esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
Furthermore, for the subCAN and estCAN groups, certain EMD components of each feature presented a significant ability to distinguish between the groups. The following paragraphs provide a more detailed description of these results. Regarding the \(\log(ASR_{area})\) of the four EMD components of each group, the posthoc analysis demonstrates that there was a significant difference between the noCAN and estCAN groups (\(p<0.01\)) for all components, a significant difference between the subCAN and estCAN groups (\(p<0.05\)) in the fourth component, and a significant difference between the noCAN and subCAN groups (\(p<0.05\)) in the first component (see Table 2 and Figure 6a). Considering the \(\log(SODP_{area})\), the posthoc analysis of the four EMD components of each group also revealed a significant difference between the noCAN and estCAN groups (\(p<0.01\)) for all components. Additionally, a significant difference between the noCAN and subCAN (\(p<0.05\)) groups was found for the \(\log(SODP_{area})\) of the third and fourth IMFs, and a significant difference between subCAN and estCAN (\(p<0.05\)) for all components, except for the third one (\(p<0.09\)) (see Table 2 and Figure 6b). The estCAN group shows a significantly higher \(SODP_{CTM}\) mean value (\(p<0.01\)) for all components compared to the noCAN group, as well as a significantly higher mean value (\(p<0.05\)) for the 3 rd and 4 th IMF components compared to the subCAN group. Similarly, the estCAN group exhibited a significantly higher \(SODP_{CTM}\) mean value (\(p<0.05\)) for the third and fourth components compared to the subCAN group (see Table 2 and Figure 6c). Figure 6: The comparison of parameters for cardiovascular autonomic neuropathy (CAN) groups for the four IMFs of the RR interval signals (mean \(\pm\) standard error of the mean): noCAN—individuals with type 2 diabetes (T2 DM) without CAN; subCAN—individuals with T2 DM with subclinical CAN; and estCAN—individuals with T2 DM with established CAN. (**a**) \(ASR_{area}\)—the area of analytic signal representation. (**b**) \(SODP_{area}\)—the area of the second-order difference plot (SODP). (**c**) \(SODP_{CTM}\)—the central tendency measure of SODP. (**d**) \(PSD_{pmax}\)—the power spectral density (PSD) band power. (**e**) \(PSD_{pmax}\)—the PSD peak amplitude. * \(p<0.05\)—significant group difference. Lastly, the posthoc analysis of PSD-derived features demonstrates significantly reduced log (\(PSD_{bpow}\)) (\(p<0.05\)) and log (\(PSD_{pkamp}\)) (\(p<0.05\)) mean values for the estCAN group compared to the noCAN and subCAN groups for all IMFs. Similarly, the subCAN group presented significantly reduced mean values for the log (\(PSD_{bpow}\)) (\(p<0.05\)) and log (\(PSD_{pkamp}\)) (\(p<0.05\)) parameters compared to the noCAN group for the 3 rd and 4 th IMF components (see Table 2, Figure 6d,e).
## 2 Methods and Performance Figures
### Performance Figures  
Some figures of merit were used for the evaluation of the system's thermal performance and the variation of the behaviour using different control logics.  
* Collectors' efficiency \"\(\eta_{coll}\)\": the ratio of the energy collected in the reference period (\(T=\{\)year, month\(\}\)) by the solar system over the energy that hits the collectors. \[\eta_{coll}=\frac{\int_{T}Q_{coll}\mathrm{d}t}{\int_{T}I_{G}A_{coll}\mathrm{d} t}[-]\] (8)
* Gross solar yield \"GSY\": represents the energy captured from the solar field per square meter of collectors. Like the efficiency, this parameter shows how efficiently the collectors do their work. \[GSY=\frac{\int_{T}\dot{Q_{coll}}\mathrm{d}t}{A_{coll}}\Big{[}\mathrm{kWh/m^{2}} \Big{]}\] (9)
* Seasonal performance factor of the primary circuit \"SPF\({}_{\mathrm{coll}}\)\": a seasonal index obtained by forming the ratio of the thermal energy collected by the system over the electrical energy consumed by the circulation pump of the solar circuit. \[SPF_{coll}=\frac{\int_{T}\dot{Q_{coll}}\mathrm{d}t}{\int_{T}W_{pump}\mathrm{d} t}[-]\] (10)
* Seasonal performance factor of system \"SPF\({}_{\mathrm{dhw}}\)\": the ratio of the thermal energy used to cover the DHW demand over the total electrical energy employed for fulfilling this demand. The total electrical demand is composed of the consumption of the auxiliary electrical heater and the electrical energy consumed by the circulation pump of the solar circuit (\(W_{dhw}=W_{pump}+W_{h backup}\)). \[SPF_{dhw}=\frac{\int_{T}\dot{Q_{dhw}}\mathrm{d}t}{\int_{T}W_{dhw}\mathrm{d}t}[-]\] (11)
* The global radiation on the collectors' plane \"\(R_{G}\)\": simply the integral of the global irradiance \(I_{G}\). \[R_{G}=\int_{T}I_{G}\mathrm{d}t\Big{[}\mathrm{kWh/m^{2}}\Big{]}\] (12)
* Lost heat for control purposes \(Q_{loss}\): a parameter quantifying the inconvenient situation where the difference between the inlet and outlet temperature of the internal heat exchanger is negative, which may happen during the initial phases of the system start-up or in the evenings. \[Q_{loss}=\frac{1}{2}\int_{T}\dot{\big{|}Q_{coll}\big{|}}-Q_{coll}\mathrm{d}t \big{[}\mathrm{kWh}\big{]}\] (13)
# Multi-Objective Real-Time Planning of Evacuation Routes for Underground Mine Fires
## 4 Mathematical Models and Algorithms for Emergency Escape Routes  
### Shortest Path Problem with Resource with Constraints  
The shortest path problem with resource with constraints (SPPRC) is a variant of the shortest path problem [33]. The problem intends to find a path from a network that goes from an origin to an end point and satisfies a set of constraints based on a set of resources, where resources refer to traveling time, cost, etc. For any path in the graph, from the start point \(i\) of one of the edges to the end point \(j\) of this edge, resource extension functions (REFs) should be satisfied for any kind of resource on the edge. Resource extension functions are generally defined as  
\[f_{ij(T_{i})}=T_{i}+t_{ij} \tag{3}\]  
where \(T_{i}\) is the resource vector on node. \(t_{ij}\) is the resource weight on directed edge \(<i,j>\). \(f_{ij(T_{i})}\) is the new resource vector starting from the resource vector \(T_{i}\) on point \(i\), extending to point through the directed edge \(<i,j>\).  
For any path \(P=(v_{0},v_{1},\ldots,v_{p})\) in the graph, define the resource vector \(T_{p}\in[a_{v_{p}},b_{v_{p}}]\) at the end point of \(p\) the path, where \(a_{v_{p}},b_{v_{p}}\) are the lower and upper limits of the resource at that point. Define \(\Gamma(P)\) as the set of all feasible resource vectors at the end point \(v_{p}\) of path \(P=(v_{0},v_{1},\ldots,v_{p})\). Then, the resource-constrained shortest path problem can be expressed as follows: in a path \(P\), if the resource vector at its end point \(p\) satisfies the upper and lower limits and for all points other than the end point \(i=0,\ldots,p-1\) on the path, there exists a resource vector that satisfies the resource upper and lower limits on each  
Figure 1: Emergency escape network weighting algorithm.  
point, that is \(T_{i}\in[a_{v_{i}},a_{v_{i+1}}]\); and so that for all \(i=0,\ldots,p-1\), \(f_{v_{i},v_{i+1}}(T_{i})\leq T_{i+1}\) holds true and the path \(P\) is resource feasible. The classic SPPRC can be expressed as  
\[\Gamma(P)=\Big{\{}T_{p}\in[a_{v_{p}},b_{v_{p}}]:\exists T_{i}\in[a_{v_{i}},b_{v _{i}}],f_{v_{i},v_{i+1}}(T_{i})\leq T_{i+1},i=0,\ldots,p-1\Big{\}} \tag{4}\]  
where \(i=0,\ldots,p-1\) is the node other than the end point and \(T_{i}\in[a_{v_{i}},b_{v_{i}}]\) is the resource vector on node \(i\) that meets the upper and lower limit constraints. \(f_{v_{i},v_{i+1}}(T_{i})\leq T_{i+1}\) is the constraint that the resource vector extending from node \(i\) to node \(j\) is less than or equal to the resource vector at node \(j\), ensuring that the resource consumption in the current extension direction of the path is minimal.
# MRI Diffusion Connectomics-Based Characterization of Progression in Alzheimer's Disease
## 2 Materials and Methods
### Statistics
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & \multicolumn{3}{c}{**Features after Elasticnet Regularization**} \\
**Response** & **\(\mathbf{\alpha}\)=0.5** & **\(\mathbf{\alpha}\)=0.75** & **\(\mathbf{\alpha}\)=0.8** \\ \hline ADAS-11 & 507 & 295 & 352 \\ ADAS-13 & 175 & 203 & 164 \\ CDR-SB & 353 & 155 & 40 \\ FAQ & 77 & 85 & 101 \\ FDG & 129 & 45 & 27 \\ MMSE & 12 & 6 & 9 \\ MOCA & 36 & 89 & 96 \\ RAVLT & 181 & 217 & 197 \\ Entorhinal Vol & 69 & 19 & 94 \\ Fusiform Vol & 360 & 267 & 392 \\ Hippocamp Vol & 521 & 319 & 128 \\ Mid Temp Vol & 154 & 202 & 168 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Feature reduction profile of DWI tractography data. We used repeated 10-fold cross-validation (10 iterations) to train our models for each of the neuropsychological and neurobiological indicators identified (Table 4) as being discriminative of diagnosis classification. Despite the rigorous model development process, our predictive models showed varying levels of predictive performance across different indicators (Table 6). For AV45, an imaging biomarker for amyloid plaque accumulation (mean: 1.2; IQR: 0.4), the model yielded error metrics (RMSE: 0.2; MAE: 0.15) that were lower than the sample mean and IQR. Hippocampal volume (mean: 7060.3; IQR: 1531.6) predictions resulted in error metrics of RMSE of 885.20 and MAE of 702.23, and the model for MoCA scores (mean: 24.1; IQR: 6.0) produced error metrics of RMSE of 4.09 and MAE of 3.12, suggesting a 13% error on average in assessing a patient's MoCA scores based on DWI MRI analysis alone. Most of our models had R\({}^{2}\) values below 0.4, indicating limited explanatory power. The model for FDG, a marker for glucose metabolism, captured the most variability in the data, with an R\({}^{2}\) of 0.66. While we found that there is lack of comprehensive analysis using DWI-only metrics to predict these indictors, there is alignment with several studies. A study by [PERSON] et. al. [56] found that no strong correlation was observed for any DWI measurements in any region with respect to MMSE. This was supported by [PERSON] et al. [57], who determined that white matter ADC was not predictive of poor cognitive outcomes. Correlational analysis is the most common approach to presenting the association between DWI and clinical scores. A recent study by [PERSON] et al. [58] consistently reported low correlations between DWI and many of the indicators we evaluated in this study. Q3. Does baseline WM tract volume change with cognitive decline? Our findings (Table 7) show significant decreases in both MD and FA across both the MCI and AD stages compared with CN, even after adjusting for age, with tract volume analyses revealing significant increases with the progression of cognitive decline towards the later stages of the disease (AD), after adjusting for age. There was not a significant difference in tract volume between the CN and MCI stages.
# Consistency Analysis and Accuracy Assessment of Eight Global Forest Datasets over Myanmar
## 2 Materials and Methods
### Forest Data Harmonization for Comparison and Analysis  
Data harmonization is a key step to ensure the objectivity of the comparison among different datasets. In this study, data harmonization included two steps, i.e., spatial resolution unification and legend harmonization. First, land cover datasets and DEM data were resampled to 300 m resolution by using the nearest neighbor method to match with the coarse-resolution product (CCI-LC). Then, legend harmonization was performed before comparison, because each dataset defined forest differently and according to different descriptors [35; 36]. The Hansen Global Forest Cover Dataset contains only forest land classes, and the classification systems of the other seven datasets on forests need to be combined. CCI-LC and GLC-FCS have more secondary classes and their classification system is the Land cover classification system (LCCS) scheme. In this paper, we carried out the unification of forest land classes of these two datasets according to the Intergovernmental Panel on Climate Change (IPCC) standards. FROM-GLC and GlobeLand30 only need to extract or merge (FROM-GLC2015) the forest land classes according to the data description. The specific forest land class conversion rules are shown in Table 2, and the spatial distribution data of Myanmar forests extracted from each dataset is shown in Figure 3. We chose to simplify the classification because detailed land cover type diversification may create more uncertainty [37].
# Margin CosReid Network for Pedestrian Re-Identification
## 3 Margin CosReid Network
### Margin Cosine Softmax Loss (MCSL)
#### 3.2.3 Proposed Margin Cosine Softmax Loss  
Although features between different classes can be well distinguished, those within the same class cannot be separated using CSL. Based on the work in [43], SphereFace [42] normalizes the weight vector, and the multiplicative margin compresses the sample features to a smaller space and at the same time reduces the monotone interval of the cosine function, which leads to the difficulty of optimization. To solve this problem, both ArcFace [33] and CosFace [44] introduce additive margins and only have margins on the items corresponding to the real class labels. To maximize the distance between classes, we also consider the need to introduce additive margins between different categories at the denominator and propose the margin cosine softmax loss (MCSL) defined as  
\[L_{mcsl}=-\frac{1}{N}\sum_{i}^{N}\log\frac{e^{s(\cos(\theta_{p_{i},i})-m)}}{ \sum_{j=1}^{C}e^{s(\cos(\theta_{j,i})-m)}}, \tag{4}\]  
subject to  
\[W=\frac{W^{*}}{\|W^{*}\|},x=\frac{x^{*}}{\|x^{*}\|},\cos(\theta_{j,i})=W_{j}^ {T}x_{i}, \tag{5}\]  
where \(N\) and \(C\) denote the numbers of training sample batches and the dataset category, respectively. \(x_{i}\) denotes the normalized feature vector of the \(i\)-th sample corresponding to label \(y_{i}\), and \(W_{j}\) denotes the weight vector of the category \(j\). \(\theta_{y_{i},i}\) and \(\theta_{j,i}\) denote the angle between \(x_{i}\) and the weight vectors \(W_{y_{i}}\) or \(W_{j}\), respectively. Compared to the exp-normalize trick [45], MCSL maps the pedestrian descriptor to the cosine space through a cosine function that has intrinsic consistency with softmax. The hyperparameter \(m\) is the boundary margin by introducing which MCSL can make classification stricter through controlling the cosine boundary size from \(\cos(\varphi_{1})>\cos(\varphi_{2})\) to \(\cos(\varphi_{1})-m>\cos(\varphi_{2})\). \(\varphi_{i}(i=1,2)\) denotes the angle between the feature vectors \(x_{i}\), and \(C_{i}(i=1,2)\) denotes the class label of \(x_{i}\). \(x_{i}\) is determined to belong to class \(C_{1}\) by \(\cos(\varphi_{1})-m>\cos(\varphi_{2})\) in MCSL for significant performance in distinguishing features within class. Moreover, the binary classified MCSL can also be expanded to other multiple classification problems.
# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 5 Evaluation Protocols and Metrics
### Key Metrics
#### 5.1.3 CIDEr  
CIDEr (Consensus-based Image Description Evaluation) is tailored for assessing image and video caption [10]. It gauges the level of agreement among multiple reference captions by calculating the cosine similarity between the TF-IDF vectors of the candidate caption and those of the references. This metric excels at capturing the relevance and salience of the generated captions, embodying the idea that a high-quality caption should resemble how most humans would describe the image.  
CIDEr evaluates captions by: matching \(n\)-grams (\(1-4\) words) for grammatical and semantic richness, applying TF-IDF weighting to downplay common \(n\)-grams, computing average cosine similarity for each \(n\)-gram length to measure overlap, and combining \(n\)-gram scores with uniform weights to obtain the final score. The metric's formula is as follows:  
1. TF-IDF Weighting \[g_{k}(s_{ij})=\frac{h_{k}(s_{ij})}{\sum_{\omega_{j}\in\Omega}h_{l}(s_{ij})} \cdot\log\left(\frac{|I|}{\sum_{p\in I}\min\Big{(}1,\frac{q}{h_{k}(s_{ip})} \Big{)}}\right),\] (8) where \(h_{k}(s_{ij})\) is the frequency of \(n\)-gram \(\omega_{j}\) in reference caption \(s_{ij}\), \(\Omega\) is the vocabulary of all \(n\)-grams, \(|I|\) is the total number of images, and \(q\) is a constant typically set to the average length of reference captions.
2. Cosine Similarity for \(n\)-grams of Length \(n\) \[\text{CIDEr}_{n}(c_{i},S_{i})=\frac{1}{m}\sum_{j=1}^{m}\frac{g_{n}(c_{i}) \cdot g_{n}(s_{ij})}{\|g_{n}(c_{i})\|\|g_{n}(s_{ij})\|},\] (9) where \(c_{i}\) is the candidate caption, \(S_{i}=\{s_{i1},\dots,s_{im}\}\) is the set of reference captions, and \(g_{n}(c_{i})\) and \(g_{n}(s_{ij})\) are the TF-IDF vectors for the \(n\)-grams of length \(n\) in the candidate and reference captions, respectively.
3. Combined CIDEr Score \[\text{CIDEr}(c_{i},S_{i})=\sum_{n=1}^{N}w_{n}\cdot\text{CIDEr}_{n}(c_{i},S_{ i}),\] (10)where \(N\) is the maximum \(n\)-gram length (typically 4), and \(w_{n}\) are the weights for different \(n\)-gram lengths (typically set to \(1/N\) for uniform weights).
## 3 Background
### General CubeSat Specifications
#### 3.1.1 Power Sub-System Specifications  
The CubeSat Design specification complied by California State Polytechnic university is sparse in detail with regards to the power sub-system. The document does however state that the proposed CubeSat must be able to conduct a \"dead launch\", stating that all electronic sub-systems  
Figure 4: 1U and 3U CubeSat developed by CalPoly [24].  
and components remain dormant during the deployment phase upon release from the P-POD [1]. All batteries must be either disconnected or fully discharged throughout the dead launch phase. The CubeSat must also incorporate a \"remove before flight\" pin to prevent accidental activation of any of the electrical systems during any conducted ground tests [1].  
CubeSat powered function include the variety of subsystems such as command and data handling (C&DH), RF communication, Altitude determination and control (ADC) and deployable mechanism actuation. CubeSat power systems include all battery assemblies, solar cells, and coin cell batteries. CubeSats will incorporate battery circuit protection for charging/discharging to avoid unbalanced cell conditions. The CubeSat will have one RF inhibit and RF power output of no greater than 1.5 W at the transmitting antenna's RF input [1].
## 5 Experimental Environment
### Experimental Results and Discussion  
In this section, we detail the experiments conducted on the deployed HLF network, which comprises three hospitals, two laboratories, and the MoH acting as the network administrator. We simulated a real-world healthcare scenario for sharing patient records in a healthcare federation with the participants in the network above. The main operational functionalities include querying and updating patient records for existing or registered patients and creating patient NFTs for newly registered patients. The network architecture includes multiple peers, orderers, and endorsers distributed across the participating entities. Table 4 shows the deployed network configuration in terms of resource utilization.  
The maximum CPU utilization recorded is 74.96%, which indicates the peak load on the CPU during the observed period, potentially reflecting high computational demand or processing requirements. The average CPU utilization is 31.19%, which signifies the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & **CPU\%** & **CPU\%** & **Memory** & **Memory** & **Traffic In** & **Traffic Out** & **Disc Write** \\ \cline{2-8}  & **(max)** & **(avg)** & **(max) [GB]** & **(avg) [GB]** & **[MB]** & **[MB]** & **[MB]** \\ \hline Total & 74.96 & 31.19 & 5.3433 & 4.8441 & 284.6892 & 273.69 & 765.9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Network configuration.  
typical workload on the CPU over the monitoring interval. A lower average compared to the maximum suggests fluctuations in the CPU demand. The maximum memory usage observed is 5.3433 GB (gigabytes), which reflects the peak memory consumption during the monitoring period, highlighting the maximum memory capacity required by the blockchain network. The average memory usage is 4.8441 GB, representing the typical memory utilization over time, providing insights into the network's memory requirements for regular operation. \"Traffic in\" denotes the inbound network traffic, measuring the volume of data that the network receives in megabytes (MB). The observed value of 284.6892 MB indicates the amount of data the network processed from external sources. \"Traffic out\" represents the outbound network traffic, indicating the volume of data transmitted by the network to external destinations. The value of 273.69 MB reflects the amount of data sent out by the network. Disc write captures the maximum disk writing activity observed in megabytes (MB). The recorded value of 765.9 MB indicates that the peak data rate was written to disk during the monitoring period.
## 4 The GSA in Probabilistic Evaluation of ALSC  
### Theory of GSA  
#### 4.1.1 The Idea of GSA  
The ANOVA is introduced in [33] to quantify the contribution of each independent variable \(X_{i}\) (\(i=1,2,\ldots,m\)) to the variance of the output. This method measures the influence of \(X_{i}\) on the output variable \(Y_{s}\) by the reduction in output variance when \(X_{i}\) is set to a fixed value. When \(X_{i}\) is assigned a specific value \(x_{i}\)*, the conditional variance of the output can be expressed as \(V(Y_{s}\,|\,X_{i}=x_{i}\)*). Since this conditional variance depends on \(x_{i}\)*, to account for the impact of \(X_{i}\) when it is fixed at all possible values, the expected value of the conditional variance \(E_{X_{i}}[V(Y_{s}|X_{i})]\) needs to be calculated. Combining this with the law of total variance from probability and statistics theory:  
\[V(Y_{s})=V_{X_{i}}[E(Y_{s}|X_{i})]+E_{X_{i}}[V(Y_{s}|X_{i})] \tag{17}\]  
The first-order sensitivity index (FSI) of the input variable \(X_{i}\) is defined as follows:  
\[S_{i}=\frac{V(Y_{s})-E_{X_{i}}[V(Y_{s}|X_{i})]}{V(Y_{s})}=\frac{V_{X_{i}}[E(Y _{s}|X_{i})]}{V(Y_{s})} \tag{18}\]  
In addition, the total sensitivity index (TSI) is defined as follows [34]:  
\[ST_{i}=\frac{E_{X_{-i}}[V(Y_{s}|\mathbf{X}_{-i})]}{V(Y_{s})}=1-\frac{V_{\mathbf{X}_{-i }}[E(Y_{s}|\mathbf{X}_{-i})]}{V(Y_{s})} \tag{19}\]  
where \(\mathbf{X}_{-i}\) represents the vector composed of the remaining \(m-1\) dimensional input random variables, excluding \(X_{i}\). The first-order sensitivity index \(S_{i}\) reflects the impact of a single variable on the output variance, but it is infeasible to explore the joint effects between different input variables. In contrast, the total sensitivity index \(ST_{i}\) reflects the combined influence of the input variable \(X_{i}\) as well as the interaction between \(X_{i}\) and all other input variables on the output variance. Therefore, the total sensitivity index is the main focus of this paper. These indices are called Sobol indices. More details of the Sobol indices can be found in [26].