esa-sceva/satcom-chunk-collection huggingface dataset:
# An Experimental Study on the Reflection Characteristics of Laser Echo Light Waves
## 3 Experimental Scenario and Configuration
### Measurement Link Selection  
From October 2023 to July 2024, the experimental team conducted experiments on return path laser transmission in Beilin District, Xi'an City, Shaanxi Province. During this period, two return path laser transmission measurement experimental links with different distances were established, and multiple measurement experiments were conducted at night to collect a large amount of light intensity data. The specific conditions of the two links are as follows:  
Figure 1: Schematic diagram of laser measurement experimental system.  
Link 1: The transmitting end is located on the 8 th floor of Discipline Building No. 2, Jinhua Campus, Xi'an University of Technology, and the reflecting end is located on the 11 th floor of the Teaching Building No. 6, Jinhua Campus, Xi'an University of Technology. The overall link length is 0.4 km, and return path two-way transmission is adopted. The transmission distance is 0.8 km. The link is about 40 m above the ground. The underlying surface of the transmission path is mainly composed of buildings and trees. The transmission field of view meets the requirements of the laser transmission experiment.  
Link 2: The transmitting end is located on the 8 th floor of Building 2 of the Jinhua Campus of Xi'an University of Technology, and the reflecting end is located on the rooftop of Building B of Xinyayuan Community in Beilin District, Xi'an. The overall link length is 2 km, and the return path is used for two-way transmission. The transmission distance is 4 km, and the link height is about 100 m above the ground. The underlying surface of the transmission path is mainly the cement floor of the building, and the transmission field of view meets the requirements of the laser transmission experiment.
# Review of Power System Resilience Concept, Assessment, and Enhancement Measures
## 3 Understanding Power System Resilience
### Differentiate Resilience from Similar Concepts
Reliability is assessed for a system under unspecified threats, while robustness and resilience are associated with specific threats. Reliability often assumes that system components and their actions are independent of each other, while resilience places greater emphasis on the coupling between network components. Reliability relies on historical data for analysis, while robustness and resilience analysis do not depend on this. Reliability is based on the operation of the power system and is considered a static behavior. Robustness is a passive method based on system security, while resilience is an active method that responds in real time to changes in network topology and takes immediate action. It is considered a dynamic behavior. Reliability and robustness primarily consider expected failures during system design, while resilience focuses on the impact of events without considering their probability of occurrence. The differences between reliability, robustness, and resilience are compared in Table 4[2; 7; 10; 56].
# A New Security Proof for Twin-Field Quantum Key Distribution (QKD)
## 3 Protocol
### Entanglement-Based Version  
Instead of analyzing the above protocol, we will instead analyze the following entanglement-based protocol. It is not difficult to see that security of the following entanglement-based version will imply security of the above prepare-and-measure version. The entanglement-based version operates as follows:  
1. Eve creates a quantum state \(\left|\psi\right\rangle_{ABCE}\), where the \(A\) and \(B\) portions consist of \(N\) qubits each, while the \(C\) portion lives in a Hilbert space spanned by orthonormal basis \(\mathcal{C}=\{\left|{}^{\alpha}\!c^{\prime\prime}\right\rangle\}\) for all \(c\in\{\)\"0\", \"1\", \"0\", \"2\"\(\}^{N}\) (here, \"\(\prime\)\" will denote a vacuum observation and \"2\" an \"other\" event). The \(E\) portion is arbitrary. [PERSON] and [PERSON] are given the \(A\) and \(B\) registers, while the \(C\) register is sent to a trusted third party [PERSON].
2. [PERSON] measures his entire \(C\) register in the \(\mathcal{C}\) basis, broadcasting the result to all parties. [PERSON] and [PERSON] discard all qubits rounds where the reported outcome was \"_vac_\" or \"?\". Let \(N_{c}\) be the number of remaining systems not discarded.
3. [PERSON] and [PERSON] choose a random subset \(t\subset\{1,\cdots,N_{c}\}\) of size \(m_{c}\) (which may depend on \(N_{c}\)), and measure their respective systems, indexed by this subset, in the \(X\) basis which they subsequently broadcast to determine the fidelity of their state.
4. [PERSON] and [PERSON] measure the remaining systems in the \(Z\) basis, leading to their raw key. They then further process this through error correction and privacy amplification as normal.  
Entanglement-based versions of \(\Pi\)-Zero and \(\Pi\)-One are defined similarly, with only step 2 being changed.  
Note that in the entanglement-based version, [PERSON] does not apply a Pauli correction gate, since [PERSON] gets to prepare not only [PERSON] and [PERSON]'s state but also the state that would normally have been output from the BS; it is advantageous for [PERSON] to \"simulate\" the Pauli correction before sending it to [PERSON] (though she does not have to; however, not doing so would lead to additional \(X\) basis noise). It is not difficult to see that security of the entanglement based version above will imply security of the actual TF-QKD protocol. In the next section, we show a new proof of security, deriving an entropy bound for the entanglement-based version, which will subsequently produce a key-rate bound for the TF-QKD protocol.  
We note that the protocols above are not novel; they are, at most, very slight variations of protocols from [19]. \(\Pi\)-Total is identical to prior work in [19], while \(\Pi\)-Zero and \(\Pi\)-One are only minor variations of that protocol. As discussed in the introduction, the novelty of our work is in an alternative security proof, derived in the following section.
## 4 Applications of Intelligent Acoustic Monitoring
### Smart Cities and Urban Monitoring
#### 4.2.7 Crowdsensing and Acoustic Zone Classification  
In [117], [PERSON] et al. proposed a practical application of unsupervised learning methods, specifically the k-means algorithm, to analyse long-term acoustic data collected by a network of sound sensors deployed in Barcelona. The aim was to classify urban areas based on their acoustic characteristics, using annual noise indices (such as Lday, Lnight, and Lden) without requiring labelled data. The model enabled the identification and interpretation of four main classes of acoustic environments, allowing for the tracking of seasonal changes and the impact of special events (such as lockdowns), making it a valuable tool in supporting urban noise management strategies. Notably, the comparison with the official noise map showed the high consistency of the results and the possibility of detecting new areas requiring attention. It is worth noting that that work has significant practical implications, as it demonstrates how relatively simple machine learning methods can effectively monitor and manage the acoustic environment of a large city in an automated and scalable manner.  
In [118], [PERSON] et al. developed and implemented a scalable crowdsensing system for monitoring city noise pollution, based on a mobile application, cloud computing, and big data infrastructure. The solution enabled residents to collect acoustic data from microlocations inaccessible to traditional measurement systems, either actively or passively, and then send them for real-time analysis. The experiment conducted in Belgrade confirmed the system's usefulness for citizens and city authorities, enabling the identification of local noise problems and supporting planning and environmental decisions. Compared to existing solutions, the system stood out with its technological openness, flexible methodology for selecting locations, the possibility of integration with other systems, and the use of micro-scale data analytics. Although the authors acknowledged certain limitations, including measurement quality variability and the risk of abuse, the overall assessment suggested that the platform had great potential as a cost-effective, adaptive, and socially engaging tool for smart cities.  
There is a wide variety of approaches, from extensive infrastructure systems to participatory crowdsensing applications. The common denominator is the pursuit of low-cost, scalable, and socially oriented monitoring.
# Adaptive Noise Cancellation Based on Time Delay Estimation for Low Frequency Communication
## 2 Adaptive Noise Cancellation Based on Time Delay Estimation
### Explicit Time Delay Estimation  
Suppose that the relationship between \(d\left(k\right)\) and \(x\left(k\right)\) is:  
\[d\left(k\right)=Ax\left(k-D\right) \tag{13}\]  
where \(A\) is the attenuation factor and \(D\) is the time delay between the primary channel and reference channel. As shown in Figure 2, the finite impulse response (FIR) filter coefficients are replaced by a truncated sinc function in ETDE algorithm, then \(x\left(k-D\right)\) can be expressed as:  
\[\tilde{x}\left(k-D\right)=\sum\limits_{i=-P}^{P}x\left(k-i\right)\sin c\left(i -D\right) \tag{14}\]where, \(P\) should be set large enough to reduce the time delay modeling error and  
\[\sin c\left(u\right)=\frac{\sin(\tau u)}{\tau u} \tag{15}\]  
When maximum correntropy criterion is used in ETDE algorithm, the cost function becomes:  
\[J_{ETDE}(D)=\frac{1}{L}\sum_{i=k-L+1}^{k}G_{\sigma}(d(i)-\tilde{x}\left(i-D \right)) \tag{16}\]  
Then, the time delay estimation value \(D\left(k+1\right)\) at \((k+1)\)th iteration can be obtained through:  
\[D(k+1)=D(k)-\frac{\mu}{\sigma^{2}}G_{\sigma}(e(k))e(k)\sum_{j=-P}^{p}x\left(k -j\right)f\left(j-D(k)\right) \tag{17}\]  
where \(f(v)=(cos(\tau v)-sinc(v))/v\).
# A New Defect Diagnosis Method for Wire Rope Based on CNN-Transformer and Transfer Learning
## 4 Case Studies  
### Wire Rope Dataset  
The dataset used in this paper was collected using a wire rope LMA signal detector, as illustrated in Figure 9a. The main type of defect was a broken wire rope which is indicated in Figure 9b,c. Under different working conditions, the LMA signals of four wire ropes used in the mine hoist were obtained and divided into groups A, B, C, and D, respectively. According to the image establishment method mentioned in Section 3.2, the number of wire rope feature images is presented in Table 4. It can clearly be observed that the highest ratio between the number of normal and defect signals is approximately 50:1. This serious imbalance dataset has a negative effect on source domain training and prediction tasks [34]. Therefore, sample augmentation for the defect images was necessary, and the augmentation algorithm steps are depicted in Table 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Patch Size** & **Encoder Block** & **Hidden Size** & **MLP Size** & **Heads** \\ \hline
4 \(\times\) 4 & 12 & 512 & 2048 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The parameters of DCNNT encoder variants.  
Figure 9: Illustration of non-destructive signals detector and wire ropes. (**a**) Wire rope LMA signal detector; (**b**,**c**) wire rope defects.  
In this paper, sampling performed with the overlap method was used as the augmentation strategy. As shown in Figure 10, unlike the image establishment algorithm, the extensive number increases to 99 on both sides (100 is the center of the defect). Then, the sampling window moves to the end in a stride of 10, and 11 samples were collected from the same defect sample. The start and end states are presented in Figure 10a,c, respectively, while the middle stage is presented in Figure 10b. Following the augmentation step, the defect data were expanded 11 times, compared with the original data, as shown in Table 4.
# Outdoor Content Creation for Holographic Stereograms with iPhone
## 3 Results
### Visual Comparison of Perspective Images of Four Methods  
A comparison of the four interpolation methods (Figure 10a) alongside photogrammetry (Figure 10b), Gaussian splatting (Figure 10c), and LiDAR (Figure 10d) reveals the distinct characteristics and outcomes of each method, as demonstrated by the results.  
The interpolation method yields clear and realistic images in which the textures and details of the statue, text, and flowers are well-preserved. This method excels in detail retention and accurate color representation, making it ideal for applications where visual fidelity is crucial. However, its success is contingent upon the quality of the initial images, and any errors in the alignment during capture can propagate to the final image.  
Photogrammetry results in images with medium visual quality. There is a noticeable loss of fine details (such as the glasses on the face, separation of flowers, and legibility of text) and a slight blur, due to the stitching and alignment of multiple images. This method effectively captures the 3D structure and overall form, providing a good balance between detail and depth. However, defects are evident, and color accuracy may vary, impacting visual fidelity.  
Gaussian splatting introduces color distortions and noise, particularly purple artifacts around objects. This method offers a unique textural representation that may be useful in artistic applications or where textural depth is more important than color accuracy. Nevertheless, the visual quality is compromised by noticeable artifacts, blurring, and some holes in the model, thus reducing overall realism. While the face, flowers, and text were better reconstructed than with photogrammetry, they were still inferior to the results obtained by interpolation.  
LiDAR produces images with significant color distortion and loss of detail, rendering the object less realistic. The text and flowers become unrecognizable, and the rendering of the head completely fails, making it the method with the lowest visual quality among those evaluated. Although LiDAR offers quick capture and processing times and excels at  
Figure 10: Visual comparison for the four methods; interpolations (**a**), photogrammetry (**b**), Gaussian splatting (**c**), and LiDAR (**d**).  
capturing geometric details, its poor color accuracy and lack of detail limit its suitability for applications requiring high visual fidelity.
## 3 Results and Discussion
### Extraction Parameter Optimization and Analysis Method
For HS-TWA vial sampling these parameters were combined with the other optimized HS conditions and assumed to be optimal. It is known that pH can change THM formation in natural waters [11]. Hence, it is important to maintain the same pH in the sampling vial. Ascorbic acid has been used [26] for slight pH adjustments, as it does not seem to change THM complexation. In order to re-adjust the pH to the water samples (pH-6, see Table 1) ascorbic acid was added to the vial after 10% wt./vol. of (NH4)2 SO4 salt addition. It has been noted that addition of (NH4)2 SO4 salt can enhance THM extraction [16]. In sampling collected natural waters, a 10% wt./vol. of the salt was simply added to the water sample vials before extraction and analysis. For on-site sampling or for the determination of THMs under real water conditions this is not possible. Hence SPME sampling was carried out in three ways: (1) analysis by all methods from septum-sealed vials, were the salt and ascorbic acid was added; (2) TWA-SPME sampling in septum-sealed vials, with no salt or acid added; and (3) under on-site TWA-SPME sampling, when no salt or acid could be added. Under salt added conditions, for all methods, analysis of the six THM  
Figure 5: Typical SPME fiber extraction profiles used for (**a**) HS-TWA SPME; (**b**) DI-SPME; and (c) HS-SPME methods to determine optimal sampling times for four target THMs. showed about a 10% GC peak area increase for all THM standards over similar non-salt added analysis. For on-site sampling, the natural water was found to have a conductivity of 60 \(\upmu\)S, suggesting a low salt concentration (see Table 1). Hence a set of both non-salt (at 60 \(\upmu\)S conditions) and a 10% salt-added calibration curves were created in order to relate on-site TWA and vial analyzed TWA conditions (see Table 2). The calibration curve obtained by HS-TWA vial sampling with no salt added was used for both HS-TWA sampling methods (by vial sampling and by on-site sampling) to determine the data (Table 1), for the two ponds under study.
# An Interactive Real-Time Locating System Based on Bluetooth Low-Energy Beacon Network +
## 2 Related Work
### Bluetooth Beacon Technology  
The Bluetooth beacon technology has gained significant attention in providing proximity-aware services for consumers, businesses, and industrial environments. There are mainly three \"pseudo-standards\" for Bluetooth beacons: iBeacon [12], Eddystone [28], and AltBeacon [29], proposed by Apple, Google, and Radius Networks, respectively. All three pseudo-standards are based on broadcasting BLE advertising packets, which carry specific information in the payload area. When a Bluetooth scanner receives an advertising packet, it decodes the content and takes corresponding actions. The message format of iBeacon consist of 4 fields: UUID, Major, Minor, and TX power. By comparing the received signal strength of the iBeacon message and the value of TX power in the message, the receiver can determine the approximate proximity to the iBeacon transmitter. For Eddystone, it has four message formats served for different purposes: Eddystone-UID, Eddystone-EID, Eddystone-TLM,and Eddystone-URL. Take Eddystone-URL as an example, this type of message is used to broadcast an URL that redirects the receiver to a website, which realizes the so-called physical web [30]. AltBeacon was designed for an open and interoperable specification. The message format of AltBeacon consists of Manufacturer ID, Beacon Code, Beacon ID, and Reference RSSI. Compared with iBeacon, an AltBeacon message has more user data bytes, which delivers more data per message.
###### Abstract  
Low-density parity-check (LDPC) codes are commonly used in communication systems to improve the system performance, but LDPC codes takes too long for decoding, making communication inefficient and unsuitable for short-frame data transmission methods. In underwater acoustic channels, spread-spectrum communication becomes an effective way to realize long-distance communication. This paper combines short-block LDPC codes with a direct sequence spread spectrum and soft spread spectrum in underwater acoustic communication, addressing the problem of the inapplicability of conventional LDPC codes. The applicability of the proposed method in this paper is verified through simulation tests and pool experiments. The results indicate that the proposed communication system achieves lower bit error rates compared to the classical coding methods used in underwater acoustic spread-spectrum communication systems under the same channel conditions.  
underwater acoustic communication; spread-spectrum communication; short-block LDPC codes +
Footnote †: journal: _electronics_  
0
Footnote 0: Copyright** © 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/))).
## 3 Results
### Wavelet Residual Network
The time required to train a model is proportional to the size of the dataset, so choosing a smaller number of samples can significantly reduce training time and improve efficiency. By using the t-SNE algorithm [21], Figure 15 shows the classification of data points at each level after t-SNE dimensionality reduction. Ten colors are used to represent ten categories of fault signals. By observing the relative positions and clustering structure between data points, we can analyze the classification situation more intuitively. Suppose  
Figure 14: Accuracy of the validation set under different numbers of samples. Figure 13: Accuracy of the training set under different numbers of samples. the data points of the same category are clustered together in the dimensionality-reduced space, forming a tight cluster structure. In that case, the model classification ability in this category can be considered as good. Conversely, if data points of the same category are scattered in different regions of space or those of different categories are clustered, there may be misclassification or confusion. Figure 15a shows the original vibration signal's distribution. Since these signals are not processed, the data points of different categories are randomly aliased together, and no obvious classification effect can be observed. Figure 15b shows the classification results after the original Morlet wavelet transform. It is apparent that there is aliasing between the data points of 0 and 1, and there is also aliasing between the data points of 7 and 8. This shows that the original Morlet wavelet transform has not been able to effectively separate different types of data points. Figure 15c shows the classification results of the improved Morlet wavelet transform. Compared to Figure 15b, data points with different labels are more distantly separated, and data points with the same label are more closely clustered. This shows that the improved Morlet wavelet transform has achieved some improvement in reducing the aliasing effect between features. However, a small amount of aliasing can still be observed for data points 1 and 5. Figure 15d shows the classification result after further processing by the residual network. It can be seen that data points of different types  
Figure 15: Visualization of classification results. (**a**) Ten types of original vibration signals. (**b**) After the original Morlet wavelet transform. (**c**) After improved Morlet wavelet transform. (**d**) Wavelet residual network classification results. are completely separated, and those of the same type are closely clustered. This indicates that the model has good classification performance after residual network processing. In summary, Figure 15 shows the effect of different processing steps on vibration signal classification. The introduction of improved Morlet wavelet transform and residual network helps improve classification performance and feature separation so that different types of data points can be better distinguished.