esa-sceva/satcom-chunk-collection huggingface dataset:
# Computer Vision for Fire Detection on UAVs--From Software to Hardware
## 2 Fire Detection Using Computer Vision on UAVs
### Fire Detection Framework  
Prior to beginning the mission, the navigation plan, the identification of appropriate algorithms and models must be selected and implemented in the system. The procedures that will be done through the camera, consist of taking photos or videos and preprocessing them, including image segmentation. Then, the fire detection and features extraction are due:  
_Preprocessing_: The purpose of Computer Vision is to analyze the information coming from the image in order to perform the appropriate processes to locate the fire. After the image acquisition procedures, preprocessing consists of procedures related to image enhancement or verification of its suitability for the respective method. Procedures related to image optimization through preprocessing are noise reduction, in order to reduce or remove the image's noise, normalization processes for changing the range of pixel intensity values and scaling for image's resizing.  
_Segmentation_: Once the image has been obtained and the appropriate optimization procedures have been performed through the preprocessing, the pixels must be separatedinto those that describe the object of interest from other image information. In the case of fire, the segmentation consists of pixels associate with fire. The way in which segmentation will be done differs between the methods. More specifically, these methods can be based on colors, motion or even intensities.  
_Fire Detection and Features Extractions_: Through the feature extraction, the appropriate actions are taking place on the image in order to analyze the segmented image and to identify the key points of interest. The image then is passed to a trained model in order to find the patterns that will confirm or reject the presence of fire.  
In the next step, in case of a positive result of the artificial intelligence model processes, the system sends an alarm via the UAV or ground support station to the fire protection personnel for further actions. Figure 1 shows the aforementioned flowchart.  
At this point, it is worth noting that the methodology described above is the basic way of fire detection. Deep learning techniques involved in this process have greatly simplified the processes of segmentation and feature extractions by replacing classical algorithms [15].
### Age of Information  
Age of information (AoI) [6] is a new metric that has recently been attracting a lot of attention owing to the need to characterize the freshness (or timeliness) of packets (or data) at the receiving nodes. With AoI, the freshness of packets sent by the transmitting nodes can be determined at their respective intending nodes. Such a parameter is very useful, especially when the timeliness of data at the receivers is important for efficient system performance. While the delay metric can only reflect timeliness of a packet at the time instants at which the packet is successfully decoded at the intending receiver, AoI allows an opportunity to measure the timeliness of a packet at the intending receiving node at any given time instant [7]. This implies that the timeliness of the received packet is measured from the receiving node's perspective when the AoI is used [8].  
In [7], the analysis for the average peak AoI was derived in a cognitive radio-based internet of thing (IoT) systems with a single primary IoT system and single secondary IoT system. Similarly, the authors in [8] studied the average AoI of energy harvesting SU nodes in a multiuser CRN, while the analysis for the average AoI in the primary node was obtained for a single primary transmitter-receiver pair in [9]. The spatial distribution of the mean peak AoI was characterized in [10] using the tool of stochastic geometry (SG). The SG is a very useful approach that provides efficient mathematical and statistical mechanisms useful in the study and analyses of random spatial patterns. Such random spatial patterns, knowns as point processes--defined as set of locations that are distributed within a certain designated region--are normally generated using stochastic mechanisms and are known to be important elements of SG. With the use of random point patterns, the distribution of users in CRN can be realistically captured, while the analysis resulting from such a process can be accurate and tractable, albeit under various simplifications and assumptions [2,11]. The effectiveness of SG in obtaining various tractable analytical solutions has been demonstrated in different works. Interested readers are referred to [12, 13, 14] for more details.  
Finally, the analysis for the throughput as well as the average AoI was obtained for cellular-based IoT networks in [15], while the analysis for the AoI in a static Poisson network was also obtained in [16]. As will be seen in the remaining sections of this paper, the analysis for the AoI depends on the transmission and success probabilities in the network. We adopt the tool of SG in order to obtain the analysis for this success probability and captured the aggregate interference within the considered cognitive network in the analysis.
# Real-Time Geophysical Applications with Android GNSS Raw Measurements
## 1 Introduction
PVT: Position-Velocity-Time. NMEA: National Marine Electronics Association. C/No: Carrier-to-Noise ratio. methodologies based on the variometric approach used, respectively, during the activities described here to estimate fast movement 3D velocity and to study ionospheric conditions and disturbances. This paper presents a feasibility study to define whether raw GNSS measurements coming from a Xiaomi Mi 8 can be used to detect movements of frequency and amplitude similar to seismic waves, and to monitor ionosphere conditions. This introduction is followed by the description of potential benefits for the geoscience community coming from the availability of raw Android GNSS measurements (Section 2). The same section contains a description of the main features of VADASE and VARION, the two algorithms used in this work. The last part of the paper deals with the description of data analyses (Section 3) and the discussion of the results obtained (Section 4).
# Monthly Precipitation over Northern Middle Atlas, Eastern Morocco: Homogenization and Trends
## 3 Results
### Monthly Precipitation Trend  
The application of the Mann-Kendall test on the monthly rainfall series of 22 stations over a period of 49 years shows generalized downward trends for the months of January, February, March, April, May, June and December (Table 2 and Figure 8). During these months, the percentage of series with a negative trend (not necessarily statistically significant) varies between 68% for the month of January and 100% for the month of April. These trends toward a decrease in rainfall remain insignificant, except for the stations of S5 (Belfarah), S6 (Bni Bouilloul), S7 (Berkine), S8 (Guercif), S10 (Mrija) and S22 (Rechida) (Table 2 and Figure 8), which are significantly different from zero at the 90% confidence level. It should be noted that all of these last rainfall stations are located in the southern and eastern part of the study area, sheltered from the mountain range of Jbel Bouiblane (Figure 1). In this part, April is the wettest month of the year.  
The month of July is seen as a transformation point towards positive trends. For July, 71% of the series have an increasing trend, although only five series have statistically significant trends (Table 2). For the months of August, September, October and November, generalized positive trends were observed. The percentage of series with a positive and significant trend varies between 55 and 77% (Table 2). These results show a probable transformation of a winter and spring rainfall regime towards a more autumn regime throughout the northern Middle Atlas.  
Figure 7: RMSE and SNHT values after homogenization.  
Figure 8: _Cont_.  
Figure 8: Spatialization of monthly precipitation trends of all data series.
## 4 Experiments Analysis and Discussion
### Effectiveness of Long-Term Bias Mitigating  
TSNN-ESTD takes iTransformer as the baseline model for constructing the ensemble model with long-term and short-term forecasting strategies. Therefore, this represents a universal optimization approach that is also applicable to various types of time-series models. Figure 3 shows the performance of the original version and bias mitigation of three different models. When the forecasting time step is set as 96, the MSE metric of Autoformer, PatchTST, and iTransformer is reduced by 24.7%, 28.4%, and 27.2%, respectively. In addition, the prediction error is further reduced by 33.3%, 30.4%, 30.6% with 336 time steps. This indicates that our approach serves as a universal bias-correction strategy, demonstrating effectiveness across various models, with particularly significant improvements observed in long-term predictions.  
Figure 2: Performance of long-term forecasting.
## 2 Background
### Fine-Grained Cache Attack
#### 2.2.2 Constant-Time Gather Procedure  
The root cause of the OpenSSL's vulnerability to fine-grained cache attacks comes from that with the bank-level granularity, it has secret-dependent memory access in gathering phase. To mitigate the attack, the vulnerable version of the OpenSSL library (i.e., the version 1.0.2f) has been patched in the later version 1.0.2g so that all secret-dependent accesses are eliminated. More specifically, in the modified gathering process, all the multipliers laid on a single memory line are loaded into four 128-bit SSE (Streaming SIMD Extensions) registers (e.g., xmm0-xmm3). The relevant multiplier is then selected among them by masking the register values accordingly. The masks are necessarily calculated on-the-fly based on the index of the multiplier to be used.  
The OpenSSL's countermeasure requires modifications of two gathering functions, bn_gatter5() and bn_mul_mont_gather5(), in the source file bn/x86_64-mont5.s.  
This results in 10-20% performance drops of the modular exponentiation in RSA algorithms.  
Figure 1: Memory layout of the multiplier table in OpenSSL.
## 3 MMPD Design for DB PAM4
The data is the minimum level when d\({}_{n}\)[2:0] = 000, then no lower bound judgment is made. It is the maximum level when d\({}_{n}\)[2:0] = 110 and no upper boundary judgment is made. Compared with the method of directly searching the LUT through the 8-bit raw data, the number of LUT and FIFO after the synthesis of the error sampler is reduced from 68 to 60, and the data space is reduced by 11.8%. The basic devices of the entire MMPD are reduced by 13.7%, which significantly reduces the auxiliary data and effectively reduces the space complexity of the phase-detection process. The Error Sampler circuit and logic expression will change with the change of the error boundary, and the logic expression is not unique, so no special design is carried out. The Phase Detector circuit obtains two phase difference signals Y\({}_{E}\) and Y\({}_{L}\) based on the preset phase-detection truth table according to the output mode[2:0] of the waveform selector and the error signal output by the error sampler. The circuit is shown in the Figure 10.  
The data selector circuit is specially designed to simplify the circuit structure, and the logic truth table is shown in Table 3.  
Figure 10: Logic gate circuit of Phase Detector. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mode** & **errdata\({}_{n-1}\)** & **A** & **B** & **C** \\ \hline
000 & x & 0 & 0 & 0 \\
001 & x & \(\text{errdata}_{n-1}\) & \(\text{errlow}_{n-1}\) & \(\text{errup}_{n-1}\) \\
100 & x & \(\text{errdata}_{n-1}\) & \(\text{errup}_{n-1}\) & \(\text{errlow}_{n-1}\) \\
011 & 1 & 1 & 0 & 1 \\
110 & 0 & 1 & \(\text{errdata}_{n-1}\) & \(\text{errdata}_{n-2}\) \\
110 & 1 & 1 & 1 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Data Selector truth table. Figure 9: DB PAM-4 error boundary and eye diagram relation. According to the logic truth table, the logic gate circuit expression of the Data Selector is obtained as:  
\[ \begin{split} M_{1}&=\overline{mode[2]mode[1]mode[0]}\\ M_{2}&=\underline{mode[2]mode[1]mode[0]}\\ M_{3}&=\overline{mode[2]mode[1]mode[0]}\\ M_{4}&=\underline{mode[2]mode[1]}\overline{mode[0]}\\ A&=M_{1}errdata_{n-1}+M_{2}errdata_{n-1}+M_{3}+M_{4} \\ B&=M_{1}errlow_{n-1}+M_{2}errup_{n-1}+M_{3} \overline{errdata_{n-1}errdata_{n}+M_{4}erdata_{n-1}}\\ C&=M_{1}errup_{n-1}+M_{2}errlow_{n-1}+M_{3}errdata_{n -1}+M_{4}\overline{errdata_{n-1}}errdata_{n-2}\end{split}  \tag{4}\]  
According to the logical expression, the logic gate circuit of Data Selector is shown in Figure 11.  
According to the above principle of phase-detection, referring to the MMPD phase-detection logic [8], the MMPD phase-detection logic of DB PAM-4 is obtained, as shown inTable 4.
# PUE Attack Detection by Using DNN and Entropy in Cooperative Mobile Cognitive Radio Networks
###### Abstract  
The primary user emulation (PUE) attack is one of the strongest attacks in mobile cognitive radio networks (MCRN) because the primary users (PU) and secondary users (SU) are unable to communicate if a malicious user (MU) is present. In the literature, some techniques are used to detect the attack. However, those techniques do not explore the cooperative detection of PUE attacks using deep neural networks (DNN) in one MCRN network and with experimental results on software-defined radio (SDR). In this paper, we design and implement a PUE attack in an MCRN, including a countermeasure based on the entropy of the signals, DNN, and cooperative spectrum sensing (CSS) to detect the attacks. A blacklist is included in the fusion center (FC) to record the data of the MU. The scenarios are simulated and implemented on the SDR testbed. Results show that this solution increases the probability of detection (PD) by 20% for lower signal noise ratio (SNR) values, allowing the detection of the _PUE attack_ and recording the data for future reference by the attacker, sharing the data for all the SU.  
cognitive radio networks; cooperative spectrum sensing; deep learning; multiple PUE attack; primary user emulation +
Footnote †: journal: Future Internet  
0203 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 3 Results
### Optimistic Scenario Results  
Figure 3 presents the results for the time-dependent populations using the optimistic case parameters. The plots in (a) and (b) show projections for the case when we hold PrEP usage at the lower value of 10% but change the average sexual partners per year from two to four. The results are dramatic. In both cases, the number of infections increases as time increases. After 75 years, the percentage of the population who are infected is 40% for two partners per year and rises to 60% for four partners per year. The plots in (c) and (d) show similar projection data except that PrEP use is held at 70%. The situation is significantly improved when a larger fraction of the population is using PrEP. In (c), 15% of the population is infected after 75 years with sexual partners per year at two. This produces a stable population over time. Even with four partners per year, higher PrEP usage can make a significant difference in outcomes; the long-term infected percentage is 33% lower in this case when PrEP use is increased from 10% to 70%. This can be seen by comparing Figure 3b with Figure 3d.  
Figure 3: The optimistic scenario implies strict adherence to proper PrEP usage. These graphs show the total susceptible (black line) and infected (red line) percentages as a function of time. In addition, the breakdown into the detailed makeup of each category is presented with the legend as shown in (**b**), where \(S_{tot}\) is the total number of susceptibles, \(I_{tot}\) is the total infected, \(I_{S}\) is the portion of \(I_{tot}\) using no prophylaxis, \(I_{P}\) is the portion of \(I_{tot}\) using only PrEP, \(I_{C}\) is the portion of \(I_{tot}\) using only condoms, and \(I_{B}\) is the portion of \(I_{tot}\) using both PrEP and condoms. (**a**) Projection when 10% of the population is using PrEP and the average number of sexual partners is two. (**b**) Projection when 10% of the population is using PrEP and the average number of sexual partners is four. (**c**) Projection when 70% of the population is using PrEP and the average number of sexual partners is two. (**d**) Projection when 70% of the population is using PrEP and the average number of sexual partners is four.
# Free Space Optical Communication: An Enabling Backhaul Technology for 6G Non-Terrestrial Networks
### Connectivity Technologies  
As discussed earlier, NTN nodes have the potential to support both wireless access and backhauling. While the onboard base station provides wireless access for end users, the backhauling link connects the airborne/space-borne base station to the core network. For wireless access, the typical choice before 5G cellular systems was the use of the sub-6 GHz spectrum. In 5G cellular systems, the MMW spectrum in the range of 24 GHz to 40 GHz is also utilized. In particular, World Radiocommunication Conference (WRC) (WRC-19) agreed that the ranges 24.25-27.5 GHz and 37-43.5 GHz should be globally tagged for 5G. Spectrum allocations for the MMW band are in the range of 400 Mhz and 800 MHz. Emerging applications such as extended reality and hologram type communication put further pressure on the required data rates, in the order of terabits per second. This has motivated the exploration of the Terahertz (THz) spectrum as a possible candidate for 6G, where large contiguous bandwidths of several GHz are available. Radio links that operate in the microwave and MMW bands (7 GHz to 40 GHz) are widely used for terrestrial backhauling and can also be customized for airborne backhauling. With its high data rate comparable to fiber optics and its ability to operate in an interference-free optical spectrum, Free-Space Optical (FSO) communication is ideally suited for backhauling needs in NTNs. FSO technology is based on the modulation of laser diode light intensity and involves transmitting unguided optical signals at infrared wavelengths. Commercial FSO links utilize Intensity Modulation/Direct Detection (IM/DD), allowing data rates ranging from 10 to 30 Gbps per wavelength. FSO links can attain aggregate data rates in the terabits per second range by implementing wavelength division multiplexing. In Table 3, we present a comparison of Radio Frequency (RF) and FSO solutions.
###### Abstract  
To minimise environmental impact, to avoid regulatory penalties, and to improve competitiveness, energy-intensive manufacturing firms require accurate forecasts of their energy consumption so that precautionary and mitigation measures can be taken. Deep learning is widely touted as a superior analytical technique to traditional artificial neural networks, machine learning, and other classical time-series models due to its high dimensionality and problem-solving capabilities. Despite this, research on its application in demand-side energy forecasting is limited. We compare two benchmarks (Autoregressive Integrated Moving Average (ARIMA) and an existing manual technique used at the case site) against three deep-learning models (simple Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)) and two machine-learning models (Support Vector Regression (SVR) and Random Forest) for short-term load forecasting (STLF) using data from a Brazilian thermoplastic resin manufacturing plant. We use the grid search method to identify the best configurations for each model and then use Diebold-Mariano testing to confirm the results. The results suggests that the legacy approach used at the case site is the worst performing and that the GRU model outperformed all other models tested.  
short-term load forecasting; STLF; deep learning; RNN; LSTM; GRU; machine learning; SVR; random forest; energy consumption; energy-intensive manufacturing; time-series prediction; industry +
Footnote †: journal:  
_articles 2020_, _13_, _274_; doi:10.3390/a13110274
# Incorporating Aleatoric Uncertainties in Lake Ice Mapping Using RADARSAT-2 SAR Images and CNNs
## 1 Introduction
[14] reported an average ice/water classification accuracy of 96.4% using RADARSAT-2 scenes of the Beaufort Sea, Canada and [PERSON] et al. [13] achieved overall accuracy of 90.4% based on RADARSAT-2 scenes of Lake Erie. Quantitative accuracy metric were not given in works by [PERSON] and [PERSON] [12] and [PERSON] and [PERSON] [11]. Despite relatively limited research on unsupervised classification, there have been a larger number of studies investigating supervised classification for the task of lake ice mapping from SAR. Supervised classification can be applied on backscatter values [17; 18]. However, it has been suggested that relying only on the backscatter values results in uncertainties caused by ambiguous correlations between backscatter and ice/water features; for instance, wind roughened areas of water bodies can be misclassified as ice [19]. The addition of other SAR features such as texture has been found to bring better classification accuracy to the model for sea ice mapping [14; 20]. To benefit from both unsupervised and supervised learning and mitigate the drawbacks of each approach, [PERSON] et al. [15] used an unsupervised algorithm for region-based segmentation, called Iterative Region Growing using Semantics (IRGS), followed by pixel-based random forest for labelling. In their research, the potential of the operational usage of the proposed automated method for lake ice cover mapping was demonstrated as a high average ice/water classification accuracy of 95.8% was achieved using RADARSAT-2 scenes of Great Bear Lake (Canada). Another stream of classifying ice pixels using SAR are methods that focus on learning the features directly from the data [21; 22; 23]. These methods are based on machine vision techniques that emulate human visual system's ability to identify and localize objects while assimilating information at different spatial scales based on prior knowledge. The most popular framework for this task in image processing is a convolutional neural network (CNN). The work presented herein builds on previous studies by [PERSON] et al. [23] and [PERSON] et al. [21] to address the challenge of retrieving ice and water from SAR as a result of similar backscatter signatures for ice and water in lakes. A CNN architecture is proposed that incorporates aleatoric uncertainty estimates. Algorithm settings such as input patch size and the training dates are inferred from successful experiments in the aforementioned studies. Improvement in accuracy when including bathymetry within the classification algorithm is also evaluated. Finally, potential applicability of the proposed method to other applications and suggestions to further improve the mapping accuracy are discussed.