esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Experiment 1: Overall Performance Evaluation  
In the first experiment, the performance of the proposed method was evaluated (Table 1, Figure 4). For glioblastomas, manual and IML-assisted segmentations yielded similar pairs of DSCs both for WT and individual sub-regions, thereby indicating no significant difference between them, whereas the converse was true for other cohorts. Our method achieved higher DSC on average for spleen and breast tumors, but lower for lung nodules when compared with the manual segmentations. However, our method was substantially faster than manual annotation in all cohorts (Table 1) by 53.1% on average.  
An analysis of the volume of ground truth, manual and IML-assisted segmentations (Figure 5, Table 1 \"Correlation coefficient\" column) shows that errors made by our method were mostly systematic. This is more evident in spleen images, where IML-assisted and manual segmentations revealed systematic under- and over-segmentation, respectively. Our method made some non-systematic errors in lung nodules and the ET glioblastoma sub-region, but these areas were also more erroneous in manual segmentations. Notably, ET is regarded as the most challenging area of glioblastoma, because it frequently has unclear and smooth boundaries [3].
# Lightweight and Security-Enhanced Key Agreement Protocol Using PUF for IoD Environments
## 6 Proposed Protocol
### Drone Revocation Phase
#### 7.1.7. RoR Model
Therefore, according to the birthday paradox [37], the following inequality can be derived: \[|Adv[AW_{GA_{2}}]-Adv[AW_{GA_{1}}]|\leq\frac{q_{h}^{2}}{2|Hash|}.\] (4)
* \(G_{3}\): Similar to \(G_{2}\), \(A\) employs \(Send\) and \(Puf\) queries in this game. As discussed in Section 3.2, cloning the PUF is considered highly difficult or impossible; thus, \(A\) has no advantage in \(GA_{3}\). \[|Adv[AW_{GA_{3}}]-Adv[AW_{GA_{2}}]|\leq\frac{q_{p}^{2}}{2|Puf|}.\] (5)
* \(G_{4}\): In this game, \(A\) can extract secret parameters \((PID_{D},K_{M}^{*},G_{M},r_{M})\) from the user's mobile device by performing a \(CorruptMD\) query. \(A\) attempts to compute the session key using these values. However, all parameters are masked with the mobile user's identity and password. Since it is impossible for \(A\) to calculate these values simultaneously, \(A\) cannot compute the session key. Applying [PERSON]'s law [38], the following inequality is derived: \[|Adv[AW_{GA_{4}}]-Adv[AW_{GA_{3}}]|\leq max\{C^{{}^{\prime}}\cdot q_{s}^{{}^{ \prime}},\frac{q_{s}}{2^{l}}\}.\] (6)  
After all the games are finished, \(A\) attempts to guess the bit \(r\) using the \(Test\) query. The following equation is derived:  
\[Adv[AW_{GA_{4}}]=\frac{1}{2}. \tag{7}\]  
From Equations (2) and (3), the following equation can be obtained:  
\[ \begin{split}\frac{1}{2}SC_{A}(P)&=|Adv[AW_{GA_{0}} ]-\frac{1}{2}|\\ &=|Adv[AW_{GA_{1}}]-\frac{1}{2}|.\end{split}  \tag{8}\]  
Equation (9) can be computed from Equations (7) and (8):  
\[\frac{1}{2}SC_{A}(P)=|Adv[AW_{GA_{1}}]-Adv[AW_{GA_{4}}]|. \tag{9}\]  
Applying the triangular inequality to Equation (10) yields the following:  
\[ \begin{split}\frac{1}{2}SC_{A}(P)&=|Adv[AW_{GA_{1} }]-Adv[AW_{GA_{4}}]|\\ &\leq|Adv[AW_{GA_{1}}]-Adv[AW_{GA_{3}}]|\\ &+|Adv[AW_{GA_{3}}]-Adv[AW_{GA_{4}}]|\\ &\leq|Adv[AW_{GA_{1}}]-Adv[AW_{GA_{2}}]|\\ &+|Adv[AW_{GA_{2}}]-Adv[AW_{GA_{3}}]|\\ &+|Adv[AW_{GA_{3}}]-Adv[AW_{GA_{4}}]|\\ &\leq\frac{q_{h}^{2}}{2|Hash|}+\frac{q_{p}^{2}}{2|Puf|}+max\{C^{{} ^{\prime}}\cdot q_{s}^{{}^{\prime}},\frac{q_{s}}{2^{l}}\}\end{split}  \tag{10}\]Multiplying Equation (10) by two yields the following result:  
\[SC_{A}(P)\leq\frac{q_{h}^{2}}{|Hash|}+\frac{q_{p}^{2}}{|Puf|}+2 max\{C^{{}^{\prime} }\cdot q_{s}^{v^{\prime}},\frac{q_{s}}{2^{l}}\} \tag{11}\]  
Finally, this work proves the semantic security of the proposed protocol using the RoR model.
## 3 Results and Discussion
### Hardness  
The results of the hardness measurements are shown in Table 3. The first row shows the measured values for each specimen (specimen 1, specimen 2, and specimen 3). In the second row, all values were summed to give an average hardness for each condition. The standard deviation is given as the scatter of the values.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Porosity/Inclusions [vol.-\%]** & **Circularity [\%]** & **d10 [\(\mu\)m]** & **d50 [\(\mu\)m]** & **d90 [\(\mu\)m]** \\ \hline as-sintered & 2.87 \(\pm\) 0.76 & 87.1 \(\pm\) 1.9 & 0.98 \(\pm\) 0.10 & 4.38 \(\pm\) 0.29 & 15.25 \(\pm\) 1.09 \\ \hline After HIP & 0.77 \(\pm\) 0.08 & 95.4 \(\pm\) 0.5 & 0.70 \(\pm\) 0.06 & 3.20 \(\pm\) 0.06 & 6.98 \(\pm\) 0.10 \\ \hline \end{tabular}
\end{table}
Table 2: Overview of the percentage of pores or inclusions in the different conditions (as-sintered and after HIP) as well as the measured circularity and size distribution.  
Figure 10: Backscattered electron image and EDX mappings of a specimen after HIP.  
A comparison of the hardness values of the individual specimens within the same condition shows a considerable scattering of the measured values. The lowest hardness value in the as-sintered condition is 214 HV10 and the highest is 361 HV10. A similar scatter can be seen for the HIP condition. Hardness values after HIP densification range from 259 to 425 HV10. The scattering in both conditions is explained by pores and inclusions. Overall, an increase in hardness of approximately 40 HV10 is achieved by HIP treatment. The reason for this can be seen in the reduction of porosity.
## 2 Materials and Methods
### Gas Chromatography-Ion Mobility Spectrometer (GC-IMS)  
GC-IMS was performed using a GC coupled with an ion mobility spectrometry instrument (Flavourspec(r)-G.A.S. Dortmund Company, Dortmund, Germany), similarly to previously described research [23]. Each 0.5 g tea sample was placed in a 20 mL headspace glass sampling vial, which was then incubated at 55 \({}^{\circ}\)C for 15 min at a shaking of 500 rpm. Subsequently, 500 \(\upmu\)L of headspace was automatically injected into the injection port on GC-IMS at 85 \({}^{\circ}\)C, and the compounds were separated by a WAT-Wax capillary column (30 m \(\times\) 0.53 mm) at an isothermal condition of 60 \({}^{\circ}\)C. Nitrogen (purity \(\geq\) 99.999%) was used as a carrier gas, and the flow rates were varied as follows: 0-2 min (2 mL/min), 2-10 min (2-10 mL/min), and 10-40 min (10-80 mL/min). The draft tube temperature was at 45 \({}^{\circ}\)C, and nitrogen was used as a drift gas flowed at a flow rate of 150 mL/min. The data from GC-IMS was analyzed using Laboratory Analytical Viewer (LAV) and GC \(\times\) IMS Library Search.  
Fast Switchable System between GC-O-MS and Two-Dimensional Comprehensive Gas Chromatography-Olfactometry-Mass Spectrometry Analysis (GC \(\times\) GC-O-MS)  
An Agilent 7890B GC-5975B MS (Agilent Technologies, Santa Clara, CA, USA) equipped with a solid-state modulator SSM1800 (J & X Technologies, Shanghai, China) was used, and the instrumental settings were adapted from a previous report with slight modifications [24]. The column temperature was initially set at 40 \({}^{\circ}\)C, and after a 4 min holding time, it was increased to 230 \({}^{\circ}\)C at a rate of 4 \({}^{\circ}\)C/min, at which it was kept for 0.5 min as the final holding time. Three experienced sensory panelists (two females and one male) were recruited to perform a sniffing test on the olfactory output to identifying aroma-active compounds. Ultrapure helium (99.999%, Beijing AP BAIF Gases Industry Co., Ltd., Beijing, China) was used as the carrier gas. Electron-impact mass spectra were generated at ionization energy of 70 eV and an m/z scan range of 50-350. The temperatures of MS ion source and quadrupole were set at 230 \({}^{\circ}\)C and 150 \({}^{\circ}\)C, respectively.
## 3 Results
### Interpretable Modeling Approaches  
#### 3.3.1 Ranking the Importance of Digital Technology Use  
Feature importance scores derived from the RR model were used to evaluate the relative contributions of seven digital technology tools in predicting teaching quality (Figure 3). The results showed that multimedia courseware had the highest predictive importance (0.0167 \(\pm\) 0.0094), followed by e-textbooks (0.0142 \(\pm\) 0.0080) and question banks (0.0129 \(\pm\) 0.0087). In contrast, management software demonstrated a weaker influence (0.0091 \(\pm\) 0.0129), while multimedia materials (0.0040 \(\pm\) 0.0104) and websites (0.0013 \(\pm\) 0.0125) made minimal contributions to the model's performance. Notably, subject software displayed a negative average contribution (\(-\)0.0005 \(\pm\) 0.0087), suggesting it may function as a redundant or noisy feature in the prediction of teaching quality.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Characteristics** & **RF** & **RR** & **SVM** & **AB** & **DT** & **GBDT** & **VC** & **KNN** & **MLP** & **LR** \\ \hline AUC & 0.72 & 0.75 & 0.71 & 0.73 & 0.66 & 0.73 & 0.73 & 0.68 & 0.70 & 0.75 \\ Accuracy & 0.78 & 0.78 & 0.77 & 0.77 & 0.71 & 0.76 & 0.75 & 0.70 & 0.74 & 0.76 \\ Sensitivity/Recall & 0.93 & 0.94 & 1.00 & 0.92 & 0.76 & 0.93 & 0.84 & 0.79 & 0.94 & 0.92 \\ Specificity & 0.26 & 0.24 & 0.00 & 0.28 & 0.52 & 0.22 & 0.46 & 0.41 & 0.07 & 0.26 \\ FPR & 0.74 & 0.76 & 1.00 & 0.72 & 0.48 & 0.78 & 0.54 & 0.59 & 0.93 & 0.74 \\ FNR & 0.07 & 0.06 & 0.00 & 0.08 & 0.24 & 0.07 & 0.16 & 0.21 & 0.06 & 0.08 \\ PPV & 0.81 & 0.80 & 0.77 & 0.81 & 0.84 & 0.80 & 0.84 & 0.82 & 0.77 & 0.80 \\ NPV & 0.54 & 0.54 & 0.00 & 0.50 & 0.40 & 0.48 & 0.46 & 0.37 & 0.27 & 0.48 \\ F1 score & 0.86 & 0.87 & 0.87 & 0.86 & 0.80 & 0.86 & 0.84 & 0.80 & 0.85 & 0.86 \\ \hline \hline \end{tabular} Note: RF, Random Forest; RR, Ridge Regression; SVM, Support Vector Machine; AB, AdaBoost; DT, Decision Tree; GBDT, Gradient Boosting Decision Tree; VC, Voting Classifier; KNN, K-Nearest Neighbor; MLP, Multilayer Perceptron; LR, logistic regression; AUC, area under the receiver operator curve; FPR, false positive rate; FNR, false negative rate; PPV, positive predictive value; NPV, negative predictive value.  
\end{table}
Table 1: Performance comparison of ten machine learning models across multiple classification metrics.  
Figure 3: Permutation feature importance of digital technology tools in ridge regression model.  
Figure 2: ROC curve analysis of ten machine learning models for predicting teaching quality. (**A**) Random Forest; (**B**) Ridge Regression; (**C**) Support Vector Machine; (**D**) AdaBoost; (**E**) Decision Tree; (**F**) Gradient Boosting Decision Tree; (**G**) Voting Classifier; (**H**) K-Nearest Neighbor; (**I**) Multilayer Perceptron; (**J**) logistic regression.
## 1 Introduction
### Time Domain  
Second-harmonic-generation autocorrelation trace records the intensity autocorrelation function of repetitive ultrashort laser pulses, usually obtained by measuring background-free (noncollinear) second-harmonic generation of nonlinear crystals. It is described by the formula \(I_{AC}(\tau)=\int_{-\infty}^{\infty}I(t)I(t-\tau)dt\), where \(I(t)\) is the pulse intensity and \(\tau\) denotes a time delay. In the experiment, the impinging optical pulses are split into two identical beams and then mechanical translation or rotation introduces continuous scan of the time delay of the two beams. The second-harmonic-generation intensity is closely related to the temporal overlap of the two beams. Hence its change with time delay enables us to retrieve the information of the optical pulses in time domain. Autocorrelation trace is the most commonly used measure to characterize the temporal length of an ultrashort optical pulse, by assuming an appropriate temporal profile. Similarly, we can use it for obtaining the temporal details of soliton molecules as complementary information. Figure 3b,c depicts the typical autocorrelation traces of a two- and a triple-soliton molecule respectively, obtained by numerical calculation. For comparison, the autocorrelation trace of the individual soliton atom is illustrated in Figure 3a. For a soliton molecule which is comprised of \(n\) identical solitons (only phase might differ), its autocorrelation trace shows \((2n-1)\) peaks and the peak intensity jumps from 1 to \(n\) and then back to 1 again step by step. Similar to the situation of conventional single solitons, the width of one individual peak in autocorrelation trace is proportional to the width of one single optical pulse, see Figure 3a,b. Note that autocorrelation trace enables detection of not only temporal separation between adjacent pulses but also the amplitude ratio of them, which are reflected by modulation period and modulation depth in frequency domain. Autocorrelation measurement is not able to reveal the phase relationship between neighbouring solitons, therefore unfortunately less powerful than optical spectrum recording in terms of complete characterization of a soliton molecule.
## 3 Dynamic Transaction Confirmation Sharding Protocol
### Reputation Model  
In the PBFT-based intra-shard consensus, the consistency of the alliance chain is ensured through the two state synchronizations. The intra-shard consensus of the dynamic transaction confirmation sharding protocol only performs state synchronization once. Therefore, in addition to the confirmation mechanism, a reputation model must be used to force nodes to maintain system security jointly and distinguish malicious nodes from normal nodes. Alliance chain nodes can have a one-to-one mapping relationship with enterprise entities, so economic games can be used to motivate all nodes to maintain system security. Reputation is obtained by recording and calculating behavioral information, which is used to evaluate the reliability of nodes and verify their right to speak. The reputation incentive and penalty model is shown in Figure 5.  
In order to increase the cost of malicious behavior, a node malfeasance counter is used in the model. Nodes are assigned a reputation score of \(S\) when they join the system, which will be subject to changes and retained throughout subsequent activities. When a node actively maintains system security, it receives an incentive reputation score of \(P\). Conversely, if a node's malicious behavior is detected, its reputation score will be deducted  
Figure 4: The C++ codes for multiple nodes monitor the network simultaneously, querying whether messages and blocks are sent to themselves in the network.  
Figure 3: Flow chart of dynamic transaction confirmation consensus mechanism. There are four consensus stages: _Tct_ release, transaction send, transaction confirmation, the Leader node sends microblocks to the Master node.  
by an amount represented as \(Q\). The relationship between reputation \(S\), incentive cases \(P\), deduction cases \(Q\), and the number of misoperations \(T\) is shown in Equation (3).  
\[S=S+P-QT \tag{3}\]  
For example, a Leader node has an initial reputation score \(S\) of 100 (nodes below this reputation score will be removed from the network). In each epoch, completing a normal transaction consensus will get an incentive reputation score \(P\) of 100, while an incorrect consensus and sending a transaction whose verification does not meet the \(Tct\) (Hereinafter referred to as failed transaction) will deduct a reputation score \(Q\) of 50 and 30, respectively.  
In the first epoch, the node completes the consensus, \(T=0\), \(S=100+100=200\).  
In the second epoch, the node completed the consensus but sent a failed transaction, \(T=1\), \(S=200+100-30*1=270\).  
In the third epoch, the node had a incorrect consensus, \(T=2\), \(S=270-50*2=170\), and sent failed transaction, \(T=3\), \(S=170-30*3=80<100\). Therefore, the node is removed from the network. The amount of reputation lost for doing evil is proportional to the number of times doing evil. When a node's reputation drops below a specified value, the node will be removed from the network.
## 3 Results
### Seasonal Characteristics of GPP, ET, and WUE  
WUE exhibited a generally consistent spatial pattern across the four seasons, with higher values in the northeast and lower values in the southwest (Figure S1). The seasonal WUE values in Inner Mongolia exhibited the overall characteristics of summer \(>\) autumn \(>\) winter \(>\) spring (Figure 4). Among the four seasons, the highest mean value (1.77 gC m\({}^{-2}\) mm\({}^{-1}\)) was observed in summer (June-August), with minimum and maximum values of 0.02 and 6.89 gC m\({}^{-2}\) mm\({}^{-1}\), respectively. The mean WUE values in autumn (September-November) and winter (December-February) were 1.36 gC m\({}^{-2}\) mm\({}^{-1}\) and  
Figure 2: Interannual variations of **(a)** GPP, **(b)** ET, and **(c)** WUE in Inner Mongolia from 2001 to 2020 (The red shades highlight the 95% confidence interval. The black lines are time series of regional annual GPP, ET, and WUE, while the red dashed lines represent the trend).  
0.78 gC m\({}^{-2}\) mm\({}^{-1}\), respectively. More specifically, 50% of WUE values in autumn were primarily concentrated between 0.89 and 1.71 gC m\({}^{-2}\) mm\({}^{-1}\), while 50% of WUE values in winter were primarily concentrated between 0.16 and 1.18 gC m\({}^{-2}\) mm\({}^{-1}\). However, the lowest mean WUE value was observed in spring (March-May) at 0.40 gC m\({}^{-2}\) mm\({}^{-1}\), with minimum and maximum values of 0 to 2.24 gC m\({}^{-2}\) mm\({}^{-1}\), respectively. For GPP, the highest mean value was observed in summer, with a value of 353.82 gC m\({}^{-2}\) yr\({}^{-1}\), the 50% of GPP values in summer were primarily concentrated between 131.39 and 562.15 gC m\({}^{-2}\) yr\({}^{-1}\). Whereas, the lowest mean GPP was observed in winter (20.09 gC m\({}^{-2}\) yr\({}^{-1}\)), and 50% of GPP values in winter were primarily concentrated between 11.60 and 25.65 gC m\({}^{-2}\) yr\({}^{-1}\). The seasonal GPP values showed the characteristics of summer > autumn > spring > winter. The mean ET was the highest in summer (176.28 mm yr\({}^{-1}\)), the 50% of ET values in summer were primarily concentrated between 142.44 and 213.74 mm yr\({}^{-1}\), respectively. Followed by spring and autumn, with mean values of 81.89 mm yr\({}^{-1}\) and 62.11 mm yr\({}^{-1}\), respectively. For spring and autumn, 50% of ET values in spring were primarily concentrated between 61.29 and 105.86 mm yr\({}^{-1}\), while 50% of ET values in autumn were primarily concentrated between 50.13 and 73.49 mm yr\({}^{-1}\). The lowest mean ET was observed in winter at 20.84 mm yr\({}^{-1}\), with minimum and maximum values of 0 and 44.88 mm yr\({}^{-1}\), respectively.  
Figure 3: Spatial differences of annual average (**a**) GPP, (**c**) ET, (**e**) WUE, and (**b,d,f**) their trends (significance levels of 0.05) in Inner Mongolia from 2001 to 2020.
# The Role of Correlation in the Performance of Massive MIMO Systems
## 4 Imperfect Channel Estimation Analysis
The channel vector \(\mathbf{h}_{u}\) follows a complex Gaussian distribution where the channel statistics are known at the base station side. Recalling the received training signal  
Figure 2: Flowchart of the transmission technique. Equation (9), the channel estimate \(\hat{\mathbf{h}}_{u}\) is obtained from the conditional probability density function (PDF) given an observation \(\mathbf{y}_{u}\)[37]. Since the channel statistics are known at the base station, the linear minimum mean square error channel estimate [37] can be applied to estimate the channel at the base station. As such, the downlink channel estimate can be written as  
\[\hat{\mathbf{h}}_{k} =\mathbf{G}_{u}\bigg{(}\mathbf{G}_{u}+\frac{1}{T_{\text{tr}} \rho_{\text{tr}}}\mathbf{I}\bigg{)}^{-1}\mathbf{y}_{u}^{\text{tr}} \tag{10}\] \[=\mathbf{G}_{u}\bigg{(}\mathbf{G}_{u}+\frac{1}{T_{\text{tr}}\rho_ {\text{tr}}}\mathbf{I}\bigg{)}^{-1}\Bigg{(}\mathbf{h}_{u}+\frac{1}{\sqrt{T_{ \text{tr}}\rho_{\text{tr}}}}\mathbf{s}_{u}^{\text{tr}}\Bigg{)}. \tag{11}\]  
Using the standard MMSE estimator of Gaussian random variables with independent Gaussian noise, the covariance of the MMSE channel estimation can be written as  
\[\mathbf{E}_{u}=\mathbf{G}_{u}\bigg{(}\mathbf{G}_{u}+\frac{1}{T_{\text{tr}} \rho_{\text{tr}}}\mathbf{I}\bigg{)}^{-1}\mathbf{G}_{u}. \tag{12}\]  
Note that applying the MMSE channel estimate allows the \(u\)-th UE's channel vector to be deconstructed into the channel estimate \(\hat{\mathbf{h}}_{u}\) and the channel estimation error \(\hat{\mathbf{h}}_{u}\) as shown in Equation (13). \[\mathbf{h}_{u}=\hat{\mathbf{h}}_{u}+\hat{\mathbf{h}}_{u}. \tag{13}\]  
This property is achieved due to the orthogonality principle so that the vectors \(\hat{\mathbf{h}}_{u}\) and \(\hat{\mathbf{h}}_{u}\) are uncorrelated. The statistical distribution of covariance matrices of the MMSE channel estimate and the covariance of the error per UE are given in Equations (14) and (15), respectively. \[\hat{\mathbf{h}}_{u}\sim\mathcal{CN}(\mathbf{0},\mathbf{E}_{u}) \tag{14}\]  
\[\hat{\mathbf{h}}_{u}\sim\mathcal{CN}(\mathbf{0},\mathbf{M}_{u}) \tag{15}\]  
The MSE between the actual channel and the channel estimate that can be computed through the use of Monte Carlo simulation \(\text{C}_{\text{sim}}\) is given as  
\[\text{C}_{\text{sim}}=\sum_{u=1}^{U}\mathbb{E}\left\{\ ||\mathbf{h}_{u}-\hat{ \mathbf{h}}_{u}||^{2}\right\}. \tag{16}\]  
Equations (12) and (16) characterize the output of a channel estimator that minimizes the MSE of each UE. The overall MSE performance relies on the transmit covariance matrix in addition to the training sequence length through \(T_{\text{tr}}\) and the training power \(\rho_{\text{tr}}\), which accounts for the energy used in the training stage. Algorithm 1 summarizes the algorithm used to estimate the uplink channel in a TDD massive MIMO system for a given training phase duration.
## 4 Results of Performance Evaluation and Industrial Field Application  
This study utilized the SPOT robot by Boston Dynamics (Waltham, MA, USA), which is highly mobile and capable of autonomous navigation and mission execution in recent industrial settings [18]. This robot can be equipped with various payloads, making it suitable for fire monitoring, safety inspections, patrolling tasks, and quality inspections during product manufacturing. When equipped with an arm payload, the SPOT robot can utilize a multi-joint arm, and notably, it employs the RGB and ToF cameras attached to the End Effector to capture image data for vision inspection purposes. In this research, as illustrated in Figure 7, tests were conducted on an unpainted car body using the ToF camera mounted on the SPOT robot. The 3D models created during car design and the simulation environment established in the production factory for robot operations were utilized. The viewpoint of the ToF camera was constructed using Unity [19], and depth information in the simulation was acquired.  
Figure 6: Deep learning learning process to improve ToF depth data accuracy.