esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Latest Applications in Distribution Systems
### State Estimation/Load Monitoring
On the grid edge, non-intrusive load monitoring (NILM), which aims to disaggregate whole-house power readings into appliance-wise power readings, has been a popular area of TL applications recently [40]. In [41], the effectiveness of TL in the form of seq2 point learning is demonstrated across not only different appliances but also power readings. This is made possible due to the CNN architecture extracting domain-invariant features from the power readings. As domain-invariant features can be extracted, computational costs of NILM models are significantly reduced as they do not need to be re-trained for different appliances or power readings from different regions. Privacy-preserving TL framework is introduced in [42], and a transferable tree-based ensemble model is developed and only requires training samples from the target domain, thus reliably protecting the privacy of the users in the source domain.
# Robust Hessian Locally Linear Embedding Techniques for High-Dimensional Data
## 4 Robust Hessian Locally Linear Embedding
### Fast Outlier Identifying Algorithm
#### Step 1  
Since the objective of introducing the weights is to reduce the influence of possible outliers among the \(k\) neighbors, ideally we want to set the weights such that \(w_{j}^{i}\) is small if \(x_{j}^{i}\) is considered as an outlier. Note that the outliers are usually far from the mean of the subset of the \(k\) neighbors in which outliers have been removed. Conversely, the clean samples are much closer to the above mean than the outliers. Based on the above observation, we can choose the isotropic Gaussian exponential weights defined as:  
\[w_{j}^{i}=\frac{exp(-\|x_{j}^{i}-\bar{x}_{i}\|^{2}/\sigma)}{\sum_{j=1}^{k}exp (-\|x_{j}^{i}-\bar{x}_{i}\|^{2}/\sigma)} \tag{17}\]  
where \(\bar{x}_{i}\) denotes an approximation of the mean of the clean sample points from the local patch, and \(\sigma\) is defined as the mean squared Euclidean distance of \(k\) nearest neighbors. With this definition, clean samples in the local patch have large weights, while outliers have small weights which decay exponentially with the distances to the mean of the clean samples.  
In order to approximate the mean of the clean sample points from the local patch, we first choose \(\bar{x_{i}}\) to be the mean of all the \(k\) samples from the local patch, including both the clean points and outliers. Then we update the mean \(\bar{x_{i}}\) with the weighted sample mean vector:  
\[\bar{x_{i}^{w}}=\sum_{j=1}^{k}w_{j}^{i}x_{j}^{i} \tag{18}\]  
where \(w_{j}^{i}\) is computed by Equation (17). Then the mean \(\bar{x_{i}}\) depends on the weights \(w_{j}^{i}(j=1,\ldots,k)\). However, as we can see from Equation (17), the weights in turn depend on the mean \(\bar{x_{i}}\). Because of this cyclic dependency, we exploit an iterative procedure to find the solution. This iterative procedure can be summarized as follows:  
1. Set the initial mean \(\bar{x_{i}}^{(0)}=\frac{1}{k}\sum_{j=1}^{k}x_{j}^{i}\). Set \(l=0\).
2. Repeat until convergence: 1. Compute \((w_{j}^{i})^{(}j=1,\ldots,k)\) according to Equation (17); 2. update the mean \(\bar{x_{i}}^{(l+1)}=\sum_{j=1}^{k}(w_{j}^{i})^{l}(\bar{x_{i}^{l}})^{(l)}\) 3. if \(\|\bar{x_{i}}^{(l+1)}-\bar{x_{i}}^{(l)}\|_{2}<\epsilon\) (we set \(\epsilon=0.01\) in our experiments), return; else \(l=l+1\).
## 3 Results and Discussion
### CRA-Based Optimization of the Cost  
Figure 12 illustrates the convergence behavior of a developed Coral Reef Algorithm (CRA) applied to the optimization of studied water distribution networks, where the objective is to minimize cost while satisfying hydraulic constraints. The different lines correspond to varying demand multipliers (1\(\times\), 1.5\(\times\), and 2\(\times\)), which scale the water demand at the network's demand nodes. The downward trend of all lines demonstrates the CRA's ability to iteratively improve the network design by reducing the overall cost. Notably, higher demand multipliers generally lead to lower overall costs than the base case (1\(\times\)). This unexpected result might indicate that the optimization process can potentially find more efficient solutions for higher-demand scenarios. The convergence rate also varies, with steeper initial declines indicating faster improvements in the early stages of the optimization. This suggests that the CRA effectively explores the solution space and rapidly identifies promising designs before gradually refining the solution towards a local or global optimum.
# Uniform Linear Antenna Array Beamsteering Based on Phase-Locked Loops
## 5 Measurement on ULA
### Calibration Process and Pattern Measurement  
Calibration of the transmitter array is essential before measuring the patterns in order for all phases from the elements to be aligned with respect to the receiver. The procedure is to firstly mechanically align the transmitter with receiver so as the transmitter's broadside is normal to the direction of the receiver. Each PLL is muted while leaving its oscillator on and its LNA turned off. Then, each PLL is turned on, one at a time, and the far-field power and phase resulting from each element are measured and adjusted. Once all PLLs/elements have been set, all LNAs are turned on and the pattern can be measured. As the antenna array is connected to the PLL array by coaxial cables, this allows for compensation for any phase differences resulting form cable length mismatches.  
The entire procedure is depicted in the flowchart shown in Figure 10. The progressive phase settings corresponding to different beam-pointing angles is set using the custom Windows\(-\)based program [6]. Simulated patterns, created using source code from [6] based on Equation (10), are show plotted against the measured patterns in Figure 11 and the results are in good agreement. The minor discrepancies can be attributed to fabrication tolerances and mutual coupling effect, which is left for the students to understand in depth. This completes the entire process, right from the theory, design, fabrication, and demonstration of ULA and its beam-scanning ability.  
The current hardware does not utilise any measurement of phase in the PLL array; however, the PLL used has a differential output with the negative terminal currently terminated with a 50 Ohm load. This second RF output of each PLL could be compared with a selected PLL as a reference through a phase comparator. As the micro-controller has multiple analogue inputs, the DC phase reference signal could be used to tune the DAC output controlling the 10 MHz reference phase shifter. This would allow the control software to send a desired phase offset instead of a voltage, thus allowing for different beam angles to be automatically configured without user interaction.  
Figure 10: Radiation pattern measurement procedure in a flowchart.
## 3 The Detection and Isolation Method of Data Anomalies
### Simulation and Analysis of Anomaly Signal
Concerning the variation trend, the short-duration auto-recovery anomalies present the decreasing trend when the anomalies disappear. For the sustained anomalies, the detection function values of the multiplicative fault are proportional to the absolute values of the actual measurement data, and there is no definite trend of the noise fault or of the constant drift fault.
## 1 Introduction
They concluded that for low temperature it was necessary to cause an increase of the temperature inside the cylinder during the combustion so that the post-injection had to be performed relatively close to the main injection event to ensure its ignition. Instead, when the exhaust temperature was higher it was more efficient to induce the oxidation in the catalyst so that the post-injection event should be delayed. Another important aspect to define the regeneration strategy is related to the properties of the fuel, which determine the specific composition of the HC [25] and, consequently, the DOC response [26]. [PERSON] et al. [27] concluded that the regeneration was slower when using biodiesel fuel due to the lower temperature reached in the DOC compared to a regular diesel fuel. In this sense, the research done by [PERSON] et al. [25] analysed the HC, CO and soot emissions produced by the post-injection of n-butanol and diesel fuel, highlighting the change in the composition of HC due to the different fuels and the lower soot emissions characteristic of the operation with n-butanol. Defining the optimum regeneration strategy as the one leading to the minimum fuel consumption, i.e., the optimum combined impact of post-injection rate and regeneration duration, the current work analyses the efficiency of the timing and fuel mass rate of late fuel post-injection events in terms of exhaust gas temperature increase, change in specific fuel consumption and dynamics of the active regeneration. Other important aspects for the definition of the regeneration strategy, such as the fuel dilution inside the engine oil [28] or the thermal fatigue in the particulate filter [29], are out of the scope of this work. In order to perform this study, a set of computational models are used to analyse in detail the linked engine and ATS response. An engine model implemented in GT-POWER [30] is used to predict the influence of the post-injection event on the specific fuel consumption and the gas properties at the DOC inlet. These boundary conditions are next applied as input for dedicated lumped DOC and DPFmodels, which were previously validated within a wide range of engine experimental data. Firstly, the dependence of the DPF inlet temperature on the post-injection settings is analysed, defining the efficiency of each strategy from the exhaust gas temperature increase. The influence on the dynamics of the regeneration of the temperature increase and the available O\({}_{2}\) concentration at the DPF inlet is finally studied. This is done to make the focus centre on the changes in the pre-heating, maximum reactivity and late soot oxidation stages as a function of the post-injection parameters. This way, the impact on the duration and the required fuel consumption of each stage is evaluated, making evident the variation in sensitivity to the post-injection timing and fuel mass rate of different engine operating conditions.
## 1 Introduction  
Potato peels are one of the most common vegetable by-products in Europe and are considered a zero-value waste in potato processing plants [1]. In the UK alone, potato waste accounts for 400,000 tons every year, and in Europe, this figure can reach 5 million tons [2]. Depending on the peeling process, waste can comprise up to 40% of the raw product mass [1], but the average waste produced in the processing of potatoes is 90 kg/ton (9%), apportioned as 50 kg of peels, 30 kg of starch, and 10 kg of other components, such as sprouts and roots [3]. However, potato peels contain a vast group of nutritionally and pharmaceutically interesting components, such as phenolic compounds, that can be used as natural antioxidants [4, 5, 6]. Phenolics have shown a plethora of benefits to the human health, such as antioxidant [7], antimicrobial, antivirus [8], and even anti-parasite properties [9], among others.  
Given such potential, the extraction of these compounds for valorizing an otherwise zero-value matrix is highly interesting from an industrial perspective [6, 10]. Phenolic extraction by conventional organic solvents is widely reported in the literature but, although efficient, the approach presents technological and environmental challenges, such as long extraction times, very diluted final extracts, the need for extra post-processing operations, and the toxicity and difficult disposal of some solvents [11]. Non-conventional technologies,such as microwave-, ultrasound-, enzyme-, and electric field-assisted extractions, as well as extractions via pressurized liquids, subcritical water, and supercritical fluids, have been able to circumvent many of these drawbacks and a growing number of works from the literature have reported on their application for extracting phenolic compounds from a variety of matrices [12], including potato peels [11; 13; 14; 15; 16; 17; 18].  
Given the fact that, to the best of our knowledge, there are no studies on the extraction of phenolics from potato peels by supercritical CO\({}_{2}\), the aim of this work was to assess and optimize this process via a central-composite design of experiments. The process parameters tested included extraction temperature and pressure, cosolvent concentration, and CO\({}_{2}\) flow rate, with responses being analyzed as total phenolic contents and antioxidant activities. Insights on possible causes to the observed trend of phenolic extractions by SFE usually being less efficient than other technologies were also discussed.
## 3 Results and Discussion
### Bibliometric Analysis of Research on Pavement Management (2000-2013)
#### 3.1.4 Distribution Analysis of Keywords  
The 30 most frequently used keywords appearing in the 727 considered documents are listed in Figure 2. The results indicated that \"pavements\" was the most relevant keyword, as it appeared in 483 papers (66.4% of the total amount of articles). Besides, \"pavement\" was selected as keyword by 125 papers (17.2%). Another pair of singular and plural forms appeared in Figure 2: \"pavement management systems\" appeared 212 times (3 rd place) and \"pavement management system\" 72 times, implying a joint contribution of 39.1%. The exact expression \"pavement management\", which was employed in the search engine, occupied the 2 nd place (in 252 documents). Nevertheless, other highly related expressions, including the word \"pavement\", were employed as keywords with a high incidence: \"pavement performance\" (75 times), \"pavement condition\" (73), \"concrete pavements\" (60), \"pavement maintenance\" (58), \"pavement overlays\" (56), \"flexible pavements\" (53), or \"asphal pavements\" (51). In addition, the analysis of the main keywords, which did not include the \"pavement\" word, supported the previously exposed idea: the improvement of the classical PMSs by incorporation of new optimization strategies to ensure a better planning and administration framework, as the presence of keywords such as \"maintenance\", \"highway administration\", \"decision making\", \"mathematical models\", or \"optimization\" demonstrated. A further and deeper comment of the most relevant research hot topics was carried out in the Section 3.2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ranking** & **Journal** & **Documents** & **Percentage (\%)** & **IF (2016)** & **SJR (2016)** \\ \hline
1 & Transportation Research Record & 152 & 20.9 & 0.522 & 0.557 \\
2 & Journal of Transportation Engineering & 58 & 8.0 & 0.962 & 0.774 \\
3 & Journal of Infrastructure Systems & 26 & 3.6 & 1.516 & 0.859 \\
4 & International Journal of Pavement Engineering & 21 & 2.9 & 1.832 & 0.824 \\
5 & International Journal of Pavement Research and Technology & 18 & 2.5 & - & 0.375 \\
6 & Advanced Materials Research & 16 & 2.2 & - & 0.120 \\
7 & Tongji Daxue Xuebao–Journal of Tongji University & 9 & 1.2 & - & 0.252 \\
8 & Better Roads & 9 & 1.2 & - & 0.100 \\
9 & Road and Transport Research & 7 & 1.0 & 0.021 & 0.175 \\
10 & Canadian Journal of Civil Engineering & 6 & 0.8 & 0.591 & 0.406 \\
11 & Computer Aided Civil and Infrastructure Eng. & 6 & 0.8 & 5.786 & 1.180 \\
12 & KSC Journal of Civil Engineering & 6 & 0.8 & 0.812 & 0.360 \\
13 & Journal of Computing in Civil Engineering & 6 & 0.8 & 2.31 & 0.739 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The top most productive journals.
## 3 The OpenFlow Protocol
### OpenFlow Specifications
#### 3.2.2 OpenFlow 1.1
The group types define which of them are applied. Four group types exist, and we illustrate the use of two of them. The group type \"all\" is used to implement broadcast and multicast. Packets of this group will be processed by all action buckets. The actions of each bucket are applied to the packet consecutively. For example, a group table entry with the type \"all\" contains two action buckets. The first bucket consists of the action \"forward to Port 1\". The second bucket consists of the action \"forward to Port 2\". Then, the switch sends the packet both to Port 1 and Port 2. The group type \"fast failover\" is used to implement backup paths. We first explain the concept of liveness for illustration purposes. An action bucket is considered live if all actions of the bucket are considered live. The liveness of an action depends on the liveness of its associated port. However, the liveness property of a port is managed outside of the OpenFlow protocol. The OpenFlow standard only specifies the minimum requirement that a port should not be considered live if the port or the link is down. A group with the type \"fast failover\" executes the first live action bucket, and we explain this by the following example. The primary path to a flow's destination follows Port 3, while its backup path follows Port 4. This may be configured by a group table with the first action bucket containing the  
Figure 5: OpenFlow pipeline. Figure 6: Group table entries for OpenFlow 1.1 and later. forwarding action towards Port 3 and a second action bucket containing the forwarding action towards Port 4. If Port 3 is up, packets belonging to this group are forwarded using Port 3; otherwise, they are forwarded using Port 4. Thus, the \"fast failover\" group type supports reroute decisions that do not require immediate controller interaction. Thus, a fast reroute mechanisms can be implemented that ensures minimum packet loss in failure cases. Fast reroute mechanisms, such as the MPLS fast reroute [24] or the IP fast reroute [25] can be implemented with OpenFlow group tables. As an optional feature, OpenFlow 1.1 performs matching of MPLS labels and traffic classes. Furthermore, MPLS-specific actions, like pushing and popping MPLS labels, are supported. In general, the number of supported actions for OpenFlow 1.1 is larger than for OpenFlow 1.0. For example, the Time-To-Live (TTL) field in the IP header can be decremented, which is unsupported in OpenFlow 1.0. OpenFlow 1.1 provides additional statistics fields due to the changed switch architecture. Controllers can query statistics for the group table and group entries, as well as for action buckets.
# Combined Use of LoRaWAN Medium Access Control Protocols for IoT Applications
## 6 Performance Evaluation
In this case, each end-device must individually manage its LoRa parameters. For this reason, we implement a custom algorithm that runs on each end-device instead of the ADR algorithm. The custom algorithm works as follows. During the configuration phase, a test application runs on a mobile end-device. This end-device moves in the sensing area and calculates the parameters that guarantee a message delivery ratio higher than a configurable threshold (e.g., 96% in our simulations) at multiple points, which correspond to multiple distances \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type of End-Devices** & **Case L** & **Case M** & **Case H** \\ \hline Stationary & ED with mean 120 s & ED with mean 60 s & ED with mean 30 s \\ Mobile & ED with mean 60 s & ED with mean 30 s & ED with mean 15 s \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Message generation patterns. from the GW. For example, in the simulations we evaluated distances from the GW in steps of 10 m (a configurable value in the simulator). Based on the findings of the algorithm described above, the network designer configures the mobile end-devices to switch their LoRa configuration according to the distance from the GW. The simulation time is 43,200 s, i.e., 12 h, to gather a significant amount of data. Moreover, each simulation is repeated ten times, varying the seed for random number generation to obtain statistically significant results. Table 7 shows the simulation parameters. In the following, we show and discuss the results of the evaluated scenarios. In particular, Section 6.1 presents the evaluation of a LoRaWAN network with 100 end-devices that use Pure ALOHA and 100 end-devices that adopt LBT AFA. Among the end-devices, 50% are stationary, while the other 50% are mobile. In the assessment in Section 6.1, we consider both the scenarios discussed above (i.e., SHC and SPC) and the channel configurations (A and B) under different traffic loads. The SHC scenario is the most challenging one, because the end-devices that use Pure ALOHA and those that use LBT AFA compete for the same channels. Furthermore, differently from the SPC scenario, the SHC one does not require to split in two different sets the channels that can be used by each of the two protocols, thus allowing the end-devices to use all the available channels. For this reason, both Sections 6.2 and 6.3 focus on the performance of the two protocols in the SHC scenario. In particular, Section 6.2 evaluates a LoRaWAN network with an equal number of stationary and mobile end-devices with varying percentages of end-devices that use Pure ALOHA and LBT AFA, respectively. Section 6.3 instead evaluates a LoRaWAN network with an equal number of end-devices that use ALOHA and LBT AFA with varying percentages of stationary and mobile end-devices, respectively. Finally, Section 6.4 summarizes the results obtained.
## 2 Study Site and Datasets
### Remote Sensing Data  
The primary remote sensing data for this study came from the Harmonized Sentinel-2 image collection in Google Earth Engine (GEE). Sentinel-2 MSI data have 13 bands covering the visible, near-, and mid-infrared parts of the spectrum. The MSI sensors provide data with 12-bit radiometric resolution with variable spatial resolution across the 13 spectral  
Figure 1: Study area: the red points indicate New York State lakes where in situ data were collected through the CSLAP program. Inset: New York State location in the USA.  
bands--four bands have 10 m GSD, six have 20 m GSD, and three bands have 60 m GSD. With a two-satellite constellation, Sentinel-2 imagery is acquired over a single site every five days. While Sentinel-2 data are available directly from the Copernicus Open Access Hub ([[https://scihub.copernicus.eu/](https://scihub.copernicus.eu/)]([https://scihub.copernicus.eu/](https://scihub.copernicus.eu/)), accessed on 19 September 2024), for convenience we used the Harmonized Sentinel-2 L2A data that are available in GEE. We used 26 Sentinel-2 granules for both 2019 and 2020, including 1086 images from 2019 and 1189 images from 2020 (May to October). Using GEE as a cloud-based platform facilitates the processing of a large volume of images without the need to download and store them locally. Starting from 25 January 2022, Sentinel-2 scenes with a processing baseline '04.00' or above have a shift in digital number (DN) values by 1000. GEE has a harmonized Sentinel-2 collection available that adjusts the newer data to match the DN range in the older scenes. This study also used the Cloud Score+ S2 Harmonized V1 dataset. We used these data to compare the influence of cloud mitigation approaches on model performance. This dataset includes two QA bands that score the cloud presence for each pixel [58].