esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Discussion
### Experiments and Results  
The experiment code was programmed in Python 3.7. The import packages included pyhanlp for Chinese segmentation, pomeranate for HMM modeling, pytorch for CNN modeling, collections for statistics and matplotlib for figures. The program ran on a laptop with Intel Core i7-7820 CPU and 16 GB memory. The performance results were measured with well-known metrics, including precision (P), recall (R), F-measure (F1), accuracy (A) and area under the curve (AUC) [28].  
In the experiment, the accuracy consistently increases during the first six epochs. Then, the accuracy tends to be stable. The average accuracy on these stable epochs turns out to be 96.5%. The confusion matrix of the experiment's results is shown in Table 4. Table 5 outlines the evaluation metrics.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Actual** & **Predicted** & **Predicted \%** & **AUC** \\ \hline \multirow{3}{*}{The proposed method} & \multirow{3}{*}{Spam} & Ham & Spam & Ham & Spam \\  & & 3 & 20 & 13.0\% & 86.9\% \\ \cline{1-1}  & Ham & 122 & 2 & 98.4\% & 1.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Confusion matrix of the experiment’s results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Ham** & **Spam** & **Total** \\ \hline Total number & 381 & 59 & 440 \\ \hline Percentage & 86.6\% & 13.4\% & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of the experimental RCS messages.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Training Set** & **Testing Set** & **Total** \\ \hline Total number & 293 & 147 & 440 \\ \hline Percentage & 66.6\% & 33.4\% & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Division of the experimental RCS messages.
## 5 Results and Discussion
### Implications of the Choice of the HAN Communication Scenario
A possible way of avoiding the implementation of further HKS would be to make use of the option to change the metering point operator running the SMGW via the well-defined market process. This way, a third-party company providing value-added services could limit its interoperability to certain metering point operators, and hence enforce the installation of only SMGWs of manufacturers supporting certain HKS. The change of an installed SMGW and metering point operator, however, is associated with high effort and costs, as the metering point operator is the owner of the installed iMSys. Interoperability, as one major purpose for the detailed technical requirements, is therefore not granted. Even though the first certified SMGW from PPC offers all three HKSs, the second certified SMGW from Sagemcom [PERSON] does not match these features and only offers HKS4 and HKS5. Hence, real world application building on top of HKS3 is not possible with this first certified SMGW offered by Sagemcom [PERSON]. In summary, we demonstrated that a secured bidirectional channel can be set up with the help of the SMGW, and that the infrastructure can be used by DSMgrs to securely connect to EMSs or DERs in order to exploit their flexibility or monitor their operation. However, the exact implementation depends on which HAN communication scenario is used, and yet not all of them are supported by all SMGW manufacturers.
## 3 Results and Discussion
### Analysis of Starch Solubility and Swelling Power  
The solubility and swelling degree of C, US, and US-Q are shown in Table 2. The order of the starch solubility and swelling power is as follows: US > C > US-Q. The broken rice starch had relatively high levels of solubility and swelling power, and both the solubility and swelling power of the US increased. The ultrasonic effect causes amylose to be released into the aqueous medium, thereby increasing the solubility. Ultrasonic treatment can cause changes in the physical geometry of holes and channels on the surfaces of starch granules, which is consistent with the results of SEM. This makes it easier for water molecules to penetrate large-volume granules, thereby increasing the solubility of the granules and enhancing the binding ability of starch and water molecules. Some studies suggested that the increase in swelling power caused by ultrasonic treatment might be due to the decomposition of intermolecular bonds and the destruction of the internal crystalline molecular structure of the starch [55]. When water molecules are connected to the free  
Figure 6: Dynamic rheological properties of ground rice starch, ultrasonically modified starch, and modified starch composite: (**a**) energy storage modulus (G\({}^{\prime}\)); (**b**) loss modulus (G\({}^{\prime\prime}\)); (**c**) depletion coefficient tan \(\delta\).  
hydroxyl groups of amylose and amylopectin through hydrogen bonds, starch granule clusters are broken and the structure is changed, resulting in an increase in water absorption by starch granules. After compounding with quercetin, the solubility decreased from 23.27% to 12.73%, and the swelling power decreased from 10.26 to 9.85, significantly lower than the results for C and US. This may be because the addition of quercetin can limit the leaching of amylose during the heating and gelatinization process. Meanwhile, the hydroxyl groups of quercetin can interact with the hydrogen bonds between water molecules, thereby slowing down the movement speed of water molecules and inhibiting the water absorption and swelling of the starch during heating. It is also possible that because quercetin has relatively low solubility in water, water-dispersed quercetin is adsorbed by broken rice starch, which to some extent hinders the interaction between starch and water molecules, leading to a decrease in starch solubility [56].
## 2 Materials and Methods
### Modeling Evaluation  
For classification problems, evaluating the model's classification performance is crucial. Currently, we mainly use four metrics--_Accuracy_, _Precision_, _Precision_, and \(F\)1--in combination to comprehensively assess the model's classification performance [48]. Therefore, this study selected _Accuracy_, _Precision_, _Precision_, and \(F\)1 as the four metrics to quantitatively evaluate the model's effectiveness. For this study, the formula for calculating accuracy is as follows:  
\[Accuracy\ =\ \frac{TP+TN}{TP+TN+FP+FN} \tag{1}\]  
\[Precision\ =\ \frac{TP}{TP+FP} \tag{2}\]  
\[Recall\ =\ \frac{TP}{TP+FN} \tag{3}\]  
\[F1\ =\ 2\times\frac{Precision\times Recall}{Precision+Recall} \tag{4}\]  
where \(TP\) represents the number of positive samples correctly classified as positive, \(TN\) represents the number of negative samples correctly classified as negative, \(FP\) represents the number of negative samples incorrectly classified as positive, and \(FN\) represents the number of positive samples incorrectly classified as negative.  
In this study, the macro-average method is used to aggregate the performance results of different metrics across all classes. Specifically, this method averages the evaluation metrics (such as _Precision_, _Recall_, \(F\)1) by summing them for each class and then dividing by the number of classes. This approach gives equal weight to all classes, treating each class equally. It also fully considers rare classes with small proportions in the dataset [49].  
For the farm-scale cultivated land quality grade dataset, there is a significant disparity in the distribution of different land quality grades. High-grade and low-grade cultivated land often accounts for only a small proportion of the data, yet these grades are precisely the ones that are of particular interest in the study, holding significant practical relevance. Therefore, the macro-average method is highly suitable for such imbalanced class distributions.
# SDN Anomalous Traffic Detection Based on Temporal Convolutional Network
## 3 Proposed Method  
In this paper, we propose a TCN based SDN abnormal traffic detection method, as shown in Figure 2. Through lightweight feature design and efficient time series modeling, accurate identification of abnormal traffic in dynamic network environment is realized.  
At the feature engineering level, the packet length sequence is used as the core feature, combined with the five-element group grouping strategy, which reduces the computational complexity of traditional multi-dimensional feature extraction and retains the key dynamic information of traffic behavior. As for the model architecture, through the synergistic mechanism of causal convolution and dilation convolution, long-term dependencies are captured under the premise of ensuring temporal causality, which solves the problems of low training efficiency and gradient vanishing caused by sequence dependency in traditional RNN-like models. Meanwhile, the introduction of the residual connection structure further optimizes the stability of gradient propagation in the deep network, which significantly improves the convergence speed and generalization ability of the model. During the training process, the cross-entropy loss function and Adam optimizer are used, combined with dropout regularization technique to effectively suppress the overfitting phenomenon.
## 4 Experimental Validation
### Discussion of Results  
Figure 7 shows a comparison between the two proposed models. It can be noted that the GP model follows more accurately the measured shaft angular speed than the WANARX model, in particular during sharp changes. The overall performance was assessed using the quality indicators of Equations (19) and (20). The results are reported in Table 3. It can be observed that both models are characterized by a FIT value higher than 80%, with low RMSE values of 1.2955 rad/s for the WANARX model and 0.7169 rad/s for the GP-NARX. The GP-NARX model is more accurate in the prediction, with a high confidence in its estimation demonstrated by the narrow width of the uncertainty band around the average value.  
The performance of the WANARX model is also good but a lower performance can be observed compared with the GP method. Interesting considerations can be made if we look at the size of the training dataset: doubling the training samples and retraining the network while keeping the regressor structure unchanged, the FIT value refers to the same testing dataset rises to 88.1142% against a value of 92.457% for the GP-NARX. This shows that the WANARX model increases its predictive capability as the number of training points increases, but also that the GP is very effective even with small datasets. Both methods suffer from the \"curse of dimensionality\", in particular the GP-NARX model doubles the execution time on the training dataset with respect to WANARX.  
Nevertheless, it proves to be an accurate model for describing the relationship between PWM and angular velocity.
## 3 Results
### Projected Frontal Area Prediction Based on Anthropometrics and Joint Angles  
The regression model to predict the projected frontal area (m\({}^{2}\)) using anthropometric data (cm), 2D joint angles (\({}^{\circ}\)) from the optical mocap, and the scores associated with the 20 principal components of the 3D body shape is shown in Equation (2). The regression model based on inertial joint angles is shown in Equation (3). The adjusted R\({}^{2}\) value corresponding to Equation (2) is 0.78 (\(p<0.001\)) and 0.81 (\(p<0.001\)) for Equation (3).  
\[\begin{array}{l}\text{Projected Frontal Area (Optical)}=0.166678+0.000505\times\text{Back Flexion}+0.000075\times\\ Score+0.005565\times\text{Upper Arm Length}+0.000326\times\text{Left}\text{ elbow Flexion}+\\ 0.001023\times\text{Neck Circumference (tight)}-0.000188\times\text{Head Flexion}-0.00335\times\text{ Hip}\\ \text{Breadth (sitting)}+0.000805\times\text{Left Hip Flexion}+0.000393\times\text{Left} \text{Knee Flexion}-\\ 0.002276\times\text{Lower Arm Length}+0.000176\times\text{Left Shoulder Flexion},\end{array} \tag{2}\]  
\[\begin{array}{l}\text{Projected Frontal Area (Inertial)}=-0.322372+0.006888\times\text{ Neck Circumference (tight)}\\ -0.000998\times\text{ Chest Anterior Tilt}-0.00027\times\text{ Neck Flexion}+0.002636\times\text{ Shoulder}\\ \text{Breadth (Acromion)}+0.005635\times\text{ Lower Arm Length}+0.000704\times\text{ Chest Lateral Tilt}\\ +0.012827\times\text{ Upper Leg Length}+0.000426\times\text{ Left elbow Flexion}-0.00047\times\text{ Right}\\ \text{Knee Flexion}-0.000388\times\text{ Left Knee Flexion}-0.005606\times\text{ Back Length }+0.000708\times\\ \text{Chest Rotation}-0.000349\times\text{ Right Shoulder Flexion}+0.000296\times\text{ Right Elbow}\\ \text{Supination}+0.001107\times\text{ Neck Circumference (Shirt)}+0.000187\times\text{ Left Shoulder}\\ \text{Internal Rotation}+0.000072\times\text{ Right Shoulder Internal Rotation},\end{array} \tag{3}\]  
Table 4 shows the beta coefficients for each included variable for both the optical and inertial regression model. Table 5 shows the accuracy of the regression models after cross validation in terms of ICC, RMSE, and relative error.  
Figure 8: Area obtained from the projected frontal area camera (orange) and 3D models (black) are compared in this figure for (**a**) _TT_, (**b**) _Hoods_, and (**c**) _Drops_. The area is plotted on the y-axis, with units m\({}^{2}\) against the participant numbers on theee x-axis.  
Figure 7: Drag (N, in blue) and area obtained from the projected frontal area camera (m\({}^{2}\), in orange) for the 10 participants are plotted on primary and secondary axes, respectively. (**a**) _TT_, (**b**) _Hoods_, and (**c**) _Drops_ pose.
## 3 Theoretical Analysis
### Power Consumption  
Our approach to evaluating power consumption for the purpose of comparing WuRs is to analyze how effectively they implement the given wake-up protocol [24] in terms of total power consumption and latency. The wake-up protocol as illustrated in Figure 1 will show that the PER is an important metric. We find the optimal preamble time \(T_{pre}^{opt}\) to achieve the minimum power consumption through a single-hop network with neighbors.  
We assume the number of WuRs \(N_{surr}\) to be the number of neighbor nodes \(N_{n}\). The optimal preamble time duration, \(T_{pre}^{opt}\), is given by [24]:  
\[T_{pre}^{opt}=\sqrt{\frac{P_{surr}\,T_{surr}N_{surr}}{\left(\left(V_{ccd}I_{td} \right)+\frac{N_{surr}P_{surr}}{2}\right)\text{PR}\,E[N_{tx}]}} \tag{14}\]  
where \(T_{pre}^{opt}\) includes consecutive wake-up packets to the receiver's WuR, \(P_{surr}\) is the WuR's power consumption, \(V_{ccd}\,I_{td}\) is the data radio's transmission power, PR is the packet rate, \(E[N_{tx}]\) is the expected transmission time and \(T_{surr}\) is the wake-up packet's time duration.  
The total power consumption, \(P_{t}\), is given by [24]:  
\[P_{t}=\left(q_{1}\,P_{1}+q_{2}\,P_{2}+q_{3}\,P_{3}\right)E[N_{rx}]+P_{idle} \tag{15}\]  
where \(q_{1},q_{2},q_{3}\) represent the probabilities of three cases: WuR with packet error, data radio with packet error and WuR and data radio both with all correct packets, respectively. \(P_{1},P_{2},P_{3}\) represent the respective power consumption, and \(P_{idle}\) is the idle listening power consumption.  
Equation (15) shows that the high \(PER_{wur}\) will have more expected transmission times \(E[N_{tx}]\), then \(P_{t}\) will increase. Higher PER WuRs will increase power consumption significantly, as they have more \(E[N_{rx}]\) and wake-up packet retransmissions \(Re_{tx}^{wur}\). Moreover, they wake up the respective higher-power data radios.
# FPGA-Based Manchester Decoder for IEEE 802.15.7 Visible Light Communications
## 2 Architecture of the Receiver  
### The Method  
The signal modulated according to the Manchester code is a 2-level signal like that represented in the example of Figure 1. In the example, a white noise is added (with a SNR  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Method** & **AD Conv.** & **Rates** & **Device** & **Resources** & **BER** & **Ref.** \\ \hline uC-based & 1-bit & \textless{}200 kb/s & uC & Low & Medium & [10,27] \\ \hline proposed & 12-bit & \textless{}1 Gb/s &
\begin{tabular}{c} FPGA, \\ ASIC \\ \end{tabular} & High & Low & This Study \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._of 30 dB) to the signal to give a more realistic representation. Each bit is transmitted for a time \(T_{b}\). Formally, the signal can be expressed as:  
\[s(t)=\sum_{n_{b}=0}^{N_{b}-1}b_{nb}\cdot\text{one}(t-n_{b}T_{b})+(1-b_{nb})\cdot \text{zero}(t-n_{b}T_{b}) \tag{1}\]  
where \(N_{b}\) is the number of transmitted bits, \(b_{nb}\) is the value (0 or 1) of the bit in position \(n_{b}\), and the functions one(t) and zero(t) represent the single bits of amplitude \(A\):  
\[\text{one}(t)=A\cdot\text{rect}\bigg{(}\frac{t-3T_{b}/4}{T_{b}/2}\bigg{)}; \quad\text{zero}(t)=A\cdot\text{rect}\bigg{(}\frac{t-T_{b}/4}{T_{b}/2}\bigg{)}; \tag{2}\]  
The signal is AD converted with a rate \(f_{c}\), generating the sequence \(s_{i}=s(i/f_{c})\). Each bit is composed by \(N=T_{b}\cdot f_{c}\) samples. It is convenient--but not mandatory--that the sampling frequency \(f_{c}\) is a multiple of the transmission rate \(f_{s}=1/T_{b}\). The input signal is subdivided into slices \(sl\) of \(N\) sample each, so that each slice of index \(h\) lasts for the duration of a bit:  
\[sl_{h}=\Big{[}s_{h\cdot N+off},s_{h\cdot N+1+off},\dots,s_{N(h+1)-1+\zeta_{0}f }\Big{]}. \tag{3}\]  
Please note that in the bit boundaries (i.e., the first sample of each bit) are located in the samples \(s_{i\cdot N}\) for each \(i=\) 0,1,2,3, etc., while the slices \(sl_{h}\) in (3) are aligned to the sample \(s_{h\cdot N+\zeta_{0}f}\), i.e., the slice, in general, is not aligned to the bit boundaries, but is affected by an offset of \(\zeta_{0}f\) samples.  
In the representation of Figure 1, the subdivision of the sequence \(s_{i}\) in the slices \(sl_{h}\) is performed by the \"Sync\" block. The \"Sync\" block has no a priori knowledge on where the bit boundaries are located along the signal, and cutting the slices so that they are centered on the true bit boundaries (\(\zeta_{0}f=0\)) is not a trivial task. This is a general problem that most of the receivers must face, and it is known as \"synchronization\". A wide literature is present that investigates possible solutions that apply to different modulations [32]. We will return to this later; for now, let us imagine that the slices are perfectly centered over the bits, i.e., the receiver is synchronized and \(\zeta_{0}f=0\).
# Multi-Pedestrian Tracking Based on KC-YOLO Detection and Identity Validity Discrimination Module
## 4 Experiments and Analyses
### Experimental Dataset and Evaluation Index
#### 4.2.2 Evaluation Metrics  
* Pedestrian Detection Evaluation Metrics: These are quantitative measures utilized to assess the performance and accuracy of algorithms and models designed to detect pedestrians in images or videos. They offer in-depth insights into a system's ability to recognize pedestrians within a given dataset. Common evaluation metrics for pedestrian detection include precision, recall, and _mAP_. Precision is the ratio of true positives to the total number of predicted positives, where true positives are the instances where the prediction is correct. Recall calculates the ratio of instances where the prediction is correct to the total number of actual positives. _mAP_ is the sum of the average precision values for all classes divided by the number of classes. In other words, it represents the average of the average precisions for all classes in the dataset. The mathematical expressions for the above evaluation indicators are as follows: \[Precision=\frac{TP}{TP+FP}\] (7) \[Recall=\frac{TP}{TP+FN}\] (8) \[mAP=\frac{1}{R}\int_{0}^{1}P(R)dR\] (9) where _TP_ represents the true positives, _FP_ represents the false positives, and _FN_ represents the false negatives.
* Pedestrian Tracking Evaluation Metrics: In order to test our proposed multi-target pedestrian tracking method, we use five criteria as evaluation metrics: the multi-objective tracking accuracy (_MOTA_) [36], which is commonly expressed as a percentage, ranging from 0% to 100%, where a higher score indicates the superior performance of the tracking algorithm; the ratio of the average of the number of correctly recognized ground-truth detections to the number of computed detections (_IDF_1) [37]; the Majority of Tracked (_MT_); Major Lost Targets (_ML_); and Identity Switches (_IDS_). The mathematical expressions are as follows: \[MOTA=1-\frac{\sum_{t}(FN_{t}+FP_{t}+IDS_{w_{t}})}{\sum_{t}GT_{t}}\times 100\%\] (10) \[IDF_{1}=\frac{2 IDTP}{2 IDTP+IDFP+IDFN}\times 100\%\] (11) where \(t\) represents the index of each frame of the video, and _GT_ is the number of real labeled targets in the image. _IDSw_ denotes the total number of _ID_ switches occurring in the tracked target in frame \(t\).
# Instability Risk Assessment for Deep Excavation of Soil-Rock Combinations Containing Groundwater
###### Abstract  
Dynamic risk assessment is a pivotal tool for enhancing construction safety and minimizing the potential for partial failure during deep and extensive excavation projects. To enhance the efficacy of dynamic risk assessment in deep excavation, this study introduces a novel risk assessment model designed to evaluate instability risk in extensive excavations. It comprises a risk factor selection model for identifying the most pertinent factors and an instability risk assessment model for gauging the extent of instability risk throughout the construction process. Then, the model was deployed in the construction of Anshan Road Station of the Qingdao Metro. To pinpoint the factors with the most pronounced impact on excavation instability, a risk factor selection model was employed, yielding a comprehensive risk evaluation index system. For real-time assessment of risk, the monitoring data were used as the primary source of evidence. A comprehensive comparative analysis involving actual data and predictions from conventional RBF and back propagation neural networks was performed. The outcome of this analysis underscored the superior accuracy and predictive capabilities of the assessment model. The instability risk assessment model offers the ability to dynamically evaluate the instability risk associated with extensive excavations featuring a combination of soil and rock. It can serve as a valuable methodological tool, furnishing essential support for the systematic prevention and mitigation of excavation instability disasters.  
deep excavation; soil-rock combination; theory fusion; dynamic assessment; field monitoring Article