esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Picking Test and Result Analysis
### Multi-Target Clustered Crabapples Picking Experiment
#### 6.4.3. Crabapples Picking Test  
In order to verify the feasibility of the picking test platform for crabapple picking, validation tests of the overall picking operation effect were conducted after completing the construction of the test platform. The artificial simulation crabapple tree was used for the picking tests, and the crabapple picking test progress is shown in Figure 20.  
Figure 19: Crabapples picking test platform.  
Figure 20: The crabapple picking test progress. **(a)** Initial position of the robotic arm. **(b)** Arrive at the first picking point. **(c)** Grab the first clustered crabapples. **(d)** Finish the picking. **(e)** Move to the second picking point. **(f)** Arrive at the second picking point. **(g)** Grab the second clustered crabapples. **(h)** Finish the picking. **(i)** Move to the third picking point. **(j)** Arrive at the third picking point. **(k)** Grab the third clustered crabapples. **(l)** Finish the picking. **(m)** Return to the starting position.  
From Figure 20, it can be seen that the end effector moves to the first picking point from the initial position, the soft fingers are used to envelop the clustered crabapples, and then the clustered crabapples are picked through the end effector movement. As the fingers are released, the picked crabapples will be transported to the collection basket through the conveyor pipe. By the improved RRT obstacle avoidance path planning, the end effector is moved to the second picking point to realize the picking of the second clustered crabapples. Then, the picking of other clustered crabapples on the tree is completed sequentially, and finally, the end effector is returned to the initial position to wait for a new picking task.  
To further verify the feasibility and accuracy of the clustered crabapple picking test, multiple groups of different clusters (ranging from 1 to 6) were placed at random positions. The success rate of recognizing, positioning, and picking clustered crabapples, as well as the number of collisions between the robotic arm and the tree, were used as evaluation indicators for the crabapples-picking robot. The experimental results are shown in Table 8.  
From the experimental data in Table 8, it can be seen that the average recognition success rate of the robot for clustered crabapples (1 to 6) is 95.83%, the average positioning success rate is 93.75%, and the average picking success rate is 89.58%. Moreover, there are no collisions between the robotic arm and the main branches of the tree during the picking process, which meets the actual picking needs. Therefore, it can be concluded that the algorithm proposed in this article for recognizing, positioning, and planning the robotic arm's picking path of clustered crabapples is effective and feasible.
## 4 Case Study
To prevent the valuable parts of the signal from being excessively removed, the energy ratio (\(ER\)) is introduced to evaluate its energy change before and after processing. It is defined as follows:  
\[ER=\frac{E_{1}}{E_{0}} \tag{16}\]  
Figure 9: Waveforms and time-frequency diagrams of MS signal before and after noise reduction by EEMD, CEEMD, VMD and VMD-AIC. (**a**) Original noisy MS signal; (**b**) the signal denoised by EEMD; (**c**) the signal denoised by CEEMD; (**d**) the signal denoised by VMD; (**e**) the signal denoised by VMD-AIC. E1 means the end position of the first calculation, and E2 is the result of its second relocation. where \(E_{1}\) and \(E_{0}\) are the energy before and after reconstructing, respectively. When the amplitude of the effective time series is weakened, the energy will be changed, resulting in a corresponding decrease of \(ER\). Therefore, as long as \(ER\) is always kept at a high level, the size of \(NR\) indicates the effect of noise reduction. Figure 11a,b are the box diagrams of their \(NR\) and \(ER\) obtained by different methods. It can be seen from the distribution of \(ER\) that the whole data of VMD and VMD-AIC are greater than 0.8, and the main bodies of their boxes are even above 0.85. It means their reconstructed signals have little distortion. However, in EEMD and CEEMD, there are a few controversial points whose energy changes are excessive, resulting in their \(ER\) values being lower than the threshold. The distributions of \(NR\) manifest that the result processed by VMD-AIC is the best. Its median and quarter point are above 85, which is slightly greater than the median of VMD. The median of EEMD and CEEMD are even below 75. It means their processing results of MS signals are not particularly good. In addition, the box area of VMD-AIC, which is further reduced on the basis of VMD, is significantly smaller than that of EEMD and CEEMD. Its intensive distribution reflects the fantastic robustness of the algorithm in decomposing various MS signals. Whether it is the denoising effect or the stability of the algorithm, the proposed method can get better result than the classical algorithms in suppressing different noisy MS signals. Figure 10: The AIC time series of the entire, front and rear time window, respectively. (**a**) The AIC of the entire waveform; (**b**) The AIC of the front time window segment; (**c**) The AIC of the rear time window segment. S-AIC and E-AIC are the recalculation results of the two dashed boxes in (**a**).
## 2 Results and Discussion
### Effect of HPP and TS Processing on the Quality of Marinated Lotus Root Slices
#### 2.1.2 Color, Texture, and TVC  
Table 2 shows the color, texture, and microbiological properties of HPP and TS samples. In contrast to the TS treatment, HPP increased the hardness value of marinated lotus root slices. [PERSON] and [PERSON] [27] defined instantaneous pressure softening (IPS), i.e., the loss in texture that occurs at the instant of HPP (zero holding time), by measuring and fitting the hardness of HPP-treated fruits and vegetables treated at 100-400 MPa for 5-60 min. The IPS will be recovered to varying degrees with increasing pressurization time. The HPP conditions at 550 MPa with a holding time of 20 min in this study seem to have accomplished this textural recovery. In addition, as reported in previous studies, HPP also increases the hardness of some other marinated products, such as marinated fermented radish [1], marinated beef [11], and calcium-infused baby carrots [28]. This may be due to the aggregation of proteins/peptides [29] or marination-induced changes in cell microstructure, such as membrane permeability, cellular rearrangement, and cell collapse [1; 28; 30], which alter the effect of HPP on cell volume compression. The TVC in samples before HPP or TS was 3.98 log\({}_{10}\) CFU/g and immediately reduced to 1.68 and 1.60 log\({}_{10}\) CFU/g after HPP and TS treatments, respectively. The main mechanism of action of HPP in microorganisms involves altering cell structure and physiological functions, breaking DNA strands, disrupting cell membrane integrity, inactivating key enzymes, and irreversible denaturation of proteins, resulting in the loss of membrane selectivity [31]. It can be inferred that the bacteria with tolerance to the thermal process may be the thermal-tolerance spores, such as Bacillus and Clostridium [32]. Additionally, the TVC result of HPP samples was not significantly (\(p>0.05\)) different from that of TS
## 3 Results and Discussions
### Workability
#### 3.1.3 Influence of AAS Content  
The slump values of group A and C were compared to study the influence of AAS content on workability of FS-GPC mixes. In groups A and C, the mixes were prepared with 40% and 35% AAS content respectively. The workability of the group C mixes (35% AAS) were observed to be much lower than the group A mixes (40% AAS). The slump values of the group A mixes are between 90-130 mm, whereas the group C mixes are between 35-70 mm.  
Furthermore, the influence of AAS content can be noted in mixes with different slag replacement levels. The slump values of group C mixes, C1-S10, C2-S20, C3-S30, and C4-S50 were decreased by 46%, 50%, 55%, and 61% respectively as compared to their counterparts of group A, i.e., A1-S10, A2-S20, A3-S30, and A4-S50. These lower slump values can be attributed to the decreased consistency of the mix due to reduced alkaline activator content. It can be concluded, therefore, that workability of FS-GPC mixes is strongly affected by AAS content, in agreement with the previous studies [28,34,36].
# An Iterative Closest Point Method for Lidar Odometry with Fused Semantic Features
## 4 Methods
### Features Extraction  
#### 4.3.1 Geometric Features  
We use \(P=\{p_{1},p_{2},\ldots,p_{n}\}\) to denote a set of point clouds. Each of these points \(p_{i}=\{x,y,z,c,l\}\), where \(\{x,y,z\}\) are the 3D coordinates of the point cloud, \(c\) denotes curvature, and \(l\) is the semantic label of the point. Based on the curvature of the point cloud, the edge and plane features of a frame point cloud can be distinguished as geometric features. These features are denoted as \(Ne\) and \(Np\), respectively. Each point's curvature \(c\) is represented as follows:  
\[c=\frac{1}{n\cdot\|p_{i}\|}\sum_{j=1,j\
eq i}^{n}\|\|p_{j}\|-\|p_{i}\|\| \tag{3}\]  
where \(p_{i}\) denotes the target point and \(p_{j}\) is in the same laser beam. Then, these points are divided into edge points (curvature greater than \(\alpha\)) and plane points (curvature less than \(\alpha\)) using a threshold \(\alpha\). To obtain features uniformly, each ring is divided into six parts. In each part, \(Ne\) sharpest points are selected as edge features, and \(Np\) smoothest points that satisfy the semantic constraints are selected as plane features. Finally, the edge and plane feature points of the whole target frame are denoted as \(Ne^{all}\), \(Np^{all}\).
## 5 Generation of a Complete Multiple Beam Coverage in Ka-Band
On the other hand, the elliptical shape of the main RA matches quite well with the illuminations that are obtained from the feeds, as was shown in Figure 8.  
It is important to note that typical spot beams adopted in a multibeam coverage are circular. However, elliptical spot beams can be considered as well if there is a significant advantage. In the solution proposed in this paper, the advantage is related to the possibility to oversize only one linear dimension of the reflector (instead of oversizing both dimensions, as in [5]), which reduces the overall aperture of the antenna with improvements in terms of accommodation. Due to the elliptical shape of the beams, the adjacent beam overlap in planes \"\(v\) = constant\" occurs for a gain level around 40.2 dBi, while it is around 45 dBi in the diagonal planes forming 60\({}^{\circ}\) with the plane \"\(v\) = 0\" (note that the maximum gain of the spots varies between 49.2 dBi and 50.5 dBi). A similar multi-spot coverage based on elliptical spots arranged in a rectangular lattice has been proposed in [7] for a MFPB reflector  
Figure 12: Multi-spot coverage produced by the designed DRAA after the projection over the Earth’s surface: (**a**) relative contour patterns and (**b**) absolute gain levels (in dBi) at 20 GHz. system, but the antenna has the drawback of its greater hardware complexity, due to the use of a MFPB architecture. The performance of the proposed multibeam DRAA could be improved by means of phase optimization techniques, in order to broaden the beams only in one plane, so that the adjacent beam overlap would occur at a higher gain level. For example, phase-only synthesis based on the intersection approach technique [36; 37] could be applied to broaden the beams in the elevation plane, without disturbing the bifocal phases that provide the required beam spacing reduction. The co-polar isolation for the resultant multi-spot coverage has been estimated as the minimum C/I ratio within a 40 dBi gain beamwidth, where the interference is produced by the radiation of the neighbor beams produced in the same combination of frequency and polarization. Some examples of the interference produced among reuse beams are included in Figures 6 and 7. Note that the adjacent feed-horns will alternate in frequency, so the adjacent beams in the same plane \"\(v\) = constant\" (generated in the same polarization by different horns) will alternate in frequency. According to the previous C/I criterion, it has been checked that the co-polar isolation for the multi-spot coverage varies between 17.6 dB and 21.2 dB, which are typical values for practical HTS systems in Ka-band [5]. The design of the bifocal DRAA has been performed at the central frequency of 20 GHz, and it covers the complete frequency range for Tx (19.5-20.5 GHz, a 5% relative bandwidth). Similar and even larger values of bandwidth have been reported in previous works that use reflectarray cells based on orthogonal dipoles [20; 28].
## 3 Results and Discussion
### Correlations among Microbiota and Tea Components  
To explore microbial effects on tea components, correlations between chemical compounds and relative abundances of the bacterial and fungal genera were conducted and are shown in Tables 3-5. In total, 30 bacterial genera showed significant correlations to 1-8 tea compounds. For example, unclassified Actinobacteria have negative significant correlations to the contents of TPs, FAA, EGC, C, EC, TF, and TR; and have positive significant correlations to TB (Tables 3 and 4). For fungi, _Aspergillus_ showed negative significant correlations to the contents of C, EGCG, ECG, and GG; OTUs assigned to no_rank_Fungi have positive significant correlations to the contents of C, EGCG, ECG, and GG; and _Penicillium_ had positive significant correlations to the contents of EGCG, ECG, and GG (Table 5). The compounds had positive significant correlations to 2-10 bacterial genera and showed negative significant correlations to 0-5 bacterial genera (Table 3). This preliminary data suggested that microbiota with significant correlations to tea compounds may be involved in the dynamics of chemical compounds; however, the roles of these microbiota in the SSF of PEPT need further monoculture fermentation studies.  
Figure 5: Fungal communities in tea leaves at the genus level during the SSF of PEPT (**a**); PCA analysis of fungal communities in fermented tea leaves during the SSF of PEPT based on the relative abundances of the sample reads at the genus level (**b**). Minor groups are composed of the parties showing a percentage of reads <1.0% of the total reads in each sample.
## 3 Results
### Analysis of Factors Affecting SOM Content
#### 3.3.1 Difference of SOM Content under Different Parent Material  
There are eight main types of soil parent materials in the area studied, and the SOM content of different soil-forming parent materials in sequence are: lacustrine deposits > aeolian deposits > deluvium > littoral sediment > diluvial deposit > residual deposits > loess parent material > river alluvium. The highest SOM level was found in the soils developed from lacustrine deposits (17.63 g\(\cdot\)kg\({}^{-1}\)), followed by soils from the aeolian deposited (11.75 g\(\cdot\)kg\({}^{-1}\)), deluvium (11.72 g\(\cdot\)kg\({}^{-1}\)), and littoral sediment (11.71 g\(\cdot\)kg\({}^{-1}\)). Soils developed from river alluvium and loess parent material had the lowest SOM content, at 9.08 g\(\cdot\)kg\({}^{-1}\) and 9.22 g\(\cdot\)kg\({}^{-1}\), respectively. The variation coefficient of the SOM content in different types of parent material ranged from 23.92% to 112.18%. With the exception of lacustrine deposits and deluvium, the SOM content in other types of parent material showed a moderate degree of variation (Table 3).
# Design of an Isolated Bidirectional Symmetric Resonant Converter
## 6 The Measurements and Operating Efficiency Analysis
### Critical Measurements for the Forward Charging Mode  
Figure 16 shows the measurement results of \(v_{\text{C}S3}\), \(v_{\text{D}S3}\), and \(i_{\text{D}S3}\) in the cases where \(V_{\text{DC\_Grid}}\) = 400 V and charging battery voltages are \(V_{\text{B}at}\) = 280 V and \(V_{\text{B}at}\) = 403 V respectively. It is obvious that there appears a reverse current \(i_{\text{D}S3}\) inside the power switch both in Figure 16a,b which helps remove the energy on the parasitic capacitance and rapidly decline \(V_{\text{D}S3}\) to 0V, thus achieving ZVS.  
Figure 17 shows the measurement results of \(v_{\text{D}B7}\) and \(i_{\text{D}B7}\) in the cases where \(V_{\text{DC\_Grid}}\) = 400 V and charging battery voltages are \(V_{\text{B}at}\) = 280 V and \(V_{\text{B}at}\) = 403 V, respectively. By observing Figure 17a, the diode current \(i_{\text{D}B7}\) has not fallen to 0 A prior to the cut-off of diode D\({}_{\text{B}7}\) and thus leads to diode ZCS failure. Figure 17b shows that the diode current \(i_{\text{D}B7}\) has reached 0 A before diode \(D_{B7}\) turns off and thus succeeds in the ZCS of \(D_{B7}\). It is worth noting that the oscillation of \(v_{\text{D}B7}\) at the switching instant resulted from the resonance of parasitic capacitance and resonant inductance.
# Comparative Analysis of BERT and GPT for Classifying Crisis News with Sudan Conflict as an Example
## 4 Methodologies
### Evaluation Matrix
#### 4.2.2 Gpt  
The GPT-based classification models were evaluated using distinct metrics tailored to the SL-MLG and MLC frameworks. This evaluation aimed to assess GPT's ability to align with expert-labeled categories while identifying potential strengths and limitations of its classification performance.  
For SL-MLG, model performance was evaluated using an exact-match accuracy metric, which assessed whether the model's single predicted category matched any of the ground-truth labels. To ensure consistency in comparison, both the ground-truth labels and model outputs were normalized by converting text to lowercase and removing extra spaces. Each prediction was assigned a binary score: 1 if the predicted category matched any of the annotated labels, and 0 otherwise. These scores were then averaged across all test samples to produce the final accuracy score. This single-metric evaluation reflects GPT's effectiveness in selecting the single label that best describes the primary issue from multiple valid options and avoids the use of precision or recall, which are less applicable when evaluating a single predicted label against multiple ground-truth categories, as in SL-MLG.  
For MLC, model performance was measured using accuracy, precision, recall, and F1-score, calculated at both the article and category levels. Additionally, false positives (FPs), false negatives (FNs), and true positives (TPs) were tracked to analyze classification biases and potential weaknesses in specific categories. To compute these metrics, predicted categories were extracted from the model's textual outputs and compared against theground-truth labels. The evaluation process treated these extracted categories as unordered sets, ensuring that variations in category ordering did not affect results.
* Accuracy was determined by checking whether the predicted set of categories exactly matched the ground-truth set for each article. \[\text{Accuracy}=\frac{\text{Correctly Classified Categories}}{\text{Total Categories}}\] (10)
* Precision measured the proportion of correctly predicted categories among all predicted labels. \[\text{Precision}=\frac{TP}{TP+FP}\] (11)
* Recall quantified the proportion of relevant categories successfully identified by the model. \[Recall=\frac{TP}{TP+FN}\] (12)
* F1-score, the harmonic mean of precision and recall, provided a balanced assessment of classification performance. \[F1-Score=2\times\frac{Precision\times Recall}{Precision+Recall}\] (13)  
Additionally, the per-category analysis aggregated FP and FN counts across the dataset, identifying categories where the model struggled.
# Deep Learning-Based Secure MIMO Communications with Imperfect CSI for Heterogeneous Networks
## 5 Simulation Results
The BER curves of both DCNN training models are provided with \(f_{d}=0.1\) and \(f_{d}=0.05\), respectively. We can see from this figure that smaller \(f_{d}\) produces better performance with a gain of about 4 dB. The reason is that if \(f_{d}\) is smaller, the channel fading matrix changes more slowly, and the wireless channel can be learned more efficiently by the DCNN. As a result, more accurate CSI can be produced and enhancing the BER performance. Figures 10 and 11 depict the effects of the antenna number on BER performance with the normalized doppler frequency \(f_{d}=0.1\), and the correlation factor of imperfect CSI \(\xi=0.80\). Specifically, the antenna  
Figure 8: BER performance of deep learning-based MLD with SNR = 20 dB. Figure 9: BER performance of deep learning-based MLD with different \(f_{d}\). configuration in Figure 10 is \(N_{M}=4\), \(N_{U}=4\), \(N_{F}=1\), and the number of data stream \(N_{D}=3\). In other words, the spectrum efficiency is higher than the previous setup. In Figure 11, the antenna configuration is \(N_{U}=2\) and \(N_{U}=3\), respectively. We can see from the two figures that the performance gain of DCNN with larger antenna number of user is obvious compared with the smaller antenna number, especially in the higher SNR region. Specifically, with \(N_{F}=1\), the performance gain of DCNN to the standard MLD is about 6 dB. The performance gain of \(N_{U}=3\) is about 8 dB than that of \(N_{U}=2\). That reason is that the larger receiver antennas' number can introduce more freedom of space diversity, thus the BER will be decreased greatly. Figure 11: BER performance of deep learning-based MLD with different \(N_{U}\). Figure 10: BER performance of deep learning-based MLD with \(N_{F}=1\).
## 4 Simulation Environment
### Formulation of Serving Clusters  
The second step of the simulation loop is the formulation of serving clusters. This procedure is run once every \(T_{\text{scf}}\) time slots to enable stable radio resource scheduling, which relies on past user bitrates, i.e., the scheduler utilizes information about the past bitrates of users, and frequent reformulation of serving clusters could cause instability in radio resource allocation. We have implemented a state-of-the-art serving cluster formulation algorithm that takes as input the Reference Signal Received Powers (RSRPs) between the \(i\)-th UE and all RUs that are within the range of reference signals for this UE. The RSRP between the \(i\)-th UE and the \(l\)-th RU is denoted as \(\gamma_{i,l}\) and defined as follows:  
\[\gamma_{i,l}=\frac{P_{l}}{N_{\text{rb}}}\cdot\frac{\sum_{r=1}^{N_{\text{rb}}} \sum_{m=1}^{M_{l}}|h_{i,l,r,m}|^{2}}{N_{\text{rb}}M_{l}} \tag{10}\]  
where \(\frac{P_{l}}{N_{\text{rb}}}\) is the transmit power per single RB. Based on this input, the serving cluster for the \(i\)-th UE is formulated by \(J\) RUs characterized by the highest RSRP \(\gamma_{i,l}\), i.e., for these RUs, \(\mathbf{D}_{i,l}=\mathbf{I}_{M_{l}}\). For the remaining RUs, \(\mathbf{D}_{i,l}=\mathbf{0}_{M_{l}}\). Such an approach is commonly used in related works on UCCF M-MIMO networks, e.g., [20], or with slight modifications introducing an RSRP threshold in [21].