esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Component Tests
### Experimental Results
#### 3.3.2 Sandwich Panels
of the reflected pressure, shown in Figure 12b, is similar to the tests at 10 bar firing pressure, i.e., a reduction in the measured pressure consistent with the displacement of the test samples. The experiments at 15 bar driver pressure were performed in two series, because the first series of experiments showed that the applied load was close to the failure limit of the sandwich panels used in this study. Complete results from series 1 and 2 are presented in Figures 2 and 3, respectively. Mid-point displacements from all these tests are compared to the skins only tests in Figure 11c. Only two of the test components at this load level did not fail (XPS-250 and XPS-700). The mid-point displacements from these two tests settled at 45 mm and 36 mm permanent displacement for XPS-250 and XPS-700, respectively. For the failed components, the mid-point displacement continued to increase, as the back skin fails. Figure 12c shows the measured pressure for all tests at 15 bar firing pressure. For the test with skins only, a rapid pressure drop was recorded. This pressure drop was caused by the emptying of the driven section when the component fails. The same pressure drop is not observed for the failed sandwich components because the back skins in these tests did not fail, and thus a pressure reflection is measured. Figure 13 shows post-test images of the components in series 1. Here, the XPS-250 sandwich did not show any signs of failure. The XPS-400 sandwich had almost complete tearing of the back skin and substantial fracture of the core material. In comparison, the XPS-700 sandwich showed tearing of the back skin top horizontal and cracking of the core material. These results indicate that at 15 bar driver pressure, the applied blast load is at the limit of the failure load for these components. Figure 4 shows post-test images of the components in series 2. In series 2, there was more severe damage to the components. The XPS-250 sandwich exhibited complete tearing of the back skin along the vertical edges and the top horizontal edge, and the core failed along the inner edge of the clamping frame. For the XPS-400 sandwich, both the back skin and the core failed along the inner edge of the clamping frame. The XPS-700 sandwich did not display the same amount of damage as the other components in series 2, i.e., complete tearing of the back skin along the left vertical and bottom horizontal edge and partial tearing along the top horizontal edge. The core showed extensive fractures, but not a complete failure as for the XPS-250 and XPS-400 sandwich panels. In general, for tests at 15 bar driver pressure, the primary failure mechanism is face yield and consequently tearing of the back skin along the edge of the clamping frame. Depending on the extent of back skin tearing, the core tends to fracture in the centre of the blast-exposed area, with fracture along the clamping frame edge.
## 2 Experiments  
### Experimental Devices  
In this study, cyclic bending tests were performed on EMT carbon steel RHTs using a pure bending (four-point bending) machine, illustrated in Figure 1. The test setup includes two sprockets mounted on beams and a strong chain running around them, capable of accommodating tube specimens up to 1 m in length. To facilitate testing, each tube is fitted with solid rod extensions. During the bending process, the interaction between the tube and the roller allows for free axial movement. The applied load is generated by two concentrated loads from a pair of rollers. Retraction of the upper or lower cylinder initiates sprocket rotation, enabling pure bending of the test tube. Reverse bending is achieved by reversing the hydraulic circuit's flow direction. For a more comprehensive understanding of the machine, readers are directed to the detailed exposition by [PERSON] et al. (2024).  
The Curvature and Ovalization Measurement Apparatus (COMA), developed by [PERSON] et al. (2024) and depicted in Figure 2, is a valuable tool for assessing tube curvature (\(\kappa\)) and ovalization (\(\Delta D/D_{\mathrm{o}}\)), a crucial parameter in this study. Positioned near the mid-span of the tube, the COMA utilizes a fixed distance between two side-inclinometers and their angular change to accurately calculate \(\kappa\). For a more detailed understanding of the COMA, readers are referred to the comprehensive exposition by [PERSON] et al. (2024).
## 2 Literature Review
### Research Gaps and Novelties of the Current Study  
Based on the investigated literature, three main directions have been found to be under investigated.  
First, while macroeconomic indicators have been widely adopted to support demand risk predictions, they have rarely been integrated when forecasting supplier delivery risks. Indeed, only the study of [18] started to investigate this area.  
Second, predictive approaches to estimate supplier delivery risks have been mainly proposed when the problem needs to be solved in a classification manner. Only ref. [30] proposed an approach for solving the delivery risks prediction problem in a regression manner.  
Lastly, although autocorrelation in data and autoregressive models have often been applied when predicting demand risks, none of the approaches reported in Section 2.3 have considered adopting models that are specifically tailored to deal with time-series data and to consider this aspect.  
Based on this evidence, the main innovations proposed in this study thus rely on the definition of a predictive approach which for the first time addresses simultaneously the problem of forecasting supplier delivery delay risk in a regression manner, the necessity of integrating macroeconomic indicators, and the requirement to not forget the time-series nature of the problem and thus possible autocorrelation in the data. Indeed, according to Table 1 which summarizes the previous study, no one has proposed a predictive approach covering all three of these aspects simultaneously for the supplier delivery risk prediction problem.
# Exploring Channel Properties to Improve Singing Voice Detection with Convolutional Neural Networks
## 2 Methods
### Multi-Scale Input Channels
#### 2.2.2 Related Work  
Almost all of the work in the field of SVD chooses a pre-defined scale for the task, whether in CNNs or RNNs. In [11], the scale of the input channel of the LSTM was fixed with the length of 100 ms, and the length in the CNN was 46.4 ms [10].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Scale** & 512 & 1024 & 1536 & 2048 & 2560 & 3072 & 3584 & 4096 \\ \(\Delta\)**t (ms)** & 23.2 & 46.4 & 69.7 & 92.9 & 116.1 & 139.3 & 162.5 & 185.8 \\ \(\Delta\)**f (Hz)** & 43.1 & 21.5 & 14.4 & 10.8 & 8.6 & 7.2 & 6.2 & 5.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The scales and the corresponding time and frequency resolutions.  
In [16], although the scale was still fixed for the input channel of the LRCN, the comparison was conducted among the different scales, where the scales ranged from 50 ms to 2 s. It was found that the scale of 0.8 s was optimal on the Jamendo dataset, and that of 1 s was optimal on the RWC dataset. It was proved that the choice of scale had an important effect on the performance. However, we should note that this optimal scale may not be suitable for our method because, in their study, the singing voice separation was performed to preprocess the signal and the STFT was computed on the preprocessed vocal signal.
# [PERSON]: Extreme Plastic Pollution Pathways in Riparian Waterways
## 1 Introduction
### Environmental Context of the Saigon River  
The Saigon River, flowing approximately 256 km through southern Vietnam, is a vital economic artery for Ho Chi Minh City, supporting trade, transport, and industry [16]. This heavily trafficked waterway hosts over 2500 large commercial vessels daily, creating a dynamic and congested environment [17]. Its role in Vietnam's economic development is underscored by the Saigon Port, the country's primary port, capable of hosting post-Panamax ships [16; 17]. However, this economic activity comes at an environmental cost: an estimated 350,000 cubic meters of wastewater, including significant volumes of plastic waste, are discharged into the river annually [2; 10; 18]. This pollution, combined with intense human activity and tidal currents, renders the Saigon River an extreme environment for studying plastic transport [10; 11; 15; 18; 19].  
Plastic waste in the Saigon River predominantly consists of single-use items, such as bottles and packaging materials. Studies reveal contamination levels ranging from 10 to 233 plastic pieces per cubic meter [15; 20]. The complex transport dynamics of this waste, influenced by tidal flows, vessel activity, and often extreme environmental features like riparian vegetation, pose significant challenges to modeling and mitigation efforts [21]. While traditional oceanographic models have effectively predicted plastic accumulation and dispersion in marine systems, they fall short in accounting for the bidirectional flows and retention zones characteristic of tidal rivers [19; 21].
# Effect of pH Change on the Microalgae-Based Biogas Upgrading Process
## 4 Discussion  
According to the results in Figure 2A, in a counter-flow column for biogas upgrading, a pH shift takes place from 7.0 to 5.0; in light conditions, it is expected that this condition affects photosynthetic activity after 100 min. However, the counter-flow column is operated considering a residence time less than 20 min, which indicates that the decrease in photosynthetic activity by a pH shift will not take place [30]. Moreover, the absorption columns for biogas upgrading through microalgae are able to operate in dark without increasing the oxygen concentration in upgraded biogas [4,30]  
In relation to the results shown in Figure 3, it can be supposed that if a pH drop takes place, such as has been evidenced when biogas is injected into the microalgae culture in the column, the photosynthetic activity of algal cells could decrease. However, in this case, the cells do not suffer damage in their photosynthetic system, maintaining a high value of PSII quantum yield, such as the result obtained in this research. When microalgae return to the photobioreactor, the DIC concentration decreases due to photosynthesis and desorption, and the microalgal cells recover their photosynthetic activity.  
The result obtained, as shown in Figure 3, indicates that CO2 would be the inorganic carbon source preferred by _C. sorokiniana_. This result agrees with [PERSON] and [PERSON] [31], who indicated that _C. sacchiarophilla_ had an affinity for CO2, which was 160 times greater than that for HCO3\({}^{-}\). The highest affinity at pH 5 could suggest the expression of some concentrating mechanisms of CO2 that could facilitate its fixation by Rubisco. According to [PERSON], [PERSON], and [PERSON] [32], there are two possible ways by which CO2 may be supplied to the Chlorella surface: CO2 can be supplied from the culture medium by simple diffusion (direct supply of CO2) or HCO3\({}^{-}\) formed from CO2 can be converted again into CO2 via the enzyme carbonic anhydrase (CA) and incorporated by the algal cells (indirect supply of CO2).  
Additional research must be carried out to clarify the mechanisms affected in the algal cells due to the injection of a gas with a high CO2 concentration and/or a decrease in the pH, considering that CO2 is the preferred source of carbon for Chlorella. A possible substrate inhibition may occur in an enzyme involved in the mechanism of carbon consumption. On the other hand, because of the circulating flow between the photobioreactor and the column, microalgae cells can be exposed to pH and DIC gradients several times during the operation of the system. Thus, it would be interesting to study whether these repeated changes had some additional effect on microalgae activity.  
Figure 4: Photosynthetic activity curves of _Chlorella sorokiniana_ at pH 7.0 and pH 5.0, as a function of the DIC concentration.
# Source Rock Prediction Using Well Log and Seismic Data:
## 2 Regional Geological Overview
### Basin's Evolution Characteristics  
In the Early Cretaceous period, the South American plate and the African plate rotated apart [18], and the equatorial Atlantic split to form the Cote d'Ivoire Basin, which constitutes a typical transformational passive continental margin basin [19].  
The Cote d'Ivoire Basin has undergone three major stages of tectonic evolution [4; 10; 11; 12; 19]:  
1. Early rift stage: early Cretaceous Berriasian-Barremian stage. During this period, strike-slip faults and tensile movements formed extensional grabens, in which sediments accumulated rapidly and developed thicker fluvial facies, deltatic facies, and lacustrine facies.
2. The rift stage: Aptian to late Albian in the Early Cretaceous. Due to the tension-torsion movement of continental cracking and transformation faults, some small folds, inversion faults, fault blocks, and half-grabens were formed. Fluvial facies, deltatic facies, and lacustrine facies of siliceous clastic rock deposits have developed. The Albian--Cenomanian unconformity was formed in the study area during the Late Albian stage when the African and South American plates separated.
3. Late rift stage: late Cretaceous Cenomanian to Holocene. During this stage, the continental margin subsidence formed steep slopes, and a large number of shovel faults and slump structures were formed. Thick layers of marine shale were deposited in most parts of the study area, and local carbonate deposits developed [20]. In the Oligocene, the whole West African margin was in the low sea level stage, with high levels of unconformity and less unconformity between the Santonian stage and the Campanian stage. Therefore, the late rift stage can be divided into three stages: early, middle, and late (Figure 1b).
## 4 Applications for IoT Technology in Smart Buildings
### IoT for Energy Conservation  
The researchers say that around 20% of energy savings are related to the previous month's energy without energy management. Intelligent buildings with energy management systems that can regulate, track, and maximize the current energy usage of facilities. The Building Energy Management Systems (BEMS) can monitor, manage, and improve building energy use [105]. Users can remotely track and operate home appliances to conserve electricity using Zigbee wireless sensors. The framework focused on real-time energy usage mapping and building and city-scale tracking, energy efficiency measurement, and benchmarking. Zigbee is an IEEE 802.15.4-based protocol designed for low-energy, short-range communication. As a result, it is used at the network layer in various WSN frameworks. The Zigbee network layer supports star, tree, and mesh network topologies. For Zigbee, the internationally permitted ISM radio band is 2.4 GHz; however, additional frequencies are also available in different places [106]. Low cost, low power, low information rate, and self-association are all characteristics of Zigbee technology.
# RESTful API Testing Methodologies: Rationale, Challenges, and Solution Directions
## 4 Results
### RQ2--What Are the Code Coverage Concerns When It Comes to Testing RESTful APIs?  
Code coverage is one of the main objectives of unit testing. It is an important metric to measure the effectiveness of unit tests. The maximum amount of code executed by the unit tests is always desirable. For achieving high code coverage, there should be a sufficient number of unit tests with a wider scale to reach all possible parts of the code. Failing to reach the recommended level of code coverage is a sign of either the fewer number of unit tests or the limited scale of the unit tests in terms of code execution. In RESTful APIs' case, achieving high code coverage could be a difficult task to achieve. One of the major reasons is reaching out to protected APIs. As explained in RQ1, owing to the nature of the RESTful APIs' document description, it becomes very difficult to generate unit tests for the protected endpoint. Therefore, the major part of the RESTful APIs goes without having unit testsgenerated for them automatically. Manual testing and unit test generation are required for that, which always introduces the chance to miss some code, hence resulting in lower code coverage. In [25; 27; 28], the authors argued that code coverage is a good metric to measure the effectiveness of test cases and, as such, integrated it as a metric against which to measure their tools. Moreover, the authors of [11; 12] mentioned the same issue. The authors of [25] noted in their testing, however, that the code coverage was relatively low compared with manual tests owing to the high string constraints. As for [27], the authors faced the challenge of a unique checksum and ID to archive high code coverage. The same challenge was faced by the authors of [28] when attempting to improve the code coverage with UUID as input. The authors of [32] completely exclude authenticated APIs' testing and unit test generation owing to the complexity explained earlier, hence they possibly ended up with lower code coverage.
## 2 Conventional Reliability Theory
with the non-centrality parameter, and knowing the uncertainty of the sensor and the architecture of the model, it is possible to compute the MDB according to Equations (7)-(9). The MDB was further investigated for a single outlier in a singular Gauss-Markov model [34]. There are also studies covering either independent or correlated measurements [35; 36; 37; 38; 39]. It is also possible to set up for the case of multiple (simultaneous) outliers. The readers who are interested in multiple (simultaneous) outliers issue can refer to [40; 41; 42; 43; 44]. For more details about alternative models, refer to [8; 45]. To quantify the external reliability, one should propagate each MDB on the parameters. In other words, the external reliability measures the influence of an undetected outlier on the estimation of coordinates of the geodetic network, and it is given by:  
\[\
abla\mathbf{X}=(\mathbf{A}^{T}\mathbf{W}\mathbf{A})^{-1}(\mathbf{A}^{T}\mathbf{W}\mathbf{c_{i}}|\
abla_ {i}|) \tag{10}\]  
where \(\
abla\mathbf{X}\in\ \mathbb{R}^{u}\) is the influence of an undetectable outlier \(\
abla_{i}\) located at a given position according to the vector \(\mathbf{c_{i}}\) in (3) and \(\mathbf{W}\in\ \mathbb{R}^{n\times n}\) the known matrix of weights, taken as \(\mathbf{W}=\sigma_{0}^{2}\mathbf{Q_{e}}^{-1}\), where \(\sigma_{0}^{2}\) is the variance factor. Here, we compute the maximum external reliability (\(\max\left(\
abla\mathbf{X}\right)\)) as follows:  
\[\max\left\{\
abla\mathbf{X}\right\}=\max\left\{(\mathbf{A}^{T}\mathbf{W}\mathbf{A})^{-1}(\mathbf{A }^{T}\mathbf{W}\mathbf{c_{i}}MDB)\right\} \tag{11}\]  
It is important to mention that the maximum external reliability \(\max\left\{\
abla\mathbf{X}\right\}\) can be a positive or a negative value. According to Equation (1), we consider the maximum influence of an undetectable outlier \(\
abla_{i}=MDB\) on the parameters. Although here the reliability theory was applied in a specific network, it is a generally applicable method. For example, the reliability theory was used to measure the integrity of the receivers for civil aviation, which is a main tool for safety-of-life applications, see e.g., [46]. In the next section, we present an automatic procedure for designing geodetic networks. The proposed procedure was computationally developed based on reliability theory. Specifically, we apply reliability theory to automatically define the optimal location of control points.
# Single Photon Counting Performance and Noise Analysis of CMOS SPAD-Based Image Sensors
## 3 Single Photon Counting Noise Modelling and Analysis
### Regressive Modelling and Fitting Method  
The third method fits and scales the noise model described above, against a PCH (whether a single exposures of a full sensor or multiple exposures per pixel). This method has been used in [23] to graphically confirm the correct evaluation of RN and mean exposure. This method is expanded here to encompass the previous two methods. First the VPM and PSW are used (as appropriate given their respective limits) to obtain an estimate of RN and CVF to restrict the scaling and fitting \"search\" domain. Next the iterative process begins, recording the goodness of fit of the recorded PCH to the modelled PCH and continuing the regression analysis (by whichever chosen fitting method).  
Like the PSW method, this regression analysis should be performed per pixel to obtain the CVF, PRNU and FPN distributions of the image sensor. Furthermore, as in PSW, ADC non-linearity will affect the regression analysis so some method of interpolation between PCH bins may be necessary. The downside to this method, is its computationally intensive nature and the requirement to have a consistent mean number of photons for exact fitting. The Poisson distribution in Equation (1) assumes a constant mean number of photoelectrons (i.e., constant light level) through successive reads of a single pixel, and a constant light level across the array with equal sensitivity (0% PRNU). The advantage of the method is that the model can be expanded to account for known converter non-linearity or other noise sources, such as described in the following sections.
# Improved Electrochemical-Mechanical Parameter Estimation Technique for Lithium-Ion Battery Models
## 4 Results
### Teardown Configuration
In addition, Figure 5 presents the confidence ellipses of paired parameters to compare the stability of the two approaches: adding the mechanical constraints makes the algorithm more reliable, reducing parameter variability with respect to just using electrochemical objective functions. In fact, smaller ellipses indicate that the parameters present lower variance throughout the iterations,  
Figure 3: SEM analysis of graphite-LFP battery samples. (**a**) Graphite electrode, (**b,c**) graphite and LFP electrodes, and separator and current collector. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \begin{tabular}{c} **Current** \\ **Rate** \\ \end{tabular} & \begin{tabular}{c} **Symbol** \\ **Value** \\ \end{tabular} & \begin{tabular}{c} **Initial** \\ **Bound** \\ \end{tabular} & \begin{tabular}{c} **Lower** \\ **Bound** \\ \end{tabular} & \begin{tabular}{c} **Upper** \\ **Estimate** \\ \end{tabular} & \begin{tabular}{c} **EC** \\ **Estimate** \\ \end{tabular} & \
\begin{tabular}{c} **ECM** \\ **Estimate** \\ \end{tabular} &
\begin{tabular}{c} **Unit** \\ \end{tabular} \\ \hline \multirow{8}{*}{\(C/2\)} & \(D_{s,p}\) & \(5.5\cdot 10^{-19}\) & \(5.5\cdot 10^{-20}\) & \(5.5\cdot 10^{-18}\) & \(5.5\cdot 10^{-19}\) & \(5.25\cdot 10^{-18}\) & m\({}^{2}\)/s \\  & \(D_{s,n}\) & \(3\cdot 10^{-14}\) & \(3\cdot 10^{-15}\) & \(3\cdot 10^{-13}\) & \(3\cdot 10^{-14}\) & \(2.79\cdot 10^{-13}\) & m\({}^{2}\)/s \\  & \(k_{p}\) & \(1\cdot 10^{-10}\) & \(1\cdot 10^{-11}\) & \(1\cdot 10^{-9}\) & \(1.2\cdot 10^{-10}\) & \(1\cdot 10^{-11}\) & m\({}^{2.5}\)/mol\({}^{0.5}\)s \\  & \(k_{n}\) & \(5\cdot 10^{-12}\) & \(5\cdot 10^{-13}\) & \(5\cdot 10^{-11}\) & \(5\cdot 10^{-12}\) & \(6.65\cdot 10^{-12}\) & m\({}^{2.5}\)/mol\({}^{0.5}\)s \\  & \(\zeta_{p}\) & \(0.398\) & \(0.2\) & \(0.6\) & \(0.398\) & \(0.402\) & – \\  & \(\zeta_{n}\) & \(0.53\) & \(0.3\) & \(0.7\) & \(0.531\) & \(0.532\) & – \\ \hline \multirow{2}{*}{3C} & \(D_{s,p}\) & \(5.5\cdot 10^{-19}\) & \(5.5\cdot 10^{-20}\) & \(5.5\cdot 10^{-18}\) & \(5.57\cdot 10^{-19}\) & \(5.25\cdot 10^{-18}\) & m\({}^{2}\)/s \\  & \(D_{s,n}\) & \(3\cdot 10^{-14}\) & \(3\cdot 10^{-15}\) & \(3\cdot 10^{-13}\) & \(3.44\cdot 10^{-14}\) & \(1.4\cdot 10^{-13}\) & m\({}^{2}\)/s \\ \cline{1-1}  & \(R_{c}\) & \(0.002\) & \(0.001\) & \(0.1\) & \(0.0016\) & \(0.0025\) & \(\Omega\)m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Teardown scenario—parameters bound and comparison between the electrochemical (EC) and electrochemical-mechanical (ECM) estimate.