esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Posture Description  
A 3D Sensor (Microsoft Kinect V2) was used to record movement, as it is able to recognize different subjects, track their movement and create a skeleton comprising 25 points (Figure 1), which may be described by three-dimensional coordinates (i.e., by using X, Y and Z planes of motion).  
Any movement consists of a series of postures. Eighteen joints were used to describe a posture in a series of volunteer subjects. It was decided to exclude joints such as those numbered 16, 20, 21, 22, 23, 24 and 25 (Figure 1) from algorithms, as they demonstrated high inconsistency in tracking accuracy. A total of 40 parameters were therefore calculated, based on 18 points: 17 were vector lengths (Table 1) and 23 were angles. However, each algorithm used a different number of parameters, as described in Section 2.3.  
The vector lengths were calculated relative to a position on the centerline of the torso (see point \"2\", Figure 1), as it had minimal errors in tracking. As each subject had a different body shape, this meant lengths between joints were not consistent, and it was therefore decided to normalize them using the participants' heights using the following formula [24]  
\[D_{vector}=\sqrt{\frac{\left(x-x_{0}\right)^{2}+\left(y-y_{0}\right)^{2}+\left( z-z_{0}\right)^{2}}{height}}, \tag{1}\]  
where \(x_{0},y_{0}\) and \(z_{0}\) represent coordinates of the midpoint of the back, and \(x,y,z\) are the coordinates of the point for which the distance is calculated.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **Vector Length** & **No.** & **Vector Length** & **No.** & **Vector Length** \\ \hline
1 & 2-1 & 7 & 2-8 & 13 & 2-14 \\
2 & 2-3 & 8 & 2-9 & 14 & 2-15 \\
3 & 2-4 & 9 & 2-10 & 15 & 2-17 \\
4 & 2-5 & 10 & 2-11 & 16 & 2-18 \\
5 & 2-6 & 11 & 2-12 & 17 & 2-19 \\
6 & 2-7 & 12 & 2-13 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vector lengths used for the algorithm, where numbers represent the joint as shown in Figure 1.  
Figure 1: Diagram of connection of points received from the sensor.  
Eleven angles were used in algorithms to describe postures and movements, as shown in Figure 2 and Table 2. For all 11 joints, the angles were between two vectors in 3D space. However, for the shoulder, hip and knee, the angles were calculated in the frontal and sagittal planes only.  
The angles were calculated as the angle between two 3D vectors  
\[D_{angle}=\arccos\left(\frac{x_{1}x_{2}+y_{1}y_{2}+z_{1}z_{2}}{\sqrt{x_{1}^{2}+y _{1}^{2}+z_{1}^{2}}\sqrt{x_{2}^{2}+y_{2}^{2}+z_{2}^{2}}}\right), \tag{2}\]  
where \(x_{n}\), \(y_{n}\) and \(z_{n}\) are the coordinates of vectors obtained by the differences between points, according to Table 1.
## 5 Experimental Results
### Discussion  
The measured accuracy decreases for all classifiers when they are embedded. This fact can be related to the limitation of the embedded version of the LibROSA library, on which the audio feature extraction perform differently. Based on our analysis, the expected accuracy of both approaches ranges around 50-60% for the recognition of different sound categories. It represents a lower range than the CNN-based solutions proposed in [35], where accuracy ranges around 50-75% for the fine-grain sound recognition of a hierarchical dataset. Although the limited computational power available on embedded devices challenges the use of such CNN-based solutions, recent new commercial devices such as Google' Edge Tensor Processing Unit (TPU) [39] facilitate the use of this technique for edge computing.  
Our measurements show that the most time-demanding operation is the audio features extraction, an operation which requires several seconds on an embedded system. Although this time can be reduced by lowering the number of extracted features, our experiments demonstrate that it certainly affects the accuracy of the sound classifier. Further analysis is needed when selecting the audio features for a particular dataset since not all sound classifiers are equally affected when reducing the features set. Nonetheless, such analysis is beyond the scope of this paper.  
Our analysis provides valuable information about the achievable accuracy and the required execution time on embedded devices. Some discussion is needed when targeting real-time sound classification. The frame size of 1 s must be reduced to a few milliseconds and the sound classification might be performed per frame. Our results provide timing information, which can be used to select frame size, determining the time slot to perform the feature extraction and the sound classification. For instance, a frame size of around 100 ms would only provide enough time for the ANN classifier in case of the cascade approach (Table 16), but not for the feature extraction, which for Set 3 rounds to 3 s (Table 13). A frame size of around 700 ms could be enough by only using the features from the MFCC and the ANN classifier in case of the non-cascade approach (Table 17). Although we do not propose a solution for real-time sound recognition on an embedded device, our evaluation can be used for the selection of the frame size, the feature extraction or the classifiers.  
Embedded devices are often power-constrained devices. A power analysis of the different approaches would certainly help to not only select the most power-efficient technique but also to help in the selection of the embedded device. One would expect that the cascade approach presents a higher power consumption due to using two sound classifiers. However, the flexibility of this approach enables the combination of power-efficient classifiers, which can lead to low power solutions. A detailed power analysis of the feature extraction and the sound classification is one of the priorities for future work.
# TETES: Trust Based Efficient Task Execution Scheme for Fog Enabled Smart Cities
## 5 Results and Analysis  
The performance of our proposed scheme TETES is evaluated by calculating the offloaded tasks executed by a fog node in different scenarios. To evaluate its performance, a simulation environment is created by deploying number of IoT based sensor nodes in diverse areas and they are supposed to communicate with each other. To determine the performance of the \"TETES\" in determining the trust value of each of the networking node, some malicious nodes are added in the simulation environment. These IoT nodes are subdivided into different groups to make a clustering network. Each cluster comprises both legitimate and malicious nodes. These nodes are supposed to offload their tasks to the fog node that is placed in close vicinity and any far-end node can access the fog node through a maximum of 2 intermediate nodes. The data size of each offloaded task ranges from 50 kB to 100 kB and the time required to execute these tasks ranges from 1 processing cycle to 4 processing cycles. A complete list of simulation parameters is shown in Table 2.  
The results are compared with First Come First Serve (FCFS), Longest Task First (LTF), and Shortest Task First (STF) algorithms for varying numbers of offloaded tasks and varying fog node task execution capacity.
# Enabling Intelligent 6G Communications: A Scalable Deep Learning Framework for MIMO Detection
## 3 MIMO Detection: A System Model
### Conventional MIMO Detection
#### 3.1.1 Maximum Likelihood Detector  
The maximum likelihood detector (MLD) minimizes the difference between the received vector and all possible transmitted symbol vectors as follows:  
\[\mathbf{\hat{s}}_{\text{MLD}}=\arg\min_{\mathbf{s}\in\mathcal{A}^{N}}\| \mathbf{r}-\mathbf{Gs}\|^{2}, \tag{8}\]  
where \(\mathcal{A}\) is the modulation constellation, and \(N=2P_{t}\) is the dimension of \(\mathbf{s}\)[1; 2].
## 4 Methods for Improved Reliability and Robustness
### Automatic Correction  
[PERSON] et al. [67] formulate the segmentation problem as a two systems task: the first is a U-Net inspired encoder-decoder CNN predicting segmentations from the input images, the second is a fully convolutional network (FCN) working as a context feedback system. Once fed with segmentations, the FCN outputs encoded features which are integrated back into the decoder of the CNN. This context feedback loop helps the model extract high-level image features and fix uncertainties over time.  
[PERSON] et al. [97] build from their previously proposed QC technique [77] to embed anatomical awareness into CMR segmentation models. The authors assume that the QC information provided by the QC tool encapsulates expertise biophysical knowledge that can be used to provide feedback to the network. As such, predictions flagged as high quality by the QC tool are fed back into the network model to reinforce its anatomical awareness. [PERSON] et al. [98] present a segmentation framework that guarantees anatomical criteria by warping the predictions of a given model towards the closest anatomically valid cardiac shape with the use of a constrained Variational Autoencoder (cVAE). This warping step acts as the correction procedure, effectively leading to a reduced number of anatomical errors in the segmentation results. Finally, [PERSON] and [PERSON] [99] use the information from an autoencoder-based post-analysis QC tool as a proxy of a model's performance in unseen cardiac images [88]. The QC tool allows the automatic identification of Out-of-Distribution (OoD) data, which cause failures of the segmentation model. The information is then used as feedback to refine the training of the segmentation model, thus adapting to the OoD data.
## 5 Types of Phage-Based Biosensors
### Phage-Based Magnetoelastic Biosensor
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Transducer** & **Host Bacterium** & **Phage** & **Food Sample** & \begin{tabular}{c} **LOD** \\ **(CFU/mL)** \\ \end{tabular} & \begin{tabular}{c} **Assay** \\ **Time** \\ \end{tabular} & \begin{tabular}{c} **Ref.** \\ **Ref.** \\ \end{tabular} \\ \hline \multirow{4}{*}{Magnetoelastic} & _S._ Typhimuriumium & E2 & NR & 5 \(\times\) 10\({}^{2}\) & NR & [133] \\ \cline{2-6}  & _S._ Typhimuriumium & E2 & Romaine lettuce & 5 \(\times\) 10\({}^{2}\) & NR & [134] \\ \cline{2-6}  & _B. anthracis_ & JRB7 & NR & NR & NR & [135] \\ \cline{2-6}  & _S._ Typhimuriumium & NR & NR & \
\begin{tabular}{c} 1.5 \(\times\) 10\({}^{3}\) \\ CFU/mm\({}^{2}\) \\ \end{tabular} & NR & [136] \\ \hline \multirow{4}{*}{QCM-based assays} & _S._ Typhimuriumium &
\begin{tabular}{c} Filamentous \\ phage \\ \end{tabular} & Chicken wash & 10\({}^{2}\) & 3 min & [102] \\ \cline{2-6}  & _M. tuberculosis_ and & D29 & NR & 10\({}^{3}\) & NR & [103] \\ \cline{1-1} \cline{2-6}  & _M. singematis_ & 12,600 & NR & 10\({}^{4}\) & NR & [104] \\ \cline{1-1} \cline{2-6}  & _E. coli_ & T4 & Milk & NR & NR & [16] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Phage-based quartz crystal microbalance (QCM) and magnetoelastic (ME) biosensors.
## 6 Validity Threats Discussion  
Following the guidelines provided by [PERSON] et al. [53], potential threats to the experimental results and the corresponding mitigation strategies are discussed.  
**Conclusion validity** concerns the possibility of statistical errors and the adequacy of the statistical tests chosen to demonstrate the effectiveness of the LePB-SA4 RE model compared to others. To mitigate these threats, we conducted extensive experiments across multiple datasets (AWARE, BFRU, and Dabrowski) and employed robust statistical methods. Standard deviations and confidence intervals were calculated to ensure the result reliability. Additionally, ten parallel tests are conducted for each experiment to confirm consistency and robustness.  
**Construct validity** pertains to whether the chosen metrics accurately measure the concept. Different evaluation metrics can lead to varying interpretations of model performance. We addressed this threat by using well-established metrics (accuracy and F1-Score), widely accepted in SA and requirements elicitation. We also provided detailed explanations and justifications for these metrics, performing comprehensive analyses to ensure that they accurately reflect model performance.  
**Internal validity** relates to the reproducibility of our experiments, including the influence of uncontrolled variables and the precision in documenting experimental procedures. To mitigate this threat, we provided detailed descriptions of the experimental setup in Section 4, including hyperparameters, dataset splits, and hardware specifications. The code is publicly available to support reproducibility. Rigorous experimental protocols were followed to ensure consistency and control for extraneous variables. Moreover, detailed descriptions of the training processes and the implementation of LePB-SA4 RE are described in Section 5.  
**External validity** concerns the generalizability of the findings beyond the specific conditions of our study, including their applicability to other app domains, languages, and datasets. We mitigate this threat by testing on diverse datasets from different domains (productivity, social networking, games). Additionally, various BERT checkpoints are evaluated to demonstrate the robustness and adaptability of our model across different contexts. By comparing the performance of LePB-SA4 RE with different BERT versions, we provided evidence of its generalizability. Furthermore, the potential impacts of domain-specific characteristics are discussed, and the need for further testing in other contexts was highlighted to validate the broader applicability of our findings.
# Research on Blockchain Transaction Privacy Protection Methods Based on Deep Learning
## 3 Deep Learning-Based Blockchain Transaction Privacy Protection Model
### Anonymous Identity Realization  
During the initialization phase of the scheme, the regulatory authority needs to generate three public-private key pairs: firstly, using the BG algorithm to produce private key \(Sk_{BG}\) and public key \(Pk_{BG}\); secondly, as the KGC within the IBC framework, the regulatory authority creates a master public key \(MPK\) and corresponding a master private key \(MSK\); thirdly, defining the identity marker in IBC as \(ID_{a}\), considering \(ID_{a}\) as the public key, the signature private key \(MSK\) is created using the master private key \(Sk_{a}=IBC.KeyGen(MSK,ID_{a})\) based on the IBC algorithm. Then, users apply for key distribution from the regulatory authority using unique identifiable information \(ID_{u}\) (which needs to be self-proving, such as an email address, ID card number, or phone number).  
After verifying user identity information, the regulatory authority encrypts it using the public key \(PK_{BG}\) of the BG probabilistic cryptography algorithm to generate \(AID_{1}=BG.Enc(Pk_{BG},ID_{u})\). To ensure that \(ID_{u}\) is certified by the regulatory authority, it needs \(AID_{1}\) to be signed by the authority, generating \(AID_{2}=IBC.Sign(Sk_{a},AID_{1})\). Define \(AID_{u}=AID_{1}\|AID_{2}\). Since \(AID_{1}\) is obtained using the BG probabilistic public-key encryption algorithm and has good randomness, and \(AID_{2}\) is obtained through the IBC signature, \(AID_{u}\) also possesses good randomness, effectively hiding the user's real identity information \(ID_{u}\).  
Next, \(AID_{u}\) is used as the public-key identity. Utilizing the IBC algorithm, the regulatory authority generates the corresponding private key \(Sk_{u}=IBC.KeyGen(MSK,AID_{u})\) for the user. The user's verifiable true identity is denoted as \(ID_{u}\), while \(AID_{u}\) represents their calculated anonymous identity, \(Sk_{u}\) being the corresponding private key. Employing the BG algorithm enables the generation of various \(AID_{u}\) from the same \(ID_{u}\), establishing a one-to-many connection between \(ID_{u}\) and \(AID_{u}\). This relationship permits the theoretical creation of limitless \(AID_{u}\) from the same \(ID_{u}\), allowing users to continuously renew their anonymous identities.  
For ease of subsequent description, define the transaction sender and receiver's identity markers as \(ID_{s}\) and \(ID_{r}\), respectively. Through the above process, their corresponding anonymous identities \(AID_{s}\) and \(AID_{r}\), and private keys \(Sk_{s}\) and \(Sk_{r}\) can be calculated.  
When the sender transacts with the receiver, they can utilize \(Sk_{s}\) to decrypt the UTXO input script and set \(AID_{r}\) as the receiver's address, thereby maintaining identity anonymity.
# Innovative Approaches for Minimizing Disinfection
## 4 DBP Minimization Approaches
### Alternative to Chlorine Disinfectants
#### 4.7.1 Chloramination  
Chloramine is an alternative to chlorine due to its extended persistence in water distribution systems and its lower production of regulated DBPs, such as HAAs and THMs, compared to chlorine [3]. Even though chloramine is frequently employed as a disinfectant to minimize the formation of regulated DBPs, its interactions with dissolved organic compounds in water result in the production of nitrogenous DBPs (N-DBPs), which are often more harmful than THMs and HAAs [37].  
Bromide, a naturally occurring anion commonly found in raw water at concentrations ranging from less than 0.01 to 4 mg L\({}^{-1}\), plays a significant role in chloramination processes. During chloramination, chlorine or chloramine oxidizes bromide to form hypobromotic acid/hypobromite (HOBr/OBr\({}^{-}\)) or bromamines (e.g., NH\({}_{2}\)Br, NHBrCl, and NHBr\({}_{2}\)), respectively. These compounds can react with NOM in water, leading to the formation of brominated DBPs. Toxicological investigations have revealed that brominated DBPs demonstrate elevated cytotoxicity, genotoxicity, and developmental toxicity compared to their chlorinated counterparts. Furthermore, epidemiological studies have emphasized the greater concerns associated with brominated DBPs over chlorinated DBPs [3].  
The chloramination of various organic compounds has been extensively documented, including organophosphorus pesticides, nitrogenous organic compounds, algal organic matter, herbicides, and oxytetracycline. It has been noted that the levels of iodinated DBPs are higher during chloramination than chlorination. Both chlorine and chloramines can oxidize iodide to hypoiodous acid (HOI). In chlorination, the reactions that lead to the formation of iodate occur much faster than those resulting in the formation of iodated DBPs. Conversely, in chlorination, the reactions leading to the formation of iodide and iodate proceed much slower than those leading to the formation of iodinated DBPs [4].
## 3 Results and Discussion
### Adsorption Kinetics  
The effect of time on the adsorption can be expressed by the kinetic parameters. We applied the first- and second-order reaction and intraparticle diffusion models (Equations (2)-(4), respectively) to determine the kinetic parameters:  
\[log(q_{e}-q_{t})=logq_{e}-(K_{1}t/2.303) \tag{2}\]  
\[t/q_{t}=(1/q_{e})t+1/(K_{2}q_{e}{}^{2}) \tag{3}\]  
\[q_{t}=K_{id}\ ^{0.5} \tag{4}\]  
where \(q_{e}\) (mg/g) and \(q_{t}\) (mg/g) are the denoted adsorption capacities at equilibrium and time (\(t\)); \(t\) (min) is the time; \(K_{1}\) (min\({}^{-1}\)) and \(K_{2}\) (g/mg\(\cdot\)min) are the rate constants of first- and second-order adsorption; \(K_{id}\) (mg/g\(\cdot\)min\({}^{0.5}\)) is the rate constant of the intraparticle diffusion.  
The slope and the intercept of the linear gradient of the plot for the models were used to calculate the rate constants (\(K_{1}\), \(K_{2}\), and \(K_{id}\)) and calculated adsorption capacity (\(q_{e}\), (cal)). We present the kinetic parameters in Table 4.  
For all concentrations of Cu(II), the regression-coefficient (\(R^{2}\)) values of the second-order model exceed 0.99. This result indicates the second-order model could describe the adsorption process well. Moreover, the amount of the calculated adsorption capacity (\(q_{e}\), (cal)) from the second-order-model plot is relatively close to the adsorption capacity, determined by experiments (\(q_{e}\) (exp)). Therefore, the second-order chemical reaction could be controlled by the process of the adsorption system.
# Memory-Based LT Codes for Efficient 5G Networks and Beyond
## 3 Y-Network Using MBLT Algorithm
### Encoding Process of the MBLT on the Y-Network  
Initially, shaping the left degree distribution (i.e., MBLT) is presented to point-to-point communications where a single transmitter sends data to a single receiver [19]. In our proposed scheme, we use the concept of the memory-based over the Y-network, where two encoding processes have to be done: one encoding process at each source. Then, the relay applies the buffer-and-forward (BF) strategy to the incoming encoded symbols. The encoding process of the proposed approach over the Y-network is given in Algorithm 3. In the algorithm, \(x\) shows the number of sources, \(c_{i}\) is the encoded symbol at each source, and \(v_{j}\) is the encoded symbol from the relay to the destination.  
Naturally, the encoding process is similar to the conventional encoding process of LT codes using the RSD distribution. The only difference is shown once the picking degree is \(d=1\); the single source symbol is chosen uniformly from the k source symbols in the regular LT encoding. In contrast, the single source symbol, which has the highest instantaneous degrees, is chosen without repetition when using the proposed algorithm. The reason behind this is the importance of the degree-one encoded symbols (i.e., the ripple). They are essential in initiating the decoding process and keeping it going until the entirety of the source symbols are recovered. Indeed, as the source symbols connected to the ripple have high degrees, the encoding process recovers the source symbols faster and with high efficiency.  
```
Input: Source symbols \(\{u_{1},u_{2},u_{3},\ldots,u_{k}\}\), \(j=1\), \(i=1\), x={1,2}; while(There is no ACK from the destination)do for\(x\)do  Choose a random degree \(d\) depending on the degree distribution (RSD or any other degree distribution); if\(d\)=1 then  Choose the source symbol with the highest instantaneous degree, \(u_{m}\), (without choosing it another time);  The encoded symbols is \(c_{i}=u_{m}\) else  Choose \(d\) source symbols, uniformly at random, from the \(k\) source symbols, \(u_{m1},u_{m2},\ldots,u_{md}\);  Perform bitwise XOR on the chosen source symbols to generate the encoded symbol \(c_{i}=u_{m1}\oplus u_{m2}\oplus\ldots\oplus u_{md}\);  end if  Send the incoming encoded symbol \(c_{i}\) to the destination; \(v_{j}=c_{i}\); \(j++\);  end for \(i++\);  end for
```  
**Algorithm 3**MBLT encoding process at the Y-network.