esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Smartphone-Based Remote Microphone Systems  
Although not designed to be worn at an optimal distance from the talker's mouth, smartphones do not require external components or additional hardware to be used as remote microphones [13]. Furthermore, smartphones are readily used, with nearly everyone carrying one throughout the day. It has been reported that 98% of Americans own a cellphone or smartphone [14]. Moreover, Bluetooth-compatible headphones for iOS (Apple Inc., Cupertino, CA, USA) and Android (Open Handset Alliance, Mountain View, CA, USA) operating systemshave been introduced, making it easier to integrate RM system functionality directly through smartphones [15]. However, there are limitations to using Bluetooth receivers as part of an RM system because of the delay caused by Bluetooth transmission. This delay has been reported to range from 30 to 274 ms [7; 16]. [PERSON] reported that the average AP Bluetooth latency across 19 measurements was 144 ms compared to earlier AirPods versions that had latencies up to 274 ms [16]. [PERSON] et al. [17] reported a tolerable delay limit of 10 ms for TH listeners (n = 20) and 10-30 ms for listeners with hearing loss (n = 20), with annoyance ratings significantly increasing beyond these values. These findings suggest that Bluetooth transmission delays may affect the use of smartphone-based RM systems.  
One smartphone-based RM system involves the iPhone and AP earphones (Apple Inc., Cupertino, CA, USA). It is noteworthy that the software available on compatible versions of the AP was recently approved by the U.S. Food and Drug Administration in 2024 as an over-the-counter HA for adults with mild-to-moderate hearing loss [18]. The AP can connect via Bluetooth with iPhones and Android phones and serve as receivers worn by the listener, with the smartphone functioning as a transmitter/microphone when placed close to the talker. In Android phones (i.e., Samsung, Google Pixel), the RM features that can be used for enhancing hearing perception in noise include \"Hearing Enhancement\" in the Samsung series, and \"Sound Amplifier\" on Google Pixel. In iPhone devices, Live Listen (LL) is an additional native feature, available since 2014, that enables microphone transmission to compatible hearing devices such as HA or AP headphones with Bluetooth protocols. When activated, LL uses the iPhone's microphone to capture sound and transmit it wirelessly from the iPhone to the receiver (i.e., AP), amplifying and clarifying the sounds in their immediate environment for users [19]. The AP can be set at three modes: transparency (TP; lets outside sound in), noise cancellation (NC; cancels the external sounds), and off. This could potentially be a lower-cost alternative to a dedicated RM system designed for persons with TH. One such RM system, a Phonak Roger Touchscreen transmitter used with a Roger Focus II receiver, was shown to enhance speech intelligibility by an average of 53% in 16 children (ages 8-16 years) with unilateral hearing loss, compared to their peers with TH [20].
## 5 Evaluation Methodology
### Smart Money Corda DLT Environment
\begin{table}
\begin{tabular}{c c} \hline \hline
**Node Name** & **Functionality** \\ \hline \multirow{7}{*}{HMRCCWP} & Validates payments for invoices issued from accounts registered to \\  & SellerCWP and paid by accounts registered to BuyerCWP. \\  & Issues UC money (tokens) to accounts registered to BuyerCWP. \\  & Requests smart warrants from LegalCWP for accounts registered \\  & to SellerCWP. \\  & Executes smart warrants authorised by LegalCWP. \\  & Creates accounts to BuyerCWP, SellerCWP, and LegalCWP and shares \\  & those accounts with other nodes. \\  & Hosts VATPAs account (for payment validation). \\  & Hosts VATPAs account (for smart warrant execution) \\  & Pays invoices on behalf of accounts registered to BuyerCWP which are \\ BuyerCWP & issued by accounts registered to SellerCWP. \\  & Hosts buyer accounts which hold balances (in Current and UC accounts). \\  & Issues invoices on behalf of accounts registered to SellerCWP which are \\  & to be paid by accounts registered to BuyerCWP. \\  & Hosts seller accounts which hold balances (in Current accounts). \\  & Authorises smart warrants issued from the account registered \\  & to HMRCCWP. \\  & Notary & Ensures uniqueness to transactions through validation. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Corda nodes. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Node Name** & **Description** & **CPU Cores** & **RAM Size** & **IP Address** \\ \hline \multirow{3}{*}{HMRCCWP} & Operated by HMRC for validating payments & & & \\  & and conducting investigations through smart & 8 & 16 GB & 192.168.1.110 \\  & warrants authorised by LegalCWP. & & & \\ \multirow{3}{*}{SellerCWP} & Operated by a CWP which hosts accounts of & & & \\  & businesses which make sales. & 8 & 64 GB & 192.168.1.111 \\ BuyerCWP & Operated by a CWP which hosts accounts of & & & \\  & consumers which make purchases. & 8 & 64 GB & 192.168.1.112 \\ LegalCWP & Operated by a legal authority for authorising & & & \\  & smart warrants to HMRC & & & \\ \multirow{3}{*}{Notary} & Operated by a legal authority for authorising & & & \\  & smart warrants to HMRC & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Corda nodes in the experimental setup. only through the nodes they are registered on. An account's parent node is responsible for transmitting the message to the destination node which in turn, notifies the destination account. An RPC (remote procedure call) client application is used for interfacing a user/account with a node. That RPC is submitted to the target node through the network; hence, the user can reside in any network station that can communicate with the node through TCP/IP. For the purpose of this experiment, the users reside within the node they are registered on, i.e., requests for payment are submitted from clients executed within the same VM which hosts the _SellerCWP_. Similarly, payments are submitted from clients executed within the same VM which hosts the _BuyerCWP_.
## 3 Energy-Efficient Optimal Power Allocation
### Energy-Efficient Optimal Power Allocation for Non-Real-Time Applications  
In this section, we propose two energy-efficient optimal power allocation schemes for non-real-time applications. To regulate the transmit power limit of the satellite user, average transmit power (ATP) constraint and peak transmit power (PTP) constraint are adopted in the two schemes, respectively. From the perspective of guaranteeing the primary terrestrial user's communication quality, it is necessary to impose interference power constraint on the satellite user. Compared with the peak interference power (PIP) constraint, the average interference power (AIP) constraint can not only protect PU better, but also provide higher capacity for SU [38]. Thus, we employ AIP constraint in both schemes herein.
## 3 Results and Discussion
### Comparison of Different VFAs Exploitation Methods  
The results of the dark fermentation process under different pH, along with the final products from VFAs utilization are summarized in Table 4. The amount of NaOH used for pH adjustment during dark fermentation is also presented, as it may have a strong impact in the feasibility of the process. As it can be observed, in order to adjust the pH to seven, the amount of NaOH increases almost tenfold compared to the amount used for pH 4.5. Hydrogen production is presented in MJ equivalents, with 142 MJ/gH2. The results of the VFAs exploitation methods correspond to the equivalent products from the VFAs reported, with the methods discussed thus far. Everything is expressed per kg of VS of substrate used in the dark fermentation process.  
Even though pH 7 seems optimal for the examined processes from the amount of equivalent products, a basic economic balance can be carried out with the data presented in Table 4, in order to compare the cost of NaOH with the value of the products. The prices used were 0.52 EUR/kg of NaOH [40], 0.8 EUR/kg of biodiesel [41], 0.0637 EUR/MJ of electric energy [42], and 0.0198 EUR/MJ of thermal energy [43]. For hydrogen and methane, 40% was calculated as electric energy, 50% as thermal energy and 10% losses during combustion. Here it must be noted that this is a simplified economic balance that does not take into account the operational costs of the VFAs exploitation process that can be vastly different, especially for emerging technologies when compared to well established technologies, as well as the cost of initial investment. The results refer only to the conversion of the produced VFAs from the substrate and does not take into account the amount of substrate that was not converted but could be utilized, like the lipids and proteins for the production of methane with anaerobic digestion. From the result of each process per pH, the cost of NaOH has been subtracted and the profit from hydrogen gas has been added. The results are presented in Figure 4.
## 3 Open Systems  
We extend our description from the observed system to its environment by assuming that the full system obeys a closed dynamics, defined by the total Hamiltonian, \(H_{tot}=H_{s}+H_{e}+H_{l}\), where the terms in the sum correspond to the system, the environment, and their interactions. The pure states make up the direct product Hilbert space, \(\mathcal{H}_{tot}=\mathcal{H}_{s}\otimes\mathcal{H}_{e}\), where the first and the second factor denote the space of system and environment states, respectively. The state of the observed system is given by the reduced density matrix, \(\rho_{s}(t)=\text{Tr}_{e}[\rho(t)]\), where \(\rho(t)=e^{-\frac{i}{H}H(t-t_{i})}\rho(t_{i})e^{\frac{i}{H}H(t-t_{i})}\). A further assumption is that there are no correlations between the system and the environment in the initial state, i.e., the initial density matrix is factorized, \(\rho(t_{i})=\rho_{s,i}\otimes\rho_{e,i}\).  
Let us consider a symmetry group \(G\) of the full closed dynamics, represented by basis transformations, \(H_{tot}=U(g)H_{tot}U^{\dagger}(g)\). While the Neumann equation of the full dynamics is symmetrical with respect to the extended transformations,  
\[U(g_{-})\rho(t)U^{\dagger}(g_{+})=e^{-\frac{i}{H}H(t-t_{i})}U(g_{-})\rho(t_{ i})U^{\dagger}(g_{+})e^{\frac{i}{H}H(t-t_{i})}, \tag{3}\]  
the symmetry group of the effective system dynamics is usually more restricted. To find out the latter, we assume that \(U(g)\) acts without mixing the system and the environment states, \(U(g)=U_{s}(g)\otimes U_{e}(g)\). The invariance of the trace under basis transformation implies the identity \(\rho_{s}=\text{Tr}_{e}[\rho]=\text{Tr}_{e}[U_{e}\rho U_{e}^{\dagger}]\), holding for any unitary environment operator, \(U_{e}\). From this identity follows:  
\[U_{s,-}\rho_{s}(t)U_{s,+}^{\dagger}=\text{Tr}_{e}[e^{-\frac{i}{H}H(t-t_{i})}U _{s,-}\rho_{s,i}U_{s,+}^{\dagger}\otimes U_{e}\rho_{e,i}U_{e}^{\dagger}e^{ \frac{i}{H}H(t-t_{i})}] \tag{4}\]  
if \(U_{s,\pm}\) and \(U_{e}\) are symmetries of each term in the Hamiltonian one-by-one, \(H_{s}=U_{s,\pm}H_{s}U_{s,\pm}^{\dagger}\), \(H_{e}=U_{e}H_{e}U_{e}^{\dagger}\), and \(H_{i}=U_{s,\pm}\otimes U_{e}H_{i}U_{s,\pm}^{\dagger}\otimes U_{e}^{\dagger}\). The necessity of the last condition shows that the symmetry with respect to the extended transformation, \(\rho_{s}\to U_{s,-}\rho_{s}U_{s,+}^{\dagger}\) with \(U_{s,+}\
eq U_{s,-}\), is usually no longer a symmetry in the presence of system-environment interactions. The condition of preserving the full symmetry group for the diagonal basis transformations, \(g_{+}=g_{-}\), is the symmetry of the environment initial condition, \(\rho_{e,i}=U_{e}(g)\rho_{e,i}U_{e}^{\dagger}(g)\).  
The system-environment entanglement induces a nonfactorizable density matrix; hence, a signature of the openness of the dynamics is that the quantum fluctuations of the bra and the ket components are correlated. Such correlations reduce the symmetry of the mixed state description to the diagonal subgroup of \(G\otimes G\), \(g_{+}=g_{-}\). Therefore, such a symmetry breaking serves as an indication of an open dynamics.
## 3 Results and Discussion
### Economy
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Mixes**} & \multirow{2}{*}{**Characteristics \({}^{\bullet}\)**} & \multicolumn{6}{c}{**Cost (\(\upxi\)/m\({}^{3}\))**} \\ \cline{3-10}  & & **CEM 1** & **SP** & **FA** & **Water** & **Fine** & **Coarse** & **Coarse** & **Fine** & **Total Material** & **Final** & **CEM 1** \\  & & & **NA** & **NA** & **RCA** & **RCA** & **Cost \({}^{\text{b}}\)** & **Cost \({}^{\text{b,c}}\)** & **Cost \({}^{\text{b,c}}\)** & **Cost \({}^{\text{b,c}}\)** \\ \hline M1 & COFROAO & 33.93 & & 0.28 & 5.1 & 8.23 & & 47.54 & 79.93 & 1.00 \\ M2 & COFSOFA0 & 33.93 & & 0.31 & 2.5 & 8. Figure 4: Relative cost of 1 m\({}^{3}\) concrete mixes with increasing incorporation levels of fine RCA and FA with **(a)** 0% and 0% **(b)** 100% and 0%, **(c)** 0% and 1%, and **(d)** 100% and 1% of coarse RCA and SP, respectively. All figures are relative to the reference concrete “M1”. (Black dots represent relative results; shades represent relative cost). Figure 5: Relationship between compressive strength at 28 days and cost of 1 m\({}^{3}\) of concrete. \(f_{\text{cm,cube}}\) was converted to \(f_{\text{cm}}\) according to EN 1992-1-1 [39]. Figure 7 shows the importance of considering the cost and compressive strength simultaneously to select concrete mixes based on the target application. The cost and compressive strength of the concrete mixes with conventional concrete (M1) are compared. For example, M13 presents a similar strength value but a lower cost than that of conventional concrete. On the contrary, M5 sp has a similar cost but a higher strength. Finally, the selection of one of the others concrete mixes depends on the selected application. For example, the mixes located in \"lower cost and lower strength\" zone are preferable for residential houses, and the mixes located in \"higher cost and higher strength\" zone for high-rise buildings. However, to select suitable mixes within each of these sub-groups, it is important to make a more comprehensive cost efficiency optimisation considering cost and MPR, such as proposed in Section 3.3. Figure 6: Base-case cost versus calculated cost. Figure 7: Relationship between compressive strength at 365 days and cost of 1 m\({}^{3}\) of concrete. The mixes are divided into four zones according to the cost and strength of reference concrete (M1).
# Blockchain Solutions for Enhancing Security and Privacy in Industrial IoT
## 6 Discussion
### Evaluating Consensus Mechanisms  
The choice of consensus mechanism is a crucial factor in determining the suitability of blockchain for Industrial Internet of Things (IIoT) applications. While Proof-of-Stake (PoS) offers significant advantages over traditional Proof-of-Work (PoW) in terms of energy efficiency and scalability, it introduces challenges such as potential latency in validator selection, which may limit its ability to handle high-frequency Industrial Internet of Things (IIoT) sensor data.  
Alternative consensus mechanisms and blockchain platforms are evaluated to address these concerns. For example, Solana [48] and IOTA [49] are considered for scalability, efficiency, and real-time capabilities. Solana's high throughput and low latency make it an ideal candidate for IIoT applications requiring rapid transaction processing. Similarly, IOTA's feeless transactions and scalable architecture offer significant advantages for large-scale industrial deployments, where cost effectiveness and performance are paramount.
# Malicious Cognitive User Identification Algorithm in Centralized Spectrum Sensing System
## 5 Conclusions  
We mainly studied the security problem of centralized collaborative spectrum sensing under SSDF attacks. In order to solve this problem, we established a model of a malicious user identification system  
Figure 7: The false alarm probability under different attack probabilities.  
with multi-channel cooperation. The model uses the characteristics of cognitive users having the same performance on the different sub-channels in the same perceived gap. This model then collaborates the cognitive user's perceived results on multi-channels and reduces the probability of misjudgment.  
On this basis, we propose a malicious user identification algorithm under multi-channel cooperative conditions that are based on the \(\beta\)-function and the feedback iteration mathematical method. The cognitive user's prestige value is updated by feedback iterations and the malicious user is distinguished according to the prestige value. The simulation results show that when the attack probability is the same, the \(\beta\)-MIAMC algorithm proposed in this paper can identify the malicious users more accurately than the traditional algorithms. The proposed algorithm reduces the false alarm probability of malicious users by more than 20%. When the attack probability is greater than 7%, the proposed algorithm can distinguish the malicious users with 100% certainty.  
This work was supported by the Chinese National Science Foundation under Grants 61501078 and 61231006, the Fundamental Research Funds for the Central Universities under Grants 3132016208.  
[PERSON] designed the maritime cognitive radio communication system model, the dynamic spectrum allocation algorithm based on the Cournot game and the organization of the paper, [PERSON] carried out the simulations and was responsible for the writing and editing of the manuscript and [PERSON] supervised the work and contributed to the writing of the paper.  
The authors declare no conflict of interest.
# One-Dimensional Convolutional Neural Network for Pipe Jacking EPB TBM Cutter Wear Prediction
###### Abstract  
An earth pressure balance (EPB) TBM is used in soft ground conditions, and these conditions lead to the fluctuation and instability of machine parameters. Machine parameters influence cutter wear and tunnel excavation. For this reason, to evaluate and predict the cutter wear of an EPB TBM, a 1D CNN model was used to provide machine-parameter-based cutter wear prediction using an EPB TBM operational dataset. The machine parameters were split into 80% training and 20% test datasets. Compared to traditional machine learning applications and two deep neural network models, the proposed model provided reliable results with a reasonable computational time. The correlation coefficient was 89.6% \(R^{2}\), the mean squared error (MSE) was 57.6, the mean absolute error (MAE) was 1.6, and the computational wall time was 3 min 22 s.  
EPB TBM; tool wear; deep learning; soft ground tunnelling; cutter life; operational parameters; convolutional neural network +
Footnote †: journal: Applied  
Article
## 5 Results and Discussion  
### Results  
At this point, from the perspective of adversary \(A\), the attack advantage \(Adv_{I}^{prf}(A)\) of distinguishability between the random function family and the extraction function family \(J\) is defined as follows [22; 23].  
\[Adv_{I}^{prf}(A) \tag{1}\] \[=Pr[Exp_{I}^{prf-0}(A)=0]-Pr[Exp_{I}^{prf-1}(A)=0]\] \[=Pr[A^{g}=0|g\gets Func(D,R)]-Pr[A^{g}=0|g\gets J]\]  
\(Adv_{I}^{prf}(A)\) represents the difference between the probability that \(A\) determines the outcome of experiment 0 as 0 (the success probability of distinguishing) and the probability that \(A\) determines the result of experiment 1 as 0 (the failure probability of distinguishing). Since \(g(x_{i})\) outputted by \(g\gets J\) in lines 11 to 17 of Algorithm 1 can be distinguished accurately, it is only necessary to consider the case of distinguishing failure, \(J\subset Func(D,R)\). That is, since \(|J|=\binom{20}{4}\times(\frac{32}{4})=174,226,200\), the advantage of \(A\) is:  
\[Adv_{I}^{prf}(A) \tag{2}\] \[=1-\frac{174,226,200}{10^{6\times 2^{24}}}\] \[\approx 1-10^{-3\times 2^{23}}\] \[\approx 1.\]  
Depending on the values derived in this way, the OTP extraction function defined in Ref. [17] is distinguishable from random functions, because the adversary can directly construct effective algorithms with an advantage that is almost 1. Therefore, financial institutions and mobile systems that use this vulnerable extraction algorithm in their OTP generation mechanism should review whether there are similar vulnerabilities; it is critical to replace it with other extraction algorithms that have not yet been found to have vulnerabilities.
## 4 Conclusions  
We demonstrated a substantial PL enhancement in the bilayer MoS\({}_{2}\). The original bilayer MoS\({}_{2}\) showed a weak PL intensity with the signal of the indirect band gap. A key finding in this paper is that by both UV irradiation and TFSI treatment, the bilayer MoS\({}_{2}\) demonstrates a ML-like PL with a single signal at the direct band gap. In addition, the combined process induces more than several hundred times PL enhancement from the bilayer MoS\({}_{2}\) compared to the as-exfoliated bilayer flakes. The enhancement mechanism requires more study, but it would involve the separation of the top and bottom layers in the bilayer MoS\({}_{2}\). This situation would be due to a sort of misalignment of the top and bottom layers caused by pressing the weight and sliding each layer in the exfoliation process. If the bilayer is twisted, the interlayer separation between the top and bottom layers would happen easily, which would lead to an indirect-to-direct band gap conversion via the process. These phenomena are also supported by treating a conventional bilayer MoS\({}_{2}\) flake; there is no PL enhancement in the conventional case. This technique is useful for preparing high-optical-quality MoS\({}_{2}\) materials and devices. Since the sample is bilayer, the top layer is separated from the substrate; the bottom MoS\({}_{2}\) layer would serve as an interface layer between the top layer with a strong PL and the substrate. This structure would be effective in removing substrate effects. This work would open another strategy to obtain a strong PL intensity from 2D materials and be useful in optical device applications.  
**Supplementary Materials:** The following are available online at [[https://www.mdpi.com/article/10.3390/app11083530/s1](https://www.mdpi.com/article/10.3390/app11083530/s1)]([https://www.mdpi.com/article/10.3390/app11083530/s1](https://www.mdpi.com/article/10.3390/app11083530/s1)), Figure S1: PL enhancement via the process of TFSI treatment followed by UV irradiation for the bilayer MoS\({}_{2}\) and Figure S2: Raman spectra for the UV-irradiated bilayer MoS\({}_{2}\).  
**Author Contributions:** Conducted all experiments, Y.Y.; designed the experiments and wrote the manuscript, Y.Y. and D.K. All authors discussed continuously and contributed to the writing of the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by JSPS KAKENHI (20H02574), the Research Foundation for Opto-Science and Technology, the Konica Minolta Science and Technology Foundation, and the Sumitomo Foundation.  
**Institutional Review Board Statement:** This project does not include biological items requiring ethical approval.  
**Informed Consent Statement:** It is not required.  
**Data Availability Statement:** The study data is available from the corresponding author on request.  
**Conflicts of Interest:** The authors declare no conflict of interest.