm e}\): 0.11% cf. Table 3) are considered. The opposite trend was observed in case of economic allocation based on data from the United States (C\({}_{\rm e}\): 0.98% cf. Table 3) and Canada (C\({}_{\rm e}\): 4.30% cf. Table 3). Economic allocation by 4.30% imposes overall carbon footprints for both fly ash-based mortars (HVFA: 643 kg CO\({}_{2}\) eq; CEM II/B-V 42.5 N: 542 kg CO\({}_{2}\) eq) that exceed the Portland cement-based reference mortar (CEM I 42.5 N: 418 kg CO\({}_{2}\) eq.) by far. For this calculation approach, it seems no longer  
Figure 3: Production-related global warming potential GWP (kg CO\({}_{2}\) eq) for the binder fraction of 1 m\({}^{3}\) of the HVFA mortar (**a**) and CEM II/B-V 42.5 N mortar (**b**) in relation to the one of reference CEM I 42.5 N mortar, using various approaches for assigning impact for their fly ash content: No impact (No), impact of basic treatment (Tr), impact of basic treatment and economic allocation (Tr + Ce).  
sustainable to incorporate fly ash in the binder system, even when the amounts remain limited to the typical Portland clinker replacement levels of commercial composite cements.
# Analysis of Human Body Shadowing Effect on Wireless Sensor Networks Operating in the 2.4 GHz Band
###### Abstract  
Miniaturized wireless sensors are designed to run on limited power resources, requiring minimization of transmit power and lowering of the fade margin in the link budget. One factor that has an important impact on wireless sensor network design is path loss between the transmitter and the receiver. This paper presents an analysis of the influence of human bodies on path loss in the 2.4 GHz band, which is commonly used for wireless sensor networks. The effect of body shadowing was first analyzed in full wave computer simulations using the finite-difference time-domain method. Due to the high numerical burden, the simulations were limited to only a small region around the human body. To analyze the performance of networks in larger indoor environments, a human body model is proposed that can be used for simulations with a ray-based computer program. The proposed model of human body is the main contribution of this paper. It was used to analyze the body shadowing effect in a typical indoor environment. The results were found to be in good agreement with measurements.  
wireless sensor networks; body shadowing; path loss; computational electromagnetics; FDTD; UTD +
Footnote †: journal: Sensors
## 2 Literature Review
### Research Trends in Architecture  
Generally, the contents of the papers were related to data utilization for collecting or predicting AI-based fire information. Based on this, research was mainly conducted to predict the risk of architectural structures vulnerable to fire [6]. To be specific, studies on data utilization have constructed a smart framework that can predict smoke movement and availability in the event of a fire in a building. Further, the framework research has been developed for the construction of algorithms that can secure time for safe evacuation (available safe egress time; ASET). In the process of the construction of algorithms, smoke movement was predicted by constructing a database by preparing profiles, such as the length of the atrium that can perform ventilation, fire size, ventilation conditions, and time after ignition [7]. In addition, other related studies have conducted the real-time prediction of temporary fire scenarios using external smoke images and deep learning algorithms. A comprehensive collection of 1845 large-scale databases was formed, showing that hidden fire information can be determined in real time by training deep learning algorithms with the smoke images simulated using convolutional neural network (CNN) models. The large potential of smart firefighting was also demonstrated. This verified the possibility of using AI for firefighting performance-based design (PBD), which can reduce the time and cost required to create a fire safety architectural environment [8].  
In addition, reducing fire damage by deriving elements vulnerable to fire in architectural structures based on the developed AI was considered. An AI-based cognitive framework was constructed to track the reactions of concrete structures among building structures to high temperatures. This algorithm can successfully understand the natural and complex behavior of reinforced concrete (RC) structural members exposed to fire. Furthermore, it considers the characteristics of concrete and steel reinforcement at high temperatures and related phenomena [9]. In other studies, a series of ML models were constructed to predict the fire risks of buildings based on their structural characteristics. Approximately two-thirds of fires that occurred at research sites were accurately classified through learning with data, including the structures of fire buildings and building-level information. These algorithms are expected to help reduce fire damage by excluding uncertain factors and utilizing data that can be objectively measured in data analysis for building fire prediction.
# Fabrication Tolerances' Impact on an ODAC-Based PAM-4 Transmitter
## 4 Conclusions  
In this paper, we have presented a hybrid model to study the effects of fabrication tolerance resulting from passive variation in PIC-based devices. Using the flexibility of FFPGA, an oDAC-based PAM-4 telecommunication transmitter was mimed with passive variances ranging from 0 to 12% while mimicking the production of 1000 oDAC-based PAM-4 transmitters. A Monte Carlo simulation of the device was carried out to predict the production yield at given instances of passive variation. While benchmarking the SER to \(10^{-3}\), the number of failed and passed PICs have been determined at every instance of passive variance we have tested. Using the SER as a performance metric, the effect of passive variation and the SER gives a linear relationship in the number of failed and passed  
Figure 10: Percentage of failed and passed chips at different passive variances.  
Figure 9: SER versus the number of PIC mimed at \(\sigma_{n}\) = (a) 6%, (b) 12%.  
PICs of the oDAC-based PAM-4 transmitter after fabrication. These findings enable robust designs which consider critically the sensitive effect of passive variability on designed components and can be formulated into new design rules.  
A.E.A.: writing--original draft, visualization, conceptualization, methodology, investigation; A.T.: visualization, conceptualization, conceptualization, supervision, funding acquisition; M.L.: supervision, review; J.S.: investigation, software, methodology; T.C.: review, visualization, methodology, investigation; F.R.: software, review, visualization. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the European Regional Development Fund (FEDER), through the Competitiveness and Internationalization Operational Program (COMPETE 2020) of the Portugal 2020 framework [Project POWER with Nr. 070365 (POCI-01-0247-FEDER- 070365)]. The work is also supported by the European Commission through the HORIZON-JU-SNS-2022 FLEX-SCALE project with Grant Agreement number 101096909.  
Not applicable.  
The original contributions presented in the study are included in the article.  
Authors [PERSON], [PERSON], [PERSON] and [PERSON] are employed by PICAdvanced. Author [PERSON] was employed by the company the West Bengal Power Development Corporation Limited (WBPDCL). The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 3 Model Structure
### Construct Graph Structure
#### 3.2.1 Construct Intra-Frame Graph Structure
The superpixel segmentation method was used to convert the original image data into graph structure data. This data type not only captured the spatial relationships of the tracked target but also contained the inherent features of the image data, while also reducing the computational overhead.
## 3 High-Performance Modulation Devices Based on Graphene
### E-O Modulation
Although the modulation depths in the above-mentioned devices were optimized to an impressive scale, the modulation bandwidth was limited to less than 1 GHz. This is mostly due to the large RC constant of the circuit. To address this issue, the [PERSON] group proposed a graphene modulator with a graphene monolayer replacing the metal pad within the classical capacitor structure of the modulator, allowing the ultra-high carrier mobility of graphene to be fully utilized. This resulted in a high modulation bandwidth of 30 GHz and a strong modulation depth of 15 dB [19]. Moreover, they also reported a high-performance graphene modulator at cryogenic temperature with an intrinsic bandwidth of 200 GHz [50]. Most recently, a high-performance modulator utilizing a 2D-3D dielectric integration in a high-quality encapsulated graphene device was used to exhibit a \(\sim\)39 GHz bandwidth, resulting in a three-fold increase in modulation efficiency when compared  
Figure 2: Typical graphene/Silicon electro-optical modulators. (**a**) The schematic and the performance of double-layer graphene modulator. Reprinted with permission from _Nano Lett._ 2012, 12, 3, 1482–1485. Copyright 2012 American Chemical Society. (**b**) Graphene modulator based on photonic crystal nanocavity. Reprinted with permission from _Nano Lett._ 2013, 13, 2, 691–696. Copyright 2013 American Chemical Society. (**c**) Graphene modulator based on MRR. Reprinted with permission from _Nano Lett._ 2014, 14, 12, 6811–6815. Copyright 2014 American Chemical Society. (**d**) High modulation depth graphene modulator based on altering the coupling condition of MRR. Reprinted with permission from _Nano Lett._ 2015, 15, 7, 4393–4400. Copyright 2015 American Chemical Society. to previously reported high-speed modulators [20]. Meanwhile, the electro absorption graphene modulator could also be an ideal candidate as a photonic neuron thresholder, as reported by [PERSON]'s group in 2021 [51]. Besides the intensity modulator, the phase modulator based on graphene with a modulation efficiency of 0.28 V-cm and an operation bandwidth of 5 GHz has also been demonstrated [52].
# Energy Consumption Analysis of ISO/IEC 29192-2 Standard Lightweight Ciphers
## 1 Introduction
Conventional encryption techniques are often impractical in IoT data security due to device processors and memory limitations [4], because when conventional cryptographic methods are added to the activity already performed by the devices, a severe energy consumption problem arises. In IoT systems, more efficient lightweight cryptographic algorithms are used to assure data communication security. Many lightweight encryption algorithms have been developed in this context. However, most of these encryption algorithms remain experimental and have not been used in industry. IoT manufacturers in industry prefer to work based on standards like ISO because it provides significant advantages in terms of security, interoperability, and market access. The ISO standards ensure that IoT devices meet high levels of data protection and are resistant to cyber threats. They also improve device compatibility by offering a unified framework, enabling the seamless integration of products from different vendors. Compliance with international standards facilitates global market entry and ensures adherence to regulations, reducing legal risks and speeding up time-to-market. Furthermore, ISO standards promote consistent quality, reliability, and customer satisfaction, strengthening brand reputation. By implementing standardized processes, manufacturers improve operational efficiency, reduce costs, and support sustainability efforts, making their products more appealing in a competitive global market [5]. In this paper, the energy consumption behavior of encryption algorithms defined by the international ISO standard will be examined in depth. In this context, the energy consumption of lightweight encryption algorithms proposed in the ISO/IEC 29192-2 standard will be analyzed and their suitability for their application areas will be evaluated. The rest of the paper is structured as follows. Section 2 presents related works and the rationale for focusing on standardized lightweight ciphers. Section 3 describes the encryption algorithms and their internal structures in detail. Section 4 outlines the experimental setup, test scenario, and measurement methodology. Section 5 provides a comprehensive analysis of the results obtained from energy, runtime, and current measurements. Section 6 offers recommendations for enhancing ISO/IEC 29192-2 algorithms. Finally, Section 7 presents conclusions and discusses potential directions for future work.
# Dengue and Flavivirus Co-Infections: Challenges in Diagnosis, Treatment, and Disease Management
## 3 Co-Infection: Occurrence and Clinical Relevance
Co-infections implicating DENV with ZIKV are continuously reported in endemic areas [30; 31]. Comparatively, co-infections with other flaviviruses such as JEV and SLEV, on the other hand, have only been occasionally reported, especially in regions where the viruses are co-endemic [32; 33]. Additionally, previous studies on the situation found that co-infections lead to either viral interference or enhanced replication, depending on the timing of the number of infections [34]. One of the major challenges in managing flaviviral co-infections is diagnostic inaccuracies as the clinical symptoms of flaviviral infections often overlap. Table 1 highlights the clinical similarities commonly observed in flaviviral infections, implying the need for more accurate differential diagnoses. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Disease** & **Dengue** & **Chikugunya** & **Zika** \\ \hline Fever & ++++ & ++++ & ++++ \\ Myalgia/Arthralgia & ++ & ++++ & ++ \\ Oedema in limbs & \(-\) & \(-\) & ++ \\ Maculopapular exanthema & ++ & ++ & ++ \\ Retro-orbital pain & ++ & + & ++ \\ Conjunctivitis & \(-\) & + & ++ \\ Lymphadenopathy & ++ & ++ & + \\ Hepatomegaly & \(-\) & ++++ & \(-\) \\ Bleeding & + & \(-\) & \(-\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of clinical symptoms of dengue, Chikugunya, and Zika (adapted from [PERSON] et al. [35]). Although the WHO 2012 guideline encompassing clinical parameters, haematology, and dengue-specific diagnostics has been acknowledged as a routine diagnostic practice [36], misattributing a flaviviral infection with another due to clinical similarities can lead to the omission of necessary treatments, resulting in poorer outcomes. As a result, further investigations into more specific diagnostic approaches are warranted [37]. Table 2 presents a comprehensive summary of flavivirus co-infections in which the strengths and controversies in individual articles are explored.
## 2 Related Work
For applications like traffic forecasting, action recognition, CNN or RNN is used for temporal dependency alongside graph convolutional neural networks. Spatial-temporal graph neural network [39] address the time series prediction problem in traffic domain by applying graph convolutional neural network to both spatial and temporal dependency. However, the method uses adjacency matrix only for spatial dimension. In the method, convectional convolutional neural network is used across temporal dimension that do not capture the features of skeleton dataset very well. Our method defines a single adjacency matrix that considers both temporal and spatial dimension. Moreover, the method gives a much faster training speed with fewer parameters. Based on the landmark obtained by OpenPose [4], deep learning methods show promising results in gait analysis. Although OpenPose demonstrate higher location error of between 20 and 40 mm for laboratory based gait analysis comparing to the marker-based motion capture system, the method estimate a good landmark that can be used for further analysis [40]. CNN based method [11] predicts gait metrics that approach the theoretical limits for accuracy imposed by natural variability within the dataset prepared in Gillette Children Speciality Healthcare. However, still there is a room for improvement, where our method, i.e., graph convolutional neural network (GCNN) that fit the nature of the data improves the gait metrics. The aim of this paper is producing gait metrics of video based system that mimics the metrics calculated from marker-based optical motion capture system. Beside using minimum squared error (MSE) during training, the application of information theory [41; 42; 43; 44; 45] namely correlation is used to show how the predicted value from our method is related to the ground truth value. For our dataset it turned out that the correlation values approach the maximum limit imposed by natural variability in the gait metrics.
# Real-Time Implementation of Multiband Spectrum Sensing Using SDR Technology
## 4 Real-Time Experiments and Results  
This section presents the development environment, the parameters considered, and the results to validate the real-time MBSS using SDR devices through a controlled scenario.