esa-sceva/satcom-chunk-collection huggingface dataset:
# Predicting Hard Disk Failure by Means of Automatized Labeling and Machine Learning Approach
## 4 Proposed Approach
### RGF-Based Machine Learning Approach
#### 4.2.1 Training of Models
Usually, the goal is generally to minimize false negatives to avoid risk targets being incorrectly predicted, thus assuring a conservative behavior from an operating point of view. The accuracy index is often used in contexts of methodologies based on predictors to give a quantization of the goodness of the model. Accuracy is defined as  
\[Accuracy=\frac{\textit{True\, Positives}+\textit{True\, Negatives}}{\textit{Total\, Population}}, \tag{2}\]  
where True Positives and True Negatives are the number of disks well predicted as healthy or non-healthy, respectively. Vice-versa, the number or disk wrongly predicted are False Positives (in case of an healthy disk predicted as to be replaced) and False Negatives (for disks that need to be replaced and actually predicted as healthy). The accuracy is usually used to have a first feeling on the effectiveness of the predictor since it returns the fraction of correctly predicted test cases out of the totality of all test cases. Although this index shows correctly executed predictions, it does not measure the predictor's sensitivity to the distinctions between positive and negative cases. The method proposed in this work uses _Recall_, _False Positive Rate_ (FPR) and _Positive Likelihood Ratio_ (LR+) to better evaluate the predictor's performance. The Recall index, defined as  
\[Recall=\frac{\textit{True\, Positives}}{\textit{True\, Positive}+\textit{False\, Negatives}} \tag{3}\]  
is often preferred in this context; the higher the recall value, the better the reduction of false negatives. As regards data-centers storage systems, characterized by tens of thousands of hard disks, a single percentage point of false positives corresponds, instead, to several hundred hard disks wrongly classified as \"to be replaced\" even if they are not. This way, in the considered application field, minimizing the false positive occurrences turns out to be as fundamental as maximizing the recall. The index associated with the risk of false positives is the False Positive Rate defined as:  
\[FPR=\frac{\textit{False\, Positives}}{\textit{False\, Positive}+\textit{True\, Negatives}}. \tag{4}\]  
The Taguchi DOE approach can be exploited to assess the factors' impact on one performance index; to this aim, the _Positive Likelihood Ratio_ (\(LR+\)), defined as  
\[LR+=\frac{Recall}{FPR} \tag{5}\]and capable of simultaneously taking into account Recall and FPR, has been exploited. Before carrying out the training stage of the RGF model, input data have to suitably shuffled in order to guarantee independence from the temporal distribution of the measures and prevent biases related to the dataset \(S_{d}\). Both training and test experiments are required for each of the plan configurations; to this aim, the whole dataset \(S_{d}\) is split according to a ratio of 70%-30%, for training and testing, respectively.
# Forecasting Taxi Demands Using Generative Adversarial Networks with Multi-Source Data
## 4 Methodology
### Features Extraction and Analysis
#### 4.2.1 Taxi Trips  
To enhance data accuracy and deal with invalid data such as incomplete, noisy data and outliers, etc. Raw GPS data have been manipulated and prepared carefully. The raw data have been explored using with outstanding packages and libraries in R language and Python such as Pandas, skit-learn and stats. Considering Wuhan graphical coordinates (longitude: 113 deg40'-115 deg04' and latitude: 29 deg60-31 deg20'), the GPS records located outside this range deleted. Besides, GPS data with a taxi status of 0 or 2 (0: invalid device, 2: temporarily stopped) and repeated records were removed. GPS records of more than five sequential timestamps with zero speed value have been deleted.  
In addition, data caused by the device errors and noises, such as GPS records with zero or null values of longitude, latitude, timestamp, were also deleted.  
As in [27; 28], taking into account taxi states during working operation, we can consider two trips: occupied (O) and vacant (V). The empty trip means that the taxi status value is 1, and the driver is cruising without a passenger, whereas in the occupied trip, the taxi is crossing the roads with a passenger. Figure 4 shows an example of the two trips randomly extracted from GPS trajectory taxi.  
Figure 4: An example of the two trips randomly extracted from a GPS taxi trajectory. The occupied trip crossed Tuanjie road and ends at the gate of Hubei university and dropped off the passenger, and after several meters another passenger is picked up and anther occupied trip begins.  
Figure 3: Weather categories from historical Weather data.  
As shown in Figure 4, small green circles form an occupied trip with a giant green circle (represents a taxi pick up) and end up with a big red circle (represents a taxi drop off). In contrast, small red circles together are considered a vacant trip. In this study, taxi demand prediction focuses on occupied trips. Table 3 shows variables related to occupied trips.  
For the sake of more accurate results, the trips with a distance less than 200 m or trip period less than 3 min or number of GPS records less than five have been removed and are not considered in this study. Finally, the dataset considered 29,984,635 trips, and this number is sufficient for performing taxi demand prediction. Figure 5 shows three selected study areas in the city of Wuhan, (a) Jiedokou, (b) GuangGu area, and (c) Wuhan railway station area.
## 4 Key Design Requirements and System Components
### Layer 1: NW Broker  
Layer 1 is the NW brokers, which are wireless communication networking agents representing trusted entities in the wireless NW, including MNOs, service providers, and/or trusted third-party agents. The primary function of the NW brokers is to manage identities within the wireless NW. This involves issuing and authorizing digital identities for both new subscribers and resources. By doing so, they ensure that each entity within the NW is verified and authorized to engage in communications, thereby protecting the NW against unauthorized access and potential security threats. NW brokers are also responsible for managing subscriptions and overseeing resource allocation, ensuring that the NW's capacity is optimally utilized while maintaining high service quality standards.
# Novel Equalization Techniques for Space Division Multiplexing Based on Stokes Space Update Rule
###### Abstract  
Space division multiplexing (SDM) is a promising technology that aims to overcome the capacity crunch of optical communications. In this paper, we introduce the multiple-input multiple-output (MIMO) Stokes Space Algorithm (SSA) implemented in frequency domain, a novel equalization technique for space division multiplexing (SDM). Although different papers have been published about the SSA and its MIMO implementation, we provide for the first time an analysis of the of the convergence speed and frequency offset of the SSA compared to the least mean square (LMS). SSA algorithm can deal with higher frequency offsets and linewidths than LMS, being suitable for optical communications with higher phase noise. SSA does not need pre-compensation of frequency offset, which can be compensated after equalization without penalties. On the other hand, due to reduced convergence speed, SSA requires longer training sequences than LMS.  
frequency domain equalization; space division multiplexing; coherent technologies +
Footnote †: journal: _photonics_
## 5 Feasibility Study Through System Simulations
### Generalization for Moving Tiles and the Optimization Metric  
The training phase operates with a defined optimizer and optimization metric, training the input dataset FTT of fixed tiles. However, in the end, the model must generalize for moving tiles. We emphasize that the model cannot be trained with moving tiles directly because the proposed approach is based on the tiling of the positioning area with fixed tiles.  
In order to measure the performance of the model for moving tiles, we adopt a custom-made optimization metric. Then, to select the model that generalizes best for moving tiles, the training process is iterated with the FTT dataset and different initial random weights in each iteration. The final selected model is the one that minimizes this custom-made metric for the MTV dataset. This process is explained in detail in Section 5.6. The number of iterations among different initial random weights is assigned in the parameter \(N_{I}\).  
To generalize for moving tiles, we aim to minimize the 95 th percentile of the Cumulative Distribution Function (CDF) of the error distances for all of the moving tiles in the MTV dataset. The selection of this metric is decided by the use-case, usually based on the expected performance of the positioning method. Another metric, like aiming to minimize the mean squared error, can be used. In the end, it is just a matter of selecting the metric most suitable for the problem to be solved. In this case, we opt for improving the performance of the model for 95 percent of the samples at the cost of possibly worsening the performance for the remaining 5 percent. The 95 th percentile of the CDF is assigned in the parameter \(P_{R}\).
## 5 Discussion
### Technical Analysis and Comparative Effectiveness of ML and DL Models
#### 5.2.4 Extreme Gradient Boosting (XGBoost)  
Technical Details: XGBoost is a highly efficient and scalable implementation of the gradient-boosting framework. It supports parallel tree development, allowing it to solve many data science problems quickly and accurately by optimizing both the loss function and model complexity.  
Performance: According to [PERSON] et al. [14], XGBoost models consistently achieve higher accuracy and faster computation times compared to other ensemble methods in demand forecasting tasks. For instance, in sales forecasting for e-commerce platforms, XGBoost models outperformed traditional boosting methods by 8-10% in terms of accuracy and reduced training times by 30%.
# GRACE/GFO and Swarm Observation Analysis of the 2023-2024 Extreme Drought in the Amazon River Basin
## 3 Data and Methods
### Data
#### 3.1.4 ERA5-Land Data  
ERA5-Land is an enhanced reanalysis dataset of the global land component provided by medium-range weather forecasts based on ERA5, covering the period from January 1950 to the present. Reanalysis used the laws of physics to combine modeled data with observations from around the world to form a globally complete and consistent dataset. This provides a consistent view of water and energy cycles on land at a higher spatial resolution than ERA5 [39]. In this study, we extracted 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) monthly evapotranspiration (ET), SM, surface runoff and subsurface runoff gridded data from this dataset. ET data were used to calculate the natural net recharge (P-ET) and analyze the causes of drought, while SM, surface runoff, and subsurface runoff data were normalized to analyze the propagation of drought across different components of terrestrial water storage.
# The Recycling of Carbon Components and the Reuse of Carbon Fibers for Concrete Reinforcements
## 4 Results and Discussion
### Yarn Tensile Tests
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Number**} & **Impregnation** & **Force** & **Displacement** \\  & **[-]** & **[N]** & **[mm]** \\ \hline
1 & duromer & 649.17 & 1.09 \\
2 & duromer & 1433.65 & 1.69 \\
3 & duromer & 1463.64 & 2.14 \\
4 & duromer & 1093.33 & 1.91 \\
5 & duromer & 1073.05 & 1.80 \\
6 & polymer & 1069.04 & 1.74 \\
7 & polymer & 943.72 & 1.78 \\
8 & polymer & 769.52 & 1.68 \\
9 & polymer & 792.07 & 1.94 \\
10 & polymer & 691.51 & 1.43 \\
11 & geopolymer & 430.39 & 1.66 \\
12 & geopolymer & 356.55 & 1.35 \\
13 & geopolymer & 649.55 & 2.21 \\
14 & geopolymer & 563.99 & 2.28 \\
15 & geopolymer & 477.57 & 1.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the yarn tensile tests. The tensile strength of the yarns is directly proportional to the saturation of the yarn. The better the saturation of the yarns, the better the occurring tensile forces can be dissipated over the individual fibers. As can be seen in Figure 7 (left), the non-fully saturated yarn is stretched. The yarn saturated with geopolymer also has the lowest tensile strength and the highest machine travel. Based on the behavior during the tests (core extraction) and the measured values, it can be inferred that the impregnation of the yarn was not complete. The larger machine path compared to the epoxy-impregnated yarn can be explained by the yarn itself. In the case of an unimpregnated core, the yarn is stretched again. This increases the elongation or machine path. As the tensile forces are only absorbed by the outer impregnated yarn area, a complete activation of the yarn is not possible. The yarn impregnated with the polymer matrix achieves a similarly high-tensile load compared to the yarn impregnated with epoxy resin. Only machine path or elongation is larger in direct comparison to the yarn impregnated with epoxy resin. A direct comparison with other yarns made from recycled carbon fibers is only partially possible. Recent studies have shown that the processability of yarns made from recycled carbon fibers and thermoplastic fibers yielded better results [22]. In [23], a yarn made from recycled carbon and thermoplastic fibers was produced and examined. According to [23], the tensile strength of the hybrid yarn is approximately 1.4 GPa. Both the hybrid yarn and the yarn examined in the experiments were produced in a similar manner at ITM. With an average yarn diameter of 1.5 mm, this results in a tensile strength of 0.43 GPa.
# Coordination in Emergency Response System Design:
## Abbreviations  
* _Sets_ G(V, E) the urban transportation network with vertices \(V\) and edge \(E\).
* _EAS_ the set of existing ambulance stations.
* _EFS_ the set of existing fire stations.
* _AAS_ the set of alternative ambulance station sites.
* _AFS_ the set of alternative fire station sites.
* _A\({}^{Poor}\)_ the set of nodes in poor response time areas.
* _A\({}^{Adeq}\)_ the set of nodes in adequate response time areas.
* _A\({}^{Prof}\)_ the set of nodes in preferred response time areas.
* _Parameters_ NA the number of ambulance stations to be established (existing + new)
* _NF_ the number of fire stations to be established (existing + new)
* _DH\({}_{ik}\)_ the shortest travel time from ambulance station \(i\in EAS\cup AAS\) to node \(k\in V\).
* _DF\({}_{jk}\)_ the shortest travel time from fire station \(j\in EFS\cup AFS\) to node \(k\in V\).
* _RTAS\({}_{k}\)_ the response time to node k \(\in\) V from the closest ambulance station, where
* _RTFS\({}_{k}\)_ the response time to node k \(\in\) V from the closest fire station, where
* _RTFS\({}_{k}\)_ = min\(\left\{DF_{jk},j\in EFS\right\}\)
* _WT\({}_{k}\)_ incident scene first, where
* _WT\({}_{k}\)_ = \(|RTFS_{k}-RTAS_{k}|\)
* _Decision Variables_
* _y\({}_{k}\)_ 1 if node \(k\in A^{Poor}\) moves to the preferred zone in the optimal solution;
* _y\({}_{k}\)_ 0, otherwise.
* _z\({}_{l}\)_ 1 if node \(l\in A^{Adeq}\) moves to the preferred zone in the optimal solution;
* _z\({}_{l}\)_ 0, otherwise.
* _o\({}_{i}\)_ 1 if ambulance station \(i\in AAS\) is established; 0 otherwise.
* _p\({}_{j}\)_ 1 if fire station \(j\in AFS\) is established; 0 otherwise.
## 2 System Analysis and Design Methodology
### Requirements and Design Considerations
Of course, \(I_{sp}\) depends on both combustion temperature and molecular mass of a given propellant, and high \(I_{sp}\) can still be acquired at relatively low temperatures if the molecular mass of decomposition products is lighter and molecularly simpler. Therefore, choosing an optimal specific impulse value, not necessarily a high value, for a given propellant that tends to have lower adiabatic flame temperature will impact positively on the propulsion system's overall performance, cost, and project lead-time. A thruster with low-weight materials might not necessarily have a great impact on the propulsion system mass reduction, however, in the case of enabling Additive Manufacturing (AM) techniques, a further limitation on combustion temperature is imposed to respect the melting point of certain 3D printing materials such as Inconel-625(-1563.15 K). In the study phase presented in this article a commercial thruster model operating on high combustion temperatures was considered for the preliminary design. Further project phases will consider the design and development of metal 3D-printed thrusters that operate only low adiabatic flame temperature monopropellants. The physical constraints set on the design imply maintaining a standard CubeSat size of 1U while considering the extra protrusion for the thruster referred to as the \"Tuna Can\" volume. The size of this extra volume occupies the ejection spring of the CubeSat deployer and varies from one model to another and depends on the manufacturer [37; 38; 39]. A suitable deployer allowing a protrusion volume of \(\phi\)86.0-78.0 mm was considered. The initial Beginning Of Life (BOL) mass requirement set was \(\leq\)1.5 kg for 1U in order to have a competitive advantage over state-of-the-art commercial propulsion systems; it will be shown in the following design sections that this requirement was partially fulfilled since reducing the propellant mass for the denser propellants was required to maintain this value (i.e., \(\leq\)1.5 kg). Otherwise, the requirement can be modified by increasing the constraint to get the use of allowable propellant volume in the tank. As for the electric power requirements, a system power of \(\leq\)20 W and nominal 12 VDC was considered after studying the electrical properties of the various system parts and such details will be briefly highlighted in the mass budget table.
## 2 Methodology
### Tunnel Axis Segmentation  
According to the planning requirements regarding tunnel safety, the tunnel generally includes linear parts and nonlinear/circular segments with fixed curvature [3]. Once the tunnel axis has been extracted, it should be further segmented into linear and nonlinear segments. For the linear segments, we refit them by the linear least squares regression. However, we use a high order polynomial model to fit the nonlinear segments. The advantage of axis segmentation is twofold: (1) The tangent and  
Figure 1: Extraction of tunnel axis in the two projected spaces. (**a**) tunnel axis points in _xy_-plane; (**b**) tunnel axis points in the _xz_-plane. The gray points represent the raw tunnel point clouds and the red points represent the calculated tunnel axis points.  
Figure 2: Geometric relationships between the designed and the extracted axes.  
directional vectors corresponding to circular and linear segments can be easily determined. This means the axis direction for any point along the axis can be estimated, which prepares the conditions for the subsequent extraction of cross sections as the tunnel axis should be orthogonal to the cross sections. (2) The axis segmentation can help us to achieve the tunnel's geometric representation at multiple levels of details (LoDs). More specifically, for linear segments, a few tunnel cross sections are enough to fit/represent the tunnel geometry. In contrast, for circular segments or the transition zones between linear and circular segments, the high density of cross sections are needed to achieve an accurate geometric representation. Through tunnel axis segmentation, we can enhance the flexibility of geometric representation via using the concept of LoD representation. Meanwhile, we can also make a good balance between the tunnel's geometric accuracy and compactness via using a few tunnel cross sections.  
The proposed axis segmentation algorithm consists of three stages: (1) We first restore the topological relationships of tunnel axis points using MST. (2) The sequence axis points are then provided as input to the smoothing step which uses the Savitzky-Golay (SG) [21] filtering algorithm to refine the axis. (3) In the final step, the axis has been divided into linear and nonlinear segments using an enhanced RANSAC [22] algorithm, which works at the tangent space rather than the data space defined in the traditional sense.
# Sound Source Localization Indoors Based on Two-Level Reference Points Matching
## 2 Fingerprint-Based SSL
### Offline Sampling Phase  
Generally, the offline phase includes three steps. First, the coordinates of the samples are determined according to the environment and precise requirements of the positioning service area. Then, the positioning signal is released at each sampling location and received by the sound source positioning system with four microphones, M1, M2, M3 and M4, as shown in Figure 1. Finally, the RPs are made up of the coordinates of samples and the corresponding location features extracted from the positioning signal. The RPs are also known as position fingerprints:  
\[\begin{split} S_{n}&=[\mathbf{L}_{n},\mathbf{F}_{n}]^{T}, \quad n=1,2,\ldots,N.\end{split} \tag{1}\]  
Figure 1: Illustration of fingerprint-based SSL process.  
where \(\mathbf{S}_{n}\) corresponds to the fingerprint collected at the \(n\)th sampling point, and \(N\) is the total quantity of the sampling point in the positioning service area; \(\mathbf{L}_{n}\)=\([x_{n},y_{n}]\) and \(\mathbf{F}_{n}\) = \([f_{n}^{1},f_{n}^{2},\cdots,f_{n}^{M}]\) mean the coordinates and the feature vectors of the nth RPs. \(M\) is the total number of the positioning features and \(f_{n}^{m}\) means the \(m\)th feature in feature vector \(\mathbf{F}_{n}\).  
Sound intensity, frequency spectrum and time difference of arrival (TDOA) are closely related features of sound source position [34; 35; 36; 37]. Among them, TDOA is widely used in real-time positioning for the characteristics of the low computational complexity and a small amount of data [38]. In this work, there are four microphones in the system, and we choose TDOA as the positioning feature. Thus \(\mathbf{F}_{n}\) = \([\Delta t_{n1},\Delta t_{n2},\Delta t_{n3}]\). \(\Delta t_{ni}\) represents the TDOA value of the received signal between the reference microphone and the other three microphones at the \(i\)th reference point. We collect the fingerprint at each sampling point and establish the positioning database defined as follows:  
\[\mathbf{D}\)=\([\mathbf{S}_{1},\mathbf{S}_{2},\cdots,\mathbf{S}_{N}] \tag{2}\]