esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimating Significant Wave Height from SAR with Long Integration Times
## 4 Validation of the Experimental Results With ECMWF and Sensitivity Analysis
### Validation of the Experimental Results With ECMWF

Table 3: Comparison of SAR image significant wave height estimation results and ECMWF data.
## 3 Simulation Results  
The VCO output frequency with multiple bands is shown in Figure 16a. The Vctr range is 0.4-2.1 V, with a VCO tuning range of 0.84 V. The center frequency of the designed VCO is 330 MHz, with a variation absorption of \(\pm\)10%, i.e., 297-363 MHz. The frequency sub-band step size is 8.25 MHz, with an average Kvco of 23 MHz. To ensure the wide-range frequency continuity, the two adjacent bands are overlapped with each other up to 30%. Figure 16b depicts a typical VCO gain characteristic at a typical 010 frequency sub-band case.  
Figure 17 shows the whole PLL closed-loop output and lock-in timing. After converting the time domain output into the frequency domain, the graph shows a stable 33 MHz output frequency.  
Figure 16: (a) VCO multiple bands; (b) VCO frequency vs. Vctr for 010 band.  
Figure 17: Transient simulation result for whole PLL.
## 3. Mathematic Model of CDR on the Ground and Water Surface  
A CDR is a nonholonomic constrained underactuated structure on the ground and water surface. Therefore, this kind of robot is incapable of moving laterally. In the dynamic model, uncertainties from air resistance and mechanical friction losses disturb the robot as it moves on the ground. However, when the robot moves on the water surface, in addition to air resistance and mechanical friction losses, water resistance and wind interference should also be considered.  
Before discussing the mathematical model, the following assumptions are made:  
**Assumption 1**.: _The robot is a rigid body, whose mass distribution is homogeneous, and the shape structure is a port/straboard symmetric._  
**Assumption 2**.: _The center of gravity of the robot's body coincides with the geometric center._  
**Assumption 3**.: _The heave, pitch, and roll motions on the horizontal plane are neglected._  
**Assumption 4**.: _The non-diagonal terms of inertial and drag matrices are small compared to the main diagonal terms, and can be neglected._  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Unit** & **Value** \\ \hline Mass & kg & 6.5 \\ Max length & cm & 85 \\ Max width & cm & 55 \\ Height & cm & 75 \\ Max buoyancy & kg & 7.3 \\ Wheel radius & cm & 8 \\ Length of the axle & cm & 50 \\ Rotational inertia of Z-axis & kg\(\cdot\)m\({}^{2}\) & 0.108 \\ \hline \hline \end{tabular}
\end{table}
Table 1. The main parameters of the CDR.  
Figure 3. The CDR moves on the water surface and the ground. The robot follows the desired trajectory from the ground and returns to the starting navigation point after reaching the navigation point on the water surface.  
**Assumption 5**.: _The drive force generated by the motors meets the performance requirements of the robot._
## 3 Development of the DOFS Integrated Power Transformer and Platform Setup  
### Design of the Distributed Optical Fiber Sensor Laying Scheme  
The transformer prototype developed in this paper adopts a layered winding structure and its specific parameters are listed in Table 1. The detecting equipment is a commercial product (BY-DTS-4020, Weihai Beiyang Optoelectronic Info-Tech Co. Ltd., Weihai, China) and the relative parameters of the optical fiber and the instrument are also listed below. The high voltage (HV) winding utilizes the round copper wire while the low voltage (LV) winding uses the composite wire. The optical fiber laying scheme on the windings is shown in Figure 3.  
Figure 3: Fiber laying scheme (cross-section structure) on (**a**) low voltage (LV) winding; (**b**) high voltage (HV) winding.  
Figure 2: Power transformer temperature monitoring principle based on distributed optical fiber sensing.  
The LV winding of the 35 kV transformer is composed of composite conductors, the unit of which consists of six parallelly flat copper wires. This leaves no oil ducts between each adjacent wire (as shown in Figure 3a). Temperature sensing can be realized by attaching the optical fiber to the wire surface of the outermost turn. Actual field experience shows that the optical fiber will be highly integrated with the winding due to the friction and stress generated from the laying process. In our experiment, the fiber did not break and the mechanical stress was moderate all the time. Meanwhile, a layer of insulating paper will be wrapped around the fiber composite wire to weaken the negative deviation of measurement caused by the cooling medium.  
The HV winding is made up by enameled round copper conductors, which also means the direct contact between adjacent wires. For the convenience of fiber laying, a layer of insulating paper (0.2 mm) will be applied to the surface of the winding in advance, after which the optical fiber will be spirally winded on the paper closely attached to the wire (as exhibited in Figure 3b). The remaining steps are the same as aforesaid.  
Through the high integration and close contact of the DOFS and the winding wire, the temperature of the designed optical fiber will be synchronized with the adjacent conductor during the actual operation of a transformer. Thus, the real time distributed temperature monitoring of the whole winding can be achieved. Meanwhile, a pulley guide should be utilized during the fiber laying process to ensure that the DOFS is evenly winded and closely adheres to the winding. The optical fiber was laid between the wire and the insulating paper, which will maintain the original winding structure and helps to buffer the possible vibrations and knocks during the manufacturing process. The iron core limbs and the inner wall of the oil tank were also uniformly winded by the distributed optical fiber sensors. The whole system is based on Raman scattering and it is only temperature-related, which means that the mechanical stress or the possible vibrations will not cause any interference.
## 4 Conclusions  
The occurrence of Hg and MeHg at the Nalon River estuary still represents an issue of environmental concern due to the legacy of long-term mining activity along the river drainage basin. The interaction between the river freshwater inputs and the tidal currents, as well as the relatively shallow water depth, appeared to be the primary environmental agents regulating the distribution of both dissolved and particulate Hg and MeHg. The estuary was dominated by oxidative conditions along the water column which promoted the precipitation and/or adsorption processes responsible for Hg and MeHg preferential partitioning in the suspended particles. However, the partitioning behaviour of Hg and MeHg was also affected by different seasonal hydrodynamic conditions. Indeed, results suggested that the dissolved fraction commonly prevailed under conditions of low river discharge (summer), whereas the particulate fraction dominated the water column when the river flow was high (autumn). Particulate MeHg was found to be the only exception since the effects of the tidal currents, which were prevailing when the river discharge was low, promoted resuspension of MeHg enriched particles from bottom sediment thus increasing PMeHg concentrations in the lower sector of the estuary and at the estuary mouth. Although resuspension may increase the mobility of MeHg at the estuary mouth, its concentration both in the dissolved and the SPM was found to be extremely low with respect to the total amount of Hg thus suggesting a minimal risk of MeHg bioaccumulation \begin{table}
\begin{tabular}{l l c c c c} \hline
**River Discharge** & & **Hg** & **MeHg** & **Fe** & **Mn** \\ \hline \multirow{3}{*}{Low} & Min & 5.34 & 5.08 & 6.71 & 7.01 \\  & Max & 5.99 & 6.24 & 7.41 & 7.39 \\  & Average & 5.66 \(\pm\) 0.16 & 5.64 \(\pm\) 0.39 & 6.96 \(\pm\) 0.19 & 7.20 \(\pm\) 0.10 \\ \hline \multirow{3}{*}{Normal} & Min & 5.15 & 5.37 & 6.97 & 6.72 \\  & Max & 6.04 & 6.45 & 7.41 & 7.65 \\  & Average & 6.65 \(\pm\) 0.21 & 5.83 \(\pm\) 0.31 & 7.21 \(\pm\) 0.15 & 7.17 \(\pm\) 0.20 \\ \hline \multirow{3}{*}{High} & Min & 5.15 & 4.81 & 7.44 & 7.63 \\  & Max & 6.27 & 6.18 & 7.87 & 8.32 \\ \cline{1-1}  & Average & 5.70 \(\pm\) 0.36 & 5.57 \(\pm\) 0.36 & 7.65 \(\pm\) 0.15 & 7.89 \(\pm\) 0.21 \\ \hline \end{tabular}
\end{table} 
Table 1: Minimum, maximum and average values of logK\({}_{\rm D}\) (L Kg\({}^{-1}\)) for Hg, MeHg, Fe and Mn along the Nalon River estuary under low, normal and high river discharge. in the aquatic trophic chain. Moreover, organic carbon was found to be one of the main factors governing MeHg distribution and fate at the Nalon River estuary. This is of relevant concern since complexation with organic matter appeared to play a large role in inhibiting MeHg mobility and potential bioaccumulation.
# Several Tree-Based Solutions for Predicting Flyrock Distance Due to Mine Blasting
## 2 Model's Background  
### DT Model  
Supervised-learning DT approaches are powerful tools for performing classification and prediction modelling in machine learning and data mining. Although there are several subsets for DT, e.g., chi-squared automatic interaction detection (CHAID), quick, unbiased, efficient, and statistical trees, C5, and classification and regression trees (CART), out of these algorithms, only CART and CHAID are suitable for predicting continuous variables. Because of its white-box nature and simple interpretability, the CART algorithm makes it easy to understand the relationship between input and output parameters and is outstanding compared to other DT algorithms. Moreover, large-scale datasets do not affect CART results, and this algorithm shows its superiority when dealing with complex samples and a high number of variables [64].  
To identify the most influential input parameters, CART inherently employs principal component analysis and eliminates non-significant ones [65]. CART DT can be developed as a classification tree (CT) or regression tree (RT). The performance of a CART can be affected by choosing the right partition for a database based on its main indices. There are three main indices for a CT: the Gini criterion, entropy, and the Twoing criterion. Moreover, due to the non-parametric temperament of CART, the assumption of a distribution for variables is not required. Generally, each CART consists of four parts, including the root, branch, node, and leaf. Each tree starts from the root node (i.e., the first node), which is located at the upper level of each tree and is divided into branches on the left and right sides.  
To prevent a CART training algorithm from developing a complicated tree, there are three prevention criteria: (1) a minimum number of observations, (2) maximum tree depth, and (3) reaching the least error value for the estimation of a dependent parameter. The verification dataset will prune the developed tree and allow it to reach the optimal subtree. The pruning procedure also reduces the occurrence of overtraining. As shown in Figure 1, a DT consists of a root node, decision (interior) nodes, and terminal (leaf) nodes. Each sample in a dataset is classified from the root node until it can no longer be divided into decision nodes and reaches a terminal node.
## 3 Local Consensus-Based Bundle Algorithm Method Considering Sensor Switching
### Sensor-Switching Strategy  
When a satellite bids on a set of tasks with temporal correlation, the time it takes for infrared sensors to switch between HGVs must be considered.  
The minimum time \(T_{min}\) required to switch between tasks is calculated first. Then, the UR width \(L_{w}\) considering the maximum rotation time \(T_{r}\) needed for the UR of the vehicle's intention is determined. The best switching window for the satellite is selected. Sensors can rotate to the tracking position before the UR becomes wider.  
As shown in Figure 5, the trajectory of the HGV can be divided into the maneuver period time (red area) and the maneuver gap period time (yellow area). According to the sequence of maneuver intentions of the vehicle, the widths of URs are compared and the interval with a smaller width is considered as the maneuver gap period time. The maneuver gap period time is a special case when the vehicle continuously maneuvers. The tracking accuracy of the satellite \(Sat_{n}\) is constrained by the sensor's performance, the observation angle, and the distance. The green segment in the figure, \"accuracy down\" is a tracking accuracy degradation problem at the end of the visible time window. The bidding satellites need to consider the timing arrangement of the new task beam. The minimum time \(T_{f}\) required for sensor back sweep is then calculated. The optimal switching window \([T_{s}^{s},T_{s}^{c}]\) is calculated as shown in Algorithm 1:  
Figure 5: Sensor switching decision process.  
where \([T_{i}^{s},T_{i}^{s}]\) is the maneuver gap time of the vehicle, \([T_{d}^{s},T_{d}^{e}]\) is the tracking sensor accuracy degradation period time, and \(T_{a}\) is the earliest arrival time.  
The computational complexity of the task consensus phase in the SS-LCBBA algorithm is \(O\Big{(}\frac{\left(n^{2}\hat{n}\right)^{2}}{2}\Big{)}\), \(\hat{m}=\max\limits_{i=1}^{n}\max\limits_{g=1}^{n}\left(m_{ig}\right)\), where \(n\) is the number of satellites. \(m_{ig}\) is the number of communication time windows between two satellites. \(\hat{m}\) indicates the maximum number of communication time windows between satellites during the mission. The computational complexity of the independent decision algorithm for each satellite is \(O\big{(}u^{2}+(u-1)\cdot N\cdot d^{2}\big{)}\), where \(u\) is the number of HGVs, \(N\) is the length of the neural network input sequence, and \(d\) is the dimension of each element in the input sequence. Hence, the computational complexity of the SS-LCBBA is \(O\bigg{(}n\cdot\frac{\left(n^{2}\hat{n}\right)^{2}}{2}\cdot\left(u^{2}+N\cdot d ^{2}\cdot(u-1)\right)\bigg{)}\).
# On the Rollout of Network Slicing in Carrier Networks: A Technology Radar
## 8 Oss Domain
### Oam
If changes are too quick, the stability of the operator's network could be compromised. To avoid this, it is critical that the decision on slice placement and resource allocation (i) minimizes the probability of modifying the slice at operation time, and (ii) optimizes resource usage. * **Baseline slice assurance**. The assurance represents the ability of the operator to retrieve management data from individual slices, using them as input for SLA verification. Examples of these data includes S-NSSAI level information on slice status (e.g., activated, de-activated), performance measurements and KPIs (e.g., UL/DL throughput, latency and packet loss rate) [104; 105], notifications on fault events and alarms (e.g., threshold crossing) [106] and trace data. The operator may start with S-NSSAI level management data based on the aggregation of metrics collected from the 5 GC (via NWDAF) and NFV MANO (via SOL005). These data will then be aggregated to check the health of the slice, verifying whether it meets the SLA requirements. In case of SLA violation, the operator will trigger corrective/remediation actions (e.g., capacity increase, re-configuration) on the cor responding slice. The number of actions available by then is expected to be limited, according to the experience the operators are getting from the trials. In this baseline assurance solution, it is assumed that the assurance group in the OSS (see Figure 2) will include the 'data aggregation' and 'Service Quality Management' modules, but not the 'AI models and training' module. Finally, in the longer run (explore ring in the radar), the operator focus will be on having an **advanced slice assurance** toolkit available in their OSS stack. To that end, operators are scouting solutions in two main directions. On the one hand, the incorporation of the RIC and the SDTN controller as new data sources, building on the two already in place (NWDAF and NFV MANO). To transform these quite different types of data into useful information for the Service Quality Management activities, the data aggregation framework may leverage on the Management Data Analytics Service (MDAS) functionality, reported by 3 GPP in [107; 108]. On the other hand, the full automation of the assurance pipeline (data collection and aggregation \(\rightarrow\) insights \(\rightarrow\) corrective/remediation actions), using zero-touch mechanisms such as those outlined in [109]. For the insights step, the 'AI models and training' module is to be used. Once all the solutions in the radar have been discussed, one can wonder how the operators can assess the level of maturity of their OSS throughout this journey. This can be done using different OAM related KPIs, including scalability related KPIs (e.g., number of slice types, number of instances running in paralell), commissioning related KPIs (e.g., network slice instantiation time) and operation related KPIs (e.g., network scaling re-configuration time and scaling in/out time), among others.
# Real-Time Identification of Look-Alike Medical Vials Using Mixed Reality-Enabled Deep Learning
## 7 Implementation and Real-World Performance Analysis
### Challenges and Optimizations  
Several challenges were encountered during the deployment, particularly in optimizing real-time performance on a mobile device. The major challenges and corresponding solutions are summarized below:  
* High latency during initial inference: The first inference run experienced delays due to ONNX model loading overhead. This was mitigated by preloading the model at startup.
* Reduced detection accuracy in occlusions: When the vials were partially covered, the detection accuracy dropped. Future work may involve integrating depth-aware segmentation to improve the handling of occlusions.
* Stability of 3D annotations: Although most annotations remained stable, rapid head movements in MR caused a minor misalignment. This was improved by adjusting the spatial anchor update rates.
## 4 Methodology
### Machine Learning (ML) Models  
Several well-known ML models have been used in this work to detect security attacks in network communications. Next, a brief description of each ML model used is provided:  
* Multinomial Logistic Regression (LR). It is the simplest linear model [40] that has been widely applied to several and diverse tasks. For a binary classification problem, LR models the dependent variable \(y_{i}\in[0,1]\) as a linear combination of the independent variables \(\mathbf{x}_{i}\) as shown in Equation (4), \[y_{i}=f(\beta_{0}+\beta_{1}x_{i1}+\beta_{2}x_{i2}+...+\beta_{p}x_{ip}),\] (4)where \(P\) is the number of features describing one sample in the dataset, and \(f(\beta X^{T})\) is the logistic or sigmoid function. The parameters vector \(\beta\) is learned by solving the minimization problem depicted in Equation (5): \[\beta^{\star}=\underset{\beta}{\arg\min}\ \ ||y-f(\beta X^{T})||_{2}^{2}\] (5) Since the problem addressed consists of several (\(>2\)) labels or classes, a _One-vs-All_ approach was used in such a way that \(K\) logistic regression models are trained (\(K\) being the number of labels or classes), each one focusing on solving the corresponding binary classification problem, and the overall prediction of the LR model is computed using the _softmax_ function depicted in Equation (2).
* Support Vector Machine (SVC). It is a kernel-based method that uses a kernel function (radial basis, linear, polynomial, or any other) to map the original input space into a new space where predictions can be made more accurately. In this sense, the performance of an SVC is determined by the type of kernel function. In this work, the Linear Function (_SVC-L_) and Radial Basis Function (_SVC-RBF_) were tested. Linear and Gaussian kernel functions are depicted in Equations (6) and (7), respectively. \[K(x,z) =x^{T}z\] (6) \[K(x,z) =exp\left(\frac{-||x-z||^{2}}{2\sigma^{2}}\right)\] (7)
* Random Forest (RF). It is a tree-based non-linear bagging model for which multiple decision trees are fitted to different views of the observed data [41]. In this sense, each decision tree is fitted to a subset of the \(N\) samples (randomly sampled). Moreover, a random subset of the \(P\) input features is used within each node of a tree to determine which of them is used to expand it further. Overall RF predictions are computed by calculating the average (or weighted average according to the performance of each single decision tree on the out-of-bag samples) of the individual predictions provided by the multiple decision trees.
## 3 Evaluation of Flow Performance
### Gravimetric Test Bench  
As shown in Figure 9a, a gravimetric (weighing) test bench was enclosed by a wind shield to avoid any wind effect. A pneumatically liftable stage supported a weighing balance at a variable height for flow rate measurement at different head pressures. Figure 9b shows a specially designed weighing vessel sitting on a weighing plate. The weighing range of the weighing balance was up to 6 kg, with a resolution of 0.01 g. As shown in Figure 9c, the weighing vessel had a special feature, in which an inner vessel stood on the bottom surface of the weighing vessel. One method of avoiding liquid dropping from the tube into the weighing vessel could be to immerse the tube into the liquid, but this would create immersion effects, such as a buoyancy effect, of the immersed tube beneath the rising liquid level. To maintain a constant immersed depth of the tube, many studies have adopted an overflowing inner vessel where a tube or a needle is immersed into the overflowing inner vessel [56; 57; 58; 59]. Another method could be to maintain a constant liquid bridge or stream connecting the tube outlet and a liquid-absorbing material, such as glass filter or foam mounted inside a weighing vessel [60]. For liquid collection in this study, we combined the two methods, by not overflowing the inner vessel but filling it with liquid-absorbing spongy material so that the liquid stream from the tube outlet was absorbed. Liquid then wove through the sponge, down the inner vessel, and flowed out from the openings at the bottom of the inner vessel (see Figure 9c).  
The dynamic weighing method was adopted for the test bench, in which the increasing liquid mass collected inside the weighing vessel was measured by the weighing balance as a function of time, to ultimately obtain the mass flow rate. The flow rate was determined by a simple ratio of the differences in mass and time over a time interval. Though fitting methods such as linear fit over a time window will give a smoother curve with reduced noise, we considered that the noise was not so significant in the gravimetric system, and the present calculation method was good enough to see the flow performance of the infusion pump prototypes. Taking into consideration uncertainty factors such as liquid evaporation, resolution of weighing balance, buoyancy effect and so on, we performed a preliminary  
Figure 9: Gravimetric test bench for flow rate measurement. (**a**) A weighing balance sits on a liftable stage inside a wind shield enclosure. (**b**) A specially designed weighing vessel sits on a weighing plate. (**c**) Features of the weighing vessel.  
uncertainty analysis of the test bench, obtaining an expanded uncertainty of about 3.3% for the smallest flow rate of 3.0 g/min. The dominant uncertainty factor in the flow rate determination was the resolution of the weighing balance. Nevertheless, the amplitude order of the measurement uncertainty (3.3%) was considered sufficient in the context of the present study, seeing that the flow rate accuracy requirement for non-electrically driven infusion pumps is \(\pm\)20%.