esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 EMF-Aware Routing Framework for WMNs
### Theoretical Approach of EMF Exposure on WMNs
#### 3.1.2 Computing the Exposure on WMNs  
Let us focus on the accumulated exposure induced by a particular node \(k\) up to time \(t\) (\(\xi_{k}(t)\)), and let us assume that both the first and second derivative functions (\(\xi^{\prime\prime}_{k}(t)\), \(\xi^{\prime\prime\prime}_{k}(t)\), respectively) exist. Under these hypotheses we can establish the following properties:  
1. Since the exposure is accumulative, \(\xi_{k}(t+\delta t)\geq\xi_{k}(t),\quad\forall k,\delta t>0\);
2. Consequently, \(\xi^{\prime}_{k}(t)\geq 0\), corresponding to this growing function over \(t\);
3. Depending on the value of \(\xi^{\prime\prime}_{k}(t)\), we can establish various circumstances, as can be seen below: * If \(\xi^{\prime\prime}_{k}(t)>0\), the rhythm of accumulating exposure on node \(k\) is increasing;
* If \(\xi^{\prime\prime}_{k}(t)=0\), the pace of accumulating exposure on node \(k\) is constant;
* If \(\xi^{\prime\prime}_{k}(t)<0\), the rhythm of accumulating exposure on node \(k\) is decreasing.  
Based on these three properties, we can find an appropriate proxy for the exposure, which can be used as a means to design EMF-aware routing mechanisms over multi-hop wireless networks. We assume, without loss of generality, that time is slotted. We do not impose any limitation on the number of packets transmitted within one slot. Hence, the exposure induced by node \(k\) will increase between two consecutive time slots, provided it transmits any packet in-between; otherwise it will remain constant. In addition, the pace at which \(\xi_{k}\) increases depends on both the number of transmitted packets and the transmission power required for the node \(k\)'s packet to reach its destination. Based on these observations, Theorem 1 defines the exposure for continuous time.  
**Theorem 1**.: _The exposure induced by node \(k\) in \(t\) can be calculated as follows_  
\[\xi_{k}(t)=\xi_{k}(t_{0})+\xi^{\prime}_{k}(t_{0})(t-t_{0})+\int_{t_{0}}^{t} \int_{t_{0}}^{x}\xi^{\prime\prime}_{k}(t)dtdx \tag{3}\]  
_where \(\xi_{k  
second, that the rhythm of exposure accumulation \(\xi_{k}^{\prime\prime}\) is compensated among all nodes within the network.  
In order to consider the EMF exposure within a routing protocol, there are two key aspects to take into account: first, minimizing the total transmission power of a route, since it would reduce the overall EMF exposure within the whole scenario. On the other hand, by considering the exposure accumulation pace as a routing cost, the routing protocol is able to balance the traffic between various routes, avoiding exposure peaks over a particular area. The latter would be the consequence of keeping the same route, as it would be the case of traditional routing mechanisms. This exposure model allows considering dynamic route changes, by means of taking into account the temporal evolution of the exposure accumulation pace.
# Geometric Similarity Measurement Method for Micro Scene Generalization
## 2 Methodology
### Shape Similarity  
The extraction algorithm for shape features needs to convert the original node sequence into a feature description sequence [25]; the more detailed the descriptions of the morphological features, the more accurate the similarity calculation results [26]. In addition, the algorithm should include the extraction of global and local features [27]. The former involves the use statistical methods to extract global features, such as area, perimeter, circumscribed rectangle, and Hausdorff distance, from geometric figures; the latter describes the local structure of the graph based on curvature [28], key points [29], concave-convex morphology [30], Hough variation [31], etc.  
To ensure the accuracy of calculation and to describe complex shapes, this paper proposes a hybrid features description method that combines Included Angle Chain and Hausdorff distance.  
We treated polygons as closed line features to calculate the shape similarity of the micro scenes, and the direction Voronoi graph and position graph were constructed. The micro scene similarities were calculated through shape similarity, direction similarity and position similarity.
# Excavation and Construction Technology of Diversion Tunnel under Complex Geological Conditions
## 9 Test and Analysis  
Due to the fact that the groundwater in this project is mainly infiltrated by gravity, when there is heavy rainfall in the engineering area, there will be seasonal abundant water inside the tunnel, which is affected by climate and leads to unstable water levels in the diversion tunnel. Therefore, in order to analyze the performance of the excavation and construction technology of the diversion tunnel under the complex geological conditions above in terms of wall resistance, the following tests were conducted using detection technology.  
Point measurement is combined with line measurement and multiple measurement lines and points are simultaneously arranged on the palm and sidewall of the hand, as shown in Figure 8.  
In the diversion tunnel project, shielded dual body antennas with detection frequencies of 100 MHz and 300 MHz are used to identify adverse geological bodies such as hard rock layers and high-karst geology at the location of the side walls.  
The detection parameters during the testing process are shown in Table 3.  
On the basis of ensuring the stability of detecting unfavorable geological bodies near the tunnel face and side walls, it is also necessary to ensure that the tunnel side walls have greater resistance to damage. The antidamage coefficient of the side wall during the construction of the diversion tunnel is shown in Figure 9.  
Figure 8: Layout of measuring lines and points.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Parameter** & **Numerical Value** \\ \hline  & The movement speed of the detection antenna & 10 cm/s \\ When measuring lines and points are & Sampling points & 1000 \\ arranged inside the face of the water & The spacing between measuring points & 30 cm \\ diversion tunnel & Detection scanning rate & 40/s \\  & Time window & 500 ns \\ \hline  & Sampling points & 500 \\ When measuring lines and points are & Distance between measuring points & 30 cm \\ arranged on the side walls of the & Detection scanning rate & 40 scans/s \\ diversion tunnel & Time window & 500 ns \\ \hline \end{tabular}
\end{table}
Table 3: Detection parameters during the testing process.  
The results in Figure 9 show that after using the technology proposed in this paper, the resistance coefficient of the side wall is above 0.9. Therefore, it indicates that the excavation construction technology designed in this paper can ensure the support capacity of the side wall of the diversion tunnel, which is suitable for the excavation of the main tunnel in the construction of the diversion tunnel. The reason for this result is that in order to address the differences in geological conditions and tunnel sections in terms of geological structure, stress state, excavation sequence, and other aspects, this study specifically designed various excavation and support technologies. Especially through the design of excavation grouting technology, the bearing capacity and stability of the surrounding soil around the tunnel can be enhanced, thereby improving the antidestructive ability of the side wall.
## 3 Results  
### Simulation Results at SP  
The results based on the surveyed forest age show that the 3-PG model can accurately simulate DBH, H, and ACS of eucalyptus plantations (Figure 5), with R\({}^{2}\) values ranging from 0.80 to 0.93. Taking thinning information into account can further improve the simulation accuracy. The R\({}^{2}\) of ACS, DBH, and H increase by 0.09, 0.06, and 0.07, respectively, and the RRMSE decreases by 8.54%, 6.75%, and 4.2%, respectively. The simulation results based on forest age data from Landsat also achieve high accuracy, with R\({}^{2}\) of DBH, H, and carbon stock all higher than 0.85, and RRMSE less than 20% (Figure 6). They are generally better than the simulated results not considering thinning, and are closer to the simulated results with thinning information considered.  
Figure 5: Validation of simulated forest structure at the SP. Plots (**a1–c1**) are simulated diameter at breast height, height, aboveground carbon stock of not considering thinning in the model; (**a2–c2**) are simulated diameter at breast height, height, aboveground carbon stock of considering thinning. No thinning and thinning denote whether the SP thinned or not during the growth.
## 5 Conclusions and Future Work  
In this paper, a multi-agent framework was developed, which to the best of our knowledge, is the first one used to assist many existing routing approaches to improve their routing performance. In addition, this framework is decentralised and does not need global information. The simulation results exhibited the effectiveness of this framework in both static and open and dynamic WSNs.  
In the future, we first intend to overcome the drawback that an addressing scheme has to be used. Another flaw is that this framework uses a 'unit disk' communication model, where sensors can communicate bi-directionally if they are in each other's communication radius. However, in real-world WSNs, several physical links are uni-directional. Additionally, more existing routing approaches have to be taken into our simulation to further validate the effectiveness of our framework, and it will be very interesting to allow nodes to use different routing approaches in different situations. We are also interested in validating the framework in heterogeneous WSNs, where different sensors have different sensing coverage and initial energy. Finally, when such studies are finished, we intend to test this framework on a specific platform, e.g., NS2 [43], and further in a real environment.
# Minimum-Cost Offloading for Collaborative Task Execution of MEC-Assisted Platooning
## 3 System Model
### Task Execution Model
#### 3.3.4 MEC Server Data Transmission  
If the task \(k\) needs to be offloaded and executed on the MEC server, the data transmission time for offloading the task \(k\) from the vehicle \(v_{1}\) to the MEC server is computed by  
\[t^{k}_{v_{1},0}=\frac{d_{k}}{R^{k}_{v_{1},1}}+\frac{d_{k}}{R^{k}_{1,0}} \tag{11}\]  
If the task \(k\) needs to be executed on a platoon member, the data transmission time for offloading the task \(k\) from the MEC server to the vehicle \(v_{2}\) is represented by  
\[t^{k}_{0,v_{2}}=\frac{d_{k}}{R^{k}_{1,v_{2}}}+\frac{d_{k}}{R^{k}_{0,1}} \tag{12}\]  
\(R^{k}_{1,0}\) and \(R^{k}_{0,1}\) indicate the transmission data rate from the leader to the MEC server and from the MEC server to the leader respectively. We have  
\[R^{k}_{1,0}=B_{2}\log_{2}\left(1+\frac{\xi_{v}\mathrm{PL}_{\mathrm{MEC}}(l_{1, 0})}{\mathrm{N}_{0}}\right) \tag{13}\]  
and  
\[R^{k}_{0,1}=B_{2}\log_{2}\left(1+\frac{\xi_{\mathrm{MEC}}\mathrm{PL}_{\mathrm{ MEC}}(l_{0,1})}{\mathrm{N}_{0}}\right) \tag{14}\]  
where \(\xi_{\mathrm{MEC}}\) is the transmission power of the MEC server and \(B_{2}\) is the bandwidth for V2I communication. Because the platoon members cannot communicate directly with the BS, the data needs to be transmitted to the BS through the leader. The connection between BS and MEC is wired, and the time of wired transmission is ignored here.
## 3 Results
### Evaluation of Environmental Impacts
\begin{table}
\begin{tabular}{c c c c c} \hline  & & **P1** & **R2** & **R3** & **S3** \\ \hline Climate & [kg CO\({}_{2}\) eq.] & \(1.72\times 10^{-1}\) & \(1.24\times 10^{1}\) & \(1.33\times 10^{1}\) & \(1.65\times 10^{0}\) \\ Change (CC) & [kg CO\({}_{2}\) eq.] & \(1.78\times 10^{-1}\) & \(1.23\times 10^{1}\) & \(1.32\times 10^{1}\) & \(1.44\times 10^{0}\) \\ CC (biogenic) & [kg CO\({}_{2}\) eq.] & \(-6.17\times 10^{-3}\) & \(1.19\times 10^{-1}\) & \(2.50\times 10^{-2}\) & \(-2.67\times 10^{-1}\) \\ CC (land use & [kg CO\({}_{2}\) eq.] & \(2.22\times 10^{-4}\) & \(1.44\times 10^{-2}\) & \(1.47\times 10^{-1}\) & \(4.80\times 10^{-1}\) \\ change) & [kg CFC\({}_{2}\)\(-\)11 & \(9.37\times 10^{-9}\) & \(6.94\times 10^{-7}\) & \(1.12\times 10^{-6}\) & \(9.28\times 10^{7}\) \\ Acidification & [Mole of H\({}_{+}\) & \(8.18\times 10^{-4}\) & \(1.09\times 10^{0}\) & \(1.78\times 10^{-1}\) & \(2.01\times 10^{-2}\) \\ freshwater & & & & \\ Eutrophication & [kg P eq.] & \(6.42\times 10^{-5}\) & \(1.31\times 10^{-1}\) & \(6.72\times 10^{-3}\) & \(5.60\times 10^{-4}\) \\ freshwater & [kg N eq.] & \(1.60\times 10^{-4}\) & \(5.47\times 10^{-2}\) & \(1.41\times 10^{-2}\) & \(2.91\times 10^{-3}\) \\ Eutrophication & [Mole of N eq.] & \(1.61\times 10^{-3}\) & \(7.80\times 10^{-1}\) & \(1.50\times 10^{-1}\) & \(1.67\times 10^{-2}\) \\ terrestrial & [Mole of N eq.] & \(5.90\times 10^{-4}\) & \(2.07\times 10^{-1}\) & \(4.90\times 10^{-2}\) & \(6.55\times 10^{-3}\) \\ formation–human & eq.] & & & & \\ health & & & & \\ Resource use, & & & & \\ mineral and & [kg Sb eq.] & \(2.29\times 10^{-6}\) & \(1.07\times 10^{-2}\) & \(2.31\times 10^{-4}\) & \(2.80\times 10^{-5}\) \\ metals & & & & \\ Resource use, & & & & \\ energy carriers & [M]] & \(4.25\times 10^{0}\) & \(1.68\times 10^{2}\) & \(1.93\times 10^{2}\) & \(2.46\times 10^{1}\) \\ Water scarcity & [m\({}^{3}\) world eq.] & \(1.94\times 10^{-1}\) & \(9.80\times 10^{0}\) & \(1.23\times 10^{1}\) & \(1.38\times 10^{0}\) \\ \hline \end{tabular}
\end{table} 
Table 4: Total environmental impacts of the compared absorbers (per 1 m\({}^{2}\)) in categories defined by EN 15804. Red indicates the highest, and green the lowest value in each impact category. Figure 4: Shares of individual materials and processes relative to the sum of normalized environmental impacts.
## 2 Related Work
### RS-HLAR Using Proximity Sensors  
Another kind of sensors that has been used for RS-HLAR is proximity sensors. These are electromagnetic-radiation-based sensors that can sense and recognize activities without any physical contact. Some examples of proximity sensors are acoustic sensors, infrared sensors, and radars. A lot of research has been done on RS-HLAR using proximity sensors. [PERSON] et al. [40] used a two-dimensional array of acoustic sensors that consisted of 4 transmitters and 256 receivers. The transmitters sent ultrasonic sinusoidal waves, and the receivers received reflected waves. In this way, the samples were gathered in the form of tri-axial acoustic data to extract frequency-domain and time-domain features. Then they forwarded those features to a vanilla CNN for the classification of walking, sitting, falling, and standing. [PERSON] et al. [41] used acoustic sensors to detect the locomotion activities of humans at bus stops and parks and generated perceptual features. They used an ensemble of one-class classifiers that were based on fuzzy rules. Additionally, they validated their method using real data and then compared the results using an SVM classifier. [PERSON] et al. [42] proposed a human activity recognition model based on a micro-doppler radar system. A data augmentation method was utilized that consisted of three major operations, i.e., frequency disturbance, time shift, and frequency shift. Radar data was recorded while targeting the subject performing locomotion and then these data were converted into a spectrogram. The activity was recognized based on the change in speed and frequency. They used various deep architectures and compared their performances using their augmentation concept. The networks they experimented with include [PERSON] and [PERSON]'s architecture [43], Inception-ResNet [44], [PERSON]'s net [45], ResNet-18 [46], and CNN-RNN [47].  
[PERSON] et al. [48] worked on human activity recognition based on radar. Following the traditional approach, they acquired radar signals from the targets and then generated spectrograms. Their major contribution was that they proposed a transfer-learning-based semi-supervised algorithm. The algorithm consisted of two modules, i.e., supervised semantic transfer and unsupervised domain adaptation. They labeled every few spectrograms and achieve good results in the classification of a total of six locomotion activities.
# Bias and Variance Analysis of Contemporary Symbolic Regression Methods
## 3 Experiments
### SRBench Algorithms and Models  
The SR algorithms tested in SRBench provide different approaches to limit the size and/or structure of their produced models to counteract over-fitting and therefore reduce the algorithm's variance. Therefore, we expect clear differences. E.g., GP implementations such as GP-GOMEA [22], Operon [23], AFP [24] and AFP-FE [25] adapt their search procedure. GP-GOMEA identifies building blocks that cover essential dependencies. Operon, AFP and AFP-FE use a multi-objective search procedure to incorporate both accuracy and parsimony in their objective function [23; 24; 25]. EPLEX, AFP and AFP-FE use the \(\epsilon\)-lexicaseselection in their GP procedure [26], in which instead of one aggregated error value multiple tests are performed over different regions of the training data. ITEA [27] deliberately restricts the structure of their models. gplearm (gplearm.readthedocs.io, accessed on 21 November 2024) provides a GP implementation that is close to the very first ideas about GP-based SR by [PERSON] [5]. DSR [28] is a non-GP-based algorithm that considers SR as a reinforcement learning problem and uses a neural network, another high-variance method, to produce a distribution of small symbolic models. FFX [29] and AIFeynman 2.0 [19] do also not build upon GP but run deterministic search strategies in restricted search spaces. We expect that both determinism and restrictions in the search space result in higher bias and therefore lower variance [1].  
We reuse the string representation of all models from the published SRBench GitHub repository. The strings are parsed in the same way as in SRBench [8] with the SymPy [30] Python framework. However, certain algorithms, such as Operon, lack in precision in their string representation, which printed only three decimal places for real-valued parameters in the model. We re-tuned those parameters for models that didn't achieve the in SRBench reported accuracy when just evaluating the model using the reported parameter values. We re-tune the parameters with the L-BFGS-B algorithm [31; 32] as implemented in the SciPy library [33]. We constrain the optimization so that we only optimize decimal places of the reported parameters, e.g., the optimization of the reported value 5.5 is constrained to the interval [5; 6]. The goal is to change the original model as little as possible and prevent further distortion of the overall results, while still be able to reuse results from SRBench.  
We skip algorithms, where we could not reproduce their reported accuracy with the corresponding reported string representation of their models. We excluded Bayesian Symbolic Regression (BSR) [34], Feature Engineering Automation Tool (FEAT) [35], Multiple Regression GP (MRGP) [36] and Semantic Backpropagation GP (SBGP) [37].
## 1 Introduction  
Doubanjiang is a traditional fermented red pepper paste with a Chinese protected geographical indication (PGI) that is produced with red pepper (_Capsicum annuum_ L.), broad bean (_Vicia faba_ L.), wheat flour, and salt [1]. It has been appreciated as a uniquely flavored seasoning for several centuries in China [2, 3] and has become popular worldwide [4, 5]. In general, the traditional manufacturing process consists of three phases: (1) fermentation of broad beans with salt (12-14% _w/w_) to make doubanjiang-meju [6], (2) fermentation of red peppers (approx. 1-2 cm) with salt (14-16% _w/w_) to yield red pepper moromi, and (3) aged fermentation for more than six months in the natural environment of a mix of doubanjiang-meju with red pepper moromi at a ratio of 4:6 to improve flavor and taste characteristics. The average of annual production value of doubanjiang is $1.5 billion with the export value exceeding $100 million, according to local government statistics in 2016.  
The volatile composition of doubanjiang is quite complex. Several studies on the identification and quantitation of volatile compounds in doubanjiang have been reported [3, 7, 8, 9]. However, the key aroma-active compounds in doubanjiang are still controversial; in particular, the contribution of key odorants to the overall aroma remains unclear.  
Solvent-assisted flavor evaporation (SAFE) is a reliable solvent technique for volatile extraction and could direct the isolation of aroma compounds from complex food matrices [10], whereas headspace solid-phase microextraction (HS-SPME) is a solvent-free technique for volatile extraction present in the sample headspace [11]. Both solvent-assisted and solvent-free extraction methods have been widely applied to the analysis of aroma compounds in many kinds of foods, such as doenjang [12], daqu [13], various sauce [14] and gochujang [15]. Additionally, gas chromatography-olfactometry (GC-O), aroma extract dilution analysis (AEDA), and omission experiments have been used to investigate the key odorants extensively and successfully in many foods such as soy sauce [11,14], gochujang [15], and light aroma type Chinese liquor [16].  
The aims of this study were to (1) profile the volatile compounds in doubanjiang using SAFE and HS-SMPE, coupled with gas chromatography-mass spectrometry (GC-MS), (2) identify the aroma-active compounds using AEDA and GC-O, and (3) evaluate the most odor-active compounds in doubanjiang. The results will provide a better understanding of the key aroma-active compounds in doubanjiang and could be beneficial in improving the flavor of doubanjiang in the industry.
## 3 Measurement Conditions
### Ir Measurements  
Before imaging, basic coefficients enabling IR measurements were determined. The ambient temperature was defined using an Assman psychrometer. The dry thermometer read \(-20\)\({}^{\circ}\)C, while the wet bulb read \(-12.8\)\({}^{\circ}\)C. These readings allowed to determine, based on psychrometric tables, the relative air humidity at the level of 43%. The reflected temperature value (20 \({}^{\circ}\)C) was determined using the Lambert radiator equivalent (crumpled aluminum branch) with the emissivity coefficient equal to 1. The value of the emissivity coefficient for the tested object (conveyor belt, rubber, black \(-0.95\)) was adopted according to the emissivity table available in the device options. The object was imaged continuously from a distance of 2 m for about 2 min of a belt conveyor operation.  
Figure 8: Torque on the drive pulley shaft.  
Figure 7: Compressive forces on the piston rods of hydraulic cylinders of the belt tensioning mechanism.  
Data was captured with parameters set as specified: 2560 \(\times\) 1440 recording resolution, 60 frames per second. The camera has been mounted on the handheld and placed on a stand isolated from the conveyor frame, opposite the drum of the conveyor belt. Therefore mechanical vibrations from the working machine had no impact on the measurement.  
The point of view has been centered on the left edge of the conveyor cylinder since in the case of temperature measurement only this side of the belt was directly affected. The exemplary frame can be seen in Figure 10. Temperature changes have been tracked based on the bar scale visible on the right side of the same figure.  
Figure 10: Example frame with temperature scale obtained from the camera.  
Figure 9: Electrical parameters of the drive motor: (**a**) current, (**b**) power.  
The IR recording software (FLIR tools) allows for control over the scale in the already archived video with the possibility to set both its limit values. This grants the possibility to adjust the contrast. First, the bar with the temperature scale reference has been mapped into a single vector with both RGB and its analogical temperature. Afterward, a window of fixed size that tracked the position of the belt have been created. The actual temperature of the belt edge has been deducted from the maximum pixel value inside the tracking box concerning the temperature in created vector. This measurement has been done for each frame of the recorded video.