esa-sceva/satcom-chunk-collection huggingface dataset:
# Balloon: An Elastic Data Management Strategy for Interlaced Magnetic Recording
## 4 Balloon Design
### Adaptive Write Interference Data Placement
#### 4.2.2 Lightweight Track Hotness Scoring  
The existing methods for evaluating track hotness capture track hotness by setting a cumulative update counter for each track [10; 11; 15]. For terabyte-capacity IMR disks, this method of identifying hot and cold data imposes significant computational and storage overhead. To trade off identification accuracy and memory overhead, Balloon uses multiple Bloom filters instead of a cumulative update counter to evaluate track hotness. However, traditional multiple Bloom filters are designed to evaluate the hotness of data blocks in limited-capacity flash memory, and this fine-grained decision-making approach is not suitable for large-capacity IMR disks, where the track allocator is expected to know hotness information at the track level rather than the block level. For this reason, the coarse-grained track-level decision replaces the fine-grained block-level decision.  
Figure 6 shows the proposed track-level multiple Bloom filter (TMBF), which consists of M hash functions and N-independent Bloom filters (BFs), where each BF contains P bits to store M hash values. When an update request arrives, the target track number is entered into the TMBF, and then the hash value generated by each hash function is inserted into the corresponding bit in the specified BF. If the bit of the current BF is \"positive\" (i.e., the corresponding bit of the track in the BF is 1), the TMBF selects the next BF in a round-robin fashion. As shown in Figure 6, for track 2, the corresponding bit in \(BF_{1}\) is positive (indicated by the green rectangle) and \(BF_{2}\) is not yet set, so \(BF_{2}\) is selected to set the bits. In practice, the number of consecutive positive results in the TMBF indicates the frequency of track updates. Since the number of BFs is limited, a freezing method [19; 20] is used to prevent bit overflow if the corresponding bits in all BFs are positive. In addition, the TMBF periodically zeroes all bits of the oldest BF in a polled fashion to capture the recency of track hotness. To further improve the classification accuracy, the hotness of tracks is classified as frozen, warm, and hot. Specifically, in the TMBF, a frozen track has no \"positive\" results; a warm track has several consecutive \"positive\" results between (0, N); and a hot track has at least N positive results. The reason for this is that our scheme approximates the hotness of tracks by checking whether the track has been updated rather than the exact number of updates. Therefore, for a frozen track that has never been updated, we can assume that it is unlikely to be updated.
## 2 Materials and Methods
### Chemical Treatment  
Citric acid and tartaric acid, acidic aqueous solutions, previously used by [PERSON] et al. [29] to reduce VOC emissions from wood-based construction materials, and alkalic sodium bicarbonate [30] aqueous solution were used to modify the hemp shive in order to further reduce its VOC emissions. Low chemical concentrations applied at room temperature were chosen in order not to compromise mechanical properties of the shive. Citric acid, tartaric acid and sodium bicarbonate were dissolved to get 6% (Table 1) in 0.5 L glass beakers, dry hemp shives were added and pressed under the liquid surface using another, smaller beaker. After 24 h of treatment, treated hemp shives were dried in a circulating air oven at 40 \({}^{\circ}\)C overnight to a constant water content (10 wt.%) and placed into open headspace vials (each containing a 0.5 g of a sample)--Figure 1. Spruce strands, as well as part of hemp shives, were dried and placed into vials without any chemical pre-treatment--in this study spruce strands served only as a reference. The samples placed in open vials were stored in closed glass desicators (volume of 24 L) connected to a supply of dry and purified air with a constant flow of 0.5 L-min\({}^{-1}\) at 23 \({}^{\circ}\)C. This approach allowed a controlled air exchange while observing material degradation and volatile emissions [29].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**SAMPLE/Designation**} & \multicolumn{5}{c}{**Spruce Hemp**} \\ \cline{2-5}  & **S** & **H** & **H\({}_{\textbf{A}}\)** & **H\({}_{\textbf{B}}\)** & **H\({}_{\textbf{C}}\)** \\ \hline Treatment & - & - & _Tartaric acid_ & _Citric acid_ & _Sodium bicarbonate_ \\ Solution concentration (wt.\%) & - & - & 6 & 6 & 6 \\ Soaking time (h) & - & - & 24 & 24 & 24 \\ \hline \hline \end{tabular} _Note:_ all samples were dried up to 10% moisture content prior inserted into vials.  
\end{table}
Table 1: Spruce strands and hemp shive modifications (S—spruce strands, H—untreated hemp shive, H\({}_{\textbf{A}}\)—hemp shive after tartaric acid treatment, H\({}_{\textbf{B}}\)—hemp shive after citric acid treatment, H\({}_{\textbf{C}}\)—hemp shive after sodium bicarbonate treatment).
## 1 Introduction
### Detection of Aqueous Cr(VI) by MA-Capped Au NPs  
Briefly, 10 \(\mu\)L samples of Cr(VI) or other metal ion solutions including Ba\({}^{2+}\), Ca\({}^{2+}\), Cd\({}^{2+}\), Co\({}^{2+}\), Cu\({}^{2+}\), Cr(III), Mg\({}^{2+}\), Mn\({}^{2+}\), Ni\({}^{2+}\), Pb\({}^{2+}\), Zn\({}^{2+}\), and Al\({}^{3+}\) stock solution prepared in water were added into 1 mL of MA-capped Au NPs and mixed thoroughly for 2 min by a pipette. A UV-Vis spectrophotometer was then used to collect the absorption spectrum of the solution. A smartphone equipped with color scan application software (ColorAssist Ver. 2.4, 2020 from Apple Store) was used to take the MA-capped Au NPs solution photo at room temperature. Typically, for photograph acquiring, the vial should be placed on a white paper together with white background, and glare should be avoided by changing the photographing direction. The camera should be parallel to the vial, and the distance can be adjusted according to the square pattern displayed by the app software; one need not adjust amount. The square pattern should just cover the whole transverse part of the vial. For real sample detection by the smartphone-based colorimetric method, aqueous samples collected from three local lakes (South Lake, Tangxun Lake, and Yezi Lake) were first filtered by a 0.22 \(\mu\)m Super filter. Atomic Absorption Spectroscopy method was then used to measure the absolute concentration of Cr(VI) in the three samples. As for smartphone sensing, 10 \(\mu\)L of the sample was added to 1 mL of MA-capped Au NPs, and the mixture was allowed to react for 2 min before photographing. Each measurement was done three times, and the average was presented with its standard deviation.
## 5 Algorithm
### Intra-Cluster Block Allocation
```
0: The set of all blocks \(B\), an RSU cluster \(C_{l}\), the set of vehicles within the coverage of each RSU \(V^{i}\)
0: Block allocation strategy \(X\) in the RSU cluster \(C_{l}\)
1:for\(r_{i}\) in \(C_{l}\)do
2: Compute the service priority of each vehicle in \(r_{i}\)'s coverage based on its dwell time: \(PR(t_{m}^{d})=\sigma(t_{m}^{d}-\tau_{0})\cdot\left(1+\mu e^{-\omega(t_{m}^{d}- \tau_{0})}\right)\)
3: Compute the block access probability \(P_{mk}\) based on the correlation between vehicles and blocks and the baseline query probability, then obtain the block demand of \(r_{i}\): \(\Omega_{ik}=\sum\limits_{\tau_{m}\in V^{i}}P_{mk}\cdot PR(t_{m}^{d})\)
4:endfor
5: Initialize \(X=\{\phi\}\); that is, the initial allocation state is empty. 6: Sort blocks in descending order based on \(\Omega_{ik}\) to obtain \(B_{sort}\)
7:for\(b_{k}\) in \(B_{sort}\)do
8: Select the RSU node \(r_{i}\) with the highest demand function value \(Q(r_{i},b_{k})\) for block \(b_{k}\), where the block size is given by \(s_{k}\)
9:if the node \(r_{i}\)'s remaining storage capacity \(rc_{i}>s_{k}\)then
10: Set \(x_{ik}=1\), allocate block \(b_{k}\) to RSU node \(r_{i}\)
11: Update remaining storage: \(rc_{i}\gets rc_{i}-s_{k}\)
12: Update the demand function \(Q^{*}\) for \(r_{i}\) and its neighboring RSUs \(C_{i}^{neighbor}\)
13:else
14: Select \(r_{j}\) in descending order based on \(Q(r_{j},b_{k})\) and repeat the process until \(rc_{j}>s_{k}\)
15:endif
16:endfor
17:while any RSU node in cluster \(C_{l}\) has remaining available storage capacity \(rc>0\)do
18: Select the RSU node \(r_{i}\) and block \(b_{k}\) with the highest demand function value \(Q(r_{i},b_{k})\)
19:if the node \(r_{i}\)'s remaining storage capacity \(rc_{i}>s_{k}\)then
20: Set \(x_{ik}=1\), allocate block \(b_{k}\) to RSU node \(r_{i}\)
21: Update remaining storage: \(rc_{i}\gets rc_{i}-s_{k}\)
22: Update the demand function \(Q^{*}\) for \(r_{i}\) and its neighboring RSUs \(C_{i}^{neighbor}\)
23:endif
24:endwhile
```  
**Algorithm 2** Vehicle Service Priority-based Greedy Block Allocation Algorithm (VSPGBAA)  
For the complexity analysis, first, VSPGBAA defines the block demand for each RSU based on the association between the vehicles it serves and the blocks and then sorts the blocks by demand. The complexity of this step is \(O(N_{l}M_{i}K+K\log K)\), where \(M_{i}\) is the number of vehicles served by RSU \(r_{i}\), and \(K\) is the number of blocks to be allocated. Then, VSPGBAA performs the initial allocation of each block and updates the block demand function of RSU nodes, with a complexity of \(O(KN_{l})\). Finally, VSPGBAA utilizes the remaining available storage space in the cluster to perform redundant block replication for further access cost optimization, with a complexity of \(O(K\bar{N}_{l})\), where \(\bar{N}_{l}(\bar{N}_{l}\ll N_{l})\) represents the average number of neighboring RSUs for each RSU. Thus, the overall complexity of VSPGBAA is \(O(N_{l}M_{i}K+K\log K)\).
# Evaluating LLMs for Automated Scoring in Formative Assessments
## 3 Results
### Comparative Analysis of LLM Performance  
The grades assigned by both the open-source and premium LLMs were analyzed. Tests for normality, including the Kolmogorov-Smirnov and Shapiro-Wilk tests, yielded \(p\)-values less than 0.001 for both models, indicating significant deviations from normality. Consequently, non-parametric methods were employed for the subsequent analysis.  
Descriptive statistics showed that the median grade for the open-source LLM was 62.50, with an IQR of 100.00. The premium LLM exhibited a slightly higher median of 68.18, with an IQR of 82.16. Both models displayed wide interquartile ranges, reflecting a broad spread of grades. Histograms revealed bimodal distributions for both models, with notable peaks at the extremes of 0 and 100.  
A Mann-Whitney U test was conducted to evaluate the differences in scoring patterns between the two models. The results showed no statistically significant difference between the grades assigned by the open-source and premium LLMs (U = 22,764.5, \(p\) = 0.257). The effect size, calculated as r \(\approx\) 0.0557, was very small. The mean rank values were 201.03 for the open-source LLM and 213.97 for the premium LLM.  
A Spearman correlation was conducted to assess the relationship between the grades assigned by the two models. The results showed a very strong positive correlation (\(p\) = 0.953, N = 207, \(p\) < 0.001).  
Equivalence testing evaluated the practical significance of differences between the models. For the \(-5\) equivalence threshold, the mean difference was \(-5.03\) (standard deviation = 14.40), with a t-statistic of \(-0.027\) and a two-sided \(p\)-value of 0.978. The 95% confidence interval ranged from \(-2.00\) to 1.95. [PERSON]'s d was \(-0.002\). For the 5 equivalence threshold, the mean difference was \(-10.03\) (standard deviation = 14.40), with a t-statistic of \(-10.02\) and a two-sided \(p\)-value of \(<\)0.001. The 95% confidence interval ranged from \(-12.00\) to \(-8.05\), and [PERSON]'s d was \(-0.696\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Question Type** & **Evaluator** & **SD** & **Var** \\ \hline \multirow{3}{*}{Short-Answer} & Teacher & 4.438 & 74.843 \\  & Open Source LLM & 1.197 & 12.670 \\  & Premium LLM & 0.873 & 6.540 \\ \hline \multirow{3}{*}{Code} & Teacher & 6.350 & 83.566 \\  & Open Source LLM & 1.020 & 4.207 \\ \cline{1-1}  & Premium LLM & 0.710 & 3.874 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Variability in scoring consistency by question type and evaluator.
## 3 Results  
To evaluate the performance of the algorithm, six groups of CBCT datasets were randomly collected and manually segmented by two experts. The CBCT datasets were acquired using the MEYER SS_X9010 DPro-3 DE CBCT system (HEFEI MEYER OPTOELECTRONIC TECHNOLOGY INC., China), whose voxel size is 0.2658 \(\times\) 0.2658 \(\times\) 0.2515 mm [3] and FOV is 127 mm \(\times\) 127 mm \(\times\) 80 mm. The proposed method was implemented using MATLAB codes on a Lenovo desktop (Win 10, Intel Core i7-9700K 3.60 GHz CPU, 16 GB RAM). The precision, recall, dice coefficient, Hausdorff distance (HD), and the root mean square symmetric surface distance (RMSD) were used to verify the accuracy of the proposed algorithm.  
Table 1 presents the evaluation metrics of the mandible and maxilla that were segmented by the proposed algorithm. These mean and standard deviation values illustrate that the segmented results have a high level of accuracy and the accuracy is stable. As mentioned in the Introduction section, the structure of the maxilla is more complex than that of the mandible, and the accuracy of the maxilla is lower than the mandible.  
Figures 10 and 11 show the segmentation results of the proposed algorithm, the corresponding ground truth, and the superimposing volume of the mandible and maxilla, respectively. The volumes are visualized by the 3D plugin of ImageJ software. In the superimposing volumes, the blue voxels are the background that were wrongly classified into the jaw (false positives), and red voxels are the jaw wrongly that were classified into the background (false negatives). When the mouth is closed, the algorithm can also segment the mandible and maxilla as shown in Figure 12.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Mandible** & **Maxilla** \\ \hline
**Precision** & 0.9709 \(\pm\) 0.0093 & 0.9249 \(\pm\) 0.0124 \\
**Recall** & 0.9710 \(\pm\) 0.0081 & 0.9599 \(\pm\) 0.0125 \\
**Dice Coefficient** & 0.9709 \(\pm\) 0.0026 & 0.9420 \(\pm\) 0.0065 \\
**HD/mm** & 5.2174 \(\pm\) 1.3466 & 7.3647 \(\pm\) 4.3813 \\
**RMSD/mm** & 0.3887 \(\pm\) 0.1011 & 0.6489 \(\pm\) 0.1275 \\ \hline \end{tabular}
\end{table}
Table 1: The evaluation metrics of the mandible and maxilla that were segmented by the proposed algorithm (values indicate the mean \(\pm\) standard deviation).  
Figure 9: An axial image of maxilla from Step III.
## 4 Evaluation
### Quantifying Error for Bounding Box and Segmentation Mask Depth Predictions  
Although the mean squared error (MSE) [17; 30] is commonly used in research, the root mean squared error (RMSE) [7; 31; 32] is chosen in this study due to its interpretability in the same unit as the original data, which in this case is distance (meters). RMSE provides a more sensitive measure of prediction accuracy, making it particularly useful for evaluating the performance of depth estimation. The process begins by extracting the object distances separately using the bounding box and segmentation mask data. Afterward, the truth table is used to compute the RMSE for both the bounding box and segmentation mask. The truth table provides the actual ground truth distances for comparison, while the distances obtained from the predicted bounding box and segmentation mask are compared separately. The RMSE for the bounding box and segmentation mask is then calculated using the following formula:  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(y_{i}-\hat{y}_{i})^{2}} \tag{19}\]  
where \(y_{i}\) represents the actual distance from the truth table for the \(i\)-th data point, \(\hat{y}_{i}\) represents the predicted distance either from the bounding box or the segmentation mask, and \(N\) is the total number of data points. By calculating RMSE separately for the bounding box and segmentation mask, we can quantify the error in the predicted depth values for each method. Lower RMSE values indicate better alignment between the predicted and actual distances, reflecting improved performance in the model's depth extraction process.
###### Abstract  
A flight-proven electrical bus system for the 1U CubeSat platform was designed in the BIRDS satellite program at the Kyushu Institute of Technology. The bus utilizes a backplane board as the mechanical and electrical interface between the subsystems and the payloads. The electrical routes on the backplane are configured by software using a complex programmable logic device (CPLD). It allows for reusability in multiple CubeSat projects while lowering costs and development time; as a result, resources can be directed toward developing the mission payloads. Lastly, it provides more time for integration and system-level verification, which are critical for a reliable and successful mission. The current trend of CubeSat launches is focused on 3U and 6U platforms due to their capability to accommodate multiple and complex payloads. Hence, a demonstration of the electrical bus system to adapt to larger platforms is necessary. This study demonstrates the configurable electrical interface board's scalability in two cases: the capability to accommodate (1) multiple missions and (2) complex payload requirements. In the first case, a 3U-size configurable backplane prototype was designed to handle 13 mission payloads. Four CPLDs were used to manage the limited number of digital interfaces between the existing bus system and the mission payloads. The measured transmission delay was up to 20 ns, which is acceptable for simple serial communications such as UART and SPI. Furthermore, the measured energy consumption of the backplane per ISS orbit was only 28 mWh. Lastly, the designed backplane was proven to be highly reliable as no bit errors were detected throughout the functionality tests. In the second case, a configurable backplane was implemented in a 6U CubeSat with complex payload requirements compared to the 1U CubeSat platform. The CubeSat was deployed in ISS orbit, and the initial on-orbit results indicated that the designed backplane supported missions without issues.  
CubeSat; electrical interface; scalability; bus system development Article  
Scalable and Configurable Electrical Interface Board for Bus System Development of Different CubeSat Platforms  
[PERSON], [PERSON], [PERSON], N.C.; [PERSON] [PERSON]; [PERSON], [PERSON] Solable and Configurable Electrical Interface Board for Bus System Development of Different CubeSat Platforms  
1 Laboratory of Lean Satellite Enterprises and In-Orbit Experiments (LaSEINE), Department of Electrical and Space Systems Engineering, Kyushu Institute of Technology, Kitakyushu 804-8550, Japan 1  
[PERSON], [PERSON], [PERSON], N.C.; [PERSON] [PERSON]; [PERSON], [PERSON] Solable and Configurable Electrical Interface Board for Bus System Development of Different CubeSat Platforms
# Tracking Extended Targets: Novel TPMB Filter Driven by Model and Data Collaboration
## 3 Model-Driven IMM-TPMB Filter  
The TPMBM obtains a computationally less intensive TPMB posterior density through KLD approximation. However, during the approximation process, the uncertainty in the marginal density of the PMB trajectory set increases, and the uncertainty of the global hypotheses has been marginalized. For methods based on gating and sampling, negligible hypotheses can be ignored by truncating weights to avoid exhaustive data association. Nevertheless, in scenarios with high data association uncertainty, performance can degrade due to information loss. Therefore, to prevent the loss of information from local association hypotheses caused by explicit enumeration, a data association method that computes the multi-target marginal posterior is used to marginalize the unknown data association uncertainty. The model established in this paper is based on the factor graph probability model in [17; 18], which enables SPA to have good computational complexity and utilizes the accuracy and flexibility of an SCKF-based implementation.  
We define the measurement-vector association factor for multi-target trajectories. Due to the uncertainty in the posterior marginal association probability for each measurement \(j\in\{1,\ldots,m_{k}\}\), the indicator factor for the Bernoulli component generated by each measurement is defined as follows:  
\[\beta_{k}=\begin{cases}\beta_{k}^{j}&\beta_{k}^{j}\in\left\{1,\ldots,n_{k|k-1} \right\}\\ 0&\text{otherwise}\end{cases} \tag{23}\]  
where \(\beta_{k}^{j}=i>0\) only when the measurement \(z_{k}^{j}\) is associated with the \(i^{\text{th}}\) potential target; when \(\beta_{k}^{j}=0\), it indicates that the \(j^{\text{th}}\) measurement is a false alarm.  
The joint posterior probability of the trajectory set and the measurement vector association factor can be factored into a factor graph. According to [10], the joint posterior of the trajectory set at time step \(k\) and the measurement vector association model during the TPMB model update process is  
\[\vec{f}_{jk}\big{(}\tilde{\mathbf{x}}_{k},\beta_{k}\big{)}\propto\underbrace{ \vec{f}^{p}_{jk}(\mathbf{x}_{k})}_{1}\underbrace{\prod_{i=1}^{n_{k|k-1}} \Big{[}\vec
# Analysis of Inductive Displacement Sensors with Large Range and Nanoscale Resolution
## 4 Q Value and Excitation Frequency Analysis
### Core Loss and Its Influence on \(Q\) Value of Inductive Sensors  
Core loss mainly includes hysteresis loss and eddy current loss [11], which can be embodied as the equivalent hysteresis loss resistance \(R_{\mathrm{h}}\) and the equivalent eddy current loss \(R_{\mathrm{e}}\) resistance parallel to the coil inductance \(L^{*}\), respectively [12; 13]. The equivalent circuit of an inductive coil is shown in Figure 13a, where \(R_{\mathrm{c}}\) is the DC resistance of sensor coil and \(C\) is the parasitic capacitance. Through the impedance test, it is found that \(C\) is very small and can be ignored, as Figure 13b shows. Further, it can be equivalent to inductance and resistance in series, as shown in Figure 13c. The relevant derivation equation of the coil equivalent circuit parameters is as follows  
The increase of excitation frequency will lead to the increase of core loss, so as to reduce \(R_{\mathrm{p}}\), and then increase \(R_{\mathrm{p}}\)' and decrease \(L^{*}\). Generally, when the excitation frequency below 100 kHz, \(R_{\mathrm{p}}\) is much greater than \(\omega L^{*}\), so the change of \(L^{*}\) is very small according to Equation (21), and that is \(L^{*}\)\(\approx\)\(L^{*}\), as shown in Figure 8c from FEM and Figure 22 from experimental results. Therefore, the main embodiment of the core loss is to increase the resistance \(r\), which will cause the \(Q\) value of the inductive sensor to decrease according to Equation (18).  
\[R_{\mathrm{p}}=\frac{R_{\mathrm{e}}R_{\mathrm{h}}}{R_{\mathrm{e}}+R_{ \mathrm{h}}} \tag{19}\]  
\[R_{\mathrm{p}}r=\frac{R_{\mathrm{p}}}{1+\left(R_{\mathrm{p}}/\omega L^{*} \right)^{2}} \tag{20}\]  
Figure 13: Equivalent circuit of the inductive coil: (**a**) Equivalent circuit considering the copper loss resistance \(R_{\mathrm{c}}\), the eddy current loss resistance \(R_{\mathrm{e}}\), the hysteresis loss resistance \(R_{\mathrm{h}}\); (**b**) Equivalent circuit ignoring the parasitic capacitance. (**c**) Equivalent series circuit.  
\[L=\frac{L^{*}}{1+\left(\omega L^{*}/R_{\text{p}}\right)^{2}} \tag{21}\] \[r=R_{\text{c}}+R_{\text{p}}r \tag{22}\]