esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Network Attack Detection Method Based on LightGBM  
#### 2.3.1 The Features of the LightGBM Algorithm  
LightGBM is an ensemble learning algorithm based on a decision tree. The algorithm is an improved version of the gradient boosting decision tree (GBDT) algorithm. The gradient boosting decision tree (GBDT) is an ensemble learning framework based on decision trees. The GBDT uses gradient descent to approximate each decision tree in an additive model, as shown in Figure 4.  
Figure 4: The framework of the GBDT algorithm.  
Figure 3: The construction method of the pseudo sample database.  
To improve prediction accuracy, a new decision tree was created in each iteration to minimize the loss function in the direction of the fastest decreasing loss function--the negative gradient direction. The GBDT model composed of \(m\) decision trees are as shown in Equation (2):  
\[F_{M}(x)=\sum_{m=0}^{M}\beta_{m}h(x;a_{m}) \tag{2}\]  
The \(\beta_{m}\) represents the linear combination coefficient of the \(m\) decision trees, \(h(x;a_{m})\) represents a decision tree model.  
LightGBM is an improved GBDT that overcomes the shortcomings of GBDT overfitting and slow training speed. This improvement facilitates the detection of CPPS network attacks and failures [32]. The key improvement strategy is shown in Figure 5. The advantages of the LightGBM algorithm are shown below:  
1. The mechanism that supports parallel training reduces the training time.
2. The algorithm supports a self-defined loss function, sets a reasonable loss function according to the business background, and constantly corrects errors in the iterative process, which improves the training precision.
3. The deep growth strategy controls the complexity of the model and reduces the risk of overfitting.
4. Data optimization uses the histogram algorithm to reduce model complexity.
## 3 Three-Dimensional Silicon Carbide Crystal Growth Furnace  
Figure 1 shows a three-dimensional geometry and meshes for silicon carbide crystal growth furnace. To decrease calculation capacity, the electromagnetic and temperature fields are decoupled. Their physical models are presented in Figure 1a,b, respectively. Figure 1c indicates the mesh for physical model in Figure 1a. As shown in the figure, more fine meshes are used for silicon carbide crystal, silicon carbide powder and graphite crucible to get more accurate simulation results. It can be seen from Figure 1a that the coil is quasi-sprial, and the size of its cross section is 10 \(\times\) 10 mm. The silicon carbide crystal is 50 mm in diameter and 5 mm in height. Graphite crucible is 125 mm in inner diameter, 6 mm thick in r-direction, and 20 mm thick in z-direction. Silicon carbide powder and growth chamber are 55 mm and 25 mm in height, respectively. The furnace is cooled by both top hole with diameter of 20 mm and bottom hole with diameter of 10 mm in diameter. The convection heat transfer coefficient of cooling water (T = 300 K) is 1000 W/(m\({}^{2}\cdot\)K). The felt is 37 mm thick in r-direction and 40 mm thick in z-direction. The convection heat transfer coefficient of air outside of the furnace (T = 300 K) is 50 W/(m\({}^{2}\cdot\)K).  
Table 1 lists properties of several materials, such as density, heat capacity, heat conductivity coefficient. Properties of porous silicon carbide powder are calculated by \(\varphi_{\text{SiC\_powder}}=\varepsilon\varphi_{\text{SiC}}+(1-\varepsilon) \varphi_{\text{fluid}}\), where \(\varepsilon\) is void fraction and equals to 0.45.
## 4 The Role of UAV-Base Station in Wireless Communications
### Load Balancing  
Due to the movement of mobile users from one place to another as well as variation in user traffic demands, the traffic loads of BSs vary both temporally and spatially. This makes some BSs to be lightly loaded while others are heavily or overloaded, thereby leading to service denial or poor QoS from the overloaded BSs [86]. Although small cells help to reduce the level of traffic imbalance in cellular networks, due to the static nature of their deployment, they cannot respond to sudden changes in traffic demands that may be occasioned by large events taking place in an area for a short time or other impromptu surges in network traffic that require quick intervention to reduce the pressure on the available BSs. In such cases, UAV-BSs can be of great help because they can be quickly deployed to respond to such traffic demands and ensure that load is properly distributed among the various BS in order to avoid network congestion [87].  
In this regard, the authors in [88] proposed a learning-based framework for UAV deployment to crowded regions of the network during periods of peak traffic to assist in load balancing, thereby preventing degradation in the QoS of users. In the proposed framework, both auto-regressive integrated moving average (ARIMA) and extreme gradient boosting (XGBOOST) ML algorithms were employed to forecast future high-traffic intensity regions based on historical data for proactive UAV deployment. The work in [89] considered UAV deployment for load balancing to reduce communication latency between IoT devices and macro BSs (MBSs) during periods of high traffic load on the MBS. Heuristic algorithms were developed to determine the optimal location of the UAVs, and association strategy for the IoT devices. The work in [90] considered the application of UAV-BSs for load balancing and capacity enhancement in high-traffic areas via efficient trajectory design. A deep reinforcement learning algorithm was proposed to determine the optimal trajectories of the UAVs that would efficiently balance the load among the UAV-BSs in order to achieve high spectral efficiency.
# Mode-Selective Photonic Lanterns for Orbital Angular Momentum Mode Division Multiplexing
## 2 Principle
It has been shown that there is a periodic variation of the generating OAM\({}_{\pm 1}\) modes at a half cycle of 1.3 cm as the taper length increases. Hence, we can obtain a designed OAM-MSPL if the \(\mathrm{LP_{11}}\) modes satisfies the \(\pi\)/2 phase difference. Moreover, we could also optimize taper ratios and V values of the SMF cores to control the phase difference variation in order to obtain low-loss OAM-MSPLs. Figure 4: The BPM simulation output mode profiles of the PLs with different taper lengths when selectively injecting light into the Fiber 2 and 3 cores. Figure 3: The FEM simulation mode indexes’ evolution along the taper of the mode-group-selective PL (**a**). The comparison of the FEM/BPM simulated mode profiles at different positions along the taper when selectively injecting light into the Fiber 2 and 3 cores of the mode-group-selective PL with the taper length of (**b**) 2, (**c**) 2.65, and (**d**) 3.3 cm.
## 4 Conclusions
\begin{tabular}{l l} A\({}_{\text{a}}\) & Collector aperture, m\({}^{2}\) \\ C & Component capital cost, \(\xi\) \\ C\({}_{0}\) & System capital cost, \(\xi\) \\ c\({}_{\text{p}}\) & Specific heat capacity under constant pressure, J/kg\(\cdot\)K \\ CF & Cash Flow, \(\xi\) \\ E\({}_{\text{act}}\) & Activation energy, J/mol \\ ex & Specific exergy, J/kg or J/mol \\ Ex & Exergy rate, W \\ F & Objective function, - \\ G & [PERSON]’s free energy, J/mol \\ G\({}_{\text{bn}}\) & Solar beam irradiation, W/m\({}^{2}\) \\ h & Specific enthalpy, J/kg or J/mol \\ i & Discount factor, \% \\ J & Current density, A/m\({}^{2}\) \\ K & Cost, \(\xi\) \\ K(\(\theta\)) & Incident angle modifier, - \\ L & Membrane thickness, mm \\ LHV & Low Heating Value, kJ/kg \\ m & Mass flow rate, kg/s \\ N & Lifetime, years \\ N & Molar flow rate, mol/s \\ NPV & Net Present Value, \(\xi\) \\ P & Pressure, bar \\ P\({}_{\text{el}}\) & Electricity rate, W \\ PBP & Payback Period, years \\ Q & Energy rate, W \\ q & Thermal losses per collector aperture, W/m\({}^{2}\) \\ R & Ohmic resistance, \(\Omega\) \\ R\({}_{\text{g}}\) & Gas constant, J/(mol\(\cdot\)K) \\ s & Entropy, J/mol or J/kg \\ SPBP & Simple Payback Period, years \\ T & Temperature, \({}^{\text{o}}\)C or K \\ t & Time, s \\ U & Heat loss coefficient, W/m\({}^{2}\)K \\ u & Velocity, m/s \\ V & Potential, V \\ V\({}_{\text{T}}\) & Thermal storage tank volume, m\({}^{3}\) \\ V & Volumetric flow rate, m\({}^{3}\)/s \\ Y & Yearly parameter, - \\
**Greek symbols** \\ \(\Delta\) & Difference \\ \(\eta\) & Efficiency, - \\ \(\theta\) & Incident angle, \({}^{\text{o}}\) \\ \(\lambda\)(x) & Water content, \(\Omega^{-1}\) \\ \(\rho\) & Density, kg/m\({}^{3}\) \\ \(\sigma\) & Entropy generation, J/mol \\ \(\sigma\)(x) & Local ionic conductivity of the membrane, s/m \\ x & Distance from the cathode-membrane interface, mm \\ \end{tabular}   
**Subscripts and superscripts**  
0 & Reference condition  
act & Activation  
air & Air  
amb & Ambient  
an & Anode  
c & Collector  
cath & Cathode  
chem & Chemical  
comp & Compressor  
cool & Coolant  
crit & Critical point  
el & Electricity  
en & Energy  
ex & Energy  
fluid & Fluid  
HC & Hydrogen compressor  
heat & Heating  
HEX & Heat exchanger  
high & High value  
HST & Hydrogen storage tank  
hydr & Hydrogen  
in & Inlet  
is & Isentropic  
loss & Losses  
low & Low value  
max & Maximum value  
mg & Electro-mechanical  
min & Minimum value  
motor & Motor  
net & Net value  
O&M & Operation and maintenance  
ohm & Ohmic  
oil & Thermal oil  
ORC & Organic Rankine Cycle  
out & Outlet  
ox & Oxygen  
PEM & Proton exchange membrane  
phy & Physical  
PTC & Parabolic trough collector  
pump & Pump  
reacted & Reacted  
rec & Recuperator  
ref & Reference  
sat & Saturation  
sh & Superheating  
solar & Solar energy  
st & Stored in the tank  
sun & Sun  
T & Tank  
th & Thermal  
turb & Turbine  
u & Useful  
w & Water  
WE & Water electrolyzer
## 2 Materials and Methods
### Data Analysis  
Landmark data was first standardized through a Generalized Procrustes Analysis (GPA) [34; 35]. GPA is an effective means of extracting morphological data quantitatively, allowing for the characterization of _shape_ and _form_ patterns across different configurations (orientation, translation, and scaling) [36; 37]. Before any further hypothesis testing, allometric analyses were first considered to evaluate the impact that the tooth pit size might have had on the morphological variance. To achieve this aim, the logarithm of centroid sizes was calculated and used to perform regression on shape variables [38]. In case shape-size relationships proved to be of importance, final superimpositions were performed excluding the scaling step of the GPA procedure (otherwise known as _form_).  
After normalizing, landmark coordinates were processed using dimensionally reduction in the form of Principal Component Analysis (PCA). PCA is a common visualization technique used to assess patterns in morphological trends. Each of these graphs were coupled with the computation of Thin Plate Splines [36; 39] to visualize and assess these variations across each PC score. The PC scores representing the highest morphological variability (!95%) were then extracted for further processing.  
The degree of statistical differences and similarities between samples was assessed using Two-One Sided equivalency Tests (TOST). This test assesses the magnitude of equivalency between samples according to [PERSON]'s \(\delta\)[40]. For parametric versions of TOST, [PERSON]'s \(t\)-statistic was used [41]. In cases where non-parametric tests were needed, the Yuen trimmed robust \(t\)-statistic was used [42; 43]. It is important to note that, contrary to many other analyses of variance, both variants of TOST consider the Null Hypothesis (\(H_{0}\)) to indicate differences between samples.  
Results of statistical hypothesis tests were additionally evaluated considering values of \(p\) < 0.003 (i.e., \(p\) < 3\(\sigma\)) to be a robust indicator of notable differences between samples [21; 22; 44; 45]. \(p\)-values accompanied by calculations of their False Positive Risk (FPR) [45] supported this. FPR values are used to assess the probability that an observed \(p\)-value is a false positive, otherwise known as a Type I statistical error [45]. For FPR calculations a prior probability of 0.5 was adopted, assuming complete randomness [45].  
The R programming language (v.4.0) was used to perform all statistics.  
According to [22], we excluded the tooth scores from this analysis because this type of tooth mark shows intraspecific variability, and additional analysis is needed to evaluate the resolution of this type of tooth mark between the carnivores in captivity.
# A Novel High-Precision Imaging Radar for Quality Inspection of Building Insulation Layers
## 3 Radar System Design
### Ultra-Wideband High-Bunching Antenna Design  
#### 3.3.1 Antenna Selection  
The radar system uses two antenna units for transmission and reception, and the transmitting antenna and the receiving antenna can be of the same form. The transmitting antenna unit is mainly used to efficiently radiate the high-speed pulse output by the power amplifier to produce as little tailing and oscillation as possible. The receiving antenna is used to receive the reflected echo signals, and the receiving antenna is expected to have high directivity. The high-precision imaging radar system transmits high-speed pulse signals, and the signal energy is mainly concentrated in 7.25-10.35 GHz. The requirements of the radar system for antenna units can be summarized as follows: ultra-wideband, narrow beam, and miniaturized antenna. At the same time, the antenna system and the transmitting module can be well-matched. Therefore, antenna design is the focus and difficulty of this radar design.  
The ultra-wideband planar sinusoidal antenna is an antenna that combines ultra-wideband technology and sinusoidal antenna design. It has a wide frequency band, good radiation characteristics, and a compact structure, making it suitable for high-frequency and wide-bandwidth communication and measurement applications. However, sinusoidal antennas typically do not have the same directivity as high-gain directional antennas and therefore perform poorly in applications that require high-gain and long-range transmission.  
A lens antenna is a kind of antenna that utilizes the lens principle to focus electromagnetic waves, and its main features are better directionality and gain. Compared with  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Parameter** & **Test Level** & **Min.** & **Typ.** & **Max.** & **Unit** \\ \hline \multicolumn{5}{c}{Output port match, S22} \\ \hline
6.0–10.2 GHz & III (vn-tr) & \(-\)3.8 & & dB \\ \hline \multicolumn{5}{c}{Output pulse center frequency} \\ \hline PGSelect = 9 & III (vn-ti) & 8.2 & & GHz \\ \hline PGSelect = 10 & III (vn-ti) & 8.8 & & GHz \\ \hline \multicolumn{5}{c}{Bandwidth, \(-\)10 dB} \\ \hline PGSelect = 9 & III (vn-ti) & 2.35 & 2.65 & 3.20 & GHz \\ \hline PGSelect = 10 & III (vn-ti) & 2.65 & 3.10 & 4.40 & GHz \\ \hline \end{tabular}
\end{table}
Table 2: The parameters of the X2 impulse radar transceiver.  
Figure 4: Output pulse frequency spectra.  
conventional antennas, lens antennas have the advantages of high gain, high directivity, smaller side-lobe, low distortion, and excellent beam control.  
Therefore, in this paper, an ultra-wideband planar sinusoidal antenna with a reflecting plate and lens is used to design an ultra-wideband high-bunching antenna to ensure that the directionality of the antenna is enhanced while satisfying the ultra-wideband [33].
# An ANN-Based Approach for Real-Time Scheduling in Cloud Manufacturing
## 2 Mathematical Modeling for the CMfg Scheduling Problem
### Optimization Objectives for Real-Time Scheduling  
For each MP, the scheduling optimization objectives include three aspects, namely cost, make-span, and service quality. Without loss of generality, we proceed from the perspective of \(\mathbb{P}_{c}\).  
First of all, the service usage cost for any MP is incurred when the demander requests and uses specific MRs included in the type-matched MSs, it is equal to the sum of the usage costs of MTs inside the MP. The service usage cost for MT is determined by the unit cost of the allocated MR and the requirement for task processing. Specifically, if \(\mathbb{T}_{j}^{(c)}\) is allocated to \(\mathbb{R}_{i}^{(k)}\) for processing, the service usage cost is determined by Equation (17).  
\[z_{j,m}^{(c)}=p_{j}^{(c)}v_{j}^{(c)}\sum_{i\in\mathcal{I}}x_{i,j}^{(c)}C_{i}^{(k)} \tag{17}\]  
Then, the service usage cost for \(\mathbb{P}_{c}\) will be Equation (18)  
\[Z_{m}^{(c)}=\sum_{j\in\mathcal{J}_{c}}z_{j,m}^{(c)} \tag{18}\]  
Secondly, the make-span of MP refers to the overall processing time to complete all its MTs. It needs to consider the logistics duration for all the MTs inside the MP. According to the activity-on-vertex of \(\mathbb{P}_{c}\) as shown in Figure 3, its make-span can be expressed as Equation(19), where \(e_{-1}^{(c)}\) belongs to dummy \(\mathbb{T}_{-1}^{(c)}\).  
\[Z_{d}^{(c)}=e_{-1}^{(c)} \tag{19}\]  
Thirdly, the quality of the product refers to the satisfaction of manufacturing \(\mathbb{P}_{c}\), and it is determined by the intrinsic service quality of specific MRs. Since the satisfaction of processing \(\mathbb{T}_{j}^{(c)}\) can be expressed as Equation (20), the quality of the product for processing \(\mathbb{P}_{c}\) can be defined as Equation (21), which is the lowest satisfaction obtained by processing MTs included in \(\mathbb{P}_{c}\).  
\[z_{j,\mu}^{(c)}=\sum_{i\in\mathcal{I}}x_{i,j}^{(c)}p_{i}^{(k)} \tag{20}\]  
\[Z_{u}^{(c)}=\min_{j\in\mathcal{J}_{c}}\left\{z_{j,\mu}^{(c)}\right\} \tag{21}\]
## 3 Platform Design Criteria
### Gateway Design Criteria
#### 3.2.2 Gateway Energy Supply  
Long-distance communication of the gateways typically requires much more energy than the short-range in-field one of the sensor nodes. Gateways also have more hardware resources, such as larger program and data storage capacity, more powerful microcontrollers or microprocessors and accurate, always-running timers. All of these combined require significantly more energy than the sensor nodes.  
Thus, the gateways generally have higher energy reserves and may also have ambient energy harvesting devices to increase their lifespan between scheduled maintenance (which may be needed for, e.g., the replacement of the (rechargeable) battery or for the cleaning of the energy harvesting devices). Even if the gateways may require scheduled maintenance more often than the sensor nodes, its cost may be acceptable: a WSN deployment for pervasive environmental monitoring typically has fewer gateways than sensor nodes, and the gateways are generally deployed in locations that are more accessible than the sensor nodes (see Section 2.2), which further reduces the cost of gateway maintenance.  
Given that gateway energy reserves are far larger than those of the sensor nodes and that gateway maintenance is less expensive than the maintenance of the sensor nodes, it can be cost-effective at thenetwork-level to bias the energy consumption towards the gateways if this helps with reducing the cost of the sensor nodes and their scheduled maintenance requirements.
# Dog Identification Method Based on Muzzle Pattern Image
## 4 Results and Discussion
### Evaluation of the Robustness of the Proposed Method
For the perspective change, both SIFT and ORB were the best and the EER was 0.3%. For the noise, ORB was the best and the EER was 0.4%. In the robustness evaluation with the all deformed images, ORB was the best and the EER was 0.35%. The experimental results shows that ORB is the proper general-purpose feature extraction algorithm for the dog muzzle recognition, and that it is relatively robust to the image deformation which may occur in image acquisition than other algorithms. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Alg. **} & \multirow{2}{*}{ \begin{tabular}{c} **Evaluation** \\ **Item** \\ \end{tabular}  } & \multicolumn{2}{c}{**Rotation**} & \multicolumn{2}{c}{**Intensity**} & \multicolumn{2}{c}{**Perspective**} & \multicolumn{2}{c}{**Noise**} & \multicolumn{2}{c}{**Total**} \\ \cline{3-10}  & & **Genuine** & **Imposter** & **Genuine** & **Imposter** & **Genuine** & **Imposter** & **Genuine** & **Imposter** & **Genuine** & **Imposter** \\ \hline \multirow{4}{*}{SIFT} & Min & 3 & 0 & 5 & 0 & 3 & 0 & 0 & 0 & 0 \\ \cline{2-10}  & Max & 1367 & 7 & 1578 & 7 & 1207 & 7 & 1403 & 7 & 1578 & 8 \\ \cline{2-10}  & Average & 290 & 4 & 338 & 4 & 266 & 4 & 218 & 4 & 266 & 4 \\ \cline{2-10}  & False matching & 21 & 24 & 1 & 7 & 11 & 30 & 26 & 420 & 394 & 123 \\ \cline{2-10}  & Optimal & \multirow{2}{*}{5.9} & \multirow{2}{*}{6} & \multirow{2}{*}{5.2} & \multirow{2}{*}{5.6} & \multirow{2}{*}{5.9} & \multirow{2}{*}{5.2} & \multirow{2}{*}{5.6} \\  & threshold & & & & & & & & & \\ \cline{2-10}  & EER(\%) & & 0.3 & & 0.0 & & 0.3 & 0.9 & 0.66 \\ \hline \multirow{4}{*}{S SURF} & Min & 1 & 0 & 2 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \cline{2-10}  & Max & 619 & 8
# Interplay Between Wear and Thermal Expansion in 6082 Aluminum: A Simulation and Experimental Study
## 2 Materials and Methods
### Thermal Model
#### 2.3.3 Heat Convection  
Since air flows over the disc surface, the forced thermal convection from the disc surface to an external airflow can be described by Equation (9):  
\[\begin{split}&-\mathbf{n}\cdot\mathbf{q}=q_{0}\\ & q_{0}=h(T_{\rm ext}-T)\\ & h=\begin{cases}2\frac{k}{L}\frac{0.0338\mathrm{Pr}\mathrm{r} ^{1/3}\mathbf{Re}_{L}^{1/2}}{\left(1+\big{(}\frac{0.0468}{\mathrm{r}}\big{)}^{ 2/3}\right)^{1/4}}&\text{if }\mathrm{Re}_{L}\leq 5\cdot 10^{5}\\ 2\frac{k}{L}\mathrm{Pr}^{1/3}\bigg{(}0.037\mathrm{Re}_{L}^{4/5}-871\bigg{)}& \text{if }\mathrm{Re}_{L}>5\cdot 10^{5}\end{cases}\end{split} \tag{9}\]  
\(-\mathbf{n}\cdot\mathbf{q}=q_{0}\) represents the heat flux normal to the surface, \(q_{0}\) is the convective heat flux, \(q_{0}=h(T_{\rm ext}-T)\) indicates that the convective heat flux is proportional to the temperature difference between the external fluid (\(T_{\rm ext}\)) and the surface (\(T\)). The convective heat transfer coefficient \(h\) is determined based on the Reynolds number (\(\mathrm{Re}_{L}\)) and the Prandtl number (\(\mathrm{Pr}\)): \(k\) is the thermal conductivity of the fluid, \(L\) is the characteristic length, \(\mathrm{Pr}\) is the Prandtl number--a dimensionless number that characterizes the relative thickness of the velocity and thermal boundary layers--Re\({}_{L}\) is the Reynolds number, a dimensionless number that characterizes the flow regime (laminar or turbulent) based on the fluid velocity, characteristic length, and kinematic viscosity. These equations provide the framework for calculating the convective heat transfer coefficient \(h\) based on the flow conditions and fluid properties.  
Since the pin is stationary, the \(h\) under natural convection is given by  
\[h=\frac{k}{D}\left(2+\frac{0.589\text{Ra}_{D}^{1/4}}{\left(1+\left(\frac{0.469} {\text{Pr}}\right)^{9/16}\right)^{4/9}}\right) \tag{10}\]  
This equation describes the convective heat transfer coefficient \(h\) for natural convection around a spherical object, where \(h\) is the convective heat transfer coefficient, \(k\) is the thermal conductivity of the fluid, and \(D\) is the diameter of the pin, Ra\({}_{D}\) is the Rayleigh number, a dimensionless number that characterizes the natural convection flow. Pr is the Prandtl number, a dimensionless number that represents the ratio of momentum diffusivity (kinematic viscosity) to thermal diffusivity.