esa-sceva/satcom-chunk-collection huggingface dataset:
# Fatigue Reliability Analysis of Rib-To-Deck Joints Using Test Data and In-Situ Measurements
## 2 Fatigue Tests of Rib-To-Deck Joints  
### Design of Specimens  
As widely acknowledged, the fatigue test is the most reliable and somehow inevitable approach in the study on fatigue performance of OSDs [28]. Since extensively welded joints are used in OSDs, various types of fatigue-critical details existed [29], including the rib-to-deck joint, the rib-to-floor beam joint, and the floor beam-to-deck joint. Meanwhile, the structural behavior of OSDs is extremely complicated, because of the large number of the details in OSDs and the high integrity among the details [29]. To this end, several types of fatigue cracking may occur in the fatigue test when the segment model is employed [30]. In order to focus on the fatigue performance of RD joints, which is the main target of this study, rib-to-deck specimens are used in the fatigue test. A total of 7 specimens are fabricated using Q345 qD steel [31] (similar to the British steel grade S355 NL), including 2 CU specimens and 5 TEU specimens. The specimen consists of a 16 mm-thick deck and an 8 mm-thick U-rib, which are connected by two RD joints, as shown in Figure 2a. The RD joint in CU specimens and the joint in TEU specimens are illustrated in detail in Figure 2b. It is worth stating that the width of the U-ribs is measured at the mid-plane of the rib wall, which is the same in the two types of U-ribs. In the fabrication, 2 mm of the rib wall is not welded to avoid the negative effect of weld melt-through on fatigue strength. Under this design, the penetrated thickness of rib walls in TEU specimens is increased from 6 to 10 mm, i.e., an increment of almost 67%. At the same time, the weld leg length on the deck plate is also notably increased (from 11 to 15 mm). During the welding works, a semi-auto welding machine was employed, and the welding parameters were kept the same as in the preliminary tests [18; 19]. The welds have been made by the flux cored arc welding (FCAW) protected with CO\({}_{2}\), and the rate of shielding gas flow is 20 L/min. The designation of the welding wire is E501T-1(E71T-1C) [32; 33], with the diameter of 1.2 mm. Correspondingly, and the contact tip to work distance (CTWD) is determined as 15 times the diameter, i.e., 18 mm. The arc voltage and welding current are, respectively, 31 \(\pm\) 2 V and 290 \(\pm\) 20 A, with a travel speed of 380 \(\pm\) 20 mm/min.  
Figure 2: Schematic view of rib-to-deck specimens: (**a**) overview and (**b**) rib-to-deck joints.
# Low-Resource Neural Machine Translation Improvement Using Source-Side Monolingual Data
## 3 Overview of the Wolaytta Language
### Morphology  
Wolaytta is a language with one of the most complex morphological systems and is categorized as an agglutinative type, similarly employing both morphological inflection and derivation like other languages of this kind, leading to a huge number of variations for a single word [30; 31]. Wolaytta only depends on suffixes to form different forms of a given word. siiqa ('love'), including siiqa ('love it'), siiqa-asa ('you love'), siiqa-asu ('she loves'), siiqa-da ('fall in love'), siiqa-dasa ('you loved'), siiqa-dii ('do you love'), siiq-idda ('while loving'), siiqa-is ('love'), siiq-oosona ('they love'), and siiq-ida ('by loving'), are examples of morphological richness. These forms are derived from the root siiqa 'love' by adding the suffixes -asa, -asu, -idda, -is, and -ida. Among the word formation patterns in Wolaytta, the common one is suffixation, in which words are frequently created by combining two or more suffixes [29]. By adding one suffix on top of another, we make a long word that often has as much meaning and grammar as a whole English phrase, clause, or even sentence. For example, (i) he-g-aa-dan ('like') formed by adding three (-g, -aa, and -dan) suffixes to the root word he ('that'), (ii) bochch-enn-aa-dan ('don't touch') formed by adding three (-enn, -aa, and -dan) suffixes to the root word bochcha ('touch'). Because of the intricate nature of its grammatical structure, a single Wolaytta word may have a very wide variety of different meanings when translated into other languages.  
3.2.1. Nouns  
Wolaytta nouns are divided into four groups based on the endings they take in inflection [29; 30; 31]. The nouns ending in '-a' in the absolute case and with the action on their last syllable belong to the first class, for example, aawwa ('sun') and tuna ('truth'). Nouns with their absolute case ending '-iya' and the accent on their penultimate syllable make up the second class, for example, xalliya ('medicine'), ogiya ('road'), and siniya ('cup'). Nouns ending in '-uwa' in their absolute case constitute the third class, for example, ossuwa ('work') and giduwa ('center'). The forth class consists of nouns ending in '-(i)yo' in their absolute case. The letters mainly include terms referring to female living beings, for example, naPP-yo or na\"-yo ('the girl?') and machas-iyo ('the wife').  
3.2.2. Gender  
Like many other languages, Wolaytta nouns exhibits two genders: masculine and feminine [29; 30; 31]. Nouns belonging to the fourth class are feminine, while the nouns belonging to the other three classes are masculine. The feminine ones differ from the masculine ones by their endings; the former are characterized by the ending '-o' in the absolute case and the latter by the ending '-a' in the absolute case. Additionally, if nouns are used as a subject, they are marked with '-iy' for the feminine and '-ay' for the masculine.  
There are a few exceptions, such as possessive pronouns and some demonstratives, which may only have forms for one gender or the other.
## 3 General Findings
### Studies Distribution in Chronological Order
#### 3.1.2 Geographical Distribution of Articles  
This review study has covered six continents with 19 countries of the world as depicted in Figure 4. Overall, the continent Asia was ranked high with 33.33% studies, including Malaysia, Thailand, Taiwan, Iran, UAE, and India for the BIS research. Europe ranked as the second highest continent with 27.07% BIS published studies, this region consisted of Slovakia, Slovenia, Croatian, Portugal, Poland, Spain and the United Kingdom. In addition, North America has contributed 16.50% to the number of studies which were conducted in the USA and Canada. On the other hand, a total of 11% studies were published in the African region including South Africa and Ghana etc. Whereas, South America with two countries \"Brazil and Colombia\" is ranked low with 5.55% published articles and Australia has scored the lowest with 1% published study only. Some studies (5.55%) were conducted across the world relevant to the study topic. The findings showed that researchers have focused more on developed countries especially the USA and neglected the developing countries like Africa, Middle East, and South Asian countries for the BIS research.
# Positioning Errors Measurement of CNC Machine Tools Based on J-DBB Method
## 3 Positioning Errors Analysis of Machine Tool
### Uniform Contraction Error of Ball Screw and Linear Grating  
The uniform contraction error of ball screw and linear grating is usually due to thermal deformation. The thermal deformation is of two types: first-order uniform contraction and second-order uniform contraction. In the deformation, the contraction ratio of the ball screw and linear grating is assumed to be uniform. In addition, the contraction ratio is the first-order or second-order function of the position. It is noted that the second-order uniform contraction appears only when the ball screw and linear grating fixed at both ends are heated.  
When the contraction ratio of the ball screw and linear grating is \(a(\upmu\text{m}/\text{mm})\) on the \(x\)-axis, the positioning error on the \(x\)-axis can be expressed as:  
\[\delta_{xx}=a(-x) \tag{2}\]  
where \(x\) is the theoretical coordinates of the machine tool spindle on the \(x\)-axis direction.  
Here, other errors are assumed to be zero, and then, the error vector \(E\) can be expressed as:  
\[E=(ax,0,0) \tag{3}\]  
Substituting Equation (3) with Equation (1), we can obtain first-order uniform contraction error equation of ball screw and linear grating:  
\[\Delta R=ax_{0}\cos^{2}\theta_{3} \tag{4}\]  
When the contraction ratio of the ball screw and linear grating is \(ax(\upmu\text{m}/\text{mm})\) on the \(x\)-axis, the positioning error on the \(x\)-axis can be expressed as:  
\[\delta_{xx}=a(-x)^{2} \tag{5}\]  
Now, other errors are assumed to be zero, and we can obtain the error vector \(E\):  
\[E=\left(-ax^{2},0,0\right) \tag{6}\]  
Substituting Equation (6) into Equation (1), we can obtain the second-order uniform contraction error equation of ball screw and linear grating:  
\[\Delta R=-ax_{0}^{2}\cos^{3}\theta_{3} \tag{7}\]
## 2 Materials and Methods
### Structural Design of Microchip Laser
At the central part of the baseplate, a cavity is hollowed to accommodate a lens assembly. This non-heating component minimizes contact with the baseplate, reducing the system's heat dissipation area and consequently lowering  
Figure 1: Illustration of a passively Q-switched microchip laser. Figure 2: Schematic of imaging optical system of LD pumped laser. heat dissipation power consumption. The laser crystal is securely pressed onto the baseplate through two L-shaped structures, enveloped by indium foils to ensure efficient heat conduction. Small holes are incorporated into the baseplate to house negative temperature coefficient thermistors, enabling real-time monitoring of the microchip laser's operating temperature. Additionally, a groove is integrated into the bottom of the laser housing to accommodate a semiconductor cooler, allowing temperature control of the entire working environment and ensuring the stable operation of the microchip laser. A protective window is designed at the laser's emission port, using a flat mirror to isolate dust and moisture from the surroundings. This comprehensive design approach addresses thermal management challenges, ensuring the microchip laser's reliable and efficient performance in deep space exploration environments. As indicated in existing literature, an increase in spot size, under the same laser power density, results in a heightened spatial charge of the resulting ion beam. Considering the limited drift length in subsequent mass analyzers, this could compromise ion focusing, leading to a notable decrease in mass resolution [22]. To ensure precise excitation of the test substance, yielding singly charged ions for subsequent TOF mass spectrometry testing [23], configuring a power adjustment device becomes imperative. This device facilitates simple adjustment of the output power during testing. Consequently, to attain micro-focus laser output and streamline the adjustment of the laser's output power, an optical focusing system has been meticulously designed for the laser in this study. Illustrated in Figure 4, the optical focusing system incorporates a removable optical sleeve, providing adaptability for different application needs. The system employs optical lenses with varying refractive indices to expand and contract, aiding in minimizing the spot size, increasing the focal length, and achieving micro-focus output. A \(\lambda\)/2 waveplate and a polarizing beamsplitter (PBS) collaborate to form a variable power attenuator, enabling power adjustment within the 5% to 95% range. Tuning the waveplate allows for precise adjustment of the laser intensity output. Concurrently, a portion of the signal light can be redirected for synchronous detection. This integrated optical focusing system enhances the precision and versatility of the microchip laser for optimal performance in diverse testing environments. Figure 4: Design diagram of the optical focusing system. Figure 3: Structural diagram of microchip laser.
# An Aeromagnetic Compensation Algorithm Based on Radial Basis Function Artificial Neural Network
## 2 Compensation Models and Methods
### T-L Model
\begin{array}{c}T_{bx}\\ T_{by}\\ T_{bz}\end{array} \right]=D\left[ \begin{array}{c}T_{gx}\\ T_{gy}\\ T_{gz}\end{array} \right] \tag{6}\]  
\[D=\left[ \begin{array}{ccc}\cos\theta\cos\psi&\sin\theta\cos\psi&-\sin\psi \\ \cos\theta\sin\lambda\sin\psi-\sin\theta\cos\lambda&\sin\theta\sin\lambda\sin \psi+\cos\theta\cos\lambda&\sin\lambda\cos\psi\\ \cos\theta\cos\lambda\sin\psi+\sin\theta\sin\lambda&\sin\theta\cos\lambda\sin \psi-\cos\theta\sin\lambda&\cos\lambda\cos\psi\end{array} \right] \tag{7}\]  
\[T_{gx}=T\cos\varphi\cos\mu \tag{8}\]  
\[T_{gy}=T\cos\varphi\sin\mu \tag{9}\]  
\[T_{gz}=T\cos\varphi\sin\varphi, \tag{10}\]  
where \(\Psi\) is the side roll angle when the aircraft is flying, \(\lambda\) is the pitch angle when the aircraft is flying, \(\theta\) is the side slip angle when the aircraft is flying, \(\mu\) is the magnetization bias angle, and \(\varphi\) is the magnetization tilt angle  
Finishing Formulas (1)-(5) can be obtained, the total interference magnetic field (\(H_{t}\)) of the aircraft is:  
\[ \begin{array}{l}H_{t}=H_{p}+H_{i}+H_{ec}\\ =T(c_{1}u_{1}/T+c_{2}u_{2}/T_{t}+c_{3}u_{3}/T\\ +c_{4}u_{1}^{2}+c_{5}u_{1}u_{2}+c_{6}u_{1}u_{3}+c_{7}u_{2}^{2}+c_{8}u_{2}u_{3} +c_{9}u_{3}^{2}\\ +c_{10}u_{1}u_{1}^{\prime}+c_{11}u_{2}u_{1}^{\prime}+c_{12}u_{3}u_{1}^{\prime} +c_{13}u_{1}u_{3}^{\prime}+c_{14}u_{2}u_{3}^{\prime}\\ +c_{15}u_{3}u_{3}^{\prime}+c_{16}u_{1}u_{2}^{\prime}+c_{17}u_{2}u_{2}^{\prime} +c_{18}u_{3}u^{\prime})\end{array} , \tag{11}\]According to the relationship between \(u_{1}\), \(u_{2}\), and \(u_{3}\)  
\[ \begin{array}{c}u_{1}^{2}+u_{2}^{2}+u_{3}^{2}=1\\ u_{1}u_{1}^{\prime}+u_{2}^{\prime}u_{2}^{\prime}+u_{3}u_{3}^{\prime}=0\end{array}  \, \tag{12}\]  
After sorting out Equations (11) and (12), we get:  
\[H_{t}= \begin{bmatrix}b_{1}\\ b_{2}\\ b_{3}\\ b_{4}\\ b_{5}\\ b_{6}\\ b_{7}\\ b_{8}\\ b_{9}\\ b_{10}\\ b_{11}\\ b_{12}\\ b_{13}\\ b_{14}\\ b_{15}\\ b_{16}\end{bmatrix} ^{T} \begin{bmatrix}u_{1}/T\\ u_{2}/T\\ u_{3}/T\\ u_{1}^{2}\\ u_{1}u_{2}\\ u_{1}u_{3}\\ u_{2}u_{3}\\ u_{2}u_{1}^{2}\\ u_{1}u_{1}^{\prime}\\ u_{2}u_{1}^{\prime}\\ u_{3}u_{1}^{\prime}\\ u_{1}u_{3}^{\prime}\\ u_{2}u_{3}^{\prime}\\ u_{3}u_{2}^{\prime}\end{bmatrix}  \tag{13}\]  
where \(b_{1}\), \(b_{2}\),..., \(b_{16}\) represent 16 compensation coefficients.
# A Review of Transformer-Based Approaches for Image Captioning
## 6 Discussion and Analysis
### Training and Model Parameters  
Table 6 shows the sizes of the non-VLP models. XE is the cross-entropy loss and SCST is the Self-Critical Sequence Training; N are the number of stacked layers, d the dimensionality of the layers and h the number of heads. Many of the models employ a transformer with a size similar to the original transformer used by [PERSON] et al. [36], i.e., with the number of sub-layers, N = 6, the embedding dimensionality, d = 512 and the number of heads, h = 8. The models in Table 6 predominantly use Faster-RCNN [15] as the feature extractor. However, COS-Net [91] deviates from this pattern and uses a more recent feature extractor based on CLIP [53]. For training and optimization, most of the models used cross-entropy optimization. This method is based on the use of the cross-entropy loss during optimization. A recent approach also employed by most of the models and which has become a standard optimization for image captioning is the Self-Critical Sequence Training (SCST) optimization, pioneered by [PERSON] et al. [116]. SCST optimization makes use of reinforcement learning to specifically optimize for the CIDEr evaluation metric, which is a fundamental metric used in the comparison of different models and for reporting results on the MS COCO captioning \begin{table}
\begin{tabular}{l l c c c c} \hline \hline \multicolumn{1}{c}{**Model**} & \multicolumn{1}{c}{**Feature Extractor**} & \multicolumn{1}{c}{**Training Objective**} & \multicolumn{1}{c}{**Optimizer**} & \multicolumn{2}{c}{
\begin{tabular}{c} **Transformer Details** \\ **N** & **d** & **h** \\ \end{tabular} } \\ \hline Conceptual Captions [59] & Inception-ResNet-v2 & XE & Adagrad & 6 & 512 & 8 \\ Stacked attention transformer [77] & ResNext & XE & Adam & 6 & 512 & 8 \\ Object relation transformer [79] & Faster RCNN & XE, SCST & Adam & 6 & 512 & 8 \\ Attention on Attention [84] & Faster RCNN & XE, SCST & Adam & 6 & 1024 & 8 \\ Entangled Transformer [85] & Faster RCNN & XE, SCST & Adam & 6 & 512 & 8 \\ Meshed-Memory Transformer [86] & Faster RCNN & XE, SCST & Adam & 6 & 512 & 8 \\ X-Linear Attention Networks [87] & Faster RCNN & XE, SCST & Adam & 4 & 512 & - \\ Image Transformer [90] & Faster RCNN & XE, SCST & Adam & 3 & 1024 & 8 \\ COS-Net [91] & CLIP-grid features & XE, SCST & Adam & 6 & 512 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Training and model details for non-VLP models. XE: cross-entropy loss, SCST: self-critical sequence training (CIDEr optimization).
# Blockchain-Based Cloud-Enabled Security Monitoring Using Internet of Things in Smart Agriculture
## 8 Discussion, Limitations and Future Work  
We implemented a real-time scenario agriculture security-monitoring system, which monitors the sensor device's health status and sensor anomalies to perform precision agriculture and productive farming. However, we did not deploy the sensors in the agriculture field to capture the farmland environment conditions. We envision that the network latency will be negligible, considering the wide spread of the internet in rural areas. Our solution can even monitor the agricultural conditions in rural areas as long as an internet connection is available.  
We did not implement the IoT gateway in our work. We used the home router as an IoT gateway and connected the IoT sensor devices to the network via home WiFi. This is one of the limitations of our work. Implementing an IoT network with an IoT gateway and various sensing devices to mimic the realistic smart-agriculture environment is one of the extensions of our work.  
The current implementation only works on the Ethereum proof-of-work (POW) consensus mechanism blockchain. One future work will be implementing the current solution in the Ethereum 2.0 network, which is supported by the proof-of-stake (POS) consensus.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Authors** & **Implementation** & **Device-to-Cloud Latency (Second)** & **Cloud-to-Blockchain Latency (Second)** & **Blockchain-to-Client-Conse Latency (Second)** & **Alert Total Latency (Second)** \\ \hline
[38] & Ethereum & - & - & - & 16.55 * \\ \hline
[27] & Simulations & \textless{}0.02 (4G) & - & - & - \\ \hline
[34] & Ethereum & - & 272 * & - & - \\ \hline
[37] & Ethereum & 4 & - & - & - \\ \hline Our Work & Ethereum & 0.02 (Wifi) & 0.07 & 0.02 & 0.11 \\ \hline \hline \end{tabular}
*—partially matches our work.  
\end{table}
Table 3: Performance comparison with existing works.  
There are numerous IoT applications to monitor the IoT environment, including agriculture applications, smart homes, smart health, smart transportation applications, etc. Therefore, we envision our prototype will also be used to implement the monitoring solutions in other fields.  
The network traffic can be collected from a smart-agriculture edge gateway and store the network events data in the cloud. The network events can be used to apply machine-learning and deep-learning techniques and identify the anomaly network traffic in a smart-agriculture network. One future work will be implementing ML- and DL-based network-security monitoring solutions in smart agriculture and using blockchain to store the network anomaly events as transactions.  
The production Ethereum blockchain gas price is high. Therefore, blockchain technologies such as Cardano and Solano-based blockchain implementation are considered to design low network-latency applications and reduce the end user/farmer transaction cost in smart agriculture.
## 5 Results and Discussions
### Time History Analysis for the Dispersion of Instantaneous Lift Coefficients  
Although good agreements has been achieved for both angle-averaged lift and pressure coefficients from Figures 18 and 19, remarkable dispersion can be observed for instantaneous lift coefficients from Figures 15 and 20. Time history of azimuth angle, angle of attack, and lift coefficient in Case 1, where the pitching angle is 60\({}^{\circ}\), were analyzed and shown in Figure 21. It can be observed that both the lift coefficient and the angle of attack vary quasi-periodically, and the frequencies are same with azimuth angle. For this reason, aerodynamic behaviors in one rotation period were discussed. It is shown from Figure 22a that both the angle of attack and lift coefficient are largest when the tested blade rotates across the bottom of rotor corresponding to azimuth angle of 0\({}^{\circ}\), while they are lowest when the tested blade rotates across the top of rotor corresponding to azimuth angle of 180\({}^{\circ}\) or -180\({}^{\circ}\). In one rotation period, remarkable dynamic response phenomenon can be observed from Figure 22b. A clockwise hysteresis loop of lift coefficients was formed when angles of attack increased from -12\({}^{\circ}\) to 6\({}^{\circ}\) and decreased from 6\({}^{\circ}\) to -12\({}^{\circ}\). Obviously, the dynamic response make instantaneous lift coefficients at the same angle of attack dispersive.  
Figure 21: Time history of azimuth angle, angle of attack, and lift coefficient in Case 1.  
Figure 22: (**a**) The variation of angle of attack and lift coefficient in one rotation period, and (**b**) dynamic response of lift coefficient varying with the angle of attack.  
The reason that causes the quasi-periodically variation of angle of attack is investigated also in the present research. A comparison between yaw angle and variation amplitude of angle of attack is shown in Figure 23, where amplitude of angle of attack was multiplied by -1 to make a direct contrast with yaw angle. It can be observed that variation amplitude of angle of attack is closely correlated with yaw angle, although certain irregular deviations exists at some instantaneous time. As a whole, the variation amplitudes are determined by the yaw angles.  
If yaw angle is negative, angle of attack will be largest when the tested blade rotates across the bottom of rotor at azimuth angle of 0\({}^{\circ}\). It can also be deduced that if yaw angle is positive, angle of attack will be lowest when the tested blade is at azimuth angle of 0\({}^{\circ}\). The quasi-periodically variation of the angle of attack caused by the yawing angle, induces dynamic response of a clockwise hysteresis loop in the lift coefficient. The dynamic response is the main source of the dispersion of instantaneous lift coefficients with a standard deviation of 0.2.