esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Security Analysis and Discussion  
This section presents a comprehensive security evaluation of our quantum key agreement (QKA) protocol designed for secure communication between the Patient's Device (PD) and the Healthcare Provider (HP). The security assessment addresses two fundamental threat categories: external threats from unauthorized third parties and internal threats from potentially compromised legitimate participants. An external adversary might employ various quantum attack methodologies--including measurement-based interception followed by retransmission, quantum entanglement manipulation, or hardware-based infiltration techniques--all aimed at extracting the jointly established cryptographic key \(K\). Concurrently, the protocol must safeguard against scenarios where either the PD or HP becomes compromised and attempts to manipulate the key generation process to gain unilateral control over the resulting \(K\). The subsequent analysis demonstrates the resilience of our proposed healthcare-oriented QKA protocol against this spectrum of potential security vulnerabilities.
# Parameter Analysis and Optimization of Annular Jet Pump Based on Kriging Model
## 5 Results and Discussion
### Response Surface Analysis  
Kriging response surfaces are shown in Figure 8 to visualize the relationship between efficiency and various factors, where the efficiency \(\eta\) can reach a maximum of 33.89%. By comparing Figure 8a-c it can be seen that with the increase in the suction angle \(\alpha\), the efficiency curve of the jet pump is similar to a parabola; the efficiency reaches the maximum value with \(\alpha=26.6^{\circ}\), and the minimum values at boundaries (limits) of \(\alpha\). Comparing Figure 8a,d,e with the increase in the diffusion angle \(\beta\), the jet pump efficiency curve approximates parabola as well; the efficiency reaches the maximum value with \(\beta=4.12^{\circ}\), and the minimum value at the boundaries of \(\beta\). Comparing Figure 8b,d,f it can be found that when the area ratio, \(m\), increases, the efficiency of the jet pump increases first and then decreases by an S-curve trend; the efficiency reaches the maximum value with \(m=2.11\), and the minimum at the boundaries of \(m\). Comparing Figure 8c,e,f when the flow ratio increases, the jet pump efficiency curve approximates a parabola and fluctuates (Figure 8e); the efficiency reaches the maximum with \(q=0.6\), and the minimum value at the boundaries of \(q\), Figure 8c,e or at medium value of \(q\), Figure 8f.  
The peak efficiency does exist in the combination of parameters predicted by the approximate model. The optimal combination is: \(\alpha=26.6^{\circ}\), \(\beta=4.12^{\circ}\), \(m=2.11\), \(q=0.6\). The efficiency calculated by CFD under this condition is 34.15%. The predicted value of the optimization algorithm is in good agreement with the value calculated by CFD.  
Figure 7: Error analysis of Kriging model.  
Figure 8: Response surface of efficiency. (**a**) \(\alpha\) and \(\beta\); (**b**) \(\alpha\) and \(m\); (**c**) \(\alpha\) and \(q\); (**d**) \(\beta\) and \(m\); (**e**) \(\beta\) and \(q\); (**f**) \(m\) and \(q\).
## 4 Proposed Scheme (Multi-BSM Approach)
### Detection and Prevention of Position falsification Attack
The Figure 2 shows how a legitimate vehicle B is alerted and made aware of a malicious attacker vehicle A about the position falsification attack even before the legitimate vehicle B came into the transmission range of the attacker vehicle A due to the connectivity of the RSUs between each other. The legitimate vehicles, upon receiving the message about the attacker, store the information on the local log of flagged vehicles. As shown in Figure 2, the first step is detecting a malicious attacker vehicle by RSU. After detection, the RSU, in the next step, transmits the information regarding the malicious attacker vehicle to the neighboring RSU in the direction of the said vehicle. In the next step, the neighboring RSU, upon receiving the information, broadcasts the message to the vehicles in its vicinity. Thus, vehicle B is alerted about vehicle A even before coming into the transmission range of vehicle A. Figure 3 shows the structure of the BSM whereas Figure 4 describes the flow of information at an RSU. Figure 3: Showing the structure of a basic safety message. Figure 2: RSU detecting an attacker vehicle and alerting others.
## 3 Data Description and Data Preprocessing
### Data Preprocessing  
Due to the large differences in the sizes and value ranges of the different parameters and the inability to represent categorical variables, the original data cannot be directly input into the model, and data preprocessing is required. As shown in Figure 5, the data preprocessing process includes data normalization, one-hot encoding, and feature selection.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Depth** & **DT (Interval** & **GR (Gamma & **RHOB (Rho** & **WOB (Weight** & **ROP** &...... \\  & **Transit Time** & **Ray)** & **Bulk)** & **on Bit)** & **(m/h)** & \\ \hline
**Unit** & **(m)** & **(us/ft)** & **(API Unit)** & **(g/cm\({}^{3}\))** & **(Equivalent** & **(m/h)** & \\ \hline count & 348,702 & 348,702 & 348,702 & 348,702 & 348,702 & 348,702 &......... \\ mean & 2742.849 & 67.98826 & 22.39776 & 2.498545 & 4.939172 & 8.12864 &......... \\ std & 350.797 & 9.625563 & 14.5044 & 0.11308 & 2.063821 & 5.39589 &......... \\ min & 1504 & 41.8162 & 0.59623 & 1.72089 & 0.01 & 0.1758 &......... \\
25\% & 2505.7 & 62.2781 & 10.2852 & 2.42873 & 3.51 & 4.61538 &......... \\
50\% & 2732.3 & 66.6066 & 20.4368 & 2.52209 & 4.93 & 7.01877 &......... \\
75\% & 2946.7 & 71.0860 & 31.5935 & 2.57843 & 6.2 & 10.02 &......... \\ max & 4102.9 & 119.963 & 398.004 & 3.00439 & 41.17 & 50 &...... \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data description.
## 3 Fundamentals of GLI-Split
### Name Resolution  
To start a communication session, the initiating host resolves a DNS name (e.g., host3.other-gli-domain.net) into an IP address. If the returned IP address is a GLI-address, GLI-nodes or GLI-gateways possibly require an additional lookup to the mapping system to find an appropriate local or global locator for the ID.
###### Abstract
We utilized two different benchmark datasets for Arabic emotion recognition to evaluate our method and to compare it with other suggested methods in the literature. We can summarize the contributions of our paper as follows:  
* We propose a novel method for low-resource, fine-grained Arabic emotion recognition integrating a stacking ensemble of diverse models and self-training. * We investigate various methods (ML algorithms, few-shot methods, transfer learning, and a zero-shot classification method) for fine-grained, low-resource scenarios in Arabic emotion recognition. * We examine the use of a stacking ensemble of base models employing diverse learning paradigms for Arabic emotion recognition. * We compare the performance of our proposed method for Arabic emotion recognition with the state-of-the-art methods in the literature. Our proposed method is language-agnostic and does not incorporate modules dedicated to language-specific processing. It can be applied to any language in low-resource settings. However, we utilize pretrained language models, and their availability and diversity are critical for the effectiveness of our approach. Therefore, when considering the adoption of our proposed method, the accessibility of various large language models is vital. It is essential to recognize that the extent of resource scarcity may differ from one language to another, and this factor should be taken into account. The remainder of this paper is organized as follows. Section 2 presents an overview of the related works and available datasets for Arabic emotion recognition. In Section 3, the general framework and architecture of our novel approach to fine-grained Arabic emotion recognition are outlined. Sections 4 and 5 provide detailed explanations of each component of our proposed method, including the diverse model stacking ensemble,and the self-training process. Experiments, including the utilized dataset, data preprocessing, evaluation metrics, and the results, are presented in Section 6, followed by a discussion in Section 7. Finally, our conclusions and insights are presented in Section 8.
## 1 Introduction
### Data Resource for Seybaplaya Formation  
We present a meticulously curated database of 50 core specimens with water content, porosity, density, UCS measurements, and standardized testing protocols. This dataset supports reproducibility and future benchmarking in carbonate rock mechanics.  
Through these contributions, the study identifies the most effective ANN strategies for UCS estimation in Seybaplaya carbonates and establishes a transparent, statistically grounded framework for future machine learning applications in geomechanics.  
The paper is organized as follows. Section 2 reviews related works on UCS prediction models and ANN applications in rock mechanics. Section 3 details the materials and methods, covering study site location and sampling, rock material testing procedures, and dataset compilation. Section 4 introduces the four feed-forward neural network architectures implemented for UCS prediction. Section 5 outlines the performance evaluation metrics used to assess model accuracy and robustness. Section 6 presents the results and discussion: it begins with descriptive statistics of the experimental dataset, followed by model implementation and testing protocols for each ANN variant (RBF, Bayesian regularized, scaled conjugate gradient, and Levenberg-Marquardt), offers a comparative performance overview and sensitivity analysis, and concludes with a summary of key findings, practical implications, and suggestions for future research.
## 7 Discussion  
The work aims to analyze the performance benefits of the proposed joint-PPE scheme while utilizing a \((2\times 2)\) SDM-based optical MIMO system with PAM-4 and PAM-2 formats. The joint-PPE over MMSE PE-only achieves a \(\approx\)2 dB gain in the required optical received power \(P_{\mathrm{r}}\) to reach a BER of \(10^{-4}\). Using the PFS configuration with PAM-2 transmission, a two-tap pre-equalizer yields a \(P_{\mathrm{r}}\) gain of \(\approx\)0.8 dB at a BER of \(10^{-3}\) compared to MMSE PE-only. This is contrary to the conventional configuration in which a two-tap pre-equalizer reaches a \(P_{\mathrm{r}}\) gain of just \(\approx\)0.5 dB at a BER of \(10^{-3}\), even in the presence of minor mode coupling and higher transmission power per layer [17]. Although a substantial modal dispersion exists due to an MMF link in the PFS configuration, the proposed joint-PPE scheme improves the BER performance within the power and cost budgets. The calculation of joint-PPE coefficients requires an accurate channel estimation. During the experiments, the measured channel was very stable. However, external perturbations, such as wind and temperature, can influence channel characteristics [18; 19]. Further research must focus on the feedback mechanism for continuously adapting the joint-PPE coefficients according to the dynamic channel environment.  
Conceptualization, J.S., A.A. and S.L.; methodology, J.S., A.A. and S.L.; software, J.S.; validation, A.A. and S.L.; formal analysis, J.S.; investigation, J.S.; resources, J.S., A.A. and S.L.; data curation, J.S.; writing--original draft preparation, J.S.; writing--review and editing, A.A. and S.L.; visualization, J.S.; supervision, A.A. and S.L.; project administration, A.A. and S.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.  
The authors declare no conflict of interest.
## 1 Introduction
Wastewater in the textile industry is characterized by relatively high levels of biochemical oxygen demand (BOD) and chemical oxygen demand (COD). It is noteworthy that they contain large amounts of non-biodegradable organic compounds, especially textile dyes [32]. Around 2-3% of basic dyes do not bind to the fabric during the dyeing operation and are released into wastewater, which is commonly used, in developing countries, for the purpose of irrigation in agriculture [32; 33; 34]. In addition, dyes have been proven to exhibit mutagenic, carcinogenic and toxic properties to biota, and they can enter the human food chain and cause dysfunction in many organs [32; 35; 36; 37]. The high toxicity of basic dyes such as C.I. Basic Violet 1 (LD\({}_{50}\) = 0.05 mg/L, LD\({}_{50}\)_median lethal dose) and C.I. Basic Yellow 37 (LD\({}_{50}\) = 0.8 mg/L) was confirmed [37]. The use of appropriate yet comprehensive methods to remove dyes from wastewater is therefore a huge challenge. Adsorption takes a leading place among other physical methods of wastewater treatment [38]. It has many advantages over other techniques because it requires less energy and is flexible and easy to use with versatile designs. In addition, the adsorption method of wastewater treatment was estimated to be cost-effective. Its costs are in the range of $5.0-200 US $/m\({}^{3}\), while the costs of other technologies are in the range of $10.0-450 US $/m\({}^{3}\)[38]. In this work, we attempted to use a phosphorus monomer containing two phosphine oxide moieties in copolymerization with styrene. Monomer **6** was prepared in four steps, including the synthesis of dibromocyclohexene (Figure 1) followed by the Heck reaction of dibromide with diphenylvinylphosphine oxide **5** (Figure 2). \(1\)-Chlorocyclohexene **2** was prepared in 80% yield using a procedure described by [PERSON] and co-workers [39]. Reaction **2** with bromine and the subsequent elimination of chlorine atoms using KOH in ethanol allowed us to obtain 1,2-dibromocyclohexene **4**. With the necessary dibromide **4** in hand, we were able to complete our synthesis of **6** with 64% yield in the Heck reaction, using diphenylvinylphosphine oxide **5** and tris o-tollyl phosphine as a ligand [40]. The CyP(Ph)4-DVB obtained as a result of the crosslinking reaction of 1,2-bis((_E_)-2-diphenylphosphoriblehenyl)-cyclohex-1-ene with divinylbenzene was then applied as a potential polymeric sorbent for basic dye removal from aqueous solutions. A review of the literature allows us to conclude that polymeric adsorbents based on diphenylvinylphosphine oxide **5** have not been widely used so far to remove dyes from wastewater. Therefore, the adsorption capacities of CyP(Ph)4-DVB in comparison to poly(DVB) towards cationic dyes such as BY2 and BB3 were determined.
## 4 Numerical Results
### Influence of System Parameters  
The effects of system parameters on the performance of the proposed lifetime optimization method are analyzed in this subsection. For the sake of a clear visualization of the results in the graphs, firstly a small number of sensor nodes is considered, i.e., \(N=10\). Larger numbers of nodes are analyzed subsequently. Whenever a normalized energy value is called, it means that its value was normalized with respect to the maximum stored energy in the sensor node's battery, i.e., \(S_{\max}\). When it is stated that a single realization of random consumptions was adopted, it means that the maximum energy consumptions of the sensor nodes were generated for the whole interval of analysis according to the correlation coefficient \(\rho\) and the limits \(B_{\min}\) and \(B_{\max}\) and stored to be reused under different configurations of the system parameters. The death energy was set to 5% of \(S_{\max}\). In practice, this relatively low percentage death energy corresponds to a high percentage of the nominal battery voltage associated with the death voltage [37; 41]. For example, when the sensor's battery voltage level is still at a high percentage of its nominal value, say 70%, the energy remaining in the battery is a low percentage of the maximum, say 5% or less [41].
# A Simple Neural-Network-Based Decoder for Short Binary Linear Block Codes
## 5 Dataset Generation and Neural Network Training
### Training of Neural Network  
Python 3.8 and TensorFlow 2.5 are used to train the neural network in our simulations. The dataset of 1,000,000 records is divided into a training set of 900,000 and a validation set of 100,000 records, with a ratio of 9:1. During training, the entire training set is trained repeatedly 10 times (epochs = 10), with the weight values being updated once in each batch of 128 data points (batch size = 128). The optimizer used is ADAM [20] with a learning rate of \(10^{-3}\). The loss function is determined based on the output of the NN. Since the expected output of each node in the output layer is either 0 or 1, the binary cross entropy (BCE) loss function is utilized as shown in (16):  
\[\text{BCE}=-\frac{1}{N_{b}}\sum\
olimits_{i=1}^{N_{b}}\theta_{i}\text{log}\, \hat{\theta}_{i}+(1-\theta_{i})\text{log}\big{(}1-\hat{\theta}_{i}\big{)} \tag{16}\]  
where \(N_{b}\) is the batch size, \(\theta_{i}\) is the expected output value, and \(\hat{\theta}_{i}\) is the output value of NN.
## 3 Simulation and Experimental Analysis
### Experimental Results and Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Time (s)** & \begin{tabular}{c} **Speed (a)** \\ **Act/Est** \\ \end{tabular} & \begin{tabular}{c} **Speed (a)** \\ **Error** \\ \end{tabular} & \begin{tabular}{c} **Speed (b)** \\ **Act/Est** \\ \end{tabular} & \begin{tabular}{c} **Speed (b)** \\ **Error** \\ \end{tabular} & \begin{tabular}{c} **Position (a)** \\ **Act/Est** \\ \end{tabular} & \begin{tabular}{c} **Position** \\ **(a) Error** \\ \end{tabular} & \
\begin{tabular}{c} **Position** \\ **(b) Act/Est** \\ \end{tabular} &
\begin{tabular}{c} **Position** \\ **(a) Error** \\ \end{tabular} \\ \hline
0.1 & 812.50/814.99 & \(-\)2.49 & 965.98/964.67 & 1.31 & 5.51/4.49 & 1.02 & 4.76/5.16 & \(-\)0.4 \\
0.3 & 939.87/947.73 & \(-\)7.86 & 998.68/997.78 & 0.9 & 6.23/5.13 & 1.1 & 6.01/6.24 & \(-\)0.23 \\
0.5 & 970.27/987.90 & \(-\)17.63 & 999.97/999.06 & 0.91 & 2.18/0.98 & 1.2 & 1.79/2.18 & \(-\)0.39 \\
0.7 & 985.96/987.44 & \(-\)1.48 & 1000.01/999.11 & 0.9 & 1.34/0.08 & 1.24 & 3.89/4.28 & \(-\)0.39 \\
0.9 & 993.17/1010.23 & \(-\)17.06 & 1000.02/999.08 & 0.9 & 1.30/0.03 & 1.27 & 5.81/6.20 & \(-\)0.39 \\ \hline
1.1 & 859.71/858.22 & 1.49 & 795.70/794.77 & 0.93 & 4.73/3.61 & 1.12 & 5.83/6.27 & \(-\)0.44 \\
1.3 & 822.55/803.84 & 18.71 & 799.83/798.87 & 0.96 & 1.13/0.2 & 0.93 & 3.70/4.14 & \(-\)0.44 \\
1.5 & 808.29/802.55 & 5.74 & 799.98/799.03 & 0.95 & 1.04/0.02 & 1.02 & 1.60/2.04 & \(-\)0.44 \\
1.7 & 799.04/803.83 & \(-\)4.79 & 799.99/799.03 & 0.96 & 5.95/4.99 & 0.96 & 5.80/6.23 & \(-\)0.43 \\
1.9 & 805.53/780.67 & 24.86 & 799.99/799.02 & 0.97 & 4.05/3.04 & 1.01 & 3.70/4.14 & \(-\)0.44 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Position and speed of experiment 2. Figure 21: Experiment 2 PMSM d and q-axis currents of the (**a**) Control scheme of combining the backstepping controller with the ESO and (**b**) Control Scheme of combining the integral backstepping controller with the ELESO.
## 2 Intelligent Applications and Their Characteristics  
The growing popularity of IoT use cases in domains that rely on connectivity spanning large areas and the ability to handle a massive number of connections is driving the demand for LPWAN access technologies. With the advancement in the fields of miniaturized electronics, communication, computing, sensing, actuating, and battery technologies, it is possible to design low power, long range networking technologies with many years of battery life and tens of kilometers of coverage. These technologies have to be Internet-compatible so that data, device, and network management can be undertaken through cloud-based platforms. LPWAN technologies can be deployed for a broad range of smart and intelligent applications, including environment monitoring, smart cities, smart utilities,agriculture, healthcare, industrial automation, asset tracking, logistics and transportation, and many more, as given in Table 1[4].
## 2 Research Progress in In Situ Road Health Monitoring Technology
### Conductive Concrete Composite Materials
Moreover, strain and electrical resistivity show a good correlation with an extension of the loading time under indirect tensile creep mode. The authors also carried out research on the pressure-sensitive properties of conductive asphalt mortar with an electrical conductivity up to the order of \(10^{-4}\) S/cm by incorporation of graphite with carbon fiber [75]. The pressure sensitivity and reversibility of materials under cyclic loading mode were studied, and the change rates of resistivity under different cyclic loading times were analyzed. Asphalt-based conductive asphalt concrete has significant sticky elastic plastic due to the characteristics of asphalt viscoelastic itself. Plastic deformation is non-negligible at high temperatures and fatigue loads, leading to poor resistance output signal repeatability and an obvious hysteresis effect.
## 4 Discussion
[49], a moderately negative association was found between the porosity, flexural strength, and sealing ability of the materials, which can lead to their weakening. [PERSON] et al. [29] found that material condensation with ultrasonic activation reduced the pore amount in two of the tested CSCs (Retro MTA and BioAggregate). However, Biodentine demonstrated similar results when utilizing different placement techniques. Nowadays, **micro-computed tomography** (micro-CT) has been introduced and accepted as an innovative non-destructive method that can provide important data on the physicochemical properties of materials [30]. With high resolution, appropriate segmentation, and an image analysis approach, it may be used to quantitatively assess the interfacial gaps and filling capacity of CSCs with higher accuracy, thus overcoming some shortcomings of the SEM method [44, 50]. This new technology may be used to correlate volumetric changes with resolution and dimensional variations in endodontic materials [29, 50]. Furthermore, it allows the same specimen to be used at different time intervals of analysis, with a high level of detail [30]. **Limitations and future research:** It is acknowledged that this experimental study is a pilot and that the sample is limited. It has some other potential limitations as well. First, the volume of the retrocavities prepared was not exactly similar; however, the difference between the specimens was insignificant. It should be noted that in the larger cavity, it was easier to insert and condense the material. Second, due to artifacts resulting from the strongly absorbing particles inside the cement, some artifacts mimic voids. Thus, the volume fractions may be slightly overestimated. Moreover, the study design did not involve the use of simulated tissue fluid or other immersion media. Meanwhile, new modifications of this restorative MTA brand were proposed, apart from the MTA Universal, such as **Harvard MTA-Retro** (precisely designed for retrograde application), Harvard MTA-CAP, Harvard MTA-PT, Harvard MTA-Repair, Harvard MTA-Ortho, and Harvard MTA-RootSeal, particularly suitable for specific endodontic purposes (i.e., pulp capping, pulpotomy, filling of root perforations and root canals) [51]. Further research on the impact of different environmental conditions on the properties of this novel bioactive cement is needed, including its new variants, in order to mimic more closely the clinical scenario and gain clinically relevant results. Since the available scientific evidence is still controversial, more research is required to compare the Harvard MTA with other CSCs in order to select the most effective root-end filling material for clinical practice.