esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiment Results and Discussion
### Analysis of the Calculated Settings for Individual Users
#### 5.3.2 Rotation Interaction Mechanisms  
All participants from the physical, cognitive and neurological disorders and/or specific learning difficulties and multiple disabilities groups were assigned indirect controls for the _Rotation_ task, while participants from the auditory impairments group were mostly assigned indirect control (66.7%).  
The age groups Under 18, 45-54, and 65 or older stand out, where all participants were assigned indirect controls as an interaction mechanism for the _Rotation_ task. Participants from the remaining age groups were mostly assigned indirect controls, i.e., 81.6% of participants from the 18-24 years old group, 66.7% of participants in the 25-34 years old group, 83.3% of participants in the 35-44 years old group, and 60% of participants from the 55-64 years old group.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multirow{2}{*}{**Participants Group**} & \multicolumn{2}{c}{**Translation**} & \multicolumn{2}{c}{**Rotation**} & \multicolumn{2}{c}{**Scaling**} \\ \cline{3-8}  & & **TG** & **IC** & **TG** & **IC** & **TG** & **IC** \\ \hline \multirow{4}{*}{**S2-34**} & Auditory (N\({}_{\text{a}}=3\)) & 100 & 0 & 33.3 & 66.7 & 0 & 100 \\ \cline{2-8}  & Physical (N\({}_{\text{p}}=5\)) & 40 & 60 & 0 & 100 & 20 & 80 \\ \cline{2-8}  & Cognitive, learning, & & & & & & \\ \cline{2-8}  & neurological (N\({}_{\text{cln}}=8\)) & 37.5 & 62.5 & 0 & 100 & 25 & 75 \\ \cline{2-8}  & Multiple (N\({}_{\text{m}}=12\)) & 41.7 & 58.3 & 0 & 100 & 8.3 & 91.7 \\ \hline \multirow{4}{*}{**S3-4**} & Under 18 (N\({}_{\text{c}+18}=9\)) & 33.3 & 66.7 & 0 & 100 & 0 & 100 \\ \cline{2-8}  & 18–24 (N\({}_{\text{18-24}}=37\)) & 44.7 & 55.3 & 18.4 & 81.6 & 18.4 & 81.6 \\ \cline{2-8}  & 25–34 (N\({}_{\text{25-34}}=12\)) & 58.3 & 41.7 & 33.3 & 66.7 & 8.3 & 91.7 \\ \cline{2-8}  & 35–44 (N\({}_{\text{35-44}}=6\)) & 83.3 & 16.7 & 16.7 & 83.3 & 50 & 50 \\ \cline{2-8}  & 45–54 (N\({}_{\text{45-54}}=7\)) & 28.6 & 71.4 & 0 & 100 & 0 & 100 \\ \cline{2-8}  & 55–64 (N\({}_{\text{55-64}}=5\)) & 40 & 60 & 40 & 60 & 40 & 60 \\ \cline{2-8}  & 65 and more (N\({}_{\text{65+}}=\)3) & 66.7 & 33.3 & 0 & 100 & 0 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Summary of applied interaction mechanisms in SG2 for virtual object transformation tasks (Translation, Rotation, and Scaling) across different disability and age groups. The table presents the relative frequency (%) of participants assigned to each interaction mechanism (TG = Touch Gestures, IC = Indirect Controls) for each task.
# A Deep Learning Approach for Network Intrusion Detection Using a Small Features Vector
## 7 Conclusions  
This research investigated the classification performance of a deep-learning-based feedforward neural network classifier, when a lightweight feature vector was used to detect network traffic anomalies. The effectiveness of a small feature vector was demonstrated in detecting unsolicited and harmful network intrusion. We used the UNSW-NB15 and NSL-KDD datasets and the feedforward neural network classifier to perform the experimental study. The classification accuracy achieved confirms that the model performed better when a small feature vector was used.  
A limitation of this research is that it only used the FFNN for classification. If other deep learning architectures had been utilized, we could have presented a comparative view of their performance and potentially achieved better results.  
In the future, we intend to expand this work in the following directions: (i) perform classification using other deep learning algorithms to ascertain the degree of accuracy improvement; (ii) compute the classification accuracy of each attack type separately; (iii) implement this approach with real network traffic and evaluate the performance; (iv) for automated security, integrate this model with the continuous integration pipeline of an organization.  
Conceptualization, H.G. and B.V.; methodology, H.G.; software, H.G.; validation, H.G.; formal analysis, H.G.; investigation, H.G.; resources, H.G.; data curation, H.G.; writing--original draft preparation, H.G.; writing--review and editing--B.V. and S.S.; visualization, H.G.; supervision, S.S.; project administration, H.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability StatementData are available in a publicly accessible repository. [[https://ieee-dataport.org/](https://ieee-dataport.org/)]([https://ieee-dataport.org/](https://ieee-dataport.org/)) (accessed on 1 February 2023).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Research** & **Dataset** & **Accuracy** & **Classifier** & **Features** \\ \hline \multirow{3}{*}{[11]} & UNSW-NB15 & 78.4\% & DNN & 41 \\  & NSL-KDD & 80.1\% & DNN & 41 \\ \hline \multirow{3}{*}{[13]} & UNSW-NB15 & 90.62\% & DT & 9 \\  & NSL-KDD & 89.02\% & KNN & 11 \\ \hline Proposed & UNSW-NB15 & 91.29\% & FFNN & 9 \\ Research & NSL-KDD & 89.03\% & FFNN & 11 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the models.
# Accurate Energy Consumption Modeling of IEEE 802.15.4e TSCH Using Dual-Band OpenMote Hardware
## 2 Background and Related Work  
In this section, we briefly introduce TSCH and the open-source OpenWSN project that is used as firmware. Afterwards, the used OpenMote hardware is discussed. Finally, we compare our work to existing energy consumption models.
## 1 Introduction
In particular, it envisions the formation of _ephemeral_ local VANETs, formed by one \"primary\" vehicle surrounded by \"secondary\" vehicles. The former vehicle acts as a gateway for traffic alerts coming from a 3G/4G network, which are broadcast to the latter vehicles by means of WiFi broadcast communications. As aforementioned, the peculiar feature of X-NETAD consists in being entirely based on smartphones, without the need for dedicated OBUs. However, it is possible to easily extend the X-NETAD architecture in order to exploit the presence of dedicated OBUs within the targeted vehicles. While our previous work [10] aimed at presenting the \"big picture\" of the X-NETAD architecture without disclosing any specific implementation detail, in this manuscript, a comprehensive overview of the main research and scientific aspects of the X-NETAD project is provided. To summarize, the goal of this manuscript is twofold:  
* to provide an in-deep description of the X-NETAD architecture and its implementation on a VANET formed by an Android smartphone;
* to present the experimental results, obtained by implementing X-NETAD on a testbed of commercial off-the-shelf (COTS) Android smartphones, with no high-end requirements, shedding light on the strengths and weaknesses of the proposed architecture. This paper is structured as follows. In Section 2, we discuss related works on multihop broadcast protocols and VANET applications based on smartphones. In Section 3, the principle design behind X-NETAD is presented. The details of the dissemination protocol and its implementation on the Android platform are provided in Section 4. The experimental results are illustrated in Section 5. Finally, conclusions are drawn in Section 6.
# SAFP-YOLO: Enhanced Object Detection Speed Using Spatial Attention-Based Filter Pruning
## 4 Experimental Results
### Evaluation of Detection Performance  
In this section, we evaluate the proposed method. Tables 2 and 3 present the results of applying the proposed method to the YOLOv4 and YOLOv7 baselines for the pig dataset and vehicle dataset. The inference speeds (FPS) of the models were measured on a TX-2 embedded board [60], and the integrated performance was defined as the product of accuracy and speed. Although there was a decrease in accuracy, 84.4 to 78.6 in YOLOv4 and 87.9 to 81.2 in YOLOv7, the actual gain in inference speed was substantial: 3.9 to 20.9 in YOLOv4 and 3.8 to 20.0 in YOLOv7, which led to an increase in the integrated performance of the lightweight models. Furthermore, both YOLOv4 and YOLOv7 showed performance improvements in terms of the number of model parameters as well. This confirms the applicability of the proposed method to various models. Additionally, although CBAM and CA exhibited differences in accuracy, they both contributed to an improved integrated performance. Thus, the proposed method can be applied to various spatial attention mechanisms. Also, this confirms that the proposed method can be applicable to various dataset types.  
Figure 8 illustrates the detection results of the proposed SAFP-YOLOv7 with CA (87.5% pruning). As shown in Tables 2 and 3, the accuracy of the proposed SAFP-YOLOv7 with CA (87.5% pruning) has decreased compared to the baseline YOLOv7, yet it is confirmed that most objects were successfully detected. However, false detections were observed when objects were clustered or distant, leading to smaller sizes within the image or when obscured by infrastructure. Among the cases of false detection, it is anticipated that the detection accuracy could be improved while maintaining detection speed for those cases where objects were distant, leading to smaller sizes within the image or obscured by infrastructure, by employing the methods described in [20; 38].  
Figure 7: Test sample images of pig [58] and vehicle [59] datasets.  
Figure 8: Detection results of the proposed SAFP-YOLOv7 with CA (87.5% pruning). Even with a pruning rate of 87.5% from YOLOv7, most objects were successfully detected except occluded pigas and small vehicles.
# Noise-Tolerant Data Reconstruction Based on Convolutional Autoencoder for Wireless Sensor Network +
## 1 Introduction
However, traditional machine learning algorithms only calculate the relationship between data from the same sensor and ignore data from neighboring sensors. Therefore, a reconstruction approach based on a convolutional neural network (CNN) emerges as a potential approach for utilizing multiple directions of data from multiple sensors. A CNN combined with an autoencoder (CAE) is a popular model for reconstructing missing data. This model takes advantage of the spatiotemporal correlations in sensor data; thus, its performance should be better than that of existing data recovery methods. Therefore, in this study, we employed a CAE to address the loss of data due to noise. In a normal CAE model, before training, the convolution layer and dense layer weights are initialized randomly. However, random weight initialization may cause the optimized loss function to drop into weak local optima when the training is complete. As a result, the performance of the CAE is significantly diminished. Therefore, we proposed a stacked convolutional autoencoder (SCAE), which is more advanced. This technique pretrains the initial weights before the learning model employs them in the training process. Therefore, this method can obtain a stronger local optimum compared with a traditional CAE. Finally, we compared the performance of the SCAE with other available data reconstruction techniques in terms of error. In addition to external noise, in this research, we also consider the impact of noise caused by internal events, specifically thermal noise. The noise corrupts the data sent to the sink node, which also decreases the data transmission reliability. The proposed SCAE not only solves the problem of losing data from external event noise but also revises data changed by internal events. The main contributions of this study are as follows:* We analyzed the impact of noise caused by harsh weather conditions, such as rain, snow, and fog. We also considered the effect of noise due to internal factors, such as thermal noise. * We successfully adopted a stacked convolutional autoencoder (SCAE) to reconstruct the data affected by this noise. * We conducted extensive experiments to demonstrate the outstanding performance of the proposed model in terms of training and testing errors. The remaining sections of this paper are structured as follows: In Section 2, we discuss the recent data reconstruction investigations. Internal and external noise models are introduced in Section 3. The proposed framework for data reconstruction is presented in Section 4. The experiment conducted to evaluate the proposed model is described in Section 5. The analysis of the contributions of this study is presented in Section 6. The paper concludes in Section 7.
## 2 Composition of Berry Pomaces
### Polyphenols
#### 2.2.3 Polyphenol Content in Berry Pomaces and Their Antioxidant Activity  
Industrial chokeberry pomace consists of a solid number of bioactive compounds. Total polyphenol content (TPC) of chokeberry pomace reaches 5.5 g/100 g dm (dry mass), expressed as catechin monohydrate, determined using the Folin-Ciocalteu method. The main groups of polyphenols were found to be anthocyanins at 1.80 g/100 g dm, followed by phenolic acids at 0.31 g/100 g dm and flavonoids at 0.184 g/100 g dm. AA (antioxidant activity) was measured as 1111 umol FE/g in the ferric reducing antioxidant power (FRAP) assay [6]. Consistent results were obtained in a composition analysis of a store-bought product which contained 100% chokeberry pomace. TPC measured using the Folin-Ciocalteu method reached 4233 mg GAE/100 g dm and anthocyanins content determined by the pH differential method was 1165 mg CGE/100 g dm. AA, measured in a FRAP assay amounted to 47.38 mmol FE/100 g dm and was measured in a DPPH assay as 131.06 mmol TE/100 g dm [24]. The main phenolic compounds in chokeberry pomace, detected using HPLC, were polymeric procyanidins with a concentration of 9586 mg/100 g dm, which was two-fold higher than the result for fresh berries and almost seven-fold higher than that for juice [25].  
The TPC of industrially obtained seedless blackcurrant pomace, measured using the Folin-Ciocalteu method, was in a range of 1855.5-2241.6 mg EE/100 g pomace. Specific polyphenol composition determined using HPLC indicated that anthocyanins are the dominant components, reaching values of 344.6-1046.1 mg/100 g pomace and depending on the year of fruit harvest. AA values determined in a DPPH assay ranged from 93.3-126.5 umol TE/g pomace [26]. A comprehensive study of redcurrant, raspberry and blackberry pomaces conducted by [PERSON] et al. [27] showed that redcurrant pomace was characterized by the highest values of TPC, as determined by the Folin-Ciocalteu method, which were equal to 3446.59 mg GAE/100 g dm, followed by 2014.66 mg GAE/100 g dm for raspberry pomace and 1699.62 mg GAE/100 g dm for blackberry pomace. Anthocyanin concentrations, determined in a HPLC/MS analysis, were similar for all pomaces and ranged from 149.91 mg/100 g dm for redcurrant pomace to 188.05 mg/100 g dm for raspberry pomace. As AA was correlated with TPC, redcurrant was characterized by the highest AA (tested using ABTS), 60.83 mmol TE/100 g dm, while lower AA values for raspberry and blackberry were observed, these being, respectively, 29.75 and 22.54 mmol TE/100 g dm.  
TPC, as determined by the Folin-Ciocalteu method, in blackberry pomace extract obtained from wild fruits ranged from 48.28-50.16 mg GAE/g dm and from cultivated fruits ranged from 26.30-35.40 mg GAE/g dm, which indicates that phenolic compound concentrations are higher among wild blackberries [28]. The phenolic composition of raspberry pomace was described. TPC, measured using HPLC, was determined as 238.36 mg/100 g dm.  
Anthocyanins were the dominant phenolic compounds, reaching nearly 83% of the TPC, followed by ellagic acid and flavanols [29].
# A Network Architecture and Routing Protocol for the MEDIcal WARNING System
## 7 Simulations
Moreover, to highlight the impact of the routing protocol on the MEDIWARN network performance, we address a comparative assessment of the average RTT and PLR obtained by the MEDIWARN communication system using three different routing protocols, i.e., the MP-RPM presented here, the AODV [11] (an on-demand protocol) and the DSDV [12] (a proactive protocol). The last two protocols are implemented in the INET library. The simulation parameters and the scenario are the same used in the first simulation. The results are shown in Figure 6 and Table 5, respectively. The results in Figure 6 show that the MP-RPM obtained comparable RTT values than the DSDV although the transmissions, in our approach, are doubled (i.e., repeated over two different paths). In fact, the PLR results (presented in Table 5) show that the PLR values obtained with the DSDV and the AODV are significantly higher than those obtained with the MP-RPM protocol. The AODV protocol obtained significantly higher RTT values (tens of milliseconds), and this is because with AODV the routing path has to be discovered on-demand before starting data transmission, thus increasing the data packet delay. Figure 6: Comparative average RTT among multiple routing algorithms. The PLR results also show that, for this kind of application, proactive protocols perform better than reactive ones. Finally, thanks to the redundant routing path, the MP-RPM outperforms the other two protocols in terms of PLR.
## 2 Materials and Methods
### Estimation of Model Parameters
#### 2.3.1 Estimation of Initial Condition
The log probability \(l_{p}\) (the logarithm of the right-hand side of the Equation (10)) is evaluated internally in stan, and the parameter set from the posterior distribution samplings that gives the maximum log probability is chosen for the optimum values: \(\{e_{0}^{\text{opt}},i_{0}^{\text{opt}},\beta_{0}^{\text{opt}}\}=\{e_{0_{m}}, i_{0_{m}},f_{0_{m}}|\text{max}_{m\in[1,N_{\text{iter}}]}~{}l_{p}\}\), where \(N_{\text{iter}}\) is the number of samplings of the MCMC method. This number of samplings with the amount of Markov chains are maintained large enough to ensure the convergence of the parameters obtained: 30 Markov-chains with 5000 iterations for each chain including, 1000 warm-up iterations, and the average proposal acceptance probability adapt_delta[31] is set to 0.94. These optimum values of the parameters are used to advance one day, and to obtain the state of the variables: \((S(t_{0}+1),E(t_{0}+1),I(t_{0}+1),R(t_{0}+1))\). To solve the system of differential equations, stan uses the fourth and fifth-order Runge-Kutta method. As the parameters of transmissibility \(\beta\) are time-dependent, it can be expressed as:  
\[\beta(t)=\sum_{j=0}^{j_{\text{rand}}-W}\beta_{j}^{\text{opt}}\chi_{[t_{j},\,t_ {j+1})}(t), \tag{13}\]  
where \(\beta_{j}^{\text{opt}}\) is a specific value among the MCMC samples that gives the optimal \(l_{p}\) at time \(t_{j}\) using the time-window \([t_{j},\,t_{j}+W]\), and \(\chi\) is the indicator function defined as:  
\[\chi_{A}(t)=\left\{ \begin{array}{ll}1&\text{if }t\in A\\ 0&\text{if }t\
otin A.\end{array} \right. \tag{14}\]  
Similarly, the proportions \(\lambda_{*}\) can be defined as:  
\[\lambda_{*}(t)=\sum_{j=0}^{j_{\text{rand}}-W}\lambda_{*j}^{\text{opt}}\chi_{[t _{j},\,t_{j}+1)}(t), \tag{15}\]  
where \(\lambda_{*j}^{\text{opt}}\) are a MCMC sample which gives the optimal \(l_{p}\) at time \(t_{j}\).
## 2 Local Treatments
### Polymethyl Metheacrylate  
The most popular local treatment is an antibiotic-eluting material called polymethyl methacrylate (PMMA) or 'bone cement' [17]. This has been referred to as the gold-standard of chronic osteomyelitisitis treatment [17; 18]. Experimental studies have shown that PMMA can deliver in the order of 200 times the amount that systemic antibiotics are able to [19]. In animals, [PERSON] and [PERSON] (1993) found that PMMA beads had a 100% success rate at preventing the recurrence of osteomyelitisitis [20]. However, randomised controlled trials in humans have failed to identify a difference in outcome between PMMA and systemic antibiotics [21; 22; 23]. In addition, the high temperatures generated during the preparation of PMMA can degrade its antibiotic content and induce thermal necrosis. This means certain antibiotics such as tetracyclines cannot be used [18]. Others have described a rare 'bone cement implantation syndrome' with PMMA use [24]. This is thought to be due to a leachable MMA monomer that causes local tissue toxicity and systemic effects such as hypoxia and confusion [24]. Perhaps the most concerning disadvantages of PMMA, is that the majority of the antibiotic loaded is not released and every patient requires a second operation for it to be removed [17]. This increases morbidity,  
Figure 1: Simplified Cierny–Mader classification. Panel A: cross-sectional view of osteomyelitisitis in a long bone. Panel B: a longitudinal view with a window of bone removed. Stage 1 (medullary) osteomyelitisids is confined to the medullary cavity. Stage 2 (superficial) involves the cortical bone and most often originates from direct inoculation or contiguous infection. Stage 3 (localised) involves both cortical and medullary bone. Stage 4 (diffuse) involves the entire thickness of the bone with a loss of stability. This figure was based on the original Cierny–Mader classification [11].  
hospital costs, recovery time and the risk of perioperative complications such as infection. Attention has therefore shifted to alternative local treatments such as resorbable antibiotic scaffolds.
###### Abstract  
In recent years, structural health monitoring (SHM) has received increasing interest from both research and professional engineering communities. This is due to the limitations related to the use of traditional methods based on visual inspection for a rapid and effective assessment of structures and infrastructures when compared with the great potential offered by newly developed automatic systems. Most of these kinds of systems allow the continuous estimation of structural modal properties that are strictly correlated to the mechanical characteristics of the monitored structure. These can change as a result of material deterioration and structural damage related to earthquake shaking. Furthermore, a suitable configuration of a dense sensor network in a real-time monitoring system can allow to detect and localize structural and non-structural damage by comparing the initial and a final state of the structure after a critical event, such as a relevant earthquake. In this paper, the modal curvature evaluation method, used for damage detection and localization on framed structures, considering the mode curvature variation due to strong earthquake shaking, is further developed. The modified approach is validated by numerical and experimental case studies. The extended procedure, named \"Curvature Evolution Method\" (CEM), reduces the required computing time and the uncertainties in the results. Furthermore, in this work, an empirical relationship between curvature variation and damage index has been defined for both bare and infilled frames.  
structural damage localization; nonlinear FEM simulations; reinforced concrete framed structures; structural health monitoring; real structures; signal processing Article