esa-sceva/satcom-chunk-collection huggingface dataset:
# Similarity Model of a Rotary Lobe Expander Working with Various Fluids
## Appendix A Derivation of the Volume Transferred by the Rotors  
The volume \(\Delta V_{gcom}\) transferred by the rotors in one shaft's revolution is a basic parameter in the presented similarity analysis. In the analyzed expander type, this volume does not change along the rotor's rotation, i.e., it remains the same from the inlet to the outlet. For the particular analyzed expander, the volume \(\Delta V_{gcom}\) is derived analytically using the measured sizes of the disassembled expander, schematically shown in Figures 1 and 2. The cross-sectional geometries of the upper and the lower rotors are shown in Figure 11. The volumes transferred by the upper and the lower gas pockets in 1/2 of one shaft's revolution (i.e., of the upper rotor) are shown in grey. The measured radii are \(r_{1}\) = 36 mm, \(r_{2}\) = 90 mm, \(r_{3}\) = 29 mm, and \(r_{4}\) = 63 mm. The length of both rotors is \(L\) = 100 mm. The distance between the rotors' axes is \(r_{1}+r_{2}\) = 36 mm + 90 mm = 126 mm. The ratio of the rotational speed of the lower \(n_{l}\) and the upper rotor \(n_{u}\), connected by the gear train, is \(n_{l}/n_{u}=r_{1}/r_{2}\) = 36 mm/90 mm = 2/5. Analytical derivation of the volumes transferred by the upper and the lower rotor:  
Because of the symmetry, only the values for x \(\geq\) 0 are used in the calculation. In Figure 12, the upper halves of three circles are given by the following equations:  
\[y\ =\ \sqrt{r_{1}^{2}-x^{2}}.\ldots.\ldots.\ y\ =\ \sqrt{r_{3}^{2}-\left(x-r_{1} \right)^{2}}.\ldots.\ldots.\ y\ =\ \sqrt{r_{4}^{2}-x^{2}} \tag{12}\]  
The coordinates of points A and B are determined from the systems of equations:  
\[\left\{ \begin{array}{l}y_{A}\ =\ \sqrt{r_{1}^{2}-x_{A}^{2}}\\ y_{A}\ =\ \sqrt{r_{3}^{2}-\left(x_{A}-r_{1}\right)^{2}}\end{array} \right. \ldots.\left\{ \begin{array}{l}y_{B}\ =\ \sqrt{r_{4}^{2}-x_{B}^{2}}\\ y_{B}\ =\ \sqrt{r_{3}^{2}-\left(x_{B}-r_{1}\right)^{2}}\end{array} \right. \tag{13}\]  
which gives the following:  
\[x_{A}\ =\ \frac{2\cdot r_{1}^{2}-r_{3}^{2}}{2\cdot r_{1}}\ =\ r_{1}-\frac{r_{3}^{2}}{2 \cdot r_{1}}.\ldots.x_{B}\ =\ \frac{r_{1}^{2}+r_{4}^{2}-r_{3}^{2}}{2\cdot r_{1}}\ =\ \frac{r_{1}}{2}+\frac{r_{4}^{2}-r_{3}^{2}}{2\cdot r_{1}} \tag{14}\]In Figure 11b, the upper and the lower halves of two circles are given by the following:  
\[y\ =\ \sqrt{r_{2}^{2}-x^{2}}\ldots\ldots\.y\ =\ r_{2}-\sqrt{r_{3}^{2}-x^{2}} \tag{12}\]  
The coordinates of point E are determined from the system of equations below:  
\[\left\{ \begin{array}{rcl}y_{E}&=&\sqrt{r_{2}^{2}-x_{E}^{2}}\\ y_{E}&=&r_{2}-\sqrt{r_{3}^{2}-x_{E}^{2}}\end{array} \right.
# The Use of Autogenous Teeth for Alveolar Ridge Preservation: A Literature Review
## 6 Undemineralized Dentin Matrix (UDD)
### Clinical Studies
#### 6.2.3 Histologic and Histomorphometry Outcomes  
Regarding histologic and histomorphometry analyses, UDD generated moderate osteoblastic activity, presenting some dentin fragments as well as connective tissue, suggesting a gradual increase in the graft resorption and consequently bone formation [54; 56; 57]. Additionally, [PERSON] et al. reported a consecutive increase of the bone percentage (26.3% at 4 months and 66.5% at 6 months), and a decrease on dentin (10.4% at 4 months and 0.9% at 6 months) and connective tissue (63.3% at 4 months and 32.6% at 6 months) in the socket, substantiating a gradual bone formation along with graft resorption [57].
# Optimization of Machining Parameters for Corner Accuracy Improvement for WEDM Processing
## 3 Experiment and Discussion
### Effect of Parameter on Corner Error
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source** & **Sum of Squares** & **df** & **Mean Square** & **F-Value** & _p-Value (Prob \textgreater{} F)_ & **Remarks** \\ \hline Model & 0.008290 & 27 & 0.000307 & 33.31 & \textless{}0.0001 & Significant \\ ON & 0.005228 & 1 & 0.005228 & 567.18 & \textless{}0.0001 & Significant \\ OFF & 0.000076 & 1 & 0.000076 & 8.28 & 0.0079 & Significant \\ OV & 0.000188 & 1 & 0.000188 & 20.41 & 0.0001 & Significant \\ SV & 0.000077 & 1 & 0.000077 & 8.36 & 0.0077 & Significant \\ WT & 0.001702 & 1 & 0.001702 & 184.68 & \textless{}0.0001 & Significant \\ WA & 0.000154 & 1 & 0.000154 & 16.67 & 0.0004 & Significant \\ ON OFF & 0.000018 & 1 & 0.000018 & 1.95 & 0.1741 & \\ ON OV & 0.000002 & 1 & 0.000002 & 0.17 & 0.6813 & \\ ON SV & 0.000011 & 1 & 0.000011 & 1.15 & 0.2943 & \\ ON WT & 0.000015 & 1 & 0.000015 & 1.64 & 0.2115 & \\ ON WA & 0.000000 & 1 & 0.000000 & 0.00 & 0.9969 & \\ OFF OV & 0.000001 & 1 & 0.000001 & 0.06 & 0.8028 & \\ OFF SV & 0.000000 & 1 & 0.000000 & 0.05 & 0.8177 & \\ OFF WT & 0.000000 & 1 & 0.000000 & 0.00 & 0.9523 & \\ OFF WA & 0.000000 & 1 & 0.000000 & 0.02 & 0.8930 & \\ OV SV & 0.000000 & 1 & 0.000000 & 0.00 & 1.0000 & \\ OV WT & 0.000000 & 1 & 0.000000 & 0.00 & 0.9847 & \\ OV WA & 0.000003 & 1 & 0.000003 & 0.36 & 0.5530 & \\ SV WT & 0.000003 & 1 & 0.000003 & 0.34 & 0.5654 & \\ SV WA & 0.000008 & 1 & 0.000008 & 0.87 & 0.3601 & \\ WT WA & 0.000003 & 1 & 0.000003 & 0.36 & 0.5526 & \\ ON\({}^{2}\) & 0.000070 & 1 & 0.000070 & 7.64 & 0.0103 & Significant \\ OFF\({}^{2}\) & 0.000263 & 1 & 0.000263 & 28.54 & \textless{}0.0001 & Significant \\ OV\({}^{2}\) & 0.000056 & 1 & 0.000056 & 6.12 & 0.0203 & Significant \\ SV\({}^{2}\) & 0.000091 & 1 & 0.000091 & 9.84 & 0.0042 & Significant \\ WT\({}^{2}\) & 0.000478 & 1 & 0.000478 & 51.85 & \textless{}0.0001 & Significant \\ WA\({}^{2}\) & 0.000037 & 1 & 0.000037 & 4.03 & 0.0552 & \\ Residual & 0.000240 & 26 & 0.000009 & & & \\ Lack-of-Fit & 0.000191 & 21 & 0.000009 & 0.94 & 0.5919 & \\ Pure Error & 0.000048 & 5 & 0.000010 & & & \\ Cor. Total & 0.008529 & 53 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: ANOVA results of quadratic response surface model for 30\({}^{\circ}\) corner.
## 2 Structure Configuration and Finite Element Model Validation
### Material Models
#### 2.3.2 Annealed 304 Stainless Steel  
The Johnson-Cook (J-C) material model was elected to describe the flow stress of the annealed 304 stainless steel. This model is ideal for a material subjected to large strains, high strain rates, and high temperatures. The flow stress model is expressed by Equation (3) [29].  
\[\sigma_{y}=[A+B\bigg{(}\epsilon_{p}^{\text{eq}}\bigg{)}^{n}][1+c\ln\bigg{(} \frac{\dot{\epsilon}_{p}^{\text{eq}}}{\dot{\epsilon}_{0}}\bigg{)}][1-(T^{*})^{ m}] \tag{3}\]  
where \(\sigma_{y}\) is the dynamic flow stress, \(\epsilon_{p}^{\text{eq}}\) is the equivalent plastic strain, \(\dot{\epsilon}_{p}^{\text{eq}}\) is the equivalent plastic strain rate, and m is the thermal softening exponent. The constants \(A\), \(B\), \(m\), \(c\), \(\dot{\epsilon}_{0}\), and m are material parameters and can be determined from an empirical fit of flow stress data. \(T\) is the homologous temperature and it could be calculated from Equation (4).  
\[T^{*}=\frac{T-T_{r}}{T_{\text{m}}-T_{r}} \tag{4}\]  
\(T\) is the material temperature; \(T_{m}\) is the melting temperature of the material and \(T_{r}\) is the room temperature. In order to capture the rupture, the failure criterion based on equivalent plastic strain was adopted. This criterion has gained popularity due to its simple and effective formulation and has  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**EOS** & \(\gamma\) & **Reference Density (g/cm\({}^{3}\))** & **Reference Energy (\(\mu\)J/mg)** \\ \hline Ideal gas & 1.4 & \(1.225\times 10^{-3}\) & \(2.068\times 10^{5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ansys/Autodyn material model for the air.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\rho\) **(g/cm\({}^{3}\))** & _A (GPa)_ & _B (GPa)_ & \(R_{1}\) & \(R_{2}\) & \(\omega\) \\ \hline
1.63 & 373.75 & 3.747 & 4.15 & 0.90 & 0.35 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The JWL model used to define the TNT material model in Ansys/Autodyn.  
been proven to provide results with satisfying accuracy [24]. Table 4 shows the J-C material model parameters for the 304 stainless steel material. A fixed boundary condition was applied to the outer edges of the panel, also gauges were applied to the facing plates to track their response.
## 1 Introduction
This is achieved by (1) validating the satellite altimeter data using in situ measurements; (2) applying the EVA scheme commonly used to estimate the extreme wave heights; (3) comparing the estimated extreme wave heights with the maximum SWH measurements; and (4) investigating the difference in extreme wave height characteristics between typhoon region and non-typhoon region in the Northwest Pacific. This study also aims to discuss the limitations and precautions for estimating the extreme wave height  
Figure 1: Spatial distribution of (**a**) the annual mean and (**b**) standard deviation of significant wave height (m), (**c**) the number of typhoons in each bin, and (**d**) the typhoon tracks from 1992 to 2016 in the Northwest Pacific, where the colors denote the years 1992 to 2016.  
using satellite altimeter data and to demonstrate the necessity of in situ data to verify the satellite-derived extreme wave heights.
# Model Checking Resiliency and Sustainability of In-Vehicle Network for Real-Time Authenticity
## 4 MAuth-CAN
### Countermeasures of MAuth-CAN  
To protect from the above attacks, MAuth-CAN performs the authentication using dual-CAN controllers as shown in Figure 2.  
As the authenticator uses two CAN controllers, the reception and transmission queues are doubled. The controller has its own transmission error counter (TEC) and receive error counter (REC), thus dual-CAN controllers have two TECs and RECs for authentication. In particular, TEC is the decisive variable that determines to expel a CAN controller from CAN bus.
# MFCC Selection by LASSO for Honey Bee Classification
## 1 Introduction  
Monitoring honey bee colonies [1] is a crucial task because of their important role in providing pollination services. Today, the automated and mobile monitoring solutions introduced in beekeeping [2, 3, 4] mark the great need to develop and improve smart beekeeping systems.  
The difference between a drone and a worker bee lies in their roles and physical characteristics [5, 6, 7]. Drones are male bees whose purpose, among many others such as the thermoregulation of the nest, is to mate with a queen at drone congregation areas (DCAs) [8, 9, 10], where they travel in the late spring and early summer. They are larger in size, have larger eyes and powerful flight muscles. On the other hand, worker bees are infertile females whose primary responsibilities include pollen and nectar foraging, building and maintaining the combs in the hive, and attending to the needs of the queen and larvae. They are smaller in size, have pollen baskets, and possess a stinger for defense. The queen bee is the only fertile female in the colony, responsible for laying eggs and ensuring the survival of the colony. She is larger in size and has a longer lifespan comparedto worker bees. The queen bee is capable of laying up to 2000 eggs per day and is essential for establishing and maintaining the colony.  
The natural reproductive process in honey bee colonies is associated with two phenomena: **swarming**, which is the division of a colony into two swarms while the mother queen leaves the hive with a part of the colony [11], called a primary swarm, and the eventual later afterswarms, and visiting **drone congregation areas** by the newly emerged queen after becoming sexually mature.
## 4 Discussion
### GPP with VIs and SIF  
#### 4.3.1 Correlation at Different Time Scales  
On the whole, SIF was superior to other proxies in tracking seasonal GPP, with NIR\({}_{\rm V}\) second. This is similar to the study by [PERSON] et al. (2022) [20] in the western US ecological sub-regions. However, the hypothesis that SIF is stronger than VIs is not entirely correct. The correlation between SIF and GPP in sparse vegetation was not as good as that of VIs in other time scales except summer and autumn (Figure 8). The reason may be that the vegetation cover of sparse vegetation is low, and the real SIF signals received by the satellite are weak, which are seriously affected by the noise signals, resulting in a low signal-to-noise ratio [65; 66]. NIR\({}_{\rm V}\) and kNDVI solve the background contamination problem and better reflect the vegetation signal [22; 23]. Furthermore, SIF performance was also lower than VIs in the non-growing season of forest-shrub-grass vegetation mosaic belt and sparse vegetation and in the summer of deciduous and evergreen forests (Figure 8). The reasons for forest-shrub-grass vegetation mosaic belt and sparse vegetation may be (1) weaker SIF signals and (2) low VIs, SIF, and GPP due to the fact that vegetation basically stops growing in the non-growing season, resulting in a low correlation of GPP with VIs and SIF. The reason for the phenomenon that summer SIF performance in deciduous and evergreen forests is lower than VIs may be due to the dense vegetation canopy in the summer. SIF signal is hampered by the high-density canopy obstruction, resulting in impaired radiative transfer (low escape ratio) [67; 68; 69], and the information from SIF is not fully transmitted to the satellite. The correlation between summer VIs and GPP was also low for forest land compared to other seasonal scales. The reason for this is that GPP in forest land that is in the summer continues to increase to a large extent, with GPP increasing by more than 10 gcmin July compared to June in most years. Vegetation greenness does not change much in the summer, resulting in VIs not responding well to changes in GPP in summer. R2 has a slight increase in the entire study time scale compared to the removal of winter. In winter, VIs, SIF, and GPP are all low, and if GPP were fitted in winter alone as a correlation with VIs and SIF, the correlation would be very low. However, in the entire study time scale fitting, the winter value made up for the vacancy of low value, and the correlation increased.
## 2 Preliminaries
### Energy Consumption Model  
According to a previous study [26], the cluster-based WSN (CWSN) should be an optimal design so that energy dissipation over the entire network is equally distributed, and the lifetime of the network can be prolonged. The CoCMA was designed to follow this suggestion. In order to compare the performance of the proposed algorithm with existing ones, we use the model in the previous study [26] to measure the energy consumption for both transmission and reception. This energy consumption model is depicted in Figure 2. Given that energy consumption in radio transmission is often greater than that in general sensor operations or memory access [30, 31], we consider the energy consumption for radio transmission and ignore energy consumption for other operations.  
In Figure 2, \(E_{\textit{elec}}\) denotes the dissipated energy for the transmit circuitry or receive circuitry per bit (in nano-Joules), \(E_{\textit{amp}}\) denotes the energy consumption for the power amplifier per bit, and \(\beta\) denotes the exponent for path loss. Consequently, when a transmitter sends an \(H\)-bit packet to a receiver, the total energy consumption can be calculated by:  
\[\begin{split} E_{\textit{Tx}}(d,H)&=E_{\textit{elec} }\times H+E_{\textit{amp}}\times H\times d^{\beta}\\ E_{\textit{Rx}}(d,H)&=E_{\textit{elec}}\times H,\end{split} \tag{3}\]  
where \(E_{\textit{Tx}}\) and \(E_{\textit{Rx}}\) are the total energy consumption on transmitting and receiving an \(H\)-bit packet, respectively. Since all of the sensor nodes are assumed homogeneous, every sensor node has the same initial energy. The energy-rich sink, however, does not involve this energy consumption model; rather, it presumably comes from other power sources with plenty of supply.  
Figure 2: The energy consumption model used in this study.
# Acoustic-Based Engine Fault Diagnosis Using WPT, PCA and Bayesian Optimization
## 2 Experimental Work  
### Gasoline Engine and Data Acquisition System  
The experimental works were carried out using a Ford-Eco Boost 1.6 L which is mainly used in Ford Focus, Ford Fiesta and Transit Courier. The specifications of the ICE are provided in Table 1. The engine is fully instrumented to measure all operating conditions including speed, load, cylinder pressure, injection pressure, etc. It also has the ability to change parameters such as injection timing, pressure, etc., as well as to cut off cylinders during run, as shown in Figure 1.  
Acoustic signals were collected using microphones. It was carried out by two Vernier MCA-BTA sensors mounted 1 m from the cylinder head on left and right side, as shown in the Figure 1a. The microphones were fed to a five-channel data logger, Labquest 2, which was connected to the laptop. Acoustic signals were also captured by mobile phones, iPhone 6s, with a sampling frequency of 44.1 kHz. Each dataset comprised 10 s of vibration signals with 441,000 data points. The audio sample has a bit rate of 128 kb/s with mono-channel. Vision 4.1.4 version was used for data acquisition for varying engine parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Ford-Eco Boost 1.6L** \\ \hline Engine type & 4-stroke GDI with turbocharger \\ No. of Cylinders & 4 Cylinder \\ Compression Ratio & 10.1:1 \\ Engine capacity & 1596 cm\({}^{3}\) \\ Power & 132 KW @ 6000 rpm \\ Torque & 178 Nm @ 2500 rpm \\ Firing order & 1-3-4-2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Engine specifications.  
Three types of failures were experimented and injected into the engine, where each experiment was done at different speeds and different loading conditions. These include:  
1. Engine misfire
2. Ignition timing variation
3. Air Fuel Ratio (AFR) variation  
Engine misfire is a common defect in engines. This fault was replicated by cutting off one of the cylinders, which effectively gave no combustion into a cylinder. The next fault is ignition timing variation, which was replicated by varying the ignition timing from 15\({}^{\circ}\) retarded to 15\({}^{\circ}\) advanced from the manufacture recommended setting for different operating conditions. Lastly, AFR variation is a critical fault, as it makes real time adjustments to fuel and timing for maximum efficiency and power, and damage to AFR sensor or air flow meter can cause drop in engine efficiency and power output. This was replicated by varying the air mass flow rate. The resultant AFR and lambda, which is the ratio of actual AFR to the stoichiometric ratio, were obtained. The AFR was varied from 10.10 to 17.20 with stoichiometric at 14.45. The test engine was operated under different operating conditions to diversify the data. The various operating points are given in Table 2.
# A Distributed Real-Time Monitoring Scheme for Air Pressure Stream Data Based on Kafka
## 2 Related Works
Therefore, it can provide a high-performance information channel for real-time air pressure monitoring. Kafka producers send data to the corresponding topics in the Kafka cluster, while consumers read data from their subscribed topics (Figure 1). A Kafka cluster consists of several brokers; all partitions of the same topic are distributed across different brokers. For each partition, one replica is elected as the leader, and the others are followers. The leader of each partition maintains an in-sync replica (ISR) list to ensure data consistency. When a producer sends a message, the message is first written to the leader partition and is then replicated to all replicas in the ISR. Kafka uses this replication mechanism to achieve fault-tolerant message transmission, ensuring the cluster's ability to recover data in case of a broker failure [28]. ZooKeeper serves as the manager responsible for managing the cluster state and metadata and coordinating partition assignment and fault recovery. The ActiveMQ message broker consists of connections, producers, consumers, messages, and message queues. The broker enables data transmission between producers and consumers through message queuing services. In ActiveMQ, producers send real-time data to message queues, which consumers then retrieve. If there is only one consumer in the working environment, the destination of data transmission will be a queue; if there are multiple consumers, messages are routed using the pub/sub model [29]. In this process, consumers send an acknowledgment (ACK) message to the producer through the ACK mechanism, which indicates that the transmitted data have been received and verified without errors, ensuring message reliability. ActiveMQ also supports message transformation, which converts messages from one format to another during routing or consumption. This enables seamless integration between systems with different message formats or protocols [30]. Figure 2 illustrates the message-passing principle of ActiveMQ. Figure 1: Kafka message-passing principle. Figure 2: ActiveMQ message-passing principle.
# Rotating Target Detection Method of Concrete Bridge Crack Based on YOLO v5
## 2 YOLO v5 Model  
The YOLO v5 network architecture consists of five main components: Input, Backbone, Neck, Prediction, and Output, as shown in Figure 2. The Input module uniformly scales images to a specific size using adaptive scaling. Data augmentation for input images is performed using both Mosaic and random affine transformation methods. The Backbone encompasses the Focus, Convolution-Batch Normalization-Leak ReLU, BottleneckCSP, and Spatial Pyramid Pooling (SPP) modules [26] for feature extraction. The Focus module approximates down sampling while integrating width and height information into channels, preserving features. The BottleneckCSP employs residual connections for multi-level feature fusion through branch connections, enhancing feature extraction capabilities. The SPP module aggregates pooling layers of varying scales, enabling multi-receptive field fusion to enhance recognition in complex scenes. The Neck employs a Feature Pyramid Networks (FPNs) + Path Aggregation Network (PAN) structure [27] to effectively fuse features extracted by the Backbone. This results in three enhanced feature layers of different scales, which are then passed to the Prediction module. The Prediction module produces prediction results, which undergo Non-Maximum Suppression (NMS) [28] to obtain final prediction boxes, thereby obtaining object category and location information. The model's activation functions and loss functions are set based on the network's outputs and label information, facilitating the updating of network parameters. The latest YOLO v5 employs the Mish activation function [29] and the CIOU loss function [30].  
YOLOv5 possesses powerful real-time object detection capabilities, striking a balance between speed and accuracy, making it a mainstream choice for various object detection tasks. However, it requires a significant amount of computational resources during training and is sensitive to input image quality, necessitating careful tuning for optimal performance on specific tasks. For example, when applied to concrete crack detection, several challenges persist:  
(1) Compared to well-defined objects, crack image targets are characterized by high aspect ratios, overlapping and intersecting regions, and directional significance. Using horizontal bounding boxes fails to capture the directional information of targets and hinders accurate localization.  
(2) Crack images possess intricate backgrounds and are often accompanied by noise. YOLO v5 lacks an effective attention mechanism module, leading to suboptimal detection accuracy in complex backgrounds.  
(3) Bridge crack images have relatively low resolutions, causing feature maps to progressively decrease in size due to multiple down sampling layers in YOLO v5 architecture. Consequently, the fine-grained details of cracks are severely compromised.