esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Fundamentals of n-Pulse Scheme
### Demodulation  
For the use of this particular modulation technique at the transmitter, where each symbol has a different number of pulses, we expect to have a different number of events at the receiver side depending on the transmitted symbol. This is likely due to the response of the DVS camera, which is sensible to brightness changes as explained in Section 3. Therefore, depending on the number of events received, the data would correspond to a specific transmitted symbol. To illustrate the above, in Figure 5, we show the event distribution of positive events at the receiver side for two transmitter frequencies, 10 Hz and 100 Hz, and four different symbols (from 1 to 4 pulses in each symbol period).  
To construct the event distribution, we repetitively transmit each symbol and, at the receiver, count the occurrences of positive events during each symbol period. Subsequently, we normalize the received event count by the total number of transmitted symbols. We can note that depending on the number of received events, we can decide if the data correspond to a specific symbol. For a frequency of 10 Hz, if the received events are between 12 and 14, the transmitted symbol is likely to be symbol 1. In the same way, if the received events are between 30 and 35 events, the transmitted symbol is likely to be symbol 4. In the example with a transmitter frequency of 100 Hz, the number of pulses at the received side is the same as the transmitted symbol, meaning that if the demodulator counts 1 pulse, the received symbol would be symbol 1, and so on. These meaningful results imply that the candidate demodulator would only need to count the number of events in every period of the symbol time to estimate the transmitted symbol. In practice, counting transitions of events (from positive to negative) yields more accurate results. This straightforward method proves to be effective as long as the distribution of events on the receiver's side is distinguishable and separable.  
As a consequence, when symbols are completely distinguishable in the distribution space at the receiver's end, the demodulator will confidently classify each set of samples (events received in a symbol period) as one of the possible symbols with high probability enabling effective data demodulation. However, in cases where symbols overlap, the demodulator will classify samples with low probability, resulting in symbol error classification, and degrading the performance of the OCC link. This particular scheme reduces the demodulator complexity and energy consumption, a necessary condition for implementing OCC in real-time or constraint systems.  
Figure 5: Distribution of the events on the receiver side for different transmitted symbols, and frequencies; 10 Hz (**top**) and 100 Hz (**bottom**). In both figures, it can be seen that all symbols are completely separable on the received side.
# Comparing Commit Messages and Source Code Metrics for the Prediction Refactoring Activities
## 3 Research Methodology and Conduction
### Data Preprocessing  
After importing data as panda dataframes, data are checked for duplicate commit IDs and missing fields. To achieve better accuracy, data with duplicate values and missing values should not be considered for further analysis. We also normalized the metric values using standard deviation, randomized the dataset with random sampling, and removed null entries. Since we are dealing with commit messages from VCS, text preprocessing is a crucial step. For commit messages to be classified properly by the classifier, they need to be preprocessed and cleaned, and converted to a format that an algorithm can process. To extract keywords, we have followed the steps listed below:  
_--Tokenization:_ For text processing, we used NLTK library from python. The tokenization process breaks a text into words, phrases, symbols, or other meaningful elements called tokens. Here, tokenization is used to split commit text into its constituent set of words.  
_--Lemantization:_ The lemmatization process replaces the suffix of a word or removes the suffix of a word to obtain the basic word form. In this case of text processing, lemmatization is used for part of the speech identification and sentence separation and keyphrase extraction. Lemmatization provided the most probable form of a word. Lemmatization considers morphological analysis of words; this was one of the reason of choosing it over stemming, since stemming only works by cutting off the end or the beginning of the word and takes list of common prefixes and suffixes by considering morphological variants. Sometimes this might not provide us with the proper results where sophisticated stemming is required, giving rise to other methodologies such as porter and snowball stemming. This is one of the limitations of the stemming method.  
_--Stop Word Removal:_ Further text is processed for English stop words removal.  
_--Noise Removal:_ Since data come from the web, it is mandatory to clean HTML tags from data. The data are checked for special characters, numbers, and punctuation in order to remove any noise.  
_--Normalization:_ Text is normalized, all converted into lowercase for further processing, and the diversity of capitalization in text is remove.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Refactoring Classes** & **Count** \\ \hline Rename & 834 \\ \hline Push down & 834 \\ \hline Inline & 834 \\ \hline extract & 834 \\ \hline Pull up & 834 \\ \hline Move & 834 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of instances per class (Commit Message).
## 2 Methods
### Architecture Design of ROM Networks for Field and Scalar Prediction  
The network of the field prediction ROM is presented Figure 5. Inspired by Unet [52], the proposed network consists of two paths: the downsampling path and upsampling path, which form a U shape. Additionally, the residual blocks are inventively employed to replace all skip connections to enhance the performance of Unet.  
The downsampling path compresses the image, and at the same time extracts the different features of the image, also called a contracting path. On the contrary, the upsampling path expands the image to the original size, referred to expanding path. In both paths, there are four repeated blocks, each consisting of one dark-gray box and two light-gray boxes. The light-gray box represents the normal 3 \(\times\) 3 convolution operations followed with an activation function. In the downsampling path, the dark-gray box represents the output of the max pooling operation and in the upsampling path, the dark-gray box represents the output of a deconvolutional operation. The residual blocks take the place of skip connections, which allows part of the feature map to be convolved three more times before passing from the contracting path to the expending path. The advantages are first to provide more types of feature maps with different degrees of compression to the downsampling path and simultaneously to avoid the gradient vanishing and exploding problems in the deep neural network. The residual block first appeared in the ResNet, and there are two typical types of residual block [53]: basic block and bottleneck block. The latter is used in Unet, which consists of two 3 \(\times\) 3 normal convolutional layers and one 1 \(\times\) 1 convolutional layer in the block, which allows us to transform the input into the desired dimension. Furthermore, each convolutional layer is followed with a ReLU activation function, as presented in Figure 6.  
The network of the scalar prediction ROM is presented in Figure 7. The first half is the same as the ROM network of field prediction: four repeated blocks with one 2 \(\times\) 2 max pooling operation and two 3 \(\times\) 3 convolution operations. Meanwhile, for the remaining part, two fully connected layers replace the upsampling path, and finally, one scalar is output.  
Figure 5: The detailed structure of the proposed deep CNN-based ROM for field prediction.
# Anti-Fouling Properties of Phosphonium Ionic Liquid Coatings in the Marine Environment
## 3 Result and Discussion
### Field Testing Results
#### 3.2.3 Correlation of Free Ionic Liquid Content and Marine Anti-Fouling Performance  
The total fouling rate of each ion gel at the end of the trial period (53 and 63 days for the P444 VB and P888 VB ion gels, respectively) was calculated according to the procedure  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Contact Angle** & \multirow{2}{*}{**Sample**} & **Contact Angle** \\  & **(DEG)** & & **(DEG)** \\ \hline P444[88,10,0] & 52 \(\pm\) 3 & P888[93,0,0] & 131 \(\pm\) 8 \\ P444[82;15,0] & 33 \(\pm\) 2 & P888[84;10,0] & 129 \(\pm\) 8 \\ P444[77;21,0] & 57 \(\pm\) 3 & P888[65;28,0] & 119 \(\pm\) 4 \\ P444[67;32,0] & 36 \(\pm\) 5 & P888[48;48,0] & 83 \(\pm\) 4 \\ P44 described in Supplementary Section S6 and summarized in Figure 11. The clear difference between the control samples highlights the different rates of marine fouling under different conditions and the requirement for control samples.  
The findings of the combined investigations show that the growth of biofilm formation is somewhat inhibited by both the tributyl and trioctyl formulations, with a reduction of about 50% relative to their controls (Figure 11). This implies that these formulations successfully reduce marine fouling.  
The fouling rate for P444, P888, and P88 + biocide formulations was similar for all compositions of ionic liquid studied. While the proportion of ionic liquid is useful for controlling the durability and castability of such formulations, the increased mobility of the free ionic liquids, compared to the tethered ionic liquid moieties in the polymer backbone, does not play a role in anti-fouling performance for the early stages of fouling studied in this work.  
In contrast, the hydrophobicity of the formulation (measured via contact angle) plays a clear role in anti-fouling performance (Figure 12). As shown, the trioctylphosphonium samples display contact angles from 83 to 131\({}^{\circ}\) and total fouling rates of 10-15% after 63 days whereas tributylphosphonium only achieves 13-60\({}^{\circ}\) and has higher total fouling rates (15-30%) after 63 days. It is promising that these relatively simple trialkylphosphonium materials can achieve such high hydrophobicity and anti-fouling performance through manipulation of the alkyl substituents. The hydrophobic action is similar to foul-release coatings such as SLIPS coatings [9].  
Figure 11: Total fouling rates of tributyl(vinylbenzyl)phosphonium docusate on Day 56 and trioctyl(vinylbenzyl)phosphonium docusate and trioctyl(vinylbenzyl)phosphonium docusate with biocide on Day 63 with respect to the control samples.
## 2 Motivations for Graphene Applications in Thermal Management  
Development of high-power-density batteries, e.g., Li-ion batteries, enabled progress in mobile communications, consumer electronics, and automotive industries [14; 15; 16]. Temperature rise beyond the normal operating range negatively affects Li-ion battery performance. If overheated, the battery can suffer thermal runaway, cell rupture or explosion [17; 18; 19]. A conventional approach for thermal management of high-power-density-ion battery packs is based on the utilization of thermal PCMs. They reduce the temperature rise in the battery due to the latent heat storing and phase changes over a small temperature range [20; 21; 22].  
The common PCMs have very low thermal conductivity, \(K\), with typical values in the range of 0.17-0.35 W/mK at RT [23]. For comparison, the room-temperature (RT) thermal conductivity of Si and Cu are \(\sim\)145 W/mK and \(\sim\)381 W/mK, respectively. Conventional PCMs store heat from the batteries rather than transfer it away from the battery pack. The use of PCM in battery cells also serves the purpose of buffering the battery cell from extreme fluctuations in ambient temperature. This is different from thermal management of computer chips. To reduce the temperature rise in a computer chip, one uses thermal interface materials (TIMs) or heat spreaders that facilitate heat transfer from the chip to the heat sink [15,24,25]. The thermal conductivity of TIMs is in the range of 1-10 W/mK while that of solid graphite-based heat spreaders can be on the order of 1000 W/mK [26]. In this review we describe how these two different approaches for thermal management can be combined via the introduction of the hybrid PCM with graphene acting as filler for increased thermal conductivity. The properties that allow graphene to be an exceptional filler material are its high intrinsic thermal conductivity [1,2], and strong binding with various matrix materials [25,27-30]. The discussion of graphene applications in PCMs will mostly follow our work reported in Ref. [29].
# Drought Propagation in Brazilian Biomes Revealed by Remote Sensing
## 2 Materials and Methods
### Datasets
#### 2.2.7 Data Analysis  
All datasets were assessed for the period of 2010-2020. The data were evaluated using the native spatial resolution on a monthly scale; thus, average values in each month were calculated for variables that had a temporal resolution of less than a month. Anomalies based on the z-score were also assessed. The z-score indicated how much a variable deviated from its mean (positive or negative anomaly), according to its standard deviation. It provided the monthly anomaly values for all years of the analyzed period, which enabled visualization of periods of more extreme drought events. This type of analysis has already been performed in other studies for the detection of droughts by remote sensing products [33, 39, 74, 75]. The Z-score was calculated for each variable through Equation (1):  
\[\mathrm{Z}\ =\ \frac{\left(\overline{\mathrm{X}}_{\mathrm{m}}-\left(\overline{ \mathrm{X}}_{\mathrm{m}}\right)\right)}{\sigma(\overline{\mathrm{X}}_{\mathrm{ m}})} \tag{1}\]  
where Z is the z-score; X is the analyzed variable; \(\overline{\mathrm{X}}_{\mathrm{m}}\) is the spatial mean value for each month m; \(\left(\overline{\mathrm{X}}_{\mathrm{m}}\right)\) is the average of \(\overline{\mathrm{X}}_{\mathrm{m}}\) for the same month of the year over the 10-year period (2010 to 2020); and \(\sigma(\overline{\mathrm{X}}_{\mathrm{m}})\) is the standard deviation of the total time series over the same month of the year. The fraction of the polygon area affected by monthly negative anomalous values (z-score below or equal to \(-1\)) was obtained in each region for a 3-month composite. This result showed the amplitude and spatial extent of the drought event, as demonstrated by [PERSON] et al. [76]. This metric from monthly negative anomalous values (z-score \(\leq-1\)) was also used to indicate the most affected areas during the extreme drought event. This assessment considered the hydrological variables \(P\), _SM_, _SSM_, _ET_, and _TWSA_.  
We evaluated drought propagation by analyzing the time of drought recovery for each hydrological, climatic, and vegetation variable after onset of the drought (when _TWSA_ became extremely negative). Finally, we identified the onset, duration, and maximum intensity for all case studies. For the onset of droughts, we used the threshold of z-score equal to \"\(-1\)\" as an indicator of moderate drought [33], considering consecutive anomalous values for monthly intervals. We assumed the onset of \(P\) (for a z-score \(\leq-1\)) equal to zero, and the onsets of other variables were characterized relative to onset of \(P\). _TWSA_ were evaluated without gap-filling the data. Figure 2 summarizes the data analysis process.
## 3 Formulation of a Concept of Radar Antennas for CA Systems
### Identification of a Candidate Architecture
[49] have explored millimetre-wave CW-LFM radar systems operating in the 77-81 GHz band, employing mechanical rotation and machine learning techniques to enhance angle-of-arrival (AoA) estimation. While these approaches achieve high spatial accuracy, they typically require physically rotating radar modules or computationally intensive post-processing, which may render them unsuitable for small UAS due to their added complexity and energy demands. In contrast, considering the specific constraints of the intended host platform, we preferred a circular array over mechanically rotating systems, as it seems better suited for achieving omnidirectional coverage around the ownship. Additionally, this architecture offers flexible implementation options, including electronically steered, switched, or continuously omnidirectional (ubiquitous) modes. To ensure adequate angular accuracy, we have selected a 3D monopulse implementation. This approach, commonly used in aviation surveillance to enhance the angular accuracy of secondary surveillance radars [29; 50], combines signals received from four independent antennas or sections of a radiating aperture. We further elaborate on this concept in the following subsection.
## 3 The MQTT/MQTT-SN Based Cyber System of the UEMS
### The Quality of the Service of MQTT/MQTT-SN  
As mentioned above, the communication of the UEMS should be real-time and reliable. However, the ZigBee-based local wireless network has characteristics of low power dissipation, low-cost, low communication rate, and limited features, where stable communication cannot be always guaranteed. Hence, MQTT/MQTT-SN provides the QoS mechanism to support the real-time requirement of messages so that the probability of successful reception can be improved.  
Table 1 describes various QoS levels in MQTT-SN.
# Reduced Overhead Routing in Short-Range Low-Power and Lossy Wireless Networks +
## 5 Performance Evaluation
### ERPL's P2P Routing and Data Forwarding Performance Evaluation
#### 5.1.3 Testbed Results  
To validate our simulation-based results, we evaluated the protocols using the FlockLAB [24] test-bed. The experiments were performed using the FlockLAB's TelosB motes. In the test-bed based experiments, each experiment duration and traffic generation model were the same as we used in our simulation based experiments. The current FlockLAB deployment consists of 30 nodes, however at the time of our experiments only twenty seven TelosB motes were operational. Twenty three nodes were deployed indoors across one floor in an office building, distributed in offices, hallways, and store rooms. Four nodes were deployed outside, placed on the roof of an adjacent building a few meters beneath the floor with the indoor nodes. In the test-bed based experiments, a node at the edge of the network was selected as the gateway.  
Figure 10a shows the PDR achieved by the evaluated protocols. The PDR results obtained through the test-bed validate our simulation based PDR results as the trends are similar, i.e., in all cases ERPL outperforms the standard RPL protocol. With a decrease in the packet generation interval the performance gap between the two protocols increases and both protocols show higher PDR with an increase in the packet generation interval.  
Figure 9: Protocols’ Performances with Grid Topology (Gateway at Network Center).  
Figure 9(b) shows the delay demonstrated by the two protocols. The delay results obtained through the test-bed again validate the results obtained through simulations as the trends are similar, i.e., in all cases ERPL outperforms the standard RPL protocol. Due to lower contention with an increase in the packet generation interval, both protocols demonstrate lower delay.  
Figure 9(c),d shows the performance of the two protocols with regard to the number of times P2P packets that are relayed in the network and the total number of re-transmissions. The test-bed results validate the simulation based results as again the trends are similar, i.e., ERPL demonstrates lower total P2P transmissions and re-transmission. Lower P2P transmissions associated with ERPL result in the protocol's improved performance in all three communication types.
## 5 Experiments
### Localization Using the Compact Maps
#### 5.3.2 Localization in the Freiburg Environment  
As in the previous case (clustering task), with the aim of corroborating the results obtained in Quorum V, an evaluation of the localization task is carried out in the COLD environments. These environments present trajectory maps instead of grid maps. The two COLD environments present  
Figure 17: Results of the localization process with FS, HOG, and _gist_ used to describe the representatives of the clusters and the test images: average computing time vs. number of clusters. Quorum V environment.  
a similar configuration and also similar results. This way, only the results obtained in one of them are shown. Freiburg is chosen because this environment presents more rooms and also is more challenging due to the fact that the building presents many glass walls. Moreover, as Figure 16 shows, since the FS descriptor has presented the worst results, this descriptor is discarded in subsequent localization experiments. Furthermore, the Euclidean distance results are omitted in this section because it presented the worst outcomes. Figure 18 shows the average localization error (cm) obtained when HOG (first row) and _gist_ (second row) are used respectively as the descriptor. The case of no compaction is also considered (\(n_{c}=519\)).  
In this case, some differences are noticed between the results collected in the Quorum V environment and the results in the Freiburg environment. When the number of clusters is low (\(n_{c}=[15,25,40]\)), the localization task presents a lower average localization error with _gist_. If this number is higher than 40, the localization error is very similar for HOG and _gist_. Comparing the results obtained with the two evaluated types of distances, no remarkable differences are found. Nevertheless, a slight improvement can be noticed when the cosine distance is used. For instance, the average error value when \(n_{c}=40\) in HOG is lower with the cosine than with correlation.  
Additionally, the value of \(k_{2}\) in HOG is very important. The average error varies significantly according to it. Therefore, in order to solve the localization in an environment whose properties are similar to Freiburg or Saarbrucken (information along a trajectory), the optimal values are reached through the use of HOG descriptor with \(k_{2}=[16,32]\) and cosine distance.