esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model and Transmission Process
### System Model  
As shown in Figure 1, we consider a communication scenario working in underlay mode. The primary network consists of a pair of primary users, the primary transmitter (PT) and the primary receiver (PR). The secondary network is a downlink NOMA system,consisting of a base station (BS), multiple relays (\(R_{n}\), \(n=1\), \(2\),..., N), a near user (U\({}_{1}\)) and a far user (U\({}_{2}\)). While PT sends a message to PR, BS broadcasts a superimposed signal composed of U\({}_{1}\) and U\({}_{2}\) messages. U\({}_{1}\) can receive the signal via the direct link BS\(\rightarrow\)U\({}_{1}\). However, due to serious shadow or path loss, U\({}_{2}\) obtains the signal via relay nodes. The characteristic of the system model is that if [PERSON]\({}_{1}\) fails to decode the signal from the direct link, relays are first sought for help. Otherwise, [PERSON]\({}_{1}\) has a chance to become a candidate to assist [PERSON]\({}_{2}\). The system model is also based on the following assumptions:  
* All communication links and interference links are quasi-static Rayleigh fading channels, i.e., \(h_{XY}\sim CN(0,\lambda_{XY})\) where \(XY\) denotes the link \(X\to Y\) between the transmitting node \(X\) and the receiving node \(Y\);
* Each channel is affected by additive white Gaussian noise (AWGN) with the mean of 0 and the variance of \(\sigma^{2}\);
* All nodes (including transmitters, receivers and DF-based relays) with single antenna work in half-duplex (HD) mode and can perfectly receive channel state information (CSI);
* BS uses fixed power allocation while relays can adaptively adjust their power allocation coefficient;
* The transmit power of BS, \(R_{n}\) and U\({}_{1}\) is adaptive constrained in underlay mode.
## 4 Theoretical and Experimental Analyses
### Security  
#### 4.3.1 Analysis of Attack Resistance  
In the scenario where the attacker acquires any \(T-1\) secret shares, the reconstruction of the polynomial becomes infeasible. At most, only the \(T-1\) equations can be used to solve for \(T\) unknowns. An exhaustive enumeration of a given unknown \(p\) times yields \(p\) solutions, and since there is no correlation between individual pixels, the attacker strikes are unable to determine the correct solution to the equation from \(p\) different solutions. Assuming that the attacker collects any secret share greater than or equal to \(T\) shares, \(F(x,y)\) can be reconstructed by the Lagrange interpolation formula, which can recover the coefficients. Nevertheless, lacking an extraction key, the attacker remains incapable of determining the carrier pixel's location or extracting the embedded secret.
# A Systematic Review of Cyber Range Taxonomies: Trends, Gaps, and a Proposed Taxonomy
## 3 Proposed Taxonomy
### Phase I: Taxonomy Reviews, Systematic Literature Reviews, and Baseline Taxonomy
#### 3.1.1 Taxonomy Reviews
Their taxonomy built on [PERSON] et al. 's [11] work, but enhanced it to handle mobile devices. They divided the taxonomy into two main categories--_Management_ and _Training Environment_. 1. **Management** * **User Interface/API Gateway:** Interfaces or APIs for easier interaction with management functions. * **Exercise Design:** Tools that facilitate building complex, heterogeneous infrastructures and produce key scenario events. * **Competency Management:** Conducts skill-gap analyses and user profiling and designs learning paths. 2. **Training Environment** * **Scenario:** Emphasizes the software/hardware needed to run a scenario; includes physical systems like IoT devices. * **Support Services:** Enhances realism by interacting directly with the _Scenario_ dimension. * **Toolset:** Attack and defence tools for both Red and Blue teams. In addition, they added _Orchestration_, which received inputs from _Exercise Design_, creating a sandbox to match specifications, and _Data Collection_, signifying data extraction (service status, logs, etc.) from the _Training Environment_. The work by [PERSON] et al. [31] was not strictly a taxonomy paper, but presented the TARGET framework for the Cyber Range Exercise (CRX) design. This framework built on [PERSON] et al. 's [11] taxonomy, so we include it here. Their systematic review of the CRX design literature (2016-2022) underpinned the framework, making it a hybrid approach using a partial SLR and partial taxonomy-based evaluation tool. [PERSON] et al. [31] aimed to support evaluating CRX design more systematically, ultimately enriching the practical applications of earlier taxonomy work. In summary, research in this domain is notably diverse; some taxonomies are built on comprehensive systematic reviews, others rely on practical or domain-specific insights, and still others combine both approaches. [PERSON] et al. [11] laid a broad foundation, covering major areas like _Scenario_, _Monitoring_, and _Learning_, while [PERSON] et al. [12] refined it by adding dimensions for _Econometrics_, _Recovery_, and _Testbeds_. From the rest of the reviewed works, [PERSON] et al. [30] emphasized target-audience factors and proficiency levels, while [PERSON] et al. [13] enhanced [PERSON] et al. 's [11] model to better integrate mobile devices, adding dimensions like _Orchestration_ and _Data Collection_. Finally, [PERSON] et al. [31] created a framework for CRX design, informed by [PERSON] et al. 's [11] taxonomy, though it was not strictly a taxonomy in itself. Table 2 provides a structured overview, illustrating clearly how these key dimensions are represented across the selected taxonomies.
## 3 Experiment Setup and Evaluation
### Influence of Visual Contamination on the Analyzed Algorithms  
Figure 17 illustrates rock detection in various evaluated scenarios of visual contamination. The presented machine learning algorithms exhibit superior results compared to their variants. The ACF algorithm showed a significant number of _FPs_. It was observed that visual contamination from snow resulted in the highest number of _FPs_, followed by rain, while others maintained relatively similar amounts of _FPs_. Regarding the Viola-Jones algorithm, few _FPs_ were observed in the LBP feature, demonstrating its efficiency in capturing rock characteristics across all scenarios. In the normal scenario, _FP_ occurrences were  
Figure 16: Average detection time.  
Figure 15: P-R curve for the deep learning algorithms.  
observed at larger scales due to the processing performed by the algorithm, which utilizes a sliding window at multiple scales.  
Regarding deep learning algorithms, all showed promising results; however, YOLO v4 demonstrated excellent performance. The Faster R-CNN algorithm exhibited \(FP\) occurrences, while the SSD algorithm had no \(FP\)s but failed to detect some objects. In all cases, the YOLO v4 algorithm correctly detected rocks. Figure 18 illustrates a case where the YOLO v4 algorithm fails to detect one rock, primarily due to the absence of a well-defined boundary between two rocks and their overlapping level. The scenarios that most affected the algorithms overall were snowy and rainy conditions.  
Figure 17: Rock detection in various evaluated scenarios of visual contamination.
# Microstructure of Joint between Stranded Wire and Substrate Weld by Ultrasonic Welding
## 1 Introduction  
A coaxial connector is used to conduct high frequency signals to some devices. Recently, high quality connectors with low loss transmission have been required to achieve high capacity and high-speed communication. One of the key factors is the welding between the coaxial connector and coaxial cable. The coaxial cable consists of the outer conductors and the core stranded wires. In general, crimping is widely used to join the connector with both the outer conductors and the stranded wires. However, the mechanical strength and conductivity of the joint between the stranded wires and substrates tends to decrease at high temperature aging due to the oxidation at the interface [1; 2; 3]. For single Cu wire or ribbon welding, ultrasonic welding is an attractive method and has been studied by several researchers [4; 5; 6; 7; 8; 9; 10; 11; 12]. Within this method, the joint is quickly obtained without an external heat source in the air. Cu wires can be directly welded to Cu substrates [5; 10; 11; 12]. Further, coating of Cu wires or substrates by several metal layers for ultrasonic welding was developed to improve the weldability [4; 8; 9; 13; 14; 15]. To develop the welding techniques for the joint between the coaxial connector and the wire with low loss transmission of high frequency signal, we firstly applied the ultrasonic welding to the fine stranded Cu wire. In this paper, the joint between the fine stranded Cu wire with the Ag coating and a Cu substrate with Ni and Au coating layer was investigated. To make sure of the effectiveness of the welding, the microstructure of the welded interface was investigated using several electron microscopy techniques.
## 4 Result and Discussion
### Experimental Study  
Experiments were carried out to investigate the dynamic performance and verify the simulation method. The cooling characteristics of the refrigerator with energy consumption were studied experimentally. The operating parameters of the tests and simulations are listed in Table 2. In two tests, the refrigerator run under the same expansion ratio and boundary conditions. With optimal expansion efficiency, the expander operated under optimal characteristic ratio through the feedback control [4]. With certain brake pressure, the blower inlet pressure was fixed in the cooling process. Each experimental data was the mean of three replicates.  
As shown in Figure 11, the simulated results agreed well with the experimental data under two operation modes. The dynamic simulation method and analysis in Section 3 were verified. The refrigerator in test 1 had a faster cooling speed than that in test 2. The refrigerator ultimate temperature stabilized at 115.3 K in test 1 finally while it stabilized at 117.2 K in test 2. The ultimate temperatures in simulations 1 and 2 were 110.8 and 112.1 K, respectively. The relative deviations of the ultimate temperature in simulations 1 and 2 were 3.9% and 4.5%. The refrigerator achieved a  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Test 1** & **Simulation 1** & **Test 2** & **Simulation 2** \\ \hline Expander inlet & & & & \\ pressure (MPa) & 0.398–0.401 & 0.400 & 0.398–0.400 & 0.400 \\ Expander inlet & & & & \\ temperature (K) & & & & \\ Expander outlet & & & & \\ pressure (MPa) & 0.107 & 0.107 & 0.107 & 0.107 \\ Operation method & Feedback control & & \(p_{B}=0.100\) MPa & \\ \hline \hline \end{tabular}
\end{table}
Table 2: The operating parameters of test and simulation.  
Figure 10: Compression work and total energy: (**a**) Under different rotating speed; (**b**) total energy at 170 krpm; (**c**) total energy at 190 krpm; (**d**) total energy at 210 krpm.  
better cooling effect under optimal expansion efficiency. But with a proper constant brake pressure, the refrigerator could still obtain fairly good results. It verified the conclusions in Section 4.1 and showed that the simulation model can be applied in actual applications.  
There were errors between the simulated and tested results. That is because the models of expander and heat exchanger both adopted some simplifications and assumptions. To simplify the calculation, the heat exchanger was simulated using a one-dimensional method, which caused the deviation. Also, the expansion process was considered as quasi-steady during the cooling process. These resulted in the limitations of the simulation method. The simulation method is more suitable to be applied in the working condition without dramatic change. The simulated results will be satisfactory for a long running turbo-based refrigerator.
## 3 Methodology
### Extended W2 reg for Multi-Class Classification
#### 3.3.3 Proposed Training Procedure  
The proposed strategy to train multi-class classifiers with mitigated algorithmic biases on specific classes' prediction was motivated by the future need of certifying that automatic decision models are not discriminatory. In order to make absolutely clear our strategy, we detail it in Algorithm 1.  
```
0: Training observations \((x_{i},s_{i},y_{i})_{i=1,\dots,n}\), where \(x_{i}\in\mathbb{R}^{p}\), \(s_{i}\in\{0,1\}\) and \(y_{i}\in\{0,1\}^{K}\), plus a multi-class neural network model \(f_{\theta}\).
1:[Detection of the output classes with discriminatory predictions]
2: Train the baseline parameters \(\theta^{bl}\) of \(f\) on \((x_{i},s_{i},y_{i})_{i=1,\dots,n}\) with no specific regularisation.
3: Find the output classes \(\{c_{1},\dots,c_{C}\}\) on which the model \(f_{\theta^{bl}}\) has unacceptable True Positive Rate gaps (TPRg) using Equation (6).
4:[Multi-class W2 reg training]
5: Re-initialise the training parameters \(\theta\).
6:for\(e\) in epochs do
7:for\(b\) in batches do
8: Draw the batch observations \((x_{i},s_{i},y_{i})_{i\in B}\), where \(B\) is a subset of \(\{1,\dots,n\}\).
9: Compute the mini-batch predictions \(f_{\theta}(x_{i})\), \(i\in B\).
10: Detect the output classes \(\{\widehat{c_{1}},\dots,\widehat{c_{D}}\}\) among \(\{c_{1},\dots,c_{C}\}\) for which at least a \(y_{i}^{j}=1\), with \(j\in\{1,\dots,C\}\) and \(i\in B\).
11: For each \(j\in\{1,\dots,D\}\), pre-compute \(H_{\widehat{c_{j}},0}\) and \(H_{\widehat{c_{j}},1}\) using \(m\) output predictions \(f_{\theta}(x_{i})\), where \(i\
otin B\) and \(y_{i}^{\widehat{c_{j}}}=1\).
12: Compute the empirical risk and its derivatives with respect to \(f_{\theta}(x_{i})\), \(i\in B\).
13: Compute the pseudo-derivatives of the discretised \(W_{2}^{2}(h_{\theta,0}^{\widehat{c_{j}}},h_{\theta,1}^{\widehat{c_{j}}})\) with respect to the pertinent mini-batch outputs \(f_{\theta}(x_{i})^{\widehat{c_{j}}}\) using Equation (8).
14: Backpropagate the risk derivatives and the pseudo-derivatives of the \(W_{2}^{2}\) terms.
15: Update the parameters \(\theta\).
16:endfor
17:endfor
18:return Trained neural network \(f_{\theta}\) with mitigated biases.
```  
**Algorithm 1** Procedure to train bias mitigation multi-class neural network classifiers.
# Digital Pre-Distortion of Carrier Frequency Offset for Reliable Wi-Fi Enabled IoTs
## 3 Problematic RF Impairments in a Narrowband
### DC Offset  
DC offset arises from nonlinearity and self-mixing in the front-end [8; 14]. Large DC offset at the output of the mixer is amplified by large gain and causes saturation of the following RX path. When this happens, the DC offset will cause the receiver to fail. Typically, DC offset cancellation circuits can be included in the signal path in order to remove DC components. For typical WLAN direct conversion receivers, most DC offsets have to be mitigated in the analog domain to prevent clipping signals due to large DC offsets at high gain settings; then, a digital DC canceller compensates for the residual DC offset. Popular DC offset compensation schemes include offset estimation/cancelling, AC-coupling, and many other methods that use adaptive techniques [8].  
However, if the high pass filter (HPF) pole is placed too high in frequency domain, it may cause a severe degradation in the error vector magnitude (EVM) of the lower subcarriers. At the same time, the filter poles cannot be placed too low in frequency because this would result in a very slow transient response. Direct conversion receivers reduce the circuit complexity and power consumption of superheterodyne receivers. Direct conversion receivers, however, are susceptible to noise from sources including DC offset and carrier frequency offset.
# Polyimide Encapsulation of Spider-Inspired Crack-Based Sensors for Durability Improvement
## 2 Experimental Section  
### The Fabrication of an Encapsulated Thin Metal Layered Crack Sensor  
The basic features of the encapsulated crack sensor are demonstrated in Figure 1a. The sensor was 5 mm in width and 50 mm in length, and the gauge length was 30 mm. It was composed of five layers: 7.5 \(\upmu\)m thick PI film (3022-5 Kapton thin film, Chemplex, Palm City, FL, USA) or 6 \(\upmu\)m thick PET film (3026 Mylar thin film, Chemplex, Palm City, FL, USA) as the bottom substrate layer, 5 nm thick molybdenum trioxide (MoO\({}_{3}\)) as an adhesion layer, 50 nm thick chromium (Cr) as a crack inducing layer, 30 nm thick gold (Au) for an electrical conductor, and lastly 650 nm thick cured polyimide film (Poly(pyromellitic dianhydride-co-4,4\"-oxydianiline), amic acid solution, Sigma-Aldrich, St. Louis, MO, USA ) on the top layer for the encapsulation of the total system. The PI precursor was spin-coated on the cracked metal films and it was cured on a hot plate in the N\({}_{2}\) atmosphere (80 \({}^{\circ}\)C for 30 min and 290 \({}^{\circ}\)C for 30 min). MoO\({}_{3}\) is known as an adhesion layer between metal and polymer [17], and we simply tested it with \"scotch tape test\" [22]. It was confirmed that no defect occurred when the scotch tape was attached and detached on the metal surface (Figure S1\(\dagger\)). The thin film layers (MoO\({}_{3}\), Cr, and Au) on the polymer substrate are deposited by the thermal evaporation system (Thermal Evaporation System, DD High Tech. Co., Gimpo-si, Gyeonggi-do, Korea). The thickness of deposited metals and polymers are measured by AFM (Atomic Force Microscopy) measurement.  
Figure 1: Schematic illustrations and scanning electron microscope (SEM) images of polyimide (PI) encapsulated crack sensor. (**a**) The schematic illustration and optical microscope image of the PI encapsulated crack sensor. (**b**) SEM image of the crack sensor before encapsulation (left), and cross-sectional SEM image after encapsulation (right). (**c**) The schematic illustration of generated cracks on metal layer before stretching. (**d**) The schematic illustration of widened cracks after stretching.
## 7 Applications and Future Research Directions of RIS
### RIS-Aided Millimeter (mmWave) MIMO Systems
#### 7.2.1 Channel Estimation for mmWave MIMO Channels Using RIS  
By passively modifying the directions of impinging electromagnetic waves, a RIS can shape radio propagation for mmWave MIMO Channels. However, perfect channel state information (CSI) of all links between the BS and the MS via the RIS is required for optimal RIS control. As a result, channel (parameter) estimation and the associated message feedback mechanism are required at the BS/MS. The authors in [106] suggested a two-stage channel estimation approach for RIS-aided mmWave MIMO channels, which uses an iterative reweighted method to estimate the channel parameters sequentially.
## 3 Proposed Design
### Mapping of Proposed A-MPDU Mechanism with Superframe  
To adjust the aggregation mechanism with superframe of IEEE 802.15.4, we need to compute such the superframe duration (SD) where at least one or more than one A-MPDU could transmit. The possible maximum size in our scenario is 120 bytes as mentioned in Equation (8) and the transmission time for a single A-MPDU is 3.84 as mentioned in Equation (9). The superframe duration (SD) represents the duration of superframe which consists of 16 time slots [31] is:  
\[\mathrm{SD}\ =\ \mathrm{aBaseSuperframeDuration}\ \times\ 2^{\mathrm{SO}}\ \mathrm{symbols} \tag{14}\]  
where superframe order is represented by SO. aBaseSuperframeDuration is calculated as  
\[\mathrm{aBaseSuperframeDuration}\ =\mathrm{aBaseSlotDuration}\ \times\ \mathrm{aNumSuperframeSlots}\ \times\ \mathrm{symbolTime} \tag{15}\]  
Symbol time using 2.4 GHz frequency band and symbol rate 4 is  
\[\begin{array}{c}\mathrm{symbolTime}\ =\ 1\ /\ (\mathrm{phyDataRate}\ \times\ 1000\ /\ \mathrm{phyBitsPerSymbol})\\ \mathrm{symbolTime}\ =\ 1\ /\ (\ 250\times\ 1000\ /\ 4)\ =\ 0.016\ \mathrm{ms}\\ \mathrm{The default value of\ aBaseSuperframeDuration\ with\ (\mathrm{SO}=0)=\ 60\times\ 16\times\ 0.016\ =15.36\ \mathrm{ms}}\end{array} \tag{16}\]  
The single slot time should be at least 3.84 ms as mentioned in Equation (9). Using Equation (14) the suitable value of SD is computed which fulfils the slot duration requirement.  
\[\mathrm{SD}\ =\ 15.36\times\ 2^{2}=61.44\ \mathrm{ms}\]  
The single slot duration is computed as  
\[\mathrm{Single\ slot\ duration}\ =\ \mathrm{Total\ SD\ time}/\mathrm{Number\ of\ slots}\ =\ 61.44/16\ =\ 3.84\ \mathrm{ms} \tag{17}\]  
It is noted that 3.84 ms is the same time as computed in Equation (9), which means that SO should be at least 2 or greater than 2 so that a complete A-MPDU could be transmitted.
## 3 Simulation Results
### Noise Reduction with Respect to the Noise Source Angle  
Based on the results presented thus far, the advantage of using a circular array is that the global noise reduction performance is acceptable, and the proposed ANC system configuration is simpler than the ANC system when 3D spatial information is used. However, the greatest drawback is that, when the vertical component of the noise source increases in the ROI, the circular array does not adequately reproduce the wave field of the noise source. This disadvantage will yield the same results in the ANC system. The arrangement of the microphones and loudspeakers is the same as the existing simulation, and Figure 9 shows how noise reduction is achieved depending on a noise source angle that is 5 m away from the origin. The \"0 \({}^{\circ}\)\" case implies that the noise source exists in the x-y plane, and the \"90 \({}^{\circ}\)\" case implies that it exists at [0 m, 0 m, and 5 m]. The angle at which noise is no longer reduced is different depending on the radius of the microphone array, and the radii of the loudspeaker array all yielded similar results. This information demonstrates the possibility of using a circular array for the angle of incoming noise when installed in an indoor space made of a sound-absorbing material (ceiling and floor).  
Figure 8: (**a–c**) Residual wave fields and (**d**) average noise reduction spectrum.
## 2 Materials and Methods
### Data Processing Module
#### 2.1.2 Data Normalization and Transformation  
Cellular data are considerably complex and are composed of underlying signals with vastly different properties. Moreover, cell data is distributed randomly. To alleviate the effect of outliers from our analyses, min-max normalization is considered. This technique scales data into the range (0, 1). The formula used to achieve this is as follows [28]:  
\[x_{normalized}=\frac{x-min\;(x)}{max\;(x)-min\;(x)} \tag{1}\]where \(min\)\((x)\) is the minimum of the data and \(max\)\((x)\) is the maximum value of the data traffic. Real-time prediction of data traffic requires continuous data input and learning. Hence, we employ the notion of the sliding window, which indicates a fixed number of previous timeslots to learn and then predict the current data traffic. Finally, we split the processed data into two sets: training and testing. Our feature set should contain the traffic values for the last 24 h, while the label or dependent variable should be the traffic value at the 25 th hour. In order to train the LSTM (Long Short-Term Memory) model by use of a training dataset, data needs to be converted into the shape acceptable for LSTM architecture. Therefore, data require reshaping into a three-dimensional format. The first dimension is the number of records or rows in the dataset, which in this case is 8732. The second dimension is the number of time steps, which is 24, and the last dimension is the number of indicators that are equal to 1 for the given dataset.