esa-sceva/satcom-chunk-collection huggingface dataset:
# End-of-Life Tyres: Comparative Life Cycle Assessment of Treatment Scenarios
## 2 Materials and Methods
### Goal and Scope  
As it emerges from literature, different studies have provided data on the environmental profile of different CR recycling routes. However, often the reproducibility of the studies is hindered by the lack of a detailed LCI, or refers to very specific applications or are referred to different geographies. In this context, this paper provides inventory data and life cycle impact assessments (LCIA) on the two common end-of-life (EOL) treatment methods for scrap tyres, with reference to the Italian context: the recycling for asphalt production and the energy recovery through the incineration.  
The functional unit of the developed LCA is the treatment of 1 t of tyres.  
Figures 2 and 3 show the system boundaries of the two analyzed scenarios. Specifically, the recycling scenario includes the impacts due to the tyres collection, the shredding into CR, the transports and the production of wearing course with CR and the benefits deriving from the avoided production of a standard wearing course with bituminous mixture. In this scenario it is therefore considered the difference in environmental impacts between the two alternative asphalts. The energy recovery scenario considers the tyres collection, the shredding into tyre shreds, the transports and the incineration process. This latter requires the use of materials and energy and produces electricity, heat, as well as emissions and wastes. To allow a fair comparison with the recycling scenario, the burdens avoided trough the substitution of electricity and heat from the European mix are considered.  
Figure 3: System boundaries of the energy recovery scenario.  
Figure 2: System boundaries of the recycling scenario.  
When available primary data have been used. These have been integrated, when necessary, with secondary data from literature and Ecoinvent 3.4 database. For secondary data, particular attention has been paid to the geographical, technological and temporal representativeness, as specified in the next paragraphs. The LCA model has been developed with Simapro software.  
The scenarios have then been analysed with the ILCD midpoint method for all the available impact categories, with particular attention to the categories of climate change (CC), particulate matter (PM), human toxicity-non cancer (HT-nc) and Mineral, fossil & renewable resource depletion (RD). These categories have been chosen because of their relevance for the analysed systems or for the interest in the international policies.  
A contribution analysis has been developed as well to identify the processes that mostly contribute to the impacts or benefits of the analysed ELT scenarios.
# A Research on Manipulator-Path Tracking Based on Deep Reinforcement Learning
## 2 Method
### Deep-Reinforcement-Learning Algorithm Combined with Manipulator
#### 2.3.3 Application of Redundant Manipulator  
The algorithm proposed in this paper was also applied on a 7-degree-of-freedom redundant manipulator for path tracking. The simulation system of the 7-DOF redundant manipulator is shown in Figure 4. The simulation platform still used V-REP PRO EDU 3.6.0, and the simulated manipulator used the KUKA LBR ii wa 7 R800 redundant manipulator. The setting of actions was \(a:=[a_{1},\ a_{2},\ a_{3},\ a_{4},\ a_{5},\ a_{6},\ a_{7}]\in\mathbb{R}^{7}\), and states were set as \(s:=\left[\theta_{1},\ \theta_{2},\ \theta_{3},\ \theta_{4},\ \theta_{5},\ \theta_{6},\ \theta_{7},\ \hat{ \theta}_{1},\ \hat{\theta}_{2},\ \hat{\theta}_{3},\ \hat{\theta}_{4},\ \hat{\theta}_{5},\ \hat{\theta}_{6},\ \hat{ \theta}_{7},\ \Delta x,\ \Delta y,\ \Delta z\right]\in\mathbb{R}^{17}\) where \(\theta_{i},\ \hat{\theta}_{i}\) is the angle and angular velocity of the first joint and \(\Delta x,\ \Delta y,\ \Delta z\) is the distance between the end-point \(p\) of the manipulator and the corresponding desired target point \(p^{*}\). The initial position of the end-point was [0.0044, 0.0001, 1.1743], and the initial position of the target point was [0.0193, 0.4008, 0.6715]. The expected path was an arc trajectory generated by the path-generation algorithm, and the step size was set to 50.  
The setup of the redundant-manipulator path-tracking experiment was exactly the same as that of UR5. The experiment still used the continuous-control reinforcement-learning algorithm SAC, and all network structures were also the same as in the UR5 setup. That is, each network contained two hidden layers, the number of nodes in each layer was 200, and the activation function of the hidden layer was set to ReLu. The hyperparameter settings were also the same: replay buffer = 1 \(\times\) 10\({}^{6}\), discount factor = 0.99, batch size = 128, the update between the \(Q\) network and the target \(Q\) network adopts the soft update method, the soft parameter tau = 0.01, the learning rate of Actor and Critic network are both set to learning-rate = 1 \(\times\) 10\({}^{-3}\), and the weight coefficient \(\alpha\) = 1 \(\times\) 10\({}^{-3}\) of policy entropy during the whole training process.  
The reward settings were also the same as those used before. The only difference was that the redundant-manipulator path tracking-experiment was conducted to verify the generalization of the algorithm, so it did not involve the results when other hyperparameter conditions changed. Therefore, the default separation distance in the reward setting was \(n\) = 1.
## 4 Results and Discussion
Figure 4: The multiscale \(K_{m}\equiv K_{m,t}\) for several reference signals, as well as for PPG signals, acquired from five individuals chosen at random **[computation time per signal]** in a server cluster consisting of 10 computing nodes, with a total of 64 cores and Intel Xeon architecture, and 166 GB of memory: **6 h 7 min** and 7 s]. (a) Multiscale \(K_{m}\) for two periodic signals, one quasi-periodic signal, one aperiodic signal, one chaotic signal, and a random signal; almost all the signals have recurrent dynamics at all different scales; (b) PPG samples (five individuals chosen at random). All of them, regardless of their dynamics on a small timescale, trend to a \(K_{m,t}\approx 1\) on a large timescale. PPG signals have not excessively marked fractal character. They always have a Hurst exponent [54]\(H=2-D_{F}>0.5\) (see Table 3 for individual results), which attributes to the physiological process a long-term memory (persistence)--in conformity with the low-frequency spectrum of a typical PPG cycle, as [PERSON] et al. clarify [47]; the higher the persistence, the higher the \(H\) value. A signal waveform with a considerable value of H involves a smooth time evolution from a geometric perspective.
## 5 Conclusions  
With the rapid growth of technology, medical instruments that are widely used anywhere should increase security policies through resource limitations. Moreover, the patients probably face risks of different forms of physical harm because of the IoMT device attacks. In this study, we presented a novel IoMT framework with machine learning for intrusion detection based on GA-RF algorithm. We provided our model using the GA-RF algorithm to achieve intrusion categorization based on the existing protocols as guest login status and the server error rate with the highest accuracy in comparison with the other applied ML algorithms. The simulation results using the WEKA tool showed that the performance of the GA-RF algorithm according to the precision, recall, F1-score, and accuracy parameters is higher than the other ML algorithms. The GA-RF algorithm achieved 99.999% accuracy and 99.9% precision. Moreover, the random forest algorithm obtained 100% recall and a 99.9% F1-score. Also, the GA-RF algorithm obtained minimum error rates of 0.0027 MAE rate and 0.0284 RMSE rate for the NSL-KDD data set. It means that the average volume of the errors in a set of detected malicious activities is the lowest in the GA-RF algorithm  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Accuracy** & **Precision** & **Recall** \\ \hline PSO-RNN [15] & 96.08\% & 85.63\% & 85.63\% \\ \hline RF-SMOTE [29] & 98.31\% & 98.61\% & 98.41\% \\ \hline PSO-KNN [15] & 98.9\% & 98.89\% & 92.33\% \\ \hline EGA-PSO [30] & 98.97\% & 99.84\% & 96.12\% \\ \hline PSO-RF [15] & 99.76\% & 99.75 & 96.45 \\ \hline CNN-cuDNNLSTM [16] & 99.99\% & 99.83\% & 99.33\% \\ \hline The proposed GA-RF & 99.999\% & 100\% & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison results for intrusion detection with the proposed method and other case studies.  
Figure 9: Anomaly detection activities based on the applied protocols in UNSW_2018_IoT_Botnet.  
for the proposed data set. Finally, the GA-RF algorithm achieved 99.9999% accuracy, 100% precision, and 100% recall for the UNSW_2018_IoT_Botnet data set.  
M.N.: Writing--original draft, visualization, resources, methodology; Z.G.-A.: Validation, writing--review and editing; O.C.T.: Validation; M.Y.Y.: validation; M.A.A.: Validation, writing--review and editing; A.S.: Writing--review and editing, visualization, formal analysis. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not Applicable.  
The data sets are available as open source materials in Kaggle.  
The authors declare no conflict of interest.
## 5 Validation
### Experiment A: Evaluating the Improvement in the Hit Rate Using the Haptic Tuning Fork
#### 5.2.3 Results  
After analyzing the results using the Student's t-test, we observe that the mean difference in the total hit rate identifying both the shape and the frequencies when using the tuning fork vs. not using the tuning fork is 20% and it is statistically significant (\(p\)-value \(<\) 0.05), again, the haptic tuning fork proves useful. The mean difference in the frequency hit rate, it is of 14.55% and it is statistically significant (\(p\)-value \(<\) 0.001). Regarding the hit rate when determining the shape of the haptic icon, the difference was not statistically significant (\(p\)-value \(=\) 0.083), see Figure 5(a). Further studies need to be done regarding shape identification. In Figure 5(b), the higher hit rate regarding the identification of both the frequency and the shape can be observed. All subjects indicated that they preferred to perform the exploration task using the tuning fork.  
Figure 6: Experiment B results.  
Experiment C: Evaluation of the Improvement in the Hit Rate When Users Discriminate between Haptic Frequencies in an Environment with Divided Attention Using a Haptic Tuning Fork  
In the environments of experiments, A and B, users are focusing on feeling the stimuli and the haptic scales, which allows paying almost total attention to the stimuli. While this environment was useful to design and validate the scales and the haptic tuning fork, this experimental framework does not reflect a real situation in which the haptic icons are used for exploring a more complex scenario. In a real environment, the user will be performing other tasks when the haptic icon will be perceived. For this reason, we designed experiment C in which the user has to perceive the haptic icons while he or she has part of the attention focused on performing a different task. Again, we evaluate if including the haptic tuning fork still improves the hit rate.
# Neural-Network-Assisted Polar Code Decoding Schemes
## 2 Preliminaries
### Decoding of Traditional Special Nodes  
This section introduces four special nodes: R0 node, R1 node, Rep node, and SPC node, which are distinguished by the positions of the frozen bits and information bits. A node that has all frozen bits is an R0 node; a node that has all information bits is an R1 node; a node that has only one information bit at the last bit position is a Rep node; a node that has only one frozen bit at the first bit position is an SPC node. Assuming that a polar code \(s_{1}^{N_{S}}=(s_{1},s_{2},\cdots,s_{N_{S}}),s_{i}\in\{0,1\}\) with length \(N_{S}\) is transmitted, after the BDMC with transition probability \(\Pr(y|s),s\in\{0,1\}\), the received signal \(y=(y_{1},y_{2},\cdots,y_{N_{S}})\) is chosen to obtain the sequence \((\beta_{1},\beta_{2},\cdots\beta_{N_{S}})\), where  
\[\beta_{i}=(1-sign(\ln\frac{\Pr(y_{i}|0)}{\Pr(y_{i}|1)}))/2, \tag{7}\]  
If the node is R1, \((\beta_{1},\beta_{2},\cdots\beta_{N_{S}})\) is its fast decoding result. If the node is R0, there is no need to decode. The decoding method of the Rep node and SPC node are as follows.
## 3 Results and Discussion  
### DTA Analysis  
To investigate the thermal properties and stability of the synthesized glasses, differential thermal analysis (DTA) measurements were conducted. The results are presented in Figure 2. It can be observed that the glass transition temperature is influenced by B\({}_{2}\)O\({}_{3}\) content. The glass transition temperatures (Tg) are approximately 426 \({}^{\circ}\)C, 443 \({}^{\circ}\)C and 460 \({}^{\circ}\)C for the respective B\({}_{2}\)O\({}_{3}\):Bi\({}_{2}\)O\({}_{3}\) molar ratios of 50:50, 60:40 and 70:50. The onset of the exothermic peak (Tx) associated with the crystallization process of the matrix shifts toward higher temperatures with the increase in B\({}_{2}\)O\({}_{3}\) amount only for B\({}_{2}\)O\({}_{3}\):Bi\({}_{2}\)O\({}_{3}\) molar ratios of 70:50 and is as follows: 485 \({}^{\circ}\)C, 484 \({}^{\circ}\)C and 510 \({}^{\circ}\)C (for 50:50, 60:40 and 70:50 ratios, respectively). None of the tested glass compositions exhibit a high-intensity peak. Taking the difference between Tx and Tg in order to define the glass stability region, it can be concluded that the increased B2O3 content relative to Bi2O3 positively influences the thermal stability of glasses. These measurements were performed mainly to investigate the glass stability region, which is a critical parameter in the formation of Ag nanoparticles. Their synthesis typically occurs at temperatures below the first exothermic peak but above the glass transition temperature. Above Tg, glass properties such as viscosity and ion mobility undergo significant changes, promoting the agglomeration of Ag nanoparticles. Silver ion Ag\({}^{+}\) can be reduced to metallic silver Ag\({}^{0}\) through a one-step process (Ag\({}^{+}+1\)e\({}^{-}\rightarrow\) Ag\({}^{0}\)), with a reduction potential of E0 = 0.7996 V [5]. Moreover, this low-temperature annealing prevents crystallization of the glass matrix, as observed in B2O3-Bi2O3 glasses [19]. The synthesis of Ag nanoparticles was carried out within the temperature range between Tg and Tx at 450 \({}^{\circ}\)C, 480 \({}^{\circ}\)C and at the temperature at which (for the two compositions) the crystallization process had started: 500 \({}^{\circ}\)C. These temperatures should be sufficient to initiate the migration of Ag ions, leading to the formation of Ag nanoparticles.
# Weather-Aware Long-Range Traffic Forecast Using Multi-Module Deep Neural Network
## 3 New Traffic Forecasting Model Using Multi-Module Deep Neural Networks
The predictor works in a manner similar to that of a discrete simulation model; it updates the input by the current values to proceed to the next time interval. Both predictors contain two blocks of neural network modules. Each module serves with the different input data classes, i.e., traffic data, link information, date, time, and weather. The neural network with multiple modules has the following characteristics [25]. * Because the number of input features is reduced and simplified, the neural network can be easily optimized. * By assigning each input feature to the proper network architecture, prior knowledge of the data can be well integrated into the predictor. To find out the optimal network architecture of predictor, 1% of training dataset is uniformly sampled as a _validation set_. The validation set is not used for training and testing. It helps to select the best predictor among the group of the predictor with randomly designed network architecture.
## 3 Assessment of Precise MGEX Orbit and Clock Products  
In this section, multi-GNSS products from 1 January 2018 to 1 November 2019 are collected. The consistency is assessed by orbit and clock comparisons among different ACs, and the orbit accuracy is further evaluated by the external SLR validations.
## Conflicts of Interest: The authors declare no conflict of interest. Abbreviations  
\begin{tabular}{l l} \(B\) & Energy stored in battery at the beginning of the step \\ \(B^{+}\) & Energy stored at the end of the step \\ \(B^{\prime}\), \(B^{\prime\prime}\) & Intermediate results related to stored energy \\ \(B_{C}\) & Minimum level of energy in the battery to allow cooperating \\ \(B_{L}\) & Minimum accepted level of battery \\ \(B_{M}\) & Maximum battery capacity in kWh \\ \(B_{r}\) & Rated battery capacity \\ \(D\) & Demanded energy \\ Di & Dwelling j, \(j\in\{1,2\}\) \\ \(E\) & Energy surplus. If \(E\geq 0\), there is an energy surplus. If \(E<0\), there is an energy deficit. \\ \(ENS\) & Energy not supplied \\ \(ENU\) & Energy not used \\ ESS & Energy storage system \\ E\({}_{y}\) & Annual energy consumption \\ \(G\) & Generated energy \\ HNU & Hours with energy not used \\ \(i\) & Subscript, referring to each step \\ \(j\) & Superscript, referring to each participant \\ LOLP & Loss of load probability index \\ \(N\) & Energy needed to supply the demand and to increase the energy stored to \(B_{C}\) \\ \(p\) & Available energy to be sent to other participant \\ \(P_{d}(peak)\) & Maximum demanded power in dwelling j \\ \(PV_{peak}\) & Rated power installed in the photovoltaic panels \\ SAPV & Stand-alone photovoltaic system \\ SMC & Sequential Monte Carlo \\ \(SOC\) & State of charge \\ \(SOC_{C}\) & Minimum level of state of charge to allow cooperation \\ \(SOC_{L}\) & Minimum allowed level of state of charge \\ \(SOC_{M}\) & Maximum allowed level of state of charge \\ S1 & Scenario 1 (base scenario without sharing resources) \\ S2\({}_{x}\) & Scenario 2\({}_{x}\) (cooperative scenario) \\ TTF & Time to failure \\ TTR & Time to repair \\ \(T^{\prime j\to k}\) & Intermediate result related to energy to be transferred from user \(j\) to user \(k\) \\ \(T^{k\to j}\) & Energy received in user \(j\) from user \(k\) \\ \(U\) & Usable surplus. If \(U\geq 0\), the demand is covered. If \(U<0\), the demand is not covered \\ VPP & Virtual power plant \\ \(\eta_{c}\) & Efficiency of battery charging \\ \(\eta_{d}\) & Efficiency of battery discharging \\ \(\eta_{t}\) & Efficiency of energy transmission \\ \(\lambda_{c}\) & Photovoltaic panel array and battery controller failure rate per year \\ \end{tabular}
# High-Accuracy Clock Offsets Estimation Strategy of BDS-3 Using Using Multi-Source Observations
## 2 Materials and Methods
### The New Strategy
#### 2.2.3 Estimation and Elimination of the Bias of Two-Way Clock Offsets  
By the method in Sections 2.2.1 and 2.2.2, the new strategy can estimate the two-way clock offsets with higher accuracy in real-time. However, bias still exists between the two-way estimated clock offsets and ODTS clocks offsets. This bias includes the zero value of signal transmitting and receiving of Ka-band antennas and L-band antennas. However, BDS-3 users use one-way downlinks observation for positioning and timing. Therefore,  
Figure 7: The estimation errors of the second least square estimation (since 19 May 2022), where the reference satellite is GEO-3.  
to align the two-way estimated clock offsets with the one-way downlinks signal, the new strategy should finally correct the bias in the two-way estimated clock offsets.  
The effective way to estimate the bias is using ODTS clock offsets. The duration of BDS-3 orbit determination is three days, and the specific strategy of BDS-3 orbit determination can refer to [PERSON] et al. In order to suppress the influence of ODTS clocks offset errors on the bias estimation. The new strategy uses multiple-day ODTS clock offsets to estimate the bias. As shown in Figure 8, the estimation and elimination of the bias of the two-way estimated clock offsets include three steps. Firstly, as shown in Figure 8, splice the original ODTS clock offsets of multiple days to obtain the ODTS clock offsets (at present, the number of days is 3, which are consistent with the two-way superimposition strategy). Secondly, make the difference between the ODTS clock offsets and two-way estimated clock offsets, and average their differences to estimate the bias. Thirdly, deduct the bias in two-way estimated clock offsets so as to realize the alignment of the two-way clock offsets with the one-way downlink signal.
# Managing a Smart City Integrated Model through Smart Program Management
###### Abstract  
Context:A Smart city is intended as a city able to offer advanced integrated services, based on information and communication technology (ICT) technologies and intelligent (smart) use of urban infrastructures for improving the quality of life of its citizens. This goal is pursued by numerous cities worldwide, through smart projects that should contribute to the realization of an integrated vision capable of harmonizing the technologies used and the services developed in various application domains on which a Smart city operates. However, the current scenario is quite different. The projects carried out are independent of each other, often redundant in the services provided, unable to fully exploit the available technologies and reuse the results already obtained in previous projects. Each project is more like a silo than a brick that contributes to the creation of an integrated vision. Therefore, reference models and managerial practices are needed to bring together the efforts in progress towards a shared, integrated, and intelligent vision of a Smart city. _Objective_. Given these premises, the goal of this research work is to propose a Smart City Integrated Model together with a Smart Program Management approach for managing the interdependencies between project, strategy, and execution, and investigate the potential benefits that derive from using them. _Method_. Starting from a Smart city worldwide analysis, the Italian scenario was selected, and we carried out a retrospective analysis on a set of 378 projects belonging to nine different Italian Smart cities. Each project was evaluated according to three different perspectives: application domain transversality, technological depth, and interdependences. _Results_. The results obtained show that the current scenario is far from being considered \"smart\" and motivates the adoption of a Smart integrated model and Smart program management in the context of a Smart city. _Conclusions_. The development of a Smart city requires the use of Smart program management, which may significantly improve the level of integration between the application domain transversality and technological depth.  
Smart city; program management; integrated model +
Footnote †: journal: Applied Sciences