esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Current Capabilities of Modern FPGAs
### Features and Platforms of FPGA Vendors
#### 2.2.3. Actel  
Actel focuses on low-power and mixed-signal FPGAs. Their FPGAs are based on FLASH memory instead of SRAM. As a consequence, they have limited number of writings, are non-volatile and have a reduced power consumption, since the FPGAs are made up of less transistors and therefore their leakage-current is reduced in comparison to SRAM. Moreover, the proprietary Flash*Freeze technology allows to maintain their FPGAs (particularly IGLOO) in ultra low-power mode (\(\mu\)W) without data loss. Therefore, the IGLOO platform fits completely in the sensor node architectures with on-board processing.  
Furthermore, same as Xilinx and Altera, Actel supports the Cortex M1 soft-processor as well as the Cortex M3 as HIP. Actel's FPGAs are classified in three series: IGLOO (low-power), Pro ASIC (low-cost) and Smart Fusion (full-featured and mixed-signal focused) [19]. IGLOO is based on low-power, works between 1.2 V and 1.5 V and consumes between 2 \(\mu\)W and 5 \(\mu\)W in standby. This is extremely low compared to the two other series, ProASIC (3 mW) and SmartFusion (17 mW). Smart Fusion FPGAs are based on a HIP processor (Cortex M3) and signal processing blocks such as 12-bit ADCs/DACs, analog comparators and current monitors.  
Table 3 depicts the static power consumption of the main FPGAs from Actel. It is worth noting that Actel provides two modes of turning off their platforms during execution, _i.e._, Flash*Freeze and sleep, both reducing the power consumption enormously. The parameters from Table 3 have been estimated via the Actel Power Calculator tool.  
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Platform** & **Model** & **Core Voltage (V)** & **Number of LUTs** & **Block RAM (KB)** & **Static Power Consumption (mW)** \\ \hline Cyclone & EP1C6 & \(1.5\) & \(5,980\) & \(92\) & \(60\) \\ Cyclone II & EP2C8 & \(1.5\) & \(8,256\) & \(165\) & \(40\) \\ Cyclone V & 5 CEFA9 & \(1.10\) & \(301,000\) & \(12,200\) & \(206\) \\ Stratix & EP1S25 & \(1.5\) & \(25,660\) & \(635\) & \(450\) \\ Stratix II & EP230 & \(1.5\) & \(33,880\) & \(663\) & \(86\) \\ Stratix V & 5 SE29 & \(1\) & \(840,000\) & \(12,800\) & \(880\) \\ Arria V & 57 GXMA1D & \(1\) & \(75,000\) & \(8,000\) & \(197\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Power consumption and operating voltage of Altera FPGAs.  
\begin{table}
\begin{tabular}{l l c c c c c} \hline \hline  & & & & & \multicolumn{3}{c}{**Static Power Consumption (mW)**} \\ \cline{4-7}
**Platform** & **Model** & **Core Voltage (V)** & **Number of FFs** & **Block RAM (KB)** & **Static** & **Flash*Freeze** & **Sleep** \\ \hline IGLOO & AGL060V5 & \(1.5\) & \(1,536\) & \(18\) & \(0.030\) & \(0.018\) & \(0.005\) \\ IGLOO & AGL060V2 & \(1.2\) & \(1,536\) & \(18\) & \(0.012\) & \(0.012\) & \(0.004\) \\ ProASIC3 & A3P060 & \(1.5\) & \(1,536\) & \(18\) & \(3\) & \(-\) & \(0.10\) \\ SmartFusion & A2F500 & \(1.5\) & \(11,520\) & \(24\) & \(17.43\) & \(-\) & \(-\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Power consumption and operating voltage of Actel FPGAs.
## 2 Previous and Related Works  
In this project we are looking for a connectivity solution in a remote, usually disconnected site, near a river in underdeveloped countries, to connect a Wireless Sensor Network (WSN) solution for pollution monitoring. A possible solution for this scenario are Delay/Disruption Tolerant Networks (DTN). DTNs have been used in rural applications for several years. Although DTNs started as an alternative to spatial communications led by NASA, they have expanded to several applications in Earth-based communications; for example, Wireless Sensor Networks, Remote and Extreme Environments Monitoring, and Rural Communications. The following subsections describe the DTN Architecture, the Bundle Protocol, some routing and forwarding algorithms, and some rural connectivity technologies.
## 6 Experimental Results
### Dealing with Large Datasets
#### 6.5.1 Joint-Approach  
The three different weighting schemas (Boolean-IDF, TF-IDF, LogTF-IDF) are tested with ESCAPE and the obtained results are shown in Table 13. In general, the average and global silhouette values corresponding to the selected best configurations are, for all the datasets, in the range between 0.2 and 0.5, suggesting that the partitions are good.  
Figure 4: Document probability distributions in each topic for weighting TF-IDF (**left**) and LogTF-Entropy (**right**).  
these graphs; in fact, the shape of the Boolean-IDF clusters seems to be more defined with respect to LogTF-IDF.
## 1 Introduction
### UAV (Unmanned Aerial Vehicle)
The effectiveness of the use of UAVs in the activity of providing information necessary for monitoring, simulating, modeling, and preventing floods has been demonstrated in numerous case studies that concern various geographical areas. Thus, The Drone Watch project brought together the commercial flood modeling and risk specialist Ambiental Ltd. (Amersfoort, The Netherlands) and UAV imagery specialists Caintech Ltd. (Inverness, UK) and the University of Leicester, Loughborough University, and Imperial College London to use UAV technology to obtain information almost in real-time about the floods that occurred after winter storms in Cumbria (UK) in the winter of 2015. The near real-time accurate mapping of the flood extent and property and infrastructure damage in urban and rural areas was required for the estimation of insurance loss and insurance claim validation [37]. In Queensland, Australia, in 2019, and in Luxembourg in 2022, UAVs were used to obtain necessary information for accurate modeling with high-quality elevation data. Considering the nature of information retrieval, drone mapping, and photogrammetry can provide finer-resolution elevation data than can be achieved through mapping with manned aircraft [38]. To protect the lives and livelihoods of people, The Gambia has partnered with UNESCO to establish a state-of-the-art, end-to-end early warning system for floods that relies heavily on drones. The observation area is mainly based on the drainage basin of the Gambia River, the most sensitive area from the point of view of flooding in The Gambia. This partnership with UNESCO was established because, in 2010-2018, about 35,000 Gambians were directly affected by the consequences of floods. Thus, the drones managed to quickly identify any safe sites, unsafe areas, and evacuation routes. In addition, drones help with the rapid identification of damaged and at-risk individuals in post-disaster situations [39]. The Utah Water Research Laboratory used UAVs on 25 May 2023 to obtain information with the help of over five flight sessions that spanned 19 m. Drone imagery revealed how the floodwaters affected surrounding neighborhoods, farmland, and grassland. The data collected from drones during a flood helped fine-tune flood predictions and modeling [40].
# Spatiotemporal Feature Enhancement for Lip-Reading: A Survey
## 2 Classification of Lip-Reading Methods
### Lip-Reading Methods Based on Spatiotemporal Features  
The essence of spatiotemporal features based on deep learning, as illustrated in Figure 4c, lies in extracting spatiotemporal features from lip movements, particularly capturing the dynamic interplay between spatial and temporal dimensions, which is essential for accurate lip-reading.  
By replacing ResNet networks with more complex models, richer temporal information can be captured. Such a recognition process consists of four main stages: spatiotemporal feature extraction, spatiotemporal feature enhancement, temporal feature modeling, and classification. Initially, 2D CNNs act as an extractor to preserve spatial features, whereas 3D CNNs capture spatiotemporal features. Next, these models, such as TSM [48] and Transformer [49], which process both temporal and spatial features, are employed to enhance spatiotemporal features. Temporal feature modeling is then performed by using GRU, Bi-GRU [50; 51], LSTM, Bi-LSTM [52], Transformer, and DC-TCN [53]. Finally, classification is carried out using SoftMax and CTC [54].  
The concept of spatiotemporal feature enhancement aims to strengthen the comprehensive representation of the visual, semantic, and temporal information of the lips, capturing both the temporal and spatial information of the lip image sequence. This enhancement plays a crucial role in subsequent temporal feature modeling since its performance directly impacts the recognition accuracy, generalization capability, and robustness of the models.
# TR-GPT-CF: A Topic Refinement Method Using GPT and Coherence Filtering
## 3 Results and Discussion
### Evaluating Topic Coherence Improvement Across Datasets
#### 3.1.6 Improvement of Topic Coherence in Science Article Dataset
The small gain is likely related to the informal nature and diversity of the Newsgroup dataset. AGNews demonstrates limited potential for enhancement, as the topics are already well-structured, providing limited opportunity for additional refinement. The hierarchical organization of WordNet aligns more closely with the structure of Yahoo Answers dataset compared to GPT, leading to higher enhancement with WordNet. While both WordNet and GPT perform well individually for TagMyNews, their combination does not yield additional benefits. WordNet demonstrates superiority in this context, highlighting its proficiency in datasets characterized by succinct and clearly delineated content. The combined approach \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Before Refinement** & **After Refinement** & **Improvement (\%)** \\ \hline LDA & 0.526 & 0.544 & 3.42 \\ BERTopic & 0.731 & 0.740 & 1.23 \\ G-BAT & 0.265 & 0.341 & 28.68 \\ NMF & 0.614 & 0.619 & 0.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Coherence score for the Science Article dataset. is particularly effective for SMS Spam, as it utilizes both semantic understanding and contextual nuance to address brief, spam-related phrases. Datasets with more structured content, such as Science Article and Yahoo Answers, tend to favor WordNet. In contrast, less structured or short-text datasets, such as SMS Spam and TagMyNews, benefit more from GPT and combined approaches. The combination of WordNet and GPT demonstrates the most significant improvements in datasets that present specific challenges, like SMS Spam. However, some datasets, such as AGNews and TagMyNews, show no significant advantage for the combined approach, as individual methods already achieve high coherence. The comparative efficiency of WordNet, GPT, and their combined use vary by dataset. WordNet performs consistently, achieving its highest improvement of 8.3% in the SMS Spam dataset, most likely due to its dependence on semantic similarity, which helps create a more organized context. In contrast, GPT alone performed comparably but with slightly lower performance. For example, it achieved only a 3.4% improvement in the Science Articles dataset, versus WordNet's 4.5%. The combination of WordNet and GPT, however, yields the highest improvement percentages in most cases, such as 10.8% in SMS Spam and 4.6% in Science Articles. This result consistently shows that combining WordNet and GPT leads to greater improvements, implying that semantic relationships (WordNet) and contextual representations (GPT) complement each other effectively. This suggests that combining semantic clarity with contextual fluency creates a more effective mechanism for candidate word replacement, thereby improving overall topic coherence.
## 5 Experimental Design and Result Analysis  
### The Source and Composition of Sound Sample Data  
The experimental data include the sound from the network and the audio clips collected in the laboratory. Audio data contain the following pests, which can change forest and urban ecosystems: iron beetle (Anthurium andraeanus), mountain skin beetle, red-necked longicorn beetle, Asian longhorn beetle and Chinese awnless beetle.  
The data acquisition process from the laboratory is as follows. In order to pick up the faint vibration of trunk borers with high sensitivity, we chose the piezoelectric accelerometer YD-189-5 as the signal acquisition equipment. With the same accuracy, sensitivity and measurement, piezoelectric accelerometer YD-189-5 has the advantage of a low price. The specific parameters are as follows: sensitivity: 5.015 v/g; frequency sensing range: 0.2-5000 Hz; maximum lateral sensitivity: <5%; maximum allowable acceleration: 5 g. The temperature of signal acquisition is controlled at 26-36 \({}^{\circ}\)C. This is to prevent the trunk borer from going into hibernation, so that it is unable to effectively collect the signal. Then, we remove the bark from the tree and glue the sensor to the flat trunk. The collected signal is stored in the storage device after being amplified by a power amplifier. For follow-up experiments, we have prepared some audio data that do not contain the sound of trunk borer pests. Inserting the prob into a tree free of trunk borer, the recording of sound clips from trunk borers can easily be obtained from trees that are not infested by trunk borers.  
We also found an interesting dataset on the Internet. In this dataset, trunk borer sound from different acoustic environments, including jungles, urban motorcycle roads, urban pedestrian alleys, and roadsides where children laugh, was collected. It records audio data from the past six months. The data of the dataset were from a record database provided by a device that periodically records and wirelessly transmits short records of the internal vibration of trees to the cloud server. Specifically, the device includes a piezoelectric sensor that records vibrations caused by pests eating trees. Vibrations were picked up by drills or metal foil acting as acoustic couplers, recorded, compressed in Ogg format, and then uploaded to the cloud server (as is shown in Figure 7), where they were decompressed, recorded and classified. The server was set up to record the time of uploading by listening equipment and the time of uploading content at the same time, and users can monitor remotely to infer the state of trees infested by withered wood insects. The device's SIM card has global coverage, so the device placed on the tree can communicate with the server at any time. The listening device has embedded solar panels, which can provide sufficient power for its own low-power electronic circuits. Therefore, it can be placed on the target tree for a long time. Some devices with GPS location function will be displayed on the server's world map.
## 1 Introduction
### Evaluation of Distributed Fault Displacement Hazard  
[PERSON] et al., 2011 [18] suggested the equation below equation for the calculation of annual rate of distributed fault contribution to the PGD hazard:  
\[\lambda(d\geq d_{0})_{xyz}=\alpha(m)\int_{m,s}f_{M,S}(m,s)P[sr\
eq 0|m]\times \int_{r}P[d\
eq 0|r,z,sr\
eq 0\,]\times P[d\geq d_{0}|r,m,d\
eq 0]f_{R}(r)drdmds. \tag{12}\]  
Here the \(\lambda(d\geq d_{0})_{xyz}\) is the annual probability of the off-fault displacement \(d\geq d_{0}\) at a location \((x;y)\) within area \(z\). The meaning of the probabilities and marginal distribution of distances is identical to those in Equation (1). [PERSON] et al. [18] suggested the following equation for the direct calculation of \(d\):  
\[\ln(d)=1.4016*m-0.1671*\ln(r)-6.7991 \tag{13}\]  
Here, \(d\) is in centimeters and \(r\) in meters, and with standard deviation of \(1.1193\) in \(ln\) units. Like the considerations above, the following equation can be written for the probability of \(d\geq d_{0}\) at the hazard level \(\lambda\):  
\[P(d\geq d_{0})|_{\lambda}=\left.\left[\int_{m_{0}}^{m_{u}}P(sr\
eq 0|m)\int_{0}^ {r_{u}}P(d\geq d_{0}|r,m)\times p_{\lambda}(m,r)drdm\right]\right|_{\lambda}. \tag{14}\]Equation (14) can be written for the probability of \(d\geq d_{0}\) for distributed faulting as follows:  
\[P(d\geq d)|_{\lambda}=\left.\left[\sum_{i=0}^{i=i_{q}}\sum_{j=0}^{j=i_{u}}P\big{(} sr\
eq 0|m_{j}\big{)}P\big{(}d\geq d_{0}|r_{j};m_{i}\big{)}W_{i,j}\right]\right|_{ \lambda}. \tag{15}\]  
The expected value of displacement, \(\overline{d}\Big{|}_{\lambda}\) for given hazard level \(\lambda\) will then be:  
\[\overline{d}\Big{|}_{\lambda}=\left[\sum_{i=0}^{i=i_{q}}\sum_{j=0}^{j=i_{u}}P \big{(}sr\
eq 0|m_{j}\big{)}exp\big{(}1.4016m_{i}-0.1671\ln\big{(}r_{j} \big{)}-6.7991\big{)}W_{i,j}\right]|_{\lambda} \tag{16}\]  
Here, the findings of [PERSON] et al. (2011) and [PERSON] et al. (2017) regarding potential area to be accounted for while the contribution of distributed faulting is calculated. Therefore, the calculation via Equation (16) is performed for all distance bins.
# Evaluation of 6 LoWPAN Generic Header Compression in the Context of a RPL Network
## 3 Preliminaries
Application gateways can easily translate between HTTP and CoAP requests and responses. The 6 LoWPAN adaptation layer fulfils three tasks: (1) compressing headers, (2) fragmenting compressed packets to fit in the Maximum Transmission Unit (MTU) of IEEE 802.15.4, and (3) forwarding them. It is important to specify that 6 LoWPAN only defines the format of forwarding headers and not a forwarding mechanism.
# A Binaural MFCC-CNN Sound Quality Model of High-Speed Train
## 4 Results and Discussions
### Training Results of the Binaural MFCC-CNN Model  
The training progress of the binaural MFCC-CNN model is shown in Figure 15. After training, the accuracy of the proposed model was evaluated by testing the data set. The accuracy of the testing data set reached 96.2%, indicating that the proposed model was feasible for sound quality evaluation and that the CNN method was appropriate. Figure 16 displays the accuracy matrix of the testing data set to measure the effectiveness of the proposed model. The diagonal elements represent the number of samples for which the predicted evaluation is equal to the actual evaluation. The testing data set contained 132 noise samples, and the model made a total of 132 evaluations.  
From the subjective test results in Figure 14, the evaluated noise samples were categorized into four scales. If the mean value was smaller than 1.5, the measuring points were labeled as 'Not at all'. For measuring points with a mean value between 1.5 and 2.5, they were rated as 'Slightly'. Then, the points with a mean value between 2.5 and 3.5 were rated as 'Moderately'. The points were rated as 'Very' if the mean value was between 3.5 and 4.5. Because the highest mean value was 4.27 at point 22, no points were rated as 'Extremely'.  
Figure 14: Subjective evaluation of each measuring point.  
Figure 13: Normality test: (**a**) Subjective responses’ distribution of point 11; (**b**) Hypothesis test results of each measuring point.  
The binaural MFCC-CNN model made an accurate assessment. It predicted 'Not at all' 31 times, 'Slightly' 36 times, 'Moderately' 34 times, and 'Very' 31 times. The model made 127 accurate evaluations and missed 5, so the testing accuracy was 127/132 (96.2%). One 'Not at all' was wrongly evaluated as 'Slightly', and two 'Slightly' were wrongly evaluated as 'Not at all'. To improve the accuracy, it is important to increase the number of samples of 'Not at all' and 'Slightly' to train the model better.
## 2 Research Methodology  
The Monte Carlo method is a useful algorithm that has been applied in many aspects. It consists of repeated random sampling to generate a series of data. Then, these data are input into a model/system to obtain a numerical result. In principle, the Monte Carlo method takes advantage of its stochastic nature to solve complex problems. The typical application of the Monte Carlo method includes sampling, estimation and optimization [13]. This method has been applied in the field of petroleum exploration, including seismic processing, inversion, reservoir identification and uncertainty quantification [14; 15; 16; 17].  
The Monte Carlo method is used in the forwarding model in this study. The range of random sampling can be adjusted conveniently, and the combination of the variables can cover most scenarios when the number of samples is sufficient. The responses of these scenarios can be visualized and analysed in the different methods of hydrocarbon prediction.  
Some empirical equations are used for Monte Carlo modelling. Three empirical relationships [18] are used to derive \(V_{p}\), \(V_{s}\), and the density of sand in this model for the sand layer:  
\[V_{p}=5.59-6.93*\Phi-2.18*V_{clay}, \tag{8}\]  
\[V_{s}=3.52-4.91*\Phi-1.89*V_{clay}, \tag{9}\]  
\[\rho=-0.0115*V_{p}^{2}+0.261*V_{p}+1.515, \tag{10}\]  
In addition, the equations for the shale layer which is used for the analysis of the interface-domain attribute are as follows:  
\[V_{s\_shale}=0.862*V_{p\_shale}-1.172, \tag{11}\]  
\[\rho_{shale}=-0.0261*V_{p\_shale}^{2}+0.373*V_{p\_shale}+1.458, \tag{12}\]  
Hydrocarbon prediction can be regarded as binary classification which contains bool values, true and false. One of the standard ways to judge the performance is the confusion matrix. A confusion matrix consists of the counts of each predicted label: true positive (TP), false positive (FP), false negative (FN) and true negative (TN). TP and TN refer to correct predictions, and FP and FN are incorrect predictions. The metrics which are usually used are the precision, recall, accuracy and F-measure as shown in Table 1. Each metric ranges from 0 to 1, with 1 representing best performance.  
The receiver operating characteristic (ROC) curve is usually used to evaluate the binary classifier. The AUC is equal to the probability that a classifier will rank a randomly chosen positive instance higher than a randomly chosen negative one (assuming 'positive' ranks higher than 'negative') [19]. The AUC which equals 1 means the best prediction. A good classifier has the ROC which is above the line of no-discrimination (a line from the left bottom to the top right corner, also called 'random guess line'). The ROC and AUC analysis, which are based on the evaluation metrics and confusion matrix, output a threshold for a specific binary classification problem.
## 4 Feature Extraction Techniques
### Principal Component Analysis (PCA)
#### 4.1.1 Standardisation  
The standardisation step is a scaling process that allows all the variables or sampling points in a continuous signal \(S_{i}\) to evenly participate in the feature analysis. This step limits any bias in the feature analysis process, in case there are large differences between the dataset variables. Given a continuous signal, \(S_{i}=(s_{1},s_{2},\ldots,s_{i})\), the standardisation step is achieved as:  
\[\bar{S_{i}}=\left(\frac{s_{1}-M(S_{i})}{D(S_{i})},\frac{s_{2}-M(S_{i})}{D(S_{ i})},\ldots,\frac{s_{i}-M(S_{i})}{D(S_{i})}\right)=(s_{1},s_{2},\ldots,\bar{s _{i}}), \tag{1}\]  
where \(\bar{S_{i}}\) is the standardised continuous signal, \(M\) is the mean of \(S_{i}\), and \(D\) is the standard deviation of \(S_{i}\).