esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Last Day (Ldc)--Last Week (Lwc) Consumption
#### 2.2.2 Temperature Data Set (T\({}_{\rm eff}\))  
The literature research shows that there is a very strong relationship between weather condition changes and the electricity consumption amount. Temperature changes directly affect electricity consumption [21]. The correlation between air temperature and load consumption for the period between 1 and 4 April 2013 is given in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Date** & **Time Period (Distance)** & **Load Consumption (MWh)** & \(\textbf{x}^{2}\) & \(\textbf{x}^{2}\textbf{y}\) \\ \hline
**25 December 2011** & 1 & 26,175 & 1 & 26,175 \\ \hline
**26 December 2011** & 2 & 24,386 & 4 & 97,544 \\ \hline
**27 December 2011** & 3 & 26,412 & 9 & 237,708 \\ \hline
**28 December 2011** & 4 & 26,493 & 16 & 423,888 \\ \hline
**29 December 2011** & 5 & 26,345 & 25 & 658,625 \\ \hline
**30 December 2011** & 6 & 26,463 & 36 & 952,668 \\ \hline
**31 December 2011** & 7 & 26,083 & 49 & 1,278,067 \\ \hline
**1 January 2012** & & **26,595** & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: 25 December 2011–31 December 2011 load consumption data.  
Figure 5: Calculated electricity load using the least square method and trend line for the period between 25 December 2011 and 1 January 2012.  
Figure 6: Comparison of normalized (0–1) hourly-based temperature and load consumption data between 1–4 April 2013.  
Usually, there is a linear correlation between temperature and load consumption. When temperature increases, load consumption increases synchronously. Temperature changes are crucial factors for forecasting but are not the only indicators to use.
# CNN for User Activity Detection Using Encrypted In-App Mobile Data
## 3 Proposed Methodology
### Image Classification Using CNN  
The CNN architecture proposed to identify in-app activities was composed of four main parts: model input, traffic feature extraction, prediction, and model output as shown in Figure 3.  
The input layer was then connected to a combination of convolution and pooling layers to learn image features. In this proposed architecture, three convolutional layers were used, where each convolution layer was followed by a pooling layer that selects the most important features from its receptive region and reduces the number of parameters required to train the model. Pooling layers reduce the size of the output through the convolutional process and cancel noise. In our model, the max-pooling function was used, which outputs the maximum value in a rectangular neighborhood of the previous layer.  
The fully connected layers at the end of CNN concatenated the output of the convolution layer into a dense vector. This was passed to the model prediction phase.  
The dense vector was then transformed into model outputs through a fully connected layer, which had 92 outputs indicating the number of classes (in-app activities) used to classify the input images. This output layer was a SoftMax layer, which output a K-dimensional probability distribution vector of values in the range of 0-1, where K is the number of classes (K = 92). Each node value represents a class score. The class with the highest score was selected and the corresponding class label was assigned.  
The hyperparameters, such as the number of convolutional layers, number of fully connected layers, number of filters for each hidden layer, filter and stride size, and activation functions, were selected through comprehensive tests involving numerous parameter combinations. The depth of the CNN should be neither too large nor too small [31], and thus it is able to learn complex relations while maintaining model's convergence. To determine the suitable value for the model's depth, different values from small to large were assigned to test the CNN model until the best model was found. The rationale behind  
Figure 3: Proposed CNN architecture to identify in-app activities. The traffic flow data transferred into pixelized images are used as the input to the CNN model with input dimension of \(28\times 28\).  
using this architecture was based on the experiments we conducted in which we discovered that this architecture was the best fit model for our problem.
# Dynamic Gesture Recognition System with Gesture Spotting Based on Self-Organizing Maps
## 3 Gesture Recognition System
### SOM-Hebb Classifier for Sequence Vector Classification  
The SOM-Hebb classifier classifies the sequence vector \(\overset{\rightarrow}{X}\) and identifies the input gesture. This classifier is the same one that was proposed in our previous work [3]. The SOM-2 in this classifier consists of \(M_{2}\times M_{2}\) neurons and \(2F\)-dimensional weight vectors are included in the neurons. The SOM-2 is trained in the same way as was explained in the previous section. Note that \(V_{2}(c)\) is the vector distance of the winner neuron's weight vector to the input vector that is the sequence vector \(\overset{\rightarrow}{X}\), and \(V_{2}(c)\) is used to implement the gesture spotting function.  
During the recall phase, the class to which the input vector belongs can be identified from the winner neuron of the SOM-2. Here, \(H\) represents the number of classes. The Hebb network generates its output \(\hat{\mathcal{C}}_{h}\) from the winner neuron. Each neuron represents a single cluster in the input vector space. Since a single gesture class may consist of combination of multiple clusters, multiple neurons must be associated to the single class in that case.  
Figure 3: Example of operation of the proposed system, **(A)** Input gesture frames, **(B)** Winner neurons in the SOM-1, **(C)** Development of the shift registers, **(D)** Vector distance \(V(c)\) of winner neurons in the SOM-2.  
Selection of the neurons belonging to the same class is done by a single layer feedforward network. This network is trained by the Hebbian training algorithm, which is a supervised training. During the Hebb training, training vectors with their class data are sequentially fed to the network. Every training vector makes one of the neurons the winner. If strong correlation is found between training vectors in class \(h\) and neuron \(j\), then the neuron \(j\) is assigned to the class \(h\). In practice, the class of the input vectors with which the neuron \(j\) won the most, is associated to the neuron.  
The SOM-2 must have appropriate number of neurons for the SOM-Hebb network to work properly. It happens that some neurons may have no connection to any gesture class. Obviously, the selection of such neuron as the winner in the recognition phase causes false recognition. To avoid this situation, neurons without connections to class ID are culled. The culling replaces the weight vectors of these neurons with huge vectors so that they never win.
# XBeats: A Real-Time Electrocardiogram Monitoring and Analysis System
## 5 Experimental Results and Discussions  
This section presents the evaluation techniques and tests to verify and validate all the components in the XBeats platform. These include measurements about the operation modes, useful sampling rates, and energy consumption analysis concerning the hardware constraints. The hardware constraints in each of the following experiments are mainly related to the data acquisition time constraints, the quality and correctness of the acquired ECG signals, the accuracy of the classification algorithm, and the overall battery lifetime. The experiments are organized in the same order we presented the XBeats platform in terms of the functional components. Therefore, the first experiment evaluates the ECG data acquisition under different operation modes while running the data logging subroutine, followed by data transmission using BLE. Then, we assess the ECG data classification module, and, lastly, we determine the analytical energy consumption of the ECG patch over time. But, first, we explain the data sources used to conduct our experiments. The experimental setup consists of the following steps:  
1. Collect ECG data in real-time using the prototype hardware of the ECG patch. The experiment includes acquiring ECG data at different modes of operation: standard 12-Lead ECG data under the \"continuous\" mode of operation; one and three ECG leads under the triggered mode of operation; standard 12-lead ECG data under the offline mode of operation;  
Evaluate the effective ECG data sampling rate compared to the theoretical data acquisition values provided by the analog to digital converter. The evaluation is performed on each of the operation modes above;
3. Evaluate the proposed ECG classification service implemented on an edge node. The evaluation steps include comparing the accuracy and processing time of six different techniques, which is concluded by the selected classification techniques for our edge classification service; and
4. Calculate the power consumption footprint and the energy-saving of applying the triggered operation mode while activating the edge classification service.
## 4 Discussion
### Implications and Future Steps  
This study combined MODIS and Landsat imagery to examine the impacts of climate and management interactions on grassland phenology and productivity. The method can be utilized to study field-scale management and climate impacts in other regions. With the auxiliary climate data and management records, assessment of the impacts of specific climate events (e.g., drought) and management (e.g., burning, grazing, or baling) on grassland phenology and productivity can be accomplished. It also allows for inspection of the interactions of climate and management. Combining MODIS and Landsat imagery to study localized disturbances on vegetation can offer new insights for the understanding of the extent, type, and consequences of disturbances.  
Intensively managed grassland (both native prairie and introduced pasture) is a major forage source for beef cattle in the region. Thus, higher productivity in grasslands is of great importance to the beef industry. By utilizing satellite data, climate data, and management records, this study demonstrated that the interactions of climate and management can significantly affect forage productivity. Ranchers should consider both management and climate when trying to maximize forage productivity. The drought plus management in the same year caused the lowest production (Table 3), which indicates the necessity of drought preparedness. Adjusting management strategies by considering climate conditions is a better practice than a static or predefined management plan [67].  
As demonstrated in the results, the interactions of climate and management are intricate and dynamic. Further observations over a longer period are necessary to develop effective strategies for forage production in different scenarios. Multisite analysis, such as using data from FLUXNET and AmeriFlux [68], can help understand the interactions in a large geographic domain and potentially produce more general discoveries. MODIS-derived NDVI and EVI were used to calculate phenological metrics. The mixed pixel problem could affect the accuracy of the phenology metrics. Ground observations such as in situ PhenoCam digital images can be used to evaluate them. Moreover, one can fuse the Landsat and Sentinel-2 data to reduce the impacts of the mixed pixel problem. However, the interactions of climate and management could happen fast (e.g., rapid green-up after burning and rainfall). Evening fusing of two optical sensors might miss the critical detecting window [69].
# Development of Cementitious Mortars for Aerial Additive Manufacturing
## 3 Results
### Mechanical Tests--Strength and Creep  
The 28-day compressive and flexural strengths for mixes \(A\)-\(E\) are shown in Figure 5a and b, respectively. Specimens were tested to failure and the graph shows the mean results with standard deviation. Mix \(D\) performed well in mechanical tests and showed the highest compressive strength, consistently in excess of 60 MPa and capable of rising above 70 MPa. Mix \(C\), with the polyol resin achieved a lower compressive strength of just below 30 MPa. Mix \(E\), with the limeX70, remained competitive despite requiring a significantly higher water:binder ratio of 0.47 for suitable workability. Coefficients of variation for compressive strength ranged from 4% - 8%.  
There is a direct positive correlation between compressive strength and density. For mortar mixes \(B\)-\(E\), it can be ascertained that as compressive strength increases, workability decreases, with cement paste mix \(A\) an exception to this statement.  
Flexural specimens exhibited variation in strength, with coefficients of variation ranging from 8% (mix B) to 23% (mix D). Specimens failed in a sudden, brittle manner, breaking into two. An exception to this was the fibrous Mix \(D\), where specimens were held together by the presence of the polypropylene fibres as shown in Figure 5c, preventing them from fully breaking into two. This contrasts with the broken specimens of the other mixes shown in Figure 5c.  
The results of the creep rig showing strain over a period of 50 days, along with the recorded environmental variations in temperature and humidity, are shown in Figure 5d and e, respectively. Whilst there was minor difference in temperature, there was significant variation in humidity and this has had a clear impact upon all samples, with a rise in humidity corresponding with expansion in the cylindrical specimens. The cement paste mix \(A\) varied significantly with humidity, while mix \(C\), with the added polyol resin, exhibited the least variation with humidity but the greatest overall strain. Mix \(D\) with the added polypropylene fibres exhibited the least strain. For all five mixes, there is a direct negative correlation between deformation and density (Table 4).
# An Efficient Approach for LBS Privacy Preservation in Mobile Social Networks
## 2 Related Work
### Architectures of LBS Privacy Preservation  
There are two main categories proposed by most recent works for protecting privacy in LBS from the perspective of the system architecture: TTP-based approach [19] and TTP-free approach [20]. TTP-based approach takes an anonymizer as a trusted third part (TTP), and the anonymizer usually plays a role to confuse LBSP. The anonymizer often adopts some technologies, such as Cloaking and Dummy, to protect users' information before sending the requests to LBSP [21,22]. TTP-free approach does not rely on a trusted third part, users usually adopt some methods, such as P2P [23,24] or encounter scheme [5,25], to achieve k-anonymity, and users' information is often exchanged to confuse LBSP. It is really hard to say which of the two approaches mentioned above is better. TTP-based approach can be applied to almost all scenarios no matter users' distribution. However, the disadvantage is also obvious, that is the third part can be the single point of failure and the bottleneck of the whole system. The amount of private information exposed to a third part can be decreased by adopting TTP-free approach. However, due to the limitation of communication range of users' mobile devices, TTP-free approach may not satisfy the requirement for every mobile user. But with the development of smart mobile and communication technology, the application of TTP-free approach would be more extensive due to the advantages of distributed architecture.
# Measuring the Lense-Thirring Orbital Precession and the Neutron Star Moment of Inertia with Pulsars
## 3 Evolution of Binary Pulsars
### Suitability for Measurements of LT Precession  
Despite the better timing precision of MSPs, and their much larger angular momenta (and, thus, potential LT effects), DNS systems are preferable laboratories for testing the LT effect. This is because the small eccentricities observed in the vast majority of MSP-WD systems (10\({}^{-7}\) to 10\({}^{-4}\)) make it impossible to measure \(\dot{\omega}\) precisely and measure the LT contribution to that effect. Furthermore, as discussed above, the spin of the recycled pulsar and the orbital angular momentum remain aligned, resulting in a null value of \(\dot{x}^{\rm LT}\). Therefore, MSP-WD systems are generally not suitable for detecting the LT precession. An exception is PSR J1141\(-\)6545, which has undergone a different evolutionary scenario, resulting in a slow pulsar spin, a relatively high eccentricity, and a fast spin of the massive WD. This is discussed in more detail in Section 4.1.1.  
However, some fast-spinning MSPs might still be useful for these purposes. Because of the very high stellar densities of globular clusters, an MSP in that environment can exchange its low-mass WD companion with a much more massive companion (see, e.g., [71; 72]). The resulting orbits will generally be very eccentric and misaligned with the spin of the MSP. This and the high-precision timing of MSPs could make such systems promising for measuring the relatively large \(\dot{x}^{\rm LT}\) that results from their large spins, especially if the systems are compact. Things become even more interesting if the massive companion is a fast-spinning BH--a possibility in the case of the NGC 1851E binary MSP, discussed in Section 5--then the LT precession would be a promising way of measuring the spin of the BH companion.  
Finally, we note that the aforementioned binary pulsars with a main-sequence star companion are inadequate for this sort of measurement. First, because they are slowly spinning normal pulsars, their timing precision is generally much worse than recycled pulsars. Second, those systems are also much wider, making the detection of relativistic spin-orbit coupling impossible even with high-precision timing. Third, any relativistic spin-orbit coupling would originate from the much larger angular momentum of the main-sequence star. Finally, in those systems, the classical spin-orbit coupling and tidal effects are many orders of magnitude larger than the relativistic spin-orbit coupling [73], and have been detected in the timing [64; 74].
# Sampling Rate Impact on Precise Point Positioning with a Low-Cost GNSS Receiver
## 2 Methodology  
The methodology applied for this research is presented in Figure 1. First, the observations with the low-cost GNSS receiver and the geodetic receiver were made at different dates, but it was considered that the same day and time should be used with the aim to maintain similar conditions. Second, the value of the signal-to-noise ratio (SNR) and multipath (MP) were extracted to inspect the intensity of the received signal and the influence of the multipath effect on the observations at a sampling frequency of 0.1 and 0.2 s (original sampling rate) for the low-cost GNSS receiver and geodetic receiver, respectively. Likewise, the treatment of the RINEX [34] files was performed with TEQC (translate/edit/quality check) software [35] in order to obtain different sampling rates for the proposed comparative analysis. Third, the data were processed using the PPP method in the online software CSRS-PPP [36; 37] considering the static and kinematic mode according to the observation epoch. Finally, the comparison was made considering the difference between the precise coordinates obtained from a previous survey campaign [20], processed using the Trimble Business Center software [38], and estimations derived from the CSRS-PPP software. Coordinates obtained from a previous survey from a geodetic receiver were taken as a reference, wherein the lower the difference the better the behavior was deemed to be [21]. Additionally, the precision reached was considered for the comparison.
# Multi-Source Satellite Imagery and Machine Learning
## 5 Experiments and Results
### Combination of Geographic Parameter Data and OLI Spectrometric Parameter Data (DGP-DOLIP)  
Several algorithms were used to evaluate the combination of geographic parameter data with OLI spectrometric parameters. Table 8 presents the performances of these algorithms according to different criteria.  
The results show that XGBoost and Random Forest achieve the best performance, with an accuracy of 51.53%. XGBoost stands out slightly with an MCC of 40.35%, while Random Forest achieves an MCC of 40.28% while offering the lowest execution time (3.16 ms). While performing well, Gradient Boosting shows slightly lower accuracy (50.51%) and an MCC of 39.14%, with a higher execution time (14.08 ms).  
These results demonstrate the effectiveness of combining OLI geographic and spectrometric parameters for classification tasks. The XGBoost algorithm achieves optimal performance, and Random Forest offers remarkable speed.
## 4 Experiments and Results: Performance Analysis  
This section is dedicated to a rigorous empirical assessment of the proposed framework, thus specifically examining its efficacy under a gamut of operational scenarios.  
To facilitate an exhaustive evaluation, a prototype of the proposed system has been instantiated. This subsection delineates the experimental apparatus, thereby encapsulating both hardware and software configurations. Furthermore, we elucidate the methodologies employed for data acquisition and specify the evaluation metrics chosen to quantify the system's performance.
## 3 E-barter Case Study  
In this paper, the design and the implementation of SWS-enabled agent-based E-barter system were realized using JACK agent language [20] and OWL-S SWS technology [29].  
JACK is a BDI oriented MAS development language providing a framework in Java. It is a third-generation agent platform building on the experiences of the Procedural Reasoning System (PRS) [34] and Distributed Multi-Agent Reasoning System (dMARS) [35]. JACK is one of the MAS platforms that uses the BDI software model and provides its own Java-based plan language and graphical planning tools.  
OWL-S (Semantic Markup for Web Services) enables the discovery, invocation, interoperation, composition, and verification of services. It builds on the formerly developed DAML-S [36] and was the first submission for describing SWS submitted to the W3C. Each SWS in OWL-S consists of a service profile, a service model, and a grounding. The service profile describes what the service does and is used to advertise the service. The service model answers the question \"how it is used?\" and describes how the service works internally. Finally, the service grounding specifies how to access the service. OWL-S is based on the Web Ontology Language OWL [37] and supplies web service providers with a core set of markup language constructs for describing the properties and capabilities of their web services in an unambiguous, computer-interpretable form.  
The following subsections discuss the details of analysis, design, generation, and implementation of E-barter case study using the detailed methodology proposed in this study and benefiting from SEA_ML platform.