esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Data Gathering and Processing  
Data gathering of the electricity and water consumption data was done by using Granlund Manager [37], a facility management software to which smart metered data was stored. The facility management software obtained data from the building management system and the utility companies in the hourly interval. Electricity data was collected in kilowatt-hours consumed, and water in cubic meters consumed. For this work, measured electricity consumption for equipment and the lighting was used on the case study floor. Additionally, for the supervised study, a sum of both sub-metered electricity consumptions as a variable called total sub-metered electricity was used as well. Ground truth data on the occupancy was collected from the camera with people counting software in 30-min intervals, which was summed to hourly intervals so that it could be compared with the consumption dataset. In Figure 4, the correlation matrix shows that selected predictors had a very good correlation with the ground truth of which equipment electricity consumption had the highest.  
The acquired dataset was for the period between the 1 st of February and 31 st of August 2017. Before analytics was performed, the dataset had to be cleaned. First, the weekends were taken out of the dataset, since the floor was very rarely occupied during weekends. This was decided since weekend occupancy is sporadic and occupant type was often different than during the week. It was more common that cleaning, maintenance or security personnel passed through the building than the regular company employees. Different occupant types used electricity and water in a different pattern than the regular occupant, which decreased the model performance. Second, since there were no occupants on the floor during night time, it was decided to use only the data between 06:00 and 21:00.  
Additionally, the water meter had missing data during some days, and those days were removed from the dataset. Dataset processed in this way was used in both supervised and unsupervised methods of this paper.  
Figure 4: [PERSON]’s correlation matrix between lighting, equipment and water consumption and occupancy ground truth.
# PDTR: Probabilistic and Deterministic Tree-based Routing for Wireless Sensor Networks
## 6 Simulation Results
### Importance of Implementing the Control-Parameters Sets on PDTR  
#### 6.3.1 On the Delay  
Achieving the low delay when sending and receiving data can be reached by minimizing the number of hops in the routing protocol. Hence, the control parameter of the hop-count distribution \(\alpha\) should have greater value than the two other control parameters.  
In the first scenario, 14 parameters set are selected such that the value of \(\alpha=2,\;\text{while}\;\gamma=\beta=\{0,0.2,0.4,0.6,0.8,1,1.2,1.4,1.6,1.8,2,2.2,2.4,2.6\}\). Each source node \(1<\text{s}<100\), sends one packet each \(0.1\text{ s}\), and the simulation time is \(300\text{ s}\), for the parameter values given by Table 2, such that the communication range is R = 50. The results show that the minimum number of hops in the routing protocol is reached when \(\gamma=\beta=0\), (see Figure 5a).  
In the second scenario, 14 parameters set are selected such that the value of \(\alpha=3,\;\text{while}\;\gamma=\beta=\{0,0.2,0.4,0.6,0.8,1,1.2,1.4,1.6,1.8,2,2.2,2.4,2.6\}\). Each source node \(1<\text{s}<100\), sends 1 packet every \(0.1\text{ s}\), and the simulation time is \(300\text{ s}\), for the parameter values defined and given by Tables 1 and 2, such that the communication range is R = 50. The results show that the minimum number of hops in the routing protocol is reached when \(\gamma=\beta=0\), (see Figure 5b).  
From the above two scenarios, we can conclude that; first, the minimum number of hops in the proposed PDTR protocol is reached when \(\gamma=\beta=0\). Second, the greater hop-count control parameter, the lower the number of hops needed in the PDTR routing protocol.  
This conclusion is maintained under the condition: \(\alpha+\gamma+\beta\leq U_{b}\) (Experimental view).
# Smart Poly(acrylic acid)/Poly(acrylamide) Microgels with Interpenetrating Polymer Network Structure
## 3 Results
### pH-Dependent Zeta Potential (ZP) of Mg  
The pH dependence of ZP for the PAA MGs (PC100, Figure 4A) supports the results obtained for the pH dependence of their R\({}_{\text{h}}\) (Figure 2A). In the following section, the absolute value of zeta potential \(\mid\)ZP \(\mid\), reflecting the charge density, is discussed. Generally, the \(\mid\)ZP \(\mid\) of both PAA and PAAM MGs increased with increasing pH. The increase in \(\mid\)ZP \(\mid\) for PAA MG was due to the ionization of carboxyl groups, which reached a maximum at pH 6 and remained constant at higher pH levels. It was enhanced by the adsorption of negative ions (OH\({}^{-}\)), especially at low pH, when PAA was only weakly ionized. The isoelectric point (iep) at pH \(<\) 3 is typical for surfaces with dissociable acidic groups. Surfaces without dissociable groups normally exhibit an iep around pH 4 and--above pH 4--an increase in the \(\mid\)ZP \(\mid\) with increasing pH due to the adsorption of OH- ions on all surfaces. This should also account for PAAm. However, the measured iep of PAAM at pH \(<<\) 2 is too low. An iep lower than that of the acidic PAA means that PAAM is more acidic than PAA. This low iep can therefore only be an effect of acidic contaminations by the surfactant or initiator--which, vice versa, could also affect the charge of the surface.  
Figure 3: Relative increase in the R\({}_{\text{h}}\) of IPN MGs as a function of pH (compared to their initial radius at pH 2.6) calculated by the R\({}_{\text{h}}\) data obtained by DLS.  
The ZP of the PAA MGs reflects the increased ionization of COOH groups with the increasing pH. As result of the ionization, PAA became more hydrophilic, and the negatively charged PAA chains repelled each other. Therefore, the MG absorbed water and swelled. The \(|\)ZP \(|\) of PAAM also increased not due to ionization, but due to OH-adsorption, if impurities are neglected. Therefore, the polymer chains remained unchanged, and the MGs did not swell.  
For the different IPNs, there was a marginal tendency towards lower \(|\)ZP \(|\) for higher PAA content at lower pH levels (except for PC66). However, this tendency was very small and almost invisible in the alkaline range due to the large measurement error. The small influence of the PAA content on the zeta potential (and also on the swelling ratio) was probably due to the fact that the ionized PAA chains swelled and determined the surface charge, while the hydrophobic part of PAA remained inside the MGs.
# The New CAP Theorem on Blockchain Consensus Systems
## 1 Introduction
### Contribution  
In this work we introduce the _new CAP theorem_ in the context of distributed consensus engineering.  
In direct semantic analogy to the [PERSON]'s CAP theorem, which formalizes the tradeoff between Consistency, Availability, and Partition tolerance in database systems, the new CAP theorem_ highlights the fundamental constraints existing between Autonomy (A), Consensus achievement (C), and entropic Performance (P) in Blockchain consensus systems.  
In this work we study the process of consensus as a distinct self-containing process. We introduce a set of novel quantitative definitions:  
* _Autonomy_ is defined in the _atomic scale_ as the fraction of the memory of each node reserved to serve local operations.
* _Consensus achievement_ is defined at the _system scale_ as the fraction of nodes required to reach agreement, i.e., to consent on new events for the system to function.
* _Entropic Performance_ is introduced, following [14], as a metric for the efficiency of the consensus process. It is given as the overall reduction in the information entropy of the system per unit of consumed energy. It measures how efficiently a Blockchain consensus system reduces uncertainty, in the sense of data disorder, relative to the energy it consumes.  
A witnessing node is responsible for recording and validating the event data. Throughout this work, we recognize the notion of _witness_ as the essential link among the Autonomy (A), the Consensus (C), and the entropic Performance (P) of the system.  
* We prove that of these three essential properties, two at the most can be optimized simultaneously at any given time in the generic case. For this, we formally prove that there exists at least one peer system consideration for which (C), (A), and (P) cannot be simultaneously optimal, and we validate the results through empirical real-world Blockchain systems data benchmarking.
* We demonstrate that this trait comes in a direct analogy and has the same semantic origins as [PERSON]'s CAP theorem.  
The _new CAP theorem_, as presented in this work, establishes a fundamental theoretical constraint on distributed consensus mechanisms. Just like [PERSON]'s CAP theorem in distributed databases, the _new CAP theorem_ in blockchain consensus holds independently of the specific blockchain architectures and considerations.  
The main focus of this paper is on proving the intrinsic limitations of blockchain consensus mechanisms. The impact of the revealed limitations on the prevailing Blockchain systems is also studied in Sections 5.2 and 5.3. This further reinforces the generality of the findings in this work, which is semantically based on the foundations of the common perception and witnessing that takes place among the consenters and lies beneath every consensus mechanism, irrespective of architectural considerations and system implementation details [2; 14; 15; 16; 17; 18].
###### Abstract  
In the Fifth Generation (5G) wireless standard, the Internet of Things (IoT) will interconnect billions of Machine Type Communications (MTC) devices. Fixed and mobile wearable devices and sensors are expected to contribute to the majority of IoT traffic. MTC device mobility has been considered with three speeds, namely zero (fixed) and medium and high speeds of 30 and 100 kmph. Different values for device mobility are used to simulate the impact of device mobility on MTC traffic. This work demonstrates the gain of using distributed antennas on MTC traffic in terms of spectral efficiency and fairness among MTC devices, which affects the number of devices that can be successfully connected. The mutual use of Distributed Base Stations (DBS) with Remote Radio Units (RRU) and the adoption of the millimetre wave band, particularly in the 26 GHz range, have been considered the key enabling technologies for addressing MTC traffic growth. An algorithm has been set to schedule this type of traffic and to show whether MTC devices completed their traffic upload or failed to reach the margin. The gains of the new architecture have been demonstrated in terms of spectral efficiency, data throughput and the fairness index.  
5G network; millimetre wave; MTC; IoT; Distributed Base Station +
Footnote †: journal: Computer Science & Electronic Engineering Conference
# Experimental Demonstration of Secure Relay in Quantum Secure Direct Communication Network
## 2 Experiment
AMZI: asymmetric Mach–Zehnder interferometer; Attn: attenuator; PC: polarization controller; PM: phase modulator; BS: beam splitter; PBS: polarization beam splitter; CIR: circulator; FR: Faraday rotator; SPD: single-photon detector. Figure 2: The architecture of the computationally secure relay. Rec. : receiver; Rconsys: receiver control system; Trans. : transmitter; Tconsys: transmitter control system; LDPC-H: low-density parity check and Hadamard code. sequence of qubits randomly in one of four states, namely, \(|0\rangle\) or \(|1\rangle\) for the Z basis, \(|+\rangle\) or \(|-\rangle\) for the X basis. Then, they send the prepared qubits to the transmitter. Second, the transmitter randomly chooses some of the qubits and measures them in the Z basis or the X basis randomly. Then, they communicate with the legitimate receiver to estimate the qubit error rate (QBER) of the quantum channel. Third, if the QBER is typically below the security threshold, the transmitter encodes the message with the remaining qubits by taking advantage of the low-density parity check and Hadamard code (LDPC-H). The encoded qubits are sent back to the legitimate receiver. Finally, the legitimate receiver decodes the message from the received qubits when the error rate is below the correcting capability of the LDPC-H code. In the experiment, [PERSON] and [PERSON] are each connected to the computationally secure relay through independent 5 km and 10 km spooled standard fiber links. [PERSON] uses the public key of KYBER-512 to encrypt the message. After receiving the optical pulses from the relay, [PERSON] takes advantage of two single-photon detectors (SPDta0 and SPDta1) to monitor the quantum channel. If the quantum bit error rates are typically below the security threshold, [PERSON] encodes the ciphertext into quantum states and returns the quantum signals to the relay. The relay takes advantage of SPDa0 and SPDa1 to decode the quantum signals. In the case of the private key not being available, the relay cannot decrypt the ciphertext. Thus, the security of the message is well protected by KYBER. Then, the relay communicates with [PERSON] in the same manner. SPDtr0 and SPDtr1 are used to monitor the QBERs of the quantum channel between [PERSON] and the relay. After [PERSON] receives the quantum signals, he decodes them with SPDb0 and SPDb1. Finally, [PERSON] decrypts the ciphertext to recover the message with his private key of KYBER.
## 2 Method  
The DMDct method is derived for real-valued two-dimensional problems defined over a rectangular mesh of equally-spaced nodes as depicted in Figure 1. For each snapshot, only a subset of its elements is observed which is obtained by applying a pre-defined random sampling mask. The mask is defined as a set of pairs of \((i,i^{\prime})\) indices, shown as \(M\), for which the samples are taken. All observed elements of each snapshot \(\mathbf{S}_{k}\) are vectorized and represented as a real-valued data vector \(\mathbf{s}_{k}\) of length \(N_{s}\), where \(N_{s}\) is the number of sampling points. The data vectors \(\mathbf{s}_{k}\) are taken as the input to the DMDct algorithm. First, the \(N_{s}\times m\) matrix \(\mathbf{Z}_{s}=[\mathbf{s}_{0}\ \dots\ \mathbf{s}_{m-1}]\) is constructed and the exact DMD method is applied to that to obtain DMD eigenvalues \(\boldsymbol{\lambda}\) (Section 2.1). Then, the spatial component of DMD is reconstructed based on the DCT basis vectors by taking random samples from the fully-sampled dataset while maintaining the sparsity of reconstruction coefficient matrices through an \(l_{1}\)-regularization scheme (Section 2.2). Finally, each snapshot is reconstructed in full using the calculated reconstruction coefficient matrices and the DCT basis vectors.
### Network Architecture and Requirement of Uwsn  
The current section will discuss the basic UWSN topology (which provides the foundation for creating UWSN applications) in brief and the next section provides the basic requirement for UWSNs. Figure 1 shows a simplified architecture of UWSNs [8,9].
## 2 Materials and Experiments
### Mechanical Performance of ECC  
The tensile test and bending test were conducted to evaluate the mechanical performance of ECC, as shown in Figure 3. The size parameters of tensile specimen are, as follows: The total length is 330 mm; the gauge is 80 mm; the width is 60 mm; the narrow width is 30 mm; and, the thickness is 13 mm. The loading rate in the tensile test is 0.5 mm/min. The size of four-point bending specimen is 400 mm \(\times\) 70 mm \(\times\) 16 mm. The loading rate in the bending test is 1.0 mm/min.
## 3 Materials and Methods
### Baseline Model: BiGRU  
To establish a performance benchmark, the researchers first employed a baseline BiGRU model without additional enhancements. The BiGRU analyzes input sequences in both forward and backward orientations, enabling it to record bidirectional context for each word in the sentence. However, despite its bidirectional structure, the baseline BiGRU may face limitations in handling the complex contextual dependencies required for fine-grained word sense disambiguation, particularly for polysemous words, where subtle contextual nuances define meaning. Using this baseline, the researchers aimed to quantify the enhancements offered by additional model components in subsequent configurations.  
BiGRU was chosen as the baseline model despite the fact that transformer-based models like BERT and RoBERTa are frequently used to establish robust benchmarks due to their high computational cost and substantial hardware requirements for both training  
Figure 4: Taxonomy of lexical ambiguity in Sesotho sa Leboa.  
and inference. As a recurrent neural network (RNN) variation, BiGRU is more effective and lightweight, which makes it suitable for low-resource settings. BiGRU is essential for WSD because it can capture bidirectional dependencies in textual sequences. It enhances contextual knowledge by enabling the model to preserve both past and future context when making logical predictions. Additionally, assessing the additional impact of transformer embeddings is made possible using BiGRU as a baseline, which offers a robust but interpretable standard. Incorporating contextual embeddings into the model aids in separating their distinct contributions.  
BiGRU and transformer embeddings work well together to enable a hybrid strategy that improves sense disambiguation by processing contextualized representations from transformers in a sequential manner.
# Effect of Ajwa Date and Germinated Barley on Lipid Profile in Rats Fed High-Fat Diet
## 3 Results  
### Chemical Composition of GB  
The chemical composition of the barley before and after germination was analyzed to measure the most abundant compounds in germinated barley. As seen in Table 1, the moisture percentage, protein, and vitamin C content were higher in the germinated barley than in the bare seed. Moreover, the results showed a decrease in the ash percentage from 1.59 to 1.26% after germination. Likewise, there was a decrease in the carbohydrate content from 78.47 to 75.59%, and the fat content of GB compared to normal barley decreased from 3.25 to 2.03%.  
\begin{table}
\begin{tabular}{l c c} \hline
**Item** \({}^{*}\) & **Barley** & **Germinated Barley** \\ \hline Moisture (\%) & 7.23 \(\pm\) 0.14 & 10.14 \(\pm\) 0.65 \\ Ash (\%) & 1.59 \(\pm\) 0.33 & 1.26 \(\pm\) 0.12 \\ Carbohydrate (\%) & 78.47 \(\pm\) 1.94 & 75.59 \(\pm\) 1.14 \\ Protein (\%) & 9.37 \(\pm\) 0.94 & 11.22 \(\pm\) 2.01 \\ Fat (\%) & 3.25 \(\pm\) 1.64 & 2.03 \(\pm\) 0.00 \\ Vitamin C (mg.100 g) & 18.13 \(\pm\) 1.96 & 23.21 \(\pm\) 4.73 \\ \(\beta\)-glucan (g.100 g) & 5.89 \(\pm\) 0.13 & 2.70 \(\pm\) 0.22 \\ \hline \end{tabular} \({}^{*}\) Results are expressed as mean \(\pm\) standard error.  
\end{table}
Table 1: Chemical composition of barley before and after germination.
# Comprehensive Review of RF MEMS Switches in Satellite Communications
## 3 Strategies for Optimizing RF MEMS Switch Performance
### High Reliability
#### 3.1.3 Improvement of Contact Materials and Structures
Subsequently, [PERSON] et al. [128] designed a new type of three-state non-contact RF MEMS switch. Figure 7f displays the structure of the non-contact switch, which is based on a variable capacitance between the signal line and a movable ground electrode controlled by an electrostatic actuator. The proposed switch does not require any ohmic contacts prone to stiction or dielectric layers susceptible to dielectric charging, thus enhancing the device's reliability.
## 3 Framework
### Cyber-Attack Model  
With the mathematical concepts presented in Section 3.1, a FDI in line parameters can be modeled. Consider a line connecting buses \(k\) and \(m\) has a FDI in its parameter model. Then, this FDI can be modeled as follows:  
\[g_{km}^{pert}=g_{km}+\Delta g_{km}. \tag{17}\]  
\[b_{km}^{pert}=b_{km}+\Delta b_{km}. \tag{18}\]  
\[b_{km}^{sh,pert}=b_{km}^{sh}+\Delta b_{km}^{sh}. \tag{19}\]where \(g_{km}\), \(b_{km}\), and \(b_{km}^{sh}\) are the true line parameters, \(\Delta g_{km}\), \(\Delta b_{km}\), and \(\Delta b_{km}^{sh}\) are the deviation (due to attack) in line parameters, and \(g_{km}^{pert}\), \(b_{km}^{pert}\), and \(b_{km}^{sh,pert}\) are the perturbed quantities. By substituting (17)-(19) into (9) and (10) one can derive:  
\[P_{km}^{pert}=(V_{k}^{2}-V_{k}V_{m}\cos\theta_{km})g_{km}^{pert}-(V_{k}V_{m} \sin\theta_{km})b_{km}^{pert}. \tag{20}\]  
\[Q_{km}^{pert}=(-V_{k}V_{m}sin\theta_{km})g_{km}^{pert}+(-V_{k}^{2})b_{km}^{sh, pert}+(V_{k}V_{m}cos\theta_{km}-V_{k}^{2})b_{km}^{pert}. \tag{21}\]  
where \(P_{km}^{pert}\) and \(Q_{km}^{pert}\) are the attacked (deviated) real and reactive power, considering values obtained in (9) and (10), respectively, due to a FDI in line parameter(s). Note that the voltages at buses \(k\) and \(m\) are the same as the ones estimated to obtain \(P_{km}\) and \(Q_{km}\) in (9) and (10). Hence, with this notion, the system operators can make use of data already available from SE to further secure the state estimator routine over time. In addition, it can be viewed as a filtering stage prior to run SE routine to validate system database after initialization. Hence, any flag from SE after validating system database would be identified to measurement set considering a previously defined confidence level.
## 2 Experimental Program
### Direct Shear Test  
A direct shear test was intended to identify the effect of decontamination on the shear strength of treated soil. This direct shear test was carried out under the consolidated, drained condition in accordance with ASTM D3080 [12], for which the soil sample was compacted with 95% of the maximum dry unit weight (16.0, 15.9 and 16.0 kN/m\({}^{3}\) for soils contaminated and treated for 24 and 48 h, respectively). The void ratios of the tested soils (soils contaminated and treated for 24 and 48 h) were 0.56, 0.58 and 0.57, respectively.  
Normal stress (\(\sigma\)) applied to a direct shear test should be designed according to the surface load considered at the construction site. As shown in Figure 9, the direct shear test was conducted with normal stresses of 49, 98 and 147 kPa, at which maximum shear stress was clearly determined as shear strength on a Mohr-Coulomb failure envelope.  
As shown in Table 4, the cohesion of contaminated silty sand was 7.1 kPa, and the internal friction angle was 27.9\({}^{\circ}\). The cohesion of remediati  
Figure 9: Shear strain–shear stress responses and Mohr–Coulomb failure envelope: (**a**) Contaminated soil; (**b**) treated soil (24 h); (**c**) treated soil (48 h).  
the internal friction angle was 30.2\({}^{\circ}\) after treatment of 48 h, indicating that cohesion was reduced after remediation, while the internal friction angle was increased. In other words, it was found that while the viscosity according to oil concentration increased, the internal friction angle decreased. As shown in Figure 10, treated soils showed a more obvious dilative behavior under lower shear strain than in contaminated silty sand due to the particle climbing action (interlocking) during shearing.  
Moreover, it was found that decontamination caused an increase in the secant shear modulus (\(G_{sec}\)), which commonly represents soil stiffness, as shown in Figure 11:  
\[G_{sec}=\frac{\tau}{\gamma} \tag{1}\]  
Here, \(\tau\) = shear stress and \(\gamma\) = shear strain.