esa-sceva/satcom-chunk-collection huggingface dataset:
# Nematic Liquid Crystal Composite Materials for DC and RF Switching
## 3 Electrical Conductivity of Liquid Crystal and Carbon Nanotube Composites
### Local Heating and Phase Transition during Switching
showed that minutes amount (5 \(\times\) 10\({}^{-4}\) and 10\({}^{-3}\) wt.%) of CNT doping in a twisted nematic crystal cell do not perturb its orientational properties (cell capacitance, voltage holding ratio, etc.) in both the on and off states, but the residual DC field greatly reduced as CNTs trap ions [54]. To further establish the idea that Joule heating is responsible for the transition, [PERSON] et. al. performed the field-induced switching at elevated temperatures [53]. They found that switching is faster at a higher temperature, which is in agreement with their reasoning. [PERSON] et al. mentioned an additional source of local heating during field-induced switching [20]. They argued that applying an electric field to the LC-CNT composite may cause a dielectric breakdown after reaching a certain threshold value, which leads to local heating. Along with reorientation, this dielectric breakdown effect also increased the electrical conductivity of the composite. They found about an increase in conductivity, \(\sigma\), by four orders of magnitude for the E7-0.05% SWCNT system. However, the dielectric constant shows erratic behaviors above 5 V and even goes negative above 8 V. In the Current-Voltage (I-V) measurement, a dramatic  
Figure 8: The nematic–isotropic transition temperature (T\({}_{\text{NI}}\)) versus weight percent of MWCNT: This phase diagram shows chimney type characteristics (OM = Optical Microscopy, DSC = Differential Scanning Calorimetry) [52]. increase in the current is observed, which is indeed a characteristic of a dielectric breakdown [55]. Using a polarizing optical microscope, they observed an N-I transition (for the 8 CB-CNT system) at 7.9 V and at a 36 \({}^{\circ}\)C cell temperature [56]. Using an electric-field-induced heating model without considering the effect of CNT doping, they calculated the temperature increase of the composite that is enough (10 \({}^{\circ}\)C increase at 7 V) to derive the system into an isotropic phase. The peak length of the CNT used here is 500 nm, which is much smaller than the cell gap at 70 \(\upmu\)m. Therefore, a shortening of the cell electrode is not possible by a single nanotube as it happened for longer tubes [53]. [PERSON] et al. suggested that a loosely connected network CNT formed between the cell elctrodes, but this group argued that this alone cannot give such a large increase in conductivity [15; 19]. Considering all these issues, they strongly claimed that a dielectric breakdown of the LC composite is largely responsible for the high conductivity change (not the inherent property of CNT). No other research group working on a similar LC-CNT composite has mentioned this aspect yet. A further investigation is required to explain the dielectric breakdown effect on conductivity. However, a dielectric breakdown and/or local heating is not desirable. If composites are subjected to these effects for a long time, chemical degradation may occur.
## 3 Results and Discussion
### Bioactive Compound Analysis in Propolis Residue
For instance, volatile oil extracted from Brazilian brown propolisis showed a DPPH IC\({}_{50}\) of 25.0 mg/mL, which is considerably lower than the IC\({}_{50}\) values typically reported for fruit-derived by-products, such as strawberry extracts (IC\({}_{50}\)\(\approx\) 150-230 \(\mu\)g/mL) and apple pomace (IC\({}_{50}\)\(\approx\) 200-400 \(\mu\)g/mL), supporting the enhanced antioxidant potential of propolis residue [39]. Based on the studies by [40], the concentration of \(p\)-coumaric acid in raw propolisis, prior to extraction, is significantly higher than the values reported in other studies. For instance, [41] identified concentrations were greater than 300 mg/kg. Our findings of 637.80 \(\pm\) 27.35 mg/kg for \(p\)-coumaric acid in propolis residues align more closely with these studies, supporting the idea that propolisis richer in this compound. In contrast, [42] found European propolis to have a lower kaempfer concentration up to 50 mg/kg. In comparison, this study identified a higher kaempfer content (295.82 \(\pm\) 11.67 mg/kg), suggesting greater antioxidant potential in the examined propolisis residues. Moreover, comparing these results with berries such as blueberries, raspberries, and strawberries [43], in which the total content of phenolic compounds ranges from 1500 to 5000 mg/kg fresh weight, propolis residues were shown to be richer in these compounds, with a total phenolic content of 2041.34 mg/kg dry weight, underscoring propolis residue's richness as a natural antioxidant source. Then, vegetables like spinach, cabbage, and broccoli are known for their phenolic content, with values varying significantly from 1000 to 2500 mg/kg fresh weight [44]. These analyses underscore the superior antioxidant potential of propolis residue, exceeding levels found in some natural sources and highlighting its value for sustainable applications. \begin{table}
\begin{tabular}{c c} \hline
**Compounds** & **Propolis Residue 2,3** \\ \hline Phloretin & n.d. \\ Kaempfer & 295.82 \(\pm\) 11.67 e \\ Isorhametin & 5.91 \(\pm\) 0.39 a \\ TOTAL & 2041.34 \\ Artepillin-C & 56.56 \(\pm\) 1.75 mg/kg \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._Figure 1: Bioactive and volatile compounds in Brazilian propolis residue. This figure illustrates the predominant phenolic acids, such as \(p\)-coumaric acid (637.80 \(\pm\) 27.35 mg/kg), chlorogenic acid (497.93 \(\pm\) 28.33 mg/kg), and kaempfer (295.82 \(\pm\) 11.67 mg/kg), along with significant concentrations of volatile compounds like \(\alpha\)-pinene (20.09%) and carophyllene (9.76%), in propolis residue. These components highlight the residue’s potential for antioxidant activity and applications in fragrance and flavor industries.
# Robust Low-Sidelobe Transmit Beamforming under Peak-to-Average-Power Ratio Constraint
## 3 The Proposed Algorithm
Using the triangle inequality and Cauchy-Schwartz inequality, a tight upper bound of is determined as  
\[\left|\mathbf{\omega}^{H}\hat{\mathbf{a}}\big{(}\theta_{\text{q}}\big{)}\right|= \left|\mathbf{\omega}^{H}(\mathbf{a}(\theta)+\mathbf{a}_{\text{e}}(\theta))\right| \leq\left|\mathbf{\omega}^{H}\mathbf{a}(\theta)\right|+\left|\mathbf{\omega}^{H} \mathbf{a}_{\text{e}}(\theta)\right| \tag{21}\]  
According to (21) and \(\|\mathbf{a  
According to inequality (22), we can reformulate problem (20) into  
\[ \begin{split}\min_{\mathbf{\omega}}&\max_{\theta_{\text{q}} \in\Theta_{\text{G}}}\left|\mathbf{\omega}^{H}\mathbf{a}\big{(}\theta_{\text{q}} \big{)}\right|+\epsilon_{\text{e}}\|\mathbf{\omega}\|\\ &\text{s.t.}&\left|\mathbf{\omega}^{H}\mathbf{a}(\theta_{ \text{q}})\right|=1\\ &\left|\omega_{i}\right|\leq\sqrt{\xi}/N,\quad\forall i\\ &\mathbf{\omega}^{H}\mathbf{\omega}\leq b\end{split}  \tag{23}\]  
With the combination of the triangle inequality and Cauchy-Schwartz inequality, problem (20) is finally transformed into the following convex optimization problem:  
\[ \begin{split}\min_{\mathbf{\omega},f}& t+\epsilon_{ \text{e}}\|\mathbf{\omega}\|\\ \text{s.t.}&\left|\mathbf{\omega}^{H}\mathbf{a}(\theta_{ \text{0}})\right|=1\\ &\left|\omega_{i}\right|\leq\sqrt{\xi}/N,\quad\forall i\\ &\mathbf{\omega}^{H}\mathbf{\omega}\leq b\\ &\left|\mathbf{\omega}^{H}\mathbf{a}\big{(}\theta_{\text{q}}\big{)} \right|\leq t,\theta_{\text{q}}\in\Theta_{\text{G}}\end{split}  \tag{24}\]  
where \(t\) is an auxiliary variable. Note that (24) is an SOCP problem, which is a subclass of the convex programming problem. We can therefore solve it by using off-the-shelf convex optimization toolboxes, such as CVX.
# Victim Detection and Localization in Emergencies
## 4 Victim Detection  
Immediately after a catastrophe, a key aspect is to detect and account for the victims trapped under the rubble, after which localization can be performed to further concentrate the efforts of the first responders. Several techniques have been proposed for this task:  
* Visual recognition with rescue dogs: the traditional task of finding victim is usually performed either by direct observation or with the help of trained rescue dogs [3].
* Human body image detection: when the victims are incapatitated, image recognition may help to find individuals that are on the surface [19; 21].
* Audio-processing-based human detection: when victims are trapped under rubble and there is no visual or imaging recognition, drones may integrate microphones to detect any distress calls and notify the rescue services [14].
* Vital signs detection: Passive Infrared (PIR) sensor for detecting victims that are buried close to the surface [15]. PIR reacts only to certain energy sources such as human body heat. Low-power microwave radar signals can be used to detect the heartbeat and breathing of underground victims. The limitation of such devices is the short coverage of some meters [22].
* Localization using radio signals coming from devices that victims carry (e.g., smartphones) or wear (e.g., smartwatches): this paper relies on this method for detecting and then locating the victims [23].
# On the Utilization of Reversible Colour Transforms for Lossless 2-D Data Compression
## 4 Experimental Setup And Results  
All the simulations and experimentations of the proposed method were performed over machines equipped with an Intel Core i7 processor, Quad Core CPU running @ 2.9 GHz, 8 GB RAM, 2 GB virtual memory executing programs and applications over the Microsoft Windows operating system. Algorithms were developed in MATLAB and were evaluated using the following image datasets.  
* Kodak colour image dataset
* University of Loughborough Uncompressed Colour Image Database (UCID)
* RASTER Maps Images
* Colour Filter Array (CFA) Images
* EEG Images  
Simulations were performed by applying the proposed technique to these image sets and comparing against benchmark schemes. Six different colour transforms that include RDgDb, LDgDb, LDgEb, YA2 UA2 VA2, YA6 UA6 VA6, and YA7 UA7 VA7 [43] were utilized. Simulation results are presented as follows.
# Privacy-Preserving Data Aggregation with Dynamic Billing in Fog-Based Smart Grid
## 4 Our Proposal
### Data Reading  
1. When it receives \(\mathcal{T}_{i,m}\) from all covered FNs, AC performs the batch verification to verify whether all \(\mathcal{T}_{i,m}\) satisfy the following condition: \[e(\prod\limits_{i=1}^{k}\sigma_{FN_{i,m}},g)=\prod\limits_{i=1}^{k}e(H(ID_{ FN_{i}}||u_{i,m}||v_{i,m}||T_{FN_{i,m}}),pk_{FN_{i}}).\] (2)
2. After checking the validity, AC uses its private key to perform pre-decryption as \(C^{\prime}_{i,m}=\frac{v_{i,m}}{(u_{i,m})^{AC}}\).
3. AC utilizes its private key \(x_{AC}\) to generate the signature \[\sigma_{AC_{i,m}}=H(ID_{AC}\parallel u_{i,m}\parallel C^{\prime}_{i,m} \parallel T_{AC_{i,m}})^{x_{AC}}.\]
4. AC sends \(\mathcal{T}_{AC_{i,m}}=(ID_{AC},u_{i,m},C^{\prime}_{i,m^{\prime}}T_{AC_{i,m}},\sigma_{AC_{i,m}})\) to SP.
5. Upon receiving \(\mathcal{T}_{AC_{i,m}}\) from AC, the SP accepts \(\mathcal{T}_{AC_{i,m}}\) if it satisfies the following condition: \[e(\prod\limits_{i=1}^{k}\sigma_{AC_{i,m}},g)=\prod\limits_{i=1}^{k}e(H(ID_{ AC}\parallel u_{i,m}\parallel C^{\prime}_{i,m}\parallel T_{AC_{i,m}}),pk_{AC}).\] (3)
6. After verification, the SP computes \[g^{\sum\limits_{j=1}^{n}c_{ij,m}}=\frac{C^{\prime}_{i,m}}{(u_{i,m})^{x_{SP}}}.\] (4) After retrieving \(g^{\sum\limits_{j=1}^{n}c_{ij,m}}\), the SP first applies the pollard lambda method [35] to obtain \(\sum\limits_{j=1}^{n}c_{ij,m}\) and then utilizes the decoding function in [6] to decode \(\sum\limits_{j=1}^{n}c_{ij,m}\) as \(\sum\limits_{j=1}^{n}c_{ij,m}=\sum\limits_{j=1}^{n}m_{1}^{ij,m}||\sum\limits_ {j=1}^{n}m_{1-1}^{ij,m}||\cdots||\sum\limits_{j=1}^{n}m_{2}^{ij,m}||\sum\limits _{j=1}^{n}m_{1}^{ij,m}\). The length of \(\sum\limits_{j=1}^{n}m_{w}^{ij,m}(w=1,2,\cdots l)\) is \((\log_{2}n+z)\), which represents the total amount of data of the same type consumed by all users covered by \(FN_{i}\) in time slot \(m\). The specific form of \(\sum\limits_{j=1}^{n}c_{ij,m}\) and the decoding process with \(l=4\) are shown in Figure 4.  
Obviously, the SP can obtain the total electricity consumption data of all users in each sub-region during time slot \(m(m=1,2,\cdots,t)\). The SP analyses the obtained data to adjust generation and distribution, then anticipates the electricity price \(p_{m+1}\) for the next time slot and broadcast \(p_{m+1}\) before the start of the next time slot (giving a specific pricing process is not our focus). Users can schedule their electricity consumption according to the price in different times of the day.
## 7 Discussion
The aminolaevulinic acid is the precursor of protoporphyrin IX (PpIX), it is normally synthesized in mitochondria, thanks to the condensation reaction between glycine and succinyl-CoA [72], but it can also be administrated exogenously as a prodrug [51]. The ALA molecules are better absorbed intracellularly than the PpIX, and their accumulation and photoactivation in porphyrins (PAPs) is higher in cells with greater metabolic activity, such as tumor cells, bacteria, and inflammatory cells [73]. ALADENT device allows, therefore, to maximize the penetration of the PS in the bacterial membrane. The effectiveness of ALA PDT depends on several factors: Bacteria cell wall composition, pH value, concentration, and duration of exposure to the PS [74; 75; 76]. Our study highlighted that ALADENT cannot only be effective in destroying pathogenic microorganisms colonizing the subgingival area but also in significantly improving CAL, PD, and BOP after six months of follow-up. The suppression or eradication of these microbes and the positive effect on periodontal clinical parameters result in periodontal health improvement. Moreover, the novel ALADENT contains the preservatives, potassium sorbate and sodium benzoate, enhancing the bactericidal effect in periodontal pockets [77; 78], and it was demonstrated that the use of a red led, irradiating ALA, allows the antimicrobial effect to be longer maintained [79]. Thanks to its properties, the use of ALADENT enhances the control of the periodontal pockets during the non-surgical treatment of periodontitis. It was also demonstrated that ALADENT may significantly reduce the progression of periodontitis and the destruction of periodontal tissues in animal models. Besides ALADENT, the literature also presented other interesting innovative strategies in the treatment of periodontal disease: The clinical trial by [PERSON] et al. [80] proposed the use of a probiotic-based toothpaste and chewing gum in addition to SRP treatment, demonstrating that it could be a valid support option against periodontal microorganisms, improving several clinical periodontal indexes. Another study by the same author [81] compared the effectiveness of a chlorhexidine-based and postbiotics-based periodontal gel used after SRP, recording a significant reduction of probing pocket depth, recession, dental mobility, and bleeding on probing after both treatments. Probiotics were largely recommended as complementary periodontal therapy since symbiotic bacteria are able to decrease the level of proinflammatory cytokines, potentiating the effect of SRP [82]. Postbiotics derive from microbial fermentation and metabolic activity of microorganisms, providing antiseptic, antioxidant, and anti-inflammatory effects [83]. Finally, beside SRP, the adjunctive treatment with low-level laser therapy (LLLT) obtaining photobiomodulation (PBM) was demonstrated to be more effective in reducing probing pocket depth, bleeding on probing, and plaque index compared to the ozone therapy [84] since it promotes gingival fibroblast proliferation and FGF-b and type-1 collagen expression, improving healing processes through bio-stimulation.
## 4 Experiment and Results
### Data Preparation
#### 4.1.2 Data Augmentation  
Based on Section 3.2.1, it can be inferred that the neural network of YOLO used in this study has a multi-layer structure that contains millions of parameters. To make these parameters work well, a great deal of data is needed to be trained. Otherwise, overfitting may occur during the training process and then there will be a poor performance of component detection on the new image. Therefore, to avoid the overfitting problem when training on a small dataset and improving the generalization ability of the YOLO model, image augmentation technique was implemented in this study to increase the size of the dataset by randomly cropping and translating images in the original dataset. A total of 1000 new images were generated in this phase, and all original and newly generated images were mixed together to form an augmented dataset. After that, this augmented dataset was used for training and testing to prevent classification bias in the process of structural component detection.
# Routing Protocols for Delay Tolerant Networks:
## 3 Performance Evaluation
### Evaluation Using Real-World Mobility Traces  
We continue by evaluating the performance of the protocols by conducting an experiment using real-world traces. The motion of the nodes is dictated either by the traces acquired by the Haggle experiment [23] or by the traces acquired by the Rome Taxi experiment [24]. Independent of the movement, at any given time, a node that decides to generate a new message selects a random destination and invokes the underlying DTN routing protocol. Messages never expire, namely their TTL is infinity. The simulation settings are summarized in Table 3. It is worth noting that metrics on synthetic traces are obtained averaging the results over fifteen runs in which the underlying connection graph is different in each run. This is clearly not possible for real traces, in which the underlying connection graph is obtained from the traces, as explained earlier.  
In Figures 5-7, we include the results for the real traces and the Levy Walk mobility model. We decided to include the results using the Levy Walk as a reference when comparing the results with those of the previous section. A more in-depth comparison of the results acquired from each model is presented in Section 3.4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Indoor Environment** & **Outdoor Environment** \\ \hline Simulation time & 4 days & 30 days \\ Transmission speed & 25 kBps & 25 kBps \\ Transmission range & 30 m & 100 m \\ Message size & 100 B \(\ldots\)200 B & 100 B \(\ldots\)200 B \\ Message creation interval & 2, 4, 6, 10 (min) & 5, 10, 15 (min) \\ Buffer size & 10,100 (KB) & 100 KB, 1 MB \\ Number of Nodes & 78 & 320 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation settings.  
Figure 5: Simple protocols: Levy Walk (LW) (**a,c,e**) _vs_. Infocom (**b,d,f**). For the sake of readability: if the difference between the 100-KB and 10-KB plots for a protocol is within 5%, we do not show the 100-KB plot.  
Figure 6: _Cont._  
Figure 6: Single technique protocols: LW (**a**,**c**,**e**) _vs._ Infocom (**b**,**d**,**f**). For the sake of readability: if the difference between the 100-KB and 10-KB cases for a protocol is within 5%, we do not show the 100-KB case.  
Figure 7: _Cont._  
Figure 8: Performance of simple protocols in a real-world outdoor setting.  
Figure 7: Advanced protocols: LW (**a**,\(c\),_e_) _vs_. Infocom (**b**,\(d\),_f_). For the sake of readability: if the difference between the 100-KB and 10-KB cases for a protocol is within 5%, we delete the 100-KB case.  
Figure 9: _Cont._  
In Figures 8-10, we include the results for the real traces based on the Rome Taxi experiment. The outdoor traces cannot be compared to the Levy Walk mobility model or any other synthetic mobility model, since all of them are used to model the motion of human walking, while the real-world trace is based on the mobility of cars.
# Analysis of AMI Communication Methods in Various Field Environments
## 6 Discussions
### Comparison of the AMI Communication Methods  
In wireless communication methods, the Wi-SUN method is better than the ZigBee method in terms of the communication distance. Furthermore, the Wi-SUN method is usually better than the HS PLC method for the overhead power line case. However, for the underground power line case, the wireless communication method suffers from radio signal propagation problems due to metallic closures. Because new PLC techniques, such as HPGP and IoT PLC, can provide better communication performances than the HS PLC case [37], independent of such propagation problems, the PLC method can provide better performance compared to the wireless communication method, especially for the underground power line case. Even for the overhead power line case, the wireless communication method cannot provide a stable communication performance depending on the positions of mounted wireless antennas. Therefore, if the coverage distance is relatively short, then the HS PLC method can have an advantage over the Wi-SUN method. On the other hand, for long distances with a small number of customers, employing the wireless communication method is preferable.
## 7 Overview of Deep Learning in 5G Security
### Importance and Role of Deep Learning  
In the context of 5G security, the importance and role of DL cannot be overstated. One of the significant challenges faced by 5G networks is the vast amount of data being transmitted and received, which calls for advanced, efficient, and intelligent security solutions. With its ability to model high-level abstractions in data through the use of multiple processing layers, deep learning has emerged as a powerful tool for handling the intricacies of 5G security [137]. Primarily, deep learning excels in identifying, classifying, and mitigating potential security threats in 5G networks. Fifth-generation networks consist of numerous interconnected nodes, each producing and receiving a significant amount of multi-dimensional data. This data complexity and volume can be overwhelming for traditional security methods, resulting in missed or false alarms [138]. Deep learning, however, can analyze these data with a high degree of accuracy, using its complex, multi-layered neural networks to identify subtle patterns and anomalies that may be indicative of an impending attack or an ongoing security breach [111]. Moreover, deep learning has a self-learning or adaptive learning capability that sets it apart from traditional machine learning methods. This capability allows deep learning models to 'learn' from new information, continuously updating their knowledge and improving their performance over time. In the context of 5G security, this means that the network's defense mechanism can continually enhance itself, becoming more effective and efficient at identifying and responding to emerging threats [139]. This proactive and adaptive approach to security is particularly vital given the rapid evolution of cyber threats and the increasing complexity of 5G networks.
## 3 Implementation of the Methodology Using the NoLOAD Software
### Variable Selectivity: Improvement Compared to CADES  
One of the notable advancements of NoLOAD over the CADES software employed in G2 Elab [35] lies in the selective approach it adopts for choosing the variables to differentiate. Since NoLOAD is implemented in Python, a scripting language that offers greater user intuitiveness and does not enforce variable typing, it dynamically assesses whether a variable warrants differentiation. In this context, the specifications allow for the implicit designation of the sizing outputs to be differentiated with respect to the sizing inputs. Consequently, unlike CADES [35], the size of the Jacobian matrix is markedly reduced, leading to a significant enhancement in computational efficiency.