esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Future Work
### Prosumer Communities
#### Prosumer Management  
The prosumer management area attracts significant attention among researchers and several existing research projects address critical challenges such as energy sharing approaches, prosumer groupings, prosumer motivations, and incentive strategies. However, there is a need to discover and develop novel and innovative approaches that address these challenges more effectively and efficiently.  
[PERSON]'s published articles cover significant areas which are helpful in gaining a firm understanding of prosumer communities and their management schemes. These include an analysis of prosumers' energy behavior profiles [8], a framework to manage multiple goals [4], and a methodology to form PCGs [4; 7; 10], among others. However, a thorough examination of the related challenges involved in managing PCGs is limited. [PERSON] enumerated six open challenges which need to be further investigated namely: \"lack of approaches that take energy sharing between prosumers and consumers into account; lack of systematic methods for grouping of prosumers; lack of prosumer inspiration; limited approaches to identify and attract more dynamic prosumers; lack of approaches to identify risks associated with negative behaviors of prosumers; and lack of comprehensive prosumer rewarding schemes, including non-financial aspects\" [5]. These are areas to be explored for future work which can contribute to developing sustainable PCGs.  
Existing research on prosumer management strategies is focused primarily on residential consumers. In the future, the proposed methods could be explored and optimized for different business types and industries such as restaurants, hotels, universities, and factories [55]. In the future, actual dataset obtained from Future Energy Management System or other available dataset from existing energy systems could also be used for testing the general usability of the proposed methods [55].
## 8 Practical Applications  
Based on the results obtained, it was found that monitoring hormonal responses during season planning can yield valuable information regarding the level of stress caused by sports training and competitions, as well as the ways in which athletes adapt to stress induced by sports activities. This information can be used to manage training load and plan training cycles more effectively throughout the competitive season.
## 4 Role of sRNA in Antibiotic Resistance
### Biofilm-Mediated Increased Drug Resistance in Bacterial Pathogens
#### 4.4.1 As1974  
The 127-nt sRNA AS1974 has been implicated in biofilm-associated antibiotic resistance in _Pseudomonas_ sp. Hfq RIP-seq analysis identified AS1974 as being downregulated in MDR _P. aeruginosa_ isolates, with its overexpression sensitizing cells to aminoglycos, cephalospor, and carbapenems [222; 223], suggesting AS1974 as a negative regulator of antimicrobial resistance. This is achieved by AS1974-mediated repression of _ndvB_, a gene encoding a biofilm-associated protein required for the synthesis of periplasmic glucans that sequester antibiotics and prevent their entry into the bacterial cell. The deletion of _ndvB_ sensitizes biofilms to gentamicin and ciprofloxacin [223]. Transcriptomic analysis revealed that AS1974 sRNA plays a regulatory role in multiple drug resistance pathways by downregulating key resistance genes, including _mexD_, a component of multi-drug efflux system MexC-MexD-OprJ, _prc_, periplasmic tail specific protease, an enzyme involved in the processing of C-terminal region of penicillin binding protein 3, and _chtA_, a TonB-dependent siderophore receptor (Figure 3b).  
To investigate the regulation of _AS1974_, researchers analyzed its promoter region in drug-susceptible and multi-drug-resistant (MDR) strains. Chromatin immunoprecipitation followed by qPCR (ChIP-qPCR) revealed stronger RNA polymerase binding to the _AS1974_ promoter in susceptible strains, hinting toward a possible epigenetic regulation of AS1974 that downregulates its transcription in resistant strains akin to the methylation-mediated regulation of P-fimbirae (discussed in Section 2.1.1). Bisulfite genomic sequencing identified three methylation sites upstream of the _AS1974_ promoter in resistant strains, which are missing in susceptible strains. These sites were located at \(-16\), \(-66\), and \(-73\) relative to the transcriptional start site (TSS) of AS1974 (Figure 3b). A GFP reporter assay demonstrated that _AS1974_ transcription was suppressed in resistant strains, confirming a functional impact of methylation. These results suggest that promoter methylation inhibits _AS1974_ transcription by restricting RNA polymerase binding, contributing to drug resistance in MDR strains [223].
## 3 Methodology
### Classical Channel Simulation  
As can be seen in Figure 1, in a precisely designed series of actions, [PERSON] and [PERSON] independently generate their chosen digital signature public and private keys. They right away exchange the public keys between them, and [PERSON] goes a step further by generating [PERSON]'s public and private keys. She applies her chosen digital signature algorithm to sign her public key, marking a critical stage in the secure key exchange process. [PERSON] then takes on the role of the verifier, receiving [PERSON]'s Kyber public key and employing the designated digital signature algorithm to authenticate its origin, thereby confirming [PERSON]'s identity and preserving the integrity of the transmitted data. Demonstrating cryptographic sophistication, [PERSON] generates a robust 256-bit random string. He encapsulates this string using [PERSON]'s public key and appends a digital signature, ensuring the security of the entire package, which represents a securely determined secret dispatched back to [PERSON]. Upon receipt of this encapsulated key packet, [PERSON] initiates a series of cryptographic operations to validate its authenticity. She subjects the encapsulated data to rigorous verification processes, all relying on the public key associated with the chosen digital signature algorithm. Once the integrity of the data is confirmed, she proceeds to decrypt the encapsulated string with her private Kyber key. Through these intricate algorithms and complex mechanisms, including encapsulation, signing, and verification, [PERSON] and [PERSON] successfully share a reliable 256-bit key, serving as the cornerstone of their secure digital communication. This key ensures data confidentiality, authentication, and trustworthiness. Finally, with the shared key in hand, both parties can employ AES symmetric encryption with utmost confidence in the security and privacy of their information exchange, thanks to their meticulous orchestration of cryptographic protocols, which has forged an unbreakable bond of trust in their digital communications.
## 4 Experiments and Analysis
### Output Performance of Two Kinds of Harvester in Different Environments  
The energy harvesting performance of two types of energy harvesters in different environments was evaluated separately, as shown in Figure 9. The solid curves in the figure correspond to the voltage signal (left axis), and the curves with hollow points correspond to the average output power (right axis). Under the condition of up-and-down vibration excitation (Figure 9a), the peak value of circuit output voltage increases with the increase of load resistance, while the peak output power shows a trend of initially increasing and then decreasing. Within the experimental range, when the external resistance of the circuit is 22 k\(\Omega\), the peak output power of the circuit reaches a maximum state of 0.534 mW (7.45 Hz). In the condition of wind-induced vibration excitation (Figure 9b), the power output of the energy harvester showed the same variation pattern, with a peak power value of 0.545 mW. The natural frequency of the energy harvester structure and the properties of the PVDF (capacitance) determine the optimum load resistance value to be used during the testing of the device [42]. The average power \(P_{avg}\) is calculated as \(P_{avg}=\frac{V_{max}^{2}}{R}\), where \(V_{rms}=\sqrt{\frac{1}{T_{2}-T_{1}}\int_{T_{1}}^{T_{2}}V2 dt}\) denotes the root mean square (RMS) voltage (\(V_{rms}\)). (where \(V\) is the voltage and \(R\) is the resistance). The conclusion is that, as the external resistance value of the circuit increases, the output power of the energy harvester in both environments first increases, then reaches a maximum when the external resistance reaches 22 k\(\Omega\), and eventually decreases.  
To verify the energy harvesting capability of the two energy harvesters, a 470 u capacitor was used to collect energy with an excitation frequency of 7.45 Hz. A full bridge circuit to change alternating current (AC) to direct current (DC), as shown in Figure 10. After 100 seconds of charging, the capacitor voltage reached 1210 mV with the energy harvester at up-down excitation environment (7.45 Hz), and the capacitor charging voltage with the energy harvester at wind excitation environment (7.45 Hz) increased by 26.4% to 1530 mV. Further, after 200 seconds of charging (7.45 Hz), the capacitor voltages are 1820 mV and 2290 mV, respectively. In the same environmental excitation frequency, the energy harvester efficiency of the S-type generator beam in a wind environment is better than that of up-and-down vibration excitation environment. This is because in wind environment, the force of VIV received by the blocking fluid can be more fully transmitted to the middle of S-type generator beam in the form of torque, resulting in more high-frequency vibration.  
Figure 8: The voltage signal histogram of the harvester: (**a**) Peak histogram; (**b**) Mean histogram.
# Comparing Application-Level Hardening Techniques for Neural Networks on GPUs
## 2 Background
### Software Solutions for Reliability Enhancement of NNs
#### 2.3.3 Swap ReLU6  
Swap ReLu6 changes the order of batch normalization and _ReLU_ activation function positions [33]. The ReLU activation functions are bounded to 6 (i.e., _ReLU6_). Afterwards, the new architecture is fine-tuned with a new hyperparameter configuration. The convolutional layer is the most computationally intensive component of a neural network, which makes it the most susceptible to faults. Consequently, applying batch normalization to corrupted feature maps can alter the distribution of those feature maps. This alteration may lead to faster and more significant error propagation throughout the entire batch normalization output. Consequently, the authors hypothesize and prove that placing an upper bounded activation function right after the convolutional layer allows them to counteract the effect of faults.
# Blockchain-Enabled Secure and Interoperable Authentication Scheme for Metaverse Environments
## 4 System Model
Mapping a physical ID with each virtual identity will avoid attacks like avatar impersonation. Figure 2 shows the low-level design of the Meta-ID. The procedural sequences of the proposed scheme are outlined as follows:  
* Initialization: In the initialization phase, the CA initializes all of the system parameters, default users, avatars, etc. It syncs up all of the metaverse platforms, and all of the smart contracts are deployed in the MIF. * User registration: Physical users can choose to register for a metaverse platform. In this phase, a user ID, a password, and other information is collected. The user is synced up with MIF through the metaverse platform server. The CA authorizes the user and then assigns a Meta-ID. This Meta-ID, a public key, and other relevant information about the user is stored in the blockchain for later verification. * Device registration: A device registration process is required whenever any new device is identified for a user. A device ID, together with a user's physical ID, is sent for registration to the MIF. The MIF maps physical IDs, avatars, and device IDs. MIF verifies a device and assigns a Meta-ID to it. * Avatar generation: When the user has been registered to the metaverse platform and wants to navigate across the virtual world, avatar generation is required. One physical user might be associated with multiple avatars. An avatar ID is created and mapped to the user's physical ID, and the relevant Meta-ID is assigned to an avatar. Using a public key, a session key, and an avatar ID, the user navigates across multiple metaverses. * Login: During the user-registration phase, the user is assigned a Meta-ID that can be used in the login and authentication phases to uniquely identify this user. A session key is used during a logged-in session. * Avatar and device mutual authentication: In the metaverse, avatars and devices can identify and interact with other avatars and devices using a Meta-ID. By using a Meta-ID, avatars and devices can interact not just within the same virtual world but within others as well.
## 4 Evaluation Model Based on AH-Entropy and Normal Cloud Model
### Cloud Model-Related Theories
#### 4.1.1 Definition of Normal Cloud Model  
**Definition 1**.: _Consider U as the universe of discourse, and let C be a qualitative concept in U. If \(x\in\)U is a random instantiation of concept C, where \(x\) follows a distribution \(x\sim\)N(Ex, En\({}^{\prime 2}\)), En\({}^{\prime}\sim\)N(En, He\({}^{2}\)), then the degree of certainty that \(x\) belongs to concept C is satisfied by:_  
\[\mu(x)=\exp\left\{-\frac{(x-Ex)^{2}}{2 Ex^{\prime 2}}\right\} \tag{5}\]  
The distribution of \(x\) within the universe U is referred to as a normal cloud or a second-order normal cloud.  
From Definition 1, it is evident that the normal cloud model can capture not only the fuzziness of concepts through membership functions \(u(x)\) but also the randomness associated with these membership functions \(u(x)\). This highlights a key distinction between the normal cloud model and type-2 fuzzy sets: the normal cloud model is capable of characterizing both the fuzziness of uncertain concepts and their inherent randomness.
# Operation Margin of the ITER Central Solenoid During the Plasma Scenario
## 5 Result and Discussion
### Performance of the Conductor During the Plasma Scenario
#### 5.1.2 Mass Flow Rate Evolution in the Pancakes  
Between \(t\) = 0 s and \(t\) = 10 s, helium backflow is observed at the inlets of modules CS3L, CS1L and CS1U. The reverse flow--which is not observed in the other modules--is due to the higher loss deposition in these modules than in the others during the start of the plasma discharge (\(t\) = 0 s); see Figure 8a.  
Figure 10: Temperature of the bundle in pancake #39, module CS1U, at the inlet (\(x\) = 0.0 m), middle point (\(x\) = 73.75 m) and outlet (\(x\) = 147.5 m).  
An example is shown in Figure 11a for the mass flow rate in the spiral of pancake #2, module CS3L. This module has the highest losses at \(t\) = 0 s. An insight into the mass flow rate profile until \(t\) = 25 s is shown in Figure 11b. After a transient time of about 10 s, the circulator is able to re-establish the nominal flow with a mass flow rate of about 5 g/s in the spiral. This backflow, despite being undesired, does not have a remarkable impact on the performance of the conductor, as shown by the temperature increase discussed in the previous paragraph.  
During the scenario, the mass flow rate in the bundle of the modules manufactured with KAT strands, i.e., CS2L and CS3U, is 16% higher than in the modules with JAS and FUR strands, i.e., CS3L, CS1L, CS1U and CS2U. The modules manufactured with KAT strands are in fact characterised by a lower friction factor than those manufactured with JAS and FUR strands.  
Figure 11: Mass flow rate in the spiral at the inlet, middle point and outlet of (**a**) pancake #2, module CS3L; (**b**) insight into helium backflow until \(t\) = 25 s.  
In the modules manufactured with KAT strands, a portion corresponding to 52% of the helium flows in the spiral, while 48% flows in the bundle. In the modules with JAS and FUR strands, the flow repartition is slightly different, with 56% in the spiral and 44% in the bundle.
## 3 Methods
### Cornea Segmentation
#### 3.1.3 Hough Circle Transform  
The HCT is an adaptation of the Hough Transform algorithm. This technique is used to detect circular objects in images [24; 25]. The algorithm utilizes edge detection to identify potential edges within the image and evaluates all possible coordinates that are able to fit the center and radius of the circles. In general, the cornea and iris are different structural components; however, they share the same boundary in the front portion of the eye [26]. The characteristics of both components are almost circular in shape; therefore, the HCT is widely used for iris segmentation tasks and yields excellent results [3; 10; 27; 28; 29]. In this research, we employ the HCT with the output images from the image processing module to refine the segmented cornea area into a circular shape. The algorithm detects all circles in the input images and stores them in a circle list. Then, the circles with low confidence scores are eliminated by thresholding. The remaining circles are stored with their center coordinates and radius values as the cornea candidates. Finally, we select a circle \(C\) that maximizes our proposed objective function \(F_{\alpha}(C)\) to represent the cornea area, as shown in Figure 3. The proposed objective function is defined as follows:  
\[F_{\alpha}(C)=\frac{N_{C}/M}{(1+(r_{C}/W))^{\alpha}} \tag{1}\]  
where \(N_{C}\) denotes the cornea predicted area that intersects with the circle from HCT, M denotes the cornea predicted area, \(r_{C}\) denotes the radius of the circle from the HCT, W denotes the input image width, and \(\alpha\) denotes the fine-tuning parameter.
# Adsorption of Coxsackievirus in Sediments: Influencing Factors, Kinetics, and Isotherm Modeling
## 3 Results and Discussion
### Effect of Different Factors on Virus Adsorption
#### 3.1.5 Initial Concentrations  
In the adsorption experiments, CA16 at various concentrations (1 \(\times\) 10\({}^{8}\), 1 \(\times\) 10\({}^{7}\), 4.6 \(\times\) 10\({}^{6}\), 2.1 \(\times\) 10\({}^{5}\), 4.6 \(\times\) 10\({}^{4}\)) was utilized. The adsorption isotherm of CA16 on clay is presented in Figure 3e. The graph demonstrates an inverse relationship between virus concentration and the relative adsorption capacity of clay for the virus. This observation can be explained by considering that at lower concentrations of CA16, the surfaces of sediment particles provide an abundance of available adsorption sites, and as a result, the virus has a higher adsorption capacity. Meanwhile, as the concentration of the CA16 suspension increases, the adsorption capacity decreases due to limited available adsorption sites on the surfaces when the CA16 concentration in the solution is high. Consequently, some viruses may remain in a free state without being adsorbed. It is notably that continuous shaking during the experiment may induce the detachment of loosely adsorbed CA16 under shear forces, contributing to the observed decrease in adsorption capacity at higher virus concentrations [56; 57; 58]. In addition, the variation in results observed among the different experiments (1, 6, 9, 12, and 15) under the same condition can be attributed to the difference in initial concentration of the virus within each group. The error within these experiments is further analyzed and demonstrated in Figure 3f. It can be seen that the error range is less than 10%, indicating that the TCID\({}_{50}\) method yields consistent results within an acceptable range of error.
## 1 Introduction
### Spectrometer  
The spectrometer is an optical instrument that measures the spectral responses of different materials. The basic function of a spectrometer is to disperse and measure the electromagnetic energy (mostly solar) reflected or emitted by an object into its spectral components and digitize the signal as a function of wavelength; thus, the physical and chemical properties of the object emitting the light can be inferred by analyzing its spectral components. In the context of this experiment, water pipes and soil provide spectral reflectance. Numerous studies have attempted to predict various soil properties, such as soil moisture content, texture, salinity, organic content, etc., by analyzing its spectral reflectance. A leak in a water infrastructure leads to an increase in the moisture content of the surrounding soil as the water from the leak penetrates the soil; therefore, the soil moisture content is the soil property of interest when dealing with spectrometers. Table 3 summarizes some of the studies that used spectrometers to identify the soil moisture content. Therefore, although there are no studies conducted on the use of spectrometers for water leak detection, there is a strong reason to believe that this technology has potential as a water leak detection method given its ability to detect changes in the water content within soil layers. Preliminary experimentation was conducted by the authors prior to main testing to determine if spectrometers could detect pipe leaks. The results of the preliminary testing were promising. As the leak time increased, the moisture content of the soil surrounding the leak increased. This resulted in a decreased reflectance of the wavelength, specifically higher than 700 nm (infrared band) [26; 27; 28].
# Delay-Sensitive NOMA-HARQ for Short Packet Communications
## 5 Conclusions  
In this paper, we proposed a multiuser uplink strategy for delay-sensitive applications. NOMA was used to allow simultaneous transmission of users' packets and also to allow retransmission to share resources with new arriving packets to limit delay. In this way, the target reliability is achieved without causing queuing to any of the users. We analyzed the throughput, PER, and delay performance of the proposed scheme by using a MM. We also defined and solved an optimization problem to find power-sharing parameters to minimize the PER. Results show that the proposed scheme significantly outperforms the standard NOMA-HARQ scheme in terms of the packet delivery delay.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**CC-HARQ**} & \multicolumn{2}{c}{**IR-HARQ**} \\ \cline{3-6} \(\mathbf{w_{1}}\) & **SNR** & \(\mathbf{(\alpha_{1},\alpha_{2})}\) & \(\mathbf{(\min\max_{i}\xi_{i})}\) & \(\mathbf{(\alpha_{1},\alpha_{2})}\) & \(\mathbf{(\min\max_{i}\xi_{i})}\) \\ \hline  & 3 dB & 0.5, 0.5 & 0.00117 & 0.5, 0.5 & 0.00058 \\
0.55 & 4 dB & 0.6, 0.57 & \(5.67\times 10^{-5}\) & 0.6, 0.57 & \(1.58\times 10^{-5}\) \\  & 5 dB & 0.6, 0.6 & \(1.197\times 10^{-6}\) & 0.6, 0.6 & \(4.4\times 10^{-7}\) \\ \hline  & 3 dB & 0.5, 0.3 & 0.000165 & 0.5, 0.3 & \(6.68\times 10^{-5}\) \\
0.6 & 4 dB & 0.55, 0.4 & \(3.023\times 10^{-6}\) & 0.6, 0.4 & \(1.03\times 10^{-6}\) \\  & 5 dB & 0.6, 0.5 & \(5.839\times 10^{-8}\) & 0.6, 0.4 & \(1.22\times 10^{-8}\) \\ \hline  & 3 dB & 0.6, 0.36 & 0.00058 & 0.7, 0.4 & 0.0003 \\
0.65 & 4 dB & 0.6, 0.45 & \(4.8\times 10^{-6}\) & 0.7, 0.46 & \(1.98\times 10^{-6}\) \\  & 5 dB & 0.6, 0.44 & \(1.313\times 10^{-8}\) & 0.7, 0.46 & \(5.75\times 10^{-9}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Optimal parameters for D-NOMA-HARQ.  
**Author Contributions:** Conceptualization, F.N. and M.S.; Formal analysis, F.N.; Investigation, F.N.; Methodology, F.N. and M.S.; Project administration, M.S., Y.L. and B.V.; Supervision, M.S., Y.L. and B.V.; Validation, M.S., Y.L. and B.V.; Writing--original draft, F.N.; Writing--review and editing, M.S., F.N. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Australian Research Council through the Discovery Projects under Grants DP180100606 and DP190101988.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.