esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Cr(III) Oxidation by Thermal Treatment  
Regarding the oxidation mechanism of Cr(III), heat treatment under aerobic conditions is necessary, as in the case of pyrolysis (i.e., under anaerobic conditions) this will not take place [5]. Initially, the dominant form of Cr(III) in the raw sludge is considered to be Cr(OH)\({}_{3}\), since this was precipitated from the wastewater during its primary treatment. However, through the dezydroxylation process in the temperature range 150-250 \({}^{\circ}\)C, the requirements for the oxidation of Cr\({}_{2}\)O\({}_{3}\) were fulfilled [6], according to Reaction (1). Cr(III) is oxidized in the presence of atmospheric oxygen, while the application of thermal/heating treatment increased both the reaction rate and efficiency [29].  
\[2\text{Cr}_{2}\text{O}_{3}+3\text{O}_{2}\to 4\text{CrO}_{3} \tag{1}\]  
\[\text{CaCO}_{3}\to\text{CaO}+\text{CO}_{2} \tag{2}\]  
\[\text{Cr}_{2}\text{O}_{3}+2\text{CaO}+3/2\text{O}_{2}\to 2\text{CaCrO}_{4} \tag{3}\]  
\[4\text{CaCrO}_{4}+2\text{MgO}\to 2\text{MgCr}_{2}\text{O}_{4}+4\text{CaO}+3 \text{O}_{2} \tag{4}\]  
Furthermore, the presence of other metal oxides enhances the oxidation reaction [30]. As the chemical characterization of the sample has proven, the tannery sludge contained high amounts of calcium [31]. However, the dominant form in the sludge is CaCO\({}_{3}\) and thus, in order for Reaction (3) to proceed, a prior step is required, namely Reaction (2). The decomposition of CaCO\({}_{3}\) was essential and became possible in the temperature range examined [4]. The combination of Reactions (2) and (3) is considered to be the dominant oxidation mechanism, since the Cr(VI) is present in the thermally treated samples as CaCO\({}_{4}\), according to XRD spectra. In addition, the CaCO\({}_{3}\) content in the corresponding ashes was inversely proportional to the determined Cr(VI) percentage up to 700 \({}^{\circ}\)C (Figure 3). A further increase in treatment temperature (e.g., over 750 \({}^{\circ}\)C) led to a partial reduction of Cr(VI) to Cr(II), since the more stable compound of MgCr\({}_{2}\)O\({}_{4}\) was formed, due to the presence of MgO, according to Reaction (4) [32].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**Treatment Temperature (\({}^{\circ}\)C)**} \\ \hline  & **Initial** & **500** & **550** & **600** & **650** & **700** & **750** \\ \cline{2-8}  & & \multicolumn{8}{c}{**\% w/w**} \\ \hline Ca & 14.8 & 28.8 & 29.1 & 29.3 & 29.7 & 29.8 & 30 \\ Mg & 2.4 & 4.9 & 5 & 5.1 & 5.1 & 5.1 & 5.1 \\ Na & 1.5 & 3 & 3 & 3.1 & 3.2 & 3.2 & 3.2 \\ Al & 0.5 & 1.1 & 1.1 & 1.2 & 1.2 & 1.2 & 1.2 \\ Fe & 0.46 & 0.5 & 0.5 & 0.5 & 0.5 & 0.6 & 0.6 \\ K & 0.12 & 0.25 & 0.25 & 0.26 & 0.26 & 0.27 & 0.27 \\ Zn & 0.06 & 0.14 & 0.14 & 0.14 & 0.15 & 0.16 & 0.16 \\ \hline pH & 9.1 & 9.2 & 9.2 & 9.2 & 9.2 & 9.2 & 9.2 \\ Conductivity & 0.96 & 7.35 & 7.38 & 7.4 & 7.43 & 7.44 & 7.43 \\ (mS/cm) & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._
# A Review of Pedestrian Indoor Positioning Systems for Mass Market Applications
## 3 Inertial Based Systems
### Simultaneous Localization and Mapping  
The Simultaneous Localization and Mapping (SLAM) extends the localization problem including the estimation of a map. It was developed by the robotics community and the key idea is that a mobile robot moving in and indoor environment can build a consistent map of the building at the same time that determines its own position without prior information about the position or the map [93; 94]. In 2012, [PERSON] et al. developed the FootSLAM system resulting from the application of the SLAM problem to the localization of a pedestrian in an indoor environment [44]. The FootSLAM system maps the environment with a regular grid of hexagons and builds a probabilistic map computing the probability that a pedestrian crosses the transition between two adjacent hexagons. The idea beyond this system is that it is probable that a pedestrian walking in an indoor environment passes different times by the same place and thus the estimation can be enhanced considering that the user goes and returns along the same path. Figure 10 shows the concept of the FootSLAM where the more likely hexagons are highlighted. The FootSLAM system computes the odometry of the user using a foot mounted IMU and uses the movement of the user between two epochs to update the particles of a Rao-Blackwellized particle filter. Each particle takes into account a possible path of the user and computes the corresponding hexagonal probabilistic map. At every epoch, the estimated path and probabilistic map of each particle are updated with the measured movement of the user, that is, the probabilities of the transitions between hexagons crossed due to the movement of the user are increased. Thus, whenever the user closes the loop and returns to the origin the filter will reward those particles that have gone and returned along the same path. With this method the drift of the inertial sensors can be eliminated but the filter has the dependence on the closure of the loops. If the walk of the pedestrian does not return to the same place the error in the position estimations will grow as in the typical step and heading systems. The main disadvantage of the system is the computational complexity as every particle must store a probabilistic map of all the environment which can lead to high computational complexity for large environments. Recently, there appeared works in the literature based on the FootSLAM system, such as the FeetSLAM where the maps of different users are combined [45] or the PocketSLAM where the inertial measurements are obtained from an smartphone placed on the pocket of the user [46]. Although SLAM approaches obtain highly accurate position estimations when the user walks in loops and the cost of the systems is reduced to the cost of the IMU, the computational complexity grows with the size of the map and therefore the scalability of the system is limited by the area of the environment.
# The Effect of Non-Transferred Plasma Torch Electrodes on Plasma Jet: A Computational Study
## 2 Computational Modeling
### Geometry and Mesh
#### 2.2.1 Cathode Shapes  
The shape of the cathode tip is critical, as it significantly affects arc stability and overall torch performance [31]. This study examines various cathode tip geometries, sourced from prior literature (cathodes a-d) and experimental designs at RISE (cathodes e and f) [11; 15; 20; 32]. Figure 3 illustrates the different cathode shapes used in this work. In the present study, all the cathodes are 9.1 mm in height, and the bottom radius is 4.5 mm. The top distance from the symmetry axis is adjusted based on the shape of the cathode, but the maximum distance is 3 mm.  
1. Conical cathode (Figure 3a): Features a pointed apex directed toward the anode. This shape focuses the arc, enhancing energy density and making it effective for cutting and melting.
2. Tapered cathodes (Figure 3b-d): Similar to conical tips but with a wider base and narrower end. These shapes provide a stable and focused arc, suitable for high-power operations.
3. Flat cathode (Figure 3e,f): Consists of flat, disc-shaped ends aligned perpendicular to the arc. These offer stable arc behavior and are typically used in lower-power (around 20 kW) torches.
# Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey
## 4 Research on Machine Learning-Based IDSs
### Session-Based Attack Detection
#### 4.3.1 Statistic-Based Feature Detection Methods  
Session statistical information includes the fields in packet headers, the number of packets, the proportion of packets coming from different directions, and so on. This statistical information is used to compose feature vectors suitable for shallow models. The sessions have high layer semantics; thus, they are easily described by rules. Decision tree or rule-based models may be appropriate methods. Unfortunately, the methods based on statistical features ignore the sequence information, and they have difficulties detecting intrusions related to communication content.  
Because statistical information includes the basic features of sessions, supervised learning methods can utilize such information to differentiate between normal sessions and abnormal sessions.  
The existing session-based detection methods often face problems of low accuracy and have high runtime costs. [PERSON] et al. [54] proposed a hierarchical decision tree method in which, reduce the detection time, they analyzed the frequency of different types of attacks and designed the detection system to recognize specific attacks. They used data from the CICIDS 2017 dataset that included 79-dimensional features and 15 classes. The proposed detection system had a two-layer structure. The first layer consisted of two independent classifiers (i.e., a decision tree and a rule-based model), which processed part of the features. The second layer was a random forest classifier, which processed all the features from the dataset as well as the output of the first layer. They compared multiple machine learning models on 15 classes; their proposed methods performed best on 8 of the 15 classes. Moreover, the proposed method had low time consumption, reflecting its practicability.  
Session-based detection using supervised learning models depends on expert knowledge, which is difficult to expand to new scenarios. To address this problem, [PERSON] et al. [55] proposed an unsupervised method to detect attacks in smart grids. Due to the lack of smart grid datasets, they constructed a dataset through simulation experiments. First, they captured and cached packets to construct sessions. Then, they extracted 23-dimensional features from the sessions. Next, they utilized mini batch K-means to divide the data into many clusters. Finally, they labeled the clusters. This work was based on two hypotheses. The first was that normal samples were the majority. The second one was that the distances among the normal clusters were relatively short. When the size of a cluster was less than 25% of the full sample amount or a cluster centroid was far away from all other the other cluster centroids, that cluster was judged as abnormal. No expert knowledge was required for any part of this process. The proposed methods were able to detect intrusion behaviors in smart grids effectively and locate the attack sources while holding the false alarm rate less to than 5%.
## 3 Results
### Analysis between Pairs of Days  
Data from seven days were arranged into 21 unique pairs. Classification errors were calculated for each pair using eight-fold cross validation (eight repetitions for each movement per two days, so each repetition constitutes a separate fold), which were averaged over all pairs for each subject. Results for this analysis are shown in Figure 7 as the average of 10 healthy and six amputee subjects.  
SSAE outperformed LDA by 11.93 and 21.59 percentage points for both healthy and transradial amputee subjects, respectively (Figure 6). Furthermore, SSAE achieved error rates similar to those of the corresponding data in the within-day analysis with percentage point differences of 3.55 and 11.26 for healthy and amputee subjects, respectively. Conversely, LDA performance between-days worsened significantly from the within-day analysis by 11.28 and 18.95 percentage points for healthy and amputee subjects, respectively. Mean classification errors for the 10 healthy and six amputee subjects for each pair of days are shown in Tables 1 and 2, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Healthy sEMG Data**} \\ \hline
**Day** & **D1** & **D2** & **D3** & **D4** & **D5** & **D6** & **D7** \\ \hline
**D1** & - & 2.29 & 3.06 & 2.23 & 3.09 & 2.74 & 3.88 \\
**D2** & 10.28 & - & 3.8 & 3.15 & 3.23 & 2.95 & 3.65 \\
**D3** & 10.4 & 9.96 & - & 3.74 & 4.08 & 3.57 & 4.31 \\
**D4** & 11.22 & 11.37 & 11.02 & - & 3.02 & 2.61 & 3.45 \\
**D5** & 13.16 & 12.78 & 12.68 & 11.43 & - & 2.53 & 3.46 \\
**D6** & 12.74 & 12.94 & 12.66 & 12 & 11.11 & - & 2.69 \\
**D7** & 13.54 & 13.56 & 13 & 11.62 & 11.31 & 9.95 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Stacked sparse autoencoder (SSAE) vs. linear discriminant analysis (LDA) performance and surface electromyography (sEMG) vs. intramuscular (iEMG) data comparison for healthy subjects. In each matrix, the upper diagonal triangle represents the mean classification errors obtained with SSAE and the lower diagonal triangle represents errors obtained with LDA for corresponding pairs of days.  
Figure 7: Mean (and SD) classification error obtained with SSAE and LDA for both EMG data types from healthy and amputee subjects. The diamond symbol indicates the best classifier with statistical significance (\(p<0.001\)) for each EMG data type of both healthy and amputee subjects.  
From Table 1, sEMG and iEMG classification were similar when using SSAE (percentage point difference of 1.76), whereas classification differed significantly when using LDA. sEMG was superior to iEMG with a percentage point difference 8.37. For transradial amputees, however, sEMG and iEMG were classified with similar accuracy with both SSAE and LDA (Table 2).
## 3 Results and Discussion
### GC Analysis
#### 3.2.3 HS-SPME-GC-MS  
Some of the compounds identified by the HS-SPME technique (Table 3) were described previously, since they were detected in the analysis carried out with the other techniques by GC-MS. Several aldehydes (heptanal, octanal, nonanal, decanal, 2-decenal,2-undecanal, tetradecanal, and 14-octadececal), alkanes (undecane, dodecane, tridecane, tetradecane, and heptadecane), and acids (octanoic acid, nonanoic acid, and decanoic acid) were identified in the samples.
# Key Agreement and Authentication Protocols in the Internet of Things: A Survey
## 1 Introduction
### Motivations and Contributions  
Technology surrounds us from almost every side. We use various intelligent devices that, above all, make our lives easier but also transfer large amounts of data. Often, the sent messages contain sensitive data related to users' devices. Thus, the need to secure data is an essential aspect of the operation of intelligent systems. Users require that the use of technological facilities is safe for them, both in protecting human health and life, as well as in data processing.  
As a rule, the communication process consists of several stages. Specially designed protocols are used to secure each of these steps. These, in turn, are exposed to malicious users who look for security vulnerabilities to intercept and then use the data. The user authentication and key agreement stages deserve special attention here, as the security of subsequent communication phases depends on their safe course. We are aware that technological progress also entails the development of attack techniques. Therefore it is necessary to regularly review the level of security implemented by the protocols securing individual stages of communication. Properly selected and safe protocols will certainly increase IoT devices' security level. So, in this arithmetic, we provide an overview of how to secure the authentication processes and the reconciliation and agreement of keys using security protocols in WSNs.  
We believe that studying the work of protocols that secure the authentication process and session key agreement in IoT can help readers understand the state of art in both theory and practice. We will explain what security problems and threats are exposed to IoT devices operating in such networks. In addition, we will discuss the security levels offered by the protocols used in IoT or WSNs. We will also highlight the challenges and requirements for the newly designed protocols.
## 3 Methodology and Results
### The Smart Pipe Demonstrator Unit
In order to ensure that the sensors and electronics remained operational, and to gather some initial data on the motion sensors which were affected by the burial works, the Smart Server remained powered by a 12 V battery during the burial and sensor outputs were monitored throughout the burial and backfilling processes. The ground conditions consisted of a sandy soil with many larger pieces of broken concrete and stone, and appeared to comprise made-ground, elevated above an adjacent stream behind the fence (Figure 6(a)). The smart pipe demonstrator unit was then taken to the burial location and lowered gently into place by hand (Figure 6). It was then backfilled 'gently' using the excavated material to prevent damage to the sensors and electronics. Example data gathered and the lessons learnt from the burial of the demonstrator unit are presented in the following sections.
## 3 Materials and Methods
### Radix-4 Booth Multiplier  
The Booth algorithm, specifically the radix-2 version, was introduced to enhance multiplication efficiency [28]. The radix-4 Booth algorithm, also known as the modified Booth algorithm [29], further optimizes this process by halving the number of PP rows required for multiplication. The third architecture employs the radix-4 Booth algorithm, which is an effective technique for boosting multiplication performance, particularly with two's complement operands. When implementing the radix-4 Booth algorithm, the multiplier \(Y(y_{n-1},\ y_{n-2},\cdots,y_{0})\) is divided into overlapping groups of three consecutive bits. Each group is encoded and decoded with the multiplicand \(X(x_{n-1},\ x_{n-2},\cdots,x_{0})\) to produce the corresponding PPs. Generally, the n-bit multiplication using the radix-4 Booth algorithm can be represented as  
\[\begin{array}{rcl}X\times Y&=&X\times\left(y_{n-1}2^{n-1}+\sum\limits_{k=0}^{ m}y_{k}2^{k}\right)\\ &=&X\times\sum\limits_{k=0}^{m}\left(-2y_{2k+1}+y_{2k}+y_{2k-1}\right)2^{2k}\\ &=&X\times\sum\limits_{k=0}^{m}b_{k}2^{2k}\end{array} \tag{1}\]  
where \(m=n/2-1\) and the multiplicand \(X\) and multiplier \(Y\) are \(n\)-bit two's complement numbers. Based on the continuous three bits of the multiplier \(y_{2k+1},\ y_{2k},\ y_{2k-1}\), the radix-4 Booth algorithm can generate the corresponding coefficient \(b_{k}\), which can take on five possible values (\(\pm 1\), \(\pm 2\), or \(0\)), as illustrated in Table 2. A notable characteristic of this algorithm is that the corresponding PPs are 0 s when the three consecutive bits of the multiplier \(y_{2k+1},\ y_{2k},\ y_{2k-1}\) (\(b_{k}=0\)) are identical.  
Figure 4: An 8-bit Wallace tree multiplier.  
Figure 5 shows the design of an 8-bit radix-4 Booth multiplier. Each row of PP includes one Booth encoder and \(n+1\) Booth decoders. Each decoder outputs a single bit of the PP based on the 3-bit signal (_one_, _two_, and _neg_) received from the encoder, as depicted in Figure 5. When the _neg_ signal is zero, the decoder outputs zero if both control signals, _one_ and _two_, are zero; it outputs \(X_{i}\) when _one_ is activated; and it outputs \(X_{i-1}\) when _two_ is activated, corresponding to multiplication by 0, 1, and 2, respectively. If the _neg_ signal is one, the unit reverses the output bits mentioned above. Inverting the bits results in a one's complement, and for a two's complement, _neg_ must be added to the outcome. Finally, FAs and a CLA combine all PPs to produce the final product.
# VLSI Implementation of an Efficient Lossless EEG Compression Design for Wireless Body Area Network
## 3 Methodology
### Second-Stage Prediction
#### 3.1.2 Tri-Stage Entropy Coding  
The entropy encoder incorporates both the Huffman coding and the Golomb-Rice coding algorithm. The Huffman coding encodes symbols that have higher probabilities with shorter code length according to the Huffman tree. However, as the value of the EEG signal data varies in a wide range, the Huffman tree would group up outstandingly. It is difficult to design a low-cost and a high performing VLSI architecture that has a block circuit and implements a tree with a very large depth. For this reason, a two-stage Huffman encoding technique [19] is applied to the data whose values have high probabilities. For the \(PD_{2}\) values whose probabilities are relatively low, the absolute value is not too large. As such, the Golomb-Rice coding technique can efficiently shorten the length of the code by choosing an appropriate divider. This further enhances the performance of the Golomb-Rice coding. The \(PD_{2}\) value can be transformed into a smaller value \(PD^{\prime}(n)\) using Equation (1).  
\[\begin{split} PD^{\prime}(n)=2*PD_{2}(n)-2k\text{ if }PD_{2}(n)\geq 0\\ PD^{\prime}(n)=2*PD_{2}(n)-2k+1\text{ if }PD_{2}(n)<0\end{split} \tag{1}\]  
where \(k\) is the first position in the Golomb-Rice table.
# A Multimode Fusion-Based Aviation Communication System
## 2 System Design  
In this section, a detailed description of the system design for the aviation multimode fusion communication system is provided. We will outline the overall architecture, including the integration of various communication technologies, and discuss the principles and calculations used for link budget analysis. This comprehensive overview sets the stage for understanding the system's functionality and performance metrics.
## 1 Introduction
Few studies have been reported on the contribution of the torsional effects of chords to the out-of-plane elastic buckling loads. It is noted that, in Pratt and Warren truss arches, the sectional torsional stiffness is mostly contributed by the shear stiffness of their diagonal tubes and chords, and the contribution from the torsional stiffness of chords can be ignored [30]. However, in the sectional torsional stiffness of the Vierendeel truss arches, the contribution from the torsional stiffness of chords becomes remarkable and can be comparable with that from the shear stiffness of chords. To date, the torsional effects of the chords on the out-of-plane inelastic failure mechanism of the Vierendeel truss arches are still unclear. There are scarce design recommendations on the out-of-plane stability of Vierendeel arches in the current design codes or the literatures. Taking the torsional deformation of chords into account might reduce the local buckling loads of truss arches slightly, whereas incorporating the torsional stiffness of chords will increase the sectional torsional stiffness/resistance considerably. Thus, the  
Figure 1: Timeline for researches on the out-of-plane stability of circular steel arches [4; 6; 7; 10; 14; 19; 23; 24; 25; 26; 29]. torsional effects of chords can be deliberately ignored for a conservative strength design in the truss arches with diagonal tubes. However, in the Vierendeel truss arches where the sectional torsional stiffness is small, the torsional stiffness of chords should be taken into account for achieving better architectural aesthetics and economic demand. In this study, the out-of-plane global elastic buckling loads were derived for the pin-ended and end-fixed circular steel tubular Vierendeel truss arches on different sectional stiffness assumptions. The main finding is that incorporating the torsional effects of chords can remarkably strengthen the global out-of-plane stability of the circular steel tubular Vierendeel truss arches. Further, the out-of-plane elastic-plastic failure mechanisms were investigated for the end-fixed arches under different loading cases. The paper is organized as follows: Section 2 introduces the finite element models of the typical Vierendeel truss arches used for static analyses; Section 3 derives the sectional stiffnesses analytically in the Vierendeel truss arches; Section 4 calculates the out-of-plane elastic buckling loads for the pin-ended and end-fixed circular tubular Vierendeel truss arches based on the derived and numerically fitted equations, respectively; Section 5 presents the out-of-plane inelastic buckling behaviors of the Vierendeel truss arches under different loading cases; some conclusions are drawn in Section 6.