esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Recent studies on the possible use of thermoacoustic refrigerators have provided quite significant results, which may lead to a considerable reduction in environmental pollutants and, in the future, a reduction in costs [27] due to the use of new smart-window technologies that reduce the cooling load [28]. Different search methods have been proposed for solving the OCL problem and saving energy in air conditioning systems, but the uncertainty of the cooling demand has only been discussed in [26]. Two consecutive intervals have been considered for modeling the underestimated and overestimated cooling loads in the robust optimization approach. Meanwhile, information gap decision theory (IGDT) could be applied to economic dispatch problems in order to model both risk-aversion or robustness and risk-taker or opportunistic aspects of risk-constrained OCL strategy. This paper implements the IGDT algorithm on a benchmark five-chiller plant in order to solve a mixed-integer non-linear programming problem with a generalized algebraic mathematical modeling system (GAMS). In the risk-aversion OCL problem, the system operator enters a critical cost value. The cooling load increase is then maximized in such a way that the sum of the refrigeration production of the chillers is higher than or equal to the overestimated cooling demand, and the daily energy cost of the test system is smaller than the given cost function. In other words, the total electricity cost over the study horizon is not considered to be the objective function. It is restricted so that it is smaller than the critical cost function. The maximum value of the cooling demand increase that can be satisfied by chillers is then calculated as the objective function. In the risk-taker or opportunistic decision-making process,the minimum value of the cooling load decrease is calculated in order to reduce the total energy cost so that it is as low as the given cost function. The refrigeration capacity constraints of the chillers and load-generation balance criterion are also modeled in robustness and risk-taker modes. The on/off status, partial load ratio, cooling generation, and electricity consumption of each chiller are selected as the decision variables that are found in the base case study and the robustness and opportunistic OCL problem. In all reviewed works, the total power consumption of the chiller plants was minimized as the objective function, but real-time electricity prices should be considered for minimization of the total energy cost of multi-chiller systems. The novel contributions of this paper can be summarized as follows:  
* The IGDT method was applied to the OCL problem to model the uncertainty of the cooling demand. * The robustness or risk-aversion decision-making approach was used for maximizing the value of the cooling load increase while minimizing the daily energy cost so that it was as low as the critical energy cost. * The opportunistic or risk-taker OCL strategy was used to find the best operating point of the chillers so that the minimum value of the cooling load decrease that results in target cost saving was found.
## 3 Proposed Sparse SLMS Algorithms
### Fourth Proposed Algorithm: SLMS-LP  
\(\ell_{p}\)-norm sparse penalty is a nonconvex function to exploit sparse prior information. In [5], LMS-LP based channel estimation algorithms was developed as  
\[G_{LP}\left(\mathbf{w}(n)\right)=\left(1/2\right)e^{2}(n)+\lambda_{LP}\left| \left|\left.\mathbf{w}(n)\right|\right|\right|_{p} \tag{28}\]  
where \(\lambda_{LP}>0\) is a positive parameter. The update function of LMS-LP is given as  
\[\begin{split}\mathbf{w}(n+1)&=\mathbf{w}(n)-\mu\frac{ \partial G_{LP}(\mathbf{w}(n))}{\partial\mathbf{w}(n)}\\ &=\mathbf{w}(n)+\mu e(n)\mathbf{x}(n)-\frac{\rho_{LP}\left|\left.\mathbf{w}(n )\right|\right|_{p}^{1-p}\text{sgn}\left(\mathbf{w}(n)\right)}{\epsilon_{LP}+\left| \mathbf{w}(n)\right|^{1-p}}\end{split} \tag{29}\]  
where \(\epsilon_{LP}>0\) is a threshold parameter and \(\rho_{LP}=\mu\lambda_{LP}\). To remove the S\(\alpha\)S noise, SLMS-LP based robust adaptive channel estimation is written as  
\[\mathbf{w}(n+1)=\mathbf{w}(n)+\mu\text{sgn}\left(e(n)\right)\mathbf{x}(n)-\frac{\rho_{LP} \left|\left.\mathbf{w}(n)\right|\right|_{p}^{1-p}\text{sgn}\left(\mathbf{w}(n)\right) }{\epsilon_{LP}+\left|\mathbf{w}(n)\right|^{1-p}} \tag{30}\]
# Numerical Integration Schemes Based on Composition of Adjoint Multistep Methods
## 5 Experimental Investigation
### Energy Preservation  
A numerical test for energy preservation was carried out for the following Hamiltonian system with non-separable Hamiltonian:  
\[H(p,q)=\frac{1}{2}(p^{2}+1)(q^{2}+1),\]  
which leads to the following nonlinear ODE system:  
\[\dot{p} =p(q^{2}+1) \tag{24}\] \[\dot{q} =q(p^{2}+1).\]  
In Figures 6 and 7 the variations of Hamiltonian for test system solved by two-step Adams-Bashforth method, two-step Adams-Moulton and corresponding composition schemes are presented. Initial conditions were \((p_{0},q_{0})=(2,0)\), integration step was set as \(h=0.1\). It can be clearly observed, that the composition version of the methods allows preserving Hamiltonian of the test system in both cases, which indicates the presence of desired geometrical properties of designed methods.  
Figure 5: Efficiency plot for two-step Adams-Bashforth method (AB2), two-step Adams-Moulton method (AM2), composition schemes based on them (AB2 comp and AM2 comp) and three-step Adams-Moulton method (AM3).
# Involvement of _E. coli_ 6S RNA in Oxidative Stress Response
## 2 Results  
### The Oxidative Stress Phenotype of the 6S RNA-Deficient Strain  
We previously constructed an _E. coli_ MG1655 \(\Delta ssrS\) strain where we replaced the 6S RNA gene (\(ssrS\)) with a kanamycin resistance cassette [34]. In the course of phenotypic strain analyses, we noticed an effect of _E. coli_ 6S RNA gene deletion on cell growth and survival under oxidative stress conditions. Upon inoculation of fresh medium containing 5 mM H\({}_{2}\)O\({}_{2}\) with stationary phase cells (overnight culture), we observed an extended delay in outgrowth for the \(\Delta ssrS\) strain relative to the parental MG1655 (wild type, WT) strain (Figure 1).  
This relative outgrowth delay reached 1 to 2 h (Figure 1) or even up to 4 h in other experiments (Figure S1). The exact period of the lag phase varied between individual experiments, possibly due to fluctuations in media preparations that might affect the rate of H\({}_{2}\)O\({}_{2}\) reduction. The phenotype was reproducibly observed for growth in liquid culture flasks (Figure 1a) and 96-well plates (Figure 1b) despite the differences between these two setups, including medium volume, stirring speed and aeration. In the presence of 2 mM H\({}_{2}\)O\({}_{2}\) we observed the same effect, although the lag phase extension of the \(\Delta ssrS\) strain tended to be somewhat shorter (\(\sim\) 1 h; Figure S2). The presence of 10 mM H\({}_{2}\)O\({}_{2}\) caused very long (>12 h) outgrowth delays for both, the WT and \(\Delta ssrS\) strain, with increasing variations between replicates (data not shown), thus excluding the calculation of meaningful average growth curves.  
Figure 1: Lack of 6S RNA leads to an extended delay in outgrowth of _E. coli_ cell cultures in the presence of 5 mM H\({}_{2}\)O\({}_{2}\). Growth curves of _E. coli_ MG1655 WT (green symbols) and \(\Delta ssrS\) bacteria (red symbols) in LB medium in the absence or presence of 5 mM H\({}_{2}\)O\({}_{2}\). Cells were either grown in liquid culture flasks (**a**) or in a 96-well microtiter plate format (**b**) with manual monitoring of optical density at 600 nm (OD\({}_{600}\)) in three and six biological replicates, respectively.  
To explore whether retarded growth of the knockout strain resulted from a higher frequency of cell death, we analyzed the number of survivor colonies after H\({}_{2}\)O\({}_{2}\) treatment by plating of serial culture dilutions. To further accentuate the difference between the two strains, we increased the concentration of H\({}_{2}\)O\({}_{2}\) in the culture medium to 50 mM and withdrew culture aliquots just after 30 min of incubation. In these experiments, \(\Delta ssrS\) bacteria showed at least 10-fold lower viability than the parental WT strain (Figure 2a). We additionally tested the influence of H\({}_{2}\)O\({}_{2}\) on _E. coli_ growth on solid medium by a conventional inhibition zone assay. This also demonstrated a reproducibly higher sensitivity of the \(\Delta ssrS\) strain to H\({}_{2}\)O\({}_{2}\) (Figure 2b).
# CL-TAD: A Contrastive-Learning-Based Method for Time Series Anomaly Detection
## 4 Experiments and Discussions
### Benchmark Datasets  
The proposed method was tested on a combination of two univariate datasets--UCR and Power Demand--as well as seven multivariate datasets, namely ECG, 2D-Gesture, PSM, SMD, MSL, SWaT, and WADI. Power Demand [21; 27; 54] is a univariate dataset that records the yearly power demand at a Dutch research facility. The UCR (HexagolML)  
Figure 11: Illustration of point adjustment technique.  
dataset [43; 55] was a part of the KDD-21 competition and serves as a univariate anomaly detection benchmark. It is divided into four subsets: 135_136_137_ and 138_.  
The ECG dataset [21; 27; 54], consisting of two-dimensional electrocardiogram readings, is further segmented into six sub-problems: chfdb_chf01_275 (ECG-A), chfdb_chf13_45590 (ECG-B), chfdbchf15 (ECG-C), lststdb_20221_43 (ECG-D), lststdb_20321_240 (ECG-E), and mitdb_100_180 (ECG-F). The 2D-Gesture dataset [21; 27; 54] records two-dimensional X-Y coordinates of hand gestures captured in videos. The PSM dataset [42; 56], with 25 dimensions, logs data from various application server nodes at eBay. The SMD dataset [43; 57] is a 38-dimensional dataset capturing five weeks of resource utilization across 28 computers. Only the non-trivial traces labeled as _machine-1-1, 2-1, 3-2, 3-7_ were used in our experiments as per [43; 57]. The MSL dataset [43; 58], 55-dimensional in nature, comprises soil samples from the NASA Mars rover. Only the non-trivial trace, C2, was considered for anomaly detection, consistent with [43; 58]. The SWaT dataset [42; 59] has 51 dimensions and was gathered over a week of regular operations and four days of irregular operations at a real-world water treatment plant. Lastly, WADI [42; 59] is an extended version of SWaT, boasting 127 dimensions, and encompasses data from a broader array of sensors and actuators. Table 1 presents the statistics of those datasets.  
All datasets have been normalized to fit within the range \([0,1]\). For datasets comprising multiple time series, each series underwent independent normalization. The normalization method is elaborated upon in Algorithm 10 located in Appendix A. Figure 12 offers a visual representation of the normalization for a time series from the two-dimensional ECG dataset. The green sections in the figure mark an anomalous event.  
Figure 12: Normalization of a time series in the two-dimensional ECG data where each colored curve shows a trace of a signal. **Top left**: an original normal time series; **Top right**: an original time series with an anomaly; **Bottom left**: the corresponding normalized normal time series; **Bottom right**: the corresponding normalized time series with an anomaly.
# Industrial Internet of Things for a Wirelessly Controlled Water Distribution Network
## 8 Simulation
### Trusted Wireless Network
#### 8.2.3 Cluster Head T/1/677/0/96  
Figure 23 shows that the connection between the CH and well T/1/656/0/96 is feasible, with the required margin being achieved at 14 dBm. Although not part of the initial design, the link between CH T/1/677/0/96 and well T/1/678/0/96 also performed well, reaching the margin at about 15 dBm using the same 9 dBi gain for both locations. This effective connectivity is partly attributed to the 15 dBi gain antenna used to connect the CH with the main tank, broadening its range to include remote nodes at a distance. The final link parameters are summarized in Table 12.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sector/Link**} & **Distance** & \multirow{2}{*}{**Antenna Type**} & **TX Power** & **Antenna** & **RSSI** & **Fresnel** & \multirow{2}{*}{**Status**} \\  & **(km)** & & **Antenna Type** & **(dBm)** & **Height (m)** & **Margin (dB)** & **Clearance** & **Status** \\ \hline Cluster Head & & & & & & & \\   ww2 & & & & & & & \\ \hline \multirow{2}{*}{ww1} & \multirow{2}{*}{1.26} & Allendale Electronics’ & \multirow{2}{*}{13} & \multirow{2}{*}{5} & \multirow{2}{*}{15.1} & \multirow{2}{*}{-80} & \multirow{2}{*}{Feasible} \\  & & Omnidirectional & & & & & \\ \hline T/1/676/0/96 & \multirow{2}{*}{0.47} & Allendale Electronics’ & \multirow{2}{*}{13} & \multirow{2}{*}{5} & \multirow{2}{*}{15.1} & \multirow{2}{*}{-80} & \multirow{2}{*}{Feasible} \\  & Omnidirectional & & & & & & \\ \hline \multirow{2}{*}{T/1/676/0/96} & \multirow{2}{*}{1.20} & Allendale Electronics’ & \multirow{2}{*}{13} & \multirow{2}{*}{5} & \multirow{2}{*}{15.7} & \multirow{2}{*}{-80} & \multirow{2}{*}{Feasible} \\  & Omnidirectional & & & & & & \\ \hline \multirow{2}{*}{T/1/678/0/96} & \multirow{2}{*}{0.47} & Allendale Electronics’ & \multirow{2}{*}{12} & \multirow{2}{*}{5} & \multirow{2}{*}{15.1} & \multirow{2}{*}{-80} & \multirow{2}{*}{Feasible} \\  & Omnidirectional & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 11: Link Parameters for Cluster Head ww2.  
Figure 23: Transmit Power vs. RSSI plot for CH T/1/677/0/96.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sector/Link**} & **Distance** & \multirow{2}{*}{**Antenna Type**} & **TX Power** & **Antenna** & **RSSI** & **Fresnel** & \multirow{2}{*}{**Status**} \\  & **(km)** & & **Antenna Type** & **(dBm)** & **Height (m)** & **Margin (dB)** & **Clearance** & **Status** \\ \hline Cluster Head & & & & & & & \\ T/1/677/0/96 & & Omnidirectional & & & & & \\ \hline T/1/656/0/96 & 1.20 & RAD-ISM-2400-ANT-OMNI-9 & 14 & 5 & 15.7 & \(\sim\)80 & Feasible \\ \hline \multirow{2}{*}{T/1/678/0/96} & \multirow{2}{*}{1.87} & RAD-ISM-2400-ANT-OMNI-9 & 15 & 5 & \(\sim\)15 & \(\sim\)80 & Feasible \\  & Omnidirectional & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 12: Link Parameters for Cluster Head T/1/677/0/96.
# NOMA-Based VLC Systems: A Comprehensive Review
## 1 Introduction
### Organization of the Paper  
This work is arranged as follows. Section 2 discusses the associated NOMA-based VLC research contributions. Section 3 discusses the integration of NOMA-based VLC with emerging technologies. Section 4 highlights several challenges and open research issues. Section 5 concentrates on future research directions. Lastly, Section 6 brings the paper to a close.
## 4 Fault Experiment Signal Analysis
### Case Western Reserve University (CWRU) Dataset  
#### 4.1.1 Description of Experimental Equipment and Bearing Data  
Data from the Electrical Engineering Laboratory at Case Western Reserve University were utilized to further demonstrate the effectiveness of the proposed method on actual rolling bearing fault signals [46]. The experimental setup, illustrated in Figure 10, included an electric motor, a torque transducer, and a dynamometer, etc. Table 2 displays the dimensional parameters of the test bearing. An accelerometer, with a sampling frequency of 12 kHz, was installed on the electric motor. In the experiment, an EDM at 1797 r/min was used to induce three single-point faults on the bearing: an inner fault, an outer fault, and a ball fault. Each fault type was characterized by three diameters, namely the early (0.1778 mm), middle (0.3556 mm), and late (0.5334 mm) stages, resulting in nine different states and one healthy state (see Table 3). The acquired samples were allocated into training, validation, and test sets, with ratios of 70%
## 5 Simulation  
### Simulation Setup  
In this section, we reveal the results of our novel optimization technique using computer simulations, focusing on addressing the resource allocation problem within communication networks for electric vehicle charging, enhanced by RISs. The results underscore the efficacy of our approach in enhancing the aggregate transmission rate, thus highlighting its competitive edge in addressing this pertinent issue.  
We consider an RIS-assisted electric vehicle charging network communication system, where a base station equipped with N = 4 antennas serves L = 4 vehicle users, and the auxiliary RIS is equipped with M = 8 \(\times\) 8 reflector elements. The vehicles are uniformly distributed and move freely on a road with a width of 10 m and a length of 20 m at a speed of vs. = 18 km/h. The carrier frequency and bandwidth are set to fc = 5.9 GHz and B = 20 MHz, respectively. The channel matrices between the base station and RIS and between the base station and users, denoted as \(\mathbf{H}_{BR}\) and \(\mathbf{H}_{RR}\), respectively, follow dynamic Rayleigh distributions. The system parameters are summarized in Table 2.  
For comparison, we employ four benchmark schemes: (1) a phase control scheme based on deep deterministic policy gradient (DDPG) technology, (2) a phase control scheme  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline BS antenna num. \(N\) & 4 & RIS element number & \(8\times 8\) \\ \hline Number of electric & 4 & Bandwidth B & 20 MHz \\ vehicle users & & & \\ \hline BS transmission & 20 dBm & \
\begin{tabular}{c} VU hardware cost \\ power \\ \end{tabular} & 10 dBm \\ \hline RIS hardware power & 10 dBm & Path loss/1 m & \(-\)30 dBm \\ \hline Target SINR threshold & 20 dBm & Power of noise & \(-\)80 dBm \\ \hline Hidden layer & 256 &
\begin{tabular}{c} Hidden layer \\ size (critic) \\ \end{tabular} & 256 \\ \hline learning rate (actor) & 0.005 & Learning rate (critic) & 0.01 \\ \hline Discount factor & 0.99 & Optimizer & Adam \\ \hline Max training steps & 10,000 & Training batch size & 54 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
based on joint transmit beamforming and a phase shift design method, (3) a random phase shifting scheme, where the phase shifting of RIS reflector elements is randomly configured, and (4) a traditional scheme without RISs, utilizing only direct links between the base station and users.
# A Survey on the Impact of Intelligent Surfaces in the Terahertz Communication Channel Models
## 5 Discussions and Research Directions
### Research Directions
IS-aided WiNoC can be additionally integrated into machine learning models to enable the collaboration of multi-core chips [173; 174]. The communication channels, including the IS-to-WiNoC, can also be better modeled to incorporate objects, such as racks, blades, chassis, and data centers [175; 176]. Finally, other mmWave and THz ranges are worthy of exploration because they can enrich the present results of investigating IS-supported WiNoC using THz signals for communication, as there are still a few published results [133; 152].
## 3 Security Implications and Fundamental Approaches of Blockchain-Based Data Storage Methods
### The Role of Cryptographic Methods in Blockchain-Based Data Storage Systems
#### 3.2.2 Symmetric Encryption  
Symmetric encryption employs a single key for both encryption and decryption, making it significantly faster and computationally efficient. However, secure key management and distribution remain critical challenges [109; 115]. This approach is particularly useful for encrypting large datasets and supporting high-speed data transmission. For instance, symmetric encryption facilitates the secure and rapid transfer of large-scale healthcare records, preserving data confidentiality and integrity. Additionally, cryptographic hash functions are often used to verify data integrity and detect unauthorized modifications [58; 110; 113].  
Symmetric encryption is predominantly applied in off-chain solutions, where its speed advantage is crucial for large-scale data transfers [122; 123]. It is widely used in sectors such as education and logistics, where significant volumes of off-chain data require secure storage and transmission [124]. By ensuring high-speed data processing, symmetric encryption enhances scalability, despite its complex key management requirements [116; 117]. However, for seamless integration with blockchain, symmetric encryption requires additional security layers [112]. While it is not directly suitable for on-chain use, it remains an effective tool for securing off-chain data [125].  
In Table 6, the relationship between these storage methods and blockchain systems is analyzed in terms of storage environments and security considerations.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline
**Reference Number** & **Cryptographic Method** & **Storage Type** & **Application Domain** & **Security Requirement** & **Sectoral Applications** & **Advantages** & **Disadvantages** \\
[114; 118; 126; 127; 128] & Asymmetric Encryption & On-chain, Off-chain & Authentication and data integrity & High & Healthcare, finance & High security, authorized access & Processing time is high \\
[117; 122; 124; 129] & Symmetric Encryption & Off-chain & Big data transfer & Medium & Healthcare, education, logistic & Provides fast data transfer & Key management issues \\
[56; 109; 115; 119; 130; 131; 132] & Zero-Knowledge Proof (ZKP) & On-chain, Off-chain, Hybrid & Authentication and access control & High & Public sector, finance & Ensures privacy, verifies data & High computational cost \\ \hline \end{tabular}
\end{table}
Table 6: Blockchain-based cryptographic solutions and applications for data security.