esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Eye-Tracking Results of the Pairwise Comparison
#### 3.1.2 Results of the Blue Buttons  
The observed frequencies of the winners and losers of the blue button versions in the pair comparison are shown in Table 4. In the last column, the sum of the wins shows that the third version (V3) won the most pair comparisons (79 out of a total of 320). This is followed in order by the second version (V2; 75 out of 320), the fourth version (V4; 73 out  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Buttons BW** & **V1** & **V2** & **V3** & **V4** & **V5** & **Wins** \\ \hline V1 & & 12 & 11 & 9 & 12 & 44 \\ V2 & 20 & & 12 & 13 & 20 & 65 \\ V3 & 21 & 20 & & 16 & 21 & 78 \\ V4 & 23 & 19 & 16 & & 18 & 76 \\ V5 & 20 & 12 & 11 & 14 & & 57 \\ \hline Losses & 84 & 63 & 50 & 52 & 71 & 320 \\ \hline \end{tabular}
\end{table}
Table 2: The observed frequencies of the wins and losses of the different wireframe buttons.  
Figure 6: Graphical representation of the coefficient estimators of the BT model for the eye tracking data of the wireframe buttons and the blue buttons. The gray line refers to the wireframe buttons and the blue line to the blue buttons.  
of 320), and the fifth version (V5; 58 out of 320). The first version (V1) resulted in the fewest wins (35 out of 320).  
Subsequently, the simple Bradley-Terry model was estimated again. The estimators for the parameters of the items and the ranks assigned to the button versions are shown in Table 5.  
The parameter for the first button version (V1) was also set to zero in this case and therefore acts as a reference object. Based on the coefficient estimators, the study participants most frequently preferred the third button version (V3). In second place was the second button version (V2), followed by the fourth (V4) and fifth (V5) button versions. In last place, the most angular and therefore first button version (V1) can be found. The blue line in Figure 6 also shows a graphical representation of the coefficient estimators for the blue buttons. As mentioned before, the first button version (V1) represents the reference object.
###### Abstract  
This study introduced a novel approach to 3D image segmentation utilizing a neural network framework applied to 2D depth map imagery, with \(Z\) axis values visualized through color gradation. This research involved comprehensive data collection from mechanically harvested wild blueberries to populate 3D and red-green-blue (RGB) images of filled totes through time-of-flight and RGB cameras, respectively. Advanced neural network models from the YOLOv8 and Detectron2 frameworks were assessed for their segmentation capabilities. Notably, the YOLOv8 models, particularly YOLOv8n-seg, demonstrated superior processing efficiency, with an average time of 18.10 ms, significantly faster than the Detectron2 models, which exceeded 57 ms, while maintaining high performance with a mean intersection over union (IoU) of 0.944 and a Matthew's correlation coefficient (MCC) of 0.957. A qualitative comparison of segmentation masks indicated that the YOLO models produced smoother and more accurate object boundaries, whereas Detectron2 showed jagged edges and under-segmentation. Statistical analyses, including ANOVA and [PERSON]'s HSD test (\(\alpha\) = 0.05), confirmed the superior segmentation performance of models on depth maps over RGB images (\(p\) < 0.001). This study concludes by recommending the YOLOv8n-seg model for real-time 3D segmentation in precision agriculture, providing insights that can enhance volume estimation, yield prediction, and resource management practices.  
Detectron2; YOLOv8; point clouds; time of flight; precision agriculture 2024  
10324
## 3 Feature Selection Using Agglomerative Hierarchical Clustering
Within this framework, another cluster is introduced, labeled as \(L\), which is part of the forest but distinct from cluster \(K\). Cluster \(L\) contains \(n_{L}\) original objects, denoted as \(L[0],\ L[1],\...,\ L[n_{L}-1]\). By utilizing the Ward variance minimization algorithm, the computation of the new entry \(D_{\textit{nard}}(K,L)\) can be executed as follows [37]:  
\[D_{\textit{Ward}}(K,L)=\frac{n_{K}n_{L}}{n_{K}+n_{L}}d_{E}(K,L) \tag{11}\]  
In (11), \(D_{\textit{Ward}}\) represents the distance between clusters \(K\) and \(L\) under consideration for merging, where \(n_{K}\) and \(n_{L}\) correspond to the number of data points within clusters \(K\) and \(L\), respectively. \(d_{E}\) is the distance between the centroids of clusters \(K\) and \(L\) using the Euclidean distance metric. However, most modern algorithms often employ a more computationally efficient formulation to derive the inter-cluster distance, which is expressed mathematically as follows [38]:  
\[D_{\textit{Ward}}(K,L)=\sqrt{\frac{(n_{L}+n_{I})d_{E}(L,I)}{S}+\frac{(n_{L}+n_ {I})d_{E}(L,I)}{S}-\frac{(n_{L})d_{E}(I,I)}{S}} \tag{12}\]  
where \(S=n_{L}+n_{I}+n_{J}\). \(n_{L}\), \(n_{I}\), and \(n_{J}\) are the numbers of data points in clusters \(L\), \(I\), and \(J\), respectively. Once the AHC is performed and the linkage matrix is obtained, the transition to flat clusters can be achieved. This transition from hierarchical to flat clusters necessitates the application of inconsistency criteria. The inconsistency criteria allow the selection of specific levels of detail by identifying where to cut branches in the dendrogram. The inconsistency coefficient at a specific merge level \(i\) in the hierarchical clustering tree can be formulated as follows [39]:  
\[\text{inconsistency coef.}=\frac{H_{Z[i,2]}-\mu_{Y[i,\rho]}}{\sigma_{Y[i,1]}} \tag{13}\]  
where \(H_{Z[i,2]}\) is the distance at which clusters are merged at level \(i\) in the AHC tree contained in the linkage matrix \(Z\), \(\mu_{Y[i,0]}\) and \(\sigma_{Y[i,1]}\) are the mean and standard deviation, respectively, of the distances of the last \(n\) merges. The inconsistency coefficient is a valuable metric for assessing the clustering consistency at each level. It quantifies the relative increase in distance at a particular level compared to the average increase in distance observed in recent mergers. This approach systematically allows the determination of the level of detail in the hierarchical structure that effectively extracts meaningful clusters. It is worth mentioning that this function is implemented in MATLAB R2024b and SciPy (version 1.13.1) libraries to evaluate and interpret hierarchical clustering results.
## 3 System Design
### Sensitive Structure Design  
As mentioned above, the sensitive structure of the IS consists of a TM and an EH. The components are shown in Figure 6, the working coordinate system is defined as well. The TM is a parallelepiped, the normal direction of the square surface with the largest area is the X-axis. The EH provides the differential electrodes around the TM. The TM is made of TC4 titanium alloy material with extremely low magnetic susceptibility (\(|\chi|\approx 1.3\times 10^{-5}\)), which can reduce the influence of the electromagnetic noise in the space environment. A CNC (Computer Numerical Control) machining center is employed to process the TM. As a result, the TM with the perpendicularity and parallelism of end faces less than \(5\times 10^{-5}\) rad (about half of the design requirements \(1\times 10^{-4}\) rad), which can effectively reduce the coupling noise between the axes. In addition, two threaded holes are prepared on the Z-direction end face of the TM for connecting with the wire suspension component and all surfaces of the TM are gold-coated.  
Electrode plate-I, high-precision spacer ring, and electrode plate-II compose a sandwich-structured electrode housing. The two electrode plates have the same structure. Materials with higher thermal conductivity can better suppress the thermal gradients-induced acceleration noise. Therefore, all electrode plates and spacers adopt the ultra-low expansion glass-ceramic Zerodur(r) material from SCHOTT and are produced with diamond tools in the CNC machining center. The flatness of the electrode surface of the electrode plate and the parallelism of the end surface of the spacer are both less than \(5\times 10^{-5}\) rad, and the thickness error of the spacer is less than 1 \(\upmu\)m. A gold layer is first coated on the entire electrode surface of the electrode plate via the Physical vapor deposition (PVD) method, and then electrodes are fabricated by laser etching. Finally, the electrode plates are bolted, assembled, aligned, and grouped with the help of a high-precision coordinate measuring machine (CMM). Each electrode plate assembly has four stop screws to prevent the TM from directly contacting the electrode surface and has three vents to avoid sealing the gas in the assembly hole during the assembly process. The finished electrode plates and assembly components are shown in Figure 7. Corresponding electrode pairs configuration is marked by different color rectangles (X1: green, X2: blue, X3: yellow).  
This sensitive structure also has the ability to simultaneously measure and control three degrees of freedom, which facilitates the electrostatic drive and simplifies the complexity and design difficulty of the FEE. The basic design parameters are listed in Table 1.  
Figure 6: Components of sensitive structure.  
Figure 7: Components of an electrode housing.
# Lean and Flexible Project Delivery
## 4 Discussion--The Utility of the Proposed Operational System
### The Evaluation Process and Project Validation under Limited Knowledge
#### 4.1.2 Evaluation of the Different Planning Approaches  
As shown in Table 2, the best possible result (with duration of 5 to a cost of 9) is achieved under (assumed) full information, with the corresponding plan in Figure 3a. This plan leads to substantial rework when the design is different from the assumed one (see Figure 3b), with over 100% increase in time and costs (duration of 11 to a cost of 23.5). With resources available for an extra cost, the overall duration can be reduced from 11 to 10 (still 100% increase as compared to the best possible) but to an even higher cost of 25.  
Further, in Case 3, postponement and reaction to real-time information (at Period 4) combined with buffering is applied, in the spirit of lean. The time before Period 4 is filled in with activity K that carries no uncertainty. The corresponding plan (Figure 3c) provides a completion time of 8 to a cost of 9, with a schedule buffer of 3 (sized to design B) as compared to the best possible plan under Case 1. This raises the question of how to size buffers. When the variation in activity duration is large, buffering may be counterproductive, and may not even perform its goal. It may be too low, or too large, increasing costs and duration, without creating the ability to react to external factors. This is an important aspect of buffering.  
In Case 4, we attempt to do better by pursuing options for flexibility, i.e., postponement and flexible two-stage designs. The corresponding plan (Figure 3d) gives a duration of 6 to a cost of 11; about 20% increase as compared to the best possible performance under full information (Case 1), but far better than that of the reactive plan (Case 2), and also better than the lean approach (Case 3).  
What is more important is that the structure of the proactive and reactive plans differ substantially (the activities to be performed and their sequencing), in that the proactive one \"prepares\" to adapt real-time information. This is achieved by staging decisions based on information arrival, an essential aspect of planning with options. First-stage decisions are taken in light of uncertainty (standardized to fit both designs before Period 4), and second-stage decisions customized to real-time preferences.
## 5 Experiment
### Experiment Environment  
The proposed system underwent testing by dividing it into two stages: the imaging stage and the data generation stage. In the imaging stage, the detection performance was measured for each image size and correction method. Additionally, in the data generation stage, data was generated, and the detection performance of the generated data was measured.  
To ensure a robust and efficient experimental environment, the experiments were conducted using Google Colaboratory's GPU environment, which helped overcome the limitations of local environments and enabled consistent research in the same environment. The hardware environment and experimental setup used for learning and implementing the model are summarized in Table 2.
## 5 Discussion  
In this article, it is assumed that the IoT node devices have less resources such as battery power, circuit complexity, memory size and most importantly lower cost for customer. There has always been a trade-off between product security and all other resources, such as hardware complexity, cost of product, etc. The linear relationship is always considered between product security and its cost. Moreover, as we have seen in this article, multiple solutions provided by researchers add security against JTAG attacks by adding extra hardware circuitry in it. Industry believes changes in software is always less expensive than that of hardware changes and the JTAG works on most core layer of the system. Thus, there should be a very fine balance when providing highest security with limited resources.  
Besides, nowadays, various IoT wireless technologies are coming to the market such as LoRaWAN, NBIoT, LTE, etc. that provides reliable end-to-end communication network link from endpoint nodes to the centralized server. In this case, most IoT endpoint nodes contain small-sized OS. As discussed in Section 3.3, we cannot escape from the fact that these operating systems are prone to malware botnets such as Mirai. These kinds of botnets usually reside in DRAM memory of the device, which is volatile. Thus, updating the firmware/OS eliminates such malware from the system. Thus, JTAG access port must be available on PCB board in worst case to debug and update software.  
Let us evaluate the JTAG solutions that we discussed one by one. First, it seems unrealistic to add extra JTAG security circuitry into the resource-constrained IoT nodes. This extra circuitry could be added to products that are not battery operated, such as game consoles, PLC machines, set top boxes, etc. Second, a credential-based solution looked promising couple of years back but exponential growth rate in the number of IoT devices will make it difficult. Usually, JTAG debugging is used by technicians in the field either to debug circuit or upgrade firmware. Thus, in practical case, technicians use common password on the field and employees sometimes move to other companies (Job change). This makes products vulnerable over time, since it would be expensive to configure each device with new password. Third, If we take secured JTAG key methods, then the database of unique keys has to be well protected and maintained. Moreover, extra protected access has to be granted to technicians, which increases system dependency. However, note that, currently, SSL or TLS secure transport is affordable and cheap in server client protocol. The main advantage of this architecture is offloading processing on server side. Thus, global unique ID for each node can strengthen overall security. Fourth, recently machine learning techniques are coming forth to provide prevention against physical threats. The solutions that are provided with learning methods are mostly based on sample lab datasets. Hence, realistic datasets with all corner cases can only improve the accuracy of threat detection.
## 3 Results and Discussion
### The Effect of Different Treatments on Cd and Pb in Soil
#### 3.1.2 The Effect of Different Treatments on Pb in Soil  
At the end of incubation, the influence of different treatments on the speciation of Pb in soil is shown in Figure 2. After the four treatments (BC, BC + FeCl\({}_{3}\), BC + FeSO\({}_{4}\) and BC + Fe), the percentages of the Pb (F\({}_{1}\)) of each treatment decreased by 41.87%, 30.68%, 38.79%, and 51.84% respectively, the percentages of Pb (F\({}_{2}\)) of each treatment increased by 24.13%, 7.48%, 12.17%, and 1.88% respectively, the percentage of Pb (F\({}_{3}\)) of each treatment increased by 0.17%, 0.93%, 0.70%, and 8.00% respectively, the percentages of Pb (F\({}_{4}\)) of each treatment increased by 13.46%, 31.77%, 31.53%, and 31.09% respectively, and the percentages of Pb (F\({}_{5}\)) of each treatment increased by 23.95%, 17.51%, 19.67%, and 29.77% respectively, compared with the percentages of these chemicals in the CK (F\({}_{1}\): 6.13%, F\({}_{2}\): 49.40%, F\({}_{3}\): 20.53%, F\({}_{4}\): 7.76%, and F\({}_{5}\): 16.18%).  
As shown in Figure 2, the percentages of Pb (F\({}_{1}\)) in different treatments significantly decrease while the percentages of other fractions to some extent increase at the end of incubation, which indicates the Pb in soil is transformed from the exchangeable fraction to more stable fractions. Different treatments have certain passivation effects on Pb in soil, and the treatment of (BC + Fe) has the best  
Figure 1: The effect of different treatments on chemical form of Cd in soil.  
Figure 2: The effect of different treatments on chemical form of Pb in soil.  
effect among all treatments. The percentages of Pb (F\({}_{4}\) and F\({}_{5}\)) increase by 30.2% and the Pb (F\({}_{1}\) and F\({}_{2}\)) decrease by 8.43% compared with those percentages of these chemicals in the control sample. The stabilization effect is (BC + Fe) > (BC + FeSO\({}_{4}\)) > (BC + FeCl\({}_{3}\)) > (BC), which indicates that adding iron-based materials can increase the stabilization effect of Pb in soil. Research show that after FeSO\({}_{4}\) is oxidized to Fe\({}_{2}\)(SO\({}_{4}\))\({}_{3}\) in soil, FeSO\({}_{4}\) and Fe\({}_{2}\)(SO\({}_{4}\))\({}_{3}\) would generate Fe\({}_{2}\)O\({}_{3}\) precipitation [45], and iron oxide has a strong metal ion adsorption and enrichment ability [46].
## 6 Conclusions  
This paper proposes the utilization of a two-axis pointing platform to achieve IMC for long-exposure space cameras. By employing homogeneous coordinate transformation, the image motion model was derived, and the required compensating pointing angles for the two-axis pointing platform were calculated. Factors that affect the precision of IMC were also analyzed. The effectiveness of IMC was verified through simulation results. In-orbit experimental results further validated the effectiveness of the proposed method, which effectively enhances the detection capability of faint stellar objects. This provides a feasible design for future in-orbit space camera studies with a long integration time.  
In the future, we will enhance the imaging quality from two aspects based on IMC technology. On the one hand, will achieve this by employing more advanced sensor fusion techniques to improve the measurement accuracy of satellites, thereby enhancing the computational precision of IMC. On the other hand, we are also committed to developing advanced control algorithms and designing more sophisticated control algorithms, such as adaptive control methods and neural network approaches, to make the compensation more accurate. These strategic enhancements will be pivotal in achieving superior imaging performance, and they will also be integrated into our future work to further bolster the efficacy of our imaging systems.  
Conceptualization, X.C. and C.H.; methodology, X.C. and C.H.; software, X.Q.; validation, Z.M., W.L. and H.N.; formal analysis, X.C.; investigation, X.C.; resources, X.C.; data curation, X.C. and C.H.; writing--original draft preparation, X.C.; writing--review and editing, X.C. and C.H.; visualization, X.C.; supervision, X.C.; project administration, C.Y.; funding acquisition, C.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant number 12103053).  
Figure 9: Images of the star targets captured by cameras in orbit. **(a1,b1)**: Original image captured by camera; **(a2,b2)**: local area of the target star; **(a3,b3)**: gray-scale three-dimensional images of the corresponding image data.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the corresponding authors on request.  
**Acknowledgments:** We wish to give our thanks to all the Project team members.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Materials and Methods
### Machine Learning
#### 3.4.2 Long Short-Term Memory (LSTM)  
The LSTM algorithm is widely recognized for its effectiveness in time series prediction, owing to its capability to capture long-term dependencies critical for time series analysis [50]. LSTM networks employ forget, input, and output gates to manage and control the flow of information throughout the network. The forget gate establishes what past information should be discarded, the input gate updates the cell state with new relevant information, and the output gate controls what information is sent as the final output. The fundamental aspect of LSTM is the cell state, a critical component that acts as a memory pipeline, preserving important information across time steps. These gates rely on activation functions like sigmoid, which figures out how much information should pass through, and tanh, which scales the input. The network is given an input (\(x_{t}\)) and the output produced by the prior hidden layer (\(h_{t-1}\)) at time instant t. Equations (10)-(15) show how to calculate an LSTM cell.  
\[\text{Forget gate }\;\;f_{t}=\sigma\Big{[}U_{f}h_{t-1}+V_{f}x_{t}+b_{f} \Big{]}\] (10) \[\text{Input gate }\;\;i_{t}=\sigma[U_{i}h_{t-1}+V_{i}x_{t}+b_{i}]\] (11) \[\text{Input activation }\;\;g_{t}=\tanh\big{[}U_{g}  
training performance, especially for intricate models. To tackle overfitting, dropout was employed as a regularization strategy. Overfitting happens when models capture noise and unnecessary details from training data, hindering their ability to perform well on unseen data. By introducing dropout, the model achieved better regularization and improved generalization.