esa-sceva/satcom-chunk-collection huggingface dataset:
# 2 CA-R2: A Hybrid MAC Protocol for Machine-Type Communications
## 7 Conclusions  
In this work, we introduced 2 CA-R\({}^{2}\), a hybrid MAC protocol for M2M communications. The proposal consists of a collision resolution interval followed by a data transmission interval. Although this structure is similar to the one used in some other hybrid protocols, what sets it apart is that contention is resolved by means of the Adaptive-2C algorithm, which dynamically adjusts the transmission probability as a function of the estimated number of contending stations.  
We modeled the protocol using a Markov chain and evaluated its performance by means of discrete event simulations. As a performance reference, we used DCF, the widely popular MAC technique. With greedy sources, the results showed that 2 CA-R\({}^{2}\) achieves higher levels of network throughput than DCF and lower access delays when there are less than 170 devices. With a larger number of devices, DCF exhibits lower access delays but in exchange for discarding more than 10% of the offered traffic (whereas our proposal was not configured to drop packets). We also measured the time it takes to transfer the data packets coming from a varying number of sources (one packet per source). 2 CA-R\({}^{2}\) was shown to be able to transfer the whole set of packets in significantly shorter times than DCF. Additional simulations with Poisson traffic were performed, and the protocol maintained its advantages.  
Under the test conditions used in this evaluation, 2 CA-R\({}^{2}\) exhibited significantly better performance than DCF. These results suggest that the proposed protocol supports the communication requirements of a large number of IoT devices better than what can be achieved with currently popular MAC techniques.  
Conceptualization, [PERSON], [PERSON], [PERSON] and L.O.-B.; methodology, [PERSON]-[PERSON]; software, [PERSON] and [PERSON]-[PERSON]; validation, [PERSON] and [PERSON]; formal analysis, [PERSON] and [PERSON]-[PERSON]; investigation, [PERSON]-[PERSON], [PERSON], [PERSON] and L.O.-B.; resources, [PERSON]-G. and L.O.-B.; writing--original draft preparation, [PERSON]-[PERSON], [PERSON] and [PERSON]; writing--review and editing, [PERSON], [PERSON] and L.O.-B.; visualization, [PERSON]-G. and [PERSON]-[PERSON]; supervision, L.O.-[PERSON]; project administration, [PERSON]; funding acquisition, [PERSON]-G. and L.O.-B. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the Spanish Ministry of Science, Education and Universities, the European Regional Development Fund, and the State Research Agency, Grant No. PID2021-123627 OB-C52.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
## 4 Experiments and Results  
SENDI was modeled and tested with the aid of the ns-3 [26], which is an Internet-oriented discrete-event network simulator licensed by GNU GPLv2 and the 6 LoWPAN module. The implementation of this module was based on RFCs4944 and 6282 and, despite not bringing to bear the entire set of standards described for 6 LoWPAN, it serves as a basis for other functionalities, as it is an official module integrated to ns-3 since ns-3.19 version.  
Unfortunately, a routing protocol for the lr-wpan module of ns-3 is not available yet, including the RPL (Section 3.2). Thus, a simplified routing protocol was implemented in ns-3 in order to better assess the performance of the propose scheme. This protocol is based on the position of each node, where a packet with a non-neighbor node as its destination is passed along to another neighbor node which has a option that is closer to that of the destination node. This path is followed until the packet reaches the destination node. Thus, the nodes also have to know their neighbors' position and the position of the nodes that will be sent a package. In addition, the position of the destination node is embedded in the sent packet. This routing protocol will not be described in detail as it is beyond the scope of this study.  
As the lr-wpan module of ns-3 does not yet have metrics for energy, the simple radio energy dissipation model described in Section 3.3 was adopted in the simulation to determine the energy consumption during the transmission and reception of data. This model was adopted because it is widely used in the literature allowing the comparison of the results obtained. The simple battery model available in ns-3 was also adopted to analyze the battery power consumption. Table 2 shows the parameters adopted in the original LEACH paper and in the simulation, as well as the amount of energy used by the leader of the cluster for each node that belongs to it. This value was adopted because of its impact on the quantity of stored energy in the leader node. Thus, it makes it really necessary to have a mechanism to control the energy, as well as to allow the use of several ML techniques that can be combined or even to handle different kinds of data collected by distinct sensors such as, for instance, images, river level, rainfall, etc.
# Jet Noise in Airframe Integration and Shielding
## 3 Options for Computation of Jet Noise
### Numerical Issues in Capturing the Unsteady Flow and Sound Scales  
Capturing the noise sources requires capturing the unsteady flow in the noise-producing region. For the exhaust noise, the source of jet noise is the turbulence created by the jet mixing with the surrounding air. Jet-mixing noise in subsonic jets is \"broadband\" in nature (that is, it spans a broad frequency range without having specific tone content), and it is centered at relatively low frequencies (\(St\sim 0.5\)). Supersonic jets have additional shock-related noise components that generally peak at a higher frequency than the mixing noise.  
The dominant noise sources in the initial region of the jet can sometimes be classified into two modes. The jet column mode, which peaks at the Strouhal number based on the diameter of about 0.5-0.8. The other is the shear-layer mode, which scales with the lip momentum thickness (about 3% of the diameter) and peaks at the Strouhal number based on the momentum thickness around 0.01. The supersonic flow may be complicated further by the presence of shock waves. The generated acoustic pressure waves are about four orders of magnitude smaller the mean flow pressure. Because of the long propagation field, numerical dispersion and dissipation can alter the sound frequency and dampen the amplitude. Therefore, to capture such small oscillations, a high-order discretization scheme is needed. Some of the schemes widely used in computational aeroacoustics include the high-order MacCormack-type schemes (Ref. [42]), compact scheme (Ref. [43]), and dispersion-relation-preserving (DRP) scheme (Ref. [44]). A review of high-order computational schemes is given in Ref. [45].  
Boundary treatments: Even if the numerical discretization scheme used is of high order, the acoustic field may not be properly captured. The computational domain is finite and boundary conditions must be applied in an approximate way at the boundaries. The latter usually downgrades the accuracy level, dampens the acoustic waves, or creates spurious modes. Therefore, various new boundary treatments were developed and tested to allow the acoustic waves to properly propagate through the boundaries.  
Figure 9: Peak of the jet noise predicted by [PERSON] and [PERSON] [40] vs. the experimental data of [PERSON] [41].  
Outflow treatments were developed based on asymptotic analysis of the linearized Euler equation. Radiation boundary conditions were developed based on the asymptotic solution of the acoustic wave radiation, among others (e.g., [43; 44; 45; 46; 47; 48; 49]). Figure 10 shows the effect of various boundary treatments in computing the sound field associated with an acoustic source in a uniform flow. As the figure shows, spurious modes are created at the inlet or outlet, which are minimized when [PERSON] and [PERSON] [44] boundary treatment is used.
## 4 Experiments and Results Discussion
Regarding the classification of archaeological pottery sherds, [PERSON] and [PERSON] [24] obtained an accuracy of 78.26% detecting 8 classes of ceramic. [PERSON] et al. [23] obtained a better accuracy detecting architectural heritage images (94.59%), but there were only 10 types of architectural elements. In our problem, the number of classes is much larger, obtaining an accuracy of 70.06% detecting 158 categories. As previously explained, when a shabti has been identified, our system shows some local data previously collected (name, dynasty, period, description) and connects the data to the cultural institutions that have similar shabtis by means of our ontology and Europeana. This connection returns similar examples of the shabti. An example of a SPARQL query to obtain shabtis for Akhenaten is shown in Listing 1, where the syntax word _SERVICE_ links our ontology with Europeana. The search is carried out with \"habti\" instead of \"Shabti\" because the term can be found with \"Ushabti\" or \"Shabti\" and it is case sensitive. Some of the results returned by this query are shown in Table 6, where it is possible to see data from different institutions: UCL, Fitzwilliam Museum or Israel Museum. There are two results from the UCL and some properties have different values, i.e., _type_, which has the value \"funerary equipment\" but also \"ushabti\" for Fitzwilliam Museum. The Israel Museum has two identifiers for each shabti. Unlike a structured database, semantic searches are open to this type of possibility, defining certain properties for an individual that may be repeated or even not present in another similar individual in the same class. A complete deployable application (see Figure 14) using Docker [50] is available on the Internet at the URL: [[https://drive.google.com/drive/folders/1](https://drive.google.com/drive/folders/1) nRn4 jAz2 RzTD_AisSJbhv0 EptJthgLL_]([https://drive.google.com/drive/folders/1](https://drive.google.com/drive/folders/1) nRn4 jAz2 RzTD_AisSJbhv0 EptJthgLL_). This application integrates the full process and returns data from our local database and known examples from Europeana. \begin{table}
\begin{tabular}{c c c c c}
**Real** & **Detected** & **Middle Kingdom** & **New Kingdom** & **Third Intermediate Period** & **Late Period** \\ \hline
**Middle Kingdom** & 1.0 & 0.00 & 0.00 & 0.00 \\
**New Kingdom** & 0.00 & 0.91 & 0.06 & 0.03 \\
**Third Intermediate Period** & 0.00 & 0.01 & 0.97 & 0.01 \\
**Late Period** & 0.00 & 0.01 & 0.00 & 0.99 \\ \hline \end{tabular}
\end{table} 
Table 5: Confusion matrix of the period detection of the FN + HN model (%). The green cells represent success in the period detection. The redder the cells are, the bigger error in the period classification.
# Demystifying DFT-Based Harmonic Phase Estimation, Transformation, and Synthesis
## 2 Robust DFT-Based Phase Estimation of Individual Sinusoids
### Bias and Variance of the Phase Estimation Error
In the latter case, the performance shown by the same estimator is quite poor, which reveals that, in that case, leakage effects due to the rectangular window are quite strong, as the results in Figures 5 and 8 easily anticipate. In particular, the performance becomes asymptotic when the SNR exceeds 10 dB, which is commensurate with the known main-to-side lobe attenuation of the rectangular window, in the order of 13 dB [15]. The performance of the sine and shifted Hanning windows fall in between the two extreme cases due to the rectangular window. In particular, for the same DFT-based filter bank and \(\Delta\) conditions, the error variance performance of the sine window clearly exceeds that of the shifted Hanning window in the sense that a closer approximation to the CRLB is reached. This is more evident under the more favorable \(\Delta\) test conditions (e.g., when \(\Delta=0.5\)) than under the more adverse \(\Delta\) test conditions (e.g., when \(\Delta=0.01\)). A possible explanation may be linked to the relationship between the main lobe width of the magnitude of the frequency responses of those two windows, the relative prominence of the spectral coefficients inside that main lobe, the association with the discrete frequencies defining the different DFT channels (or sub-bands), and the near-end and far-end leakage characteristics of each window. The most impactful implication of these results is that under more general test conditions, the phase estimation error performance of the sine window is not only closer to the CRLB but also offers a lower deviation when signal conditions are more adverse. For these reasons, it can be considered that the performance of the sine window is better behaved and, thus, it will be used in the remainder of this paper. Figure 11: Variance of the phase estimation error when \(N=128\), \(\ell=13\), and when \(\Delta\) takes on two extreme values depending on the estimator. The Cramér–Rao lower bound is also represented although it is not too visible since it is overlapped by the DFT-RECT results when \(\Delta=0.0\). As a final note, it should be highlighted that changing \(\ell\) in the simulations produces a marginal effect on the results. In particular, when \(\ell=N/4-1\), which corresponds to the'sweet spot' given that leakage effects are minimized, results do not differ appreciably from those in Figure 11. The Matlab file that generates and displays the results in Figures 10 and 11 is available (CRLBphivar.m), such that other combinations of parameters can be tried.
## 4 Data Smoothing Algorithm  
To realize the high-accuracy measurement of track geometry parameters using the inertial track geometry state detector, motion parameter measurement of the inertial/GNSS/odometer integrated navigation system is required to provide both high absolute and relative accuracy. The forward and reverse navigation solutions of the inertial navigation system are equivalent, whereas the traditional Kalman filter only uses data from the current filtering time and before filtering. Therefore, if all data from the navigation solution process can be used, the accuracy of the integrated navigation can be improved and the detection accuracy of the inertial track geometric state detector can be increased.  
In this study, the forward filtering and the Rauch-Tung-Striebel (RTS) fixed interval smoothing algorithm [36; 37; 38] were adopted to achieve data smoothing processing for the entire navigation calculation process. The basic principle of the RTS algorithm is that a fixed interval is selected, and the forward solution is based on the Kalman filter. Then, based on all of the forward solution state variables of the fixed interval, the state of each data node in the fixed interval is reverse estimated, resulting in superior data accuracy to that of the forward filter [39; 40; 41].  
The RTS fixed interval smoothing algorithm estimates the state of each data node based on all measurement information in a fixed interval. Suppose that in a fixed interval, there are N observations. First, based on the forward Kalman filter, the K = 1, 2,..., N combination filtering is carried out, and state estimation information \(\rm\widehat{X}(k)\), error estimation covariance matrix \(\rm P_{k}\), and one-step prediction error variance matrix \(\rm P_{k/k-1}\) of N times combined filtering are stored. Then, following the forward Kalman filter in a fixed interval, and based on the stored combined filtering information, RTS reverse smoothing filtering is carried out according to the sequence K = N, N \(-\) 1,..., 1.  
The RTS fixed interval inverse smoothing algorithm is as follows:  
\[\rm\widehat{x}_{k-1/N}=\rm\widehat{x}_{k-1}+A_{k-1}(\rm\widehat{x}_{k/N}-\rm \Phi_{k/k-1}\rm\widehat{x}_{k-1}) \tag{33}\]  
\[\rm P_{k-1/N}=\rm P_{k-1}+A_{k-1}(\rm P_{k/N}-\rm P_{k/k-1})A_{k-1}^{T} \tag{34}\]  
The data smooth gain matrix is obtained as follows:  
\[\rm A_{k-1}=\rm P_{k-1}\rm\Phi_{k/k-1}^{T}\rm P_{k/k-1}^{-1} \tag{35}\]  
Development and Accuracy Verification of Track Geometry State Detector Based on an Inertial/GNSS/Odometer Integrated Navigation System
# Stratographic Division Method Based on the Improved YOLOv8
## 2 Materials and Methods
### YOLOv8x-CAMDP Model Introduction
#### 2.4.2 MDPIOU Loss Function  
The MDPIOU loss function is a new type of bounding box similarity comparison metric based on the minimum point distance, directly minimizing the point distances between the top-left and bottom-right corners of the predicted and actual annotated bounding boxes. It encompasses all relevant factors found in existing loss functions, including overlapping or non-overlapping areas, center point distances, and aspect ratio deviations, thereby providing a more comprehensive measurement of bounding box similarity. Furthermore, as different wells correspond to the same stratum with scale feature variations, the MDPIOU loss function effectively ad-dresses the issue where traditional loss functions fail to optimize when the predicted and actual annotated boxes have the same aspect ratio but completely different sizes [23]. The process is computed as follows:  
\[d_{1}^{2}=\left(x_{1}^{B}-x_{1}^{A}\right)^{2}+\left(y_{1}^{B}-y_{1}^{A}\right)^ {2} \tag{7}\]  
\[d_{2}^{2}=\left(x_{2}^{B}-x_{2}^{A}\right)^{2}+\left(y_{2}^{B}-y_{2}^{A}\right) ^{2} \tag{8}\]  
\[IOU=\frac{A\cap B}{A\cup B} \tag{9}\]  
\[MDPIOU=IOU-\frac{d_{1}^{2}}{w^{2}+h^{2}}-\frac{d_{2}^{2}}{w^{2}+h^{2}} \tag{10}\]  
Here, (\(A\),\(B\)) represents the real box and the prediction box, respectively, \((x_{1},y_{1})\) represents the upper left corner of the rectangular box, \((x_{2},y_{2})\) represents the lower right corner of the rectangular box, and \((w,h)\) represents the width and height of the input picture, as shown in Figure 4. \(L_{MDPIOU}\) represents the MPDIOU loss function.  
In the original YOLOv8x network model, the CIOU loss function was replaced with the MDPIOU loss function to comprehensively measure the similarity of bounding boxes. During the iterative training process of the model, the loss values calculated from this loss function were used to adjust the model parameters, resulting in the optimally performing model. This model more closely approximates real-world conditions in the detection of stratigraphic boundaries. Compared to the CIOU loss function, the MDPIOU loss function significantly enhances the accuracy and efficiency of bounding box regression.
## 1 Introduction
Different strategies have been utilized either to change characteristic chitosan physically or artificially to boost up the sorption limit [30]. In earlier studies, it has been confirmed that the available functional amino hydroxyl entities at the second and sixth carbon locations empower an assortment of chemical alterations [31]. Chitosan (CH) has been alluring on the grounds that the available functional entities impart the cationic and chelating characteristics, which are useful for its functionalization and/or to engineer functionalized CH-based constructs. The chemically altered CH has a more prominent take-up limit with respect to substantial metal ions [32]. The presented investigation was objected to chemically modify chitosan with glycolidyl methacrylate [23], followed by reaction with 1,4-bis(3-aminopropyl) piperazine in a second step to increase the number of Lewis basic centers for decontamination from aqueous solution. The aim of the work is to prepare a green sorbent with a high sorption capacity for cations. The biodegradability and nontoxicity of the chitosan and modified products make them sustainable tools for wastewatertreatment. After characterization, the as-prepared material was subjected to investigate its sorption capacity for copper, cadmium, and lead metal ions in aqueous solutions. The chemically modified chitosan's sorption limit was additionally assessed by contemplating the equilibrium sorption isotherm for copper, cadmium, and lead with a batch-wise procedure. The sorption capacity of the newly engineered material was compared with the raw chitosan. Moreover, to get the equilibrium information, several sorption isotherms, i.e., (1) the Langmuir, (2) the Freundlich, and the (3) Temkin and parameter values of these isotherms were also acquired in this study.
# An Implementation of the HDBSCAN* Clustering Algorithm
## 2 Background and Literature Review
### HDBSCAN* Implementations
#### 2.3.1 Python HDBSCAN* Implementation  
As mentioned in Section 1, there is a well-established Python implementation of the HDBSCAN* algorithm called hdbscan [4]. Researchers in [12] indicate that this is currently the best performing implementation available. In addition, the current version of this Python module provides some innovative features beyond what is provided by the framework described in [8]. One example is the ability to predict the cluster assignment for unseen data points using a trained model. Another example of an innovative feature from hdbscan is the soft clustering functionality it provides. The version of hdbscan being reviewed and used in this work is 0.8.27 with Python 3.9.1.  
To understand the details of this particular implementation of HDBSCAN*, the source code is reviewed. The hdbscan project has a dependency on scikit-learn and uses many of its features. It uses the KDTree and BallTree classes which are nearest neighbor algorithms. The main class, HDBSCAN, inherits from scikit-learn's BaseEstimator and ClusterMixin classes. This provides a lot of functionality out of the box and gives the module a standardized API. This code also uses numpy extensively and takes advantage of its optimized numerical array operations while providing support for multithreading. Leveraging proven libraries, such as scikit-learn and numpy, certainly contribute to the good performance of this implementation. In addition to this, the hdbscan module has implemented several variations of the algorithms which calculate the core distances and construct the minimum spanning tree. Considering all these points, it is easy to understand why this HDBSCAN* implementation is the best performing implementation available. Furthermore, this module is being actively maintained and improved by the development community. The hdbscan Python module will be consulted as a reference throughout this paper.