esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model and Problem Formulation
### Task Model  
We consider any smartphone \(n\), \(n\in\mathcal{N}=\{1,2,\,\cdots,\,N\}\), which aims to obtain a well-trained model with the maximal utility (i.e., model test accuracy), denoted by \(U_{n}\), within a latency threshold, denoted by \(T_{n}\).  
A DML model is trained for the detection of potential health risks. Specifically, the goal of DML is to train a model with \(L\) possible outputs for health data classification with \(L\) data labels through \(R\) rounds of collaborations. For example, data label \(l\), \(l\in\mathcal{L}=\{1,2,\,\cdots,\,L\}\), could be \"high blood pressure\". In round \(r\in\mathcal{R}=\{1,2,\,\cdots,\,R\}\), let \(p_{n,r}\in\mathcal{P}_{r}=\{p_{1,r},\,p_{2,r},\,\cdots,\,p_{N,r}\}\) denote the observed categorical distribution of \(N\) smartphone data labels. Denote \(q\) as the constant IID categorical distribution of \(L\) labels throughout \(R\) rounds. Then, \(p_{n,r}=\{p_{1,n,r},\,p_{2,n,r},\,\cdots,\,p_{L,n,r}\}\), and \(q=\{q_{1},q_{2},\,\cdots,q_{L}\}\).  
For example, a model is trained for detecting blood pressure risk. Collected health data have three labels: \"high\", \"low\" and \"normal\". In round 3, smartphone 1 owns 10 health data samples, including 2 samples of high blood pressure, 1 low blood pressure sample and 7 samples of normal blood pressure. Then, \(p_{1,3}=\{\frac{2}{10},\,1\},\frac{7}{10}\}\) and \(q\) can be a uniform distribution or other constant distribution, e.g., \(q=\{\frac{1}{100},\,\frac{1}{100},\,\frac{49}{50}\}\). However, \(q\) can be largely unknown by smartphones and servers during DML. In this paper, we do not assume prior knowledge of \(q\) at the network edge.  
To achieve the goal of DML, any smartphone \(n\) should decide when to participate in DML and how much effort it should contribute to DML. Let \(a_{n,r}\), \(n\in\mathcal{N}\), \(r\in\mathcal{R}\), be a decision variable such that \(a_{n,r}=1\) indicates that smartphone \(n\) participates in round \(r\); otherwise, \(a_{n,r}=0\). That is,  
\[a_{n,r}\in\{0,\,1\},\forall n\in\mathcal{N},r\in\mathcal{R}. \tag{1}\]  
In practice, any smartphone \(n\) owns a limited amount of private data. Let \(\bar{d}_{n}\) and \(\mathbf{d}_{n,r}\) denote the total data size and the training dataset actually used in round \(r\), respectively. Let \(\mathbf{d}_{n}\) be the local training dataset owned by smartphone \(n\), \(\mathbf{d}_{n}=\{(\mathbf{x}_{n,1},y_{n,1}),\cdots,(\mathbf{x}_{n,\bar{d}_{n}},y_{n,\bar{d} _{n}})\}\), where \((\mathbf{x},y)\) denotes one instance that includes the raw data and label. Then,  
\[\bigcup_{r\in\mathcal{R}}a_{n,r}\mathbf{d}_{n,r}\subseteq\mathbf{d}_{n},\forall n\in \mathcal{N},r\in\mathcal{R}. \tag{2}\]  
Let \(t_{r}\) be the time consumed for DML training round \(r\), including the time for local training, model aggregation and blockchain consensus. The latency constraint for smartphone \(n\) is described as  
\[\sum_{r\in\mathcal{R}}a_{n,r}t_{r}\leq T_{n},\forall r\in\mathcal{R}. \tag{3}\]
## 5 Achieved Results and Performance Comparison  
This section illustrates the obtained results with the proposed hardware accelerator and compares them with state-of-the-art work. First, the target FPGA platforms and the software employed for functional simulation and synthesis activities are discussed in Section 5.1. Subsequently, Section 5.2 analyzes the achieved results, considering various metrics. Additionally, in Section 5.3, we compare our design to existing implementations, highlighting performance improvements. This comparison includes factors like hardware resource usage, operational clock frequency, throughput-to-area ratio, and other relevant parameters. Our aim is to provide a clear and concise presentation of our results for publication.
## 3 Sensor Modules and Deployment
### PIR Sensor Module  
Because of the relatively stable performance, the LiTaO\({}_{3}\) film pyroelectric infrared sensor is chosen as the detecting node in the system. Due to the lower receiving sensitivity of the sensor itself, each signal sensor node is covered by a Fresnel lens as shown in Figure 1. It cannot only focus the infrared heat to the sensor node, but also can increase the angle and detectable distance. It was proved in some experiments that the effective detectable range can vary from 2 to 12 or 14 m. Based on the parameters and characteristics of the D205B sensor as shown in Table 1, the PIR sensor module is designed as shown in Figure 1.  
As shown in Figure 2, the original thermal infrared signal of human target can be translated into analog signal through three steps. Firstly, the original human thermal infrared signal is focused on the pyroelectric sensor by using Fresnel lens. Then, the infrared signal is translated into weak electrical signal by PIR sensor. Finally, the analog signal can be obtained after the processing unit.  
Figure 1: A pyroelectric infrared sensor module is covered by Fresnel lens and signal modulation mask.
# A Comprehensive Review on Network Protocol Design for Autonomic Internet of Things
## 3 Review of Protocol Designs
### Protocol Designs for Security
in [41] proposed a dynamic, scalable, IoT-ready model that is based on the OAuth 2.0 protocol. It allows complete authorization to be delegated, thereby providing a service access control mechanism. To make it very light, all the information needed to perform the authentication process uses a token, therefore OAuth 2.0 ensures that the model is compatible with low-capability devices. Additionally, this model can be used with other RESTful services across the Internet as well as with other clients besides IoT devices. The proposed model meets the specific requirements of IoT devices in terms of performance, scalability, and operability as the design has been implemented using FIWARE. (5) Recursive Inter Network Architecture (RINA)  
[PERSON] and [PERSON] in [42] proposed RINA approach that can address the architectural security challenges of IoT. The security and performance aspects in network architecture can be improved with RINA. From the research conducted, RINA has an architectural solution for every problem related to IoT attacks, challenges to IoT networks, and security requirements. In addition, to expand the problem-solving mechanism, RINA can be programmed through its policies. (6) IoT-Flows based Monitor, Analyse, Plan, Execute, and Knowledge (MAPE-K)  
[PERSON] et al. in [43] proposed an IoT-Flows multilayer approach to IoT threats against attacks on an IoT environments using the Monitor, Analyse, Plan, Execute, and Knowledge method. This approach can be used to deal with widespread attacks at every layer of the network through systems that have defense system integration capabilities that act as a line of defense. In addition, to identify new attacks and take action based on the results of their detection, the proposed system can also be developed further.
## 2 Materials and Methods
### Overview
Tier 1 assessments make use of a number of conservative simplifying assumptions, like no rain, a sensitive crop, a low leaching fraction (10%), and a relatively high level of water application and, thus, constituent load, all factors that largely determine the water quality required for irrigation. Consequently, only the composition of irrigation water is required as input to generate FFU assessments, highlighting potential problems that can be expected under these specified conservative assumptions. Should a Tier 1 evaluation indicate potential problems, more rigorous, site-specific Tier 2 evaluations are indicated to determine whether these problems can be overcome when site-specific conditions are considered. 2. Tier 2 considers site-specific conditions when assessing WQRs or FFU. IrrigWQ allows a user to conduct a more in-depth water quality assessment and guideline generation by making use of a relatively sophisticated crop growth-soil water balance and chemical equilibrium model that caters for selectable, default site-specific input parameters to simulate the response of soils, crops, and irrigation infrastructure to irrigation water of a specific composition under user-defined, site-specific conditions. This provides a significantly enhanced assessment of how crop selection, climatic conditions, irrigation management, and soil texture can be expected to determine the effect of specific water composition on soil quality, crop yield and quality, or irrigation infrastructure. IrrigWQ was developed in the following phases:  
1. The compilation of a concise literature review documenting the current understanding of how water quality constituents, soils, crops, climate, irrigation systems, irrigation management, and other factors interact to impact the resultant soil quality, crop yield and quality, and the performance of irrigation infrastructure. The review provided the theoretical basis or technical framework for the development of IrrigWQ [10]. 2. The development of a _Technology Demonstrator_ that consisted of the preliminary software system that demonstrated the most important features of Tiers 1 and 2 for six water quality constituents representing different categories of constituents, namely salinity (electrical conductivity or EC) as a major inorganic constituent, Cr and Zn as trace elements, pesticides, microbial contamination, and those constituents affecting the corrosion and scaling of irrigation infrastructure. 3. A demonstration of the _Technology Demonstrator_ to potential user groups, who made several suggestions for improvement. 4. The development of a fully functional draft DSS incorporating all the features and water quality constituents envisaged for the final IrrigWQ. 5. The testing and evaluation of the draft DSS by individual water quality experts to evaluate its user friendliness and acceptability, as well as the confidence that can be placed in its ability to make reliable and realistic evaluations. 6. The release of IrrigWQ for use and evaluation by the wider irrigation community. 7. The continued introduction of enhancements to improve utility and user friendliness.
## 3 Results
### Antibacterial Activities  
Six samples of un-doped, 2.87 Ag-doped, 5.75 Ag-doped, 2.87 Ce-doped, 5.75 Ce-doped, MBG, and 2.87 Ag/2.87 Ce co-doped (mol%) MBG bioactive glasses were investigated for antibacterial activity against _E. coli_ bacteria. The results showed that 5.75 Ag-doped and 2.87 Ag/2.87 Ce- co-doped (mol%) MBG killed 100% of _E. coli_ bacteria after 24 h, whereas 2.87 Ag-doped (86.6%) > 5.75 Ce-doped (63.33%) > 2.87 Ce (6.67%)-doped > un-doped (3%) (mol%) MBG (Figure 7). Thus, the killing efficiency of doped MBGs against _E. coli_ bacteria was dependent on their concentrations.
## 6 Discussion
### Effects of Different Algorithms on Identification Results
#### 6.2.2 Discussion on the Effectiveness of Optuna  
Regarding hyperparameter optimization, XGBoost has many tunable hyperparameters, such as the learning rate, maximum depth of the tree, subsample ratio, etc., constituting a high-dimensional parameter space. It is a challenge to perform an effective search in this space. PSO is a population-based optimization algorithm with drawbacks such as high computational cost, slow convergence, and sensitive parameter settings [51]. In contrast, Optuna has an efficient search strategy. Optuna uses advanced search algorithms such as Bayesian optimization and TPE to find the optimal hyperparameters more efficiently and to find parameter combinations with better performance faster than the traditional grid search or random search. Optuna's pruning feature automatically stops the evaluation of unpromising trials. The ability to prune ineffective trials early can lead to faster convergence in a superior model configuration, contributing to the observed performance gains. In addition, Optuna's automated pruning strategy and parallelization support give Optuna the advantage of being able to explore more parameter combinations and run multiple experiments simultaneously with the same computational budget. The Optuna-XGBoost algorithm offers better classification performance, simpler training models, and higher training efficiency and is less susceptible to the effects of training data. This efficiency is likely to be a significant contributor to the improvements in performance metrics. As a result, it exhibits the best classification accuracy [52; 53]. By exploiting the above properties, Optuna can achieve significant performance gains when optimizing the hyperparameters of the XGBoost model.  
We observed an improvement in all four evaluation metrics for the Optuna-XGBoost recognition model mentioned in this study. In other studies, [PERSON] et al. [54], [PERSON] et al. [55], and [PERSON] [56] used machine learning techniques followed by hyperparameter optimization, with the Optuna algorithm obtaining the highest accuracy.
# Highly Compact 4 \(\times\) 4 Flower-Shaped MIMO Antenna for Wideband Communications
## 4 4-Port MIMO Antenna Design
Figure 8.3  
The simulated and tested outcomes of the \(ECC\) at port 1 are illustrated in Figure 8.4 The \(ECC\) has values less than 0.004 inside the obtained frequency band, which are lower than the accepted limits of the \(ECC\) (\(\leq\)0.5), with a good trend between the two results. The \(DG\) is another parameter that can be calculated to investigate the MIMO diversity capability of the suggested model, and it has higher values when the correlation between radiators is low. The \(DG\) can be calculated from Equation (2) which has a direct relation with \(ECC\)[21]. As illustrated in Figure 16, the \(DG\) achieved values greater than 9.97 dB inside the operated frequency range, which reflects the distinct isolation between the radiators. Figure 8.5  
Figure 8.6 The realized gain at port 1. Figure 8.7 The \(DG\) at port 1. Figure 8.8 The \(ECC\) at port 1. Mean effective gain (MEG) is another parameter that can be used to test the diversity capability of the suggested 4-port MIMO antenna. It is defined as the ratio of the received amount of power by the proposed MIMO antenna to the received one by an isotropic antenna, and it can be evaluated using (3), where \(P\) is the number of antenna ports [22]. It is worth noting that the ratio between \(MEG_{i}\) and \(MEG_{i}\) should not be higher than \(-3\) dB, and this is evident by the presented result in Figure 8.9 Finally, the channel capacity with respect to signal-to-noise ratio (SNR) is added in Figure 18 for the two-port and four-port MIMO antenna. It is evident from Figure 18 that the \(4\times 4\) MIMO antenna has an improved channel capacity when compared to \(2\times 2\) MIMO antenna, and for that reason, we adopted and fabricated the four-port MIMO antenna in the current work. Figure 7:0  
Figure 7:1 The MEG at port 1. Figure 7:2 The channel capacity of the MIMO antenna. To evaluate the performance of the suggested MIMO antenna, a comparative study between our MIMO antenna and the lately reported antenna is demonstrated in Figure 7:3 The comparison is performed in terms of the number of ports, size, bandwidth, isolation, and _ECC_. It can be noticed from the presented table that the suggested antenna outperforms the other MIMO antenna models since it has a very compact size that accommodates four radiators for improved impedance and radiation characteristics. Consequently, all these advantages confirm the suitability of the suggested four-port MIMO antenna for handheld devices that operates in various wideband applications.
# Energy-Efficient UAVs Deployment for QoS-Guaranteed VoWiFi Service
## 6 Solving a Dynamic Scenario  
So far, we have applied the GA-based search algorithm in Section 5 for solving the problem of the initial deployment of drones to provide service to a set of users who are assumed to be static at known positions. However, in some activities (e.g., outdoor concerts), users are free to move within a bounded area. Consequently, the problem constraints may not hold after a while due to users's movement unless deployed UAVs are continuously relocated. One approach to do this is to perform periodic updates of the user's positions every \(T=T_{sample}\) seconds, (re)solve the optimization problem, and update UAVs locations, as illustrated in Figure 5. A central assumption in this approach (and our work hypothesis) is that the set of UAVs initially deployed and their position will suffer small changes (assuming that the trajectories always minimize UAVs displacement) if the sampling period is short enough.  
Between two consecutive relocation periods \(T\), one has to consider the relocation delay, (i.e., drones updated their location), that includes:  
* The time required to solve the problem, \(T_{solve}\), which in turn depends on the performance of the algorithm used to find the solution to the problem.
* The time required to relocate UAVs, \(T_{relocate}\), which depends on the distance traveled by UAVs and their speed. Small UAVs typically travel at speeds below 15 m/s [30].  
Under the assumption that AUVs will complete self relocation quickly due to high speed and short distance, the main limiting factor for a fast sampling of user positions is the time required to solve the problem. For this reason, we want to reduce the execution time of the search algorithm for periodic relocation.  
According to [65], search algorithms based on Particle Swarm Optimization (PSO) tend to be computationally more efficient than GA-based algorithms due to faster converge. However, although both metaheuristics provide similar effectiveness (i.e., solution quality), it is also known that PSO is more prone than GA to get stuck on local solutions. For this reason, we propose to use the GA-search algorithm from Section 5 only for the initial deployment, but then use a PSO-based algorithm to solve the UAVs placement for the remainder of the mission. PSO algorithms have been successfully used to solve optimal 3D placement of UAVs before [43].
## 4 Results and Discussion
### Evaluation Indicators  
There are many evaluation indexes to evaluate the forecasting effect of the model [36, 37, 38, 39, 40], such as the mean absolute error (MAE), root-mean-square error (RMSE), mean absolute percentage error (MAPE), mean squared error (MSE), and R-squared (R2). In this paper, the three most widely used evaluation indicators MAE, RMSE and R2 were used. Specifically, MAE is used to measure the gap between the predicted value and the real value. For any size difference, the penalty of MAE is fixed. Therefore, no matter what kind of input value is used, there is a stable gradient, which does not lead to the gradient explosion problem and has a more robust solution. RMSE is the square root of the sum of squares, which enlarges the gap between the larger errors; so, RMSE is more sensitive to errors. Among them, the range of MAE and RMSE is \([0,+\infty]\). When the predicted value is completely consistent with the real value, it is equal to 0, that is, the perfect model. The greater the error, the greater the value. The range of R2 is \([0,1]\). When the predicted value is completely consistent with the real value, it is equal to 1. Their formulas are shown in Equations (10)-(12).  
\[MAE=\frac{1}{N}\underset{i=1}{\overset{N}{\sum}}|\hat{y}_{i}-y_{i}| \tag{10}\]  
\[RMSE=\sqrt{\frac{1}{N}\underset{i=1}{\overset{N}{\sum}}(\hat{y}_{i}-y_{i})^{2}} \tag{11}\]  
\[R^{2}=1-\frac{\underset{i=1}{\overset{N}{\sum}}(y_{i}-\hat{y}_{i})^{2}}{ \underset{i=1}{\overset{N}{\sum}}\big{(}y_{i}-\overline{\gamma}\big{)}^{2}} \tag{12}\]  
Specifically, \(N\) is the time steps, \(\hat{y}_{i}\) is the set of prediction values, \(y\) is the set of target values, and \(\overline{\gamma}\) is the average of the true observed values.
## 5 Discussions, Implications, and Limitations  
In this section, we establish the importance of the experimental results achieved by our model and demonstrate the correlations between the ground truth and the similarity features. We also explain the implications and limitations of our approach. To evaluate our model's results, we used a basic statistical approach and developed hypotheses. In this way, we established five hypotheses and then evaluated whether the results proved them true or false.  
1. Two songs with higher note similarity scores are expected to be more similar.
2. Two pieces of music with higher pitch similarity scores are expected to be more similar.
3. A combination of note and provenance similarity scores leads to an accurate classification.
4. A combination of pitch and census similarity scores leads to an accurate classification.
5. All of the similarity scores combined led to the most accurate classification.  
As shown in Table 10, hypotheses 1, 3, and 5 can be seen to be true, achieving scores of 54%, 60%, and 86% for accuracy, respectively. In contrast, hypotheses 2 and 4 achieved respective accuracy scores of 31% and 56%, and hence, we declared these statements false. Our model achieved an accuracy of 86% when all features of the data were considered while making the comparison.  
To understand the possible implications of our work, we first need to understand that music plagiarism is not only about analyzing the similarity between two pieces of music. It is also important to quantify the degree of similarity between the two pieces of music in question. Moreover, stealing a musical idea or not crediting the original contributors are also acts of music plagiarism. This is where our model is different from most other previous approaches, not only did we focus on accuracy and degree of accuracy, but with our provenance-based approach, we can also figure out whether two musicians have a history of working together. For example, a score based on an artist's history of collaborations, common artists, etc., along with the trustworthiness of the source, can help analyze plagiarism. Our research can also be applied to determine copyright violations in the audio used as a background to videos from members of the public similar to those seen on YouTube.  
The identification of music plagiarism is a complex task since it depends on the model's ability to determine hidden but significant similarities between the melodic fragments inside two songs. A major drawback linked to automatic music plagiarism detection is that different legislative structures around the globe have different standards for showing plagiarism. Clear legislation designed to establish general and accepted rules for detecting plagiarism has yet to arrive. Even though current laws do not emphasize this, often the deciding element in plagiarism cases is usually related to the melody of the song. Another commonly discussed issue is the quality and availability of data for model training and comparing large numbers of songs.
# Enhanced Multiple Speakers' Separation and Identification for VOIP Applications Using Deep Learning
## 4 Experiments
### Dataset
#### 4.1.2 For Identification  
We performed this identification on the separated speaker's dataset output from the previous separation block. We achieved an accuracy of 96% regarding the speaker's identification on this dataset. For training data, four speakers each accompanied 1800 voice samples. In addition, 200 voice samples were collected for testing purposes. As a result,there were a total of 2000 voice sample datasets. Each voice sample lasted about 7 s. So, we had to tune parameters in mfcc. We adjusted nfft values from 512 to 2000 to 1800 to finally, 1500. Figure 5 indicates the state transition diagram for speaker identification.