esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Design of the Feedback Controller  
To ensure that the output BER of the wireless communication system quickly stabilizes at the target value, appropriate state feedback must be used to configure the system's eigenvalues. Therefore, each subsystem's feedback loop employs continuous-time linear quadratic optimal control [16], as shown in Figure 5. The goal is to design a state feedback controller \(\widetilde{\mathbf{u}}_{1}(t)=-\mathbf{K}\widetilde{\mathbf{x}}(t)\) that minimizes the quadratic cost function of the system state, ensuring that the output \(y_{\text{r}}(t)\) of the jammed communication system quickly restores the target bit error rate in an optimal form according to the performance criteria. This approach enables a fast response to external jamming while maintaining system stability.  
Figure 4: Linear switching system.  
For convenience in analyzing the switching system, let:  
\[\mathbf{x}(t)=\left[\begin{array}{ccc}x_{1}(t)&X_{1}\leq x_{1}<X_{2}\\ x_{2}(t)&X_{2}\leq x_{2}<X_{3}\\ \vdots&\vdots\\ x_{i}(t)&X_{i}\leq x_{i}<X_{i+1}\\ \vdots&\vdots\\ x_{M}(t)&X_{M}\leq x_{M}\end{array}\right] \tag{8}\]  
The system state can thus be transformed into:  
\[\dot{\mathbf{x}}(t)=\left[\begin{array}{c}A_{1}x_{1}(t)+B_{1}u_{1}(t)\\ A_{2}x_{2}(t)+B_{2}u_{1}(t)\\ \vdots\\ A_{i}x_{3}(t)+B_{i}u_{1}(t)\\ \vdots\\ A_{M}x_{4}(t)+B_{M}u_{1}(t)\end{array}\right]=\mathbf{A}\mathbf{x}(t)+\mathbf{ Bu}_{1}(t) \tag{9}\]  
where \(\mathbf{A}=\begin{bmatrix}A_{1}&A_{2}&\cdots&A_{i}&\cdots&A_{M}\end{bmatrix}^{T} \mathbf{B}=\begin{bmatrix}B_{1}&B_{2}&\cdots&B_{i}&\cdots&B_{M}\end{bmatrix}^{T}\).  
In the absence of jamming, the equilibrium point of the wireless communication system is \((\mathbf{x}_{t}(t),\mathbf{u}_{t}(t))\), where \(\mathbf{x}_{t}(t)\) is the target SJNR, and \(\mathbf{u}_{t}(t)\) is the target transmission power. Let \(\widetilde{\mathbf{x}}(t)=\mathbf{x}(t)-\mathbf{x}_{t}(t)\), \(\widetilde{\mathbf{u}}_{1}(t)=\mathbf{u}_{1}(t)-\mathbf{u}_{t}(t)\), where \(\widetilde{\mathbf{x}}(t)\) represents the system state error and \(\widetilde{\mathbf{u}}_{1}(t)\) represents the control input error. Then, the system dynamic error equation is \(\dot{\widetilde{\mathbf{x}}}(t)=\mathbf{A}\widetilde{\mathbf{x}}(t)+\mathbf{ Bu}_{1}(t)\).  
Substituting the state feedback controller into the dynamic error equation yields:  
\[\dot{\widetilde{\mathbf{x}}}(t)=(\mathbf{A}-\mathbf{B}\mathbf{K})\widetilde{ \mathbf{x}}(t)=\mathbf{A}_{c}\widetilde{\mathbf{x}}(t) \tag{10}\]  
The quadratic performance index is:  
\[\mathbf{J}(t)=\frac{1}{2}\int_{0}^{\infty}\left(\widetilde{\mathbf{x}}(t)^{T} \mathbf{Q}\widetilde{\mathbf{x}}(t)+\mathbf{u}_{1}(t)^{T}\mathbf{R}\mathbf{u }_{1}(t)\right)dt \tag{11}\]  
where \(\mathbf{Q}\geq 0\) represents the weight of the system state variables, and \(\mathbf{R}>0\) represents the weight of the system control inputs.  
Substituting Equation (10) into the cost function \(\mathbf{J}(t)\) yields:  
\[\mathbf{J}(t)=\frac{1}{2}\int_{0}^{\infty}\widetilde{\mathbf{x}}(t)^{T}( \mathbf{Q}+\mathbf{K}^{T}\mathbf{R}\mathbf{K})\widetilde{\mathbf{x}}(t)dt \tag{12}\]  
Figure 5: Optimal controller with state feedback.
## 4 Ensemble Learning IDS for IoV: Research Trends and Gaps
### Distributed Deployment
#### 4.1.3 Based on Model Update Mechanisms  
Dynamic models are highly beneficial for the rapidly evolving IoV environment due to their ability to adjust to new data and shifting danger scenarios. Due to their continual assimilation of new data, these models are resilient and pertinent. [PERSON] et al. [57] introduced a dynamic ensemble learning system that incorporates real-time classifier upgrades in reaction to newly identified attack patterns.  
This method mitigates evolving IoV threats by enabling the IDS to remain effective against previously unrecognizable attacks. [PERSON] et al. [75] conducted dynamic updates in an edge computing environment by routinely retraining ensemble classifiers using the latest IoV traffic data. This technique effectively addresses scalability issues by reducing latency and enhancing detection accuracy in resource-constrained applications. In 2024, [PERSON] et al. [77] presented a hybrid dynamic model update approach that integrates adaptive ensemble approaches with federated learning. Their solution solved the dual challenges of real-time flexibility and privacy preservation by enabling the IDS to adjust according to evolving IoV conditions, hence safeguarding data privacy. [PERSON] and [PERSON] [39] improved the detection of previously unseen attacks with potential computational complexity due to ensemble and transfer learning integration. They introduced an explainable ensemble model tailored for IoV, addressing the challenge of zero-day attack detection. Moreover, they demonstrated high accuracy in identifying zero-day attacks with added model transparency.
# Antimicrobial Effect of Titanium Hydroxapatite in Denture Base Resin
## 4 Discussion
Under the same UVA-irradiation conditions, the antimicrobial effect of TiHA against bacterial biofilms was more pronounced than that against planktonic bacteria (Figures 2 and 3), probably because the liquid media surrounding the planktonic bacteria weakened the UVA. The denture plaque does not consist of select single bacterial species but contains a wide variety of multiple bacterial species. Therefore, evaluation of the antimicrobial effect of TiHA against multiple bacterial species from human saliva containing various bacteria from different niches of the oral cavity [39; 40; 41] was required. TiHA was highly effective against all the tested biofilms formed by bacteria present in the different saliva samples, and killed more than 90% of biofilm-forming bacteria in seven of eight samples (Figure 4). Even when the saliva was the only nutrition source for the biofilm-forming salivary bacteria, TiHA was effective in six out of eight cases. The observed antimicrobial effect of TiHA against a wide variety of oral bacteria indicates the great clinical application potential of this material. It would be interesting to observe the change of bacteria on the TiHA-containing dentures and on the oral mucosa of the wearers. If TiHA was used in clinical applications, dentures containing TiHA would have to be repeatedly irradiated and soaked in water to maintain hygiene. Hence, it was necessary to investigate the duration of the antimicrobial effect of TiHA. A 56 h UVA irradiation, corresponding to 28 d of use (2 h per day), did not result in a reduction of the antimicrobial effect of TiHA (Figure 5a). Unexpectedly, the observed antimicrobial effect was more pronounced with longer pre-irradiation times (Figure 5a). This might be associated with the decomposition of organic substances attached to TiHA, including proteins, by pre-irradiation [25]. Further studies are needed to clarify the effect of the irradiation on the decomposition of organic substances by TiHA. It was reported that 180 d soaking does not reduce the ability of TiHA to decompose methylene blue [27]. Similarly, based on the experiments presented herein, the soaking of TiHA-containing specimens in water for 28 d did not result in a reduction of the antimicrobial effect of TiHA (Figure 5b). The concentration of Ca\({}^{2+}\) released from TiHA-containing specimens increased significantly in a time-dependent manner until 21 d after soaking, whereas the concentration of Ti\({}^{4+}\) remained below the detection limits (Figure 6). Considering that the detection limits of the apparatus used in the current study were 0.087 \(\mu\)g/cm\({}^{3}\) and 0.013 \(\mu\)g/cm\({}^{3}\) for Ca\({}^{2+}\) and Ti\({}^{4+}\), respectively, it is reasonable to assume that the amount of released Ti\({}^{4+}\) was extremely limited. A hybrid orbital is apparently formed between Ti 3d and O 2p in a TiHA crystal [42], suggesting that Ti is covalently bound to PO\({}_{4}\). Thus, Ti release would be difficult and, consequently, the antimicrobial effect of TiHA would not be reduced for at least 28 d (Figure 5b).
## 2 Materials and Methods
### Training and Validation  
Training and validation were performed for each model for a set number of epochs. Each epoch can be seen as a learning cycle: at each epoch, the training data are fed to the input layer, they get weighed along the path to the hidden layers, and a predictive model is returned through the output layer. A prediction is then performed on the validation data and compared to the observed output (in our case, the observed sex of the individuals in the validation set) to evaluate the performance of the current model. In the next epoch, the weights are modified to improve the model performance according to the result in the previous epoch. The performance can be evaluated based on different metrics [53].  
In this work, the training and validation data were obtained from [PERSON]'s dataset using 10-fold cross-validation; therefore, each of the 98 models was trained 10 times, with approximately 90% of the data used for training and 10% for validating the model performance. The data were assigned to each fold using a stratified approach [54] to ensure balanced sex classes within the folds.  
The model performance is here evaluated on the validation set using the log loss metric (or cross-entropy). Since the model assigns a probability of being female or male to any new observation, we want to obtain a model whose prediction yields the highest possible probability of belonging to a given class. The log loss metric measures how close the predicted probability is to certainty (probability of 1); the smaller the divergence, the lower the log loss [55]. Ideally, we want log loss to approach zero, but for a balanced binary classification task, a realistic and useful upper-threshold value is set at 0.693--this is thenon-informative log loss, or the value of log loss when both classes are predicted with probability equal to 0.5 (same probability of assigning an observation to the female or male class). The training is therefore performed by reducing log loss along the epochs.  
Here, training and validation are performed for a maximum of 1000 epochs. To reduce the computational time, we used an early-stopping technique, which stops the training process when the performance does not change over a specified tolerance for a specified number of cycles [56]. In the present case, training is set to stop when log loss stays within a tolerance of \(1\times 10^{-4}\) for 20 consecutive cycles. Therefore, training does not usually run for the maximum number of epochs allowed. Values of log loss below 0.693 indicate that the fitted model performs better than a random prediction.
# RF Energy Harvesting Wireless Communications:
## 5 Intermittent Connection Problem in RF-EHWC Networks  
In the RF-EHWC networks, the energy harvesting module harvest energy from the ambient environment or from dedicated energy sources to provide sustainable energy for the nodes to monitor the environment or to communicate with other devices in the network. In this section, we discuss a unique challenge, called the intermittent connection problem, in RF-EHWC networks. The intermittent connectivity refers to the temporary connection loss resulted from nodes switching between \"death\" and \"resurrection\".  
Different from battery-powered devices that consume energy monotonically until the battery is exhausted, the energy level of RF-EHWC node could even rise after a data transmission due to newly harvested energy. Consequently, an RF-EHWC node will not \"die\" permanently but comes alive after energy cumulation. However, due to the thin RF energy in the environment, a small energy harvesting device usually consumes energy much faster than the energy harvesting rate [23]. Although it can resurrect with energy replenishment, an RF-EHWC node may become temporarily unavailable after continuous operation. This phenomenon is called the intermittent connections. The causes of intermittent connections and their impact on the routing protocol are analyzed in this section.
# A Combined Method for Localizing Two Overlapping Acoustic Sources Based on Deep Learning
## 3 Sound Source Separation
### Structure of the Proposed Source Separation Model  
Figure 4 shows the structure of the proposed source separation model at the training stage:  
The STFT features with dimension [Batch, 100, 129] represent the input layer that was fed into the BLSTM network. The BLSTM network had two layers; each layer contained two LSTM recurrent networks with 300 neurons, one of which processed the signal in the forward direction and the other in the backward direction. After each BLSTM layer, the dropout regularization procedure was used to avoid overfitting [24]. The output layer was a fully connected layer (FC) with 129 \(\times\) 3 neurons and a size-weight matrix [600, 129 \(\times\) 3]. The activation function of the output layer was a sigmoid function, which, in turn, produced output samples with values ranging from 0 to 1. After the fully connected layer, the normalization \(L_{2}\) was used for regularization [25].  
The final output samples could be represented as a matrix \(V\in\Re^{12900\times 3}\), which allowed deep clustering to be applied to them later at the testing stage. The Adam learning algorithm was chosen to train the model with an initial learning rate of 0.01. The model was trained to minimize the difference between the estimated affinity matrix \(VV^{T}\) and the target affinity matrix \(YY^{T}\), where \(V\in\Re^{12900\times 2}\) represented the true IBM. The loss function was calculated using the following formulas:  
\[\mathcal{L}=\left\|VV^{T}-YY^{T}\right\|_{F^{\prime}} \tag{7}\]  
\[\mathcal{L}=\left\|V^{T}V\right\|_{F}^{2}-2\left\|V^{T}Y\right\|_{F}^{2}+ \left\|Y^{T}Y\right\|_{F^{\prime}}^{2} \tag{8}\]  
where F denotes the Frobenius norm of the matrix.  
The number of training epochs was chosen to be 50 and the batch size to be 64 samples. Figure 5 shows the model testing scheme.  
During testing, the model processes extracted features from microphone signals (test data) to generate an output matrix of size (12,900, 3), where each row represented a point in 3D space. These points were clustered into two classes using the K-Means algorithm, producing binary labels (0/1) that were subsequently reshaped into the estimated IBM.  
Figure 4: The structure of the proposed source separation model at the training stage.
## 3 Capacity Analysis of the IWMSNs
### The Linear MMSE Capacity
Figures 10 and 11 respectively show the optimal capacity and MMSE capacity for the hybrid 1D beam, the hybrid 2D beam and modified hybrid 1D beam, in the case of higher SNR \(\gamma=10\); the number of sinks is \(K=1\), \(K=2\), \(K=8\), and the Rician fading factor \(\kappa=50\), versus the interference factors \(\alpha\) and \(\beta\). The optimal capacity of the three models are approximately 3.04 nats/beam and 4.39 nats/beam when \(K=2\) and \(K=8\) with \(\alpha=0\), \(\beta=0\) and \(\kappa=50\) in the higher SNR \(\gamma=10\). However, the linear MMSE capacity of the three models is approximately 1.29 nats/beam and 1.05 nats/beam when \(K=2\) and \(K=8\) with \(\alpha=0\), \(\beta=0\) and \(\kappa=50\) in the higher SNR \(\gamma=10\). Figure 10 indicates that the optimal capacity increases as the number of sinks increases, which rises about 0.66 nats/beam from \(K=1\) to \(K=2\) and about 1.35 nats/beam from \(K=2\) to \(K=8\). However, Figure 11 indicates that the linear MMSE capacity decreases as the number of sinks increases, which reduces about 0.89 nats/beam from \(K=1\) to \(K=2\) and about 0.24 nats/beam from \(K=2\) to \(K=8\) with \(\alpha=0\), \(\beta=0\) and \(\kappa=50\) in the lower SNR \(\gamma=1\). The different tendencies of the optimal and MMSE capacities toward \(K\) are mainly determined by the decoding strategies. The Rician fading factor is chosen as \(\kappa=50\), since the capacity in \(\kappa=50\) can be approximately seen as the upper bound. Figure 10: The optimal capacity of the hybrid 1D model and modified hybrid 1D beams with \(\gamma=10\) and \(N=100\), hybrid 2D model with \(\gamma=10\), \(L=50\) and \(M=50\) (\(K=1\), \(K=2\), \(K=8\) and \(\kappa=50\)) versus \(\alpha\) and \(\beta\). Overall, from Figures 10-11, we can conclude that the MMSE capacity is lower than the optimal capacity and exhibits a different tendency from the optimal capacity towards \(K\), which is mainly determined by the single decoding strategy. From Figures 4-9, we can see that the intra-beam interference factor \(\beta\) affects in an unclear manner the capacity in the higher region of the inter-beam interference factor \(\alpha\), which means \(\alpha\) determines the capacity in the higher region of \(\alpha\). The capacity of \(\hat{\mathbf{T}}_{\text{1D}}\) demonstrates that the IWMSMSN is an interference limited network. Meanwhile, the Rayleigh fading causes performance losses for the IWMSNNs, while the strong LOS benefits the performance.
## 5 Solketal Production
### Processes  
Zaharia et al. proposed an industrial flowsheet divided into two sections: in the reaction section, solketal production would be carried in a Continuously Stirred Tank Reactor (CSTR) and in the separation section, three distillation columns would purify the products and recycle the unconverted reactants. The proposed flowsheet is exhibited in Figure 10. The authors used the kinetic and thermodynamic data from Nanda et al. to estimate the mass and energy balances necessary to process a glycerol stream resultant from a biodiesel plant, aiming to reach a glycerol conversion of 76.5% with Amberlyst-35 as a catalyst [53]. The stream is considered pure glycerol and there is no information about its pre-treatment. The flowsheet was designed in Aspen Plus software. The reaction section comprises two mixers, one for each reactant, followed by a heat exchanger to assure the reactor's inlet stream is at 308 K and then the CSTR. The authors proposed that the catalyst could be retained inside the reactor by placing it in a basket or it could be later separated in a cyclone or in a decanting unit. The separation section consists of three distillation columns; in the first, acetone would be separated and recycled, in the second, water would be removed as distillate with 99.9% of purity, and in the third, solketal would be recovered as distillate also with high purity (99.8%) and the unreacted glycerol would be recycled [120]. In this study, an economic evaluation of the industrial plant is presented; however, the authors have not accounted for the reactants and catalyst costs. Moreover, they have not provided information about glycerol purification costs, which is a necessary step once Amberlyst-35 is deactivated when salts are present in crude glycerol.
# Time Series Analysis to Predict End-to-End Quality of Wireless Community Networks +
## 3 Experimental Dataset  
It is well known that selecting high-quality links in real-world networks composed by wireless channels with unpredictable conditions is a big challenge for achieving high delivery-rate and performance. Our research goal in this paper is to assess if the improvements previously achieved by applying LQ tracking and prediction techniques, are also achievable when considering the full communication-path (End-to-End Quality). To evaluate the potential benefits of this proposal, we first analyze the characteristics of a well known, free, and experimental Wireless Mesh Community Networks (WMCN) that deals with the Optimized Link State Routing (OLSR) protocol to maintain the network topology.
# Firmware Updates over the Air via LoRa: Unicast and Broadcast Combination for Boosting Update Speed
## 5 Simulation Tests and Results  
The use of a simulation tool is seen as quite convenient to obtain results that can confirm the analytical ones. The advantage of simulations is that they are repeatable and can include a high number of nodes. This section describes the building of an ns-3 simulation environment, which is used to compare the different firmware update algorithms that have previously been proposed. The simulation only considers the firmware exchange phase (the _Sending of the checksum_ and the _Checksum validation and firmware flashing_ stages are not covered, since they are the same for every method). The tests and results with the real implementation will be reported in the next section.
## 3 Results
### Behavioral Results
#### 3.1.2 1-Back Task Data
The normality test (Kolmogorov-Smirnov test) showed that the reaction time scores neither in the pre-test (\(d\) = 0.12, \(df=858\), \(p<0.001\)) nor in the post-test period (\(d\) = 0.13, \(df=858\), \(p<0.001\)) obeyed the normal distribution, and the q-q diagram was checked for further confirmation. The scores of reaction time were normalized, and 47 trials with values more than 2.5 standard deviations above or below overall the mean were excluded. A 2 (group: experimental group, control group) \(\times\) 2 (test time:  
Figure 3: (**a**) Interaction of group (control group, experimental group) and test time (pre-test, post-test) in the percentage of hits. (**b**) Interaction of group (control group, experimental group) and test time (pre-test, post-test) in the percentage of false alarms. (**c**) Interaction of group (control group, experimental group) and test time (pre-test, post-test) in reaction times. Data are mean \(\pm\) standard error (SE). pre-test, post-test) repeated ANOVA was conducted after the exclusion, see Table 4. The main effect of the time measurement was significant, but no significant difference was found between the groups. The interaction between the two variables was significant, see Figure 3c. The post hoc test showed that there was no group difference (control group: 1978.67 \(\pm\) 950.35 ms, experimental group: 2102.86 \(\pm\) 1245.97 ms) of RT in the pre-test, \(F(1,853)=2.64\), \(p=0.11\), and that the RT in the experimental group (1439.62 \(\pm\) 646.02 ms) was significantly shorter than that in the control group (1528.22 \(\pm\) 538.72 ms) in the post-test, \(F(1,853)=4.39\), \(p<0.05\), \(\eta_{\mathrm{p}}{}^{2}=0.01\).
## 4 DPS: Grid-Based Dual Phantom Node Source Location Privacy Protection Scheme  
In order to explore the impact of the number of simultaneously used PNs on the performance of the scheme, we further propose a grid-based dual phantom node source location privacy protection scheme (DPS). Compared with the SPS scheme, DPS uses two types of PN simultaneously: The sending phantom node (SPN) and the receiving phantom node (RPN). The SPN is close to the SoN, and the RPN is far from the SoN, as shown in Figures 7 and 8.  
Figure 8: The opposite side view of the phantom nodes.  
Figure 7: The same side view of the phantom nodes.  
The simulation results show that DPS can provide higher security performance for the network, but its communication overhead also increases. In practical applications, it is necessary to choose a suitable solution according to specific performance requirements.