esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Applications of AI in Modern Communication Networks
### Control Experiments to Isolate Key Variables  
To isolate the impact of latency and energy consumption on the performance of AI models, I performed control experiments under similar network conditions. Figure 8 illustrates the trade-offs observed for deep learning, reinforcement learning, federated learning, and traditional rule-based methods.  
As illustrated in Figure 8, deep learning exhibits the lowest latency (10 ms) but requires moderate energy consumption (50 J). Reinforcement learning provides slightly higher latency (12 ms) with the highest energy consumption (60 J), reflecting its computational intensity. Federated learning achieves a balance, with a latency of 15 ms and energy consumption of 45 J. Traditional rule-based methods, while consuming the least energy (30 J), exhibit the highest latency (25 ms), making them less suitable for latency-sensitive applications. These findings underscore the importance of selecting AI models based on specific network requirements, such as real-time responsiveness or energy efficiency.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Load Balancing Method** & **AI-Based Efficiency (\%)** & **Traditional Efficiency (\%)** \\ \hline Static Load Balancing & 65 & 65 \\ AI-based Load Balancing & 90 & 75 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Load Balancing performance comparison.
# Frequency-Based Haze and Rain Removal Network (FHRR-Net) with Deep Convolutional Encoder-Decoder
## 3 Experimental Result
### Comparison of Performance with Combined SOTA Methods (Derain + Dehaze)  
In this section, we conducted an experiment comparing the proposed method with the results of applying the state-of-the-art techniques DerainNet [10] and MSCNN [14] sequentially. The experiment is described in the following, Figure 10, schematic diagram.  
Figure 11 shows the results of an experiment performed as described in Figure 10 schematic diagram. Experiment 2 method result image usually appears to have excessive color enhancements. For example, in the first picture of Figure 11c,d, the color of the tree on the upper left is close to black. In addition, the color of the people in the middle picture  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Methods** & **Average PSNR** & **Average SSIM** \\ \hline DerainNet & \(\mu=18.79\), \(\sigma^{2}=1.58\) & \(\mu=0.84\), \(\sigma^{2}=0.08\) \\ MSCNN & \(\mu=20.01\), \(\sigma^{2}=1.08\) & \(\mu=0.81\), \(\sigma^{2}=0.08\) \\ FHRR-Net & \(\mu=22.02\), \(\sigma^{2}=1.20\) & \(\mu=0.91\), \(\sigma^{2}=0.06\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average PSNR (peak signal-to-noise) and SSIM (structural similarity index) results on synthesized data for state-of-arts methods.  
Figure 9: Visual comparison with the state-of-art methods on real-world road data, (**a**) Inputs, (**b**) DerainNet [10] (**c**) MSCNN [14], (**d**) FHRR-Net. Some images are obtained through Google Images; others are taken directly.  
is also close to black.Person, car, tree, or umbrella in the last picture is shown close to black. As a result, in the Figure 10, experiment 2 and experiment 3 methods excessively improve the color compared to the original image. Hence, this experiment confirm that FHRR-Net improves better than the combination of the two SOTA methods in both removing rain and haze.  
Figure 11: _Cont_.  
Figure 10: Schematic diagram: Comparison experiments with combined SOTA methods and proposed algorithm.
# Deep Learning-Based Spread-Spectrum FGSM for Underwater Communication
## 4 SS-FGSM Performance Analysis
### Energy Efficiency  
In the proposed SS-FGSM, \(\underbrace{log_{2}(M)}_{Data\ Bits}\) bits are transmitted physically, but most of transmitting bits \(\underbrace{2 log_{2}(\mathcal{N})}_{Code\ Bits}+\underbrace{(N_{t}-1)}_{Antenna\ Bits}\) are conveyed in the indices of either transmit active antennas or spreading codes. To calculate the EE of the proposed SS-FGSM scheme, consider each modulated bit requires the energy of \(E_{b}\) to be transmitted over the underwater acoustic channel. In SMTs mapping, part of the required transmitted bits is transmitted in the spatial domain to reduce the power consumed in the communication transmission. Therefore, the percentage of energy-saving \(\eta\) of the SMTs compared to the conventional M-ary modulation schemes for the same number of transmitted bits can be calculated as:  
\[\eta_{SM}=\ \frac{log_{2}(N_{t})}{log_{2}(M)+log_{2}(N_{t})}\%, \tag{26}\]  
\[\eta_{GSM}=\ \frac{\bigg{|}log_{2}\bigg{(}\begin{array}{c}N_{t}\\ N_{u}\end{array}\bigg{)}\bigg{|}}{log_{2}(M)+\bigg{|}log_{2}\bigg{(}\begin{array} []{c}N_{t}\\ N_{u}\end{array}\bigg{)}\bigg{|}}\%, \tag{27}\]  
\[\eta_{QSM}=\ \frac{2 log_{2}(N_{t})}{log_{2}(M)+2 log_{2}(N_{t})}\%, \tag{28}\]  
\[\eta_{FGSM}=\ \frac{(N_{t}-1)}{log_{2}(M)+(N_{t}-1)}\%, \tag{29}\]\[\eta_{EFGSM}=\ \frac{\left|log_{2}\sum_{N_{u}=2}^{N_{t}}\left(\begin{array}{c}N_{t} \\ N_{u}\end{array}\right)\right|}{log_{2}(M)+\ log_{2}\left(\frac{M}{2}\right)+ \ \left|log_{2}\sum_{N_{u}=2}^{N_{t}}\left(\begin{array}{c}N_{t}\\ N_{u}\end{array}\right)\right|}\%, \tag{30}\]  
\[\eta_{GCIM}=\ \frac{2 log_{2}(\mathcal{N})}{log_{2}(M)+2 log_{2}(\mathcal{N})} \%, \tag{31}\]  
\[\eta_{SS-FGSM}=\ \frac{2 log_{2}(\mathcal{N})+(N_{t}-1)}{log_{2}(M)+2\ log_{2}( \mathcal{N})+(N_{t}-1)}\% \tag{32}\]  
Clearly, as the number of bits transmitted by the index of the spatial domain of SMTs increases, the EE of the system will be improved and the transmission consumed power will be reduced.
# A TDD Framework for Automated Monitoring in Internet of Things with Machine Learning +
## 4 Proposed Framework
### Automated Test  
The proposed automated test approach is illustrated in Figure 2. There are three environments considered: the deployment, cloud, and development environment. The user interacts with IoT modules in an deployment environment, such as a smart home. The deployed modules send their status to a MQTT (Message Queuing Telemetry Transport) [26] broker in a continuous fashion. The MQTT broker available in a cloud computing environment, where other services such as storage could also be available. The IoT module status metrics are used to train an unsupervised machine learning model. This trained model is used in an automated test that the developer applies to an IoT module under development. As the model was trained with the deployment scenario data, we advocate that the resulting automated test resemblance to a real-world scenario is bigger than a manual test that the developer could create.  
ESP32 modules with different firmware versions (and even for different applications) send their data frequently to an open MQTT broker using the integrated Wi-Fi communication (1). A script is responsible for data collection from the open MQTT broker deployed in a cloud computing environment (2). The data collected are used to train the K-Means model with another script deployed in Google Colab cloud environment, with the cluster labeling (i.e., good or bad) performed by the developer (3), as detailed in Figure 3. The trained model is imported by a script responsible for inference that receives the real time data from the ESP32 modules and classify them according to the labels provided by the developer in the previous step (4). The inference procedures is detailed in the BPMN diagram of Figure 4. The K-Means clustering algorithm [32] was chosen because of its maturity. An additional motivation is that this algorithm showed good results in other applications, such as image segmentation [33].  
Figure 2: Proposed TDD scheme for IoT with automated tests.  
Figure 3: BPMN diagram of the training phase of the automated tests.  
Figure 4: BPNN diagram of the inference phase of the automated tests.
## 4 Initial Trajectory Generation  
Let us consider a nonlinear system in the form of \(\dot{\mathbf{x}}(t)=\mathbf{f}_{c}(\mathbf{x}(t),\mathbf{u}(t))\) with initial state \(\mathbf{x}(t_{0})=\mathbf{x}_{0}\), where state vector and control inputs are denoted by \(\mathbf{x}\in R^{n_{x}}\) and \(\mathbf{u}\in R^{n_{u}}\), respectively. When generating an initial trajectory (\(\Gamma\)), ensuring dynamic feasibility is a must. In other words, \(\mathbf{x}\) and \(\mathbf{u}\) satisfy the following constraints:  
\[\mathbf{x}\in X\subseteq\mathbf{R}^{n_{x}},\quad\mathbf{u}\in U\subseteq \mathbf{R}^{n_{u}}. \tag{10}\]  
In addition to these constraints, safety constraints should also be imposed after reasoning the environment, to guarantee safety. The environment or configuration space \(C\) can be decomposed into \(C_{obs}\) and \(C_{free}\). Hence, a set of constraints should be introduced for the quadrotor to always be within free space \(\mathbf{x}\in C_{free}=C/\ C_{obs}\). Hence, the initial trajectory generation process has to consider both said types of constraints simultaneously so that the quadrotor would have a smooth flying experience.
## 3 Experimental Section
### Evaluation Metrics  
To evaluate the results of lane line category recognition and measure the performance of the trained model, evaluation metrics are used. There are many types of evaluation metrics, but they all rely on a confusion matrix, as shown in Figure 8.  
The four values in the confusion matrix are primary indicators: TP (true positive) represents actual positive samples correctly predicted as positive; FN (false negative) represents actual positive samples incorrectly predicted as negative; FP (false positive) represents actual negative samples incorrectly predicted as positive; TN (true negative) represents actual negative samples correctly predicted as negative.  
To limit the values of the metrics between 0 and 1, secondary indicators are used to calculate precision and recall. Precision is calculated using the following formula:  
\[Precision=\frac{TP}{TP+FP} \tag{11}\]  
Recall is calculated using the following formula:  
\[Recall=\frac{TP}{TP+FN} \tag{12}\]  
Precision and recall are conflicting measures. When a higher precision is desired, the recall decreases and vice versa. Therefore, this paper uses [EMAIL_ADDRESS] as the evaluation metric for the model. \(AP\) represents the accuracy based on the IoU threshold of positive and negative samples, determining the correctness of sample classification based on confidence levels. The \(AP\) value is calculated by measuring the area under the precision-recall curve (\(P\)-\(R\) curve), effectively balancing the conflicting nature of the two indicators. The calculation formula for \(AP\) is  
\[AP=\int_{0}^{1}P(R)\text{d}R \tag{13}\]  
Figure 8: Confusion matrix.  
Figure 7: Sample size of all types of lane lines.  
_mAP_ represents the average _AP_ value across all classes and is calculated using the following formula:  
\[mAP=\frac{\sum_{i=1}^{k}AP_{i}}{k} \tag{14}\]  
In this paper, [EMAIL_ADDRESS] is used as the evaluation metric to represent the average _AP_ values when the IoU threshold is set to 0.5.  
The complexity of a model can be evaluated by the number of parameters it has, which is mainly related to the overall structure of the model. Parameters represent the total number of trainable parameters in the network model. The larger the number of parameters, the more complex the model structure.  
Real-time performance is an important metric for lane line category recognition in ADAS. Frames per second (FPS) is used to evaluate the real-time capability of the model. A higher FPS indicates faster detection speed and better real-time performance, while a lower FPS indicates poorer real-time performance.
# Reducing PAPR with Low Complexity Filtered NOMA Using Novel Algorithm
###### Abstract
It is seen that the PAPR algorithms used for OFDM cannot be utilized for the 5G waveforms. The author applied a hybrid algorithm (SLM-CT) on NOMA and OFDM [12]. The outcomes of the work show a significant PAPR performance improvement in 4G and 5G waveforms. In [13], the PAPR is lowered by using the companding method for a high number of QAM systems. It is seen that PAPR is significantly reduced for low-order modulation schemes. The key contributions made by this paper are:  
* The suggested scheme has ominously lowered the PAPR with low complexity. * We have analyzed the PAPR, BER, Power Spectral Density (PSD), Power Performance, and Intricacy of the proposed algorithm for a high number of QAM schemes. The article is arranged as follows: in Section 1 we discuss the background, introduction, and related work; in Section 2, we present a system model; Section 3 deals with the simulation results; finally, Section 4 includes the discussion and conclusion. Table 1 indicates the proposed work published in a similar field. \begin{table}
\begin{tabular}{c c c} \hline \hline
**References** & **PAPR Reduction (dB) at 10\({}^{-3}\)** & **Remarks** \\ \hline
[14] & 10 & A Block Coding (BC) scheme is used to reduce the PAPR of the F-OFDM. \\  & 10 & However, the complexity and applicability of the BC for advanced \\  & & waveforms is not discussed in the presented work. \\ \hline
[15] & 7 & The authors introduced a SLM for the OFDM structure. The increased \\  & 7 & number of subcarriers affects the amplifier efficacy. High BER is seen as \\  & & one of the drawbacks of the suggested work. \\ \hline
[16] & 7.2 & The authors proposed a SLM for 5G waveforms and OFDM. When applied \\  & & to OFDM, the proposed algorithms performed well, but the computation \\  & & requirements for 5G were high. \\ \hline
[17] & 6.8 & The genetic algorithm-based PAPR schemes were introduced for advanced \\  & & radio structure. The PAPR reduction is obtained at high BER. \\ \hline
[18] & & The article discussed the role of SLM in reducing the bandwidth leakage \\  & & issue for the Universal Filter Multi Carrier (UFMC) waveform. The PAPR \\  & & and BER analysis are not performed in the presented article. \\ \hline
[19] & 7.9 & The authors studied the throughput and amplifier performance of the \\  & & UFMC structure. A significant reduction in power is obtained with high \\  & & throughput. However, the computational factor is not discussed. \\ \hline Proposed Work & 2.4, 4.9 and 5.2 & A novel SLM-based F-NOMA is implemented, and significant power \\  & & savings are obtained despite the low computation resource requirements. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Related work.
## 3 Results and Discussion
### Rubber Composites Preparation and Characterization
#### 3.2.7 Structure of the Composite: TEM Analysis  
Transmission electron microscopy analyses were carried out on cured samples to investigate their structure on a nanoscale, focusing on filler dispersion. Attention was focused on the hybrid filler compounds containing either silica and pristine HSAG or silica and HSAG-SP. Twenty-five random micrographs were captured for each compound and different magnifications were used. In Figure 8, two representative micrographs of the two abovementioned compounds are shown.  
Large agglomerates of silica are visible in the Silica/HSAG compound (Figure 8a), whereas even silica dispersions can be observed in the silica/HSAG-SP compound (Figure 8b). This behavior could be related to the interaction of silica with the graphene layers edge functionalized with SP. The structure of the compound based on HSAG-SP appears to be in line with the lower viscosity, particularly upon storage. No appreciable differences can be detected for the organization of HSAG and HSAG-SP in the two compounds.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Composite** & **Neat Rubber** & **HSAG** & **Silica** & **Silica/HSAG** & **Silica/HSAG-SP** \\ \hline G\({}_{\text{IP,0.02\%}}\) & 0.344 \(\pm\) 0.018 & 0.42 \(\pm\) 0.03 & 1.892 \(\pm\) 0.006 & 2.24 \(\pm\) 0.03 & 2.09 \(\pm\) 0.09 \\ G\({}^{\text{T}}\)\({}_{\text{IT,0.02\%}}\) & 0.343 \(\pm\) 0.007 & 0.37 \(\pm\) 0.03 & 1.7 \(\pm\) 0.1 & 1.85 \(\pm\) 0.03 & 1.78 \(\pm\) 0.06 \\ G\({}^{\text{T}}\)\({}_{\text{IP,0.02\%}}\)/G\({}^{\text{T}}\)\({}_{\text{IT,0.02\%}}\) & 1.00 \(\pm\) 0.06 & 1.11 \(\pm\) 0.12 & 1.11 \(\pm\) 0.08 & 1.21 \(\pm\) 0.03 & 1.18 \(\pm\) 0.06 \\ \hline G\({}^{\text{T}}\)\({}_{\text{IP,21\%}}\) & 0.321 \(\pm\) 0.017 & 0.35 \(\pm\) 0.06 & 0.87 \(\pm\) 0.06 & 1.11 \(\pm\) 0.03 & 1.10 \(\pm\) 0.04 \\ G\({}^{\text{T}}\)\({}_{\text{TI,21\%}}\) & 0.325 \(\pm\) 0.010 & 0.35 \(\pm\) 0.03 & 0.86 \(\pm\) 0.05 & 0.986 \(\pm\) 0.008 & 0.96 \(\pm\) 0.08 \\ G\({}^{\text{T}}\)\({}_{\text{IP,21\%}}\)/G\({}_{\text{IT,21\%}}\) & 0.99 \(\pm\) 0.06 & 1.01 \(\pm\) 0.17 & 1.01 \(\pm\) 0.09 & 1.12 \(\pm\) 0.03 & 1.14 \(\pm\) 0.06 \\ \hline G\({}^{\text{T}}\)\({}_{\text{max,TP}}\) & \(\ast\) & \(\ast\) & 0.206 \(\pm\) 0.006 & 0.268 \(\pm\) 0.006 & 0.258 \(\pm\) 0.013 \\ G\({}^{\text{T}}\)\({}_{\text{max,TP}}\) & \(\ast\) & \(\ast\) & 0.193 \(\pm\) 0.013 & 0.217 \(\pm\) 0.005 & 0.212 \(\pm\) 0.004 \\ G\({}^{\text{T}}\)\({}_{\text{max,TP}}\)/G\({}^{\text{T}}\)\({}_{\text{max,TT}}\) & \(\ast\) & \(\ast\) & 1.07 \(\pm\) 0.08 & 1.23 \(\pm\) 0.04 & 1.22 \(\pm\) 0.07 \\ \hline \end{tabular} \({}^{\text{*}}\)G\({}^{\text{*}}\) is constant over the entire strain amplitude range.  
\end{table}
Table 7: Data of storage modulus G\({}^{\text{*}}\) at minimum (0.021%) shear strain amplitude, at maximum shear strain amplitude (21%), and of maximum value of loss modulus (G\({}^{\text{*}}\)\({}_{\text{max}}\)), both in IP and TT configuration, and anisotropy index of rubber composites of Table 1. Average values with standard deviations are reported.
## 2 Theoretical Background
### Patent Network  
Network analyses have been employed to simply understand the overall relationships of social actors (i.e., people, groups, and organizations) [39; 40; 41]. The network can be developed by measuring indicators that include degree, centrality, and density [42; 43]. In particular, patent networks can explore technology trends and technical cooperation in specific technical fields [44; 45; 46]. A patent network analysis can be divided according to available information (i.e., citation, classification, and textual information) [47].  
A citation-based network analysis considers patent documents as nodes (i.e., actors) and visualizes the relationships with links between nodes. A citation network can contribute to the interpretation by intuitively showing the macroscopic flow of the technical fields [37; 48] but inter-relationships cannot be identified in detail because the network only considers the frequency of citations [44]. IPC-based network analysis has been developed by employing the IPC classification developed by the WIPO to identify technical relation ships [49]. Since all patents must be classified under the IPC codes for specific technical fields, the technical content can be analyzed via an IPC-based network.  
Patent documents contain unstructured textual information. Previous studies have attempted to extract keywords in patent documents by applying text mining. Because the extracted keywords can explain the characteristics for patents [50], keyword-based network analysis has been employed to understand the most significant technology in the patent database [47]. This network can represent keywords as nodes and can indicate the relationships with links.  
In this analysis of TFTFT system technology, IPC-based network analysis and keyword-based network analysis were applied to derive an empirical concept for technology development strategies. This study suggests that it is necessary to provide practical ideas to plan R&D for new technologies. The results may be clearly interpreted from both an overall and a detailed technical perspective on technologies.
# DisKnow: A Social-Driven Disaster Support Knowledge Extraction System
Its potential, however, does not come without challenges. There is a massive scarcity of formalized data, such as domain-specific annotated data sets, as well as severe problems regarding the use of social media data, such as the lack of methods to filter trustful information and to exploit noisy social media data streams of high volume, in near real-time [22]. Although geotagging is supported on Twitter, only approximately 1% of published tweets are geotagged [23]. The short nature of tweets, having a maximum size of 280 characters, makes applying the current state-of-the-art machine learning algorithms--that build latent topic models--quite difficult. Our major approach is to handle this useful information in the context of disaster scenarios to provide knowledge to local operation authorities. This, however, requires a way to efficiently store and connect data, which can be addressed by database technologies, such as graphs. Graphs are extremely capable when extracting insight from data is a top priority, as they are mainly made of nodes and connections, and translating that to the real world, it means we can have access to a logical map of entities and their relationships. In addition to their structure, graphs are also very efficient performance-wise, as in contrast to traditional databases, their performance stays nearly constant even as their data grows. Their structure also allows for high flexibility when expanding to incorporate new data. Taking into account the work performed on disaster monitoring through social media, and these mentioned techniques, we identified a gap in the way knowledge is being delivered to stakeholders, as most systems use social media for extracting data that only later is reported as a whole. DisKnow aims to fill this gap. It improves on such systems by not only treating each piece of data as possible new information, but also automatically connecting the knowledge it extracts. This is made possible through the usage of state-of-the-art classifiers for filtering tweets that are related to disasters, and graphs that allow for natural and automated representations of both pieces of individual knowledge, and the connections between them. With these technologies combined, our system is continuously learning from tweets, and therefore expanding its knowledge. This allows DisKnow to present its contributions to disaster management stakeholders in a way that they can employ real-time updated knowledge to make the best possible decisions, in scenarios where every second matters. In this article, we will be presenting the current state-of-the-art for both disaster monitoring through social media and Disaster Extraction techniques. We will then present our system, whose pipeline will be divided and detailed. Lastly, we will provide both a comparison with other Disaster Extraction techniques, and a simulated scenario, to set forth DisKnow's contributions in what would be a real emergency.
# Traffic-Based Heuristics for Regenerator Placement in Translucent Dynamic Optical Networks
## 4 Proposed Regenerator Placement Heuristics
### Estimation of the Total Regeneration Load Offered to a Node  
The load offered to the pool of regenerators installed in the network node \(n\) can be roughly estimated by the sum of the loads of all lightpaths that need regeneration and pass through that node. This sum can be evaluated as  
\[\beta_{n}=\sum_{l=1}^{N}\sum_{j=1}^{N}G_{ij}(n), \tag{3}\]  
in which \(G_{ij}(n)=\gamma_{ij}\) if \(\exists\ m\ /\ n\in c_{ij}^{(m)}\) and 0 otherwise.  
It is possible to improve the \(\beta_{n}\) estimate by considering that the offered load \(\gamma_{ij}\) is proportionally distributed among all FRCs. The reasoning for this is that two FRCs cannot be used simultaneously. For instance, if there are three feasible combinations for \(\pi_{ij}\), then the offered load for each combination is approximately \(\gamma_{ij}/3\). The estimate of the total effective offered load to node \(n\) can be evaluated by  
\[\alpha_{n}=\sum_{i=1}^{N}\sum_{j=1}^{N}F_{ij}(n), \tag{4}\]  
in which  
\[F_{ij}(n)=\left\{\begin{array}{rl}\frac{\gamma_{ij}}{M_{ij}^{\prime}}\sum_{ m=1}^{M_{ij}^{\prime}}P_{ijm}(n),&\text{ if }n\in\pi_{ij}\\ 0,&\text{otherwise},\end{array}\right. \tag{5}\]  
with \(P_{ijm}(n)=1\) if \(n\in d_{ij}^{(m)}\) and 0 otherwise. In this paper, it is proposed to evaluate the \(pdf_{3R}(n)\) (using the notation from [2]) by either: \(\beta_{n}/\sum_{i=1}^{N}\beta_{i}\) or \(\alpha_{n}/\sum_{i=1}^{N}\alpha_{i}\).