esa-sceva/satcom-chunk-collection huggingface dataset:
# The Design and FPGA-Based Implementation of a Stream Cipher Based on a Secure Chaotic Generator
## 3 Hardware Implementation of the Proposed SCbSC and Evaluation of Its Performance
### SPCNG Resilience against Statistical Attacks  
To quantify the cryptographic properties of the pseudo-chaotic sequences generated by the proposed SPCNG, a series of tests must be applied. Each test measures a particular characteristic, such as the correlation between generated sequences or their uniformity, and the overall results of these tests give an idea of the degree of randomness of the sequences produced. The pseudo-chaotic behavior of the generated sequences is closely linked to the statistical characteristics of these sequences. The National Institute of Standards and Technology (NIST) tests [33] serve, among other things, as a reference to quantify and compare the statistical properties of binary pseudo-chaotic sequences.  
Note that the Lyapunov exponents of the three chaotic maps used are positive; however, it is not obvious to compute the Lyapunov exponents of the new stream cipher we propose here. Nevertheless, its chaotic nature is due mainly to the weak coupling of the three chaotic maps. The weak coupling mechanism of chaotic maps has been thoroughly studied [17]; it leads generally to high quality pseudo-random generators. The chaotic  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & & \multicolumn{4}{c}{**Versions**} \\ \hline  & & \multicolumn{3}{c}{**Chaotic Multiplexing**} & \multicolumn{2}{c}{**XOR Operation**} \\ \hline  & & & **Without LFSR** & **With LFSR** & **Without LFSR** & **With LFSR** \\ \hline \multirow{4}{*}{Resources used} & \multirow{2}{*}{Area} & LUTs & 3744/7.04\% & 3763/7.07\% & 3586/6.74\% & 3599/6.77\% \\  & & FFS & 1066/1\% & 1130/1.06\% & 1064/1\% & 1128/1.06\% \\  & & Slices * & 1079/8.11\% & 1087/8.17\% & 1031/7.75\% & 1029/7.74\% \\ \cline{2-6}  & \multicolumn{1}{l}{DSPs} & 25/11.36\% & 25/11.36\% & 22/10\% & 22/10\% \\ \hline \multirow{4}{*}{Speed} & WNS [ns] & \(-\)18.968 & \(-\)19.062 & \(-\)19.632 & \(-\)18.018 \\  & \multicolumn{1}{l}{Max. Freq. [Mhz]} & 37.08 & 36.95 & 36.18 & 38.43 \\ \cline{1-1}  & \multicolumn{1}{l}{Throughput} & 1186.59 & 1182.46 & 1158.07 & 1229.91 \\ \cline{1-1}  & \multicolumn{1}{l}{[Mbps]} & & & & \\ \hline Efficiency [Mbps/Slices] & 1.09 & 1.08 & 1.12 & 1.19 \\ \hline NIST & \multicolumn{1}{l}{Successful} & Successful & Successful & Successful \\ \hline \hline \end{tabular}
* Note: Each slice contains four LUTs with 6 inputs and eight FFs.  
\end{table}
Table 2: Comparison of the proposed SPCNG design versions on ZYNQ PYNQ Z2 FPGA.  
nature of it is highlighted by the histogram and figures of the uniform and uncorrelated distribution of its iterates (Figures 7 and 8).
# Quantifying Fermentable Sugars in Beer: Development and Validation of a Reliable HPLC-ELSD Method
## 2 Materials and Methods
### HPLC-ELSD Analysis  
Chromotographic analysis was performed on a 1260 Infinity HPLC system (Agilent Technologies, Soquimica, Lisbon, Portugal), equipped with a quaternary pump (1260 Quat Pump), autosampler (1260 ALS), and a thermostat-controlled column compartment (1260 TCC). The analytical column used was a Spherisorb NH\({}_{2}\) (250 \(\times\) 4.6 mm, 5 \(\upmu\)m; Waters, Waters Portugal, Porto, Portugal). Data acquisition and analysis were managed using Agilent OpenLab CDS ChemStation Edition C.01.04. Detection was carried out with a 380-ELSD detector (Agilent Technologies). The following detector parameters were optimized: gas flow (0.9-3.25 SLM), nebulizer temperature (25-90 \({}^{\circ}\)C), evaporator temperature (25-120 \({}^{\circ}\)C), light source (480 nm LED), gain (1-10), smoothing (0.1-5 s), and data rate (10, 40, or 80 Hz). Final operating conditions were set at a 1 mL/min flow rate, 30 \({}^{\circ}\)C column temperature, 10 \(\upmu\)L injection volume, 60 \({}^{\circ}\)C nebulizer temperature, 85 \({}^{\circ}\)C evaporator temperature, and 1.1 SLM nitrogen flow. The ELSD detector was stabilized under active eluent flow for at least 1 h prior to each analysis.
# A Secure and Lightweight Group Mobility Authentication Scheme for 6 LoWPAN Networks
## 6 Performance Evaluation
### Discussion
#### 6.5.4 Transmission Overhead  
Transmission overhead for low-power networks like 6 LoWPAN must be minimized, as energy consumption directly impacts device longevity. SL_GAS consumes the least energy among the compared schemes because its message size is reduced, and fewer messages are transmitted. It can be seen from Table 8 that the energy costs for SL_GAS are consistently lower than those of SGMS, regardless of whether the device group consists of 10 nodes or 50 nodes. As a result of this efficiency, 6 LoWPAN networks can extend the life of battery-powered devices, as extending their life is a major concern. A significant benefit of SL_GAS is that it reduces the amount of data transmitted during communication exchanges, thereby reducing energy consumption. This advantage results in longer battery life for 6 LoWPAN nodes and reduces the need to replace or recharge batteries frequently, which is particularly important when deployments are remote or difficult to reach. As a whole, SL_GAS has significant advantages over SGMS, SEIP, and CGM6 across various dimensions, including security, computational overhead, signaling costs, and transmission overhead. As a result of these advantages, SL_GAS is an ideal solution for secure, efficient, and sustainable group-based handovers in 6 LoWPAN environments that require both security and resource optimization. For small networks (\(n=10\)), SL_GAS consumes 6.59% less energy than SGMS. For larger networks (\(n=50\)), SL_GAS saves 7.07% energy, proving its efficiency in high-scale deployments.
## 3 The Proposed Vehicle Dataset
### The Advantages of the Proposed Dataset  
The following list presents the advantages of the proposed VehDS-LA dataset compared to related dataset generation works:  
* Generating the database does not require a long time, as in the related works, where it took days and months.
* The accuracy of the positions of vehicle samples which were produced based on Google Maps and the MATLAB simulator. It was verified that the samples are located on the LA streets without any deviation.
* There is no need to install special equipment and devices in the vehicle, such as a GPS receiver, small computer, or smartphone.
* The number of dataset samples is large and each sample has four features, which are the most important features of a vehicle for traffic simulation purposes.
* The method of generating the proposed VehDS-LA dataset introduces a general mechanism that can be followed in generating new databases in any region of the world on the basis of Google Maps.  
In fact, the VehDS-LA dataset is based on the current state of the selected streets of Los Angeles city. After a few years, the database may need to be updated according to future street-related information.
## 4 Discussion
Regarding the urban sound environment, the results for conversational noise (\"human speech-like noise\" as babble noise) and brown noise were comparable with those of previous studies. It has been shown that high-intensity white noise of 75 dB [4] and 90 dB [5] impairs creative task performance compared with a no noise condition. Furthermore, it has been reported that intermittent noise of 85 dB reduces creativity compared with continuous pink noise as measured in a poetry writing task [6]. By contrast, highly creative participants were shown to exhibit greater creativity in other tasks when exposed to white noise of 80 dB compared with 60 or 100 dB [7]. A previous study using more natural ambient noise reported that noise of 70 dB improved the performance of creative tasks compared with 50 dB [8]. In the present study, conversational noise (babble noise) exceeded a rating of \"3: moderately good conversation\", even with a SNR of +9 dB (\(L_{\text{Aeq}}\) was approximately 49 dB under this condition). Considering this \(L_{\text{Aeq}}\) level, the noise levels in the present study were 50 dB or less, suggesting that \"liveliness\" at these noise levels greatly contributes to \"good conversation\" in knowledge creative activities with multiple people (See Table 4 and Figure 4).
## 2 Unbiased Minimum Variance Receding-Horizon Fixed-Lag Smoother in Batch Form  
Consider the discrete time-varying state-space model as  
\[x_{k+1} = A_{k}x_{k}+G_{k}w_{k}, \tag{1}\] \[y_{k} = C_{k}x_{k}+v_{k}, \tag{2}\]  
where \(x_{k}\) and \(y_{k}\) are the state vector and output vector, respectively. The process noise vector \(w_{k}\) and output noise vector \(v_{k}\) are assumed as zero-mean white Gaussian noises, where the covariance matrices are denoted as \(Q_{k}\) and \(R_{k}\), respectively. It is assumed that the process and output noises are mutually uncorrelated with each other and are also uncorrelated with the initial state \(x_{k_{0}}\). The pair \((A_{k},C_{k})\) is also assumed to be observable. For the system model (1) and (2), the conventional RHF smoother for estimating the lag state \(x_{k-h}\) is expressed as a linear function of the finite measurements on the overall horizon \([k-N\;\;k-1]\) as follows [20]:  
\[\hat{x}_{k-h|k-1}=\sum_{i=k-N}^{k-1}H_{i}y_{i}=H_{k-h}Y_{k-N,k1}, \tag{3}\]  
where \(\hat{x}_{k-h|k-1}\) is the overall estimate, \(N\) is receding horizon length, \(H_{k-h}\) and \(H_{i}\) are the gain matrix and its \(i\)-th row vector of RHF smoother, and \(Y_{k-N,k-1}\) is the finite number of measurements expressed as follows:  
\[Y_{k-i,k-j} = \Big{[}\;\;y_{k-i}^{T}\;\;\;y_{ki+1}^{T}\;\;\;\cdots\;\;\;y_{k-j-1 }^{T}\;\;\;y_{k-j}^{T}\;\;\Big{]}^{T}, \tag{4}\]  
respectively. To reduce the computational complexity and memory size, the proposed UMVRHF smoother in this study is designed to be a weighted linear combination of two optimal and independent local RH estimators as  
\[\hat{x}_{k-h|k-1} = U_{1,k-h}\hat{x}_{1,k-h|k-1}+U_{2,k-h}\hat{x}_{2,k-h|k-1}, \tag{5}\]  
where \(\hat{x}_{1,k-h|k-1}\) and \(\hat{x}_{2,k-h|k1}\) are defined as the local unbiased minimum variance estimates on the sub-horizons \([k-N\;\;k-h-1]\) and \([k-h\;\;k-1]\), and \(U_{1,k-h}\) and \(U_{2,k-h}\) are constant weighting matrices for each local estimate, respectively. Because two local RH estimators are implemented independently for the two separated subsets of measurements, their estimates can be combined in an optimal manner using [PERSON]'s theorem. By using [PERSON]'s theorem, constant weighting matrices \(U_{1}\) and \(U_{2}\) can be determined from the following mean-squared criterion [23]:  
\[\min_{U_{1,k-h},U_{2,k-h}}\mathbb{E}\Big{[}(x_{k-h}-\hat{x}_{k-h})(x_{k-h}- \hat{x}_{k-h})^{T}\Big{]}, \tag{6}\]  
with unbiased constraints  
\[U_{1,k-h}+U_{2,k-h}=I. \tag{7}\]  
From (7), the overall estimate (5) can be rewritten as  
\[\hat{x}_{k-h|k-1} = U_{1,k-h}\hat{x}_{1,k-h|k-1}+(I-U_{1,k-h})\hat{x}_{2,k-h|k-1}. \tag{8}\]  
Then, the overall state error covariance matrix \(P_{k-h}\) can be represented as  
\[P_{k-h} = \mathbb{E}\Big{[}(x_{k-h}-\hat{x}_{k-h})(x_{k-h}-\hat{x}_{k-h})  
\[U_{1,k-h} = P_{2,k-h}(P_{1,k-h}+P_{2,k-h})^{-1},\] (11) \[U_{2,k-h} = P_{1,k-h}(P_{1,k-h}+P_{2,k-h})^{-1},\] (12) where the inverse of \((P_{1,k-h}+P_{2,k-h})\) always exists, since \(P_{1,k-h}\) and \(P_{2,k-h}\) are positive definite.
# Decision-Making Approach Based on Neutrosophic Rough Information
## 3 Applications to Decision-Making
### Online Reviews and Ratings  
Customer reviews are increasingly available online for a wide range of products and services. As customers search online for product information and to evaluate product alternatives, they often have access to dozens or hundreds of product reviews from other customers. These reviews are very helpful in product selection. However, only considering the good reviews about a product is not very helpful in decision-making. The customer should keep in mind bad and neutral reviews as well. We use percentages of good reviews, bad reviews and neutral reviews of a product as truth membership, false membership and indeterminacy respectively.  
Mrs. [PERSON] wants to purchase a refrigerator. For this purpose she visits web sites of different refrigerator companies. The refrigerator companies and their ratings by other customers are shown in Table 1.  
Here \(X^{*}=\{\text{Pel}(\text{P})\text{,Da Wallace}(\text{D})\text{,Haier}(\text{H})\text{,Waves}( \text{W})\text{,Orient}(\text{O})\}\) and the neutrosophic set on \(X^{*}\) according to the reviews will be \(X=\{(P,0.45,0.29,0.37),(D,0.52,0.25,0.49),(H,0.51,0.43,0.45),\\ (W,0.47,0.41,0.38)(O,0.51,0.35,0.48)\}\). The neutrosophic tolerance relation on \(X^{*}\) is given below  
\begin{table}
\begin{tabular}{c|c c c} \hline \hline \(X^{*}\) & **Good Reviews** & **Neutral** & **Bad Reviews** \\ \hline PEL & 45\% & 29\% & 37\% \\ Dawlance & 52\% & 25\% & 49\% \\ Hiaer & 51\% & 43\% & 45\% \\ Waves & 47\% & 41\% & 38\% \\ Orient & 51\% & 35\% & 48\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Companies and their ratings.  
\[T(v_{i}v_{j}) =\max_{v_{j}\in X^{*}}T_{\Sigma Y}(v_{i}v_{j})\times\max_{v_{j}\in X ^{*}}T_{\Sigma Y}(v_{i}v_{j}),\] \[I(v_{i}v_{j}) =\max_{v_{j}\in X^{*}}I_{\Sigma Y}(v_{i}v_{j})\times\max_{v_{j}\in X ^{*}}I_{\Sigma Y}(v_{i}v_{j}),\] \[F(v_{i}v_{j}) =\min_{v_{j}\in X^{*}}F_{\Sigma Y}(v_{i}v_{j})\times\min_{v_{j}\in X ^{*}}F_{\Sigma Y}(v_{i}v_{j}).\]  
By direct calculations we have  
\[S(P)=0.167,S(D)=0.156,S(H)=0.268,S(W)=0.272,S(O)=0.155.\]  
From the above calculations it is clear that Waves is the best company for refrigerator.
# LoRaWAN Physical Layer-Based Attacks and Countermeasures, A Review
## 3 Vulnerabilities
### Jamming Attacks
In order to achieve consistent jammer performance, the authors noted that the inherent latency in the setup should be stabilized by properly configuring the GNU-Radio scheduler. Alternatively, off-the-shelf LoRa transceivers can be also configured to jam selected LoRaWAN packets. In [12], the popular SX1276 transceiver was reconfigured to perform byte-by-byte LoRaWAN packet sniffing and, subsequently, to transmit the LoRa-jamming waveforms. Since SX1276 allowsfor direct inspection of reception FIFO-buffer contents, the jammer can be triggered once a desired stream of bytes is detected. Hence, the latency between detection of, e.g., victim device address and the transmission of a jamming packet, can also be minimized. In the same vein, the channel activity-detection of LoRa modems can be employed to perform triggered jamming, where jamming waveforms are sent as soon as an arbitrary LoRaWAN communication is detected.
# Energy Cost Minimization with Hybrid Energy Storage System Using Optimization Algorithm
## 1 Introduction
We approach this by enclosing the different battery technologies in a single system, a sort of energy storage black box from outside, which automatically creates the best possible environment and usage patterns for the contained technologies. In order to make the hybrid battery work, a modular EMS is created that consists of two parts: an optimizer that uses predictions and an online controller that copes with deviations from the predictions. The optimizer needs the prediction of both the energy usage and the energy production in order to determine the charging schedule for the next time frame. However, predictions are always prone to uncertainty, so an online controller considers the predictions and adjusts them for the reality of the moment. In this work, the focus is on a method to optimally determine the operation of the HESS applied to historical data rather than predictions. The reason for this is that it will show us the possibilities and impacts of the batteries, not only for peak-shaving purposes but also for economic purposes. In particular, that latter aspect is of importance: showing the economic viability of the hybrid battery on historical data allows for verification against the true results and can be used as justification for the hybrid battery concept. The goal of the presented work is to develop a control strategy for an EMS that schedules HESS with the aim of minimizing energy cost. The original contribution of this work is:  
* Design and implementation of a techno-economical model of a HESS operating in a microgrid;
* The creation of a model that includes two battery types with their respective round trip efficiencies and costs of depreciation related to battery degradation during cycling;
* The design of an optimization method that calculates a schedule for each battery in a 24 h window;* The validation and comparative analysis of a proposed method with a benchmark approach based on real life energy usage and production data of a research centre in Poland;
* The novelty of the proposed method is the considering of the multi-battery setup and the inclusion of battery depreciation cost related to its degradation, so that total operating costs are minimized.
# A Scalable Framework for Sensor Data Ingestion and Real-Time Processing in Cloud Manufacturing
## 5 Results
### Data Processing and Analytic Systems
* CPU and Memory Usage: Both systems demonstrate efficient resource utilization, with the average CPU and memory usage of Spark Streaming increasing marginally during operation. This slight increase signifies the ability of Spark to manage resources adaptively in real time, ensuring consistent performance, even under varying workloads. * Environmental Metrics: The nearly identical values for Average Temperature and Average Relative Humidity across both systems validate the integrity of data processing under similar conditions. However, the real-time aggregation of Spark Streaming offers a temporal advantage for monitoring environmental conditions in CM applications. The superior performance of Spark Streaming was further bolstered by several innovations embedded in Algorithm A5. Real-time system monitoring enables dynamic adjustments to resource constraints, whereas cumulative tracking of metrics, such as latency and processing times, facilitates iterative process optimization. These features make Spark Streaming an indispensable tool for scenarios that demand continuous, low-latency data processing, operational efficiency, and rapid decision making in CM environments. Conversely, the high throughput of Hadoop Batch Processing remains valuable for prioritizing large-scale data aggregation and offline analytics. The inclusion of enhancements such as batch size optimization and metric logging ensures that Hadoop remains relevant for traditional high-volume data processing needs, albeit at the expense of real-time responsiveness. By integrating these insights, we emphasize the necessity of balancing throughput and latency metrics when selecting data-processing systems for CM applications. The ability of Spark Streaming to deliver low-latency, real-time insights complements the high-throughput capabilities of Hadoop Batch Processing, underscoring its role in optimizing the performance and scalability of our proposed nine-layer CM framework.
## 4 Conclusions  
* The sulfur-doped, nonhomogeneous catalyst CoSN@C was produced using a one-pot synthesis followed by carbonization, and it was employed for the degradation of RhB;
* Increasing the temperature can greatly increase the rate of RhB degradation; increasing the pH value will significantly decrease the rate of RhB degradation; the best results were achieved with 20 mg L\({}^{-1}\) RhB, CoSN@C 0.05 g L\({}^{-1}\), and 1 mM PMS;
* The results of radical capture experiments and EPR tests evidenced that a large amount of SO\({}_{4}\).- and HO- radicals adsorbed on the catalyst surface and a certain amount of \({}^{1}\)O\({}_{2}\) were generated in the reaction, and that the radical and non-radical pathways collaborated to degrade RhB rapidly and efficiently;
* After four cycles, the CoSN@C catalyst maintained its high catalytic activity and structural stability, and the RhB degradation rate remained at 88.9%.  
Conceptualization, C.Z.; methodology, C.Z. and Y.C.; software, C.Z.; validation, C.Z., Y.C. and Q.M.; formal analysis, C.Z.; resources, H.C. and J.F.; data curation, C.Z.; writing--original draft preparation, C.Z.; writing--review and editing, Y.C. and Q.M.; visualization, C.Z.; supervision, Q.M., Y.C., H.C. and J.F.; project administration, J.F.; funding acquisition, J.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by State Key Laboratory of Pollution Control and Resource Reuse Foundation, grant number No. PCRRK 21003.  
Not applicable.  
The authors declare no conflict of interest.
# Acoustic Characterization of Some Steel Industry Waste Materials
## 2 Materials and Methods
### Porous Materials
The Johnson-Champoux-Allard [35,36] and the Champoux-Stinson [37] models fall into the semi-phenomenological/microstructural group and involve five non-acoustic parameters: porosity, airflow resistivity and tortuosity are common to both, whereas the JCA model uses thermal and viscous characteristic lengths, and the Champoux-Stinson model considers viscous and thermal shape factors. Subsequent implementations of the JCA model, such as the six-parameter Johnson-Champoux-Allard-Lafarge (JCAI) model or the eight-parameter model of Johnson-Champoux -Allard-Pride-Lafarge (JCAPL), involve more parameter, such as viscous and thermal tortuosities and permeabilities. Compared to the JCA model, they provide more precision at low frequencies [38]. In general, the more sophisticated models require more parameters and have better performances. Nevertheless, as a counterpart, they are more complex and demanding. All the aforementioned models require physical techniques to measure the non-acoustic parameters. Some of them involve expensive set-ups and even complex or destructive tests. Recently, multiscale analyses have been developed to compute non-acoustic parameters by means of numerical simulations at the microstructural level [21,22]. The multiscale approach, which establishes micro-macro relationships, bypasses the difficulty of direct measurements by developing specific finite-element analyses. Each model has its application field, related to the type of material its development is based on, and respective limitations and advantages. More details about these aspects and model comparisons can be found in [21,22,25,27,28,39,40,41,42,43,44,45,46,47,48,49,40,41]. In this work, the five-parameter JCA model, better described in Section 2.2, was selected to perform the investigation: it is one of the most known generalized models, suitable for the accurate description of the wide-band sound propagation in porous materials. It is a robust model as it is applicable to the random geometry of porous materials, it allows rapid calculation and the five parameters, having a physical meaning, and can be directly measured by experiments. The JCA model, coupled with the four-microphone impedance tube and inversion methods, results to be a well-established and fast technique to investigate the intrinsic properties of a material, thus being a valuable alternative whenever direct measurements are not available.