esa-sceva/satcom-chunk-collection huggingface dataset:
# Context-Aware Complex Human Activity Recognition Using Hybrid Deep Learning Models
###### Abstract
While the CNN model provides the proposed solution with the advantage of extracting discriminative features dynamically without the use of handcrafted feature extraction processes, the LSTM helps to extract temporal information from context-dependent human activities containing latent temporal and spatial information. The CNN-based approach has demonstrated its efficiency when dealing with fixed and short sequence HAR datasets, but it is not the best when dealing with long and complex HAR problems [21]. Therefore, we used the CNN-LSTM hybrid model because the convolutional layer of the proposed model uses its capability to learn the internal representations of sensor signals; the LSTM learns the long-term and short-term temporal dependencies in the context-infused time-series data. The key question therefore addressed in this article is \"can rich contextual information infused into the traditional inertial sensing data improve the accuracy of deep hybrid learning models in generalizing simple and complex activities of daily living while  
Figure 2: High-level view of the hybrid model with sensory signals from various sensors. also addressing the interclass similarity problems?\" To answer this question, the main contributions of this article are four-fold:  
* The article proposes a deep hybrid feature model capable of discriminating between simple and complex activity signal patterns by augmenting low-level inertial sensing signals with contextual data to improve recognition and generalization accuracy. * An extensive review of state-of-the-art human activity recognition, context awareness, deep learning algorithms, and interclass similarity problems in human activity recognition. * We demonstrate through extensive experiments the efficacy of the context-aware CNN-LSTM model for realizing state-of-the-art results using raw sensor signals without heuristic handcrafted features. * We demonstrate the efficacy of smartphone-rich contextual data for solving interclass similarity problems of simple and complex activities. The main novelty is the use of environmental noise level and light conditions as context information to generate context-aware CNN-LSTM models capable of discriminating between simple and complex activities, particularly those activities that exhibit similar signal patterns. The remainder of the article is structured as follows: In Section 2, we review related works focusing on aspects covered by the proposed solution. In Section 3, we present the methodology of the proposed solution. Experimental validation of the proposed solution is presented in Section 4. In Section 5, we analyze and discuss the experimental results and their significance. Finally, Section 6 concludes and presents recommendations for future work.
# Reliability-Based Design of an Aircraft Wing Using a Fuzzy-Based Metaheuristic
## 2 Possibility Safety Index Based Design Optimization (PSIBDO)
### PSIBDO Problem  
In general, deterministic design of aircraft wing using metaheuristic (MH) optimization can be modelled as shown as follows:  
\[Min\ f(\mathbf{x}) \tag{3}\]  
Subject to \(g_{i}(\mathbf{x})\geq 0\ i=1,\ 2,\ \ldots,N\)  
\(\mathbf{x}^{L}\leq\ \mathbf{x}\ \leq\ \mathbf{x}^{LI}\)  
where \(x\) is the design variables in \(N\)-dimensional, \(f\) is the objective function for optimization to minimized, and \(g_{i}(\ )\) is the \(i\)th constraint, \(x^{L}\) and \(x^{U}\) are the lower and upper boundary condition of the design variable. The solution of Equation (3) is said to be a deterministic solution, which is often unrealizable in practice due to the presence of uncertainties [17]. In cases of the uncertainties, it is performed in the form of a fuzzy \((a)\) function. To address such a problem, RBDO is an alternative choice to solving the original deterministic problem. The RBDO cooperates with the fuzzy set theory can be performed with the possibility safety index (PSI) of the constraints [9] and it is called PSIBDO. The formulation can be shown as follows.  
\[Min\ f(\mathbf{x}) \tag{4}\]  
Subject to \(\ \pi_{f}\)= Pos\(\{g_{i}(\mathbf{x},\mathbf{a})\ \leq\ 0\}\ \leq\ \pi_{f}^{max}\), \(i=1,\ 2,\ \ldots,N\)  
\(\mathbf{x}^{L}\leq\ \mathbf{x}\ \leq\ \mathbf{x}^{LI}\)  
Suppose that the fuzzy uncertainties of aircraft wing structure (\(\mathbf{a}\)) are the Young's modulus (\(E\)) and allowable transverse displacement (\(u\)). Hence, the PSIBDO of the wing structure considering allowable PSI \(\pi_{f}^{max}\) as constraints can be defined as follows:  
\[Min\ f(\mathbf{x}) \tag{5}\]  
Subject to \(\ \pi_{f}\)= Pos\(\{g_{i}(\mathbf{x},\mathbf{a})\leq\ 0\}\ \leq\ \pi_{f}^{max}\), \(i=1,\ 2,\ \ldots,N\)  
\(\mathbf{x}^{L}\leq\ \mathbf{x}\ \leq\ \mathbf{x}^{LI}\)  
\(0\ \leq\ \pi_{f}^{max}\leq\ 1\)  
The value of allowable PSI \(\pi_{f}^{max}\)\(\in\)[0, 1]The proposed PSIBDO leads to some advantages as:  
1. The new technique is in the non-probabilistic technique group, which saves time consumption in uncertainty quantification when compared with the traditional method.
2. The present technique is eased to cooperate with high-performance metaheuristics (MHs).
3. It is easy to apply to many real-world optimization problems without with the trap-pings of gradient calculation methods in the based gradient group.
# Joint Transmit Waveform and Receive Mismatched Filter Design to Suppress Range Sidelobe
## 2 Design of NLFM-CPM Integrated Waveform and Signal Processing Methods
### Weighted Time Domain Correlation Matrix Design  
Refining the design of the matched filter to attenuate sidelobe intensity constitutes a pivotal element in augmenting system efficacy. The mainlobe indicates the target's position, while sidelobes typically increase the detection threshold around the target and obscure weak signals. Therefore, it is necessary to suppress high sidelobes near the target. The diagonal matrix composed of weighted coefficients in the time domain is defined as  
\[\mathbf{w}=\text{diag}[w(-N+1),\cdots,w(-1),\ w(0),\ w(1),\cdots,w(N-1)] \tag{18}\]  
where \(w(i)\) is the weighting coefficients of manual intervention in the updating Formula (36), \(i\in\mathbb{Z}\cap[-N+1,N-1]\) corresponds to the weighted coefficients of the desired mainlobe and other sidelobe regions. We defined the \(2N-1\)-point IDFT matrix as  
\[\mathbf{F}_{2N-1}^{-1}=\frac{1}{2N-1}\begin{bmatrix}1&1&\cdots&1\\ 1&W_{2N-1}^{-1}&\cdots&W_{2N-1}^{-(2N-2)}\\ \vdots&\vdots&\ddots&\vdots\\ 1&W_{2N-1}^{-(2N-2)}&\cdots&W_{2N-1}^{-(2N-2)(2N-2)}\end{bmatrix} \tag{19}\]  
where \(W_{2N-1}^{-k}=\exp\left(j\frac{2\pi}{2N-1}k\right)\). Thus, (15) can be rewritten as  
\[\mathbf{psf}_{\text{MF}}=\mathbf{F}^{-1}\mathbf{S}_{\text{P}} \tag{20}\]  
and  
\[\mathbf{S}_{\text{P}}=\mathbf{S}\odot\mathbf{S}^{*} \tag{21}\]  
Without considering the loss of process gain (LPG) of the EWMF, the \(\mathbf{psf}_{\text{EWMF}}\) is defined as [11]  
\[\mathbf{psf}_{\text{EWMF}}=\mathbf{F}^{-1}(\mathbf{S}_{\text{P}}\odot\mathbf{ W}) \tag{22}\]  
where \(\odot\) is the Hadamard product, \(\mathbf{W}\) is the DFT of \(\mathbf{w}\).  
Some early literature often employed the minimization of the ISL criterion to reduce sidelobes. The ISL of the EWMF output is  
\[\mathbf{Y}_{\text{ISL}}=\mathbf{h}^{*}(\mathbf{\Lambda}(\mathbf{s})\mathbf{ w}_{\text{ini}}\mathbf{\Lambda}^{*}(\mathbf{s}))\mathbf{h}=\mathbf{F}^{-1}( \mathbf{S}_{\text{P}}\odot\mathbf{W}_{\text{ini}})\odot\mathbf{F}^{-1}( \mathbf{S}_{\text{P}}\odot\mathbf{W}_{\text{ini}})^{*} \tag{23}\]  
where \(\mathbf{w}_{\text{ini}}=\text{diag}(1\cdots 1\ 0\ 1\cdots 1)\) is just a linear weighting of the sidelobes.  
However, the distribution of targets in range is typically non-uniform. In order to reduce the sidelobe gain for detecting the desired range cell, it is necessary to iteratively optimize the filter coefficients.  
The weighted time domain correlation matrix (WTDCM) for the sidelobe region, based on the weighted matrix \(\mathbf{w}\), is represented as  
\[\mathbf{B}=\mathbf{\Lambda}(\mathbf{s})\mathbf{w}\mathbf{\Lambda}^{*}(\mathbf{s} )=\sum_{l=-N+1}^{N-1}w(i)\mathbf{x}_{i}\mathbf{x}_{i}^{*} \tag{24}\]  
For the initialization of \(w(i)\), we set the initial value of the manual intervention power in the sidelobe region to 1 and the initial value in the mainlobe region to 0. According to (24) and (25), the ISL output result of the EWMF can be expressed as  
\[\mathbf{Y}_{\text{ISL}}=\mathbf{h}^{*}\mathbf{B}\mathbf{h} \tag{25}\]
## 5 Experiments and Results
### Optimal Learning Rate  
To obtain a good LR, the first step is to find the range that best fits our network, as this will lead the parameters of our model to optimal solutions at a reasonable pace. If the model is trained with very high or very low learning rate values, the model responds with very low accuracy and a huge loss [27]. To this end, the SGD batch is trained with increasing LR and investigates when the model deviates. Plotting LR against loss, one can find the region of LR where the loss decreases most rapidly, as this is the steepest part of the graph. The training of the entire network consists of a range of learning rates \(10^{-5}\) and \(10^{-2}\) in 4 epochs. According to the slope of Figure 8.  
We see that the optimal values are between \(10^{-4}\) and \(10^{-3}\).
# Location Optimization of Emergency Bell Based on Coverage Analysis for Crime Prevention
## 3 Materials and Methods
### Exploratory Data Analysis and Statistical Analysis  
In this section, as part of EDA, we conducted a correlation analysis to extract ten significant variables with high correlations from the initial set of 27 variables. Additionally, using PCA, we obtained three principal components. Reflecting the results of PCA, we employed K-means clustering to divide administrative districts into three clusters based on the variables number of emergency bells and number of crimes.
## 4 Numerical Simulation
### Study of Internal Parameters  
The whole pack model is then set up and solved with different \(\delta\) and N values, separately, at certain boundary conditions.  
Figure 10 presents the influence of the cross-sectional area ratio \(\delta\) on bias rate \(\beta\), pressure loss \(\Delta\)p, and cell temperature T\({}_{\text{max}}\) and T\({}_{\text{diff}}\) at a flow rate of Q = 5 L/min when N = 0. When the cross-sectional area ratio \(\delta\) increase, the bias rate \(\beta\), the pressure loss \(\Delta\)p, the maximum temperature T\({}_{\text{max}}\), and the temperature difference T\({}_{\text{diff}}\) all decrease. The bias rate \(\beta\) and pressure loss \(\Delta\)p tend to become smaller as \(\delta\) increases, and when \(\delta\) is large enough, the trend gradually becomes slower. Similarly, the maximum temperature T\({}_{\text{max}}\) and the temperature difference T\({}_{\text{diff}}\) also become smaller as \(\delta\) increases; when \(\delta\) = 0.484, Tmax exceeds 40 \({}^{\circ}\)C, and Tdiff exceeds 5 \({}^{\circ}\)C. Considering the influence of \(\delta\) on the cooling performance and actual size of the main cooling pipe, the final selection of \(\delta\) is 0.973.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Grid level No.** & **1** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline Number of grids & 3698 & 9850 & 29,978 & 47,426 & 79,335 & 159,777 & 258,299 \\ Number of prism layers & 1 & 2 & 3 & 4 & 5 & 6 & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Grid.  
Figure 9: Grid independence. (**a**) Nusselt number, pressure loss, and maximum temperature calculated by different grid systems; (**b**) comparison of the Nusselt number and pressure loss of the theoretical and numerical results.  
Next, the heat transfer enhancement and pressure drop increase with a different number of internal walls, N, is studied (Figure 11). In general, the effect of different walls N on the pressure drop \(\Delta p\) is larger than ratio \(\delta\), but the effects on \(\beta\), T\({}_{\rm max}\), and T\({}_{\rm diff}\) are smaller than ratio \(\delta\). When the wall number N increase, the bias rate \(\beta\), the maximum temperature T\({}_{\rm max}\), and the temperature difference T\({}_{\rm diff}\) all decrease, but the pressure loss \(\Delta p\) increases dramatically. When N = 9, the maximum pressure loss reaches 3.75 kPa, thereby exceeding the maximum acceptable pressure loss limit. Thus, we fixed N at medium number 6 to balance the maximum heat transfer and pressure loss.
## 3 Results and Discussion
### Correlation Analysis  
The dependence of various biological activities (FRAP, CUPRAC, DPPH, ABTS, and AChE inh.) on the degree of hydrolysis (DH) of papain enzyme was investigated using correlation analysis. The results are summarized in the following Table 5.  
In the first column of the table, the investigated dependencies are described; in the second column the values of the correlation coefficients are given; in the third column are the values of the coefficient of determination for different types of regression (linear, exponential, logarithmic, etc.); and in the last column are the \(p\)-values. According to [PERSON]'s scale [PERSON] (1970), the correlation between DH-Ache inh is very high, DH-CUPRAC is high, between DH-DPPH and DH-ABTS is medium, and between DH-FRAP is weak. The data in the last column show that the DH-CUPRAC and DH-AChE inh. Relationships are statistically significant as \(p\)-value \(\leq\) 0.05. Therefore, these two dependencies are subjected to regression analysis and their coefficients of determination are determined in different types of regression.  
Figure 2: IC\({}_{50}\) of lupin protein hydrolysates toward acetylcholinesterasecholinesterase activeity. Values are means \(\pm\) SEM, n = 3 per treatment group. Different letters (a–e) indicate significant differences (\(p<0.05\)) between treatments as analyzed by one-way ANOVA and TUREY test.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Correlations**} & \multirow{2}{*}{**r**} & \multicolumn{4}{c}{**R\({}^{2}\)**} & \multirow{2}{*}{**\(p\)-Value**} \\ \cline{3-4} \cline{6-7}  & & **Linear** & **Exponential** & **Logarithmic** & **Polynomial** & **Power** \\ \hline DH-FRAP & \(-0.2275\) & 0.0518 & 0.0551 & 0.13 & 0.874 & 0.138 & 0.556 \\ DH-CUPRAC & 0.5886 & 0.3465 & 0.3791 & 0.29 & 0.9552 & 0.31 & 0.0095 \\ DH-DPPH & 0.3848 & 0.1480 & 0.1380 & 0.173 & 0.387 & 0.167 & 0.277 \\ DH-ABTS & \(-0.4066\) & 0.1650 & 0.1590 & 0.127 & 0.415 & 0.121 & 0.306 \\ DH-AChE inh. & \(-0.7994\) & 0.6390 & 0.6560 & 0.73 & 0.8533 & 0.747 & 0.0097 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Dependence of various biological activities on the degree of hydrolysis (DH) of papain.  
The relationship between DH-CUPRAC has the highest coefficient of determination in a fourth-order polynomial regression and is therefore best described by the following equation:  
\[\text{y}=4.10^{-6}\text{x}^{4}-0.0005\text{x}^{3}+0.0221\text{x}^{2}-0.4239\text {x}+3.1945 \tag{4}\]  
The relationship between DH-AchE inh. Has the highest coefficient of determination in a fourth-order polynomial regression and is therefore best described by the following equation:  
\[\text{y}=0.0002\text{x}^{4}-0.0352\text{x}^{3}+2.0624\text{x}^{2}-53.092\text {x}+677.12 \tag{5}\]
# Denial-of-Service Attacks on Permissioned Blockchains: A Practical Study
## 5 Experiments and Results
### Attack Simulation
#### 5.2.7 Architectural Attack  
In HLF, each peer node can be configured to use CouchDB as its state database, enabling powerful querying on the current world state. While this enhances the data access flexibility, it introduces architectural dependencies that may be exploited by adversaries. Specifically, each peer's CouchDB instance runs independently, often as a Docker con  
Figure 11: Raft protocol attack scenario where the attacker increases the tick interval from the default 500 ms value to disrupt consensus.  
Figure 12: Comparison of delay and throughput in Raft consensus protocol attack.  
tainer. This means that the availability of the peer is directly tied to the availability of its CouchDB instance.  
In this experiment, we evaluated the impact of an architectural attack targeting the CouchDB container. By forcibly stopping the CouchDB Docker container on a peer, we observed that the affected peer could no longer process transactions or serve read requests. In scenarios where CouchDB is shared between multiple peers, the consequences become more severe, i.e., a single point of failure can cascade across the network, potentially disrupting multiple components.  
Figure 13 illustrates such an attack scenario where a user was denied access to the ledger due to CouchDB unavailability. Our test confirmed that once the CouchDB service was disabled, the client application was unable to read from or write to the blockchain, which effectively resulted in a DoS condition.  
This experiment highlighted a critical architectural vulnerability in HLF deployments that rely on CouchDB. The extent of disruption depends on the redundancy and fault-tolerance mechanisms in place, but the reliance on external state database services such as CouchDB represents a significant attack surface. If not properly mitigated, such a dependency can be exploited to compromise the availability of the network.
## 4 LBT-Based MAC Protocol for LoRaWAN
### LBT AFA Operation Mode  
The LBT AFA MAC protocol assessed in this paper operates following the steps shown in Figure 4, where \(MAC_{BE}^{min}\), \(MAC_{BE}^{max}\), \(MAC_{nBackoff}^{max}\), \(U_{backoff}\) are configuration parameters.  
For each transmission attempt the ED uses two variables, called NB (backoff number) and BE (backoff exponent), respectively. NB represents the number of times the transmission attempt of a given message is deferred. NB is initialized to zero before each new message transmission attempt. BE determines the number of backoff units (\(U_{backoff}\)) a device must wait before running a new CCA procedure. BE is initialized to the value of \(MAC_{BE}^{min}\).  
Each ED can start a transmission attempt on one of the channels available for transmission according to the \(ToA_{200\text{kHz}}^{tot/h}\) parameter discussed in the previous subsection. The list that contains these channels is calculated when the LBT AFA procedure starts and after each backoff.  
Figure 4: LBT AFA operation mode.  
Once a channel is randomly chosen, the LBT AFA MAC protocol requires that the ED checks whether the channel is busy or not through a CCA procedure before transmitting. If the average signal level detected over the CCA interval, here called \(\mathcal{T}_{CCA}\), is below the CCA threshold (i.e., a configurable value), the LBT AFA procedure succeeds and the ED begins transmitting the message. Otherwise, i.e., if the channel is sensed busy, the ED applies the AFA technique or performs an exponential backoff. In particular, if there are other available channels that have not been assessed, the ED switches to one of them and runs again the CCA. If no transmission is detected on the new assessed channel, the ED starts transmitting. Conversely, it runs the AFA technique again, if possible. If the ED detects that all the available channels are busy, the ED updates the values of NB and BE, as shown in Figure 4. If the maximum number of backoffs \(MAC_{nBackoff}^{max}\) has not been reached, the ED runs an exponential backoff algorithm that delays a new message transmission attempt for a deferral interval equal to a random value between 0 and \(2^{BE}-1\), multiplied by \(U_{backoff}\). Otherwise, the LBT AFA procedure fails and the message is discarded.
###### Abstract  
Free-space optical communication (FSO) technology has wide prospects in deep space exploration, but it will encounter coronal turbulence during superior solar conjunction, and solar scintillation will seriously affect the communication quality. In this paper, we propose a terrestrial-deep space hybrid radio frequency (RF)/FSO system with the hybrid \(L\)-pulse position modulation-binary phase shift keying-subcarrier intensity modulation (\(L\)-PPM-BPSK-SIM) scheme, where the RF channel of the satellite-terrestrial relay follows the Rayleigh distribution, and the FSO channel of the relay satellite to the deep space probe adopts Gamma-Gamma distribution. Considering the pointing error, the expression of the bit error rate (BER), the outage probability, and the average channel capacity of the hybrid system are derived. In addition, we evaluated the influence of coronal turbulence parameters on the system through amplitude fluctuations. The simulation results demonstrate that the hybrid RF/FSO system improves the BER performance by 10 to 30 times in a deep space environment, and the use of a hybrid modulation can further reduce the BER. The non-Kolmogorov spectral index, outer scale, solar wind density fluctuation factor, and optical wavelength comprehensively affect the BER through amplitude fluctuations. Our research results have potential application value for evaluating the link performance of future deep space communications.  
deep space communication; RF/FSO system; solar scintillation; PPM-BPSK-SIM; bit error rate; pointing error +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
\begin{table}
\begin{tabular}{c c} \hline \hline
**Compound Name** & **Compound Name** \\ \hline Allopurinol & Procaine \\ Ambroxol & Promethazine \\ Aminophenazone & Proglenone \\ Amitriptyline & Propofol \\ Amoxapine & Propranolol \\ Antipyrine & Pyrazinamide \\ Astemizole & Quetiapine \\ Azelastine & Quinine \\ Beclomethasone dipropionate & Rabeprazole \\ Benzbromarone & Salicylic acid \\ Benzoic acid & Sarafloxacin \\ Betahistine & Sulfacetamide \\ Betaxolol & Sulfachloropyridazine \\ Bicalutamide & Sulfadiazine \\ Bisacodyl & Sulfadimethoxine \\ Bisoprolol & Sulfadoxine \\ Budesonide & Sulfaquidine \\ Caffeine & Sulfamerazine \\ Carbamazepine & Sulfamethazine \\ Carvediol & Sulfamethizole \\ Cefadoxol & Sulfamethoxazole \\ Chloradinone acetate & Sulfamethoxypyridazine \\ Chloroquine & Sulfamonomethoxine \\ Chlorphenamine & Sulfamoxole \\ Chlorpromazine & Sulfanilamide \\ Cinoxacin & Sulfapyridine \\ Ciprofloxacinacin & Sulfaquinoxaline \\ Citalopram & Sulfathiazole \\ Clomipramine & Sulfisoxazole \\ Clonidine & Sulindac \\ Cloazepine & Zalcitabine \\ Cromoglic acid & Zaleplon \\ Danofloxacin & Tacrine \\ Dapsone & Tenidap \\ Desipramine & Theobromine \\ Dexamethasone & Theophylline \\ Diclofenac & Thiabendazole \\ Dicloxacinillin & Thiamphenicol \\ Diffoxacin & Thiethylperazine \\ Diflunisal & Thioridazine \\ Dihydroergotomine & Tiagabine \\ Diliazem & Timolol \\ Dimetindene & Tizanidine \\ Disopyramide & Tofisopam \\ Dolasetron & Tolbutamide \\ Domperidone & Tolperisone \\ Doxazosin & Tolterodine \\ Doxepin & Tramadol \\ Enoxacin & Trazodone \\ Enrofloxacin & Triamcinolone \\ Ethinyl estradiol & Trimethoprim \\ Etoricoxib & Tropisetron \\ Famotidine & Valsartan \\ Fenoterol & Warfarin \\ Fluconazolele & Verapamil \\ Flumequine & \\ Fluorescence & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._Given the heterogeneous nature of the analyzed compounds, their permeating capability changes as the pH varies. According to the literature [11], the inspected molecules were labelled as _high-permeable_ (logP\({}_{\rm e}\geq-6.2\)) or _low-permeable_ (logP\({}_{\rm e}<6.2\)). This collection of empirical data led to the organization of four different datasets, one for each pH level. Eventually, a last set of information was obtained by evaluating the intrinsic permeability (IP) of the compounds [2], leading to a total of five diverse datasets. The distribution of the molecules into the two classes according to the diverse pH levels, together with IP-based outcome, is shown in Figure 1. In the plot, red and blue bars represent low- and high-permeability, respectively. As expected, (and in agreement with the literature), a general increase in permeability can be observed as the pH moves to higher values.