esa-sceva/satcom-chunk-collection huggingface dataset:
# Metabolite Analysis of Hangzhou Gongmei White Tea of Different Varieties
## 3 Results and Discussion
### Differential Analysis of Volatile Metabolites  
The volatile and non-volatile substances of FD, JK, LJ, and YS with high scores were measured. A total of 92 aroma components were identified through a process of matching with the NIST 20 mass spectrometry library and retention index (Table 1). The categorisation of these components was conducted based on their compositional characteristics, resulting in the identification of ten distinct categories: alcohols (27), aldehydes (23), ketones (13), esters (5), terpenes (6), alkanes (4), benzenes (5), furans (5), sulphur-containing compounds, (2) and others (2). PCA was performed on the content of different aroma components (Figure 3a). The first two principal components were found to account for 72.6% of the variability in the original data. The distance between JK and LJ was relatively close, while the other two were clearly distinguished, which was basically similar to the sensory evaluation results. The OPLS-DA method was utilised to construct a discriminant model (Figure 3b), and the four varieties were effectively discriminated, with R2Y and Q2 values of 0.977 and 0.937, respectively. The model demonstrated no signs of overfitting, as evidenced by the 200 prediction analyses (Figure 3c). The VIP analysis results (Figure 3d) demonstrated that linalool, geraniol, 2-ethylhexanol, hexanal, methyl salicate, linalool oxide I, (_E_)-hex-2-enal, \(\beta\)-myrcene, (_Z_)-hex-3-en-1-ol, phenylethanol, benzaldehyde, (_E_)-citral, nonanal, and _trans_-\(\beta\)-ionone were the main discriminating components. It is noteworthy that the majority of these substances align with the key aroma components of Fujian white tea, as reported in the literature [25; 37].
# Deep Learning-Based Super-Resolution Reconstruction and Segmentation of Photoacoustic Images
## 2 Materials and Methods
### Photoacoustic Signal Generation and Reconstruction  
Photoacoustic signals are generated by illuminating tissue with a nanosecond laser pulse \(\delta(t)\). The light-absorbing molecules in the tissue undergo thermoelastic expansion and generate a photoacoustic pressure wave [33]. Assuming that thermal diffusion and volume expansion during illumination are negligible, the initial photoacoustic pressure \(x\) can be defined as  
\[x(r)=\Gamma(r)A(r) \tag{1}\]  
where \(A(r)\) is the spatial absorption function and \(\Gamma(r)\) is the Gruneisen coefficient describing the conversion efficiency from heat to pressure [34]. The photoacoustic pressure wave \(p(r,t)\) at position \(r\) and time \(t\) can be modelled as an initial-value problem for the fluctuation equation, where \(c\) is the speed of sound [35].  
\[\left(\
abla^{2}-\frac{1}{c^{2}}\frac{\partial^{2}}{\partial t^{2}}\right)p(r,t)=-x(r)\frac{d\delta(t)}{dt} \tag{2}\]  
The transient signal is measured by a sensor located on the measurement surface \(S_{0}\). The linear operator M acts on \(p(r,t)\) confined to the boundary of the computational domain \(\Omega\) for a finite time \(T\) and provides linear mapping from the initial pressure \(x\) to the measured transient signal \(y\).  
\[y=\mathrm{M}_{p|\partial\Omega\times(0,T)}=Ax \tag{3}\]  
Time-reversal reconstruction [36] is a robust photoacoustic image reconstruction method for homogeneous and heterogeneous media and any arbitrary detection geometry. The method utilizes a time-reversal algorithm to achieve image reconstruction by running the numerical model of the forward problem in reverse, i.e., inverting the signal propagation process in the time domain while keeping the spatial coordinates constant. Compared to other imaging algorithms, the time-reversal reconstruction method is less susceptible to image artefacts and can achieve more desirable reconstruction results [37].  
Figure 1: Schematic diagram of the deep-learning structure for PAI.
# A Study of LoRa: Long Range & Low Power Networks for the Internet of Things
## 3 The LoRa Physical Layer  
The LoRa modulation is a Semtech proprietary technology and is as such not fully open. This section presents an analysis (of the parts of LoRa that are open) and an experimental evaluation (of the proprietary parts of LoRa) with the purpose of understanding if the advertised performance of LoRa is observed in practice.  
Figure 1: LoRa network architecture.
## Appendix A Appendix A
### Example of Violation of the Triangle Inequality  
We know that a normalized similarity metric is given by a relation \(s_{n}(x,y)=1-d_{n}(x,y)\). Hence, we prove only that the term \(d_{n}(x,y)=\frac{d(x,y)}{max(|x|,|y|)}\) is a distance metric. Recall the definition.  
A metric space is an ordered pair, \((\mathbf{X},d)\), where \(\mathbf{X}\) is a set and \(s\) is a function \(s\colon\mathbf{X}\times\mathbf{X}\to\mathbb{R}^{+}\) that is a metric, i.e., such that, for any \(x,y,z\in\mathbf{X}\), the following hold:  
1. \(d(x,y)=d(y,x)\) (symmetry);
2. \(d(x,z)\leq d(x,y)+d(y,z)\) (triangle inequality);
3. \(d(x,y)=0\Leftrightarrow x=y\) (identity of indiscernibles).  
The edit distance is a solution to a dynamic programming problem and can only be obtained numerically. The task of the embeddability of the edit distance into \(l_{p}\) norms is still an open problem. It has been shown that such a metric cannot be embedded into the \(l_{1}\) norm (with arbitrary dimension) with distortion better than \(\frac{3}{2}\)[61].  
Let us illustrate this rather with the example of the strings \(X=\) \"\(ab\)\", \(Y=\) \"\(abc\)\", and \(Z=\) \"\(bc\)\". Then, we obtain  
\[d(X,Z)\leq d(X,Y)+d(Y,Z),\] \[\frac{d(X,Z)}{max\{|X|,|Z|\}}\leq\frac{d(X,Y)}{max\{|X|,|Y|\}}+ \frac{d(Y,Z)}{max\{|Y|,|Z|\}}, \tag{10}\] \[\frac{2}{2}\
leq\frac{1}{3}+\frac{1}{3}.\]
# LoRaWAN Physical Layer-Based Attacks and Countermeasures, A Review
## 4 Protection Techniques
### Device Fingerprinting
#### 4.4.2 Case Study  
Twenty commercial off-the-shelf LoPy4 devices and a USRP N210 software-defined radio (SDR) platform are used to experimentally demonstrate how a LoRa-RFFI system works. The carrier frequency of LoPy4 transmitters is set to 868.1 MHz. The transmission is configured with a spreading factor of seven and a bandwidth of 125 kHz. The sampling rate of the USRP N210 receiver is set to 1 MHz. In the experiments, 400 packets are collected in turn from each LoPy4 device and stored in the training dataset, i.e., the dataset contains a total of 8000 packets. The packets are then preprocessed for RFFI, including carrier frequency offset (CFO) compensation, preamble extraction and conversion to channel-independent spectrograms. The CFO compensation is to ensure system stability [15; 16], preamble extraction is to prevent learning protocol-specific information and a channel-independent spectrogram is to mitigate the impact of wireless channel [17]. The preprocessed training dataset is then used to train a CNN whose architecture can be found in [17]. After the CNN training stops, we can use it to predict the newly received LoRa packets.  
After sufficient training, another 100 packets are collected in turn from each LoPy4 device and preprocessed for evaluation/inference. The confusion matrix and overall accuracy are common evaluation metrics for a classification task, which are both presented in Figure 10. As shown in the confusion matrix, most of the predicted labels match the true labels since the predictions distributed along the diagonal, and the overall classification accuracy can reach 97.75%. The result shows that the RFFI system can classify 20 LoPy4 devices with high accuracy, even if they are from the same manufacturer.  
Figure 10: Classification result on LoRaWAN Radio frequency fingerprint identification with the overall accuracy of 97.75%.  
Figure 9: Overview of an RFFI system.
## 3 Results
### NDRT Performance  
The program recorded all NDRT results. After data organization, the correctness of the NDRTs were presented as percentages. The data revealed no outliers, but were not normally distributed (\(p<0.05\)). Tests showed that there was a significant effect of SAT on the correctness of the NDRT, i.e., \(\chi^{2}(2)=8.475\), \(p<0.05\), mean rank = 58.63, 80.25, and 78.63 for SAT level 1, SAT level 1 + 3, and SAT level 1 + 2 + 3, respectively (Figure 6). Pairwise comparisons showed that the difference between the SAT level 1 + 3 condition and the SAT level 1 condition and the SAT level 1 + 2 + 3 condition and the SAT level 1 condition were significant. However, tests revealed that there was no significant effect on correctness of NDRT in regards to scenario urgency and driving stage (\(p>0.05\)).  
Although the effects of the scenario urgency and driving stage were not significant, from the descriptive statistics, the correctness of the NDRT was higher under the low-urgency condition (M = 95.49, SD = 4.207) than under the high-urgency condition (M = 95.49, SD = 4.570), and was higher in stage 2 (M = 95.90, SD = 4.139) than in stage 1 (M = 95.56, SD = 4.639).
## 3 Experimental Tests  
### Case Study  
A 3 \(\times\) 30 m IAB in Shenzhen (China), shown in Figure 2[37], was selected as the case study. The girder-integral abutment-RC pile represents the reference structure of this investigation. The main features of the bridge are given in Table 1. The concrete grade for the girder and abutments is C50 with a 50 MPa cube strength (\(f_{\text{cu}}\)) and 3.45 \(\times\) 10\({}^{4}\) MPa Young's modulus (\(E_{\text{c}}\)). The concrete grade for the piers is C40 with a 40 MPa \(f_{\text{cu}}\) and 3.25 \(\times\) 10\({}^{4}\) MPa \(E_{\text{c}}\). The concrete grade for the pile caps and piles is C30 with a 30 MPa/\(f_{\text{cu}}\) and 3.0 \(\times\) 10\({}^{4}\) MPa \(E_{\text{c}}\).  
Figure 1: Experiment diagram: (**a**) IAP [36]; (**b**) GIAP.  
Because of the spatial limitations of the laboratory, a specimen with a \(1/10\) scale ratio was designed and fabricated. Based on the law of similitude [38], the scale ratios of the gravity acceleration (\(S_{\mathrm{g}}\)), elastic modulus (\(S_{\mathrm{E}}\)), stress (\(S_{\mathrm{\sigma}}\)), strain (\(S_{\mathrm{\epsilon}}\)), and density (\(S_{\mathrm{\rho}}\)) were \(1/1\); the length (\(S_{\mathrm{L}}\)) and displacement (\(S_{\mathrm{d}}\)) were \(1/10\); and the moment of inertia (\(I\)) was (\(1/10\))4.
## 4 Holochain Experiment for Healthcare IoT Network
### Experimental Setup and Configuration  
The experiment was carried out by creating a custom DNA to manage the sensor data (Listing 1). The development environment was prepared using the Holochain Launcher  
Figure 4: Holochain-based healthcare IoT system model.  
and CLI tools, with Rust as the primary language. A local test network was deployed with three simulated healthcare agent nodes connected to the same DNA instance. Each agent node was assigned a unique AID and key pair. Secure messaging and authenticated data operations were performed using these credentials.  
A new DNA project was initialized as follows:  
```
1 healthcare-id-dna/
2-mad.yaml
3-1_ames/
4|-health_sensors/
5|-src/
6|-|-lbsr
7|-Cargo.toml
```  
Listing 1: DNA Structure.  
Zome logic was implemented to allow the capture and sharing of healthcare data such as heart rate, blood pressure, and glucose levels. Listing 2 represents the Rust code that defines the Zome logic for handling sensor inputs:  
```
8 usehdk::prelude::*;
9 uschrono::[DateTime,Utc];
10
11#[hdk_entry(id=\"health_sensor_data\")]
12 pubstructHealthSensorData{
13 pubaid:String,
14 pubheart_rate:f32,
15 pubblood_pressure:String,
16 pubglucose_level:f32,
17 pubtimestamp:DateTime<Utc>,
18 publocation:String,
19}
20
21#[hdk_extern]
22 pubfncreate_sensor_data(data:HealthSensorData)->ExternalResult<EntryHash>{
23 lethash=hash_entry(&data)?;
24 create_entry(&data)?;
25 Ok(hash)
26}
27
28#[hdk_extern]
29 pubfnget_sensor_data(hash:EntryHash)->ExternalResult<Option<HealthSensorData>>{
30 letdata:Option<HealthSensorData>=get(hash,GetOptions::default())?
31-mmp(|e|e|.entry().to_app_option())
32.transpose()?;
33 CK(data)
34}
```  
Listing 2: Zome Logic for Healthcare IoT Network.  
This logic enables each agent to publish and retrieve health data entries. The following steps demonstrate how the nodes interact:  
* Node 1: Monitored heart rate and blood pressure during a patient check-up. Data was published using the command:  
```
holochain-clicall-fn=create_sensor_data-args='{\"aid\":\"W-sensor-1\",\"heart_rate\":75.0,\"blood_pressure\":\"120/80\",\"glucose_level\":95.0,\"timestamp\":\"2025-04-16T14:23:00Z\",\"location\":\"Room101,ClinicA\"}'-id-node1
```  
Listing 3: Zome Logic for Healthcare IoT Network.  
This logic enables each agent to publish and retrieve health data entries. The following steps demonstrate how the nodes interact:  
* Node 2: Monitored heart rate and blood pressure during a patient check-up. The hash of the entry created by Node 1 was used as input for the retrieval command:holochain-cli call  --fn-get_sensor_data  --args='{\"hash\": \"<entry_hash>\"}'  --id=node2
* Node 3: Published glucose data to update the patient's monitoring status. This node also verified the integrity of the data by retrieving and validating the timestamp and physiological parameters.
## 7 Conclusions  
In this study, a machine learning-based model is proposed to automate the process of collecting transcriptional data used to evaluate children's language development level. The utterance collection model is performed in the steps of maintaining the topic and changing the topic after initiating the conversation during the conversation collection protocol. The main role of the proposed collection model is to analyze children's utterances, classify whether to maintain the topic or change it, and then maintaining the topic, generating a questioning-like sentence according to the segmentation point of the sentence. In order to perform the above role, we used previously collected data. For the analysis model, we tested which of the three models, LGBM, SVM, and DNN, is suitable. As a result, in the topic maintenance or change classification tasks, DNN showed the best performance with 94.17%, and in the task of generating imitation sentences through the segmentation point, humans directly evaluated the naturalness of the sentence. LGBM showed the best performance with an average of 76 votes. As a result of confirming the performance results of the two tasks covered in this study, LGBM showed excellent results on average in both tasks, so LGBM is judged to be a suitable analysis model.  
Based on the above conclusions, it is clearly difficult to apply the proposed method immediately in the process of the collection protocol. In the next study, we will structure the utterance data of more children and construct a model that generates questioning-like sentences more naturally by analyzing them.  
Conceptualization, [PERSON]-M.C. and [PERSON]-S.K.; data curation, [PERSON]-K.L., [PERSON]-[PERSON] and [PERSON]-Y.P.; formal analysis, [PERSON]-M.C., [PERSON]-[PERSON] and C.-Y.P.; methodology, [PERSON]-M.C., [PERSON]-[PERSON] and [PERSON]-Y.P.; validation, [PERSON]-D.K. and C.-Y.P.; resources, [PERSON] and [PERSON]-[PERSON]; visualization, [PERSON] and [PERSON]; writing--original draft preparation, [PERSON]-[PERSON]; writing--review and editing, [PERSON]-S.K.; supervision, [PERSON]-S.K.; project administration, Y.-S.K. All authors have read and agreed to the published version of the manuscript.  
This work was partly supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF) (NRF-2019S1A5A2A03052093), Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (NO.2021-0-02068, Artificial Intelligence Innovation Hub (Seoul National University)), and 'R&D Program for Forest Science Technology (Project No. 2021390A00-2123-0105)' funded by the Korea Forest Service (KFPI).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Model**} & **Topic Maintenance or Change** & **Imitation Sentence** \\  & **Classification (Accuracy)** & **Generation (Votes)** \\ \hline SVM & 79.41 & 60.375 \\ LGBM & 89.70 & 76 \\ DNN & 94.17 & 36.75 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance evaluation results of each model.
## 2 Literature Review
### Hipot (High-Potential) Failures in Electric Armatures  
Hipot testing is a crucial quality control procedure for electric armatures, ensuring their ability to withstand operational voltage stresses without breakdown. However, failures during hipot tests can be costly and time-consuming, requiring root cause analysis and corrective actions. This section reviews recent research on failures encountered during hipot testing of electric armatures.  
* Partial discharge (PD) activity: PDs are localized dielectric breakdowns within the insulation system, often indicated by increased current leakage during hipot tests. [PERSON] et al. [42] explored the use of advanced PD detection techniques to identify and locate PD sources in electric motor stator windings, aiding in early failure prediction. [PERSON] et al. [43] provided detailed experimental specifications involving PD measurements methods and PD data post-processing Algorithms. The obtained results are drawn as a useful and timely reference that enhances the understanding of the insulation PD process in SiC (silicon-carbide)-based power electronics applications.
* Automated hipot failure detection: Research by [PERSON] [44] introduced a new concept developed for high-voltage testing in electrical apparatus used in railway systems, which supports predictive maintenance. This device conducts insulation tests between contacts and the ground wherein contacts must withstand the test voltage for a speci  
Figure 5: Representation of the ISO 9001:2015 structure in the PDCA cycle.  
Figure 6: Mapping of ISO/IEC 17025:2017 clauses to the PDCA cycle for quality management in laboratories.  
fied time; it also tests several apparatus at the same time, improving efficiency in the maintenance sector.
* Moisture ingress: Moisture absorption in the insulation degrades its dielectric properties and increases the risk of hipot test failures. [PERSON] et al. [45] examined the effect of moisture content on the breakdown strength of stator coil insulation under various temperatures.
* Advanced detection and mitigation techniques: Recent research has focused on developing advanced methods for detecting and mitigating hipot test failures in electric armatures. These include (a) non-destructive testing (NDT) techniques. Research by [PERSON] et al. [46] reviewed the main aging and failure mechanisms of stator winding and methods for extending the useful life of the machine. The symptoms of each failure mechanism were discussed, and an overview of various electrical diagnostic techniques for condition assessment of stator winding insulation was presented. (b) Machine Learning for Anomaly Detection. Research by [PERSON] et al. [47] explored the application of machine learning algorithms to data for different possible types of anomalies in electric motors, such as uncoupled, overloaded, unbalanced, misaligned, and normal anomalies. The obtained results show how these algorithms can be effective in classifying the different types of anomalies and that the two models that presented the best accuracy values were k-nearest neighbor and multi-layer perceptron.
# The Role of Brownmillerite in Preparation of High-Belite Sulfoaluminate Cement Clinker
## 3 Results and Discussion  
### Thermal Evolution of HBSC Raw Materials  
TG/DTA curves of HBSC raw materials (set out in Table 1) are shown in Figure 1; the corresponding data of characteristic peaks in the DTA and TG curves are listed in Tables 5 and 6, respectively. There is an endothermic peak (Figure 1a) and slight weight loss (Figure 1b) between 100 \({}^{\circ}\)C and 130 \({}^{\circ}\)C, which was attributed to physical water evaporation. The second endothermic peak (Figure 1a) associated with an obvious weight loss (Figure 1b) located at 400-600 \({}^{\circ}\)C was attributed to the decomposition of boehmite (\(\gamma\)-AlOOH) in aluminous materials. The highest endothermic peak (Figure 1a), associated with the maximum weight loss (Figure 1b) located at 700-900 \({}^{\circ}\)C, is observed, which corresponded to the decomposition of LS. From 950 \({}^{\circ}\)C to 1280 \({}^{\circ}\)C, many of the endothermic peaks in Figure 1a appeared without weight losses in Figure 1b. From 1280 \({}^{\circ}\)C to 1330 \({}^{\circ}\)C, an exothermic peak appeared without weight losses, which was the clinker mineral formation stage accompanied by complex solid-phase reaction processes, including the formation of anhydrous C\({}_{4}\)A\({}_{3}\)S, C\({}_{2}\)S, and brownmillerite C\({}_{4}\)AF. It was noted that the endothermic temperature would be slightly decreased with the increase in designed brownmillerite content, as shown in Table 5. It can be seen from the DTA curve that the reaction peak at temperatures from 1280 \({}^{\circ}\)C to 1330 \({}^{\circ}\)C becomes evident with the increase in brownmillerite content. When the temperature increased above 1330 \({}^{\circ}\)C, another endothermic peak associated with weight loss should indicate the decomposition of some clinker minerals, especially the sulfoaluminate phases. As shown in Table 6, weight loss in this stage except for GF-1 decreased with the increase in designed brownmillerite content, and the weight loss was lower than that of the sample with brownmillerite content of less than 20% (GF-1), which preliminarily showed that the brownmillerite content could stabilize the anhydrous calcium sulfoaluminate and reduce its decomposition. According to the above analysis, the sintering temperature of HBSC clinker was mainly between 1200 \({}^{\circ}\)C and 1400 \({}^{\circ}\)C.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **Peak 1** & **Peak 2** & **Peak 3** & **Peak 4** \\ \hline GF-1 & 125.4 & 516.5 & 863.1 & 1241.9 \\ GF-2 & 121.9 & 512.2 & 864.5 & 1239.3 \\ GF-3 & 121.8 & 512.3 & 864.2 & 1238.1 \\ GF-4 & 120.7 & 510.5 & 860.4 & 1237.3 \\ GF-5 & 118.0 & 507.5 & 863.6 & 1235.8 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Key temperature point of exothermic or endothermic peaks in DTA curves (\({}^{\circ}\)C).