esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Control Strategies
### Linear Quadratic Lateral Tracking Control  
The considered BBW architecture is able to continuously track four different target pressures, to be assigned to each wheel. This allows the development of a high-level tracking control system that ensures stability and safety using an LQR approach. To achieve this, the controller aims at the direct regulations of the actual yaw rate and side-slip angle of the vehicle expressed by the single-track dynamic vehicle model, while the reference states rely on the ideal kinematic model described in the previous section. Therefore, the outputs of the optimal control are the errors between the actual and reference states, which are minimized by delivering to the vehicle body a desired yaw moment. This is applied through continuous and independent braking actuation actions on the wheels.
## 5 Implementation of the Proposed Method
### Online Alignment Phase  
The online-alignment phase identifies the best candidates in the inspection image which have an arbitrary rotation angle. Also, the rotation angle is further determined using the ring-shift projection. The precise steps conducted at multi-resolution inspection image are presented as follows:1. A multi-resolution inspection image \(S_{l}\) is constructed, where \(l=0,1,\ldots,L\), and \(L\) corresponds to the maximum image pyramid level in the template preprocessing phase.
2. The search block initializes at location (\(x=0\), \(y=0\)) in the highest inspection image pyramid level. This image is the same size as the template image on the highest image pyramid level.
3. Ring- and variance-projection transform are used to determine the ring-projection values \(C_{S,l}(x,y,r)\) and variance-projection values \(\sigma_{S,l}(x,y,r)\) from the search block at location (\(x,y\)) on image pyramid level \(l\).
4. The hybrid-projection values \(H_{S,l}(x,y,r)\) on image pyramid level \(l\) are obtained using \(C_{S,l}(x,y,r)\), \(\sigma_{S,l}(x,y,r)\) and the weighting coefficients, \(w_{m,l}(r)\) and \(w_{\sigma,l}(r)\), which are calculated in the template preprocessing phase.
5. When estimating the similarity coefficient between the template image and search block on image pyramid level \(l\), if the similarity coefficient exceeds the pre-defined threshold for similarity, the location (\(x,y\)) is stored in the candidate list.
6. The search block in the inspection image is moved and Steps 3-6 are repeated until the similarity coefficient in the inspection image is computed for all locations.  
When the candidate list for the highest image pyramid level is determined, the image pyramid search process is used to increase the speed of the candidate selection and localization processes. On the next lower image pyramid levels, the search block location inherits the location from the candidate list that has been stored on the previous pyramid level. The search block must only be searched within a small range of displacement. Steps 3-5 in the online-alignment phase are used to determine the most likely candidate on the lower image pyramid levels. The hybrid-projection transform for multi-resolution using the image pyramid search technique is described in Section 3.3. Finally, the best candidate is identified on the lowest image pyramid and then the rotation estimation process calculates the rotation angle using the ring-shifted projection for the best candidate.
## 3 Results
### Canopy Height  
Due to its echo characteristics, CASAL-derived forest height measurements represent the maximum tree height within each footprint. Therefore, ALS-Hmax was used as the reference value for comparison with CASAL-derived height estimates. We performed linear regression analysis between ALS-Hmax and each relative height metric (RH80, RH85, RH90, RH95, RH99, and RH100), with results presented in Table 5.  
Figure 6: Comparison of CASAL elevation estimates with ALS-derived DTM.  
Figure 7: Differences between CASAL and ALS ground elevations by (**a**) slope, (**b**) vegetation height, and (**c**) cover.  
As RH values increase, median and MAD values relative to ALS-Hmax progressively increase, while \(R^{2}\) initially increases before decreasing. Similarly, RMSE and Bias values first decrease before increasing. The RH90 percentile demonstrates optimal performance, achieving a maximum \(R^{2}\) (0.512) and minimum RMSE (2.70 m), indicating its superior accuracy for canopy height estimation.  
Figure 8 presents scatter plots comparing CASAL-RH90 and ALS-Hmax values. Strong correlations were observed between full-waveform RH metrics and ALS-derived canopy heights, with optimal accuracy metrics clustering near the 1:1 line. Consequently, CASAL-derived RH90 forest heights showed the strongest correlation with airborne LiDAR measurements. Footprint density peaked near the 1:1 line. With \(R^{2}\) = 0.512 and RMSE = 2.70 m, canopy height estimates exhibited a consistent \(-\)0.35 m deviation across the measurement range. Median differences between CASAL-RH90 and ALS-Hmax were \(-\)0.26 m, with MAD = 2.13 m.
## 3 Materials and Methods
### Sediment Processing
#### 3.2.5 Age Model Construction  
The age model of the core Hu90-08 was constructed by employing two methods: (1) Using five \({}^{14}\)C-AMS dates between 10.5 and 311 cm, and (2) constructing the oxygen isotope stratigraphy using the \(\delta^{18}\)O in _N. pachoderma_ (Figure 3a) in which MISs were clearly identified. Fourteen tie points older than 40 ka (Figure 3) were obtained through matching with the deep North Atlantic benthic \(\delta^{18}\)O stack of [PERSON] [47]. A total of 19 control points were used to construct the age model in which linear sedimentation rates between the \({}^{14}\)C-AMS dates/tie points are considered. As a result, it appears that the core Hu90-08 contains climate records between 3.54 and 145 ka for the depth interval between 0 and 841.5 cm (Figure 4). The estimated sedimentation rates vary between 1.62 and 8.5 cm/year, in which the Holocene period records the lowest sedimentation rates.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**No.** & **Depth** & **Uncorrected \({}^{14}\)C** & **Cal. ka BP 1\(\sigma\)** & **Species** & **Laboratory** \\ \hline  & **(cm)** & **Age (yr.) \(\pm\) 1\(\sigma\) Error** & **Age Range** & **Used** & **ID** \\ \hline
1 & 10–12 & 6532 \(\pm\) 20 & 6.807 & _G. bulloides_ & \\
2 & 109–110 & 17,960 \(\pm\) 40 & 20.757 & _N. pachoderma_ & UCI-190945 \\
3 & 161–162 & 21,285 \(\pm\) 50 & 24.591 & _N. pachoderma_ & UCI-190946 \\
4 & 220–221 & 31,270 \(\pm\) 140 & 34.868 & _N. pachoderma_ & UCI-190947 \\
5 & 310–311 & 40,510 \(\pm\) 530 & 42.851 & _G. inflata_ & UCI-219476 \\
1 1  & 213 & & 29.5  & & \\
2  & 440 & & 59.5  & & \\
3  & 505 & & 64  & & \\
4  & 595 & & 70  & & \\
5  & 630 & & 85.5  & & \\
6  & 655 & & 88.5  & & \\
7  & 670 & & 98  & & \\
8  & 730 & & 110.5  & & \\
9  & 770 & & 128  & 10  & 780 & & \\
11  & 800 & & 135  & 12  & 815 & & \\
13  & 845 & & 145.5  & 14 [FOOTNOTE:1]Footnote 1: The point; 1 [PERSON] and [PERSON] [47].  
Figure 4: Age model of core Hu90-08 (this study) using the C14-AMS dates and tie points listed in Table 2. A linear fit (discontinuous green line) with the R\({}^{2}\) = 0.949815 suggests a robust depth-age relationship, and the core contains climate records between 3.54 and 145 ka from 0 to 841.5 cm. Sedimentation rates range from 1.62 cm/kyr to 8.5 cm/kyr in which the Holocene period shows the lowest sedimentation rates.  
Figure 3: (**a**) Deep North Atlantic benthic foraminiferal \(\delta^{18}\)O stack [47] and (**b**) \(\delta^{18}\)O in the planktonic foraminifera _N. pachyderma_ of core Hu90-08 (this study) are plotted. Fourteen tie points (Table 2) were obtained by visually aligning the \(\delta^{18}\)O values of Hu90-08 with those of the \(\delta^{18}\)O values of [PERSON] and [PERSON] [47] and five \({}^{14}\)C-Accelerator Mass Spectrometry (AMS) dates (Table 2) to construct the age model of core Hu90-08. Note that the upward triangles (**b**) denote the position of \({}^{14}\)C-AMS dates.
## 2 Materials and Methods
### Quantitative Algorithms  
In this paper, partial least squares regression (PLSR), support vector machine regression (SVR) and random forest (RF) modeling algorithms are mainly used to establish the corresponding quantitative model. The linear model PLSR is the most commonly used modeling method in quantitative analysis of LIBS, while nonlinear models such as SVR and RF usually have better performance in data mining. It can be seen from the existing work of relevant researchers that the models established by these algorithms have good performance. Therefore, this paper chooses these three methods to model and compares the advantages and disadvantages of each model, so as to choose a better model.  
The following parameters are used to evaluate the performance of each model: (1) fitting degree (\(R^{2}\)), (2) root mean square error of calibration (\(RMSEC\)), (3) root mean square error of prediction (\(RMSEP\)), (4) mean absolute error (\(MAE\)), and (5) mean relative error (\(MRE\)). \(R^{2}\) represents the linear relationship between the reference value of the index andthe predicted value of the model. The closer \(R^{2}\) is to 1, the higher the correlation between the spectral signal and the index content, and the more significant the regression effect. \(R^{2}>0.7\) indicates that the data are reliably represented; \(R^{2}>0.9\), indicating a good fitting effect. \(RMSEC\) and \(RMSEP\) are used to measure the deviation between the index reference value and the predicted value of the model, which is to evaluate the performance of the model from the perspective of collation. \(MAE\) and \(MRE\) represent the mean value of absolute error and the mean value of relative error between the predicted value of the model and the reference value, respectively, which are used to evaluate the accuracy of model prediction from a single measurement. The smaller the four values above, the better the prediction performance of the model.  
The above evaluation indexes are defined as  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y_{i}})^{2}}{\sum_{i=1}^{n}(y_{i}-\overline {y_{i}})^{2}} \tag{3}\]  
\[RMSEC=\sqrt{\frac{\sum_{i=1}^{n}(y_{i}-\hat{y_{i}})^{2}}{n}} \tag{4}\]  
\[RMSEP=\sqrt{\frac{\sum_{i=1}^{n_{p}}(y_{i}-\hat{y_{i}})^{2}}{n_{P}}} \tag{5}\]  
\[MAE=\frac{1}{n}{\sum_{i=1}^{n}}|y_{i}-\hat{y_{i}}| \tag{6}\]  
\[MRE=\frac{1}{n}{\sum_{i=1}^{n}}\frac{|y_{i}-\hat{y_{i}}|}{y_{i}} \tag{7}\]  
where \(y_{i}\) is the true value, \(\hat{y_{i}}\) is the predicted value, \(\overline{y}_{i}\) is the mean of the sample true value, \(n\) is the number of training samples, and \(n_{P}\) is the number of testing samples.
## 2 Fifth-Generation Telecommunications Technology and Architecture
### The Sixth Generation: The Next Frontier in Wireless Communications  
The projected sixth generation of wireless communication systems, 6G, is expected to be more than just an incremental upgrade from its predecessor, 5G. It is predicted to usher in unprecedented speeds, lower latencies, and a level of connectivity and integration that the world has never seen [40]. Where 5G introduced the concepts of mMTC and URLLC, 6G is anticipated to amplify these features to a new level. Think of technologies such as holographic meetings, high-definition augmented and virtual reality, and ultra-connected smart cities. Moreover, terahertz frequencies, cell-free architectures, and satellite-network integration are other key expected features of 6G, making global coverage and space-to-ground communications possible [41].  
The potential of 6G is immense, but with that potential come significant security challenges. As the line between our physical and digital worlds becomes even more blurred, securing our wireless networks becomes not just a matter of data protection but a matter of personal safety [42]. Many researchers investigated 6G security and the use of AI and ML in enhancing 6G security [43, 44]. However, with 6G networks being inherently AI-driven, the defense mechanisms will also rely heavily on artificial intelligence. AI can offer proactive threat detection, analyze vast datasets in real time to identify anomalies, and adapt dynamically to emerging threats [45]. However, this reliance on AI also brings vulnerabilities. There is a potential risk of adversaries manipulating AI operations, leading to system misbehaviors.
# Relational Graph Convolutional Network for Text-Mining-Based Accident Causal Classification
## 4 Experimental Results  
Through five-fold cross-validation, the original labeled dataset was equally divided into the same quintiles, i.e., each part contained 200 accident records. For each training, four of them were used as the training set and the remaining one was regarded as the test set, ensuring that each part was treated as the test set throughout the validation process. The results of each cross-validation were measured through the average F1-score to evaluate the performance of the whole model and the model with the highest average F1-score was adopted as the final built model combining the pre-trained BERT and a R-GCN. The results of the five-fold cross-validation are illustrated in Table 2.  
As shown in Table 2, the accident causal classification model combining the pre-trained BERT and the R-GCN could achieve an average F1-score of up to 0.77. In order to more intuitively show the performance of the constructed model on each type of accident, we show the confusion matrix of the adopted model in Table 3. The corresponding procedure for calculating the average F1-score of Fold 4 is shown in Table 4.  
It can be seen, from the confusion matrix in Table 3, that the model used can achieve accurate classification of most texts, but there is still a certain error for types that also contain fall or object. This shows that the proposed text-mining-based accident cause analysis could be roughly classified, but the specific accident cause analysis results still need to be further improved. To further demonstrate the advantages of the proposed model, comparative experiments were performed to numerically evaluate the improvement of the proposed method over previous methods. Traditional text mining methods, including decision tree, k-nearest neighbors (KNN), Naive Bayes and logistic regression, were also adopted to classify text in the OSHA dataset. Deep-learning-based networks, including long short-term memory (LSTM), gate recurrent unit (GRU) and symbiotic organisms search-gate recurrent unit (SGRU) [48], were also compared. Furthermore, to explore the role of the gate mechanism in the proposed method, the results of an ablation experiment were also analyzed. It should be noted that all experiments were performed on Fold 4. Due to space constraints, only the final average results, not class-by-class results, are shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Fold 1** & **Fold 2** & **Fold 3** & **Fold 4** & **Fold 5** \\ \hline AvgF1-score & 0.69 & 0.64 & 0.72 & 0.77 & 0.75 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the five-fold cross-validation.  
From the results in Table 5, it can be seen that, although the proposed method still has some limitations, it achieved a 6% improvement of the average F1-score compared to existing research. At the same time, it can also be found from the results of the ablation experiments that the gate mechanism played a key role in the entire model. By eliminating the possible errors of the pre-trained BERT, the model achieved a 4% improvement.
# Impacts of GPS Spoofing on Path Planning of Unmanned Surface Ships
## 3 GPS Attack Designs and Spoofing Analysis  
In this section, we first introduce GPS and attacks on GPS. Then we design a GPS spoofing attack and analyze how the GPS spoofing influences the result of the path planning of unmanned ships based on the APF method in Section 2.
## 3 Compositional Data on Date Seeds and Date Seed Coffee Products
### Macronutrients
#### 3.1.3 Fiber  
Dietary fiber is defined as non-digestible carbohydrates, such as pectin, cellulose, hemicellulose, and different types of non-starch and resistant saccharides, as well as lignin. The carbohydrates found in date seeds are primarily insoluble fiber types such as cellulose and hemicellulose. Two-thirds of the fiber content in the Tunisian date cultivar _Deglet Nour_ is attributed to cellulose, while 50% and 20% are attributed to hemicellulose. Around 70% of the components of _Allig_ date seeds are fiber, with slightly higher cellulose content and lower hemicellulose content. The total fiber content of _Deglet Nour_ and _Allig_ is complemented by a low lignin content [37]. In the endosperm, there are various hemicellulose fractions, including gluco and galacto-mannan, and alkali-soluble heteroxylan. These storage compounds are slowly degraded and support the date seeds during the long germination period [13]. [PERSON] et al. reported an insoluble fiber content of 52.7%. After phenolic extraction, either with solvent water or acetone, the fiber extraction increased to 81-82 g/100 g [37]. A daily fiber intake of 25 g per day is recommended for adults to support normal laxation. In addition, other positive health effects of dietary fiber are associated with reduced risk of heart diseases and the prevention of diet-related diseases [38]. Date seed fiber is a good source of fiber for human nutrition and can be easily added to bread doughs. In summary, dates are good sources of carbohydrates and fiber, with a lower sugar content.
# Cross-Layer-Aided Opportunistic Routing for Sparse Underwater Wireless Sensor Networks
## 5 Protocol Analysis
### Effectiveness of Negotiation
In addition, spatial reuse decreases as \(d/R_{tx}\) increases until that \(d/R_{tx}\) reaches 0.7, especially with high SL. In [59], we can obtain \(\gamma\) with a value smaller than 2 when we set SL = 155 dB; however, we require a high value of SL in this paper, since we consider \(\sigma\) and \(A_{p}\) in a network scenario. In a real ocean scenario, more transmit power is required to obtain a small value of \(\gamma\). To improve the spatial reuse of a network, we should find an appropriate SL that is large enough to ensure the connectivity of network topology and that is small enough for approaching a high value of \(d/R_{tx}\). We plot \(E_{ngt}\), which varies with \(d/R_{tx}\) for different values of SL, in Figure 4(b). \(E_{ngt}\) decreases as distance \(d\) increases with a fixed \(R_{tx}\). Moreover, \(E_{ngt}\) can be improved by increasing the transmit power. When SL is set to 180 dB uPa @1m source, \(E_{ngt}\) can reach  
Figure 4: The area of region \(S_{I\cap(ngt)}\) in different scenarios: (**a**) Scenario II; (**b**) Scenario III. Figure 5: (**a**) Reuse factor \(\gamma(f,d)\) varies with normalized node separation \(d/R_{tx}\); (**b**) Effectiveness of negotiation \(E_{ngt}\) varies with normalized node separation \(d/R_{tx}\); (**c**) Optimal SL for different effectiveness of negotiation \(E_{ngt}\). 100% when \(d/R_{tx}\) is higher than 0.57, and this value is smaller than that in [59], where the threshold value is 0.74. This implies that scale factor \(\sigma\) and transmission anomaly \(A_{p}\) heavily influence the effectiveness of negotiation. To give a comprehensive analysis, we plot the optimal SL for different negotiation efficiency at a fixed distance \(d\) in Figure 5c. To ensure the 100% effectiveness of negotiation strategy for \(d=300/600\) m, SL should be larger than 157/164 dB in relation to uPa @1m source. Similarly, to obtain 80% effectiveness of the negotiation strategy, we have to set SL to a value higher than 154.7/161.3 dB re uPa @1m source for \(d=300/600\) m. Considering the effectiveness of the negotiation strategy, we should set an appropriate source level for a specific network scenario to improve the network performance.
###### Abstract  
Sun-induced chlorophyll fluorescence (F) is a novel remote sensing parameter providing an estimate of actual photosynthetic rates. A combination of this new observable and Monteith's light use efficiency (LUE) concept was suggested for an advanced modeling of gross primary productivity (GPP). In this demonstration study, we evaluate the potential of both F and the more commonly used photochemical reflectance index (PRI) to approximate the LUE term in [PERSON]'s equation and eventually improve the forward modeling of GPP diurnals. Both F and the PRI were derived from ground and airborne based spectrometer measurements over two different crops. We demonstrate that approximating dynamic changes of LUE using F and PRI significantly improves the forward modeling of GPP diurnals. Especially in sugar beet, a changing photosynthetic efficiency during the day was traceable with F and incorporating F in the forward modeling significantly improved the estimation of GPP. Airborne data were projected to produce F and PRI maps for winter wheat and sugar beet fields over the course of one day. We detected a significant variability of both, F and the PRI within one field and particularly between fields. The variability of F and PRI was higher in sugar beet, which also showed a physiological down-regulation of leaf photosynthesis. Our results underline the potential of F to serve as a superior indicator for the actual efficiency of the photosynthetic machinery, which is linked to physiological responses of vegetation.  
spectroscopy; sun-induced chlorophyll fluorescence; F\({}_{760}\); gross primary productivity; GPP; vegetation; photosynthesis; PRI +
Footnote †: journal: Remote Sensing  
0000-0001-8746-3390/rs8070574
## 8 Performance Evaluation  
To evaluate the performance of the proposed framework, we used a dataset collected with an IWR6843 SK by Texas TI Single-chip 60 GHz to 64 GHz intelligent mmWave radar sensor [50] to train and test the CNN. There are several scenarios in the collected data. The drone and pedestrian data are mixed in Scenarios A, C, and D. Scenario B includes only noise and pedestrian data. In addition, the data were divided into training and validation sets at a 5:1 ratio. Our framework recognizes three classes of targets: drone, non-drone, and drone and non-drone, with a pedestrian as the non-drone target. In this context, the accuracy of the proposed algorithms for clustering, tracking, and recognition was determined as follows.
# Real-Time Analysis of Facial Expressions for Mood Estimation
## 3 Vision-Based Mood Estimation
### Mood Estimation
In addition, as previously mentioned (see Section 2), the PAD model is defined in terms of three orthogonal dimensions: Pleasure (P), Arousal (A), and Dominance (D). Thus, from the intersection of the axes of Pleasure, Arousal, and Dominance, eight octants can be derived that represent the fundamental categories of mood. The mood estimation fits one of these eight fundamental categories and requires knowing the sign in each of the dimensions of the PAD space, which, depending on the expressiveness of the subject, may not be possible. As defined in [PERSON]'s original work, Dominance is more related to an internal feeling of control or submission, which may not have obvious or consistent facial manifestations. In the context of image sequence analysis for emotion recognition, most previous work focuses on identifying facial expressions that correspond to basic emotion categorizations or the Pleasure (Valence) and Arousal dimensions [14]. In contrast, Dominance may require additional contextual or postural information to be accurately inferred, so a second level of categorization was considered, eliminating this dimension. This second level is made up of four categories corresponding to the quadrants of the P+A plane: \"Exaltation\", \"Calm\", \"Anxiety\" and \"Bored\". Following the same criteria, a third level corresponding to the P dimension was defined. This level is made up of two categories: \"Positive\" and \"Negative\". Mood states for which it is not possible to determine the P value are considered \"Neutral\". Figure 4 shows a selection of frames from a sequence with different expression activation, the identified AUs and the estimated mood for each level. The maximum values of each level are reported as the final mood estimation.