esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Physical-Chemical Characterization of the Waste Vitreous Enamels
#### 3.1.4 Results of Determination of the Specific Surface by the BET Method  
The results of the total specific surface area (Sp) of starting samples of the waste vitreous enamels generated from the plant, are presented in Table 5.  
Figure 2: XRPD diagrams of the waste vitreous enamels: (**a**) WEP and (**b**) WETM.  
The specific surface area of the waste vitreous enamel for both investigated samples in the raw state, was below 10 m\({}^{2}\)/g generated in all months except batch 3 when it was 22.3 m\({}^{2}\)/g and 20.08 m\({}^{2}\)/g for WEP and WETM samples, respectively (Table 5). The appearance of smaller values of specific surface areas of the generated samples is in agreement with the higher degrees of agglomeration. In general, mesopores are formed between particles, while micropores are formed on the particles themselves. However, the standard \"EN 197-1-Cement--Part 1 which is applicable in the Republic of Serbia and the EU (Standard EN 13263-1), prescribes that the specific surface area of the waste material should be greater than 15 m\({}^{2}\)/g in order to be used as a type II admixture for the production of the waste material.  
Figure 3: FTIR spectra of the waste vitreous enamels: (**a**) WEP and (**b**) WETM.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Sample**} & \multicolumn{8}{c}{**WEP**} & \multicolumn{8}{c}{**WEMT**} \\ \hline \multirow{2}{*}{**Batch**} & Sp\({}_{\text{P}}\) & \(\mathbf{V_{total}}\), & \(\mathbf{V_{meso}}\), & \(\mathbf{V_{micro}}\) & \(\mathbf{D_{sr}}\) & \(\mathbf{D_{max}}\)* & Sp\({}_{\text{P}}\) & \(\mathbf{V_{total}}\), & \(\mathbf{V_{meso}}\) & \(\mathbf{V_{micro}}\) & \(\mathbf{D_{sr}}\) & \(\mathbf{D_{max}}\)* \\  & **m\({}^{2}\)/g** & **cm\({}^{3}\)/g** & **cm\({}^{3}\)/g** & **cm\({}^{3}\)/g** & **nm** & **nm** & **m\({}^{2}\)/g** & **cm\({}^{3}\)/g** & **cm\({}^{3}\)/g** & **cm\({}^{3}\)/g** & **nm** & **nm** \\ \hline
1 & 8.75 & 0.0818 & 0.0633 & 0.0185 & 10.87 & 2.48 & 6.08 & 0.0040 & 0.0002 & 0.0038 & 6.49 & 2.32 \\
2 & 7.8 & 0.0816 & 0.0562 & 0.0254 & 9.92 & 2.53 & 7.06 & 0.0057 & 0.0003 & 0.0054 & 7.03 & 2.36 \\
3 & 22.3 & 0.0708 & 0.0682 & 0.0026 & 9.80 & 2.25 & 20.08 & 0.0689 & 0.0622 & 0.0067 & 9.33 & 2.36 \\
4 & 8.92 & 0.0816 & 0.0632 & 0.0184 & 10.95 & 2.59 & 6.23 & 0.0060 & 0.0002 & 0.0058 & 6.57 & 2.15 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Porosity parameters of the waste vitreous enamels generated from the plant (batches 1–4).  
of concrete in accordance with EN 206. This would mean that, due to parameters of textural properties, only samples from batch 3 meet the criteria and can be used as a replacement of cement. In order to overcome this problem and met the required criteria, it is also to further crush all other samples at particle sizes below 45 \(\upmu\)m.
# Millimeter-Wave Smart Antenna Solutions for URLLC in Industry 4.0 and Beyond
### Paper Organization  
The rest of the paper is organized as follows: Section 2 presents the potential prospects, advantages, and challenges in 60 GHz band communication in an industrial environment. Section 3 discusses standards and protocols for the 60 GHz mmWave ISM band for smart industries. Section 4 reveals state-of-the-art antenna designs, their challenges, and design considerations in the 60 GHz band. Section 5 unveils research prospects and future directions. Section 6 concludes the paper. The organization of this paper is depicted in Figure 3.
# Unified Ciphertext-Policy Weighted Attribute-Based Encryption for Sharing Data in Cloud Computing
## 5 Proposed Unified Ciphertext-Policy Weighted Attribute-Based Encryption
### Overview of UCP-WABE  
In UCP-WABE, different providers' weighted access trees can be merged into one if and only if the trees meet two conditions. One is that there is a mutual sub-policy between the weighted access trees. The other is that the sub-trees' roots of every weighted access tree locate in a trunk. A merging process is displayed in Figure 3. The two weighted access policies belong to different providers and have a mutual sub-policy: {Attr3(3) AND (Attr1(2) OR Attr2(3))}. All roots of the left weighted access tree are in the trunk: \"AND\"-\"AND\"-\"OR\". All roots of the right weighted access tree are in the trunk: \"OR\"-\"AND\"-\"OR\". Therefore, the two conditions are met, and the weighted access trees can be merged. The structure of the new weighted access tree is a multi-root tree.
# Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications
## 4 Mathematical Framework for Anomaly Detection
In a local model, only the spatial neighbors of PUT are used for the description of the background. Quasi-local methods present a trade-off solution to global and local models.
# Candidate Waveforms for ARoF in Beyond 5G
## 2 Requirements to Waveforms for Beyond 5G
### Requirements for General Wireless Communications
According to release 15 of 3 GPP [15], the bandwidth is up to 400 MHz for carrier frequencies above 24 GHz. A portion of such a bandwidth (around 20%) is used as a guard band. Therefore, the OOB should be high enough to achieve a reduced interference between the adjacent channels and, thus, to obtain an adequate frequency multiplexing of services. For example, the OOB emission shall not exceed \(-\)5 dBm for bandwidths of 50, 100, 200 and 400 MHz in the OOB region of 0 to 5 MHz [15]. * Enabling asynchronous multiple access: Asynchronous multiple access is relevant as it allows to efficiently utilize resources. In frequency division duplex (FDD) and time division duplex (TDD) systems, asymmetric and dynamic allocation of both time and frequency resources is feasible for increasing bandwidths in order to accommodate the asymmetric traffic with higher efficiency [16]. Namely, waveform formats enabling asynchronous multiple access are connected with more efficient channel usage and corresponding higher total throughput. * Filter granularity: This factor indicates the level in which the waveform is using the filtering stage. The filter granularity is directly related to latency and OOB emissions. As a direct consequence, long filters cause a high block processing delays and thus negatively impact achievable latency. On the contrary, other waveform formats implementing shorter filter lengths do not induce high latency because they filter by sub-band (wide filter bandwidth) [17]. Therefore, a trade-off between low OOB emissions and low latency is required. Thus, a very narrow filter granularity (subcarrier) implies very low OOB emissions. However, the filter length will be very long and, in consequence, the latency will increase. * Hardware (HW) complexity: The importance of low hardware complexity is associated with both the final expense and the complexity of the system. As already mentioned in the introductory section, the number of cells will increase in the next generation of mobile networks. Therefore, the complexity and cost of the hardware in each cell is a key factor when determining the feasibility of a modulation format.
# Unified Ciphertext-Policy Weighted Attribute-Based Encryption for Sharing Data in Cloud Computing
## 1 Introduction  
As one of the most promising applications, cloud computing [1; 2; 3] provides a more efficient way for data sharing. It enables data providers to store their data remotely in a cloud, and once data consumers can access the cloud, they can access the data any time and anywhere. Despite cloud computing supplying great convenience for data sharing, it also brings the serious challenge of information security [4]. Massive data are stored in the cloud storage platforms, and the data often contain sensitive information, such as personal health records in the medical cloud [5; 6; 7] and banking transactions in the financial cloud [8; 9; 10]. An untrustworthy entity named the cloud service provider (CSP) runs the cloud storage platforms, and it may steal the sensitive information to make a profit. Therefore, how to enforce a secure and efficient data sharing in the cloud has attracted many scholars' attention [11; 12; 13; 14].  
Attribute-based encryption (ABE) [15] is a widespread cryptographic technology to protect the security of data in cloud computing. Different from traditional public key encryption, ABE does not encrypt plaintexts for an explicit consumer. Consumers' secret keys and ciphertexts are associated with a set of attributes or an access policy, respectively. A consumer can decrypt a ciphertext if and only if his/her secret key has a match with the ciphertext. Weighted attribute-basedencryption [16; 17; 18; 19; 20; 21] is a variant of ABE. It assigns different weights to attributes according to their importance. For example, we can give full professor and distinguished professor with weights one and two, denoted as \"professor(1)\" and \"professor(2)\", respectively. This can avoid a very complicated access structure and improve the efficiency of the encryption in certain scenarios.
# High-Performance Computing and Parallel Algorithms for Urban Water Demand Forecasting
## 2 Materials and Methods
### Data Preprocessing Pipeline
#### 2.3.3 Normalization and Feature Engineering  
After data cleaning, each reservoir's time series was normalized using min-max scaling, which transformed water level values into a [0, 1] range--a prerequisite for ensuring stable training across machine learning algorithms. Normalization parameters (minimum and maximum values) were computed based on the training data split to prevent data leakage, and the same scaling was applied to the validation and test sets.  
Additional features were engineered to enhance the predictive performance of the models, including hour of the day, day of the week and month indicators. These features allowed the models to account for known periodicities and consumption patterns.  
To efficiently generate these features, Spark's SQL and DataFrame operations were leveraged. Instead of iterating through reservoirs individually, feature extraction was vectorized and distributed, ensuring efficient processing across the dataset.  
The dataset was then partitioned into training, validation, and testing sets, maintaining chronological order to replicate real-world forecasting conditions. This ensured that models were always trained on past data to predict future trends. The partitioning process wasalso managed using Spark, which allowed each executor to write out a portion of the final dataset in efficient Parquet format, minimizing I/O bottlenecks [19].
# AI-Driven Phishing Detection: Enhancing Cybersecurity with Reinforcement Learning
## 7 Conclusions  
While RL provides strong adaptability for phishing detection, RL models can still be vulnerable to adversarial attacks. Attackers may craft specially designed phishing emails to manipulate the input feature space, causing the RL agent to misclassify phishing attempts as benign. Such adversarial perturbations have been shown to significantly degrade the performance of machine learning-based cybersecurity systems, as discussed in [56] with the context of smart grid security. Although our proposed RL-based model was not explicitly trained against adversarial examples, future extensions should consider adversarial training techniques, input sanitization methods, and robustness evaluation to mitigate this risk and strengthen the resilience of phishing detection agents against sophisticated evasion tactics.  
In this research, we introduced an RL-based phishing detection model, leveraging a DQN to improve adaptability and accuracy while reducing false positives. Unlike traditional ML methods, which require frequent retraining, our approach enables continuous learning through feedback mechanisms, enhancing its resilience against evolving phishing techniques. Experimental results demonstrated a 95% accuracy, 96% precision, 94% recall, and a 2% false positive rate, significantly outperforming conventional phishing detection methods. AUC analysis (0.92) further confirmed the model's robustness, showing competitive performance with DL models while maintaining computational efficiency. The validation results obtained from both real-world and synthetic datasets demonstrate the proposed framework's strong potential for practical deployment in dynamic phishing detection environments. Future research should explore real-time deployment to evaluate its performance in dynamic cybersecurity environments and integrate behavioral analysis and social engineering detection to enhance robustness. Transfer learning could improve adaptability across multiple phishing attack surfaces, while hybrid AI models combining RL with natural language processing (NLP) techniques, such as BERT or GPT-based architectures, could refine phishing text analysis. Additionally, optimizing computational efficiency and exploring federated learning would enable scalable deployment in enterprise security and mobile environments, ensuring a more resilient and adaptive phishing defense system.  
Conceptualization, [PERSON] and S.A.-J.; methodology, [PERSON]; software, [PERSON]; validation, [PERSON] and S.A.-J.; formal analysis, [PERSON]; investigation, [PERSON]; resources, S.A.-J.; data curation H.J.; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON] and S.A.-J.; visualization, [PERSON]; supervision, [PERSON]; project administration, [PERSON] and S.A.-J.; funding acquisition, [PERSON] and S.A.-J. All authors have read and agreed to the published version of the manuscript.
## 4 Core Modelling
### Equivalent Magnetic Permeability of the Core  
For the case of a one-dimensional penetration of the electromagnetic field into the ferromagnetic material, with a thickness of 2D, the only existing \(y\)-component of the magnetic intensity vector is given by Formula [30]:  
\[H_{y}(z)=H_{y0}\frac{\cosh kz}{\cosh kD}, \tag{3}\]  
where \(k=\sqrt{j\omega\mu\gamma}=\frac{(1+j)}{\delta}\) and \(\delta=\sqrt{\frac{2}{\omega\mu\gamma}}\) is the skip-depth.  
The complex permeability in the laminated material is defined by Equation [31]:  
\[\underline{\mu}=\frac{1}{H_{y0}}\cdot\overline{B}=\frac{1}{H_{y0}\cdot 2D} \int\limits_{-D}^{D}\Big{(}\mu H_{y}\Big{)}dz=\mu_{0}\mu_{\text{r}}\cdot\frac {\sinh kD}{kD\cdot\cosh kD}, \tag{4}\]  
where \(\underline{\mu}\) is the complex permeability, \(\mu_{0}\) and \(\mu_{\text{r}}\) are the vacuum and relative permeability, \(H_{y0}\) is the magnetic field intensity applied to the surface of lamination, and \(\overline{B}\) is the average magnetic flux density.  
This complex permeability can by split into real and imaginary parts:  
\[\underline{\mu}=\mu_{0}(\mu\prime+j\mu^{\prime\prime}), \tag{5}\]  
\[\mu\prime=\frac{\text{Re}\Big{(}\underline{\mu}\Big{)}}{\mu_{0}}=\frac{\mu_{r} \delta}{2D}\Bigg{(}\frac{\sinh\Big{(}\frac{2D}{\delta}\Big{)}+\sin\Big{(}\frac {2D}{\delta}\Big{)}}{\cosh\Big{(}\frac{2D}{\delta}\Big{)}+\cos\Big{(}\frac{2D }{\delta}\Big{)}}\Bigg{)} \tag{6}\]  
\[\mu^{\prime\prime}=\frac{-\text{Im}\Big{(}\underline{\mu}\Big{)}}{\mu_{0}}= \frac{\mu_{r}\delta}{2D}\Bigg{(}\frac{\sinh\Big{(}\frac{2D}{\delta}\Big{)}- \sin\Big{(}\frac{2D}{\delta}\Big{)}}{\cosh\Big{(}\frac{2D}{\delta}\Big{)}+ \cos\Big{(}\frac{2D}{\delta}\Big{)}}\Bigg{)} \tag{7}\]  
The real part of the complex permeability corresponds to the conduction of the magnetic flux through the ferromagnetic material, while the imaginary part corresponds to the core loses. The complex permeability as a function of frequency, calculated according to (4), is presented in Figure 6.  
In the computer simulation, the values of the real part of the complex permeability were entered separately as input data to the Maxwell software for each simulated frequency.
## 2 Problem Description and Model Construction
### Model Establishment for Multi-Objective Optimization  
Tables 1 and 2 show the parameters and variables defined in this paper, with superscript T for handling, superscript S for setup, and superscript a for workpiece kind. Figure 2: Diagram of a multi-equipment work center. The decision variables in this paper are shown below. \(X_{h,j,k}\): If workpiece \(j\) is machined on machine \(k\) at work center \(h\), \(X_{h,j,k}=1\), otherwise \(X_{h,j,k}=0\). \(A_{h,j,k}\): If workpiece \(j\) is of the same type as the workpiece being processed by machine \(k\) at work center \(h\), \(A_{h,j,k}=0\), otherwise \(A_{h,j,k}=1\). \(Y_{h,j}\): If the workpieces in work center \(h\) with a completion time less than \(\text{TE}_{h,j}\) belong to a set, \(Y_{h,j}=1\), otherwise \(Y_{h,j}=0\). The objective function and restrictions are as follows, with the makespan and the number of handling events as the optimization objectives. (1) Makespan. The most crucial metric in the MTO production process is still the lead time, which can be directly calculated from the makespan. The objective function is shown in Equation (1). \[ \begin{split} f(1)=\min\,Cmax\\ Cmax=\max\{\text{TE}_{h,j}\,|\,j=1,2\,\dots,n,\,h=1,2\,\dots,\,i\} \end{split}  \tag{1}\]  
(2) Minimize the number of handling events. The number of handling events in actual production indirectly impacts the amount of handling equipment, the total handling time, the handling cost, and the number of handling \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Definition** \\ \hline \(\text{t}_{h,j,k}\) & the time that the workpiece \(j\) is processed on the equipment \(k\) at the work center \(h\) \\ \(\text{H}_{h,h+1}\) & handling time from work center \(h\) to \((h+1)\) \\ CT & total handling time \\ ST & total setup time \\ CB\({}_{h,h+1,d}\) & start time of the \(q\)th handling from work center \(h\) to \((h+1)\) \\ CE\({}_{h,h+1,d}\) & end time of the \(q\)th handling from work center \(h\) to \((h+1)\) \\ CB\({}_{h,h+1}\) & handling start time of workpiece \(j\) from work center \(h\) to work center \((h+1)\) \\ CE\({}_{j,h+1}\) & handling end time of workpiece \(j\) from work center \(h\) to work center \((h+1)\) \\ TB\({}_{h,j}\) & start time of processing of workpiece \(j\) at work center \(h\) \\ TE\({}_{h,j}\) & end time of processing of workpiece \(j\) at work center \(h\) \\ S\({}_{h,j}\) & setup time of workpiece \(j\) at work center \(h\) \\ S\({}_{h,j,k}\) & setup time of the workpiece of type \(a\) on the equipment \(k\) at the work center \(h\) \\ MB\({}_{h,j,k}\) & start time for machining the \(v\)th workpiece on machine \(k\) at work center \(h\) \\ ME\({}_{h,j,k}\) & end time for machining the \(v\)th workpiece on machine \(k\) at work center \(h\) \\ J\({}_{h,d}\) & the set of workpieces for the \(q\)th handling in work center \(h\) \\ \(p(_{h,h+1})\) & number of handling events from work center \(h\) to \((h+1)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Definition of variables.
# Energy-Saving Routing Protocols for Smart Cities
## 2 Materials and Methods
### Related Works
#### 2.1.2 LoRa  
LoRa is a wireless communication technology patented by Semtech Corporation that can be applied on devices with battery restrictions, aiming at longer lifetimes and large transmission ranges [6]. The range of a LoRa-based network in an urban area is up to 15 km, and in rural areas it can be up to 30 km in normal conditions. Lora operates in the ISM (Industrial, Scientific, Medical) bands.  
LoRa modulation is based on the Chirp Spread Spectrum (CSS), which is characterized by the spectral spread of the signal to be transmitted in a given frequency range (\(f_{low}\), \(f_{high}\)), generating a signal called Compressed High Intensity Radar Pulse (Chirp) [6]. An unmodulated Chirp signal has constant amplitude, and its frequency varies inside the bandwidth (BW = \(f_{high}-f_{low}\)) by a given period of time (\(TS\) = Symbol time).  
The parameters used in LoRa modulation are the Bandwidth (BW), Spreading Factor (SF), and Code Rate (CR). Each LoRa symbol spans an entire BW and can encode bits of data defined by the SF, which can be from six to twelve. A LoRa symbol is an up-chirp (from \(f_{low}\) to \(f_{high}\)), meaning that when a frequency related to the data being transmitted is reached, it is shifted to \(f_{low}\) while maintaining the same frequency slope, causing a discontinuity point.The discontinuity point position is responsible for the encoding of the transmitted data [6].
# Absolute Capacitance Measurement by Direct Digital Fitting of Proportional Coefficient
###### Abstract  
In the realm of capacitance measurement, traditional methods that gauge capacitance through timing charge and discharge intervals frequently suffer from inaccuracies, particularly due to noise affecting voltage threshold detection. These techniques are also inefficient for high-capacitance components, as their lengthy charge-discharge cycles limit the measurable range within a given period. To this end, a method for directly sampling and analyzing the input and output signals of an RC first-order system under square wave excitation is proposed for a wide range of capacitance measurements. By establishing a proportional relationship between the differentiated output signal and the difference between input and output signals, one can deduce the capacitance. To counteract noise-induced errors during differentiation, data smoothing is applied, enhancing accuracy. This technique achieves a relative standard deviation of less than 0.9% for capacitances from 60 pF to 60,000 pF, using a 100 k\(\Omega\) reference resistor and continuous square waves. For capacitances above 800 pF, precision further improves to less than 0.2%. The approach leverages least squares fitting and outlier rejection to manage noise effectively. It remains independent of the capacitor's initial state, ensuring broad-range accuracy and faster measurement times.  
square wave excitation; high capacitance measurements; numerical fitting; data smoothing 2021
## 3 Results and Discussion
### Initial Conditions  
The choice of the initial conditions based on different conditions on velocity and displacement is detailed in the following paragraphs.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & & \multicolumn{2}{c}{**Free Modes**} & \multicolumn{2}{c}{**Stability Analysis**} \\ \hline
**Mode** & **Denomination** & **Frequency [Hz]** & **Damping Ratio [-]** & **Frequency [Hz]** & **Equivalent Damping Ratio [-]** \\ \hline
5 & 0L0 & 304.43 & \(1\times 10^{-3}\) & 304.43 & \(-1.25\times 10^{-3}\) \\
6 & 0L2 & 386.38 & \(1\times 10^{-4}\) & 386.35 & \(-4.09\times 10^{-3}\) \\
9 & 0L3 & 1028.8 & \(1\times 10^{-4}\) & 1028.79 & \(-1.49\times 10^{-3}\) \\
12 & 0L4 & 1853.9 & \(1\times 10^{-4}\) & 1853.91 & \(-7.60\times 10^{-4}\) \\
16 & - & 2369.8 & \(1\times 10^{-4}\) & 2369.8 & \(-1.43\times 10^{-5}\) \\
17 & 0L5 & 2776.3 & \(1\times 10^{-4}\) & 2776.3 & \(-4.23\times 10^{-4}\) \\
22 & 0L6 & 3752.6 & \(1\times 10^{-4}\) & 3752.59 & \(-2.55\times 10^{-4}\) \\
26 & - & 4561.5 & \(1\times 10^{-4}\) & 4561.5 & \(-3.79\times 10^{-7}\) \\
28 & 0L7 & 4762.2 & \(1\times 10^{-4}\) & 4762.19 & \(-1.53\times 10^{-4}\) \\
33 & 0L8 & 5794.7 & \(1\times 10^{-4}\) & 5794.69 & \(-9.09\times 10^{-5}\) \\ \hline \end{tabular}
\end{table}
Table 2: Natural frequency and modal damping of the free modes and the unstable modes (stability analysis).  
Figure 3: Equivalent damping ratio of wheel modes with respect to frequency (black: damping ratio of the free damped wheel modes, blue: equivalent damping ratio of stable modes, red: equivalent damping ratio of unstable modes). For purposes of visualization, several modes do not appear in the graph due to their high damping.