esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Low-Temperature Flexibility Analysis  
The low-temperature flexibility of the self-adhesive membrane was tested in accordance with the requirements of GB 23441-2009, \"Self-adhering Polymer Modified Bituminous Waterproof Sheet\" [23]. Samples were prepared for both unaged and various-degrees-of-aged specimens to conduct the low-temperature flexibility limit test. The cracking temperature of the unaged specimen was \(-\)28.5 \({}^{\circ}\)C, which met the basic specification  
Figure 4: Scanning electron micrographs before and after aging: (**a**) no aging; (**b**) aging 5 days; (**c**) aging 14 days; (**d**) aging 28 days.  
Figure 5: Variation curve of mass loss rate with aging time.  
requirements [23]. The cracking temperatures of the specimens at each stage of aging in the low-temperature flexibility test are presented in Table 2.  
As shown in Table 2, the cracking temperature in the low-temperature flexibility test increased with aging time. The cracking temperature of the specimen subjected to 28 days of combined thermo-oxidative aging and freeze-thaw cycle aging was \(-15\)\({}^{\circ}\)C. Compared to specimens aged solely by thermo-oxidative aging [12], the self-adhesive membranes exposed to both thermo-oxidative aging and freeze-thaw cycle aging were more prone to cracking at low temperatures. This is because, during thermo-oxidative aging, the membrane undergoes an oxidation reaction, which accelerates phase separation, causing the modifier in the asphalt to separate from the asphalt. As freeze-thaw cycle aging continues, the deformation resistance of the membrane worsens, and its low-temperature ductility decreases, ultimately leading to an increase in the cracking temperature.
# On Improving Reliability of SRAM-Based Physically Unclonable Functions
## 3 Proposed Technique
### Harnessing Statistical Bias for Improving Reliability  
An SRAM PUF cell is expected to produce reliable value for key generation. If the relative drive strength of the cross-coupled inverters is low, then any noise present in the cell will determine the outcome of the PUF creating errors. When an SRAM cell exhibits errors during start-up, it is possible that if the cell is powered-up several times, it will exhibit a _statistical bias_ (bias towards 0 or 1). That is, if we assume that the mean value of noise that gets coupled to the PUF cells is close to zero, multiple evaluation of the PUF's response would give the true statistical bias of the PUF response. Temporal Majority Voting (TMV) is one of the well-known techniques to extract such a bias in the presence of noise. If the statistical bias is strong, it may be detected using only a _limited_ number of experiments. Contrarily, if the bias is weak, a much larger number of start-up experiments may be necessary to detect the true bias. A problem with a larger number of start-up experiments is that the associated circuits for bookkeeping will grow in size. Our proposed technique avoids this problem while extracting such a weak bias. Through simulation and analysis, we show that this technique is superior to traditional TMV.
# Boosting the Learning for Ranking Patterns
## 4 AHP-Based Learning Approach for Ranking Patterns
### Complexity Analysis  
**Proposition 1** (Time complexity).: _Algorithm 2 has a time complexity of \(O(n\ k)\) in passive learning and \(O(n)\) in active learning when the number of interestingness measures is bounded._  
Proof.: Let \(\mathcal{S}=\{S_{1},\ldots,S_{n}\}\) be a set of \(n\) user rankings, where \(k\) is the size of the largest ranking \(S_{i}\) and \(m\) is a bounded number of measures in \(\mathcal{M}\). Once the pair-wise comparison matrix \(\mathbf{A}\) is built, the preference vector of weights \(w\) can be computed using various mathematical techniques, including the eigenvector-based method (EVM) [30]. The worst-case time complexity of the EVM approach is \(O(m^{3})\)[36], which is of constant time complexity \(O(1)\) since \(m\) is constant. The time complexity of computing the matrix at line 3 of the learnWeights function is \(O(m\ n\ k)\) since [PERSON]'s W is in \(O(k)\) (see Definition (5)). The complexity of lines 2, 5, and 11 in learnWeights are, respectively, \(O(n\ m^{2}+m\ n\ k)\), \(O(m^{2})\), and \(O(m^{3})\). Notice that in AHP, it is demonstrated in [37] that the number of criteria is recommended to be no more than seven \(\pm 2\). Thus, assuming \(m\) is a small and fixed number of measures, we can set \(m\leq 9\) and obtain an asymptotic quadratic complexity of \(O(n\ k)\) when AHP is in passive mode. In other words, the approach takes \(O(k)\) for each ranking in the given \(\mathcal{S}\). When AHP is in active mode, where queries are pairs of patterns (\(k=2\)), the time complexity is linear on the number of queries submitted to the user, i.e., \(O(n)\).  
It is important to emphasize that in practice, the set \(\mathcal{S}\) is typically reduced to a single large ranking in passive learning mode (\(n=1\) and a large \(k\)), and to a set of \(n\) queries consisting of pairs of patterns in active learning mode (large \(n\) and \(k=2\)). As a result, AHPRank can run in linear time, with a complexity that is linear in \(k\) for passive mode and linear in \(n\) for active mode. This low complexity makes AHPRank a fast approach for learning a user ranking function, as supported by the experimental evaluation in Section 6.
## 2 Materials and Methods
### Adversarial Attacks
#### 2.3.2 Carlini and Wagner (C&W)  
The C&W approach is one of the most widely used in generating adversarial examples and has been extensively used in computer vision studies. The C&W approach's goal is to generate the adversarial example (\(x^{*}\)) by optimizing the objective function for the distance between the target class (\(y\)) and the most likely class (\(y^{*}\)). In [5], seven objective functions were presented; the objective function \(f_{6}\) outperformed all functions. Thus, they selected \(f_{6}\) to perform their approach, as follows:  
\[f_{6}(x^{*})=(max_{i\
eq y^{*}}(Z(x^{*})_{i})-Z(x^{*})_{y^{*}})^{+} \tag{6}\]  
The original C&W paper proposed three norm-based attacks, whereas we focused on an \(l_{2}\)-based attack. To ensure that C&W generated a valid adversarial example, the \(\delta\) was constrained by the _box constraints_: \(0\leq x_{i}+\delta_{i}\leq 1\) for all \(i\). In C&W, different methods for _box constraints_ were evaluated, and it was found that the best method was that of the change of variables. They added a new variable \(w\) and then applied a change of variables to optimize over \(w\) instead of (\(\delta\)), as follows:  
\[\delta_{i}=\frac{1}{2}(tanh(w_{i})+1)-x_{i} \tag{7}\]  
where \(-1\leq tanh(w_{i})\leq 1\). It follows that \(0\leq x_{i}+\delta_{i}\leq 1\).  
Thus, the C&W formulation using the \(l_{2}\) distance metric can thus be defined:  
\[\begin{array}{l}\text{minimize}\left\|\frac{1}{2}(\,tanh(w)+1)-x\,\right\| _{2}^{2}+\,c.f(\,\frac{1}{2}(\,tanh(w)+1\,))\\ \text{where}\,f(x^{*})=\max\,(\,\max\,\{Z(x^{*})_{i}:i\
eq y^{*}\}-Z(x^{*})_{y ^{*}},-k)\end{array} \tag{8}\]  
where \(Z(x^{*})\) represents the _logits_, which are the outputs of the layers-unnormalized probability predictions for each class; except for the softmax layer, parameter \(k\) sets a lower limit for the loss value to generate an adversarial example with confidence that the adversarial example is correctly classified to the target class (\(y^{*}\)). The highest \(k\) value generates a high-confidence adversarial example but is far from the original input. We used \(k=0\) to conduct our experiment, as described in the original paper [5]. The C&W approach is described in Algorithm 2.
# Efficient Melody Extraction Based on Extreme Learning Machine
## 2 Preliminaries
### Constant-\(Q\) Transform  
Given a discrete time domain signal \(x(n)\), its CQT representation is defined as [24]:  
\[X(k,n)=\sum_{m=0}^{N-1}x(m)a_{k}^{*}(m-n) \tag{10}\]  
where \(k\) and \(n\) denote frequency and time indices, respectively, \(N\) is the length of the input signal \(x(n)\), and the atoms \(a_{k}^{*}(\cdot)\) are the complex conjugated window functions, defined as:  
\[a_{k}(m)=g_{k}(m)e^{i2 nmf_{k}/f_{k}},m\in\mathbb{Z} \tag{11}\]  
with a zero-centered window function \(g_{k}(m)\), bin center frequency \(f_{k}\), sampling rate \(f_{s}\), and \(i=\sqrt{-1}\).  
The center frequencies \(f_{k}\) are geometrically distributed as  
\[f_{k}=f_{0}2^{\frac{k}{2}},k=0,\dots,K-1 \tag{12}\]  
where \(f_{0}\) is the lowest frequency, \(b\) is the number of frequency bins per octave, and \(K\) is the total number of frequency bins.  
The \(Q\)-factor of CQT is constant. The frequency resolution \(\Delta f_{k}\) at the \(k\)-th frequency bin is  
\[\Delta f_{k}=\frac{f_{k}}{Q} \tag{13}\]  
Substituting Equation (13) into Equation (12) yields  
\[\Delta f_{k}=\frac{f_{0}}{Q}2^{\frac{k}{2}} \tag{14}\]  
It can be found that the frequency resolution of CQT is also geometrically spaced along the frequency bins. That is, higher frequency resolutions are obtained in the lower frequency bands, while lower frequency resolutions are obtained in the higher frequency bands, in accordance with the pitch intervals of notes.
## 2 Experimental Section
### Synthesis and Characterization  
A micro-reactor which was used for polymerization is described in detail in the Supporting Information (Figure S1, Table S1). For synthesizing, BL-01: poly(St-DPE-MAA), the micro-reactor was operated as follows: five solutions were prepared in 100 mL eggplant flasks (a)-(e) under argon atmosphere (Figure S1).  
A total of 6 M St solution: St (37.5 g, 41.5 mL) was dissolved in 18.5 mL of tetrahydrofuran (THF).  
A total of 1.2 M \(n\)-BuLi solution: \(n\)-BuLi (13.8 mL, 2.6 M) was added to 16.2 mL of \(n\)-hexane on ice.  
A total of 1.2 M DPE solution, DPE (6.50 g, 6.5 mL) was dissolved in 23.5 mL of THF.  
A total of 3.6 M \(t\)-BMA solution, \(t\)-BMA (25.6 g, 29.0 mL) was dissolved in 21.0 mL of THF.  
A total of 1.5 M methanol (MeOH) solution, MeOH (2.48 g), and THF 46.9 mL were mixed.  
Four flasks (a), (b), (c) and (d) were connected to the micro-reactor through 4 plunger pumps. The reactor temperature was set to 25 \({}^{\circ}\)C.  
Firstly, St solution in flask (a) and \(n\)-BuLi solution in flask (b) were fed into the reactor at a rate of 3.9 mL/min and 1.5 mL/min, respectively, and they were mixed in M1 to initiate the living anionic polymerization of the St. Secondly, the DPE solution in flask (c) was fed into the reactor at a rate of 1.5 mL/min to terminate the St polymerization in M2. Then, the \(t\)-BMA solution in flask (d) was fed into the reactor at a rate of 3.81 mL/min, to sequentially start the polymerization from the DPE-terminated St polymer-solution in the mixture solution in M3. Finally, the reaction mixture was discharged into the MeOH solution in flask (e) to terminate the reaction.  
The obtained block copolymer was treated with a cation-exchange resin to hydrolyze the _tert_-butoxycarbonyl group (\(t\)-Boc) of the \(t\)-BMA structures to the carboxyl group, i.e., MAA structures.  
We prepared the other 4 polymers, operating the micro-reactor in the same manner. For BL-03, poly(St-\(\alpha\)-MeSt-MAA), BL-04: poly(St-\(\alpha\)-MeSt-MAA), and BL-05: poly(St-\(\alpha\)-MeSt-MAA), \(\alpha\)-MeSt was used in flask (c) instead of DPE for terminating the St polymerizations. For BL-02, poly(St-DPE-\(n\)-BMA-MAA), \(n\)-BMA were mixed with \(t\)-BMA solutions in flask (d). The synthesis scheme and parameters for operating the micro-reactor are summarized in Figure 1 and Table S1.  
The obtained polymers were dissolved in Diethyl ether-d10 (99.5+%, Fujifilm Wako, Tokyo, Japan) and characterized by \({}^{1}\)H NMR (JNM-ECA500, JEOL, Tokyo, Japan). Number and weight average-molecular-weights were measured by size-exclusion chromatography using the HLC-8220 (TOSOH, Tokyo, Japan) system equipped with TSK gel G5000, G4000, G3000 and G2000 columns (TOSOH, Tokyo, Japan) and eluted by THF at 1.0 mL/min. Polystyrene standards were used for calibration.
## 3 Simulation Setup and Performance Evaluation
### Deadlines  
In this experiment, we determine the missed deadlines that are one of the most important features to decide whether a packet is delivered within this deadline. Another goal of determining the deadline is to regulate the consumed energy for multi-hop communication. In Figure 12, we show the number of missed deadlines that helps identify the performance of the compared protocols for real time communication. Based on the simulation results, we observe that MIMO protocol missed the maximum number of deadlines as compared with other real-time QoS routing protocols. MIMO suffers from the overhead of maintaining states and routing tables at each sensor nodes and this drawback causes the maximum number of missed deadlines. The other routing protocols have similar behavior except for PT. However, when the number of set deadlines increase, then PT starts to behave positively. The path construction process in PT is simple, which causes a minimum number of missed deadlines by increasing the number of set deadlines. In addition, the path construction is divided into a single phase instead of three phases (parallel phase, growing phase and converging phases) as used by MIMO. As all these phases require additional processing time this leads to increased missed deadlines.  
Figure 11: Average packet loss.  
Figure 12: Number of miss deadlines _vs._ number of set deadlines.
## 4 Experiment and Results
### BIMCoder Validation
#### 4.2.3 Analysis of BIMCoder Model Test Results  
In the above experiment, the BIMCoder model achieved the highest overall performance. This can be attributed primarily to the generalization capabilities of the ERNIE 3.0 model. With the assistance of the LLM-based Wrapper model, the expert model can concentrate on structured query generation by receiving cleaner, normalized inputs.  
However, Exact Match Accuracy significantly declined when the model encountered fuzzified or colloquially expressed query sets. To mitigate this issue, we adopted a two-stage strategy: first, leveraging the ERNIE 3.0 model's robust generalization ability, the system identifies key entities and semantic structures from vague user queries and converts them into more standardized representations; then, these normalized queries are passed to the fine-tuned SQLCoder model to generate precise and executable BIM queries.  
The combination of the ERNIE 3.0 and SQLCoder models forms a collaborative architecture that capitalizes on ERNIE 3.0's strengths in interpreting complex or ambiguous language and SQLCoder's specialization in generating accurate SQL syntax. This collaborative mechanism proves effective in handling a wide variety of BIM-related natural language queries, especially in scenarios where language variability and user intent ambiguity are prominent. It demonstrates that the proposed pipeline can expand the applicability of NL2 SQL techniques to real-world data analysis and BIM system integration.  
Despite these strengths, several limitations were observed in edge cases. For instance, in task T10, BIMCoder failed to generate a correct structured query. Analysis reveals that the failure is due to the model misinterpreting the relationship between the GUID and the semantic role of \"floor.\" Specifically, the fuzzy phrasing caused confusion between spatial containers (e.g., floors, zones, buildings) and component elements, resulting in a query structure that either neglected the containment hierarchy or retrieved unrelated elements. This example highlights a known limitation of the current pipeline: when queries rely on implicit IFC hierarchy knowledge or reference ambiguous spatial entities using GUIDs, the model may misrepresent entity roles or generate logically inconsistent queries.  
This failure case underscores the current limitations of the BIMCoder pipeline in accurately interpreting spatial semantics and IFC containment hierarchies. To address these issues, subsequent work should emphasize the incorporation of hierarchical reasoning mechanisms, the expansion of training data to include more hierarchy-aware examples, and the integration of domain-specific semantic validation modules. Such enhancements are anticipated to improve the model's interpretability and reliability in complex, real-world BIM environments.
# Transportation and Reserve of Emergency Medical Supplies during Public Health Events
## 3 Materials and Methods
### Allocation and Storage Model of Emergency Medical Materials
#### 3.3.1 Demand Urgency Function
\begin{table}
\begin{tabular}{c c c} \hline
**Target Layer** & **Guideline Layer** & **Indicator Layer** \\ \hline \hline \multirow{8}{*}{\begin{tabular}{c} Public health event \\ prevention and control \\ capabilities \\ \end{tabular} } & \begin{tabular}{c} X1 population density \\ (people/km\({}^{2}\)) \\ X2 strictness of prevention and control policy \\ X3 receives inbound tourists \\ \end{tabular} \\ \cline{2-3}  & \
\begin{tabular}{c} X4 enrollment rate of institutions \\ of higher learning (\%) \\ X5 median age \\ X6 vaccination rate (\%) \\ \end{tabular} \\ \cline{2-3}  &
\begin{tabular}{c} X7 number of beds for thousands \\ of people (sheets/thousands of \\ people) \\ X8 GDP per capita (US \$10,000) \\ X9 health expenditure as a \\ percentage of GDP (\%) \\ X10 consumer Spending as a \\ percentage of GDP (\%) \\ X11 UHC coverage index \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation Index System. \(k\) at time \(t\); \(I\) denotes the number of the infected population at demand point \(k\) at time \(t\); \(R\) denotes the number of the recovered population at demand point \(k\) at time \(t\); and \(D\) denotes the number of the dead population at demand point \(k\) at time \(t\). If the urgency degree of emergency medical material demand in a certain area is \(Y_{2k}(t)\), the formula of emergency medical material demand urgency degree in a certain area is obtained:  
\[y_{2k}(t)=0.3N+0.15I-0.15R-0.3D \tag{11}\]  
Finally, normalized processing is performed, and \(Y_{2K}\) represents the urgency of the demand for emergency medical materials in the k-th region among \(n\) regions. \[Y_{2k}=\frac{y_{2k}}{\sum\limits_{i=1}^{n}y_{2i}} \tag{12}\]  
The higher the urgency of the demand for epidemic prevention and control ability, the better the situation of epidemic prevention and control in this area. That is, the higher \(Y_{1}\) proves that the demand for epidemic prevention and control is low and the higher the urgency of the demand for emergency medical materials, the worse the demand for emergency medical materials in this area. That is, the higher \(Y_{2}\) proves the high demand for emergency medical materials. Mathematical processing is performed to design the total demand urgency function \(Y\) as follows:  
\[Y_{k}=-\varphi_{1}\times Y_{1k}+\varphi_{2}\times Y_{2k} \tag{13}\]  
where \(\varphi_{1}\) is the weight of the urgency function of the demand of the ability to prevent and control epidemics, and \(\varphi_{2}\) is the weight of the urgency function of the demand of emergency medical materials.
# A Novel Preprocessing Method for Dynamic Point-Cloud Compression
## 3 Proposed Method
### Overlap Extraction  
This procedure is the process of determining a duplicate vertex by comparing both point-clouds and splitting them into two vertex sets, vertex \(v_{ov}\) and \(v_{add}\). Generally, when comparing two sets where \(n\) and \(m\) elements are equal, the method of comparing each set is performed \(m\) times for \(n\) data. In this case, it will be repeated \(n\times m\) times. However, in general, such an operation is not possible in a point-cloud having one million or more elements. Therefore, we use a hash table to compare point-clouds. This hash table consists of an integer-type key and a value in the form of spatial coordinates. Since there is a way to generate spatial coordinates with a key, a point-cloud can be configured with a hash table. When the hash table is used, it is repeated \(n+m\) times, so the time used for the comparison operation is greatly reduced.  
When performing the overlap extraction for \(frame\)\(n\) and \(frame\)\(n+1\), which are two point-cloud frames, respectively, it is assumed here that the same vertex does not exist in one point-cloud. First, configure \(frame\)\(n\) with a hash table. Convert \(frame\)\(n+1\) vertex to key format. Perform a duplicate comparison using the hash table of \(frame\)\(n\) for all key values. The comparison result confirms that each vertex of \(frame\)\(n\) overlaps with \(frame\)\(n+1\). Divide into \(v_{ov}\) when duplicated, and \(v_{add}\) when not.
## 4 Experimental Evaluation  
### HCIE Dataset  
To address the absence of a dedicated dataset for indoor human body detection in existing publicly available object detection datasets, this study introduced a new dataset named the Human in Complex Indoor Environments Dataset (HCIE dataset).  
The HCIE dataset is sourced from two main channels: integrating indoor human images from existing open-source target detection datasets and collecting public indoor images from the internet. The dataset's image distribution was carefully planned from various viewpoints to ensure that models trained on it exhibit a degree of generalization.  
Following the construction principles of established object detection datasets [42; 43], the HCIE dataset was chosen based on the following specific criteria:  
* Diversity: For data diversity, the dataset encompasses diverse lighting conditions, shooting angles, backgrounds, human body sizes, and other variations within indoor scenes. This approach enhances the model's generalization capability.
* Class Balance: While focusing solely on the human class, the dataset incorporates a range of factors like diverse ages, genders, body types, and poses of human subjects in indoor settings (standing, sitting, lying down, etc.). This approach enables the model to learn features from a spectrum of categories.
* Similar targets: The dataset includes both the human body to be detected and objects that are partially similar to the human body that are not intended to be detected, but when labeled, only the target to be detected is labeled.
* Practicality: Ensure the quality of the collected data, preferably with a size close to the actual usage scenario.
* Data integrity: To ensure consistency and completeness between images and annotations in the dataset, and to avoid missing or inconsistent data, LabelImg is used to label all data in this dataset.  
The final dataset contains five thousand images, which are divided into training, validation, and testing subsets in an 8:1:1 distribution. The examples of some images in Figure 8 demonstrate that the dataset includes different postures and lighting conditions of human bodies of different age groups in indoor scenes.  
Figure 9 shows the label distribution of the HCIE dataset. In Figure 9a, the x and y coordinates denote the ratio of the bounding box center point's coordinates to the image's length and width, respectively. This description illustrates the distribution of bounding box center points within the image. In Figure 9b, the x and y coordinates indicate the ratio of the detection target's horizontal and vertical length within the bounding box to the image's horizontal and vertical length. This representation highlights the distribution of target aspect ratios in the training set.  
Figure 8: Example of indoor human detection images in HCIE dataset.