esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Methodology
### Experimental Setup
#### 6.1.1 Scenario 1  
This scenario was defined to perform a simulation of PER and BER and its evolution depending on the distance between transmitter and receiver. For these tests, 14 nodes were set, all of them placed along a straight line (Figure 7). The first node (Node 0) was placed on the origin of this virtual line, and the other 13 were uniformly placed between 19.5 and 21.5 m. No nodes were placed beyond 21.5 because taking into account Equation (6) and the propagation model of Equation (3), the theoretical PLR would be close to 1 beyond that point.  
All the nodes were assumed to use 2-FSK modems. Node 0 was configured to use 33 dBm of transmitting power and 116 bytes of packet size, whose 16 first bytes were STANAG 5066 overhead.  
Figure 6: Markov model associated to NONARQ messages.  
Figure 7: Topology of Scenario 1. Node 0 was configured to broadcast messages, and the rest of the nodes were placed on a straight line.  
All application packets carry exactly 100 bytes of payload, so no fragmentation was needed and packets were always of the same length.  
As aforementioned, the channel propagation model and the node transmission power leads to find the PER curve interest range between 20.0 and 21.0 m. Therefore, most nodes were placed among these distances, except for the two boundary nodes. The node-placement scheme can be observed in Table 3.
## 4 Results
### Comparison with an Existing Sizing Problem for Energy Storage  
For validation purposes, the proposed mathematical formulation has been compared with an existing BESS sizing algorithm for demand charge reduction [15]. Since the model of [15] only counts for DC, the energy charge and demand charge terms have been considered in the objective function of the proposed formulation, while the rest of the charges like GAC and revenues (DR and EPA) are not considered in order to maintain a consistent comparison. The sizing algorithm of [15] has been coded and tested on the data set of the considered Ontario class-A customer presented in Figure 7. The two methods are tested based on the base case assumption given in Table 1. The demand charge cost is adopted from [15] to be $13/KW. The obtained results have been compared and tabulated as follows  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Before** & **After** \\ \hline Energy cost per year & \$205,004.01 & \$296,505.21 \\ Demand Charge cost per year & \$42,470.00 & \$40,375.86 \\ Global Adjustment cost per year & \$860,176.24 & 0.0 \\ Capital cost per year & - & \$363,604.81 \\ Revenue of Energy Price & - & \$87,428.57 \\ Arbitrage per year & - & \$35,164.28 \\ Demand Response revenue per & - & \$80,176.24 \\ year & \$1,107,650.25 & \$700,485.88 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between the bill components before and after the battery deployment.  
Figure 6: Comparison between the normalized results of the greedy algorithm and optimization solver.  
in Table 3. The comparison shows that the results are very close, demonstrating that the proposed method is accurate. It is important to note that the major benefit of the proposed method is counting for multi-revenue streams; energy arbitrage and demand response, as well as the savings from utility charges; demand charges and global adjustment charges, as reported in the previous subsection.  
Different sensitivity cases are conducted to check the performance and the effectiveness of the developed battery deployment model.
# Information Security Applications in Smart Cities: A Bibliometric Analysis of Emerging Research
## 4 Findings and Discussion
### Most Active and Cited Journals  
Journals play an essential role in the development of a research area. Table 4 reports the most prominent journals in the number of publications on cybersecurity in the Scopus database and their impact factor in 2022.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Subject Areas** & **Source** & **Impact Factor 2022** & **\# of Article** \\ \hline Computers and Security & 5.6 & 262 \\ \hline Future Generation Computer Systems & 7.5 & 712 \\ \hline IEEE Access & 3.9 & 139 \\ \hline IET Information Security & 1.4 & 23 \\ \hline Computer Communications & 6 & 323 \\ \hline IEEE Security and Privacy & 1.9 & 54 \\ \hline Computers in Human Behavior & 9.9 & 60 \\ \hline Information Technology and People & 4.4 & 63 \\ \hline International Journal of Communication Systems & 2.1 & 256 \\ \hline International Journal of Software & & \\ Engineering and Knowledge & 0.9 & 12 \\ Engineering & & \\ \hline Computer Law and Security Review & 2.9 & 164 \\ \hline Technological Forecasting and Social & 12 & 346 \\ Change & 8.3 & 13 \\ \hline Technology in Society & 9.2 & 145 \\ \hline Journal of Intellectual Capital & 6 & 64 \\ \hline Behavior and Information Technology & 3.7 & 88 \\ \hline International Journal of Human & & \\ Computer Studies & 5.4 & 27 \\ \hline Business Horizons & 7.4 & 58 \\ \hline International Journal of Accounting & 4.6 & 12 \\ Information Systems & 21 & 130 \\ \hline \multirow{2}{*}{Business, Management and Accounting} & Government Information Quarterly & 7.8 & 157 \\ \cline{2-4}  & Information Technology for & 4.261 & 47 \\ \cline{2-4}  & Development & 6.363 & 33 \\ \cline{2-4}  & European Journal of Operational & & \\ Research & 6.363 & 33 \\ \cline{2-4}  & Information Sciences & 8.1 & 131 \\ \hline \multirow{3}{*}{Energy} & Energies & 3.2 & 195 \\ \cline{2-4}  & Sustainability & 3.9 & 76 \\ \cline{1-1} \cline{2-4}  & Energy Research and Social Science & 6.7 & 151 \\ \cline{1-1} \cline{2-4}  & Journal of Cleaner Production & 11.1 & 465 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Journals and Impact Factors for information security and smart cities related literature.  
It is worth mentioning that the top journals showing that the topic of information security and smart cities has attracted the attention of researchers from different fields. Because smart city is a multidisciplinary field, scholars often struggle to figure out the most appropriate outlet for their research that would have a significant impact. The information reported in this table indicates this willingness to publish in each specific area.
## 3 Results and Discussion
### Effect of Spacer Length  
#### 3.3.1 Heat Transfer  
The effects of the spacer length (\(S\) = 40, 51.77, and 62.5 mm) on the heat transfer enhancement are illustrated in Figure 5a,b. Obviously, the value of the heat transfer rate increases with the decrease of spacer length. It can be seen that the BTs with a spacer length of \(S\) = 40 mm have the highest heat transfer rate; enhanced by about 1.44-2.01 times that of the plain tube. When \(S\) = 51.77 and 62.5 mm, the use of BTs improves the heat transfer performance by approximately 1.34-1.92 and 1.26-1.84 times that of the plain tube. Furthermore, it is also found that the heat transfer enhancement with BTs is intensified with the increase of the Reynolds number. The reason for this result lies in the fact that the BT with a smaller spacer length induces stronger turbulent flow intensity, resulting in better fluid mixing and boundary disturbance. As shown in Figure 7, in the region between the balls, vortices resulting from the occurrence of reverse flows appear. It is also noted that the maximum velocity appears near the wall region instead of the central region. It results in intensification of mixing and improves the heat energy distribution in the whole pipe volume. Comparatively, the BTs with the smallest spacer length (\(S\) = 40 mm) improve the heat transfer by 7% and 16% higher than the cases of \(S\) = 51.77 and 62.5 mm.  
Figure 6: Friction factor versus Reynolds number: (**a**) \(f\); and (**b**) \(f\)/\(f_{\rm p}\).
# Device Management and Data Transport in IoT Networks Based on Visible Light Communication
## 4 Experimentation and Discussion
### Experimentation Results
#### 4.2.2 Device Monitoring  
As mentioned earlier, we perform the device monitoring operation periodically. Figure 18 shows the packet capturing results during device monitoring. Every node has a local timer to report the current mapping status. In the figure, VR sends the reports to AA for every 10 s (see packets of 16, 19, and 36), and AA reports the current mapping table to PS for every 30 s (in packets of 14 and 41).
# New Remote Care Models in Patients with Spinal Cord Injury: A Systematic Review of the Literature
## 3 Results
### General Characteristics of Included Studies  
Four RCTs, involving a total population of 270 SCI adults, were included in the present review; based on the overall population, 191 patients were male (70.7%) and 79 were female (29.3%). The minimum age was 18 years and the maximum was 75 years. Studies were conducted in three different countries: China [36; 37], Norway [34], and the USA [35]. In three cases [34; 35; 37], the RCT was preceded by a pilot study on a limited sample [34; 37] or involving a population other than SCI patients [35].  
All samples [34; 35; 36; 37] involved patients over 18 years old, with a diagnosis of stabilized SCI and indication for home care provided by nurses as part of a multidisciplinary team. Criteria to enrol SCI patients [34; 35; 36; 37] (complete or incomplete, of traumatic or non-traumatic origin) were to conform to the International Standards for Neurological Classification of Spinal Cord Injury by the American Spinal Injury Association (ASIA). A precondition for eligibility in all considered studies [34; 35; 36; 37] was the presence and ability to use electronic devices (cell phone, computer, tablet) with internet access at home.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline
**Study** & **Criteria** & **Criteria** & **Criteria** & **Criteria** & **Criteria** & **Criteria** & **Criteria** & **Criteria** & **Criteria** & **Criteria** & **Criteria** & **Criteria** & **Score** \\  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **Score** \\ \hline [PERSON] et al. (2022) [34] & Y & Y & N & Y & N & N & Y & Y & Y & Y & Y & 7/10 \\ [PERSON] et al. & & Y & Y & Y & N & N & Y & Y & Y & Y & Y & 8/10 \\ [PERSON] et al. (2021) [36] & Y & Y & N & Y & N & N & N & Y & Y & Y & Y & 6/10 \\ [PERSON] et al. (2021) [37] & Y & Y & N & Y & N & N & Y & Y & Y & Y & Y & 7/10 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quality appraisal: PEDRO Scale.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Study** & **Randomisation** & **Blinding** & **Withdrawals and Dropouts** & **Total Score** \\ \hline [PERSON] et al. (2022) [34] & \(1+1\) & 0 & 1 & \(3/5\) \\ [PERSON] et al. (2019) [35] & \(1+1\) & 1 & 1 & \(4/5\) \\ [PERSON] et al. (2021) [36] & \(1+1\) & 0 & 1 & \(3/5\) \\ [PERSON] et al. (2021) [36] & \(1+1\) & 1 & 1 & \(1\) & \(4/5\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quality appraisal—Jadad Scale.
## 2 QPN Model  
### QPNs  
QPNs can be seen as an extension of stochastic PNs. The main idea of QPNs is to add queueing and timing aspects to the net places and timed transitions. QPNs allow queues to be integrated into the places of a PN. Places (QPN) are divided into two types: ordinary and queued. A place that contains an integrated queue is called a queueing place. Queueing places contain two components: the queue and the depository of tokens that complete their service at a queue. Tokens are used to model requests and enter the queueing place through the firing of input transitions. The different possibilities of firing a transition are referred to as modes. Modes have firing weights (probabilities) that influence which mode is chosen. Tokens are inserted into a queueing place according to the queue's scheduling strategy. Timed queueing places have service distributions. In the queueing place, if a server is available, arriving tokens are first serviced. If a server is not available, the tokens have to wait in the waiting area. The time it takes to service a token is defined through a statistical distribution. After service, the tokens are immediately moved to a depository, where they become available for output transitions. When a transition fires, tokens are removed from some places and new tokens are created at others. Figure 1 shows the notation used for ordinary places and queueing places. For more details, refer to [5].  
To analyze any QPN it is necessary to determine:  
* Set of places;
* Set of queueing places with scheduling strategy;
* Set of immediate or timed transitions (QPNs allow transitions to fire in different modes (transition colors));
* Initial marking (number of tokens);
* Incidence function (routing probability) is defined per color/mode basis;
* Arcs with firing weights as relative firing frequency of the mode;
* Token color function specifies the types of tokens that can reside in the place and allows transitions to fire in different modes.  
Figure 1: QPN notation.  
The color function assigns a set of colors to each transition. Incidence functions are defined on a per color basis.  
QPN model was used to check the behavior of the token and it was developed using the QPME tool [22].
# Vibration Parameters Identification of Rotating Blades Based on Blade Tip-Timing Sensor Waveforms
## 4 Identification Accuracy Analysis of Waveform Mapping Method
### Influence of Number of Sensors on Identification Accuracy of Waveform Mapping Method  
The relative error of amplitude, relative error of constant offset and relative error of initial phase identified by the single-sensor, double-sensor and three-sensor waveform matching methods with different SNR are shown in Figures 10-12, respectively. It can be found from Figures 10-12 that when the SNR is constant, the initial excitation order  
Figure 9: Influence of excitation order on the average error of three-sensor waveform identification under different SNR: (**a**) SNR 10 db; (**b**) SNR 20 db; (**c**) SNR 30 db; (**d**) SNR 40 db; (**e**) SNR 50 db.  
of accurate recognition gradually decreases with the increase of the number of sensors, and the relative error of amplitude, relative error of constant offset and relative error of initial phase are all relatively small; that is, the recognition accuracy is high. As the SNR increases, the accurately identified initial excitation order is smaller. For a certain SNR, with the increase of excitation order, the relative error of amplitude, relative error of constant offset and relative error of initial phase are all smaller; that is, the identification accuracy of vibration amplitude, constant offset and initial phase is higher. When the excitation order is the same, the larger the SNR, the smaller relative error of amplitude, relative error of constant offset and relative error of initial phase.  
Figure 10: Relative error of amplitude identification by waveform method under different SNR: (**a**) single-sensor; (**b**) double-sensor; (**c**) three-sensor.
## 6 Implementation and Case Study
### Datasets  
#### 6.3.1 DAWN Dataset  
The DAWN dataset [11] is a comprehensive resource designed to facilitate research and development in 5G and emerging 6G network environments. It provides detailed traffic slices covering enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (URLLC), and massive Machine-Type Communication (mMTC), reflecting the broad range of use cases and performance requirements expected in next-generation networks. Beyond standard traffic, the DAWN dataset also introduces adversarial attack scenarios to simulate real-world threats and evaluate system resilience. Specifically, it includes instances of data poisoning, where 15% of the samples are deliberately corrupted to deceive ML models, as well as signaling storms, characterized by an excessive number of control requests surpassing 100 requests per second per xApp, aiming to overload and destabilize the system. A distinctive feature of the DAWN dataset is its temporal granularity, with traffic and event features recorded at a fine 1-millisecond resolution. This level of detail enables researchers to capture rapid variations in network behavior, allowing for the development and testing of advanced algorithms that require precise temporal sensitivity. For Table 6, the DAWN dataset's adversarial traces were augmented with synthetic attack vectors specifically designed to target the E2 interface and xApp APIs. The injected poisoned samples included Gaussian noise with a standard deviation of \(\sigma=0.5\), as well as label-flipped data with a flipping rate of 30%, reflecting realistic adversarial manipulation scenarios. Additionally, temporal features such as Physical Resource Block (PRB) usage and Reference Signal Received Power (RSRP) were sampled at a fine-grained 1 ms resolution to ensure the accurate capture of cross-layer interactions between network, application, and control planes during the simulated attacks. For Table 6, the DAWN dataset's adversarial traces were augmented with synthetic attack vectors specifically designed to target the E2 interface and xApp APIs. The injected poisoned samples included Gaussian noise with a standard deviation of \(\sigma=0.5\), as well as label-flipped data with a flipping rate of 30%, reflecting realistic adversarial manipulation scenarios. Additionally,temporal features such as Physical Resource Block (PRB) usage and Reference Signal Received Power (RSRP) were sampled at a fine-grained 1 ms resolution to ensure the accurate capture of cross-layer interactions between network, application, and control planes during the simulated attacks.
## 7 Discussion and Conclusions
### Practical Aspects to Decision Support Development in Industry
** It is important to emphasize that the methods presented in this article cannot replace the domain knowledge; the final decision is still made by the human experts, and the strength of the proposed tool is in its capability to process large amounts of data and highlight the information in it. It is not always possible to adjust the harmful parameter to a more favorable direction, because of the product property requirements or the overall optimization of the process; the settings that guarantee high surface quality may not guarantee other quality properties that the product has. In addition, some of the parameters describe the status of the machines; the wearing of the rolls, for example. Then, the re-scheduling of the production is the only way to optimize the parameters and to find the best process conditions for the most demanding products. **Role of the user. ** In order to gain a large group of potential users, the tool should contain different types visualizations for different user's needs. In a steel plant, the users of the decision support tools may have different needs based on their duties. In product design and process planning, the process engineers may want to test different settings and thus learn about the whole process, while the process operators have a different objective with a very short reaction time. In this case, the detailed visualizations of variable impacts and dependencies can be observed with time, while the simple presentation of possibleactions for quality improvement works, when selecting the correct process settings for a product rapidly. **Uncertainty of models. ** When in actual use, if data are not complete, the user should be informed about the uncertainty of prediction when the system is extrapolating. The other possibility would be to restrict the usage of the tool to known products only. In the case of this study, the data were not collected for research purposes and were not very representative. The models could easily be improved by systematically collecting data from production.
## 1 Introduction
### Contributions  
This work contributes to this field as follows:  
1. We present a method for transforming time domain PQD signals to time-frequency domain images based on CWT. This transformation allows deep models to more effectively identify and extract high-level disturbance features.
2. We propose an ensemble classification framework based on transfer learning with DCNN models to classify PQDs using time-frequency images. The framework includes four pre-trained DCNN models, ResNet-50, VGG-16, AlexNet, and SqueezeNet, which were selected after rigorous experimental evaluation. We evaluate their performance across a spectrum of sixteen different PQD classes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**Publication**} & \multicolumn{2}{c}{**Methodology**} & \multirow{2}{*}{**No. of PQD**} & \multicolumn{2}{c}{**Features**} \\ \cline{3-4}  & & & **Ensemble** & **Unified Model** & **Classes** & **Time** & **Frequency** \\  & & & **Approach** & **Approach** & **Domain** & **Domain** \\ \hline
[12] & 2021 & \(\times\) & DWT, MLP, SVM & 9 & \(\surd\) & \(\times\) \\
[30] & 2021 & DWT, LR, NB, DT, & \(\times\) & 9 & \(\surd\) & \(\times\) \\
[33] & 2021 & \(\times\) & Hybrid CNN & 13 & \(\surd\) & \(\surd\) \\
[24] & 2022 & Bagging-LSTM & \(\times\) & 15 & \(\surd\) & \(\times\) \\
[28] & 2022 & \(\times\) & CNN & 3 & \(\surd\) & \(\surd\) \\
[34] & 2023 & \(\times\) & CNN-LSTM & 14 & \(\surd\) & \(\times\) \\
[29] & 2023 & \(\times\) & S-transform- CNN & 16 & \(\surd\) & \(\times\) \\
[26] & 2023 & \(\times\) & HT-CNN & 16 & \(\surd\) & \(\times\) \\
[35] & 2023 & \(\times\) & CWT-CNN & 7 & \(\surd\) & \(\times\) \\ \hline \hline \end{tabular}  
* Here, DWT, LR, NB, DT, LSTM, MLP, SVM, CNN, HT, and CWT are acronyms of the discrete wavelet transform, linear regression, naive Bayes, discrete time, long short-term memory, multi-layer perceptron, support vector machine, convolution neural network, heterogenous, and continuous wavelet transform, respectively.  
\end{table}
Table 1: Summary of recent studies related to PQD classification 1.  
3. The proposed ensemble approach uses the voting approach to improve the accuracy and generalization capabilities of individual classifiers. This method aggregates predictions from multiple classifiers using a voting scheme.  
The paper is ordered as follows: the proposed approach is given in Section 2. The experiments along with the results are discussed in Section 3, while the study is concluded and future work is proposed in Section 4.
# Fabric Defect Detection Using L0 Gradient Minimization and Fuzzy C-Means
## 4 Experimental Results and Discussion
### Quantitative Comparison
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Condition**} & \multicolumn{3}{c}{**Ours**} & \multicolumn{3}{c}{**[PERSON] [5]**} & \multicolumn{3}{c}{**[PERSON] [6]**} \\ \cline{2-9}  & **ACC** & **IOU** & **ACC** & **IOU** & **ACC** & **IOU** \\ \hline Normal & 0.9673 & 0.7575 & 0.9153 & 0.7707 & 0.9349 & 0.6599 \\ \hline \multirow{3}{*}{SNR} & 20 dB & 0.9489 & 0.6512 & 0.8346 & 0.7015 & 0.8773 & 0.5973 \\  & 15 dB & 0.8976 & 0.6217 & 0.7793 & 0.6584 & 0.8315 & 0.5517 \\  & 10 dB & 0.8532 & 0.5576 & 0.7544 & 0.5542 & 0.7966 & 0.5044 \\ \hline Luminous intensity & +20\% & 0.9245 & 0.6851 & 0.8972 & 0.6645 & 0.8645 & 0.5754 \\  & \(-20\%\) & 0.9097 & 0.6544 & 0.8733 & 0.6497 & 0.8142 & 0.5945 \\ \hline Blur & Radius = 20 & 0.9456 & 0.6956 & 0.8546 & 0.6701 & 0.8599 & 0.5482 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental results with different conditions. The computational comparison result is shown in the Table 2, which reports the average computational time (in seconds) of four methods while processing plain, twill, star-patterned, dot-patterned, box-patterned, striped, and statistical fabrics. As can be seen from Table 2, our method is faster than the automatic band selection method [6] and human inspectors. Even though the EADL method is  
Figure 18: Inspection results of defect inspection methods with different methods on different types of fabric. faster than our method, it has fatal limitations. Their method cannot segment defects, as shown in Figure 17d, and cannot maintain high accuracy, as shown in Figure 18a. From the average calculation speed of the algorithm, the method proposed in this paper takes less time in comparison with the other methods when detecting various types of textured fabrics. In addition, our method performed better than [PERSON] [5] in terms of TPR, FPR, and IOU.