esa-sceva/satcom-chunk-collection huggingface dataset:
# Improvements of the xAAL Home Automation System
## 5 The xAAL Communication Protocol
### Alternative Home Automation Protocols over IP
#### 5.7.8 Confluens  
The Confluens project [57] aims to address home automation interoperability. It is both a software solution and a company created by home automation companies: CDVI (access control systems), Delta Dore (heating control devices), Hager, Legrand, Schneider-Electric (electrical equipment) and Somfy (shutters control automation). Technically, this is an inter-box communication system. The idea is that a consumer may have several products at home from different companies, each one managed by the home automation box of the corresponding company. So, there may be several home automation boxes at home. The boxes may share information and work together. Communication is based on the MQTT protocol, inside the home network. A discovery mechanism allows joining new boxes and to elect the box that is to play the role of the MQTT broker. Communications are secured by TLS. The Confluens company is the certificate authority, signs the TLS certificates, and manages their deployment on partners' boxes.  
A home automation box is a centralized solution. Confluens proposes a multi-box approach. This is less distributed than the xAAL approach, but this is a first step. The MQTT protocol is a machine-to-machine protocol designed for the global Internet, and may be heavy to be used inside a home network, a local area network. The pyramidal signing mechanism of TLS' certificates cannot allow third parties to enter the loop without prior agreement between companies. The security is in the hands of companies and not in end users' ones. On the other hand, xAAL allows users to manage their privacy.  
Confluens is now an ended project. No product was sold with this technology. According to last news from involved companies, they are willing to distribute the code to the open-source community.
# A Co-Design-Based Reliable Low-Latency and Energy-Efficient Transmission Protocol for UWSNs
## 5 Design of Data Transmission Protocol  
### Network Initialization  
In the layering phase, the sink node starts to initialize the network by propagating a probe packet. The sink node is assigned to be the layer 0 node. The sink will broadcast a probe packet, which contains its layer number and ID. The nodes that receive this probe will assign themselves as the layer 1 nodes. These nodes will increment the layer number, change the ID to their own and then propagate the probe packet after waiting for a certain time. Once these probe packets reach all the nodes in the network, each node will obtain a layer number depending on its hop from the sink.  
During this process, each node records the ID of its neighbors and the pairwise distances from them (the distance is calculated based on the path attenuation model that will be mentioned later). After building its own neighbor table, each node puts its neighbor table into the probe packet before propagating it. That is, probe packets contain the layer number, ID and neighbor table of the current node. Each node will maintain the knowledge of two-hop connectivity after receiving a probe packet from its neighbor.  
When the above process is completed, the probe packet will be broadcast from the sink once again. One reason is that the first transmission of the probe packet cannot help the node to obtain complete two-hop neighbor information. When a node decides to forward a probe packet, it may not receive a probe from all neighbors. The other reason is that two probe packets can regulate the layer numbers of all nodes. In order to avoid loops, the two probe packets will be sent after an interval, and each node will only forward a packet once in different time periods. After initialization, all nodes obtain enough information which will be used in the forwarding phase. If each node in its particular layer supposes it will forward a data packet to its LN neighbors, the packet will finally be transmitted to the sink. In other words, the distance between any source node and the sink is over an equal number of hops.  
Due to the node movement, the layer numbers of all nodes need to be updated frequently. Whenever a node receives a data packet, it will broadcast an ACK in a data channel. This is considered as a probe packet for the re-layering process for its neighbors. A node may receive multiple probe packets from different layers, but it will record the layer number as the lowest layer number plus one. Whenever a sensor node detects three data packets from a lower layer, say \(q\), and does not hear from layer \(q-1\) simultaneously, then the node will assign itself as layer \(q+1\). For the purpose of avoiding glitches [17], the nodes reassign their layer numbers every three times when they detect the probe from the lower layer. It is assumed that binary phase shift keying (BPSK) [20] is used to modulate the underwater acoustic data to avoid high bit error rates (BER).
## 5 Implementation Environment
### Optimized Recommendation Module
#### Objective Function for Optimal Project Recommendations  
In our case, the goal of the objective function is to find a project with a maximum user preference value and maximum authenticity, i.e., a project with high or extremely high credibility. Therefore, we need to design an objective function which fulfills the following criteria:  
1. Maximizes the higher credibility levels (i.e., maximizes weights for Topic_7, Topic_9, Topic_10, and Topic_11)
2. Maximizes user preferences.
3. Minimizes the lower credibility level (i.e., minimizes weights for Topic_0, Topic_1, Topic_2, Topic_3, Topic_4, Topic_5, and Topic_6).  
Thus,  
\[weight_{1}=\ \alpha(Topic\_7)+\ \beta\ (Topic\_9)+\ \delta\ (Topic\_10)+\ \mathrm{Y}\ (Topic\_11)\ +\ \omega\ (\mathrm{User\ preferences}) \tag{6}\]  
\[weight_{2}=\ \Phi(Topic\_0)+\Phi 1(Topic\_1)+\ \Phi 2\ (Topic\_2)+\ \Phi 3\ (Topic\_3)+\ \Phi 4\ (Topic\_4)+\Phi 5\ (Topic\_5)+\Phi 6\ (Topic\_6) \tag{7}\]  
In Equation (6), \(\alpha\), \(\beta\), \(\delta\), \(\mathrm{Y}\) and \(\omega\) are the weights associated with _Topic_7_ (product shipment), _Topic_9_ (product working status), _Topic_10_ (product received), _Topic_11_ (showing excitement), and user preferences respectively. Similarly, in Equation (7), \(\Phi\), \(\Phi\)1, \(\Phi\)2, \(\Phi\)3, \(\Phi\)4, \(\Phi\)5, and \(\Phi\)6, are weights associated with _Topic_0_ (waiting for rewards), _Topic_1_ (asking for refunds), Topic_2 (waiting for an update/reward), _Topic_3_ (reporting or taking legit actions), Topic_4 (product never received), _Topic_5_ (showing anger or disappointment), and _Topic_6_ (no communication/confused), respectively. The target of the objective function in PSO is to minimize the weights of topic classes from 0 to 6; and maximize the weights of user preferences along with topic classes 7, 9, 10, and 11. Hence, the objective function can be described as Equation (8) below:  
\[weight=\ Max(weight_{1})+\ Min\ (weight_{2}) \tag{8}\]  
Figure 8 shows the working flow of the PSO algorithm. In PSO, particle velocities are randomly initialized for the generated population. In the next step, their positions are randomly assigned; and based on these current positions and velocities, the fitness of each particle is estimated. Thus, it can help us formulate the objective function for the fitness estimation as it considers both the maximization function and the minimization function. In the next step, the current fitness value of the particle is compared with its individual best fitness, i.e., \(\mathrm{Y}(t)\). If the current fitness value is better than \(\mathrm{Y}\ (t)\), \(\mathrm{Y}(t)\) is updated with current fitness value else it remains the same. After this, \(\mathrm{Y}\ (t)\) is compared with the global fitness, i.e., \(\mathrm{G}(t)\). If \(\mathrm{G}(t)\) is better than \(\mathrm{Y}(t)\), \(\mathrm{Y}(t)\) is updated to \(\mathrm{G}(t)\), else it remains the same. For each particle, its velocity and positions are updated in each iteration to the best fitness values.
## 3 Visual-Predictive Data Analysis Scheme
### Evaluation  
#### 3.5.1 Models' Performance Evaluations  
The hold-out or cross-validation schemes can be used to evaluate the performances of the machine learning models. On the one hand, in the hold-out scheme, the dataset is separated into two subsets: the Training and the Testing sets. The training set is used to fit the machine learning models, while the testing set is used the evaluate the generalization performance. On the other hand, the cross-validation scheme separates the dataset into \(k\) subsets or folds, where \(k-1\) folds are used for training and the other fold is used for testing. The confusion matrix corresponds to a summary of the prediction results obtained with the machine learning model. Given \(n\) samples, the \(TP\) is the number of true positives, the \(TN\) is the number of true negatives, \(FN\) is the number of false negatives, and \(FP\) is the number of false positives. To evaluate the performance, we use the classification metrics obtained from the confusion matrix. These metrics are _Accuracy_, _Precision_, _Recall_, and \(F1\)-score, following Equations (2)-(5), which are described below.  
\[Accuracy=\frac{TP+TN}{n} \tag{2}\]  
\[Precision=\frac{TP}{TP+FP} \tag{3}\]  
\[Recall=\frac{TP}{TP+FN} \tag{4}\]  
\[F_{1}=2\frac{Precision\cdot Recall}{Precision+Recall} \tag{5}\]  
_Accuracy_ measures the proportion of samples that are correctly classified. Precision measures the proportion of predictive positives that are correct. _Recall_ measures the sensitivity of the classifier to detect the positive cases. \(F1\)-score is the harmonic mean of the _Recall_ and the _Precision_ and gives a trade-off measure between the Recall and the Precision.
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Test Location** & **Time Duration of the Study** & **Employed Parameters as Inputs** & **Machine Learning Models** & **Evaluation Criteria** \\ \hline [PERSON] et al. [13] & Ohus Island (Huozhi) & Twenty months & Oxford, England, United, & RNN, LSTM, RLISTM & RMSE less than 15\%. \\ \hline [PERSON] et al. [14] & Narenda desert, USA & One year (second analysis) & Our year (second analysis) & Semi prototype, & Gender recurrent unit (GRU) NNL & Best RMSE = 11.44 (in variance) \\ \hline [PERSON] et al. [17] & Southern Taiwan & Seventom months & Temperature, humidity, rainfall, and wind speed. & SOM, RF & Best RMSE = 1.3912 \\ \hline [PERSON] et al. [18] & South Korea & Three years & Temperature, wind speed, humidity, & CNN, LSTM & Best RMSE = 0.0007 \\  & & & and ground temperature & & \\ [PERSON] et al. [19] & USA (California) & Six months & Solar irradiance, temperature, & & & \\  & & and windspeed & & & \\ \hline [PERSON] et al. [21] & India & One year & Historical power, solar irradiance, panel temperature, & LSTM, Conv-LSTM A-6 & Best RMSE = 0.12 (Conv-LSTM-6) \\ \hline [PERSON] et al. [22] & Pakistan & One year & Temperature, solar cycle, wind speed, & Artificial Neural Network (ANN) & Average MAP, = 1.43\%. \\ \hline [PERSON] et al. [23] & Johannesburg (South Africa) & Five years & Temperature, shallow-moving, solar radiation, & LSTM, CNN, LSTM, and hybrid & Best RMSE = 7.88 (Conv-LSTM) \\  & & & and wind speed & & & \\  & & & and wind speed & & \\ \hline [PERSON] et al. [24] & Merego (Philippes) & Sn month & Handley, station temperature, ambient temperature, & ANN, CNN, Bidirectional and & Best RMSE = 4.2788 \\  & & & and wind speed & & & \\ \hline [PERSON] et al. [25] & Scotland & Five years & Temperature, precipitation, and wind speed & Bidirectional LSTM & MAE = 0.525 (Day-ahead) 0.708 \\  & & & & & (Weeks-ahead) \\ \hline [PERSON] et al. [25] & Johannesburg (South Africa) & Five years & Temperature, shallow humidity, solar radiation, & Convolutional LSTM & NARBE = 1.42\%. \\ \hline [PERSON] et al. [27] & Agascalientos (Mexico) & Sn months & Wind velocity and direction, irradiance, temperature, & SNA, Linear Regression (LRS) and & Mean Squared Error (MSE) 0.2222 \\  & & & humidity, pressure & NARB & & \\ \hline [PERSON] et al. [25] & Center Jordan & Thirty months & Solar radiation & ANN & RMSE = 0.0271 \\ \hline [PERSON] et al. [25] & South Japan & One year & Solar radiation & ANN & RMSE = 0.02707 \\ \hline [PERSON] et al. [11] & North, south, center Jordan & Twenty years & Wind, air temperature, solar radiation & ANN & MAP values = 5\%. \\ \hline This research work & West-central Jordan & Five years & The 3-2 parameters listed in Table 1 & ANPS, LSTM & RMSE in the range 0.016-0.84 \\  & & & & & & \\  & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison analysis of the research studies reported in the literature with the proposed one in this work related to solar radiation forecasting carried out in the Jordanian territory and worldwide.
## 3 Methodology of Literature Search and Selection
### Search Strategy and Data Sources  
A comprehensive literature search was conducted across five major academic databases: PubMed, Scopus, IEEE Xplore, Web of Science, and Google Scholar. The search covered peer-reviewed journal articles and conference proceedings published from January 2000 to April 2025.  
Three thematic categories guided the search query design:  
* **Disorder-related terms:** \"epilepsy\", \"epileptic seizures\", \"seizure detection\", and \"seizure prediction\";
* **Modeling techniques:** \"deep learning\", \"CNN\", \"convolutional neural network\", \"LSTM\", \"RNN\", \"Transformer\", \"graph neural network\", and \"self-supervised learning\";
* **Feature extraction approaches:** \"temporal features\", \"spectral features\", \"wavelet transform\", \"STFT\", \"Fourier transform\", \"EEG spectrogram\", and \"power spectral density\".  
Boolean operators (AND/OR) and wildcard symbols were used to refine the search and accommodate terminology variation. A full search string for each database is documented in Appendix A.
## 3 Comparison of Gaseous Lubricant
### Performance of Different Gas-Lubricated Bearings  
To compare bearing performances, several different commonly used gases--air, nitrogen, helium and hydrogen--are selected. Bearing load capacity, friction torque and critical mass are used to quantify the performance of the hydrogen gas bearings. Load capacity is the physical quantity that reflects the basic performance of the bearing. Friction torque can be used to characterize the friction loss of the bearing. Critical mass is a dynamic performance parameter that represents the maximum rotor mass that the bearing is able to dynamically support, and reflects the dynamic stability of the bearing.  
Bearing load capacities and attitude angles with rotational speed using different gases are shown in Figure 6. The bearing load capacity exhibits a linear growth trend with rotational speed. The bearing load capacity decreases in the order of helium, air, nitrogen and hydrogen. Moreover, the bearing capacities of air-, nitrogen- and helium-lubricated  
Figure 4: Dimensionless pressure field of hydrogen gas bearing (0.5 MPa, 300 K).  
Figure 5: (**a**) Density and (**b**) viscosity of air, nitrogen, helium, and hydrogen with temperature (0.3 MPa).  
bearings become closer, and are far higher than that of the hydrogen gas-lubricated bearing. Throughout the whole speed range, the attitude angle of the hydrogen gas bearing is the largest.  
The friction torque with rotational speed obtained for the four different gases is shown in Figure 7. The values of bearing friction torque using air, nitrogen and helium are relatively close to one another. Throughout the whole speed range, the bearing friction torque using hydrogen is the lowest. In addition, the friction torque increases linearly with rotational speed.  
The critical masses of the bearings with rotational speed for the four different gases are shown in Figure 8. When the rotational speed is low, the critical mass is relatively high. The critical mass begins to decrease with increasing rotational speed. The critical mass of the helium-lubricated bearing is close to those of the air- and nitrogen-lubricated bearings, and all of them are higher than that of hydrogen-lubricated bearing. The critical masses of the bearings when using different gases can be ordered from high to low as: helium, air, nitrogen and hydrogen.  
Figure 6: (a) Load capacities and (b) attitude angles with rotational speed using four different gases.  
Figure 7: Bearing friction torque with rotational speed for the four different gases.  
The influence of different gaseous lubricants on bearing performance is mainly due to differences in viscosity, which is consistent with the conclusions presented in the literature [22]. Therefore, when selecting lubricating gas, it is necessary to clarify the influence of gas viscosity under the designed conditions.
# Numerical Analysis on the Performance of a Heat Pump Powered by Surface Water Freezing
## 4 Conclusions  
In this paper, the direct solidification heat source heat pump is suggested and the effects of several factors on its performance are studied by the numerical method, including the evaporation temperature and the water velocity and temperature at the evaporator inlet. The following conclusions can be drawn as follows.  
1. The direct solidification heat source heat pump provided in this paper is simpler and more efficient, but its run-time should be about 15 min before once again deicing. Although its _COP_ during the cold period is a little lower than that of the traditional surface water source heat pump due to a lower evaporative temperature, it can be used in all winter.
2. Based on Simulink software, the numerical model and method shown in this paper are verified and practicable.
3. The evaporation temperature is negatively correlated with the exhaust temperature, isentropic power, and heating capacity and positively correlated with _COP_. The water velocity at the evaporator inlet is negatively correlated with the exhaust temperature, isentropic power, and heating capacity and positively correlated with _COP_. The water temperature at the evaporator inlet is negatively correlated with the exhaust temperature, isentropic power, and heating capacity and positively correlated with _COP_. During the stable operation of the system, when the evaporation temperature is \(-3\)\({}^{\circ}\)C, the evaporator inlet water velocity is 0.1 m/s, and the evaporator inlet water temperature is 1 \({}^{\circ}\)C, the _COP_ has a maximum value, and the maximum values are 3.02 and 3.72 and 3.86, respectively.  
Conceptualization, Y.C.; methodology, W.M.; software, X.C.; validation, Y.C., W.Z.; formal analysis, S.H.; investigation, X.L.; resources, W.Z.; data curation, X.C.; writing--original draft preparation, W.M.; writing--review and editing, Y.C. and W.Z.; visualization, W.M.; supervision, Y.C. and W.Z.; project administration, Y.C.; funding acquisition, W.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
## 1 Introduction
However, using piezoelectric actuators for high-temperature applications is difficult because they have a low Curie temperature of 150 \({}^{\circ}\)C. [PERSON] et al. studied the dynamic characteristics and position-control performance of a piezostack actuator at high temperatures up to 180 \({}^{\circ}\)C [25]. They found that the piezoelectric stack actuator could be operated at a high temperature, but both the displacement and blocking force rapidly degraded, as expected. [PERSON] et al. proposed a direct-drive valve system actuated using a piezostack subjected to high temperatures [26]. The valve system was evaluated at 100 \({}^{\circ}\)C, but the piezostack has a high operating temperature property because it is specially made for high-temperature applications; thus, an operating temperature of 120 \({}^{\circ}\)C was ensured. However, normal (or general) piezostacks used in hydraulic systems can be widely applied under 60 \({}^{\circ}\)C to guarantee high control performance. In fact, the use of normal piezostacks for hydraulic applications subjected to high temperatures is a challenge since the precise flow-rate control performance rapidly degrades when the operating temperature is over the Curie temperature of the piezoelectric actuator. Consequently, the challenges and contributions associated with this specific control problem are summarized as follows: (i) a normal piezostack actuator frequently used at room temperature is applied to the proposed piezostack actuator direct-drive valve (PADDV) system and subjected to a high operating temperature and high exciting frequency to examine its control performances at high temperatures. This is challenging since a minor mistake in the adjustment of the high temperature causes permanent damage to the actuator. In this work, several preliminary tests are undertaken by increasing the temperature with a fine resolution to determine the maximum operating temperature which is allowed to apply the piezostack actuator without failure; (ii) a proportional-integral-derivative (PID) feedback controller is designed and integrated with a fuzzy algorithm considering the changes of the operating frequency and temperature. The control gains are tuned using an empirical fuzzy rule with the input frequency and temperature. The choice of appropriate fuzzy variables is crucial to achieve high control performance at high temperatures; (iii) the control performances of the PADDV are experimentally observed in the heat chamber. In this study,the displacement of the spool is selected as a control variable because it is directly related to the flow rate of the spool valve. Hence, the performance of the valve system is evaluated by controlling the spool displacement. The displacement control of the spool is investigated with different frequencies up to 200 Hz and different temperatures up to 150 \({}^{\circ}\)C. The tests are undertaken without and with partial insulation of the piezostack using the aerogel.
# Detecting SPIT Attacks in VoIP Networks Using Convolutional Autoencoders: A Deep Learning Approach
## 2 Related Work
Furthermore, the proposed IDS framework was implemented on two benchmark datasets (i.e., NSL-KDD, and the UNSW-NB15) and used an XGBoost-based feature selection algorithm to reduce the feature space of each dataset. The results showed that the XGBoost-RNN-based IDSs outperform existing methods and achieve high accuracy. [PERSON] et al. [23] proposed an LSTM-based approach for detecting network attacks using Software Defined Network (SDN) intrusion detection systems in IoT networks. The authors analyzed the performance of different deep learning architectures and classical machine learning models using two SDN-IoT datasets. The proposed model architecture consisted of 4 LSTM layers with 64 units, 4 dropout layers, and a dense layer. This architecture was shown to be effective in the binary and multiclass classification of network attacks. In addition, this LSTM model was found to be robust and generalizable to handle various SDN attacks. Furthermore, it effectively classifies the attack types with an accuracy of 0.971%.