esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Machine Learning Technique for Detection System
### ML Algorithms  
After the process of feature selection, we selected ML algorithms for feature classification (ML classifier). A set of the three traditionally well-proven ML classifiers used herein are 1 Bayesian classification, 2 support vector machine (SVM), and 3 random forest. Hereunder is a description on the basic characteristics of each of those three ML classifiers.  
(1) Bayesian Classification Algorithm  
The Bayesian classification algorithm is based on [PERSON] theorem assuming attributes are independent given the class, where Bayesian classifiers are the probabilistic classifiers. It is an algorithm using the condition between the prior probability (likelihood of an event occurring before taking new information) and the posterior probability (likelihood of an event occurring after taking new information) of two random variables [23]. The Gaussian Naive Bayes algorithm is typically used in ML.  
(2) Support Vector Machine Algorithm  
SVM is a classifier that is based on the non-probabilistic binary linear classification algorithm, seeking the best decision boundary to be as wide as possible, in the feature space with boundaries [24]. With the margin being as large as possible, it tries to make a decision boundary that best represents two categories and identifies true and false from the outcome.  
(3) Random Forest Algorithm  
The random forest algorithm, an ML algorithm developed by [PERSON] [25], is a kind of ensemble learning method for classification, regression analysis, and other tasks. It operates by constructing a multitude of decision trees at training time, where the output of the random forest is the class selected by most decision trees or the mean prediction of the individual trees is returned. Random forest is a form that combines the output of multiple decision trees, generally having high accuracy but somewhat low speed.  
The selected ML algorithms were tested, assessed, and validated in terms of accuracy. In this study, features were set to be 4 or less in number based on the statistics result in the course of feature selection, and then applied to the individual ML algorithms. The following chapter briefly describes the method of evaluating the algorithm performance.
# Assembly Tolerance Design Based on Skin Model Shapes Considering Processing Feature Degradation
## 2 Predictive Machined Surface Modeling  
In this paper, machined surfaces are regarded as an integration of processing features and random geometric deviation. To a specific machine surface, the processing feature is determined by the basic geometric shape (plane, cylinder, sphere, etc.), the application and sequence of processing techniques (turning, milling, grinding, etc.), and the corresponding operating parameters (cutting speed, feed rate, cutting depth, etc.). Random geometric deviation depicts unpredictable and inevitable machining errors. Previous research has shown the rationality behind the idea that a stochastic process could represent the precision deterioration of a machining system. In the condition of mass production, the processing features are degrading due to the deterioration of the machining tool. Therefore, a multi-dimensional Markov model based on data training is introduced for modeling and prediction. The pattern of degradation becomes more explicit if the machined parts are from different branches.  
grows and the training error decreases. To overcome this, an \(L_{2}\) regularization term weighted as \(\frac{1}{2\delta^{2}}\) is added to the object function, as shown in Equation (6):  
\[g(w)=\frac{\partial\ln(p(y|x,t))}{\partial w_{k}}-\frac{\sum w^{2}}{2\delta^{2}}. \tag{6}\]  
As a solution to Equation (6), a quasi-Newton iteration is implemented to calculate the parameter weights, which makes \(g(w)\) converge to zero:  
1. Set the initial counter \(k=0\), initial positive definite matrix \(M^{(0)}\), and initial parameter weight \(w^{(0)}\). Calculate the initial object function value \(g^{0}\).
2. If \(g(w^{(k)})\
eq 0\), set the current search direction \(p_{k}=-{(M^{(k)})}^{-1}\cdot g(w)\).
3. Update the positive definite matrix \(M(k)\) based on the Armijo rule with calibration coefficients: \[M^{(k+1)}=M^{(k)}+\frac{{y_{k}{y_{k}}^{T}}}{{y_{k}^{T}\delta_{k}}}-\frac{M^{( k)}\delta_{k}\delta_{k}^{T}M^{(k)}}{\delta_{k}^{T}M^{(k)}\delta_{k}}\] (7)
4. Use a line search function \(h(\lambda)=f(w^{(k)}+\lambda_{k}p_{k})\). Update \(w^{(k)}\) when \(h(\lambda)\) reaches the global minimum.
5. Output \(w^{(k)}\) if \(g(w^{(k)})=0\). Otherwise, \(k=k+1\) and go back to step (2).
## 5 Conclusions  
In this study, the temperature distribution and evolution law of fractured granite was investigated using a constant temperature heat source and a multi-channel data recorder from the perspectives of temperature field distribution, temperature variation pattern, heating rate, temperature difference, and temperature gradient evolution, leading to the following primary conclusions:  
(1) The fracture significantly alters the granite's temperature field distribution. Granite's heterogeneity, coupled with the fracture's presence, governs the field's regional characteristics.  
(2) Fractured granite exhibits three distinct temperature response stages during heating: rapid heating, slow heating, and stabilization. Steady temperatures decrease nonlinearly (left fracture side) and linearly (right fracture side) with increasing distance from the heat source.  
(3) Heating rates at all monitoring points followed a unimodal trend (increase, peak, decrease) before stabilization. Peak heating rates ranged 20-120 \({}^{\circ}\)C/h above the horizontal fracture (F-F) and 10-20 \({}^{\circ}\)C/h below it.  
(4) The fracture creates maximal temperature differences on both sides of its central region and minimal differences near the top (e.g., \(\Delta T\) = 10.5 \({}^{\circ}\)C at the center and 4.5 \({}^{\circ}\)C at the top under a 80 \({}^{\circ}\)C heat source). This confirms the fracture's dominant heat transfer impedance in the central granite region.  
(5) Heating generates concentrated isothermal gradient contours around the fracture. The time to peak temperature gradient correlates inversely with heat source proximity. Peak gradients within the fracture reached 825 \({}^{\circ}\)C/m (max) and 345 \({}^{\circ}\)C/m (min) at 90 \({}^{\circ}\)C source temperature.  
Future research should prioritize quantitative studies on fracture-induced impediments to heat transfer in rock masses. This necessitates integrating laboratory experiments and numerical simulations with actual geothermal reservoir conditions and undertaking a holistic consideration of the influence of fractures' geometric parameters (e.g., length,aperture, density) and surface characteristics (e.g., roughness, contact ratio, heat exchange area) on heat transfer.  
Conceptualization, B.X.; methodology, K.L. and Q.F.; investigation, K.L., Q.F. and F.H.; data curation, K.L. and Q.F.; writing--original draft, Q.F.; writing--review and editing, B.X., K.L., F.H. and W.Z.; supervision, B.X., K.L. and W.Z.; funding acquisition, B.X. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Joint Key Project of the National Natural Science Foundation of China (Grant No. U24A2089) and the Shanxi Provincial Natural Science Foundation Project (Grant Nos. 202303021211042 and 202303011222006).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflicts of interest.
# Path Planning of Obstacle-Crossing Robot Based on Golden Sine Grey Wolf Optimizer
## 4 Application of the GSGWO in Path Planning of Obstacle-Crossing Robot
### Working Environment Model  
The working environment models of path planning include grid maps, vector maps, point cloud maps and so on. As the most widely used environmental model, a grid map has the advantages of simplicity and intuition, so a grid map is used as the environmental model of the obstacle-crossing robot.  
The core idea of the general path-planning algorithm is obstacle avoidance, so the map is simply divided into accessible areas represented by white and non-accessible areas represented by black when establishing the environmental model. This division method is not applicable in the path planning of the obstacle-crossing robot. In this paper, an environmental model is proposed to add a grey obstacle-surmountable area between the two-color areas and give it a certain passage cost. The model can quantify the obstacle-crossing action cost of the obstacle-crossing robot so that the robot can make better choices in obstacle crossing and avoiding and achieve good cooperation with the obstacle-crossing function of the robot.  
Taking the real map shown in Figure 2(a) as an example, the environment models without an obstacle-surmountable area and with an obstacle-surmountable area are shown in Figure 2(b),\(c\), respectively.  
The map adopts a discrete node processing method. Each grid represents a node. The corresponding relationship between the node number and the coordinates is as follows:  
\[p=(x+0.5)+(y-0.5)\cdot n, \tag{14}\]  
where \(n\) is the number of rows or columns of the map.
## 2 The Proposed Image Fusion Method
### DPCNN and Its Improved Model  
#### 2.3.1 DPCNN Model  
[PERSON] and [PERSON] introduced a dual-channel PCNN (DPCNN) model to guide the fusion of high frequency sub-band images, which keeps the coupling characteristics of PCNN model, and simplifies parameter setting [20]. DPCNN model (Figure 2) is a simulation process in which cerebral cortex cells respond to visual pulse signals. Its bidirectional excitation and global characteristics can enhance the important information extraction from source images, which is conducive to the selection of detailed features [32]. Its mechanism is defined as shown below.  
The neuron (\(i,j\)) receives external excitation value and coupling values of peripheral neurons \(L_{ij}\) in the receiving domain, and internal activity items \(U_{ij}^{k}\) are nonlinear modulated by link strength \(\beta_{y}^{k}\).  
\[\begin{array}{ll}F_{ij}^{k}(n)=&\Big{|}S_{ij}^{k}(n)\Big{|},\ \ \ k=\{1,2\}\\ L_{ij}(n)=e^{-\alpha_{L}}L_{ij}(n-1)+V_{L}\sum\limits_{p\
eq q}\omega_{ij,pq}Y_ {ij,pq}(n-1)\\ \mathcal{U}_{ij}^{k}(n)=&F_{ij}^{k}(n)\Big{(}CR+\beta_{ij}^{k}L_{ij}(n)\Big{)},\ \ \ k=\{1,2\}\end{array} \tag{5}\]  
where \(n\) is the iteration number. \(S_{ij}^{k}\) (\(k=1,2\)) is the external excitation. \(\alpha_{L}\) is the attenuation constant of link input. \(V_{L}\) is the amplification factor of link input. \(p\times q\) is the neighborhood range. \(\omega_{ij,pq}\) is the connection weight. \(CR\) is the non-linear coefficient ratio between link input and external excitation. \(Y_{ij,pq}\) is pulse activation value (1 or 0).  
The maximum active item \(U_{ij}\) is taken in the information fusion domain, and the dynamic threshold \(\theta_{ij}\) is judged with it to generate a corresponding output activation signal.  
\[\begin{array}{ll}U_{ij}(n)=\max\Big{\{}U_{ij}^{1}(n),U_{ij}^{2}(n)\Big{\}} \\ Y_{ij}(n)=\left\{\begin{array}{ll}1&U_{ij}(n)\geq\theta_{ij}(n-1)\\ 0&\ \ \mbox{others}\end{array}\right.\end{array} \tag{6}\]  
where \(Y_{ij}\) is the output of neurons (1 or 0).  
When the neuron is activated, the channel corresponding to the maximum activity item is selected as the fusion coefficient. Besides, dynamic threshold is updated to promote the activation of adjacent neurons, and the method of iterative updating is adopted until all neurons are ignited.  
\[\theta_{ij}(n)=e^{-\alpha_{\theta}}\theta_{ij}(n-1)+V_{\theta}Y_{ij}(n) \tag{7}\]  
where \(\alpha_{\theta}\) is an attenuation constant of threshold. \(V_{\theta}\) is a magnification factor of threshold.
# A Novel Spaceborne Sliding Spotlight Range Sweep Synthetic Aperture Radar: System and Imaging
## 5 Comparisons  
In this section, the SSS-RSSAR and the traditional SSS-SAR are to be compared in two main aspects: the total sample number for the full data acquisition and the total computational loads for the raw data focusing.
## 3 Results and Discussion  
### Design and Strategy of Method Development  
A previous study [21] confirmed that DUV has intense native fluorescence as expected from its chemical structure (Figure 1) because it contains strong fluorophoric moieties such as extended conjugations and rigid fused rings. Therefore, fluorimetry was considered in developing the method described in this study. The conventional fluorimetric methods have limited throughput because they are mostly carried out in a non-automated fashion [22; 23]. These methods also consume large volumes of costly organic solvents, and more importantly, the analysts are exposed to the toxic effects of the organic solvents [24]. Recently, our laboratory successfully adapted a fluorescence plate reader in the development of a microwell-based fluorimetric method to measure the active drug content in pharmaceutical dosage form [25]. This method displayed high throughput and consumed minimum volumes of samples and organic solvents. For these reasons, the development of this methodology for DUV was considered in this study. In most cases, liquid-liquid and/or solid-phase extraction of analytes from plasma is required before fluorimetric analysis of plasma samples [26; 27]. In fact, the several extraction steps lengthen the overall analysis process and reduce the method's accuracy. The successful development of fluorimetric methods for the accurate measurement of pharmaceuticals in plasma samples without extractive pre-treatment of the samples was disclosed in earlier publications [28; 29]. In order to create the MW-FLR approach presented here, this non-extractive method was used.
## 3 Results and Discussion
### Effect of Modification on the In Vitro Digestibility of Broken Rice Starch
also confirmed that quercetin played an important role in the interaction with starches and reduced their digestibility, increased \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Sample** & **RDS (\%)** & **SDS (\%)** & **RS (\%)** & **C\({}_{\infty}\)** & **k** \\ \hline C & 70.94 \(\pm\) 0.53 \({}^{\text{a}}\) & 22.49 \(\pm\) 0.13 \({}^{\text{a}}\) & 6.57 \(\pm\) 0.13 \({}^{\text{a}}\) & 92.6 \(\pm\) 1.30 \({}^{\text{a}}\) & 0.07 \(\pm\) 0.01 \({}^{\text{a}}\) \\ US & 76.63 \(\pm\) 0.58 \({}^{\text{c}}\) & 18.65 \(\pm\) 0.16 \({}^{\text{c}}\) & 4.72 \(\pm\) 0.14 \({}^{\text{c}}\) & 94.21 \(\pm\) 0.82 \({}^{\text{c}}\) & 0.08 \(\pm\) 0.01 \({}^{\text{c}}\) \\ US-Q & 49.51 \(\pm\) 0.42 \({}^{\text{b}}\) & 30.26 \(\pm\) 0.13 \({}^{\text{b}}\) & 20.23 \(\pm\) 0.13 \({}^{\text{b}}\) & 78.35 \(\pm\) 1.47 \({}^{\text{b}}\) & 0.05 \(\pm\) 0.01 \({}^{\text{b}}\) \\ \hline \hline \end{tabular} Note: RDS: rapidly digestible starch content; SDS: slowly digestible starch content; RS: resistant starch content; C\({}_{\infty}\): starch hydrolysis rate at the end of the reaction; k: enzymatic hydrolysis rate. Different letters within the same column denote a significant difference (\(p<0.05\)).  
\end{table} 
Table 3: Model parameters of RDS, SDS, and RS contents and starch digestion kinetics in crushed rice starch and modified starch composite. Figure 7: (**a**) Hydrolysis rates of crushed rice starch and composite modified starch. (**b**) Hydrolysis fitting curves of crushed rice starch and modified starch composite. the formation of resistant starch, inhibited digestive enzymes, and lowered the glycemic index based on in vitro or in vivo data [61]. [PERSON] et al. showed that quercetin exhibited very strong inhibitory activity on \(\alpha\)-glucosidase in in vitro and in vivo assays [62]. Furthermore, experiments involving the oral administration of quercetin-starch complexes in rats suggested that the maximum level of postprandial blood glucose was delayed and reduced. Despite quercetin being able to efficiently reduce the rate of starch digestion, its bioavailability is often relatively low (<10%) in the human digestive system [63]. This is because of its poor water solubility, chemical stability, absorption properties, and complex food matrix effects. Many efforts have been made to improve its bioavailability and efficacy, such as in emulsions, liposomes, hydrogel-based systems, protein nanoparticles, cyclodextrin complexation, and crystal engineering to control its size and physical form.
# Deterministic and Robust Optimization Approach for Single Artillery Unit Fire Scheduling Problem
## 3 Fire Scheduling Problem
### Robust Optimization Model for Fire Scheduling Problem  
It is important to note that in the DFS model described in the previous section, the enemy target threat level is an uncertain parameter. In a war, the enemy target threat level is evaluated through qualitative analysis by information gathering assets and analysts. However, because enemy targets minimize their exposure and attempt to deceive their opponents, it is impossible to assess the threat level of the enemy definitively. Therefore, the threat level of the enemy target should be considered to be an uncertain threat level (\(\widetilde{f_{j}}\)), and in this paper, it is assumed that the threat level of the enemy target belongs to an uncertainty set \(U_{j}\).  
The RO model for minimizing the total enemy threat to friendly forces when the enemy target threat level is uncertain is given by  
\[\text{minimize}\ y\ \ \ \ \ \ (\text{RFS}) \tag{5}\]  
subject to  
\[\sum_{j=1}^{n}\widetilde{f_{j}}x_{j}\leq y\ \ \ \ \ \ \widetilde{f_{j}}\in U_{j},\ j =1,2,\ \ldots,\ n \tag{6}\]  
(2), (3), and (4).  
The objective function in Equation (5) yields the best worst-case objective value for the uncertain threat level of all of the enemy targets. Equation (6) guarantees a feasible solution for all cases in which the uncertain threat level of the enemy target belongs to the uncertainty set \(U_{j}\). The uncertain threat level \(\widetilde{f_{j}}\) belongs to the uncertainty set \(U_{j}\), and its value is unknown. The robust fire scheduling (RFS) model represents a semi-infinite problem; it has infinitely many constraints. Therefore, it cannot be solved directly; instead, a tractable robust counterpart formulation with a finite number of constraints is required.
# Design and Evaluation of Steganographic Channels in Fifth-Generation New Radio +
## 4 Protocol Analysis and Covert Channel Design
### Design
#### 4.5.5 Receiver-Side Process  
The covert receiver must be able to capture the 5G data traffic and decode the received packets according to the 5G New Radio standard. The position and the values of the PDCP header are identified based on the specified protocol structures. By looking at the least significant bit of the reserved bits in the PDCP header, the covert receiver recognizes whether the PDU contains hidden information. If this bit is set to one, the remaining bits \((b_{8},b_{7},b_{6},b_{5})\) of the corresponding header element are extracted and cached. The covert receiver then monitors the data traffic again a packet is identified with the covert data flag set to one. From this PDU, the covert bits \((b_{4},b_{3},b_{2},b_{1})\) are extracted and concatenated with the cached bits in the correct order. The covert receiver gradually extracts all the transmitted characters and thus obtains the information hidden by the covert sender. An algorithmic description is depicted in Algorithm 2.  
```
1 Read input from file: \(input\leftarrow\) secret message
2 Set counter for overall transmitted PDUs: \(pdu\gets 1\)
3 Set counter for transmitted covert segments: \(seg\gets 0\)
4 For each PDU to be transmitted:
5 Covert flag of current PDU \(\leftarrow\) 0
6 if\(pdu\) modulo 10 == 0 then
7 if\(input\) is not empty then
8 ifseg modulo 2 == 0 then
9 Extract \((b_{8},b_{7},b_{6},b_{5})\) from first ASCII character of \(input\)
10 Covert flag of current PDU \(\gets\) \(1\)
11 Reserved bits of current PDU \(\leftarrow\) \((b_{8},b_{7},b_{6},b_{5},\) covert flag)
12 else
13 Extract \((b_{4},b_{3},b_{2},b_{1})\) from first ASCII character of \(input\)
14 Covert flag of current PDU \(\gets\) \(1\)
15 Reserved bits of current PDU \(\leftarrow\) \((*,*,*,*,\) covert flag)
16 Delete first ASCII character from \(input\)
17 endif
18\(seg\gets seg+1\)
19 endif
20 else
21 Reserved bits of current PDU \(\leftarrow\) \((*,*,*,*,\) covert flag)
22 endif
23\(pdu\gets pdu+1\)
```  
**Algorithm 1** Hiding method applied by covert sender.  
```
1 Create empty byte for assembly of ASCII character: \(ascii\gets 0\)
2 Set counter for received covert segments: \(seg\gets 0\)
3 For each received PDU:
4\((e_{4},e_{3},e_{2},e_{1},\)CovertFlag\()\leftarrow\) Reserved bits of current PDU
5 if Covert flag == 1 then
6 if\(seg\) modulo 2 == 0 then
7 Extract first 4 reserved bits of received PDU
8\((b_{8},b_{7},b_{6},b_{5})\) of \(ascii\leftarrow\) Extracted bits \((e_{4},e_{3},e_{2},e_{1})\)
9 else
10 Extract first 4 reserved bits of received PDU
11\((b_{4},b_{3},b_{2},b_{1})\) of \(ascii\leftarrow\) Extracted bits \((e_{4},e_{3},e_{2},e_{1})\)
12 Convert \(ascii\) to ASCII character
13 Write ASCII character to output file
14\(ascii\gets 0\)
15 endif
16\(seg\gets seg+1\)
17 endif
```  
**Algorithm 2** Hiding method applied by covert receiver.
# An Integrated Platform for the Internet of Things Based on an Open Source Ecosystem
## 4 Evaluation
### System Function  
The system function evaluation results are given in Figure 6. For the evaluation, the following were considered  
1. The integrated application development platform is described here using the system functions implementation.
2. The experiment was conducted using a campus network environment. Each measurement result was obtained by performing the test 50 times. Experiments under more complex network conditions will be conducted in the future.
3. We tested the availability of architecture that uses only one kind of gateway technology; the interoperability between different subsystems using different kinds of open source gateways will be studied further.  
As shown in Figure 5(a), the open source Cloud Computing platform Eclipse Kapua was used to implement MQTT-based IoT gateway management and MQTT broker functions, which included application deployment, system configuration, data publishing and subscription. By using Kapua, the IoT gateway could be managed by an administrator at a remote office, and the data published and subscribed by different applications running in different locations.  
Figure 5(b) shows the resource list for the smart factor from the \"well-known\" interface of the CoAP protocol by querying the P2P gateway node. These CoAP resources can be operated by the GET, PUT, and DELETE operations, which are invoked by web applications. The resources are accessed by URL. For example, the light resource of factory 1 can be invoked using \"GET Coap://192.168.1.115: 5683/factory1/light\(\backslash\)T1\(\backslash\)textquotedblright. The resource is managed by a gateway which is run as a P2P node. In our simulations, dozens of P2P nodes are running on the same gateway.  
Figure 5(c) presents the node-red visual interface for developing an IoT application. It displays the visual flow programming mode for IoT application. Figure 5(d) shows the result of Figure 5(c), which shows various environmental data with the Dashboard interface. Figure 5(c),d shows that it is easy to develop IoT applications with open source systems.  
Figure 5: An industrial IoT simulation environment.  
Figure 6: IoT application platform based on open source ecosystem: (**a**) gateway management and data collection; (**b**) CoAP resource discovery; (**c**) Node-Red development; and (**d**) Dashboard GUI.