esa-sceva/satcom-chunk-collection huggingface dataset:
# An Observation Scheduling System for Radio Telescope Array
## 3 The Overall Architecture of the System
The corresponding calibration sources visible during the specified time period will appear. When creating the observation plan, include calibration sources in the target source's surrounding area to calibrate the flux of the target source. Located at the bottom right corner of Figure 2 is the \(uv\) coverage module. To operate this module, an initial setup of observation parameters is required, including details about the telescope array, target information, and the range of observation times. Once  
Figure 2: The overall architecture and interface of the radio sources observation prediction system. the parameters are set, users can click the \"Mapping\" button within the \(uv\) coverage module to generate the \(uv\) coverage map for the target over the specified time period as observed by the telescope array. Concurrently, the system will produce \(uv\) data, which is then exported in a TXT file format. These data are essential for calculating the coverage ratio of the observation. By comparing the coverage ratios across various telescope array configurations, the optimal setup can be determined. Details of the implementation of each module are shown below.
## 3 Theory
### Multilayer Perceptron Artificial Neural Network (MLP-ANN) Model  
The multilayer perceptron artificial neural network (MLP-ANN) model is a versatile and powerful deep learning architecture widely used for regression and classification tasks. It consists of multiple layers of interconnected neurons, where each neuron receives input from the previous layer, applies a transformation function, and passes the result to the next layer [40]. MLP-ANN is capable of learning complex patterns and non-linear relationships within the data, making it well suited for modelling intricate datasets. The predicted output value using an MLP-ANN model can be formulated as follows:  
\[y_{i,\,predicted}=f_{out}\Big{(}W^{(2)}.f_{hidden}\Big{(}W^{(1)}+b^{(1)}\Big{)} +b^{(2)}\Big{)} \tag{23}\]  
where \(y_{i,\,predicted}\) represents the predicted value for the \(i\)-th data point, \(x_{i}\) denotes the input features for the \(i\)-th data point, \(f_{hidden}\) and \(f_{out}\) are activation functions applied to the hidden and output layers, respectively, \(W^{(1)}\) and \(W^{(2)}\) are weight matrices connecting the input to the hidden layer and the hidden to the output layer, respectively, and \(b^{(1)}\) and \(b^{(2)}\) are bias vectors for the hidden and output layers, respectively.  
MLP-ANNs are trained using an optimization algorithm such as stochastic gradient descent (SGD) or its variants, which iteratively adjusts the weights and biases of the network to minimize a loss function. The loss function measures the discrepancy between the predicted and actual values, and the optimization algorithm seeks to find the optimal set of parameters that minimizes this discrepancy. MLP-ANNs offer several advantages, including their ability to learn complex patterns and relationships in the data, their flexibility in handling various types of data, and their scalability to large datasets. However, they require careful tuning of hyperparameters such as the number of hidden layers, the number of neurons per layer, and the choice of activation functions to achieve optimal performance.
## 4 Experiments and Results
### Results
#### 4.4.4 Use-Case #1  
For the initial application case, we obtained images from the study conducted by [PERSON] et al. [57]. The serial images, depicting the measurements of a Wagner grade 2 wound with progressive healing in a diabetic patient (A-C), are presented in Figure 11.  
The performance of our model was evaluated using cosine similarity and Euclidean distance metrics, as illustrated in Figures 12 and 13, respectively. Upon processing image A, the model identified it to be highly similar to infection, with a cosine similarity score of 0.81 and a semantic similarity score of 0.41 using Euclidean distance. In both cases, image C was deemed similar to the anchor class \"none and healthy\".  
While the results seem obvious between images A and C, this is not the case for images A and B.  
While the radar plot gives a good visual and quick understanding of disease progression, we need to closely analyze the detailed results of both similarity metrics we are using.  
From Table 3, we observe that Image A, upon arrival, has a similarity score of 0.81 with the infection class, which aligns with the description of the wound. However, for Image B, the similarity score shows less similarity with the infection class and higher similarity with both the healthy and ischemia classes. After 12 weeks of treatment, Image C clearly exhibits similarity to neither infection nor ischemia but rather to the healthy class. Similar trends can be seen in Table 4.  
Figure 11: DFU images for Use-Case #1 [57].  
Figure 12: Longitudinal similarity for Use-Case #1: cosine similarity.  
Figure 13: Longitudinal similarity for Use-Case #1: Euclidean distance.
## 3 Results and Discussion
### Focus on Informative Targeted Analyte Signatures
For example, HSSE-TW2 achieves unit values for C6-C9 and C12, and is one of the best performing devices for these analytes. For higher  
Figure 4: Heat-map illustrating concentration factor (CF) values for the alcohol series (**A**) and unsaturated aldehydes (**B**) relative to the selected devices/approaches, with solid-phase microextraction SPME-TRIF being taken as the reference. homologs in the series, C12-C17, the highest relative concentration capacity was shown by DHS-PDMS. Interestingly, opposite trends are observed with SPME-TRIF, which discriminates this series in favor of the most volatile species, and DHS-PDMS, which better enriches the less volatile members (C13-C17). For the alcohols series (Figure 5B), the most effective approach to enrich the higher homologs, from C9 to C14, was HSSE-TW2. Complementary behavior is shown by MMSE-ODS/GC and DHS-PDMS; they effectively enrich alcohols from C2 to C8. SPME-TRIF represents this chemical class well and displays less discrimination than observed for aldehydes. Fatty acids (Figure 5C), within the C2 to C10 range, were well characterized by HSSE-TW2, which maximized the extraction for those with lower volatility. On the other hand, volatility discrimination is evident in the SPME-TRIF profile. These experimental results demonstrate how high concentration-capacity (HCC) HS can provide useful information on olive-oil volatile-fraction compositions and also enable trace and ultra-trace  
Figure 5: Relative concentration capacity of each device/approach towards selected analyte classes (saturated aldehydes—(A), alcohols—(B), and fatty acids—(C)) with the most effective approach being taken as the reference. analytes to be quantitatively recovered. However, as demonstrated by observing CF trends in absolute and relative terms, this information is partial and can only be adopted for cross-sample analyses. Any conclusion about the quantitative distribution of volatiles in the sample would be erroneous if not obtained via accurate quantitation methods [42; 43]. To quantify volatiles from a complex matrix, standard addition (SA) or multiple headspace extraction (MHE) should be adopted. These methods require headspace linearity conditions [44], for all targeted analytes, meaning that no saturation effects should compromise result accuracy. The next paragraph will focus on the evaluation of headspace linearity for a selection of potent odorants after SPME-TRIF sampling in the selected time-temperature conditions. Considerations about internal standard (IS) quantitation will also be discussed as this approach is widely, although often erroneously, adopted in several applications.
# SpaceSheep: Satellite Communications for Ovine Smart Grazing
## 3 Results
### Alarm Encoding Evaluation  
The alarm encoding evaluation focuses on the assessment of three serialization formats: Apache Arvo, MessagePack, and Protocol Buffers. For each existing type of alarm, a varied number of messages were generated through an alarm simulator created in the gateway. The maximum number of messages simulated for the two types of devices (collars and beacons) was different because a real system uses a reduced number of beacons to cover the pasture area while monitoring a herd that is typically composed of a greater number of animals.  
The tests were repeated 10 times for each combination, and the average was registered with a 95% confidence interval using a t-distribution. The performance of the encoding APIs was measured with respect to the message size through a network sniffer. Figure 3 demonstrates the encoding message sizes for each of the alarm messages supported by the system in the three encoding APIs under investigation.  
The test in the previous section revealed that using MessagePack is an adequate format to encode the information forwarded to the cloud-hosted application. Moreover, experiences show that the evolution of the system's latency in alarm message transmission using the different APIs follows the encoding efficiency. As a result, the alarm types that produce larger messages also take longer to transfer information.  
Figure 3: Analysis of the size of generated information for different alarms and encoding APIs.  
Figure 2: System alarm dispatch latency for several hours of WiFi network (**left**) and satellite network (**right**).  
Once the greater efficiency of the MessagePack API was verified, the JSON encoding API was replaced by the MessagePack API, and the tests for transferring gateway-generated alarms were repeated. To assess the impact of these changes, two metrics were measured: the volume of the traffic produced, and the corresponding latency.  
The test conditions were similar to those of the initial tests in terms of the number of alarms and the distribution of alarm types, yet the periodicity messaging was set to 60 s. These results were compared to the results obtained before the system optimization and are presented in Figure 4. Unsurprisingly, the alarm dispatching latency followed the message volume for both encodings.  
The monthly costs associated with the transmission, considering the transmission price for one MB, are represented in Figure 4 as hundreds of euros per month, and grow, as expected, alongside the size of the herd being monitored.
# A New Compact Octagonal Shape Perfect Metamaterial Absorber for Microwave Applications
## 3 Results and Discussions
### Analysis of Design Structure with Different Thickness of Metallic Materials  
Thickness of the resonator and ground plane of the PMA has an effect on the absorption of electromagnetic energy. Different thickness like 0.1 mm, 0.2 mm, 0.3 mm, and 0.4 mm have been analyzed in the design structure.  
The variation of absorption is shown in Figure 8. The absorption values of the structure are 99.04% at 8.32 GHz and 96.98% at 11.59 GHz, respectively, for metallic material with 0.1 mm thickness. Likewise, the values of the absorption are 97.96% at 8.66 GHz, and 97.39% at 11.89 GHz, correspondingly for 0.2 mm thickness. In addition, absorption values are 96.70% at 9.01 GHz and 99.71% at 12.22 GHz, respectively, for 0.3 mm thickness as well as 99.04% at 9.11 GHz and 99.92% at 12.43 GHz, respectively, for 0.4 mm thickness of copper metallic material. The highest absorption peaks are 99.04% at 9.11 GHz and 99.92% at 12.43 GHz for 0.3 mm thickness of metallic material. Due to different thickness of metallic material, the amount of absorption has been different at different frequencies. The thickness of metallic material with 0.3 mm has shown the maximum peak. However, the minimum peak has been achieved from metallic material with 0.2 mm thick. However, the variation of absorption has occurred in a small amount.
## 3 Retrieval
### Setup
#### 3.1.8 A Posteriori Error Covariance Matrix \(\hat{\mathbf{S}}\)  
Once convergence is achieved, the a posteriori error covariance matrix includes the a posteriori uncertainties of the retrieved state vector elements and their correlations. It has a dimensionality of \(n\times n\).
## 5 Conclusions  
This study aims to improve communication quality and energy efficiency in wireless communication systems under dynamic channel environments. Traditional methods, such as equal power allocation and CSI-based proportional allocation, often struggle to optimize power distribution under uneven or rapidly changing channel conditions. These limitations lead to decreased communication stability and increased unnecessary energy consumption, constraining overall communication performance. To address this issue, this paper proposes a CSI-based nonlinear power allocation method. By dynamically adjusting power distribution according to channel conditions, the proposed method adapts to channel variations, ensuring more stable communication while enhancing throughput and energy efficiency. Experimental results demonstrate that, compared to conventional methods, the proposed approach significantly improves communication performance in dynamic environments and effectively reduces overall energy consumption. However, this study has certain limitations. Future research directions include verifying the applicability of the proposed method under more diverse mobility patterns and complex channel conditions, as well as improving its implementation efficiency in real-time environments. The findings of this study contribute to the advancement of next-generation wireless communication technologies and are expected to be further refined and enhanced in the future.  
[PERSON] is the main author. [PERSON]-[PERSON], [PERSON] and [PERSON] contributed to the methodology, software, and constructive feedback on every part of the manuscript. [PERSON] is the supervisor. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Data Availability Statement: Data are contained within the articles. Conflicts of Interest: The authors declare no conflicts of interest.
# A 1 km Global Carbon Flux Dataset Using In Situ Measurements and Deep Learning
## 2 Materials and Methods
### Target Carbon Fluxes and Predictors
#### 2.1.3 Reanalysis Data  
The reanalysis data for the meteorological variables were retrieved from the ERA5-Land dataset (Land component of the fifth generation of European Reanalysis) with the spatial resolution of 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\)[33]. Data from January 1999 to June 2020 were used in this study. In accordance with remote sensing data, the original data with a three-hour time step were aggregated to the 10-day average. We processed the ERA5 data into 1 km resolution using the nearest neighbor method.
# Geo-Spatial Context Provision for Digital Twin Generation
## 2 CPS-Based Vaccine Logistics
### Using LiDAR Technology for Obtaining Geo-Spatial Context  
Assuming the intended use case, the layout of the room is known in advance. Thus, the positions of the different navigation way-points in the room and the locations that objects are picked up from or placed down at are known (with the robot keeping track of the status of each shelf position). The movement of the robot between the different way-points can thus be pre-planned, allowing for the avoidance of complex SLAM tasks.  
For situations requiring the robot to actively be aware of contextual information pertaining to its surroundings, the scanner equipped on the robot is used to obtain the current geo-spatial status of its operational environment. This allows to adapt the robot's behavior if objects are not positioned as they should be (or missing altogether) or if obstacles appeared in its path.  
During the movement between the different way-points an unexpected obstacle could block the robot. In this case, the robot is required to navigate autonomously around it, or to notify the person in charge of overseeing the process so that they may intervene. Similarly, when reaching a way-point, such as standing before a shelf, adjustments in the robots positioning could be needed to ensure that it is in the right position to interact with an object that is not placed properly. These adjustments can then be computed based on scan data. Different object detection approaches for point clouds have been proposed in the literature and can be adapted for such a task (see, e.g., approaches such as PointNet [24] and its successor PointNet++ [25], VoxelNet [26], or the approach described in [27]). Once objects in the robot's environment have been detected and classified, their distance and position in relation to the robot can be computed to determine its actions for re-positioning itself to be in the right place to interact with the object. In case re-positioning proves impossible (due to an object being placed in such a way that the robot cannot align itself to execute the intended interactions), a human needs to be called for assistance and restoring the operational environment.
# Impact of CoAP and MQTT on NB-IoT System Performance +
## 1 Introduction  
The core idea of the Internet of Things (IoT) is to use the Internet Protocol (IP), mainly IPv6, to communicate between connected devices and services in the cloud. A plethora of standards addressing the IoT on different levels of the protocol stack are available today and may be combined in different ways.  
The IoT umbrella covers many use cases. Here we focus on use cases where small data transmissions are expected from and to sensors and actuators. Such use cases are for example connected lighting, connected home sensors and actuators, smart metering, smart agriculture, etc.  
Most of the popular IoT applications today use either the Internet Engineering Task Force (IETF) standardized Constraint Application Protocol (CoAP) or the OASIS specified Message Queue Telemetry Transport (MQTT) as the data transfer of choice. Other examples of IoT application layer transfer protocols are the Hyper Text Transfer Protocol, Data Distribution Service and Advanced Message Queuing Protocol [1].  
The requirements of the use case set boundary conditions to the reliability and latency of the communication link. If the use case involves collecting large amounts of sensor data for statistical purposes, and the traffic is mostly from the sensor to the cloud, it may be acceptable to lose occasional packets. Even in such scenarios, however, reliable transport may be required on occasion when firmware updates or security patches are done on devices.  
The physical layer and the medium access control, i.e., L1 and L2, may differ greatly depending what kind of access technology is used. In the end to end path of an IoT application data flow, theremay be fast fiber links, Ethernet cabling, as well as radio access links over a cellular network. In this paper, we focus on the low power wide area technologies and especially to cellular network links realized with the narrowband IoT (NB-IoT) technology. NB-IoT is one of the cornerstone massive IoT technologies forming the baseline for 5G. As part of the 5G technology family, NB-IoT systems always have support for IP based traffic.  
Other low power wide area technologies are, for example Sigfox and LoRa. Both Sigfox and LoRa differ from NB-IoT in that they run over the unlicensed spectrum while NB-IoT runs over licensed spectrum. Both technologies have an optimized last hop where the use of TCP, or even IP, is not common. This has been identified by the Internet Engineering Task Force (IETF) where work is currently ongoing to define header compression to enable better IP support [2].  
In this work, we study how the combination of different higher layer stacks impact the NB-IoT system performance with the hypotheses that CoAP will perform better than MQTT.
# EvoSplit: An Evolutionary Approach to Split a Multi-Label Data Set into Disjoint Subsets
## 3 First Approach: Single-Objective Evolutionary Algorithm
### Results  
Next, this work presents a comparison of the performance of the proposed evolutionary approach with other alternatives to split a data set into disjoint subsets, i.e., random and stratified (SOLS) (The stratified alternative has been obtained using the algorithm provided by scikit-multilearn [35].). Similarly to the literature [12; 13], this work has evaluated the different methods considering 10-fold cross-validation of well-know multi-label data sets. For the evolutionary approaches, the parameters have been selected experimentally:  
* Size of the population (\(n\)): 50
* Individuals created by crossover (\(c\)): 10
* Individuals created by mutation (\(m\)): 10
* Number of generations without changes in the best individual (\(gen_{max}\)): 25  
The splitting has been carried out using both the Label Distribution and the Label Pair Distribution as fitness functions. For each of these alternatives, results have been obtained without and with the constraint presented in Section 3.2 that tries to ensure that, if possible, all folds have examples for all the labels. For each alternative, given the probabilistic behavior of evolutionary algorithms, the best result of five runs of the algorithm has been selected. Table 3 shows the results in the case of using the Label Distribution as fitness function. In this case, the optimization algorithm tries to create subsets in which the proportion of examples of each class is close to that in the complete data set. In both cases, whether considering or not the constraint, the evolutionary approach obtains better results than any other method. Something similar happens (Table 4) when the Label Pair Distribution is employed as fitness function, i.e., the algorithm tries to approximate theproportion of label pairs. However, in most cases, when the evolutionary algorithm tries to improve the distribution of single labels (by using \(LD\)) fails to distribute label pairs better than the Stratification method. Something similar happens when the fitness function is the \(LPD\) measure and the results are measured in terms of \(LD\). It is worth mentioning that the Stratification method does not consider the desired number of samples per fold as a hard constraint. Therefore, the final sizes of the subsets might deviate from the pre-established ones, as measured by the Examples Distribution and shown in Table 5. This is not allowed for the evolutionary approaches. Following [12], besides using the Label Distribution and the Label Pair Distribution, the result of each alternative is also measured (see Tables 6 and 7) in terms of the number of folds that contain at least one label with zero positive examples (\(FZ\)), and the number of fold-label pairs with zero positive examples (\(FLZ\)). The \(FLZ\) measure shows the effect of introducing the constraint of ensuring that subsets contain, if possible, one example of each label. In the constrained version of EvoSplit, the \(FLZ\) values are lower than with all the other methods.