esa-sceva/satcom-chunk-collection huggingface dataset:
# Leveraging Large Language Models for Sensor Data Retrieval
## 5 Evaluation
### Dataset  
The evaluation proposed aims to measure the precision and MRR of the data retrieval component. However, no existing dataset was available for testing retrieval systems involving sensor data. Hence, it was necessary to curate a dataset from scratch.  
Initially, raw sensor data were retrieved by following the architecture outlined in Section 3.1 focusing on meteorological data. The selected portal was the aforementioned Censys, which is a scanning service specifically engineered to aggregate information about Internet devices and resources. It is open source and freely available for academic purposes. The search carried out on meteorological data provided 35 datasets in HTML format.  
Subsequently, an LLM (GPT-3.5 Turbo from OpenAI) was employed to transform these datasets into CSV format, as described in Ref. [49]. The average number of tokens in each input file was 98,885.87, so most of the files to be transformed by the LLM had to be split into several files that met the maximum input tokens of the model (16,385 tokens in the case of GPT-3.5 Turbo). Once transformed, each individual file was merged back into a single file resulting in the complete transformation of the original file to CSV. The average transformation time per complete file was 2308.89 s. Note that this time depends on the infrastructure provided by OpenAI and may be constrained by API demand and file size.  
To complement this collection with additional CSV files for the evaluation, various open data portals were scanned for sensor data: Government of Spain ([[https://datos.gob.es/](https://datos.gob.es/)]([https://datos.gob.es/](https://datos.gob.es/)), accessed on 10 December 2023) (1194 tables), Government of the United States ([[https://data.gov/](https://data.gov/)]([https://data.gov/](https://data.gov/)), accessed on 10 December 2023) (54 tables), and Valencia City Hall ([[https://valencia.opendatasoft.com/](https://valencia.opendatasoft.com/)]([https://valencia.opendatasoft.com/](https://valencia.opendatasoft.com/)), accessed on 10 December 2023) (16 tables). The final dataset comprises 1299 tables in CSV format encompassing data from sensors across different domains, including environment, transportation, energy, science, and technology. Table 1 provides an overview of the primary characteristics of the compiled dataset.
# Performance Analysis of the Pilot and Data Component of a CSS Signal for LEO-PNT +
## 2 Signal Model
### Data Transmission  
Once the signal is synchronized, we can assume \(\tau=f_{\rm D}=0\), so that data reception can be achieved through a simple de-chirp process when transmitting the general CSS waveform in (1). The details for the data component will be given in Section 3.2. Here, let us give a summary of the advantages and drawbacks of different CSS modulations from a PNT signal design's point of view:  
* BOK-CSS: A digital modulation scheme sometimes considered in practice for chirp signals is the so-called BOK for communications [16]. Note that although considering up- and down-chirp signals, the BOK chirp scheme considered here is different from the one used in the pilot component [4]. In this case, the transmitted signal is written as \[s_{\rm CBOK}(t)=b(t)e^{\lvert 2\pi\int_{0}^{t}[\mu t]_{\rm B}\mathrm{d}u}+(1-b(t) )e^{-\lvert 2\pi\int_{0}^{t}[\mu t]_{\rm B}\mathrm{d}u},\] (2) with \(b(t)=\{0,1\}\) as the data bit. Note that only one bit per \(T_{\rm c}\) can be sent, which limits the data-rate capability.
* PSK-CSS: A phase shift keying (PSK)-CSS modulation is given by [17]: \[s_{\rm PSK}(t)=e^{\lvert 2\pi\int_{0}^{t}[\mu u]_{\rm B}\mathrm{d}u+\theta(t) \rvert},\] (3) where \(\theta(t)\) stands for the phase corresponding to the transmitted symbol at time \(t\). This modulation could be useful to increase the data-rate capabilities with respect to the BOK-CSS option when considering a higher-order modulation than BPSK.
* FSK-CSS: As long as the initial frequency of (1) is not used for other purposes (e.g., satellite identification), it can be used for data delivery as performed in LoRa [13]. In that way, each symbol in an \(M\)-ary constellation is assigned to a given initial frequency, \(s_{k}\). Then, the transmitted signal is written as \[s_{\rm PSK}(t)=e^{\lvert 2\pi\int_{0}^{t}f_{k}(w_{\rm s})\mathrm{d}u},\] (4) for \((k-1)T_{\rm c}\leq t\leq kT_{\rm c}\), and the instantaneous frequency given by \[f_{k}(t;s_{k})=[s_{k}+\mu t]_{B}.\] (5) This option can be useful to increase the data rate with respect to BOK-CSS, and it can provide a better alternative to PSK-CSS in terms of HW impact on current GNSSreceivers. Note, once synchronized, that the demodulation of frequency shift keying (FSK)-SS can be based on a simple de-chirp process.
# Fault Diagnosis of Intelligent Production Line Based on Digital Twin and Improved Random Forest
## 1 Introduction
The contributions of this paper are as follows:  
* We propose an improved random forest algorithm which first constructs multiple decision trees by bootstrap sampling, then refilter the decision trees by a hierarchical clustering method, calculate the differences between each tree, select the highest accuracy of each tree for integration, and finally conduct weighted voting. An improved random forest algorithm is constructed. The method is applied for fault diagnosis verification on a rear axle assembly line for automobiles, which proves that our method has higher accuracy than random forest. * In order to solve the problem of an unbalanced training dataset and the lack of a high volume of fault data in practical applications, we propose using digital twin to simulate a large amount of balanced data, and then transferring it to the physical production line. The data in the virtual space are used as a training set to train the improved random forest algorithm, and then hierarchical clustering is used to refilter the decision tree. Through the transfer of learning applied to the actual production line, we can then use real data to fine-tune and train a reliable model. * The effectiveness of our proposed method was verified by the actual automobile rear axle assembly line. The accuracy and efficiency of the DT + IRF algorithm are better than those of an ordinary machine learning algorithm. In Figure 1, we present an overview of this paper. At the physical entity layer, we will establish three-dimensional models of the tighten robot, loading robot, unloading robot, conveyor belt, four-wheel positioning device, etc., using lightweight tools for model optimization and building real-time mapping digital twin scenes. The connection between the background service and the source data storage system is established by calling relevant interfaces, so that the background service can obtain the status signals collected by PLC and sensors and display them in the visual interface. Then we will verify the data reliability to ensure that the data is not lost during transmission. Finally, the data is input into the algorithm for decision-making. A 3D visualized fault diagnosis system is established through digital twin technology and an IRF algorithm, and the fault diagnosis results are analyzed through data in real time. We will introduce the basic theory in Section 2. We describe the proposed DT + IRF method in Section 3. In Section 4 we use an automobile rear axle assembly line to verify the feasibility of the algorithm and present the experimental results. In Section 5 we compare our algorithm with others. Section 6 gives the conclusions of this paper. The abbreviations in the paper can be seen in the Abbreviation part.
## 4 Implementation and Results
### Comparison  
Now we measure the comparison of study I and study II to determine how much delay increases and throughput decreases, when there is no framework for the detection of malicious data and malicious nodes. Figure 8 and Figure 9 show that delay is much lower when SMBF is applied and throughputs also increase much more when using SMBF. All vehicles have sensors to detect the congestion and improve privacy [16].
# Extending Wireless Reschargeable Sensor Network Life without Full Knowledge
## 8 Stopping Methods
### Minimum Only Approach  
Algorithm 4 shows the Minimum Only implementation of the procedure canVisitMoreNodes. The Naive approach attempts to charge almost all the nodes it encounters until it runs out of power. Intuitively sacrificing a node from \(v\) in exchange for more exploration may lead to finding more lower powered nodes. The Minimum Only approach builds on this intuition and sacrifices high power nodes in \(v\), until the nodes with the lowest power are identified as the ones that need charging in \(v\). The algorithm uses the probabilistic bound Equations (2) and (3) to calculate the probability, \(pLo\), of a node existing at power level less than or equal to the lowest power level found in \(v\). The algorithm uses \(pLo\) to determine if further exploration should be conducted or terminated.  
The time complexity of Minimum Only (Algorithm 1) is \(O(1)\). It can be seen that almost all operations are of constant time \(O(1)\). There are two method calls finding the minimum (line 5) \(min(v)\) and probability computation findProbLess that need to be analyzed, given that finding the minimum node \(min(v)\) (line 5) in the charging list \(v\) is \(O(1)\). This is possible because the charging list is a sorted list, so accessing the minimum element takes constant time. In addition, computing the probability based on Equation (3) (findProbLess) is a constant time operation \(O(1)\). All this renders the Minimum Only approach \(O(1)\).  
The algorithm also defines a minimum probability, minThreshold, that must be exceeded, which enables the algorithm to keep exploring until it finds low power nodes. The algorithm also guarantees that it charges at least one node. This is achieved by making sure the total power needed to charge, \(C\), is greater than the power needed to charge at least the lowest power node in \(v\) identified as minNeededPower (lines 5-6). If the power used to charge is only used to charge a single node, then we terminate the exploration, since any movement further may not increase the life of the network.  
[PERSON] was not able to discover low power nodes located further down the network. While Minimum Only manages to overcome this limitation, and charge low power nodes located far from \(B_{S}\). On the other hand, there is a chance it is wasting power. The algorithm makes sure the lowest power node in \(v\) is a low power node. However, there still could be nodes in \(v\) with relatively high power levels, greater than the expected power level, rendering charging them less useful in extending the network life. The existence of high power nodes in \(v\) means either power is wasted in charging high power nodes, or power is wasted charging low power nodes to high levels preventing exploration. The next approach attempts to remove high power nodes from \(v\).
## 4 Design of the Equalizers
### Minimum Mean Square Error Equalizer  
Unlike the ZF, a minimum mean square error (MMSE) equalizer integrates the effect of noise into the criterion it attempts to optimize. Its purpose is to minimize the mean square error (MSE) between the transmitted sequence \(x(n)\) and its estimate at the output of the equalizer \(\hat{x}(n)\).  
Therefore, using the MSE criterion to determine the optimum coefficients of the equalizer is based on the minimization of the following function [15]:  
\[J=E\Big{\{}|e(n)|^{2}\Big{\}}, \tag{15}\]  
the correlation vector between the received signal and the transmitted one and \(\sigma_{\rm x}^{2}\) is the variance of the transmitted signal, and Equation (17) can be written as  
\[\rm J(C)=\sigma_{\rm x}^{2}-2P^{T}C+C^{T}RC, \tag{18}\]  
J(C) is a quadratic function of the parameters \(\rm\{C_{0},C_{1},\ldots,C_{L-1}\}\). Function J(C) has a unique minimum, obtained by taking the gradient equal to zero, such as  
\[\rm\partial J(C)/\partial C=-P+C^{T}R=0, \tag{19}\]  
from where it results in  
\[\rm C_{opt}=R^{-1}P, \tag{20}\]  
Equations (19) and (20) are known as the Wiener-Hopf equations.  
At the optimum, it can be written that  
\[\rm J(C_{opt})=\sigma_{\rm x}^{2}-2P^{T}C_{opt}+C_{opt}^{T}RC_{opt}=\sigma_{\rm x }^{2}-C_{opt}^{T}RC_{opt}=\sigma_{\rm x}^{2}-P^{T}R^{-1}P=\sigma_{\rm x}^{2}- \sigma_{\rm sopt}^{2} \tag{21}\]  
where \(\rm x_{opt}(n)=C_{opt}^{T}(n)\) is the optimal filtered signal and \(\rm\sigma_{xopt}^{2}=E\Big{\{}x_{opt}^{2}(x)\Big{\}}\) is the variance of this signal.  
Consequently, the relation (21) asserts that for the optimal filter, the MSE is the difference between the variance of the transmitted signal and the variance of the estimated signal produced by the filter.  
Thus, the value of MMSE for the optimal Wiener filter is according to  
\[\rm J_{min}=J(C_{opt}), \tag{22}\]
## 2 Materials and Methods
### Data Analysis  
Following data acquisition, MATLAB-based (version 24.1.0.2689473 (R2024a)) data processing methods were employed to refine signal quality and enhance the accuracy of the results. A series of analytical procedures were applied to address baseline normalization, peak identification, noise reduction, and data visualization, all aimed at optimizing the fluorescence signals for more reliable interpretation.  
Baseline normalization and wave subtraction was applied through integrated spectrophotometer software, to adjust for any shifts in the baseline by subtracting the minimum intensity value from each spectrum. This procedure centered the data around zero, reducing fluctuations and preparing the spectra for accurate comparison across samples. Moreover, for peak identification, a specific algorithm was employed to locate the primary fluorescence peaks associated with CRANAD-2's emission, and retrieving the targeted wavelength (Algorithm S1 in Supplementary Materials). The intensity values at these peaks were then extracted and analyzed in relation to the analyte concentration, providing insights into the quantitative relationship between fluorescence and concentration. Noise reduction was accomplished using a Savitzky-Golay smoothing filter [31]. This filter was selected for its ability to preserve the shape of the fluorescence peaks while reducing high-frequency noise. The optimal parameters for the filter were determined through iterative testing, with a polynomial order of 3 and a frame size of 77, which proved to be the best balance between noise suppression and signal preservation (Algorithm S2 in Supplementary Materials). To quantify the fluorescence response, a concentration-dependent analysis was performed. Fluorescence intensity data points were plotted against CRANAD-2 concentration, and linear regression was applied to generate a concentration-response curve. This curve provided a clear depiction of how fluorescence intensity varied with changes in analyte concentration, enabling the quantitative assessment of CRANAD-2's fluorescence behavior.
## 2 PQD Signal Visualization Processing Based on Lissajous Trajectory
### Sequence Visualization via Lissajous Trajectory
the trajectory features and have no impact on the final result). The expression of \(U_{\text{ISP}}\) is as follows:  
\[U\text{ISP}=\sin(\omega t+\frac{\pi}{2}) \tag{2}\]  
Based on the concept of LT curve synthesis, the waveform of the PQD signal defined in (1) and the ideal reference waveform in (2) are assigned as the horizontal and vertical coordinates, respectively, in a Cartesian coordinate system to generate LT characteristic curves corresponding to various PQD signals. To demonstrate the synthesis effect of LT on PQDs, nine of the most representative single disturbance signals given in IEEE Std 1159 (Table 1) are used for illustration. To align with the actual operating and measurement conditions of the power grid, the fundamental frequency is set to 50 Hz, and the sampling frequency is set to 12.8 kHz. The disturbance signals are sliced every four cycles to generate the Lissajous characteristic trajectories of nine single-PQD signals, as shown in Figure 2a-i. It can be seen in Figure 2 that the characteristic trajectories corresponding to various disturbances are clear and easily distinguishable. This not only facilitates feature extraction after the composite superposition of disturbances but also lays the foundation for accurate PQD classification using deep learning networks. Furthermore, compared to existing signal visualization methods such as GAF [11], TC [12], and ITC [13], the conversion process of LT is remarkably concise and efficient, requiring only the construction of an ideal reference signal for the synthesis of the horizontal and vertical coordinates. Figure 2: Lissajous trajectories of PQD signals. The red lines represent the LT corresponding to PQDs with waveform distortion, while the dotted lines indicate the LT corresponding to normal signal waveforms.
## 8 RAN Decomposition Scheme
### FWA System and Functional Split Implications  
In Section 8.2, we present the LLS and HLS FSOn that can be implemented between the CU and DU to meet the system requirements. The following subsection focuses on the FWA transport network and the functional split implications for both HLS and LLS options. Note that the merits and demerits of each transport network are based on factors such as the required RAN use case/deployment scenario, the accessible fiber infrastructure, as well as future migration plans. Usually, PtP fiber and PtP-WDM solutions are capable of supporting the lowest latency requirements. Similarly, in scenarios with little or inadequate fiber deployment, the PON-based schemes can be employed as viable solutions [8].  
In addition, when higher-layer RAN split options are implemented, a number of the conventional transport solutions can conveniently support the requirements per cell site by the FWA system [8]. As discussed in Section 8.2.3, this is because the imposed stringent bandwidth requirement is being relaxed by this split option. As the case may be, for the PON-based schemes, a dedicated or shared OLT can be employed to aggregate several ONUs. Moreover, dedicated or shared ONUs can also be used to aggregate some DUs [48]. However, when lower-layer RAN split options are implemented, it is highly imperative to take into account and exploit the emerging optical technologies regarding the system concepts and TRxs [8]. This is due to the imposed stringent bandwidth requirement by the split options, as discussed in Section 8.2.2. Furthermore, in an attempt to address the required system capacity for various applications, efforts are in progress on simplified and advanced higher-bit-rate optical TRx that are capable of supporting different rates such as 25, 40, 100 Gbps and even more [435].
## 4 Organizational Experimental Setup  
In the realm of security, a standard situation includes three participants: [PERSON], [PERSON], and [PERSON], depicted in Figure 1. [PERSON] wants to send [PERSON] a single confidential message, referred to as plaintext (P), while [PERSON] tries to intercept it. [PERSON] processes this plaintext to produce ciphertext (C), with both [PERSON] and [PERSON] receiving C and attempting to recover P. Their respective computations are represented by \(\mathrm{P_{[PERSON]}}\) and \(\mathrm{P_{[PERSON]}}\). Unlike [PERSON], [PERSON] and [PERSON] share a secret key (K), treated as an additional input for them. They may use one unique key per plaintext, but the lengths of K and P are not necessarily the same. Interestingly, all three parties--[PERSON], [PERSON], and [PERSON]--are modelled as neural networks with different parameters \((\theta_{A}\,\ \theta_{B},\ \theta_{C})\), which means that encryption and decryption may not be identical functions even if [PERSON] and [PERSON] have the same structure. They operate over floating-point numbers instead of sequences of bits, leading to the possibility that C, \(\mathrm{P_{[PERSON]}}\), and \(\mathrm{P_{[PERSON]}}\) might be arbitrary floating-point numbers, even though P and K may consist of 0s and 1s. Although constraints, in practice, limit these values to a specific range (e.g., \(-1\) to \(1\)), intermediate values are permitted.  
In Figure 4, we can see the flowchart of the experimental setup. Despite exploring alternative methods, the essential focus is not on those, but on a rudimentary setup that enables the possibility of [PERSON] and [PERSON] using K as a one-time pad, encrypting and decrypting simply by XORing the key K with the plaintext P and the ciphertext C, respectively. However, [PERSON] and [PERSON] are not mandated to function this way, and indeed, further experiments might reveal other schemes. This configuration, while basic, serves foundational designs, ignoring aspects like key generation from a seed or the use of randomness for probabilistic encryption. Such enhancements could be explored in future work, broadening the scope and complexity of this standard security situation.  
Figure 4: Flowchart of experimental setup.
## 2 First-Order Primal-Dual Algorithm-Based Sparse Time-Frequency Analysis
### Solution of the Model Based on the First-Order Primal-Dual Algorithm  
As the model is a set of convex optimization problems with constraints, it can be rewritten as an unconstrained optimization problem, i.e., Equation (9), which is convenient for solving, as follows:  
\[\hat{\mathbf{X}}_{m}=\underset{\mathbf{X}_{m}}{\text{argmin}}\|\mathbf{X}_{m}\|_{1}+\frac{ 1}{2}\|\mathbf{Z}_{m}\mathbf{W}_{N}\mathbf{X}_{m}-\mathbf{y}_{m}\|_{2}^{2}, \tag{9}\]  
where \(\mu\|\mathbf{X}_{m}\|_{1}\) is the sparse term, \(\mu>0\) is the sparse coefficient, and \(\frac{1}{2}\|\mathbf{Z}_{m}\mathbf{W}_{N}\mathbf{X}_{m}-\mathbf{y}_{m}\|_{2}^{2}\) is the fidelity term. Assuming \(F(\mu\mathbf{X}_{m})=\mu\|\mathbf{X}_{m}\|_{1}\) and \(G(\mathbf{X}_{m})=\frac{1}{2}\|\mathbf{Z}_{m}\mathbf{W}_{N}\mathbf{X}_{m}-\mathbf{y}_{m}\|_{2}^{2}\), according to the first-order primal-dual algorithm, the solution can be trivially given by the following sequence of convex problems:  
\[\left\{ \begin{array}{l}\mathbf{Y}_{m}{}^{(n+1)}=(\mathbf{I}+\sigma\,\partial F^{*})^ {-1}\Big{(}\mathbf{Y}_{m}{}^{(n)}+\mu\sigma\overline{\mathbf{X}}_{m}{}^{(n)}\Big{)}\\ \mathbf{X}_{m}{}^{(n+1)}=(\mathbf{I}+\tau\,\partial G)^{-1}\Big{(}\mathbf{X}_{m}{}^{(n)}- \mu\tau\mathbf{Y}_{m}{}^{(n+1)}\Big{)}\\ \overline{\mathbf{X}}_{m}{}^{(n+1)}=\mathbf{X}_{m}{}^{(n+1)}+\theta\Big{(}\mathbf{X}_{m}{} ^{(n+1)}-\mathbf{X}_{m}{}^{(n)}\Big{)}\end{array} \right., \tag{10}\]  
where \(\mathbf{I}\in\mathbf{\mathbb{R}}^{N\times N}\) is the unit matrix, \((\mathbf{I}+\sigma\,\partial F^{*})^{-1}\) and \((\mathbf{I}+\tau\,\partial G)^{-1}\) are resolvent operators, \(F^{*}\) is the convex conjugate of \(F\), \(F^{*}(\mathbf{Y}_{m})=\left\{ \begin{array}{l}0 if\,\,\mathbf{Y}_{m}\in\mathbf{Y}\\ +\infty if\,\,\mathbf{Y}_{m}\
otin\mathbf{Y}\end{array}