esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Blending Ensemble Learning-Based Landslide Susceptibility Mapping and Risk Assessment  
### Optimization Method Based on Blending Ensemble Learning  
#### 4.1.1 A Brief Introduction to XGBoost Algorithm  
XGBoost is guided by the Boosting integration concept, amalgamating multiple decision tree models to construct a more precise classifier. Owing to its superior accuracy and rapid computational speed, XGBoost has found application in landslide susceptibility analysis [30,31]. The optimization objective function of the XGBoost algorithm is formulated based on the decision tree algorithm and incorporates an additional regularization term to manage the complexity of models, encompassing both the number of leaf nodes and the sum of squares of the number of leaf nodes. Thus, the objective function comprises both the loss function and the regularization term, as depicted in the following equation:  
\[\text{Obj}=-\frac{1}{2}\sum\
olimits_{\text{T}}^{\text{j}=1}\frac{\text{G} _{\text{j}}^{2}}{\text{H}_{\text{j}}+\lambda}+\gamma\text{T} \tag{3}\]  
where \(\lambda\) represents the fixed coefficient, \(\gamma\) represents the complexity parameter, T represents the number of leaf nodes in the tree, \(\text{G}_{\text{j}}\) represents the sum of the first-order partial derivatives of the samples contained in leaf node j, and \(\text{H}_{\text{j}}\) denotes the sum of the second-order partial derivatives of the sample contained in the leaf node j.
## 3 The Third Industrial Revolution (TIR) and Energy Internet
NGLIA is a simple integration architecture that cascades 10G-EPON and LTE networks without requiring any ODN modifications. At the downside, all upstream traffic coming from collocated ONU/BSs and destined to neighboring ONU/BSs or stemming from handovers between a given pair of ONU/BSs has to traverse the OLT, resulting in an increased latency and non-negligible packet delays. Both LIA and RNIA are able to avoid this shortcoming, at the expense of architectural modifications of the ODN. In LIA, the splitter/combiner at the remote node is replaced with an \((N+1)\times(N+1)\) passive star coupler (PSC), to which each of the \(N\) ONU/BSs is connected via an additional fiber to provide a loopback path and enable direct optical communication among all ONU/BSs through the PSC without having to traverse the OLT. It is worthwhile to mention that LIA uses the same (single) upstream wavelength channel of 10G-EPON, _i.e._, does not require any additional transmitter at the ONUs. However, LIA does require that each ONU is equipped with an additional receiver to receive the loop-backed packets. Also note that LIA allows the bandwidth allocation to be handled in a distributed manner by the ONU/BSs (instead of the central OLT) due to the fact that each upstream packet is looped back to all ONU/BSs, thereby giving rise to the implementation of distributed MAC protocols. Clearly, the modified loopback architecture of LIA helps reduce latency (and free up downstream bandwidth between OLT and ONUs) by throwing hardware at the problem, which might violate given budget constraints of cost-sensitive access networks. Similarly, RNIA relies on inserting _active_ remote nodes in the distribution fibers of the ODN. These active remote nodes have the intelligence to perform MAC layer functionalities, e.g., packet filtering, storing, and forwarding. The basic idea behind RNIA is to replicate the MAC layer functionalities of the central OLT in distributed proxies and place them closer to ONU/BSs in order to decrease latency. In doing so, this results not only in additional costs but also, and arguably more importantly, in losing the trademark of PONs, their completely unpowered nature. In the following, we highlight advanced techniques that are able to significantly lower the latency in NG-PONs, while aiming at minimizing the amount of required ODN modifications and hardware upgrades as well as maintaining the passive fiber infrastructure.
# Extending Cooling Rate Performance of Fast Scanning Chip Calorimetry by Liquid Droplet Cooling
## 3 Results
### Liquid Cooling of Empty Sensors
After the whole droplet has reached the surface of the sensor and sample and starts to spread along it and evaporate, the cooling rates decreases, showing an exponential decay. In the case of a small droplet (orange line in Figure 8), as soon as all liquid is removed/evaporated from the sample, the cooling rate returns to the values of gas cooling. Consequently, for larger droplets, the temperature range of fast cooling is extended to lower temperatures. Figure 7: The cooling efficiency of different cooling agents in linear (**a**) and logarithmic (**b**) scales on the sensor XI395 with a measuring area of 60 \(\times\) 80 \(\upmu\)m\({}^{2}\). Various measurements showed that liquid droplet cooling efficiency mostly depends on the droplet size and the arrival place. By varying the injector diameter, liquid droplets with various size/volume, from approximately 1 to 200 \(\mu\)L, were tested, with their deposition on sensors with different heated areas (XI395 60 \(\times\) 80 \(\mu\)m\({}^{2}\) and UFS1 e500 \(\mu\)m). Cooling with larger droplets is more efficient, providing a higher maximum cooling rate (see Figure 8a, blue curve). Surprisingly, the droplet becomes efficient only when its size is orders of magnitude larger than the sample. A water droplet of ca. 1 \(\mu\)L has a lateral dimension of ca. 1 mm, which is much larger than the heated area of the sensor, which is in the order of 50 \(\mu\)m in lateral dimension, but its cooling is inefficient. In contrast, a ca. 200 \(\mu\)L drop, which is basically a flow of liquid around the sample, gives maximum cooling efficiency. A smaller heating area of the sensor shows better cooling performance in terms of boiling (see Figure 8b). The peak cooling rate of the XI395 sensor increased at the temperature of maximum around 500 K from \(10^{5}\) to almost 2.5 \(\times\)\(10^{6}\) K/s. On the larger UFS1 sensor, intensive boiling of pure ethanol and the ethanol-water mixture was observed because of the large heat capacity of this sensor, resulting in a reduction in the maximum cooling rate. It was found earlier that convection does not significantly contribute to the heat losses on the here relevant length and time scales [33]. The effect seems to be related to the enlarged contact area with the sensor. Interestingly, below 350 K both curves (liquid cooling on both XI495 and UFS1 sensors) coincide, proposing that if boiling is avoided, e.g., by replacing the coolant with a high boiling temperature ionic liquid [34], the droplet cooling of the UFS1 sensor may be even more efficient relative to its performance with gas cooling.
## 3 Methodology
### Dynamic Feature Samples Construction
#### 3.2.1 Dynamic Feature Distribution Samples Dataset (\(DFD\))  
Let \(DFD^{in}\) be the dynamic feature distribution of a ship whose MMSI is \(i_{m}\), then \(DFD\) is the set of \(DFD^{in}\), which is defined by Equation (11).  
\[DFD=\left\{DFD^{i_{1}},DFD^{i_{2}},\ldots,DFD^{i_{m}}\right\} \tag{11}\]  
Limited by the number of satellites, signal conflicts, or AIS receiver performance [27; 28], the dynamic data \(d_{j}^{in}\) received by satellite is insufficient to describe \(T^{in}\) completely. But in a longer period, for a trajectory \(T^{in}\), the feature distribution function of \(d_{j}^{in}\) can describe the overall motion of the ship (e.g., the distribution of latitude and longitude in Figure 7 describes the area of the ship's activity). In addition, this description can reduce the impact of outliers. For the massive amount of space-based AIS data, it is impractical to calculate the feature distribution function for each \(T^{in}\), so we use the frequency histogram to approximate the distribution function, as shown in Figure 8. For feature f of trajectory \(T^{in}\), the range of f on the dataset is sliced uniformly into n intervals, on which the frequency distribution of f (i.e., \(f_{d}\)) is calculated. The matrix \(DFD^{in}\) in Figure 8 is the combination of each feature's frequency distribution. The Time field in \(DFD^{in}\) is the number of minutes from the Time field in \(d_{j}^{in}\) to the zero point of the day. \(DFD^{in}\) takes 13 features into consideration, excluding the fields of \(speedlng^{\prime}\),\(speedlat^{\prime}\) and \(speed^{\prime}\).  
Figure 7: Process of dynamic features extraction.
## 1 Introduction
Thus, it is possible to monitor test results over time, compare gender- and sport-specific performances and determine individual deficits by the limb symmetry index (left-right comparison) [27]. The purpose of this study was to evaluate the jump height in the SLVHT as a functional parameter to assess the rehabilitation progress in patients after ACL-R when measured with digital sensor technology in a clinical setting. We hypothesised that a limb symmetry index (LSI) of the SLVHT jump height could both detect functional deficits after ACL-R and respond to improvements over the course of rehabilitation, independently of gender and age differences. Functional testing after ACL-R is complex, often time-consuming, and therefore difficult to implement in the daily routine of practitioners. The results might provide a simple option to reliably test functional capability after ACL-R and present a useful setup to monitor rehabilitation progress.
## 3 Methods
### Data Collection Experiment
#### 3.5.2 Data Collection  
In the experiment, only the left-hand side was utilized for cylinder grasping. The participants consisted of both right-handed and left-handed people, which may have affected the way they grasp the cylinder. Therefore, this condition is crucial for evaluating the performance of the employed machine-learning models in accounting for the corresponding variations in the data. Subsequently, the participants were instructed to emit a minimum power required to lift the cylinder with varying weights, which depicted the actual rehabilitation case with a patient, where achieving specific minimum power levels may be challenging for patients with certain severity levels. As mentioned in Section 3.2.3, this study has four weight variations. The data collection was repeated three times for each weight variation, constituting the three sessions. In this study, holdout data splitting was employed, where data from the first and third sessions were used as training data, while data from the second session were used as testing data. One session consisted of grasping and lifting the six cylinders with varying diameter sizes. Subsequently, for each cylinder, the participants were asked to perform five-finger conditions. The first condition was that all fingers grasp the cylinder. The second condition was that the index finger was open while the other fingers grasped the cylinder. The third to fifth conditions consisted of keeping the middle, ring, and little finger open, respectively, while the other fingers grasped the cylinder. The illustration of finger conditions is shown in Figure 11.  
A resting period was applied between finger conditions and sessions to avoid muscle fatigue and prioritize participants' safety. Additionally, a randomized order of weight variation was applied to prevent bias of muscle power toward certain cylinders' weights.  
Figure 11: Illustration of finger conditions.  
These finger conditions were employed by assuming the normal and abnormal conditions of the patient's finger, where the first condition corresponds to the normal condition. During cylinder grasping activity, the abnormal condition corresponds to the inability to perform finger flexion to grasp the object due to the dysfunctionality of the corresponding muscle [3]. Consequently, one or more fingers remain open instead of grasping the object. In this study, the assumed abnormal condition was arranged to involve only one finger, represented by the second through fifth conditions, as shown in Figure 11. This setting was employed to enable the machine learning model to learn both normal and abnormal conditions. Subsequently, the performance of the machine learning model in distinguishing between normal and abnormal conditions could be evaluated through the RMSE score. The illustration of the data collection flow is shown in Figure 12. After the experiment, each participant was asked to rate the perceived difficulty of lifting 1000 g cylinders. Accordingly, each participant provided information about the easiest and hardest cylinders.
## 4 Experiment and Results
### Performance Metrics  
Various metrics were used to evaluate the model's performance, with specific metrics designated for discrete and dimensional emotion representations.  
1. Weighted Accuracy (WA): This metric considers class imbalance by weighting the contribution of each class proportionally to its presence in the dataset. It provides a more accurate assessment of the model's performance across imbalanced datasets. The formula for Weighted Accuracy is \[\textit{Weighted Accuracy}=\frac{\sum_{1}^{k}n_{i}}{\sum_{1}^{k}N_{i}}\] where \(N_{i}\) is the number of utterances in the \(i\)th class, \(n_{i}\) is the number of correctly recognized utterances in the \(i\)th class, and \(k\) is the number of classes;
2. Unweighted Accuracy (UA): This metric measures the overall accuracy without considering class imbalance, providing a straightforward accuracy calculation. The formula for Unweighted Accuracy is \[\textit{Unweighted Accuracy}=\frac{(TP+TN)}{(TP+TN+FP+FN)}\] where \(TP\) represents True Positives, \(TN\) represents True Negatives, \(FP\) represents False Positives, and \(FN\) represents False Negatives;
3. Mean Concordance Correlation Coefficient (CCC): This metric evaluates the agreement between predicted and true dimensional values (valence, arousal, dominance).  
It provides a measure of how well the model's predictions align with the actual continuous emotion dimensions. The formula for CCC is  
\[CCC=\frac{2\rho\sigma_{x}\sigma_{y}}{\sigma_{x}^{2}+\sigma_{x}^{2}+\left(\mu_{x} -\mu_{y}\right)^{2}}\]  
where \(\rho\) is the Pearson correlation coefficient, \(\sigma_{x}\) and \(\sigma_{y}\) are the standard deviations of the predicted and true values, and \(\mu_{x}\) and \(\mu_{y}\) are the means of the predicted and true values, respectively.  
In summary, Weighted Accuracy (WA) and Unweighted Accuracy (UA) are used for evaluating the performance on discrete emotion representations, ensuring both a balanced view and a simple accuracy measure. The mean Concordance Correlation Coefficient (CCC) and Mean Absolute Error (MAE) are used for dimensional emotion representations, providing insight into how well the model predicts continuous emotional states. This combination of metrics allows for a comprehensive evaluation of the model's effectiveness in capturing both discrete and continuous aspects of emotions.
## 3 The Setup of the Simulation
### Verification of Calculation Method
#### 3.5.1 Surface Pressure Coefficient  
Figure 8 shows the numerical results of time average pressure coefficient distribution on the surface of upstream cylinder and downstream cylinder, which are compared with the experimental results of QFF and BART [20; 21; 22]. In the figure \(\theta\) is the azimuth.  
Figure 6: Schematic diagram of flow around tandem cylinders.  
Figure 7: Grid and boundary conditions.  
It can be seen from Figure 8 that the surface pressure coefficient of the upstream cylinder is in good agreement with the QFF and BART test results, and the downstream cylinder surface pressure coefficient is closer to the QFF experimental results. This is consistent with the results of literature [18; 19]. The main reason for the error at position 80\({}^{\circ}\) and 280\({}^{\circ}\) of the downstream cylinder is that the downstream cylinder is affected by the upstream cylinder, so the flow is more complex, and the spanwise length in the simulation is inconsistent with the experiment.
# Assessing the Carbon Intensity of e-fuels Production in European Countries: A Temporal Analysis
## 3 Results & Discussion
### Influence of the Considered Time Resolution  
For the exemplification of the influence of the considered time resolution, Figure 11 presents a time-series for the carbon intensity of e-H\({}_{2}\) in Austria. This case study was selected as a clear and relevant case for illustration purposes, as its carbon intensity of e-H\({}_{2}\) is sometimes below the threshold and sometimes above it. The time-series are calculated and represented for a daily, weekly, and monthly time resolution. It highlights the importance of time resolution when comparing the average value to the threshold, as it can be observed that when the monthly value is just above the threshold, such as in August, some weekly or daily values can be lower than the threshold, and the contrary can be observed in June.  
Figure 12 presents the potential load factor of low-carbon hydrogen production considering both the production and consumption mix for the different time steps, from annual values to hourly values. When considering an annual time step, the load factor is 0 or 1, depending on the annual average and if it is above the limit or not. The load factor is slightly affected by the considered time step. Indeed, as exemplified for Austria in Figure 11, even if the monthly carbon intensity is above the limit for 1 month, it is possible that within this month, there are some weeks, days, or hours for which the carbon intensity is lower than the limit. On the other hand, there might be months with a monthly intensity below the limit, and within this month, there might be some weeks, days, or hours over the limit. The larger the time step is, the higher the risk of including periods of time where the electricity is sourced from high-carbon intensity sources. Having a time step as low as an hourly time step strongly limits this risk. A larger time step may facilitate the running of an e-fuel power plant, but Figure 12 also shows that, for most countries, when looking at the consumption mix, the shorter the time step is, the higher the potential load factor is. This is due to the fact that every single hour below the limit can be used for plant operation.  
Figure 11: Time series of e-H\({}_{2}\) carbon intensity in Austria.
# Cost-Aware IoT Extension of DISSECT-CF
## 3 Cost Modeling in DISSECT-CF
### IoT Pricing
#### 3.1.1 Azure IoT Hub  
Concerning the IoT side pricing, Azure IoT Hub charges one after the chosen edition/tier. This means that there are intervals for the number of messages used in a month. Azure also comes with some additional platform services when similarly to other providers, but we only kept the general parts to allow the identification of common pricing components. There is a restriction for message sizes that depends on the chosen tiers. One can choose from four tiers, Free, S1, S2, S3. Each of them vary in price and the total messages allowed per day. Message and group size of the Free tier is significantly more limited compared to the other tiers.
# Optimization of the Drying Temperature for High Quality Dried Melissa officinalis
## 3 Results
### Color and Chlorophyll Content  
The effects of the drying temperature on _M. officinalis_ color are presented in Table 2. A lower color index was observed with drying at 35 \({}^{\circ}\)C compared to all the other temperatures. On the other hand, drying at 20 and 49 \({}^{\circ}\)C presented as plant material with a higher browning index compared to the other investigated temperatures.  
The effects of the drying temperature on _M. officinalis_ leaf chlorophyll content are shown in Table 3. Drying at 35 and 42 \({}^{\circ}\)C resulted in higher Chl b content compared to 20 and 49 \({}^{\circ}\)C. In addition, a higher Total Chl content was observed with drying at 42 \({}^{\circ}\)C as to 20 and 49 \({}^{\circ}\)C.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Temperature** & **kWh/h** & **kg CO2/h** & **kWh/kg** & **kg CO2/kg** \\ \hline
20 \({}^{\circ}\)C & 0.000 & 0.000 & 0.00 \(\pm\) 0.00 d & 0.00 \(\pm\) 00.0 d \\
35 \({}^{\circ}\)C & 0.0385 & 0.030 & 23.61 \(\pm\) 0.17 c & 18.40 \(\pm\) 0.13 c \\
42 \({}^{\circ}\)C & 0.0905 & 0.085 & 52.36 \(\pm\) 0.40 a & 49.18 \(\pm\) 0.37 a \\
49 \({}^{\circ}\)C & 0.1170 & 0.110 & 48.60 \(\pm\) 0.16 b & 45.69 \(\pm\) 0.15 b \\ \hline \end{tabular} Values represent the mean \(\pm\) standard error (n = 5). Different Latin letters in each column indicate significant differences (\(p<0.05\)).  
\end{table}
Table 1: Effects of drying temperature on the energy consumption and CO2 production per h of oven operation and per kg of dried product during the drying of _M. officinalis._  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Temperature** & **Hue Angle (\({}^{\circ}\))** & **Chroma Value** & **Color Index** & **Browning Index** \\ \hline
20 \({}^{\circ}\)C & 178.56 \(\pm\) 0.01 & 15.00 \(\pm\) 2.50 & \(-\)4.40 \(\pm\) 0.91 a & 56.14 \(\pm\) 1.93 a \\
35 \({}^{\circ}\)C & 178.81 \(\pm\) 0.05 & 16.30 \(\pm\) 1.62 & \(-\)11.01 \(\pm\) 0.18 b & 38.12 \(\pm\) 5.84 b \\
42 \({}^{\circ}\)C & 178.60 \(\pm\) 0.07 & 13.98 \(\pm\) 2.30 & \(-\)4.76 \(\pm\) 1.13 a & 43.59 \(\pm\) 4.36 ab \\
49 \({}^{\circ}\)C & 178.53 \(\pm\) 0.01 & 13.33 \(\pm\) 1.94 & \(-\)3.40 \(\pm\) 0.06 a & 55.36 \(\pm\) 2.38 a \\ \hline \end{tabular} Values represent the mean \(\pm\) standard error (n = 5). Different Latin letters in each column indicate significant differences (\(p<0.05\)).  
\end{table}
Table 2: Effects of drying temperature on _M. officinalis_ color.  
Figure 2: Relevant changes in energy consumption, CO2 production, and time duration during drying process on _M. officinalis._
# Efficient Crowd Anomaly Detection Using Sparse Feature Tracking and Neural Network
## 3 Materials and Methods
### Crowd Representation and Behaviour Descriptors
#### 3.1.1 Local Feature Extraction
Its capability to capture complex patterns enables the detection of anomalous behaviours that deviate from the expected crowd dynamics, making it particularly effective in detecting anomalies of varying sizes. AKAZE builds upon SIFT's principles while offering enhanced speed and robustness. AKAZE efficiently extracts key points across different scales and is less sensitive to noise and blur. Its adaptability to diverse environmental conditions makes it suitable for the detection of anomalies in challenging real-world scenarios, ensuring reliable performance in varied settings. FAST is designed for rapid corner detection. It can efficiently identify key features of corners within crowd images. Although not as invariant to scale and rotation as SIFT and AKAZE, FAST's speed makes it ideal for real-time anomaly detection applications, where a timely response is crucial. Its efficient detection of key points contributes to the overall effectiveness of sparse feature tracking in identifying anomalous crowd behaviours.