esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Background and Literature Review
### Blockchain and Smart Contracts
#### 2.2.2 Blockchain Algorithm: Hyperledger Fabric  
In expanding upon the section for the blockchain algorithm, we delve deeper into the intricacies of Hyperledger Fabric and its pivotal role in 5G antenna manufacturing. Selected for its enterprise-grade capabilities, Hyperledger Fabric offers a versatile platform that supports an array of components critical for building a robust blockchain ecosystem. This includes a modular architecture that is conducive to tailoring consensus protocols, managing membership services efficiently, and enabling the deployment of smart contracts through chaincode. The ability of Hyperledger Fabric to facilitate the creation of private blockchains is particularly advantageous in manufacturing contexts where data sensitivity is paramount. With security and efficiency at the core, this blockchain framework ensures that proprietary manufacturing data are managed with the utmost integrity. The platform's support for smart contracts, written in contemporary and versatile programming languages like Go (accessed at [[https://golang.org](https://golang.org)]([https://golang.org](https://golang.org)), accessed on 1 January 2024), JavaScript (information available through [[https://developer.mozilla.org/en-US/docs/Web/JavaScript](https://developer.mozilla.org/en-US/docs/Web/JavaScript)]([https://developer.mozilla.org/en-US/docs/Web/JavaScript](https://developer.mozilla.org/en-US/docs/Web/JavaScript)),accessed on 1 January 2024), and Java (details found on [[https://www.oracle.com/java/](https://www.oracle.com/java/)]([https://www.oracle.com/java/](https://www.oracle.com/java/)), accessed on 1 January 2024), empowers developers to embed sophisticated logic into the manufacturing workflow, thereby optimizing process management and quality assurance.  
Customization is another forte of Hyperledger Fabric, with its consensus mechanism being particularly adaptable to suit the specific needs of a network, which is crucial for accommodating the varying scales of operation within the manufacturing industry. This flexibility ensures that the system can grow and evolve in tandem with the enterprise, maintaining performance and scalability. The strategic selection of Hyperledger Fabric underscores a commitment to advancing the manufacturing process through enhanced security, transparency, and collaboration. By integrating this technology, we are setting the stage for a manufacturing ecosystem that not only thrives on mutual trust and streamlined operations but also stands at the forefront of innovation in industrial practices. This integration is a testament to the transformative potential of blockchain technology in the manufacturing sector, particularly within the realm of 5G antenna production, where it can serve as a catalyst for efficiency and strategic partnership among all stakeholders in the supply chain.
# Optimized Deployment of Generalized OCDM in Deep-Sea Shadow-Zone Underwater Acoustic Channels
## 4 Simulation  
### Peak-to-Average Power Ratio Performance  
In this subsection, we evaluate the PAPR performance of GOCDM through simulations. In deep-sea communication, a relatively high number of subcarriers is typically required in order to counteract the long multipath delay of the channel. We consider two cases, with subcarrier counts of 1024 and 2048. For the 1024-subcarrier case, we simulate three scenarios with \(M=128\), \(M=256\), and \(M=512\). For the 2048-subcarrier case, we use \(M=256\), \(M=512\), and \(M=1024\). The modulation scheme used is QPSK.  
We analyze the PAPR performance of different modulation schemes using the Complementary Cumulative Distribution Function (CCDF), which represents the probability that the root mean square of the signal's PAPR exceeds a given threshold \(z\). As shown in Figure 3, the PAPR of GOCDM is consistently lower than that of conventional OFDM and OCDM. In Figure 3a, it is evident that conventional OFDM and OCDM exhibit the same PAPR characteristics. However, the proposed GOCDM method significantly reduces the PAPR compared to these conventional schemes. Specifically, for cases where the PAPR exceeds 12 dB, increasing the \(M\) leads to a reduction in the probability of high PAPR occurrences in GOCDM modulation by 1 to 3 orders of magnitude compared to OFDM and OCDM.  
When \(M\) is set to \(K\), GOCDM evolves into SC-FDE and simultaneously transitions from multicarrier modulation to single-carrier modulation, whereby the PAPR reaches its minimum value. Subsequently, we compared other performance aspects of the communication system under different \(M\) values and summarized the findings in Section 6. Users may adjust the \(M\) value based on practical requirements to achieve optimal performance.  
In deep-sea shadow-zone communication, the signal-to-noise ratio (SNR) at the receiver is typically low, and the high-power portions of signals with a high PAPR are more susceptible to medium non-uniformities, such as density variations in the thermocline. This can lead to increased energy loss, refraction, or reflection. Conversely, the lower-power portions of the signal may lack sufficient energy to penetrate these non-uniform regions, resulting in inconsistent signal attenuation across different power levels and increasing  
Figure 3: The CCDF of OFDM, OCDM, SC-FDE, and GOCDM: (**a**) \(K=1024\), and (**b**) \(K=2048\).  
transmission uncertainty. Overall, the reduced PAPR of GOCDM makes it particularly well-suited for deep-sea shadow-zone communication.
## 3 Comparison Based on Numerical Simulation  
### Engineering Background and Field Measurements  
The Pengcheng cooling tower, located in Pengcheng electric power station in Xuzhou, China, stands at a height of 167 m with a smooth-walled exterior. The tower has a neighboring tower of the same size located to the south with a center-to-center distance of 1.5 times the diameter of the tower base. Additionally, there is a large building complex located to the west. When the wind affects the tower from east or north, there are no major interfering buildings or topography; thus, the flow event can be considered a free-standing tower case.  
In 2009, 36 pressure measurement transducers were evenly distributed around the tower's throat section at a height of 130 m. From 2009 to 2015, wind pressures were measured on the tower two to three times per year for a two-week period each time. Over 4000 h of effective wind pressure samples were collected on the tower under standard ABL strong winds. Samples for the free-standing tower case recorded on 29 November 2011 were chosen from this database for further use. The reason for selecting data collected at that time is explained in ref. [17], which will not be reiterated in this article. Moreover, a typical non-stationary wind velocity sample is extracted from a full-scale wind pressure sample measured in the quasi-steady region on the actual large cooling tower using the approach presented in ref. [26] for further use.
## 4 Simulations and Results
### Availability: Mean Up Time (MUT) and Mean Down Time (MDT)  
Availability is the ability of an item to carry out a particular task under specific circumstances and at a specific time [30]. Availability predictions are generally difficult since both human and logistical factors must be considered in conjunction with reliability  
Figure 11: Reliability of the different components of the RBS.  
Figure 10: Regenerative braking system’s reliability.  
and maintainability [31]. For simplicity's sake, we assume that human and logistical conditions are ideal in the present work.  
Marking size monitors are defined to evaluate the availability. They record the duration of the state when each component is broken. They are applied on **panne** places, **PMC** for the example of the master cylinder, and **System_NOK** place for the whole system.  
From the data obtained using this monitor, we can conclude the mean up times and the mean down times.  
In fact, if the component is not broken (available), this monitor records the value 0 (no token is available on the panne place) and then we can extract its associate MUT. Otherwise, the monitor returns the value 1, and then the MDT is extracted.  
MDT is the expected time of an entity's failure, including detection, intervention, reparation, and function restoration times. Therefore, MDT is the system's anticipation of downtime. If the system has only one failure state, then the following is true [32]:  
\[\text{MDT}=\text{MTTR} \tag{11}\]  
MUT and MDT average values of each component and of the whole RBS are listed in Table 5.  
From Tables 4 and 5, we note that the MTTR and MDT values are identical because we have presumed that the repair is carried out right after the failure occurs.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **MUT** & **MDT** \\ \hline Ped & 509.57 & 2.43 \\  
hline \hline \end{tabular}
\end{table}
Table 5: MUT and MDT results.
# Design of a Wearable Healthcare Emergency Detection Device for Elder Persons
## 3 Fall Detection System
### Data Pre-Processing  
When data are transmitted to the designed neural network, the size of each input datum should be the same as the number of input layer variables (in our model, the input layer size is 300). However, since the duration of each action, including falling, is different, we needed to make the sizes of the data the same. Based on this, all data were unified to have 50 values.  
We split complete dataset into four lists of gestures, namely, \"adl\", \"fall\", \"walk\" and \"run\"; we had 50 samples for each gesture. For each of these files we had to normalise input data between 0 and 1 in order to create tensors. Each row contained normalised input data, coming from acceleration and angular velocity values, and the output represented by an eye matrix encoding the expected activity value. Data pre-processing reported in the following Listing 1.  
```
1#Setafixedrandomseedvalue,forereproducibility,thiswillallowustotgetthesamerandomnumberseachtimethenotebookisrun
2 SEED=1337
3 np.random.seed(SEED)
4 tf.random.set_seed(SEED)
5
6#thelistofgesturesthatdatasavailablefor
7 GESTUREES=[\"ad1\",\"fall\",\"walk\",\"run\",]
8 NUM_GESTUREES=len(GESTUREES)
9 SAMPLES_PER_GESTURE=50
10
11#createone-hotencodedmatrixthatisusedintheoutput
12 ONE_HOT_ENCODED_GESTUREES=np.eye(NUM_GESTUREES)
13
14 inputs=[]
15 outputs=[]
16
17#readencsvfileandpushaninputandoutput
18 forgesture_indexinrange(NUM_GESTUREES):
19 gesture=GESTUREES[gesture_index]
20 print(f\"Processingindex{gesture_index}forgesture'{gesture}'.\")
21
22 output=ONE_HOT_ENCODED_GESTUREES[gesture_index]
23
24 df=pd.read_csv(\"/content/\"+gesture+\".csv\")
25
26#calculatethenumberofgesturerecordingsinthefile
27 num_recordings=int(df.shape[0]/SAMPLES_PER_GESTURE)
28
29 print(f\"\tThereare{num_recordings}recordingsofthe{gesture}gesture.\")
30
31 foriinrange(num_recordings):
32 tensor=[]
33 forjinrange(SAMPLES_PER_GESTURE):
34 index=i*SAMPLES_PER_GESTURE+j
35#normalizetheinputdata,between0 and1:
36 tensor+=[
37(df['ax'][index]+4)/8,
38(df['ax'][index]+4)/8,
39(df['ax'][index]+4)/8,
40(df['gx'][index]+1000)/2000,
41(df['gx'][index]+1000)/2000,
42(df['gx'][index]+1000)/2000
43]
44
45 inputs.append(tensor)
46 outputs.append(output)
47
48#convertthelistotnumpyarray
49 inputs=np.array(inputs)
50 outputs=np.array(outputs)
51
52 print(\"Datasetparsingandpreparationcomplete.\")
```  
**Listing 1** Dataset parsing and pre-processing.
## 3 Results and Discussion
### Scattering of a Counter-Propagating Laser Pulse from the Relativistic Electron Mirror
The contrary behavior of dependences of the reflection coefficient and hard quanta energy on the laser intensity and target width shown in Figure 6 indicates the existence of an optimum with respect to the width and intensity at which the conversion coefficient of laser radiation energy to the energy of hard radiation reaches its maximum. In Figure 7, the dependence of the energy conversion coefficient of the main laser pulse to the hard quanta on the laser intensity is shown. **Figure 6.** The dependence of the frequency \(\Omega\) **(a)** of scattered hard radiation and the reflection coefficient \(R\) on the intensity of the main laser pulse. The target width is 0.6 nm. The dependence of the frequency \(\Omega\) **(b)** of scattered hard radiation \(R\) and the reflection coefficient on the width of initial target at laser intensity of \(5\,\times\,10^{19}\) W/cm\({}^{2}\). One can clearly see that the optimal conversion can be reached at comparatively low energies of hard quanta 50 eV (\(\Omega\approx 30\) ), which is why, in our case, the generation of hard quanta of high energies (higher than 1 keV) occurs at the non-optimal coefficient of energy conversion to that of the scattered radiation. In our calculations, at an intensity of 5 \(\times\) 10\({}^{19}\) W/cm\({}^{2}\) and width of Carbon target of 0.6 nm, the conversion coefficient is \(\chi\approx 0.1\%\). Such a conversion coefficient exceeds that of laser X-ray line conversion in the same spectrum of quanta energies. Let us estimate, using Equation (3), the source luminosity corresponding to the parameters of Figure 5. The number of photons per unit area \(N_{\mathrm{s}}/S\) for a given laser pulse (16 fs, 10\({}^{18}\) W/cm\({}^{2}\), 0.8 \(\mathrm{\SIUnitSymbolMicro m}\)) amounts to 5.3 \(\times\) 10\({}^{20}\) photons/mm\({}^{2}\). In accordance with Figure 6a, the reflection coefficient is \(R\)\(\approx\) 0.05 and the value \(\Omega=4r_{\mathrm{x}}^{2}\)\(\approx\) 40. At the scattering pulse repetition rate \(f=\) 10 Hz, the average luminosity is about 1.3 \(\times\) 10\({}^{15}\) photons/(sec mm\({}^{2}\) mrad\({}^{2}\)). This is significantly higher than that of conventional X-ray tubes (10\({}^{8}\)) and laser-electron generators on a base of accelerators (10\({}^{12}\)), but lower than the average luminosity of contemporary synchrotrons (10\({}^{21}\)) in the approximate energy range of hard quanta. In Figure 5, the magnitude of the source peak luminosity reaches 3 \(\times\) 10\({}^{29}\) photons/(sec mm\({}^{2}\) mrad\({}^{2}\)), which is 8 orders higher than that of synchrotron luminosity. One can increase average luminosity by increasing the pulse repetition rate to 10 kHz, thereby leading to a doubling of the average luminosity of such a scheme.
## 3 Results and Discussion
### Study Limitations and Future Directions  
The effect of heat stress on the behavior of lactating cows raised in compost barns has been explored in a few studies; thus, as most of the research was conducted in Brazil and the United States, some care is needed in extrapolating our findings. Due to the different methodologies used in the studies to evaluate cow behavior, we could not perform a meta-analysis and, therefore, we did not assess the quality of the studies, which could influence our results. We excluded conference proceedings (articles and abstracts) and book chapters as we could not be sure that these sources had been peer-reviewed. We also excluded literature in languages other than English, as we could not critically evaluate the methods and assess the results. Thus, we cannot determine the extent to which these exclusions affected the conclusions of this review.  
Further studies (review and experiments) on the applied ethology for lactating cows housed in compost barns should consider the percentage reduction in the dry matter intake, production losses, the percentage of time cows remain at rest, standing, consuming water, moving, as well as agonistic behaviors. However, lying down behavior should be cautiously assessed due to its relationship with thermoregulatory responses, but there is little literature on this for lactating cows [28]. Furthermore, little data was found in the literature regarding the activities and behaviors of dairy cows under heat stress conditions in a confinement system [93]. Most studies on housing for lactating dairy cows were of the free-stall, tie-stall, and loose-housing models [94]. Few studies have been conducted on compost barns (such as evaluating different breeds, ventilation systems and geographical areas) mainly because it is a relatively new confinement system. This may have hampered data extraction in our study and may serve as the basis for future research.  
More precise methods to detect heat stress in feedlot systems are needed. THI does not accurately represent the thermal environment in which dairy cows are embedded, nor does using air temperature alone [95]. Thus, assessing heat stress from physiological assessments may be more adequate and accurate.
## 2 Circuit Configuration of \(LC\)-Hapf  
### Circuit Configuration and System Modeling  
Figure 2 shows a three-phase four-wire \(LC\)-HAPF with a balanced nonlinear load [9; 10; 11]. The subscript \(x\) indicates the phase \(a\), \(b\), \(c\) and \(n\). \(v_{sx}\), \(i_{sx}\), \(v_{x}\) and \(i_{lx}\) are the source voltage, source current, the point of common coupling (PCC) voltage, and load current for each phase, respectively. \(i_{cx}\), \(v_{cx}\) and \(v_{invx}\) are the compensation current, compensation capacitor voltage and inverter output voltage, respectively. \(L_{Lx}\), \(R_{Lx}\) and \(C_{Lx}\) are the inductor, resistor and capacitor of the nonlinear loads, which are the full-bridge rectifier. \(L_{cx}\), \(C_{cx}\) and \(R_{cx}\) are the inductor, capacitor and equivalent resistance of the coupling PPF. \(T_{1x}\) and \(T_{2x}\) are the trigger signals for IGPs. The upper and lower DC-link capacitor voltage, \(V_{dcU}\) and \(V_{dcL}\), satisfy \(V_{dcU}=V_{dcL}=0.5V_{dc}\), where \(V_{dc}\) is the DC-link voltage of VSI.  
Figure 2: The circuit configuration of a three-phase four-wire \(LC\)-HAPF with balanced nonlinear load.  
The PPF and VSI consist of _LC_-HAPF, which generate \(i_{cx}\) to compensate the grid harmonics and reactive power. The differential equations for _LC_-HAPF are set up by (1).  
\[\left\{\begin{array}{l}L_{cx}\frac{d_{i_{cx}}}{dt}=v_{invx}-v_{x}+v_{cx}-i_{ cx}R_{cx}\\ C_{cx}\frac{d_{i_{cx}}}{dt}=i_{cx}\end{array}\right. \tag{1}\]  
Based on (1), the state-space model of _LC_-HAPF is set up by (2).  
\[\left\{\begin{array}{l}\frac{d}{dt}\mathrm{e}=\mathrm{A}\mathrm{e}+\mathrm{B }\mathrm{u}+\mathrm{E}\mathrm{z}\\ \mathrm{y}=\mathrm{C}\mathrm{e}+\mathrm{D}\mathrm{u}\end{array}\right. \tag{2}\]  
where \(\mathrm{e},\mathrm{u},\mathrm{z}\) and \(\mathrm{y}\) indicate state, control, disturbance and output variable matrix, and they are expressed as \(\mathrm{e}=\begin{bmatrix}i_{ca}&i_{cb}&i_{cx}\end{bmatrix}^{T}\), \(\mathrm{u}=\begin{bmatrix}v_{inva}&v_{invb}&v_{invc}\end{bmatrix}^{T}\), \(\mathrm{z}=\begin{bmatrix}v_{a}-v_{ca}&v_{b}-v_{cb}&v_{c}-v_{cc}\end{bmatrix}^{T}\) and \(\mathrm{y}=\begin{bmatrix}i_{ca}&i_{cb}&i_{cc}\end{bmatrix}^{T}\). \(T\) indicates the transpose of the matrix.  
The upper equation in (2) is the system state differential equation of the _LC_-HAPF, the matrix A, B and E are state, control and disturbance matrix. The lower equation in (2) is the output equation of the _LC_-HAPF, which includes the output state matrix, C, and the output control matrix, D. The value of the A, B, C, D and E matrices for LQRC and LQRIC will be discussed in Section 3.
The behavior of carbon-polymer columns reinforced with vertically bent hollow steel beams was investigated in [21]. Sixteen curved beams with a span of 1200 mm, with four radii of curvature; 0 mm, 2000 mm, 4000 mm, and 6000 mm were tested for three-point bending. A theoretical model was also developed and validated to predict the elastic behavior of CFST reinforced tubular beams using the integral Maxwell-Mohr method. A high-strength, low-magnesia (CFST) concrete-filled tubular steel column was investigated in [22] under eccentric loading. Seven groups of columns with different eccentricity factors were experimentally studied, and the ultimate load of each test was compared with the design loads from several specifications. The results of the load-vertical deflection curves show that, with the same eccentricity, the bearing capacity of the test specimens is 12% higher than that of high-strength concrete from steel pipes. The technological features of the manufacture of concrete tubular columns, their properties, and calculation methods under static and dynamic influences are considered in [23; 24; 25; 26; 27]. The above review shows that most of the work related to the study of the stress-strain state of CFST is carried out experimentally or numerically in a simplified formulation. The results that are closest to the real work of structures are provided by finite element modeling in a volumetric setting, taking into account the physical nonlinearity of the material [28]. However, this approach requires a lot of computational time. Thus, this work aims to develop a method that considers the nonlinear properties of the CFST materials, which makes it possible to reduce the dimension of the problem of determining the stress-strain state from three-dimensional to two-dimensional without a noticeable loss of accuracy of the results. In the framework of the set goal, the following tasks are solved:  
1. Derivation of the resolving equations and computer implementation of the method for nonlinear calculation of concrete-filled steel tubular columns by reducing the three-dimensional problem to the two-dimensional one. 2. Verification of the method and the developed software by comparison with calculations in existing software complexes in the three-dimensional setting. 3. Approach of the developed method on existing experimental data. 4. Study of the peculiarities of the stress-strain state of columns at various eccentricities of the longitudinal force.
# Fidex and FidexGlo: From Local Explanations to Global Explanations of Deep Models +
## 2 Explainability Techniques
### Explainability by Visualization  
Many techniques aimed at explaining deep NN decisions in image classification are based on visualizing the areas that are most relevant to the result [15] but, as stated by [PERSON] [16], \"it does not explain anything except where the network is looking\". Grad-CAM, which stands for gradient-weighted class activation mapping, is a technique designed to highlight the most critical regions of an image used in a deep learning model's decision-making process [17]. The heat-map shows which pixels from the image are required by the model to perform the classification task. Furthermore, Grad-CAM only shows features that contribute positively to the class of interest. It does not show features that the model uses as evidence against a particular class. Finally, this technique assumes a linear relationship between the final convolutional layer and the output of the model. This may not always be the case, especially for complex models.  
Layer-wise relevance propagation (LRP) can provide more detailed and accurate explanations than Grad-CAM [18] because it can handle non-linearities in the model. It first calculates the output for a given input; then, during a backward pass, LRP propagates the output of the network (the relevance) back to the input layer. Specifically, the relevance of each neuron is distributed to its inputs according to their contribution to its activation. The resulting relevance scores indicate how much each feature in the input contributed to the final output.