esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Research Findings
### RQ1: What Techniques Are Utilized to Abuse the CPLS as C&C Communication Channels?
#### 5.2.7 Increasing Complexity  
In recent years, attackers have increasingly employed a combination of techniques, and often abuse multiple CPLS platforms concurrently to increase the resilience of the C&C infrastructure and complicate tracking and takedown efforts. For instance, we have observed cases where cybercriminals combine encoding and cryptography techniques to enhance the concealment of C&C operations. This trend implies that attackers are continually advancing their tactics in order to evade detection. The combination of techniques increases the difficulty of detection, suggesting that multi-layered security strategies are essential for effective defense.
## 5 Typical Medium Theories of Anderson Localization: Model Studies
### Off-Diagonal Disorder  
In this section, we extend the DCA and TMDCA formalisms to enable the study of off-diagonal disorder. The simplest model used to study the effects of disorder in materials is a single-band tight-binding model with a random on-site disorder potential. Such a model is justified when the disorder is introduced by substitutional impurities, as in a binary alloy where the substitution of host atoms by impurities only leads to changes of the local potential on the substitutional site and, on average, does not affect the neighbors. Then, the disorder appears only in the diagonal terms of the Hamiltonian coupling to the electronic density and hence is referred to as diagonal disorder. However, when the bandwidth of the dopant is very different from that of the pure host, such substitution results not only in the change of the local potential but may also affect the neighboring sites. A simple model to capture such effects should include both random local potentials and random hopping amplitudes which depend on the occupancy of the sites. The dependence of the hopping amplitude  
Figure 23: The TMDCA self-consistent loop.  
on the disorder configuration is usually referred to as off-diagonal disorder [173]. Of course, a proper theoretical description of realistic disordered materials requires the inclusion of both diagonal and off-diagonal randomness.  
To illustrate these ideas, we will employ a simple binary alloy model with random nearest-neighbor hoppings. Each site may be one of two types, A and B, with random diagonal potential depending on the type, \(V_{A}\) and \(V_{B}\), and hoppings between nearest neighbors \(i\) and \(j\), \(t_{ij}\), are introduced as  
\[t_{ij} = t^{AA},\text{ if }\quad i\in A,\quad j\in A \tag{52}\] \[t^{BB},\text{ if }\quad i\in B,\quad j\in B\] \[t^{AB},\text{ if }\quad i\in A,\quad j\in B\] \[t^{BA},\text{ if }\quad i\in B,\quad j\in A,\]  
with all others being zero. The hopping depends on the type of ion occupying sites \(i\) and \(j\) (Figure 24). We will assume that the alloy is completely random without clustering, with the concentration of \(A\) sites, \(c_{A}=1-c_{B}\).  
We may immediately see the difficulty that the off-diagonal disorder poses: the mean field, contained within \(\mathcal{G}\), depends upon the configuration of a site. Physically, the reason for this is clear. Consider the CPA (\(N_{c}=1\)) in our binary disorder model. Since the cluster/impurity site couples to the host only through the near-neighbor hoppings, it will depend on the occupancy of the impurity and neighboring sites. If we approximate the mean-field coupling with the Fermi's golden rule, then we might expect the coupling to depend on the square of the relevant near-neighbor hoppings multiplied by the local DOS. In the CPA with nearest-neighbor hoppings, this matrix element is just the nearest-neighbor hoppings. Since it depends on the occupancies, A or B, of the neighboring sites involved, we expect the mean-field coupling to depend strongly upon the type of impurity and its neighbors.
# The BER Performance of the LDPC-Coded MPPM over Turbulence UWOC Channels
## 4 BER Performance without the FEC  
To verify the efficiency of LDPC code to mitigate turbulence-induced fading, we compare the BERs with and without LDPC code in Section 5, prior to which approximated average BER of the MPPM UWOC system without FEC over GG-distributed fading channels are deduced.  
The average BER \(P_{b}\) is obtained by averaging the conditional BER \(p(e|\gamma)\) over the fading channels as:  
\[P_{b}=\int_{0}^{\infty}p(e|\gamma)p_{\gamma}(\gamma)d\gamma \tag{18}\]  
where \(p_{\gamma}(\gamma)\) is the PDF of instantaneous SNR, and its CDF \(F_{\gamma}(\gamma)\) can be obtained using (3) and (5), and is given as  
\[F_{\gamma}(\gamma)=\frac{\mathrm{Y}\!\left(\frac{\beta}{\rho},\left(\frac{ \gamma}{a_{\gamma}^{2}}\right)^{p/2}\right)}{\Gamma(d/p)}. \tag{19}\]  
A curve-fitting method is an alternative to derive the exact analytical expression of the conditional BER of MPPM-based systems [34; 43]. A hyper-exponential fitting technique is applied to derive the closed-form approximation expression of the conditional BER in [34]. An exponential fitting, which is a special case of hyper-exponential fitting, is adopted to obtain the tractable closed-form conditional SER expression in [31]. Here, the hyper-exponential fitting is used, and thus we assume \(p(e|\gamma)=b_{1}\exp\!\left(-b_{2}\gamma^{b_{3}}\right)\), where \(b_{1}\), \(b_{2}\) and \(b_{3}\in\mathbb{R}^{+}\) and are evaluated by fitting to the Monte Carlo-simulated BER in the absence of any fading (i.e., AWGN channel). The Monte Carlo simulation is verified to ensure the correctness prior to fitting, which is presented in Section 5. Then, with the integration by parts, change of variables and Gauss-Laguerre quadrature formulation [43], we have:  
\[P_{b}=-\int_{0}^{\infty}F_{\gamma}(\gamma)dp(e|\gamma)\] \[=b_{1}\sum\limits_{i=1}^{m}w_{i}\frac{\mathrm{Y}\left(\frac{ \delta}{P},\left[\frac{1}{a_{\gamma}^{2}\gamma}\left(\frac{w_{i}}{2}\right)^{ \frac{1}{2}}\right)^{p/2}\right)}{\Gamma(d/p)}; \tag{20}\]  
where \(x_{i}\) is the th zero of Laguerre polynomials \(L_{m}^{\beta}(x),w_{i}=\Gamma(m+\beta+1)x_{i}/\left\{m\!\left[(m+1)L_{m+1}^{ \beta}(x_{i})\right]^{2}\right\}\) is the corresponding weight coefficient and \(\beta=0\). \(m=10\) is assumed, and then values of \(x_{i}\) and \(w_{i}\) in (20) are shown in Table 1.
# Hollow-Core Optical Fibers for Telecommunications and Data Transmission
## 10 Splicing and Termination of Hollow-Core Fibers
### Splicing HCF to HCF  
When the spliced HCFs are identical, there is no Fresnel reflection and added loss due to MFD mismatch, described in Section 10.4. The main problem is reducing the length of the collapsed cladding and the resulting escape of light. The destruction of cladding can be prevented by using the \"tack-sweep-pulse\" arc fusion splicing technique, with the melting of glass only to a certain depth [105; 106]. This method also works for splicing fibers of dissimilar diameters, like HCF and SMF, but requires fitting a fusion splicer with additional devices.  
Butt-coupling without fusion can produce a zero-loss splice with proper fiber alignment and sealing of the joint. A possible solution is presented in Section 10.5.  
To minimize splice loss, fibers shall be rotated with respect to each other prior to fusion for maximum power transfer. This is because commercial HC-PBGF fibers deliberately have non-circular cores and/or small \"shunts\" (Figure 5) to suppress undesirable propagation of higher-order modes [32], while NANF fibers with few tubes and gaps between them have a non-circular mode field (Figure 12b). However, the improvement during NANF splicing is limited: [PERSON] et al. estimated the loss of the connection of six-tube NANF to SMF with an identical MFD, but full radial symmetry, to be 0.08 dB [84].
# An Overview of Modelling and Energy Management Strategies for Hybrid Electric Vehicles
## 2 Types and Characteristics of the Powertrains for HEVs
### Series Hybrid Electric Vehicle  
The series configuration has a main motor driving the vehicle forward, while the engine is connected to a generator to generate power for the main motor or battery [2]. The ICE and the propulsion wheels cannot be connected mechanically directly in this setup [29]. During the vehicle's movement, the engine is not directly involved in driving the whole vehicle, but in energy conversion in the form of driving the generator to generate electricity. During the vehicle braking process, the energy recovery function is carried out by the main motor and the braking energy is transferred to the power battery [30; 31]. A detailed picture of this HEV is depicted in Figure 1.  
Figure 1: Structural diagram of the series construction.  
The engine shaft in this design is not directly connected to the drive shaft, resulting in a straightforward construction. This allows the engine to operate in a high-efficiency range for extended periods. However, due to the multiple conversions between fuel and motor, the energy is not utilized as effectively as possible [32]. Generally speaking, this powertrain is commonly used in large vehicles, such as miners and buses, and in a few hybrid passenger cars, such as the Toyota Corolla Twin Engine and BMW i3.
# High-Throughput Line Buffer Microarchitecture for Arbitrary Sized Streaming Image Processing
## 5 Evaluation  
This section describes the experimental setup for evaluating the D-SWIM implementation. In the evaluation, we compare this work with SWIM in terms of hardware resource usage, timing, and power consumption on FPGA. Subsequently, we evaluate the D-SWIM workflow with dynamic programming for continuously processing images in different sizes. Furthermore, we present D-SWIM based hardware architectures for two real-world image applications (2D-Convolution and Harris Corner Detector). The implementations are compared to streaming architectures in prior works.
# Ku-Band Mixers Based on Random-Oriented Carbon Nanotube Films
## 2 Materials and Methods
### Measurement of S Parameter  
As RF devices tend to operate in the high-frequency band, if a normal probe station is used for testing, the connection line and the measuring equipment will incur significant power losses in the high-frequency band. Therefore, a special high-frequency probe station (Cascade Summit 1100, Cascade Microtech, Beaverton, OR, USA), coaxial cable, semiconductor analyzer (Keithley 4200 (Keithley Instruments, Cleveland, OH, USA) and Agilent B1500 (Agilent Technologies, Santa Clara, CA, USA)), GSG probe, and vector network analyzer (Agilent E8363B) were used. At the same time, during the process of RF transistor fabrication, it should be noted that the spacing of pads should meet the specification of the 150 \(\upmu\)m GSG probe. Before measurements, GSG probe heads and coaxial cables were standardized using off-wafer short-open-load-through (SOLT). DC bias \(V_{\mathrm{gs}}\) and \(V_{\mathrm{ds}}\) of CNT-based RF transistors were provided by semiconductor analyzers. The gate and drain were port \"1\" and port \"2\", respectively. The vector network analyzer was used to measure the S-parameters of the two ports (S\({}_{11}\), S\({}_{21}\), S\({}_{12}\), S\({}_{22}\)).
# An Integrated Platform for the Internet of Things Based on an Open Source Ecosystem
## 2 Open Source Ecosystem for the IoT
### Application Layer Transmission Requirements
#### 2.3.2 MQTT Protocol  
MQTT is a lightweight publish/subscribe messaging protocol that can be used for M2M IoT connections. It is used for communication via satellite link or dial-up link between the sensor node and the MQTT broker. It has the characteristics of small code size, low power consumption, minimized data packet size, and can distribute packets to multiple applications at the same time. It also achieves loose coupling of application programs and became the OASIS standard in 2014. Eclipse Paho [38] provides an open source implementation of the MQTT protocol client, supporting languages such as Java, Python, Javascript, Go, C,.Net (C#), Embedded C/C++, etc. Moquette [39] implemented the Java-based Broker function of MQTT. MQTT.js is a Node.js-based MQTT client library that can be used in web development. MQTT-sn-tools [40] implemented the MQTT protocol version for use in sensor networks. These open source systems are regularly updated at the time of writing.
## 3 Results
### Quantifying Energy Input and Electrode Loss
It focused on three key controlled factors: A--the presence of magnet (not present or present), B--mixing speed (250 or 350 rpm), and C--process duration (10 or 20 min). Each of these factors was examined at two levels, which is in accordance with the principles of the L8 orthogonal array. Table 3 shows an overview of these factors and their experimental levels and results obtained. The signal-to-noise ratio (S/N) represents a key tool in the Taguchi method. This ratio measures how the response variable varies in relation to the target value under different conditions. The Taguchi method defines three basic quality characteristics that determine the method of calculating the S/N ratio: \"larger the better\"--when the goal is to maximize the response variable, \"nominal is best\"--when the goal is to achieve a specific target value, and \"smaller the better\"--when the goal is to minimize the response variable. In this research, the goal was to find optimal conditions for maximum reduction of COD, maximum settling velocity, minimum electrode consumption, and minimum turbidity. Accordingly, for the COD decrease and settling velocity, the \"larger the better\" characteristic was applied, while for the electrode consumption and turbidity, the \"smaller the better\" characteristic was applied. Used quality characteristics are expressed by the following equations:  
\[\text{S/N}_{\text{LB}}=-10\,\text{log}\frac{\sum_{i=1}^{\text{n}}\frac{1}{y_{i }^{2}}}{\text{n}} \tag{6}\] \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Exp. Mark**} & \multicolumn{2}{c}{**Factor**} & \multicolumn{2}{c}{**COD**} & \multicolumn{1}{c}{\(\mathbf{v_{t}}\)} & \multicolumn{1}{c}{**Turbidity,**} & **Electrodes** \\  & **A** & **B** & **C** & **Reduction, \%** & **cm/min** & **NTU** & **Loss, g** \\ \hline T1 & not present & 250 rpm & 10 min & 82.06 & 2.250 & 979.67 & 0.0820 \\ T2 & not present & 250 rpm & 20 min & 86.18 & 2.667 & 40.80 & 0.1800 \\ T3 & not present & 350 rpm & 10 min & 82.31 & 2.833 & 183.00 & 0.0713 \\ T4 & not present & 350 rpm & 20 min & 85.94 & 2.833 & 23.85 & 0.1581 \\ T5 & present & 250 rpm & 10 min & 81.09 & 0.560 & 300.50 & 0.0785 \\ T6 & present & 250 rpm & 20 min & 84.97 & 0.458 & 23.30 & 0.1465 \\ T7 & present & 350 rpm & 10 min & 79.88 & 1.956 & 630.50 & 0.0843 \\ T8 & present & 350 rpm & 20 min & 82.31 & 1.956 & 3.55 & 0.1478 \\ \hline \hline \end{tabular} Note: COD reduction is expressed as percentages of mean COD values. Standard deviation from the average value for COD is in the range \(\pm\)(11.50–30.90) mg\({}_{\text{O}}\)/L, for settling velocity is in the range \(\pm\)(0.023–0.142) cm/min, for turbidity is in the range \(\pm\)(0.14–39.19) NTU, and for electrodes loss is in the range \(\pm\)(0.0035–0.0090) g.  
\end{table} 
Table 3: Design of the experiments using Taguchi L8 and results obtained (means).
## 4 Materials and Methods
### Data Representation (Feature Extraction)  
Data representation plays a crucial role in preparing textual data for machine learning and deep learning models, as it transforms the raw textual information into a format that can be understood and effectively processed by these models. In this study, various techniques were applied to extract meaningful features from Arabic texts tailored to the specific requirements of machine learning and deep learning approaches; these methods are:  
* N-gram Feature Extraction: The text data were vectorized using the TF-IDF method, with configurations for word and character N-grams.
* Word N-grams: In a text, word N-grams store groups of words and show how these words relate to each other in context. To improve the feature extraction process, this study used word N-grams between 1 and 3. Using a range of 1 to 3 allows the model to analyze single words (unigrams), pairs of consecutive words (bigrams), and three-word sequences (trigrams). This approach allows the model to understand both the meanings of individual words and the relationship of words in short lines to each other.
* Character N-grams: In character N-grams, the study used characters that span up to 6 characters. This approach is very efficient for languages that have a rich morphological structure, such as Arabic, as it captures the morphological part of words. It also detects different spellings and common spelling errors that occur in digital communication.  
Figure 3: Distribution of tweets across dataset classes.  
* Word/Character N-grams: The study investigated a hybrid approach of word and character N-grams, where word N-grams from 1 to 3 were integrated with character N-grams from 1 to 6 to take advantage of both representations.
* Word Embeddings: For the deep learning models, the study used different methods to embed words. For CNN, RNN, LSTM and BiLSTM, we experimented with text representations by using trainable embeddings and pre-trained embeddings separately. More specifically, the pre-trained embeddings were generated from AraVec. AraVec is a pre-trained word embedding model that converts Arabic words into continuous vector representations. This model has been trained on large collections of Arabic texts, so that it can capture the complex linguistic patterns that characterize the Arabic language. The trainable embeddings, on the other hand, allowed the models to directly adapt the word representations to the dataset during the training process.  
AraBERT and BiAraBERT were used in their pre-trained models for fine-tuning in the text classification task of Arabic tweet hate speech detection. The pre-training, which integrates both, provides a deep understanding of the structure of the language, while the fine-tuning adapts it to the subtleties of the dataset.
# Artificial Heart Valve Scaffold Based on Electrospun PCL/PU Three-Layer Composite Fibers
## 2 Experimental Section
### Electrospinning Composite Fiber Heart Valve  
We weighed the medication using electronic scales (ME204/02, Mettler Toledo Technology, Wuhan, China), prepared the solution, and stirred thoroughly with a magnetic stirrer (HJ-4A, GuoHUA Electric Appliance Co., Ltd., Changzhou, China). A total of 2 g of PCL was dissolved in 8 g of DMF and 2 g of PU was dissolved in 8 g of medical acetone. They were configured into 20 wt% PCL solution and 20 wt% PU solution, respectively. A total of 2 g of PCL/PU was dissolved in 8 g of DMF and acetone at the corresponding ratios of 1:2, 1:1, and 2:1. When the mass ratio of PCL/PU is 1:2, 1:1, or 2:1, the mass ratio of DMF and acetone also corresponds to 1:2, 1:1, or 2:1. The mixture was placed on a magnetometer and stirred for 3 h. PCL solution (mass fraction 20%) and PU solution (mass fraction 20%) were prepared, and PCL/PU mixed solution (mass fraction 20%) was prepared (the ratios were 1:2, 1:1, and 2:1, respectively).  
A volume of 3 mL of the PCL solution with a mass fraction of 20% was put into a 5 mL syringe. This syringe was equipped with a stainless-steel needle (19G) that had an inner diameter of 0.67 mm and was connected through a plastic tube. The heart valve mold, pre-coated with methyl silicone oil, was securely mounted on the claw of a rotating motor. A needle holder capable of axial movement was prepared to ensure alignment parallel to the mold's axis, with the needle fixed accordingly. The syringe needle was attached to the positive electrode of a high-voltage DC power supply (DW-P403-1 ACF7, Dongwen, Tianjin, China), while the motor side clamping the mold was connected to the negative electrode. The electrospinning process was conducted at a voltage of 15 kV and a motor speed of 400 rpm, with the needle holder alternating axially at a speed of 1 cm s\({}^{-1}\), maintaining a 15 cm distance between the needle and the electrospun heart valve. This setup facilitated the creation of a PCL monolayer of randomly oriented fibers.  
Subsequently, the motor speed was increased to 1800 rpm, retaining all other experimental conditions, and a second ordered fiber layer was spun using five ratios containing 4 mL of PCL/PU blended solutions in ratios of 0:1, 1:2, 1:1, 2:1, and 1:0. The initial layer procedure was then replicated to produce a third disordered layer. The valve was delicately removed from the mold by making precise incisions with a sterile scalpel, yielding electrospun heart valve samples. In subsequent testing, 15 samples were used for each experiment, and three samples were tested for each component. This three-layered structure emulates the natural human heart valve's architecture, with the ordered-disordered arrangement designed to replicate mechanical properties akin to the natural valve.
# Take-Over Time: A Cross-Cultural Study of Take-Over Responses in Highly Automated Driving
## 3 Results
### Take-Over Time  
A multiple linear regression was conducted to investigate the effects of location, gender, age (young, old), and being engaged in IVIS (distracted) at TOR on the take-over time (in milliseconds). The six participants in Seattle, WA who did not respond to TOR (see Table 2) were removed prior to the analysis. The regression results show a collective significant effect of these variables on the take-over time (\(F_{(4,27)}=6.77\), \(p<0.001\), \(R^{2}=0.50\), \(R^{2}{}_{adj}=0.43\)). A significant effect was found for location (\(\beta=0.59\), \(t=3\), \(p=0.006\)) and being engaged in IVIS (distracted) at TOR (\(\beta=0.32\), \(t=2.2\), \(p=0.034\)), with the mean take-over time for the US participants (\(n=14\); \(M=3307\), \(SD=923\)) being significantly longer (\(M=+1285\)) than for the Slovenian participants (\(n=18\); \(M=2022\), \(SD=778\)). Age (\(\beta=-0.04\), \(t=-0.17\), \(p=0.87\)) and gender (\(\beta=-0.11\), \(t=-0.54\), \(p=0.59\)) had no statistically significant effect on the model. Figure 1 shows the distribution of take-over time data across location and being distracted at TOR.  
The individual predictors were further examined separately between locations. Post hoc pairwise \(t\)-tests revealed a significant difference in the mean take-over time between the two locations for the participants engaged in IVIS at TOR (see Figure 1). The distracted US participants (\(M=3775\), \(SD=595\)) had significantly longer mean take-over time (\(M=+1596\)) than the distracted Slovenian participants (\(M=2179\), \(SD=804\)), \(t=4.97\), \(p_{corr}<0.001\), Hedge's \(g=2.10\). No statistically significant effect on the mean take-over time was found for the participants not engaged in IVIS, \(t=1.91\), \(p_{corr}=0.17\), Hedge's \(g=1.01\). However, the difference in the mean take-over time between both locations is still large, with the non-distracted US participants (\(M=2683\), \(SD=951\)) having longer mean take-over time (\(M=+906\)) as compared to the non-distracted Slovenian participants (\(M=1777\), \(SD=723\)).  
The effect of being distracted with IVIS at TOR was also investigated within each location. The mean take-over time was compared per location for the participants who were engaged in IVIS (US: \(M=3775\), \(SD=595\); SI: \(M=2179\), \(SD=804\)) and those who were not (US: \(M=2683\), \(SD=951\); SI: \(M=1776\), \(SD=723\)). No statistically significant differences in the mean take-over time were found for either location (US: \(t=2.47\), \(p_{corr}=0.07\); SI: \(t=1.10\), \(p_{corr}=0.57\)). However, the mean take-over time was longer for the participants who were engaged in IVIS, and particularly for participants in Seattle, WA (US: \(M=+1092\); SI: \(M=+402\)).  
Figure 1: Take-over time per location and being distracted at TOR (with marked quartiles of the distributions).