esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Comparison of Application Cases and Methods
### Comparison of Time Series Complexity Metrics  
The effectiveness of arrangement entropy in quantifying fault information for diagnosing gear-loosening faults during gearbox transmission is critically assessed. For a thorough evaluation, time series complexity indices including arrangement entropy, multi-scale arrangement entropy, approximate entropy, among others, are applied to the modal components decomposed by VMD.  
The CSA-optimized LSTM algorithm is employed for fault diagnosis, facilitating an intricate analysis and classification of the data. This ensures that the assessment is both comprehensive and precise, illuminating the nuanced fault characteristics and their implications.  
The diagnostic outcomes, which offer a detailed insight into the capability of arrangement entropy and comparable indices in capturing and quantifying fault information, are vividly displayed in Figure 5 and itemized in Table 3. This detailed presentation aids in the meticulous examination of the indices' performance, offering robust data to inform optimized fault diagnosis and maintenance strategies.  
Insights gleaned from Figure 5 and Table 3 reveal a nuanced pattern in the performance of arrangement entropy in fault diagnosis. As the scale of arrangement entropy expands, there is a noted decline in the diagnostic accuracy for incomplete wear and broken gears. Despite this, arrangement entropy outperforms multi-scale arrangement entropy in quantifying fault information.  
A comparative analysis with other indices underscores the superior efficacy of arrangement entropy, boasting a correct diagnosis rate that peaks at an impressive 100%. This remarkable precision is consistent, underscoring the robustness of arrangement entropy in capturing and articulating fault nuances.  
Figure 5: Permutation entropy of different scales.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Targets** & **Training Sample** & **Test Sample** & **Correct Sample** & **Test Set Correctness/\%** \\ \hline entropy of disorder (physics) & 600 & 150 & 150 & 100 \\ \hline Approximate entropy & 600 & 150 & 143 & 95.3 \\ \hline Sample entropy (physics) & 600 & 150 & 145 & 96.67 \\ \hline Fuzzy entropy (physics) & 600 & 150 & 147 & 98 \\ \hline \end{tabular}
\end{table}
Table 3: Complexity index of different time series.  
These findings corroborate the assertion that arrangement entropy excels in dynamic detection performance during the diagnosis of loosening faults in gearbox transmission, outpacing other indicators. The metric's adeptness in encapsulating intricate fault dynamics underscores its pivotal role in enhancing the precision and reliability of diagnostic protocols in gearbox maintenance and repair.
## Abbreviations  
* [1]
* [2] AA Accuracy Assessment
* [3] AEA Albert Equal Area
* [4] AGDD Accumulate Growing Degree-Days
* [5] ARD Analysis Ready Data
* [6] CDL Cropland Data Layer
* [7] CxQ Convex Quadratic
* [8] DOY Day of Year
* [9] ETM+ Enhanced Thematic Mapper plus
* [10] EVI2 2-band Enhanced Vegetation Index
* [11] HLS Harmonized Landsat Sentinel-2
* [12] HPLM Hybrid Piecewise Logistic Model
* [13] LULC Land Use/Land Cover
* [14] LSP Land Surface Phenology
* [15] LST Land Surface Temperature
* [16] MODIS MODerate resolution Imaging Spectroradiometer
* [17] MSI MultiSpectral Instrument
* [18] NAD83 North American Datum 1983
* [19] NASS National Agricultural Statistics Service
* [20] NBAR Nadir-BRDF Adjusted Reflectance
* [21] NDVI Normalized Difference Vegetation Index
* [22] NLCD National Land Cover Database
* [23] OA Overall Accuracy
* [24] OLI Operational Land Imager
* [25] PA Producer's Accuracy
* [26] RFC Random Forest Classifier
* [27] SR Surface Reflectance
* [28] TM Thematic Mapper
* [29] UA User's Accuracy
* [30] USDA United States Department of Agriculture
* [31] UTM Universal Transverse Mercator
* [32] WGS84 World Geodetic System 1984
## 6 Further Benefits
_Enhanced security and billing_: An end user is always authenticated by the ISP to grant access to the network. This authentication can be further extended through a Single Sign-On (SSO) technique to access restricted content. Another key issue that threatens the revenue of the CPs is Digital Right Management (DRM). The ISPs could also control the delivery of copyrighted content to an illegitimate user. The ISPs are also capable of implementing fine-grained billing for the usage of commercial content and could supply the billing information to the CPs.
# ODIN112-AI-Assisted Emergency Services in Romania
## 2 Related Work
### Automatic Speech Recognition  
Automatic Speech Recognition has been a subject of study for a long time. However, significant improvements to make it viable for text transcription were made only in the last decade. The underlying process is made up of several steps, the most common and important ones being feature extraction, phoneme detection, word composition, and text transcription.  
The conventional implementation used to be a hard-coded pipeline and acoustic model tweaked manually until the accuracy was good enough for the specific domain it was used in. As the pipeline was built on top of different modules that used various algorithms for a particular task, optimizing the system implied adjusting each component.  
The optimization process was laborious and sometimes reached the local optimum for each component instead of the global optimum [3].  
The newer methods leverage end-to-end learning, training a single Deep Neural Network (DNN) model with multiple layers that replace the traditional pipelines. Neural networks can process inputs and outputs of arbitrary length and take advantage of local spatial coherence extracting more useful information with a relatively low computational cost. These are just two of the characteristics that make them ideal for audio and speech analysis. Furthermore, these systems can be classified into regular DNN-based models or hybrid models, which leverage Deep Neural Networks and any other set of technologies, such as the long-established Hidden Markov Models.
## 2 Nonlinear Model Predictive Control
### Mathematical Model of Nonlinear System  
#### 2.2.1 System State Equation  
A nonlinear system can be expressed using the following equation:  
\[\left\{\begin{array}{l}\dot{X(\tau)}=\ f(X(\tau),\ u(\tau))\\ X(0)=X_{0}\\ Y(\tau)=h(X(\tau),\ u(\tau))\end{array}\right. \tag{1}\]  
where \(X\) is the system state, \(u\) is the system input, \(Y\) is the system output, \(f(X,u)\) describes the dynamic system, and \(h(X,u)\) defines the system output.  
A future system state can be predicted using a numerical integration algorithm:  
\[X(t+1)=g(X(t),\ u(t)) \tag{2}\]  
where \(g(X,u)\) is a numerical integration method.  
In this paper, a vehicle kinematic model is employed to describe the system state.
# Using Machine Learning for Quantum Annealing Accuracy Prediction
## 2 Methods
### Regression  
In contrast to predicting solvability alone, we also attempt to predict the actual clique size found by the D-Wave 2000Q device from graph and annealer features alone. Therefore, in this section, our aim is not the prediction of a binary decision (solvable, not solvable), but predicting an integer response (precisely, an integer within the range of possible clique sizes \(\{0,\ldots,|V|\}\)). For this task, no ground truth (via a classical solver) is needed.  
As in the previous section, several regression models are suitable for this task. We chose to predict the clique size returned by D-Wave 2000Q with gradient boosting, a popular machine learning regression model. Whereas random forests build deep independent trees, gradient boosting works by constructing an ensemble of dependent shallow trees, each one improving on the previous one. Although shallow trees by themselves are usually only weak predictive models, combining (or \"boosting\") them in an ensemble allows for powerful machine learning models. We employed the _GradientBoostingRegressor_() class [21] in _scikit-learn_[3]. Similarly to the classification setting, we performed a grid search across selected parameters, and set _n_estimators=200_, and _random_state=0_. All other parameters were kept at their default values.  
The graph and annealer features included in the model are the same as the ones outlined in the previous section. One caveat is worth mentioning. It is not always true that the set of vertices (given by the bitstring indicating the clique vertices with a 1) returned by the D-Wave 2000Q annealer actually forms a clique. Therefore, to train the model, we verify first if the result returned by D-Wave 2000Q is a clique. If it is, we use the clique size in the regression, otherwise we use a value of zero.
# Smart Intravenous Infusion Dosing System
## 3 Problem Description
### IV Chemotherapy  
Testicular cancer most often arises from germ cells--seminoma- and non-seminoma-type. At diagnosis, approximately 70% of cases are detected in the first stage of the disease when the cure rate is extremely high. The success of the treatment is also attributed to the fact that this type of cancer is very sensitive to IV chemotherapy and radiotherapy, which is why these treatment modalities are widely used in clinical practice [83]. Platinum-based chemotherapy is most commonly used [84]. The combination of cytostatics, cisplatin/etoposide/bleomycin, the so-called PEB protocol, is the standard in treatment after the initial surgical procedure for seminoma- and non-seminoma-type tumors. When there are poor prognostic parameters, 1-2 cycles of chemotherapy are applied, while for metastatic disease, 3-4 cycles are used [85; 86]. However, if the dis ease relapses or is refractory to the PEB protocol, then another combination of cytostatics, cisplatin/etoposide/ifosfamide, the so-called PEI protocol, is used.  
The PEB protocol involves the intravenous administration of cisplatin and etoposide in the appropriate dose, from the first to the fifth day of the cycle, as well as the intravenous administration of bleomycin in the form of an intravenous bolus on the 1 st, 8 th, and 15 th days of the cycle. The PEI cycle involves the intravenous administration of cisplatin, etoposide, and ifosfamide in the appropriate dose, from the first to the fifth day of the cycle [87]. In addition, before and after the IV infusion, the patients receive appropriate premedication as an intravenous bolus using corticosteroids and antiemetics. One cycle of chemotherapy lasts a total of 21 days, after which the therapy is repeated.  
Table 1 shows the parameters of daily IV chemotherapy for both protocols. In addition to the order of administration of IV bottles and the appropriate cytostatics, the recommended drip times for each IV bottle are shown separately. Therefore, the total recommended infusion drip time for the PEB protocol is 195-220 min, while for the PEI protocol it is slightly longer at 245-270 min.  
On the basis of this, we conclude that the treatment of patients with testicular cancer using cytostatics is intravenous, very long, complex, and exhausting, but largely a known process, which is unthinkable without the use of IV infusion, whose properties we want to improve. In addition, the time of IV infusion drip required for the full effect of chemotherapy, despite its length and regardless of the number of cycles, should be imperative, which is only possible by using modern systems for detection, monitoring, regulation, and stop of IV infusion flow.
# Evaluation of Low-Cost GNSS Receiver under Demanding Conditions in RTK Network Mode
## 2 Materials and Methods
### GNSS Antennas  
Since the C099-F9P is an application board with the receiver itself, an active GNSS antenna must be connected to it. The manufacturer includes a panel antenna (model ANN-MB-00), which should provide the required accuracy in conditions with good visibility of satellites. It is small (its dimensions are only 60.0 \(\times\) 82.0 \(\times\) 22.5 mm) and weatherproof (protection level IP67).  
To reduce the number of reflections from other objects and/or the environment reaching the antenna, when mounting the above antenna, it should be ensured that it is placed on a plate made of a conductive metal (so-called ground plane). For the following tests, a special 4 mm thick disc with a diameter of 200 mm and a bracket for it were self-made of aluminum. The assembly is shown in Figure 1a. The whole set provides a coaxial mounting with the second antenna and the Leica GS18T receiver, allowing straightforward interpretation of the results later in the tests.  
To check the performance of the ANN-MB-00 antenna in multipath reduction, measurements were additionally performed with a professional surveying antenna Leica AS10, which was applied in another similar test [14; 30]. This uses, among other things, NovAtel's advanced Pinwheel technology [31] (Figure 1b), which, as will be shown later, helped to obtain better results.  
To achieve the best possible stability of the antennas during the survey, geodetic tripods were used together with Leica tribrachs and antenna carriers.  
During precise geodetic measurements, PCO (Phase Center Offset) should also be taken into account. According to the manufacturer's specifications [32], the PCO of the u-blox ANN-MB-00 antenna is 8.9 mm and 7.6 mm in the vertical (V) plane for L1 and L2 signals, respectively, and less than 5 mm in the horizontal (Hz) plane for both frequencies. For the Leica AS10 antenna, on the other hand, the values are 58.3 mm and 55.5 mm for L1 and L2 signals (V) and less than 3 mm (Hz) for both frequencies [33].  
Unfortunately, it is not possible to upload an antenna calibration file to the ZED-F9P receiver. Due to the accuracy of the RTK measurement and the small PCO values in the horizontal plane, they were omitted. However, in the vertical plane, these values were included. Because the differences between the PCO for L1 and L2 are less than 3 mm, and the PCVs (phase center variations) are of the order of 5 and 10 mm for L1 and L2 [32], the PCO values for the L2 signal were used in post-processing.  
Figure 1: Antennas used in conducted experiment: (**a**) u-blox ANN-MB-00 antenna with self-made aluminum ground plane; (**b**) survey-grade antenna Leica AS10 outside and inside (NovAtel Pinwheel technology) view.
# Dynamic Characteristics Analysis of a Rigid Body System with Spatial Multi-Point Supports
## 6 The Influence of Support Stiffness on the System Inherent Characteristics  
Taking a simplified rigid body system with four supports as an example, the influence of each support stiffness on the system's inherent characteristics was studied. The stiffness range of each support was from 1 \(\times\) 10\({}^{8}\) (N/m) to 9 \(\times\) 10\({}^{8}\) (N/m), and the first two natural frequencies of a simplified rigid body system under the corresponding stiffness were calculated. Figure 11a-c shows the variation curves of the first two natural frequencies of a simplified rigid body system when the axial stiffness \(k_{x}\), normal stiffness \(k_{y}\) and tangential stiffness \(k_{z}\) of each support change, where the solid line represents the first order frequency, and the dotted line represents the second order frequency. It can be observed that the system dynamic characteristics are most sensitive to the axial stiffness \(k_{x}\) of the four supports, followed by the tangential stiffness \(k_{z}\), and least sensitive to the normal stiffness \(k_{y}\). This is due to the axial distance (\(d_{mx}\)) from each support to the center of mass being larger than the normal distance (\(d_{my}\)) and the tangential distance (\(d_{nz}\)), resulting in a more obvious moment of inertia effect, which further affects the system's inherent characteristics. From Figure 11a, the sensitivity of system dynamic characteristics is similar to the axial stiffness, \(k_{x}\), of the four supports.
# IMPA versus Cloud Analysis and IDA: Different Methods to Evaluate Structural Seismic Fragility
## 2 Methodology  
### Choice of Engineering Demand Parameter and Intensity Measure  
The first-mode spectral acceleration \(S_{a}(T_{1}\zeta=5\%)\) is commonly used as an intensity measure (_IM_) parameter [20,21]. [PERSON] et al. [24] stated that the nonlinear response of an MDOF structure dominated by the first mode of vibration depends on the \"intensity\" of the records at the first period of vibration, while magnitude and distance play a minor role in it. For these reasons, the 5% damped spectral acceleration at the structure's first-mode period \(S_{a}(T_{1}\zeta=5\%)\) or simply \(S_{a}\) is adopted as the _IM_ in this work, since the structure selected as the case study is dominated by the first mode of vibration (structure's first-mode period of vibration \(T_{1}=0.62\) s and the modal mass participation at first-mode is 82%, see Section 3.1 for more details).  
In the literature, various engineering demand parameters (EDPs) have been proposed [24], somehow representative of the structure's local or global damaged state. In this study, the critical demand to the capacity ratio for the desired limit state (_LS_), denoted as \(DCR_{LS}\)[23,41], is assumed to be the EDP. It represents the demand-to-capacity ratio which brings the system closer to the onset of limit state (herein, the life-safety limit state). The weakest-link formulation is adopted to evaluate the \(DCR_{LS}\) (Equation (1)), which means that if the demand-to-capacity ratio \(D_{jl}/C_{jl}\) is equal to or higher than unity in just one element, then the structure attains the expected limit state for the \(l\)th mechanism.  
\[DCR_{LS}=\ \max_{l}^{N_{mech}}\max_{j}^{N_{c}}\left(\frac{D_{jl}}{C_{jl}(LS)}\right) \tag{1}\]  
where \(N_{mech}\) and \(N_{e}\) are the numbers of the considered potential mechanism of failure and the number of the elements taking part in the \(l\)th mechanism, respectively. \(D_{jl}\) and \(C_{jl}(LS)\) are the demand and the limit state capacity, respectively, evaluated for the \(j\)th element of the \(l\)th mechanism.  
In particular, in this work, only a ductile failure mechanism in columns and beams is considered as a potential failure mechanism (\(N_{mech}=1\)). Therefore, in this deformation-based critical \(DCR_{LS}\), the demand \(D\) is expressed in terms of maximum chord rotation in the \(j\)th component. Instead, the capacity \(C\) in terms of chord rotation is evaluated according to guidelines reported in the Commentary [42] of NTC 2018. Namely, the chord rotation for life-safety limit state is defined as \(\frac{3}{4}\) of that corresponding to near-collapse limit state \(\theta_{u}\), evaluated according to Equation 11.7.2.5 of the Commentary (Equation (2)).  
\[\theta_{u}=\left(\theta_{y}+\left(\phi_{u}-\phi_{y}\right)L_{pl}\left(1-\frac {0.5L_{pl}}{L_{v}}\right)\right) \tag{2}\]  
where \(\theta_{y}\) is the yield chord rotation, \(\phi_{u}\) and \(\phi_{y}\) are the ultimate and yield curvature, respectively, \(L_{pl}\) is the plastic hinge length, and \(L_{v}\) is the shear length.
# Integrating a Path Planner and an Adaptive Motion Controller for Navigation in Dynamic Environments
## 4 Experiments and Results
### Training Environment Settings  
The training environment is a simple indoor environment whose size is 20 by 20 (Figure 6). A start point, a goal point, and the motion states of moving obstacles are randomly initialized in every episode so as to cover the entire state space as soon as possible. Moving obstacles denote people, robots, and other moving objects, which adopt the uniform rectilinear motion. The range of moving obstacles' velocities is [0.08, 0.1]. The initial position is at the edge of the maximum detection distance of the robot, and the initial direction is roughly toward the robot. When the robot arrives at the goal or collides with obstacles, the current episode ends, and the next episode starts.  
Figure 6: Training environment—20 by 20.
# Quasi-Experimental Design for Medical Studies with the Method of the Fuzzy Pseudo-Control Group
## 3 The Method of the Fuzzy Pseudo-Control Group
### MFPCG Algorithm and Flowchat of the Method  
Let the continuous parameters, \(X_{1}\), \(X_{2}\), \(\ldots\), and \(X_{n}\), be proxies for the outcome of intervention \(R\) over the target population \(P\) compared with the pseudo-control population \(Q\) where \(R\) was not applied.  
MFPCG Algorithm  
1. Select the significance level, \(\alpha\), and the number of pseudo-realities, \(N\), of the Bootstrap statistical tests.
2. Set \(i=1\).
3. Choose \(X=X_{i}\).
4. Extract from the database the fuzzy samples \(E_{b}\), \(\mathcal{C}_{b}\), \(E_{e}\), and \(\mathcal{C}_{e}\) for this \(X\).  
\begin{table}
\begin{tabular}{l l l l} \hline \(\mathcal{C}_{M}\) & \(\mathcal{C}_{\text{med}}\) & **Generalized Condition** & \(\mathcal{C}_{t}\) & **Rule** \\ \hline M\({}_{-1}\) & \(\text{MED}_{-1/2}\) & \(\text{C}_{-1}\) & \(5\) \\ \hline M\({}_{-1}\) & \(\text{MED}_{-1}\) & \(\text{C}_{-1}\) & \(5\) \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._Perform stage 1 of MFPCG and expertly determine for \(X\) the optimal value margins \(X_{d,opt}\), \(X_{u,opt}\), and the insignificant change threshold \(\Delta X\).
* Perform stage 2 of MFPCG for \(E_{b}\) and \(PC_{b}\).
* Perform stage 2 of MFPCG for \(E_{e}\) and \(PC_{e}\).
* Repeat for Bootstrap Modification BM\(k\) (\(k=1\), 2, 3, 4):
* Perform stage 3 of MFPCG for \(E_{b}\) and \(PC_{b}\) using Bootstrap Modification BM\(k\) with \(N\) pseudo-realities for each Bootstrap test using significance level \(\alpha\).
* Perform stage 4 of MFPCG for \(E_{b}\) and \(PC_{b}\) using Bootstrap Modification BM\(k\) with \(N\) pseudo-realities for each Bootstrap test using significance level \(\alpha\).
* Perform stage 4 of MFPCG for \(E_{e}\) and \(PC_{e}\) using Bootstrap Modification BM\(k\).
* Perform stage 5 of MFPCG for Bootstrap Modification BM\(k\) and find the BM\(k\) class.
* Set \(i=i+1\).
* If \(i<n\), then go to step 3. Otherwise, end the algorithm.  
Apart from the method's main stages, the steps of the above algorithm are self-explanatory and trivial. In their entirety, the results answer the question of how \(R\) influences the continuous proxies \(X_{1}\), \(X_{2}\), \(\ldots\), and \(X_{n}\). To determine \(R\)'s overall influence, all \(4n\) influences (for each Bootstrap Modification and each variable) need to be accumulated.  
The flowchart of the method is given in Figure 1.  
Figure 1: Flowchart of the MFPCG.  
The MFPCG Algorithm and flowchart show the MFPCG results in four qualitative assessments for the favorability of intervention \(R\) over the target population for each of the proxies, \(X_{i}\), of the intervention outcome. MFPCG does not need numerous assumptions to justify its conclusion, if any. The only notable exception is the hill preference assumption that, on the one hand, is easily proven if present and, on the other hand, is a significant relaxation of the monotonic preference assumption required in DID.