esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Related Work
### DMPF Algorithm  
Several paths between the client and the server are available for communication in a network topology. Figure 4 shows the schematic communication topology using multi-path TCP in an SDN environment. By default, all layers are transmitted when a client requests SVC layers using a single path between the client and the server. The reliability of the connection between the client and the server reduces if multiple SVC layers are transmitted on a single path. This results in overloading a single path and under-utilization of multiple paths that are available to transmit multiple SVC layers. SVC is composed of layers, and each SVC layer can be sent over different routes in a network. SVC based streaming requires a base layer to view the lowest video representation. Sending each SVC layer on different paths helps to distribute the network bandwidth load, improved connection reliability and graceful degradation in view quality. However, it also increases the number of paths found, path length, bandwidth and computing resources. It is difficult to find an optimal mechanism that can accommodate the maximum number of client requests with the least use of bandwidth. We propose a Dynamic Multi Path Finder (DMPF) algorithm in our previous work [14] to forward all requested SVC layers of each client on a different path in a network.  
Figure 4: Using Multi-path Transmission Control Protocol (TCP) in a Software Defined Networking (SDN) environment.
## 1 Introduction
### Literature Overview
#### 1.2.2 Case When Forecast Errors Are Not Normal i.i.d  
Besides theoretical models assuming that the forecast errors are i.i.d, in practice this is not the case given the complexity of the supply chain; the use or at least the test of empirical approaches is crucial. When the deviation of the i.i.d assumptions is presented, other techniques, such as generalized autoregressive conditional heteroskedasticity (GARCH) [14], kernel density estimation (KDE) [15], and historical simulation [16] can be useful for forecasting the size of the uncertainty. Within a supply chain case study, [PERSON] and [PERSON] [17] showed that ignoring temporal Heteroskedasticity can increase inventory costs by up to 30% when demand autocorrelation is highly positive. [PERSON] and [PERSON] [18] examined the empirical performances of alternative forecasting methods by estimating the variability of the lead time forecast errors for improving the overall performance of the system. [PERSON] et al. [13] determined the variability of the lead time forecast error parametric models by applying GARCH models. Using simulated and real data, they showed that the forecast error standard deviation presented temporal autocorrelation for high lead times and that GARCH models yielded promising results.  
In addition, usually, the demand distribution is assumed to be known despite this being not the case in practice, and future demand must be forecasted. Consequently, forecast errors are among the different factors required to calculate the SS [19], and the determination of their accurate standard deviation is particularly challenging. Some authors have criticized these assumptions, such as [PERSON] et al. [20] and [PERSON] et al. [21] that can lead to mistakes in decisions regarding safety stocks and service objectives not being achieved. [PERSON] et al. [13; 14; 15; 16; 17; 18; 19; 20; 21; 22] have used empirical methods based on non-parametric KDE and the parametric GARCH models for dimensioning the SS. They showed that the kernel density estimation technique gives better results in the case of shorter lead times because the no-normality is dominant. For longer lead times, conditional heteroscedasticity is more important and GARCH models are more suitable [22]. For continuous demand data, nonparametric approaches, such as simulation and KDE, take into account the nonnormality of forecast errors. These techniques can be used when the forecast error distribution is not known. The effectiveness of these methods has been shown, for example, in the works of [PERSON] and [PERSON] [23], [PERSON] et al. [24] and [PERSON] et al. [13]. [PERSON] et al. [24] tried to correct the impact of forecast bias, non-normal forecast errors, and heterogeneous forecast errors in the Intel company. The authors found SS reductions of approximately 15%. [PERSON] and [PERSON] [23] used an empirical dataset from the pharmaceutical industry to adjust statistical forecasts for 'fast' demand items. They provide information on the combined forecast and inventory performance of discretionary estimates.
## 3 Results and Discussion
### Method Validation
The solid-phase micro-extraction (SPME) technique is a non-exhaustive extraction method. Relative recovery can be calculated as the ratio of peak areas (GC) of spiked water sample extracts to spiked ultra-pure water extracts. The real water samples were spiked with pesticide standards (1 and 2 \(\mu\)g L\({}^{-1}\)) to perform relative recovery tests, and the results are given in Table 3. The percentage recoveries found at 1 \(\mu\)g L\({}^{-1}\) and at 2 \(\mu\)g L\({}^{-1}\) concentrations of pesticides ranged from 87.5% to 106.0% and 88.5% to 109.2%, respectively. At 1 \(\mu\)g L\({}^{-1}\) level concentration, the minimum percent recovery value found was for 4,4\({}^{\prime}\)-DDT (87.5%) and maximum percent recovery value found was for \(\beta\)-BHC (106.0%). However, at 2 \(\mu\)g L\({}^{-1}\) level concentration, the minimum percent recovery value found was for \(\alpha\)-chlordane (88.5%) and maximum percent recovery value found was for endrin ketone (109.2%). The percentage recoveries investigated by other researchers for different organochlorine pesticides were ranged from 44.6% to 121.6% [19], 75% to 101% [43] and 69% to 107% [45]. All these results show that the SPME-GC-ECD technique is an excellent and convenient method for chlorinated pesticides analysis in real water samples.
## 3 Results and Discussion
### Identification of Spatiotemporal Characteristics in the Data from Seomijn River  
We portrayed the data ordination according to time and space (Figure 6a). It was remarkable to primarily characterize the data characteristics between May and September. Two data points were associated with forest areas. In comparison with Figure 5a, it was certain that these data points were correlated with _Asellus_ spp. (Isopoda) and _Gammarus_ spp. (Amphipoda), respectively. However, most data points characterized their clear separation across the vertical axis, which implies that benthic macroinvertebrate communities were affected by the temporal change of water quality. Through this pattern, the key macroinvertebrates Coleoptera _Elmidae_ spp. and Tricoptera _Hydrosyche_ spp. were relatively abundant in May, while Coleoptera _Eubrianax_ spp. were relatively abundant in September (Figures 5a and 6a). Interestingly, the Ephemeroptera groups were closely associated with September compared to other key macroinvertebrates (Figures 5b and 6a).  
We also question what major factors could drive the water quality changes between May and September. Given the unique climatological features of East Asia, such as summer monsoons and typhoons [18; 19], we conjecture that a significant factor of water quality change would be the precipitation between two time periods (Figure 2). Since our CCA did not accommodate the precipitation data, we put more emphasis on the intensity of rainfall as a key factor for benthic macroinvertebrate communities, especially in East Asian countries. There have also been plenty of literatures related to the effects of flooding on benthic macroinvertebrates on a global scale [59; 60].  
Keeping this clear temporal pattern of data ordination in mind, we estimated the dissimilarity of data samples between May and September. Since the amount of rainfall was spatially distinct and the corresponding land-use coverage differs, there was a conspicuous deviation of data ordination between May and September (Figure 6b). The OS and SDU sites showed a larger disparity of data compared to the YC and SL sites. While the SDU site is in a headstream area, the SL site is near the river mouth (Figure 1). In this respect, their sensitivity to rainfall could be distinct. This pattern was clearer at the SDU site, which is a forest-dominated area and was consistent with the data ordination from CCA (Figure 5).
## 6 Conclusions  
Researchers use data augmentation to increase the available sample size for training deep learning models. In computer vision tasks, this means generating synthetic images  
Figure 8: Receiver operating characteristics curve.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Work** & **Dataset** & **Method** & **Accuracy** & **Sensitivity** & **Recall** \\ \hline
**[PERSON] et al. (2018)** [13] & Chest X-ray & - & 92.8 & 93.2 & 90.1 \\
**[PERSON] et al. (2017)** [6] & Chest X-ray & DCGAN & 92.1 & - & - \\
**[PERSON] et al. (2018)** [8] & Chest X-ray & DCGAN & 84.19 & - & - \\
**[PERSON] et al. (2018)** [23] & CT Lesions & GAN & - & 94.4 & 87.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative measures on prior work.  
from the original dataset. While there are numerous methods to achieve this, imbalanced small sample sets create further challenges in the medical domain. The generation of a large number of high-resolution synthetic images can improve classifier performance significantly. This work demonstrates the utility of DCGAN methods for achieving the same. Hence, researchers working in the medical image analysis domain can use DCGAN for data augmentation and achieve better accuracy than traditional methods. In this study, we achieved high-quality synthetic image generation using the cross-entropy loss function. The future work can include the use of Wasserstein loss or Wasserstein loss with gradient penalty to replace the minimax loss to improve the quality of the generated images. In addition, combination methods such as stacking GAN samples with random cropping to further enhance the dataset can be attempted.
# Measuring Gas Flaring in Russia with Multispectral VIIRS Nightfire
## 3 Results
### Context of the Major Oil Producing and Gas Flaring Countries  
In Figure 4, the world's largest flaring nations are listed by flaring volumes and number of gas flares at the upstream facilities only. As is evident from Figure 4b, the highest flaring volumes are annually detected in Russia, while the maximum number of gas flares is detected in the USA. Even if only the oil upstream sector was taken into consideration, Russia would still be the highest flaring nation, with flaring estimates being comparable with Iraq or Iran in 2017-2018.  
Most countries experienced an increase in gas flare tally in 2020, which can be attributed to sudden shortages in processing facility capacities following the COVID-19 pandemic conditions. However, most countries, excluding Russia, Mexico and China, also experienced a drop in total flaring volumes, which is likely induced by the drop in worldwide oil production. With the exception of Russia, highest flaring volumes in 2020 were registered in Iraq (17 3 BCM), Iran (13.3 BCM), USA (11.7 BCM), Algeria and Venezuela (8.6 BCM). Compared with 2019, a conspicuous drop in flaring volumes can be observed in the USA (from 17.2 to 11.7 BCM), Libya (from 4.9 to 2.3 BCM), and Angola (2.4 to 1.3 BCM). Estimated global annual upstream gas flaring in 2020 dropped from 146 to 138.5 BCM, while overall flaring dropped from 160.5 to 152 BCM.  
Figure S3 in Supplementary Materials text section presents scattergram detection frequency vs. gas flaring volumes for Russia and USA.
## 3 Material and Methods
### Multivariate Hotelling's \(T^{2}\) Control Chart  
This section briefly summarizes the classic multivariate Hotelling's \(T^{2}\) control chart. Hotelling's \(T^{2}\) is a multivariate control chart, a generalization of the \(t\)-student distribution, that can be used to monitor the process mean. Let \(\mathbf{x}_{i}\) where \(i=1,\ 2,\ \ldots,\ n\) are identic and independently random vectors that follow the multivariate normal distribution \(\mathbf{x}_{i}\sim N_{p}(\mu,\mathbf{\Sigma})\). The data structure can be written as \(\mathbf{X}=\begin{bmatrix}\mathbf{x}_{1}^{T}&\mathbf{x}_{2}^{T}&\cdots&\mathbf{x}_{n}^{T}\end{bmatrix}\) with mean vector \(\overline{x}=\frac{1}{n-1}\Sigma\mathbf{x}_{i}\) and covariance matrix \(\mathbf{S}=\frac{1}{n-1}\sum{(x_{i}-\overline{x})(x_{i}-\overline{x})^{T}}\). The \(T_{i}^{2}\) statistics can be calculated as follows [6]:  
\[T_{i}^{2}=(\mathbf{x}_{i}-\overline{\mathbf{x}})^{\prime}\mathbf{S}^{-1}(\mathbf{x}_{i}- \overline{x}) \tag{1}\]  
Conventional Hotelling's \(T^{2}\) chart follows the assumption of multivariate normal distribution [38], so the control limit can be generated by following \(F\)-distribution with the following equation:  
\[CL=\frac{p(n+1)(n-1)}{n^{2}-np}\Gamma_{\mathbf{x};p:n-p} \tag{2}\]  
where \(n\) is the total number of observations and \(p\) is the variable quantity, with \(\alpha\) is the false alarm rate. The monitoring process is considered controlled if \(T^{2}\) statistics are not greater than the control limit.  
\begin{table}
\begin{tabular}{l r r} \hline \hline \multicolumn{1}{c}{**Label**} & \multicolumn{1}{c}{**Number of Records**} & \multicolumn{1}{c}{**Percentage**} \\ \hline Normal & 5000 & 31.94 \\ Intrusion & 119,341 & 68.06 \\ - Analysis & 2000 & 1.14 \\ - Backdoor & 1746 & 1.00 \\ - DoS & 12,264 & 6.99 \\ - Exploits & 33,393 & 19.04 \\ - Fuzzers & 18,184 & 10.37 \\ - Generic & 40,000 & 22.81 \\ - Reconnaissance & 10,491 & 5.98 \\ - Shellcode & 1133 & 0.65 \\ - Worms & 130 & 0.07 \\ \hline Total & 175,341 & 100.00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of UNSW-NB15 dataset.
## 3 Simulated Results and Error Analysis
### Discussion and Analysis of Reconstruction Simulation Results
#### 3.3.1 Axisymmetric Simulation Distribution
In fact, it can be seen in Figure 6 that, even if the integral projection value contains 8% random noise, the temperature reconstruction ARE is less than 0.05, and for ARMSE, it is lower than 0.06 over the temperature range of 600\(\sim\)1400 K.  
Figure 5: Comparison of reconstruction results of axisymmetric peak center section: (**a**) temperature reconstruction; (**b**) concentration reconstruction. For the concentration range of 0.02-0.05 mol/mol, the concentration reconstruction ARE is lower than 0.04, and ARMSE is below 0.06. The temperature reconstruction using the ART, Landweber and MAART algorithms with different noise levels can be seen in Figure 6. There is no stable error pattern identified, but in the concentration reconstruction process, the error increases significantly when the MAART algorithm is applied. The main reason is that MAART is sensitive to the initial values, and taking different initial values has a greater impact on its reconstruction process, whereas although MIARO was developed based on MAART, its initial values are obtained through the Landweber algorithm with the optimized regularization process. Compared with MAART, the errors in the temperature and concentration reconstructions with the application of the MIARO algorithm are reduced by 0.011 and 0.034, respectively; in terms of the standard deviation, the error reductions for temperature and concentration are 36.1% and 42.1%. Figure 6: Temperature reconstruction results for axisymmetric distribution of different noise levels. Figure 7: Concentration reconstruction results for different noise levels with axisymmetric distribution.
# Remote Sensing for Irrigation of Horticultural Crops
## 3 Sensors and Platforms
### Remote Sensing Platforms  
The platform on which a particular sensor is lodged states the distance of the sensor from the target, timing, periodicity, location, and extent of coverage of the acquired image. The detailed information obtained is strictly related to the distance between the object of interest and the type of platform. Remote sensing platforms can be divided into three groups: satellite, airborne and Unmanned Aerial Vehicles (UAVs), and ground based solutions. According to [PERSON] et al. [51], each of these platforms and sensors shows strengths and weaknesses related to technological, operational and economic factors.
# Synovial Sarcoma of the Extremities: A Literature Review
## 4 Discussion  
The potential aggressive behavior of synovial sarcoma makes the pathological and radiological evaluations very important for an appropriate staging and an adequate therapeutical decision.  
Additional testing has to be performed in order to confirm the diagnosis and the severity of the condition and to decide on the appropriate treatment options. These tests may include imaging studies, such as an X-ray, CT scan and/or MRI scan; genetic testing to detect the specific chromosomal translocation between chromosome 18 and chromosome X that is identified in most cases of synovial sarcoma; and a biopsy of the tumor. Cross-sectional images are suggestive, but not specific, for synovial sarcoma, and the main sign is the polilobulated aspect, with a \"triple sign\", i.e., hemorrhage, fluid levels and septa (\"bowl of grapes\" sign) [20; 21; 22].  
Usually, the tumor presents as a palpable soft-tissue mass or swelling, with a long period between clinical onset and the diagnosis, particularly due to the initial slow growth of the lesion (average 2-4 years).  
Moreover, the presented case displayed the same elements of positive and differential diagnosis, but with a final diagnosis that was established only via a biopsy.  
The certain diagnosis of synovial sarcoma remains a challenge and the recognition of the disease should be based upon a combination of findings, including traditional morphology, detection of the chromosomal t (X; 18) translocation and a panel of immunohistochemical markers [23; 24].  
The potential prognostic values were determined by studying many clinical factors. For example, some authors found age to predict survival, where young patients have a better prognosis than the elderly [25; 26]. Most studies reported no effect of gender, except for one study that found worse survival rates for males [27]. The tumor size frequently  
Figure 10: Therapeutic algorithm in synovial sarcoma of extremities.  
predicted local recurrence and survival in many studies [6; 25]. Central locations seem to have a worse prognosis than peripheral locations [6; 28]. Histopathological features, such as undifferentiated tumors, necrosis, high histologic grades and increased mitotic rates, were associated with a worse prognosis [16; 17; 18; 29]. Furthermore, local recurrence was associated with a greater risk of metastasis and shorter survival [13; 30].  
The proper therapeutic algorithm takes both the staging of the tumor and the prognostic factors into account. Surgical techniques are similar to those recommended for soft tissue sarcomas in general, consisting of a large tumoral resection that includes the whole compartment [31; 32].
# Optimization of RIS-Assisted 6G NTN Architectures for High-Mobility UAV Communication Scenarios
## 4 Joint Optimization Framework for RIS Configuration and UAV Mobility
### Performance Evaluation and Trade-Off Analysis
#### 4.3.2 Energy Efficiency  
Energy efficiency is a key performance metric that quantifies how effectively the system converts consumed power into useful throughput. It is defined as the ratio of the system's throughput to its total power consumption:  
\[\text{Energy Efficiency}=\frac{\text{Throughput}}{P_{\text{total}}}, \tag{19}\]  
where \(P_{\text{total}}\) denotes the total power consumed by the system.  
The total power consumption is modeled as  
\[P_{\text{total}}=P_{\text{NTN}}+M\cdot P_{\text{RIS}}, \tag{20}\]  
where \(P_{\text{NTN}}\) is the transmit power of the NTN node, \(P_{\text{RIS}}\) is the power consumed per RIS element, and \(M\) represents the total number of RIS elements deployed in the system.  
By intelligently optimizing the RIS phase shifts and efficiently managing power resources at both the NTN and RIS sides, the proposed framework significantly improves energy efficiency while maintaining high data rates.
## 3 Case Studies  
The case studies analysed in this work are based on the finite element model (FEM) of the Body-Freedom-Flutter (BFF), an unmanned flying-wing research aircraft model of total mass \(M=5.44\) Kg (without tanks), half-chord \(b=0.2\) m, wingspan equal to 3.048 m and projected wing surface area equal to 1.084 m\({}^{2}\). The model takes its name from its flutter mode involving both the vibrations and rigid-body motion of the aircraft (Ref. [30]). The first six flexible modes, with their respective frequencies, are shown in Figure 3.  
Figure 2: Time-Delay Neural Network flowchartThe effects of sloshing dynamics on the aircraft are investigated in two different configurations, one with a single tank under the center of mass (see Figure 4) and the other with two tanks symmetrically placed under the wings (see Figure 5). The geometric dimensions of the tank in Case study 1 (half-filled with a liquid with a density of \(\rho=650\) kg/m\({}^{3}\)) are listed in Table 1 and are such that the total mass of liquid contained accounts for 25% of the structural mass of the aircraft. The configuration in Figure 4 will hereafter be denoted as Case study 1.  
In the present analysis, three sloshing modes were considered in a linear lateral analysis for both the dynamics along \(x\) and \(y\) (in plane dimensions). The natural frequencies of linear sloshing are reported in Table 2 (when the tank is detached from the structure).  
Case study 2 was realised by considering two tanks created by halving the tank shown in Table 1 in the \(y\)-direction to ensure that the same amount of liquid was carried by the aircraft (see Table 3). The natural frequencies of sloshing in this case differ depending on the planar direction of the tanks under consideration, as can be seen in Table 4. This implies the presence of three modal coordinates describing the linear sloshing, referred to as \(r_{1}^{(i)}\), \(r_{2}^{(i)}\) and \(r_{3}^{(i)}\), for both tanks.  
From the point of view of the FEM solver, the presence of the tanks in both configurations was managed by creating a node located in their geometrical center, linked to the body of the aircraft by means of the rigid body element (RBE). A concentrated mass is considered to represent the structural mass of the tanks.  
On the other hand, the small disturbance unsteady aerodynamics is modeled via the Doublet Lattice Method (DLM) available in MSC Nastran for unsteady linear aeroelastic analysis (Ref. [28]). As can be seen in Figure 1, linear sloshing was included in the same block as the structural and flight dynamics model of the aircraft. Indeed, combining Equations (9), (14) and (16), it becomes possible to obtain an augmented model with additional modal variables represented precisely by the added state-space vector \(r\).  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value (m)** \\ \hline side in direction \(x\) & 0.18 \\ side in direction \(y\) & 0.18 \\ \(h\) (height) & 0.13 \\ \(h_{f}\) (filling level 50\%) & 0.065 \\ \hline \end{tabular}
\end{table}
Table 1: Tank geometry—Case study 1.  
Figure 4: Configuration with one box-shaped tank placed underneath the BFF center of mass.