esa-sceva/satcom-chunk-collection huggingface dataset:
# Error Performance of Amplitude Shift Keying-Type Asymmetric Quantum Communication Systems
## 2 Basic Theory
### Photon Number State  
The most typical quantum state of light is the photon number state \(|n\rangle\), which represents a state in which the number of photons is \(n\), and it forms the following orthonormal basis:  
\[\sum_{n=0}^{\infty}|n\rangle\langle n|=\mathbb{I},\langle n|m\rangle=\delta_{mn}, \tag{2}\]  
where \(\mathbb{I}\) is the identity operator on Hilbert space, and \(\delta_{mn}\) is the Kronecker delta.
## 3 Results
### Commonly Used Datasets  
Several open-source datasets, as shown in Table 2, are widely used in PdM and fault diagnosis due to their accessibility and relevance. CMAPSS and NASA turbofan datasets offer multivariate time-series data for RUL prediction. MIMI provides audio and vibration signals from faulty and normal industrial machines for anomaly detection. SECOM contains 591 features from semiconductor processes for quality classification. AI4I 2020 includes telemetry, error logs, and failure labels for prognostics. NASA's milling and bearing datasets deliver sensor data on tool wear and bearing degradation, supporting diagnostics and condition monitoring.  
Figure 7: Frequently employed metrics for study evaluation.  
The remaining datasets are derived from real-world industrial operations and typically contain time-series sensor data such as vibration, temperature, pressure, or flow, along with maintenance logs, inspection reports, and system telemetry. Most are proprietary due to confidentiality agreements, commercial sensitivity, or infrastructure concerns, and are collected through internal monitoring systems or research collaborations. Some are synthetically generated using simulation platforms but remain restricted to private use or licensed access.  
Across both public and private datasets, a key challenge lies in the aggregation and preprocessing of heterogeneous data. PdM systems, particularly in industrial Internet of Things (IIoT) and large-scale manufacturing environments, depend on effective integration of multi-source information. In modern architectures, data are typically collected at the edge (from sensors, devices, or loggers) and streamed centrally using real-time platforms like Apache Kafka and Spark. These tools support temporal synchronization via micro-batch processing and map-reduce operations, which structure raw inputs into logically ordered data streams [76].  
Moreover, data privacy and consistency become central concerns, especially in federated settings. Privacy-preserving schemes such as differential privacy and homomorphic encryption are increasingly used to ensure secure data aggregation, particularly in IIoT federated learning architectures. Datasets are preprocessed using techniques like normalization and enhanced through strategies to address missing values, misaligned timestamps, and cross-sensor fusion. These steps are foundational for ensuring dataset quality, whether the source is a public benchmark or a proprietary industrial stream [77].
# Erosion Resistance Properties of Iron-Carbon Composite Plating to Molten Lead-Free Solder
## 3 Results and Discussion
### Erosion-Resistance Test  
The results of the erosion-resistance test are shown in Figure 9. The Fe-MWCNT composite plating layer shows the best erosion resistance in this study. The erosion depths of the Fe-graphite composite plating layer and the Fe plating layer were 10 and 100 times larger than that of the Fe-MWCNT composite plating layer, respectively. Since carbon does not react with solder, it does not erode even if it is in contact with molten solder for a long time. Thus, the diffusion of Fe into SAC305 was greatly reduced when composite carbon filler was combined with Fe. Since MWCNT has superior mechanical and thermal properties compared to graphite, the erosion resistance of the Fe-MWCNT plating layer is superior to that of the Fe-graphite one.  
EBSD analysis was carried out to analyze the improvement effect of the erosion resistance of the Fe-graphite and the Fe-MWCNT composite plating layers. EBSD analysis results are shown in  
Figure 8: Temperature dependence on spread rate for several plating layers.  
Figure 9: Results of erosion-resistance test at 623 K.  
Figure 10. A trace amount of Fe\({}_{3}\)C was detected at the interfaces of \(\alpha\)-Fe grains. It is considered that a part of Fe and the compounded carbon filler reacts together, and thus, Fe\({}_{3}\)C compounds form by heating at 623 K. It has been reported that Fe\({}_{3}\)C particles suppress the diffusion of Fe into SAC305 solder [11; 12]. In this study, a similar tendency was observed in the erosion-resistance test. Therefore, it is expected that the composite plating layer could possess good erosion resistance due to Fe\({}_{3}\)C by optimizing the plating conditions and annealing conditions.
# Identification of Factors Influencing Fluoride Content in Tea Infusions: A Systematic Review
###### Abstract  
Tea is one of the most widely consumed beverages globally and a significant dietary source of fluoride. This systematic review aimed to identify and evaluate the factors influencing fluoride concentration in tea infusions. A comprehensive literature search was conducted in March 2025 across PubMed, Scopus, and Web of Science databases, following PRISMA guidelines (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and the PICO framework. Eligible studies investigated fluoride release in tea infusions, published in English from the year 2000 onward. Thirty articles met the inclusion criteria, and the risk of bias in the articles was assessed using the Joanna Briggs Institute (JBI) quality checklist. Fluoride concentration in tea infusions varied widely across studies, ranging from 0.008 to over 8 mg/L. Key factors influencing fluoride release included tea type (with black and green teas showing the highest values), leaf form (powdered and bagged teas released more fluoride than loose leaves), brewing time and temperature, water composition, and the presence of additives such as spices. A longer brewing time and higher temperature consistently increased fluoride extraction. Lower pH or water hardness also significantly affected fluoride availability. Regional origin of tea and production methods were additional sources of variation. Fluoride release in tea is influenced by a complex interplay of botanical, environmental, and preparation-related factors. These findings are clinically relevant, particularly for populations at risk of fluoride overexposure. Further standardized research is needed to inform safe consumption guidelines and public health recommendations.  
b
# Handover Management for Drones in Future Mobile Networks--A Survey
## 2 Drone Networks  
Drones connected to mobile networks play a key role in enabling a wide range of services throughout various fields. The necessity for steady communication links while moving is a major challenge that must be thoroughly investigated. Therefore, this section provides a brief background on drone networks connected to mobile networks.
# A Wireless EEG System for Neurofeedback Training
## 3 System Architecture of the EEG Device
### Transmission Module
#### 3.2.3 Microcontroller  
The choice of a high-performance central processing unit (CPU) was inappropriate for the present battery-powered EEG device. The selected CPU should reach an analog front-end sampling speed of 1 ksps to communicate with the device's wireless modules. A microcontroller PIC18F2420 [28] was chosen for a CPU at 16 MHz. It can operate at a supply voltage of 3.3 V, with the low power consumption in operating mode being up to 40 MHz. The microcontroller can switch to sleep mode (1 MHz). Its hardware SPI port made programming the system very easy. The supply current was passed through the pin of the bypass capacitor and then to the supply pin for a more effective bypass. Potential crosstalk between multiple ADCs on the same printed circuit board (PCB) was minimized by broad power-supply traces. Minimizing the power-supply and reference-return trace couplings involved routing both traces separately on the AVSS pin as a star connection.  
Overall, the fine performance of the ADS1298/9 comprised high input impedance and satisfactory temporal accuracy of simultaneous acquisition of multi-channel EEG data.
## 2 System Model
### Communication over WLAN  
IEEE 802.11b protocol [26] specifies two medium access coordination functions, the mandatory distributed coordination function (DCF) that is based on Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) and the optional point coordination function (PCF). Unlike wired nodes, wireless nodes cannot detect collisions because they are half-duplex, i.e. they cannot send and receive signals at the same time. CSMA/CA delivers a best effort service, thereby providing no bandwidth and delay guarantees.  
In IEEE 802.11, each node senses the medium before starting a transmission. If the medium is idle for at least a DCF interframe space (DIFS), the packet is transmitted immediately. If the medium is sensed busy, the node waits for the end of the current transmission and then starts the contention, also called backoff process. The node selects a random backoff time. During the backoff process, the backoff timer is decremented in terms of slot time as long as the medium is idle. When the medium is busy, the timer is frozen. When its backoff timer expires, if the network is still idle, the data packet is sent out. The node having the shortest contention delay wins and transmits its packet. The other nodes just wait for the next contention. If another collision occurs, a new backoff time is chosen and the backoff procedure starts over again until some time limit is exceeded.
## 3 Overview of LPWAN
### Wide or Extended Coverage  
Wide coverage is also one of the paramount strongpoints for LPWAN mass deployments. The extended coverage range allows end devices to stay connected to base stations that are probably kilometers away. Thus, the network infrastructure deployment is suited for varieties of applications that require long range connectivity such as smart cities, power grid management, and smart agriculture [40] and applications such as underground pipelines where existing conventional communication technologies struggled in reaching.
## 2 Latent Thermal Storage Design  
The latent thermal energy storage system described in this paper was specifically designed for the integration of PCM into a vapor compression system, in order to reduce the complexity of the integration of such components, as well as the bulkiness of a combined heat pump + TES system. It was manufactured by AKG (Hofgeismar, Germany) and is completely made of dip brazed aluminum 3000 alloy. It mainly consists of a central block, in which the passages for the three fluids are located: the PCM, the HTF, and the refrigerant of the vapor compression system. These consist of an array of parallel channels integrated in a unique container, which removes the need for extra components, as in the case of an external tank. In addition, the distributor for the HTF and the refrigerant are welded. The HTF and refrigerant distributors and manifolds are arranged in order to have a counter flow and, therefore, improve the heat transfer.  
The ordered channel distribution is shown in Figure 1 and was optimized according to the experimental activity described in [25]. The external dimensions of the LTES and the volumes of the fluids for the HEX tested are listed in Table 1. The overall number of channels is 76: 44 for the PCM, 22 for the HTF, and 10 for the refrigerant. The LTES was insulated with 20-mm-thick mineral wool.  
As visible in Figure 1, several layers of PCM can be placed in between the HTF and the refrigerant passages. Each layer corresponds to a fin  
\begin{table}
\begin{tabular}{c c c c} \hline  & **PCM** & **Refrigerant** & **Coulant** \\ \hline volume (dm\({}^{3}\)) & 52.5 & 2.2 & 5.4 \\ \hline hydraulic diameter (m) & 0.0035 & 0.0022 & 0.0024 \\ \hline cross sectional area (m\({}^{2}\)) & 0.0534 & 0.0021 & 0.0054 \\ \hline heat transfer area (m\({}^{2}\)) & 57.7 & 3.94 & 8.83 \\ \hline number of passages & 44 & 10 & 22 \\ \hline core length \(\times\) width \(\times\) depth (m) & \multicolumn{3}{c}{\(1\times 0.60\times 0.16\)} \\ \hline empty weight (kg) (Al) & \multicolumn{3}{c}{\(110\)} \\ \hline \end{tabular}
\end{table}
Table 1: Main features of the latent TES investigated.  
Figure 1: Channel distribution in the latent storage. Coolant indicates the HTF.  
dimensions shown in Figure 1. Stacking of multiple layers is achieved by brazing of the different channels.  
The chosen PCM for the application is a commercial paraffin produced by RubiTherm, whose main properties are summarized in Table 2. For the HTF and the refrigerant, properties from the TIL Media package were used, which were in turn taken from CoolProp library [27].
# Urban Flood Risk Assessment Based on a Combination of Subjective and Objective Multi-Weight Methods
## 2 Materials and Methods
### Combination of Multi-Weight Methods
#### 2.3.2 CRITIC Weighting Method to Determine Objective Weights  
The CRITIC weighting method is an objective weighting method proposed by [PERSON], which combines independence weight and information weight [47]. The CRITIC weighting method is an objective weighting method that takes advantage of the variability of and correlations between the data to determine the weights, mainly through the two aspects of the comparative strength of and conflict between the indicators [48].  
1. The comparative strength of indicators was calculated. In the CRITIC weighting method, a standard deviation analysis can be used to judge and express the internal values of and data difference changes in each indicator. The greater the difference value, the greater the change in the number and difference of the indicator, and the more information can be reflected from it. The stronger the evaluation intensity of the indicator itself, the more weight should be allocated to the indicator itself. The formula is as follows: \[s_{j}=\sqrt{\frac{1}{m-1}\sum\limits_{i=1}^{m}\left(r_{ij}-\overline{r_{j}} \right)^{2}}\] (7) where \(\overline{r_{j}}\) is the mean value of the \(j\)th indicator after standardization and \(m\) is the number of evaluation objects.
2. Indicator conflictability was calculated.  
In the CRITIC weighting method, if the correlation coefficient is used to express the information correlation between indicators, the information correlation with other indicators is stronger. It can be explained that the smaller the information conflict between the indicator and other indicators, the more the same amount of information will be reflected and the more repetitive the evaluation content can be, which will weaken the comprehensive evaluation intensity of the indicator to a certain extent. The distribution of the comprehensive weight of the indicator should be reduced as much as possible. The formula is as follows:  
\[R_{j}=\sum\limits_{i=1}^{p}\left(1-\frac{\text{cov}(j,i)}{s_{i}s_{j}}\right) \tag{8}\]  
where \(i=1,2,\ldots,p,\text{cov}(j,i)\) is the covariance between the \(j\)th indicator and the \(i\)th indicator, and \(p\) is the number of indicators.  
The integrated information was calculated.  
\[C_{j}=S_{j}R_{j}\left(j\ =\ 1,\ 2,\ \ldots,\ p\right) \tag{9}\]  
A larger \(C_{j}\) means that the \(j\)th evaluation index has a greater impact on the whole comprehensive evaluation index system and that more weight should be assigned to it.  
CRITIC method indicator weights were determined.  
\[w_{Cj}=\frac{C_{j}}{\sum\limits_{j=1}^{p}C_{j}}\left(j\ =\ 1,\ 2,\ \ldots,\ p\right) \tag{10}\]  
Through CRITIC-entropy weight combination assignment, the combined weight of each secondary indicator was obtained as follows:  
\[w_{j}=\frac{w_{Cj}\times w_{Ej}}{\sum\limits_{j=1}^{p}w_{Cj}\times w_{Ej}} \tag{11}\]
# An Application of BOTDR to the Measurement of the Curing of a Bored Pile
## 1 Introduction  
In the pile foundation engineering, the heat generated by the concrete curing of a bored pile is released, which obviously leads to the deformation of a bored pile. A large amount of heat is released in the hydration of cement, which causes the volume of concrete to change. The strain of pile will increase; it can affect the quality of the pile. Due to the constraint effect of surrounding soil, it causes the generation of constraint stress. The occurrence of pile tension seriously affects the bearing performance of the bored pile. Some experiments have yielded some interesting results. [PERSON] et al. used a vibrating string strain gauge to monitor the temperature and stress of the bored pile in a backfill project. It is found that the temperature and strain of the pile at the hydration stage of concrete cannot be neglected [1]. [PERSON] et al. conducted an on-site monitoring test of the dissipation process of hydration heat after pouring concrete of the pile; the temperature change and constrained stress distribution law of the concrete bored pile are obtained, and the study shows that the maximum residual stress of the pile cannot be neglected [2]. [PERSON] et al. conducted the monitoring tests on bored cast-in-place piles of an extra-large bridge, and the study preliminarily showed the distribution of strain and temperature of the pile along the depth direction caused by concrete hydration [3]. [PERSON] et al. studied the influence of hydration heat on the bored pile and permafrost in Hoh Xil area; the influence of hydration heat on the freezing stability of permafrost was studied [4].  
To sum up, a large number of studies have shown that the hydration heat of the cast-in-place pile has a non-negligible influence on the deformation; the stress and temperature of the pile are particularly prominent [5; 6; 7; 8; 9; 10]. However, the research on the hydration of concrete bored piles mostly uses the traditional temperature or strain point sensor, which is complicated in laying out and less accurate. In particular, the point monitoring method can only measure a little data of the pile; it is difficult to learn the temperature and strain change of the pile. The Brillouin Optical Time Domain Reflectometer (BOTDR) technology has the characteristics of being distributed, long-distance, real-time, high precision, and having more durability [11; 12; 13]; it can be used to measure the strain of the pile.  
In this paper, the field test experiment is carried out to monitor the strain of the pile during concrete curing; the project is a large business office building in Suzhou city, China, and the BOTDR distributed optical fiber sensing technology is used. The results will provide the references for further research on the hydration effect of the pile during concrete curing.
## 2 Optical Scanning Holography  
The system of optical scanning holography that we have employed for hologram acquisition is shown in Figure 1. A linearly polarized laser of wavelength \(\lambda\) is passed through a half-wave plate (HWP), which rotates the polarization direction of the laser beam along the bisector of the two principal axes of the electro-optic crystal of the electro-optic modulator (EOM). Since there is birefringence induced along one of the principal axes through external electrical saw-tooth signal, the output beam of the EOM contains two orthogonal polarizations with a frequency difference \(\Omega\). The beam of light with orthogonal polarization is split into two beams with polarizing beamsplitter PBS1.  
Figure 1: An optical scanning holography (OSH) system.  
One beam, expanded by beam expander BE1, is reflected to beamsplitter BS2 by mirror M1. The other beam passes through another HWP, and is reflected by mirror M2 to BS2 through beam expander BE2 and lens L1. Subsequently the pair of beams goes through lens L2, and impinges on the test sample after passing beamsplitter BS3. Note that one beam projecting on the sample is a plane wave (dotted lines) while the other beam is a spherical wave (solid lines). Hence on the sample, we have a time-dependent Fresnel zone plate (TD-FZP) as the interference of a plane wave and a spherical wave gives an FZP and the frequency difference \(\Omega\) of the two beams gives running fringes within the overall scanning beam on the sample [1,2]. A \(x\)-\(y\) table is utilized to move the sample along a zigzag scan path. At each position of the scan path, the optical waves scattered by the sample is directed to photodetector PD1 through beamsplitter BS3 and lens L3. The output of PD1 is band-pass filtered at \(\Omega\) to give the Signal output. At the same time, the pair of beams is also combined in beamsplitter BS2, impinging on photodetector PD2. The output of PD2 is band-pass filtered at \(\Omega\) and taken to be the Reference for the lock-in amplifier. The lock-in amplifier mixes the Signal and the Reference to form the hologram which is composing of a sine hologram, \(H_{\sin}(x,y)\), and a cosine hologram, \(H_{\cos}(x,y)\)[1,2]. Mathematically, suppose the 3-D object is divided into \(N\) uniformly separated image planes that are parallel to the hologram, and the \(j^{th}\) image plane that is located at an axial distance \(z_{j}\) to the hologram is denoted by \(I_{0}\big{(}x,y;z_{j}\big{)}\), the hologram acquired with OSH is given by  
\[H(x,y)=H_{\cos}(x,y)+iH_{\sin}(x,y)=\sum_{j=0}^{N-1}H_{k}\big{(}x,y;z_{j}\big{)} \tag{1}\]  
where \(H_{k}\big{(}x,y;z_{j}\big{)}=I_{0}\big{(}x,y;z_{j}\big{)}\otimes F\big{(}x,y;z _{j}\big{)}\) with \(\otimes\) denoting the operation of convolution, and  
\[F\big{(}x,y;z_{j}\big{)}=\frac{1}{i\lambda z_{j}}\exp\left[\frac{i2\pi}{ \lambda}\sqrt{\left(x\delta\right)^{2}+\left(y\delta\right)^{2}+z_{j}^{2}}\right] \tag{2}\]  
is the spatial impulse response of propagation of light [1], where \(\delta\) is the pixel size of the hologram.
## 3 Methodology
### System Model
#### 3.1.1 Power Consumption Modeling  
The power consumption of the PM is described as a scale of CPU utilization with linear relation. So, a server's power consumption is a function of its CPU utilization, as shown below:  
\[\mathbb{P}(h)=\begin{cases}\mathbb{P}^{iide}(h)+(\mathbb{P}^{max}(h)-\mathbb{P}^ {iide}(h))\times\mathbb{U}^{cpu}(h),&if\ \mathbb{U}^{cpu}(h)>0,\\ 0,&otherwise,\end{cases} \tag{1}\]  
where \(\mathbb{P}^{iide}(h)\) and \(\mathbb{P}^{max}(h)\) are the power usage of host \(h\) when it is within an idle and 100% utilization state. \(\mathbb{U}^{cpu}(h)\) denotes the normalized CPU utilization. The overall power draw for the data center will be the summation of all the PMs in the data center.
## 2 Materials and Methods
### Feature Importance  
In order to gain insight into the data and the focus given by each machine learning algorithm to the different features, for each of the one thousand trials we computed the relevance and the frequency of the variables employed by each model. Feature importance was obtained with Scikit Learn's 'feature_importances_' property, which returns the relative importance scores for each input variable. For DT, this is calculated as the improvement in performance in a node, weighted by the proportion of samples in that same node, whereas for the ensemble methods RF and XGB, the feature importance is averaged across all trees within the model. These scores represent the usefulness of each variable in predicting each class label and provide information on their function in the predictive model. For each feature with a relative importance score different from zero, we also counted its frequency across the trials and represented their distribution as histograms.