esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Performance Test in High-Temperature Sodium Salt  
A 400 mL sample of the freshwater-based slurry was taken, and 3% synthetic tackifier was added while stirring at 3000-6000 r/min for 20 min. The rheological parameters were then measured using a six-speed rotary viscometer. Different amounts of NaCl (5%, 15%, and 25%) were added to the freshwater-based slurry, which was then aged at 200 \({}^{\circ}\)C for 16 h to investigate the differences in viscosity changes. See Table 5 for the data.  
As the NaCl content increases, Table 5 shows that the initial viscosity of the base slurry gradually rises. However, after aging at 200 \({}^{\circ}\)C for 16 h, the viscosity decreases as the NaCl content increases. The increase in initial viscosity may be attributed to the tackifier's salt-sensitive nature; as the salt content rises, the viscosity also increases within a certain range. Another possibility is the aggregation of hydrophobic groups, which contributes to the increase in viscosity. However, high-temperature aging decreases the pH value, further affecting the hydration groups and diminishing the viscosity-enhancing effect of  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Age** & **Age** & **AV** & **PV** & **YP** & **YP/PV** & **Viscosity** \\
**Condition** & **Cyclic** & **mPa-S** & **mPa-S** & **Pa** & **Pa/mPa-S** & **Retention** \\  & & & & & & **Rate (\%)** \\ \hline room temperature & 0 & 11.5 & 10 & 1.5 & 0.15 & - \\
240 \({}^{\circ}\)C & 1 & 10.5 & 8 & 2.6 & 0.32 & 91.3 \\ \hline \hline \end{tabular} Note: The absence of the second, third, or fourth aging cycle at 220–240 \({}^{\circ}\)C is due to the large number of bubbles generated after high-temperature aging cooling and stirring with a high-frequency high-speed mixer, which cannot measure its viscosity.  
\end{table}
Table 4: Effect of aging time on the viscosity enhancement performance at 240 \({}^{\circ}\)C.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Sample** & **Age** & **AV** & **PV** & **YP** & **YP/PV** & **Viscosity** \\
**Condition** & **mPa-S** & **mPa-S** & **Pa** & **Pa/mPa-S** & **Retention** \\  & 0\%NaCl & & & & & **Rate (\%)** \\  & Before aging & 7 & 6 & 1.02 & 0.17 & - \\  & After aging at 200 \({}^{\circ}\)C & 30 & 20 & 10.2 & 0.51 & 428 \\ \cline{2-6}  & 5\%NaCl & & & & & \\ Freshwater-based & Before aging & 8.5 & 6 & 2.56 & 0.43 & - \\ pulp +3\%DT & & 23 & 16 & 7.15 & 0.45 & 270.6 \\  & 15\%NaCl & & & & & \\ Before aging & 12 & 9 & 3.07 & 0.34 & - \\ After aging at 200 \({}^{\circ}\)C & & & & & & \\ \cline{2-6}  & 25\%NaCl Before aging & 10 & 9 & 1.02 & 0.11 & - \\ After aging at 200 \({}^{\circ}\)C & 13 & 10 & 3.07 & 0.31 & 130 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Viscosity changes of different contents of NaCl added to 200 \({}^{\circ}\)C freshwater-based slurry.  
the tackifier. In this process, it can be observed that a higher salt content correlates with a more significant decrease in viscosity.
## 3 IEEE 802.15.6: The Key-Enabling Standard for WBANs
### IEEE 802.15.6 MAC Layer
#### 3.3.1 MAC Frame Format  
The MAC Protocol Data Unit (MPDU) is an ordered sequence of fields delivered to or from the PHY Service Access Point (PHY SAP). The MAC frame consists of a fixed-length MAC header (seven octets), a variable-length MAC frame body and a fixed-length Frame Check Sequence (FCS) field (two octets), as shown in Figure 8.  
The MAC frame body has an octet length _L FB_ such that _0 \(\ll\)L FB \(\ll\)pMaxFrameBodyLength_, and is present only if it has a nonzero length, where '_pMaxFrameBodyLength_' is the maximum frame body length at the physical layer. The Low-Order Security Sequence Number and Message Integrity Code (MIC) fields are not present in unsecured frames. Management, Control, and Data type are the three MAC frames that are described in detail in the standard. Each of them implies a different composition of the MAC Frame Body, in particular for the Frame Payload Field.  
Figure 8: IEEE 802.15.6 MAC Frame Format [9].
# Detection of Threats to Farm Animals Using Deep Learning Models: A Comparative Study
## 3 Materials and Methods
### Experimental Environment  
The experimental investigations were executed in a meticulously configured computational setting to ensure the integrity and reproducibility of the results. Utilizing MATLAB (R2020a), renowned for its robust capabilities in data analysis and algorithm development, the computational framework was designed to support intensive processing tasks. The system was equipped with an NVIDIA RTX 2080 GPU, (Santa Clara, CA, USA) acclaimed for its exceptional processing power and efficiency in handling parallel computations--essential for deep learning and complex image processing. Additionally, the computational setup included 32 GB of RAM, enhancing the system's ability to manage and manipulate large datasets effectively. At the heart of the hardware configuration was an Intel Core i7 (Santa Clara, CA, USA) processor, providing the computational velocity and multitasking efficiency required for the study. This integration of advanced GPU capabilities, substantial memory capacity, and a high-performance CPU created an optimal environment for executing our research's sophisticated deep-learning models and image processing algorithms, ensuring that the models operated at peak performance without compromising speed or accuracy.  
As seen in the configuration details in Table 2, YOLOv8 utilizes an advanced backbone and an anchorless split Ultralytics head, enhancing detection efficiency and accuracy. YOLOv8 employs Neural Architecture Search (NAS) to fine-tune the architecture for optimal balance between speed and accuracy. Fast-RNN features a simplified network structure with weighted residual connections, ensuring stable training and effective learning of temporal dynamics.
# Satellite Network Security Routing Technology Based on Deep Learning and Trust Management
## 4 Experimental Simulation and Analysis
Therefore, under different types of malicious attacks, the performance of different algorithms can be compared and analyzed under evaluation indexes such as average end-to-end delay, packet loss rate, and throughput.
## 2 Operating Modes of a DC/DC Converter. Definition of Parameters  
Next, in order to make the paper self-contained, the two operating modes (depending on current \(i_{L}\) through its inductor) of every DC/DC converter regardless of its topology are explained briefly. In addition, some parameters of interest will also be defined. The operating modes are:  
Continuous conduction mode (CCM): current always flows through the inductor (see Figure 2a).  
Discontinuous conduction mode (DCM): current flows at controlled intervals through the inductor (see Figure 2b).  
Note in Figure 2 that the current through the converter inductor is periodical with period \(T\). This current in turn depends on the current supplied \(\left(i_{g}\right)\) by the generator connected to the converter. The following parameters are defined (see Figures 1-3).  
\[\text{Switching period}\;:\;T. \tag{1}\]  
\[\text{Switching frequency}\;:\;f=1/T. \tag{2}\]  
\[\text{Duty cycle}\;:\;\delta=T_{ON}/T=T_{ON}f. \tag{3}\]  
\[\text{Rise time}\;(i_{L}):\;T_{ON}=\delta T=\delta/f. \tag{4}\]  
\[\text{Fall time}\;:\;T_{X}-T_{ON}=\delta_{f}T=\delta_{f}/f. \tag{5}\]  
\[\text{Current rise}\;:\;\Delta I=I_{2}-I_{1}. \tag{6}\]  
Figure 2: Current \(i_{L}\) through the inductor of the DC/DC converter: (**a**) CCM and (**b**) DCM.  
\[\text{Voltage rise}\;:\;\Delta V=V_{g}-V_{o}. \tag{7}\]  
\[\text{Ideal voltage rise}\;:\;\Delta V^{*}=V_{g}-V_{o}^{*}=V_{g}-\;\;(V_{0}+V_{X})\;. \tag{8}\]  
Figure 2 shows that, in CCM, \(T_{X}\) = \(T\), so:  
\[\delta_{f}\Big{|}_{\text{CCM}}=\frac{T_{X}-T_{ON}}{T}=\frac{T-\delta T}{T}=1-\delta. \tag{9}\]  
However, in DCM, \(T_{X}\) < \(T\), so:  
\[\delta_{f}\Big{|}_{\text{DCM}}=\frac{T_{X}-T_{ON}}{T}=\frac{T_{X}-\delta T}{T} =\frac{T_{X}}{T}-\delta<1-\delta. \tag{10}\]  
In the following study, with the aim of making currents and voltages independent from time, these will be characterized according to their mean value (indicated by a capital letter). Thus, for \(I_{g}\) (Figures 1 and 2) during a switching period:  
\[I_{g}=\frac{1}{T}\int\limits_{0}^{T}i_{g}\;dt=\frac{1}{T}\int\limits_{0}^{T_{ ON}}i_{g}\;dt+\frac{1}{T}\int\limits_{T_{ON}}^{T_{X}}i_{g}\;dt+\frac{1}{T} \int\limits_{T_{X}}^{T}i_{g}\;dt. \tag{11}\]  
Both in CCM and DCM the third summing of (11) is null (Figure 2). Finally, the following hypothesis will be assumed: converters always operate in steady state regime (constant input voltage and fixed duty cycle according its operating conditions), the capacitor value \(C\) is to be assumed large enough and its losses negligible (very low equivalent series resistance, ESR). This allows consideration of a practically constant output voltage \(V_{0}\) (Figure 3).
## 2 Literature Review
There have been arguments on what is better between a Host Intrusion Detection System (HIDS) and a Network Intrusion Detection System (NIDS); however, due to the sophisticated nature of modern-day attacks, a mixed solution is preferred [9]. Apart from the HIDS, Endpoint Detection and Response (EDR) systems have gained more attention recently because they constantly offer threat monitoring and rapid response, thus ensuring that an entire enterprise is protected at all times [13]. The major advantage of an EDR over the HIDS is that it combines the prevention component, enables investigation after detection, and responds within a single platform, hence providing unmatched security and operational effectiveness [13]. Traditionally, computer forensics is aimed at examining the duplicate bit-copy equivalent of a disk extracted out of memory, file and web history, network connections, jump lists, and link files, which proffer a basic understanding of the activities that would have been performed on a victim's electronic device before it was shut down [14]. Digital forensics can also be performed on live hosts using specialised software. An author in [14] proposed a collection technique in a virtualised environment involving taking snapshots of a virtual machine either through a new virtual machine instance user transfer or the use of an algorithm to determine an incident timeline to take a snapshot. However, traditional digital forensic tools are becoming outdated as technology evolves, and tools such as Forensic Tool Kit (FTK) [15] and Encase [16] might no longer handle the complicated nature of modern systems and applications [17]. Some of the drawbacks of these techniques are that they are unable to extract pre-incidental evidence and they are not very capable of being used remotely, as the communication overhead can largely impact the quality of forensic results [17; 18]. The authors in [19] suggest that, for an organisation to be considered digital-forensics-ready, there should be a \"communication channel, Encryption, compression, authentication of log data and proof of integrity, authenticating the client and server, and timestamping\". In the cloud, there is a point where cloud security and digital forensics converge, and, hence, unifying security and digital forensics may improve the forensic capabilities as well as the security of cloud environments and/or networked systems [20].
# Biophotonics in Dentistry
## 3 Applications in Dentistry
### Lasers
#### 3.4.3 Peri-Implantitis  
Peri-implant infections can be difficult to treat because of contamination of the implant surface and adjacent tissues. [PERSON] and [PERSON] found that a CO2 laser can be used to decontaminate and treat peri-implantitis [128]. In 2015, [PERSON] et al. confirmed laser treatment is important in the treatment of periodontal and peri-implant infections [129].  
Numerous other studies investigated the effects of LASER therapy on implant surfaces [130]. In a 2008 in vitro study, [PERSON] et al. utilized CO2 and Er,Cr:YSGG lasers to evaluate cellular response on cells similar to osteoblasts when planted on titanium discs. The results demonstrated a significant effect on the proliferation rate of the discs irradiated with either one of the two lasers [131]. Er:YAG lasers offer promising results for the treatment of peri-implantitis. Er:YAG laser has the potential to remove plaque and calculus from the abutments of implants without damaging the surface [132]. Studies evaluated the effects of lasers and compared them with conventional curette groups. The erbium laser showed promising results for the debridement and removal of granulation tissues; moreover, histological results revealed desirable bone formation and a greater contact between the implant and bone of the laser-treated group [120].  
The literature suggests the beneficial effects of CO2 lasers for the treatment of peri-implant defects when used in conjunction with collagen membranes or other bone-grafting materials [128].
# A Review of Fault Diagnosing Methods in Power Transmission Systems
## 4 Fault-Types Classification (FC)
### FC Based on Fuzzy Interface Systems (FIS)  
Fuzzy logics are applied by performing an interference operation based on fuzzy if-then rules in fuzzy interference systems (FIS). This varies from Boolean logic in the manner that fuzzy logic lets the truth be represented by [0,1]. Here 0 represents the absolute falseness whereas 1 represents the absolute truth. A basic FIS is distributed into three stages such as; fuzzification, inference and defuzzification stage as shown in Figure 6[75]. In [76], samples of three-phase currents for the post-fault conditions  
Figure 4: Probabilistic neural network layout.  
Figure 5: Simple Chebyshev neural network structure.  
are analyzed to evaluate the characteristic features for the fuzzy rules. The features are assessed as a difference of normalized ratios for maximums of the phase currents. [PERSON] developed an initial fuzzy rule from already trained decision tree (DT) and then rules are simplified by using genetic algorithm (GA) and similarity measured [77]. The E-algorithm is proposed in [78], to distinguish faults caused by lightning, animals, and trees via imbalanced data, which is a heuristic manner to discover the optimal fuzzy rules. [PERSON] et al. proposed a fuzzy-neural approach for TL fault classification [79]. Fuzzy-neural is a combination of fuzzy and neural logics. Negative, zero and positive sequence current components are the inputs [79]. Adaptive network-based FIS (ANFIS) is used for fault classification in [80; 81]. [PERSON] confirmed the effectiveness, precision, and robustness of ANFIS by adding white noise to the test data [82].
# Translating Speech to Indian Sign Language Using Natural Language Processing
## 2 Literature Survey  
### Sign Language in English  
A two-way communication system is suggested in the paper [4] but the authors are only able to convert 26 alphabets and three characters with an accuracy rate of 99.78% using CNN models. The authors only suggest that future work must be conducted in the field of natural language processing to convert speech into sign language.  
In the paper [5], the authors propose a system that converts sign language into English and Malayalam. The authors of the paper suggest using an Arduino Uno, which uses a pair of gloves to recognize the signs and translates the signs from ISL to the preferred language. The system is useful, as it recognizes two-hand and motion signs.  
The Indian Sign Language interpreter presented in the paper [6] uses hybrid CNN models to detect multiple sign gestures and then goes on to predict the sentence that the user is trying to gesture by using natural language processing techniques. The system is able to achieve 80-95% accuracy under various conditions.  
In another study, the HSR model is used by the authors in converting ISL signs into text. The HSR model gives an advantage over RGB-based models, but this system has an accuracy ranging from 30 to 100% depending upon the illumination, hand position, finger position [7] etc.  
The authors of [8] paper propose a system that recognizes 26 ASL signs and converts them into English text. They use principal component analysis to detect the signs in MATLAB.  
The ASL to sign language synthesis tool uses VRML avatars and plays them using a BAP player. The major problem with the system is that many complex movements are not possible using the current VRML avatars. For example, touching the hand to any part of the body is not possible in the current system [9].  
In another study mentioned in [10], one video-based sign language translation system converts signs from ISL, BSL, and ASL with an overall accuracy of 92.4%. The software utilizes CNN and RNN for the real-time recognition of dynamic signs. The system then converts the signs into text and then uses text-speech API to give an audio output to the user.  
The authors of another paper first use the Microsoft Kinect 360 camera to capture the movement of the ISL signs. A unity engine is used to display the Blender 3D animation created by the authors. Although the system can successfully convert words into sign language, it is not able to convert phrases/multiple words into ISL.
# Design of Positioning Mechanism Fit Clearances Based on On-Orbit Re-Orientation Accuracy
## 3 Rotation Angle Measurement Principle and Error Analysis
### Measurement Error Analysis
In other words, if the two real angles were both not more than \(300^{\prime\prime}\), the difference between the azimuth angles of theodolite2 before and after rotating was nearly equal to the real rotation angle of the unit around the \(x\)-axis, and the difference between the pitch angles of theodolite2 before and after rotating was nearly equal to the real rotation angle of the unit around the \(y\)-axis. For the same reason, when the real angles around the \(x\)-and \(z\)-axes were both not more than \(300^{\prime\prime}\), the difference between the azimuth angles of theodolite1 before and after rotating was nearly equal to the real rotation angle of the unit around the \(x\)-axis, and the difference between the pitch angles of theodolite1 before and after rotating was nearly equal to the real rotation angle of the unit around the \(z\)-axis.
# Dual-Frequency Linear-to-Circular Polarization Converter for Ka-Band Applications
## 2 Design of the Unit Cell  
The selection of the most appropriate dual-band unit cell configuration must consider several aspects. In particular, high polarization isolation between the x-and y-polarizations should be guaranteed. This is achieved by adopting centrally connected elements such as classical or Jerusalem crosses [14; 19; 24; 25; 26] that have been widely used in these types of applications. Another important aspect is to guarantee a relative independence between the two bands.  
Figure 1 shows the selected configuration. The cell is formed by six substrate layers separated by five foam spacers. The three layers on the bottom side (Layers #1, #2 and #3) are realized with three identical Jerusalem crosses (JC), the three top layers (#4, #5 and #6) are also identical and contain \(\Upsilon\)-type strips. The three identical Jerusalem crosses are printed on 0.13 mm-thick (_tsub_) Rogers Ro3003 substrate layers separated by 1.5 mm of foam (_lfoam2_) spacer. The three identical \(\Upsilon\)-type strips are printed on the same substrate and separated by 3.5 mm of foam (_lfoam1_) spacer. The combination of the \(\Upsilon\)'-type strips with Jerusalem crosses is used to achieve the desired dual band response.  
Figure 1: Configuration of the elementary cell.  
A linearly polarized (LP) wave tilted 45\({}^{\circ}\) relative to the \(x\)- and \(y\)-axis of the converter is used to illuminate its surface. This wave can be seen as the superposition of two orthogonal components (\(E_{x}^{i}\)) and (\(E_{y}^{i}\)). Neglecting the mutual coupling between horizontal and the vertical components, the orthogonal components of the transmitted wave can be expressed in terms of the transmission coefficients of the unit cell for the \(x\) (\(T_{x}\)) and \(y\) polarizations (\(T_{y}\)) [24]:  
\[E_{x}^{t}=T_{x}E_{x}^{i};\quad E_{y}^{t}=T_{y}E_{y}^{i} \tag{1}\]  
where \(T_{x}=|T_{x}|e^{i\angle T_{x}}\) and \(T_{y}=\left|T_{y}\right|e^{i\angle T_{y}}\) are the transmission coefficients of the two orthogonal components. The circular polarization is generated imposing the following conditions in the band of interest:  
\[\left|T_{x}\right|=\left|T_{y}\right| \tag{2}\]  
\[\angle T_{x}=\angle T_{y}\pm 90^{\circ} \tag{3}\]  
These conditions can be imposed using a single- or multi-layer Frequency Selective Surface (FSS) structure. However, the single-layer configuration typically suffers from narrow bandwidth due to the single resonant structure [27]. In contrast, the use of several metallization levels can increase the bandwidth of a generic FSS [28; 29]. For this reason, a configuration with six dielectric layers separated by spacers of foam was employed in this work to ensure an optimal compromise between manufacturing complexity and AR bandwidth.
# Effective Fabric Defect Detection Model for High-Resolution Images
## 2 Proposed Model Based on Cascade R-CNN
### Improvement of FPN Model
#### 2.5.2 Carafe  
Nearest neighbor interpolation and bilinear interpolation are the most frequently used up-sampling operators in the FPN. However, they only consider sub-pixel neighborhoods and cannot utilize the semantic information of feature maps. Therefore, we introduced the CARAFE method [33] to accomplish feature up-sampling.  
As shown in Figure 11, CARAFE can be mainly divided into two modules, namely kernel prediction and content-aware reassembly. CARAFE uses the kernel prediction module to predict the up-sampling kernel, which is then used in the content-aware reassembly module to complete the up-sampling operation. In the kernel prediction module, to reduce the amount of calculation, the 1 \(\times\) 1 convolutional layer is used to compress channels from C to Cm. Then, the Kencoder \(\times\) Kencoder convolutional layer is used to generate reassembly kernels based on input features, and the output channels are changed to \(\sigma\)2 Kup2. Finally, each reassembly kernel is normalized in the channel direction using a softmax function. In the content-aware reassembly module, the corresponding position of each output pixel in the input feature map is identified and the input position is selected as the center of the Kencoder x(\(\times\)) Kencoder region; accordingly, the output pixel value from the product between the region and up-sampling kernel is obtained.  
In comparison to nearest neighbor and bilinear interpolations, CARAFE exhibits many advantages. The up-sampling kernel used to generate output feature maps is related to input semantic information, and CARAFE introduces minimal computation. This up-sampling method can restore the defect information of small defects more accurately, which is beneficial to the recognition of small defects.  
Figure 10: Improved FPN model.
## 2 Critical Literature Review
### Data Types Associated with Autonomous Vessels
#### 2.5.1 Standardized or Non-Standardized Data  
The data produced by conventional vessels' operations comprise a mixture of uncategorized primary standardized and non-standardized data. Therefore, autonomous vesselsmust be standardized according to the framework agreed upon by the International Maritime Organization, the International Electrical Union, the International Standardization Organization, and the International Communications Union, to enable commercial trade through effective transmission, receipt, and reprocessing [50]. Standardized data are those that follow an established protocol for coding and decoding information, such as the ship's location, operational efficiency, fuel consumption patterns, weather conditions, and so on [23]. Non-standardized data are those with forms, sizes, or confidentiality clearances that differ from those of standardized data [15].