esa-sceva/satcom-chunk-collection huggingface dataset:
# Pulsed Thermography Dataset for Training Deep Learning Models
## 4 Experiments with Deep Models for Defect Segmentation
### Experiments on the Original Dataset  
In this study, the data are divided into three distinct sets: training, validation, and test. The training set consisted of 13 measurements, while the validation and test sets comprised of three measurements each. To evaluate the impact of pre-training on performance, several models are employed, including modified versions of U-Net and SegNet, as well as U-Net and SegNet models that utilized a VGG11 encoder backbone [41].  
Stochastic gradient descent (SGD) is employed as the optimizer for training the models, which are trained for 3000 epochs. The loss function used in this study is binary cross-entropy, calculated according to the formula:  
\[L=-\frac{1}{N}\sum_{i=1}^{N}y_{i}\cdot log(p(y_{i}))+(1-y_{i})\cdot log(1-p(y_ {i})) \tag{9}\]  
where \(y_{i}\in\{0,1\}\) is the model's binary classification prediction, \(p(y_{i})\in[0,1]\) is the model's prediction of the probability, and \(N\) represents the number of samples.  
The quantitative results of the models' performance on the original test set are presented in Table 1. The evaluation metric used is Intersection over Union (IoU), and the average IoU is calculated for the three test samples. U-Net with a VGG11 encoder achieves the highest average IoU of 0.722. Interestingly, U-Net with a pre-trained VGG11 encoder produces comparable results, suggesting that pre-training does not significantly affect performance. To provide visual insight into the models' performance, Figure 8 displays sample segmentations obtained by U-Net and SegNet. Both models produce satisfactory segmentations of the defects, with SegNet tending to classify more pixels as foreground than U-Net. Finally, the processing speed of the models is evaluated. The results indicate that they are relatively slow, achieving 1-2 frames per second (FPS) on a machine with two  
Figure 7: SegNet architecture adapted from a figure in [9], which is licensed under CC BY 3.0.  
Intel Xeon E5-2630 v4 @ 2.20 GHz CPUs. This suggests that the models may not be suitable for industrial deployment where faster processing speeds are often required.
## 5 Performance Evaluation
### Simulation Results  
In the following experiments, we select two hosts in two pods that are farthest apart in the fat-tree topology, one as the server for _iPerf3_ and the other as the client for _iPerf3_. As stated in Section 5.1, there are two types of flows. The number of flows increases from 2 to 34, including 2, 4, 6, 10, 16, 24, and 34 flows, and the types of flows include the bandwidth-demanding and latency-sensitive flows. For instance, when the number of flows is set to be 2, there is one bandwidth-demanding flow and one latency-sensitive flow. Note that the amount of data transmitted is sufficient to meet the requirements of the experiment. Considering that the network topology we adopt belongs to a relatively small  
Figure 7: 6-pod fat-tree topology.  
network scale, we reduce the size of each packet as well. More specifically, we change the default TCP packet size of _iperf3_ from 128 KB to 1 KB, and the UDP packet size from 8 KB to 1 KB, which is more convenient for us to infer the recovery time from the number of lost packets. In order to eliminate the errors in the simulation experiment, we repeat each group of experiments, i.e., one method for the same number of flows, for seven times in total, exclude outliers, and calculate the mean value. Note that, in each group of experiments, we keep the network topology, the pairs of hosts, the number of flows, and the failed link unchanged, to avoid result bias to the best extent. Last, but not least, in each set of experiments, the entire experimental environment is reset to avoid possible interference from data caching.
# Matching of Energy Provisions in Multihop Wireless Infra-Structures
## 3 System Model
### Energy Consumption Model  
Next we present a model of energy consumption by wireless communication. A radio device is assumed to have a fixed operating voltage \(V\) and an operating current that depends on whether it is transmitting, receiving, or idle. The transmit current is \(I_{TX}\), the receiving current is \(I_{RX}\) and the idle current \(I_{I}\). We assume that all BS transmit at the same power and the currents are the same for all BS. Note that a radio device consumes energy receiving packets even if they are not addressed to it. We will call this \"overhearing\".
# Using Noise Level to Detect Frame Repetition Forgery in Video Frame Rate Up-Conversion
## 2 Background
### Similarity Detection  
Similarity detection calculates the SSIM value between two adjacent frames as follows,  
\[s[t]\ =\ \text{SSIM}(f[t],f[t+1])\ =\ \frac{\left(2\mu_{t}\mu_{t+1}+c_{1} \right)+\left(2\lambda_{t,t+1}+c_{2}\right)}{\left(\mu_{t}^{2}+\mu_{t+1}^{2}+c _{1}\right)\left(\lambda_{t}^{2}+\lambda_{t+1}^{2}+c_{2}\right)} \tag{6}\]  
where \(\mu_{t}\) and \(\mu_{t+1}\) are the means of \(f[t]\) and \(f[t+1]\) respectively, \(\lambda_{t}\) and \(\lambda_{t+1}\) are the variances of \(f[t]\) and \(f[t+1]\) respectively, \(\lambda_{t,t+1}\) is the covariance between \(f[t]\) and \(f[t+1]\), \(c_{1}\) and \(c_{2}\) are constants. The SSIM value ranges in [0, 1], if it is set to be 1, showing that the two frames are the same, and the smaller the value is, the less similar the two frames are. Periodically inserting repetitive frames, the SSIM value  
Figure 1: Illustration of frame repetition (FR) forgery.  
will be 1 periodically, so it can be proved that the video is forged by FR once the SSIM value is seen to increase to 1 periodically. When transmitting a forged video, the noise is mixed inevitably as follows,  
\[\overline{f}[t]\ =f[t]+n[t] \tag{7}\]  
\[\overline{f}[t+1\ ]=f[t+1]+n[t+1] \tag{8}\]  
where \(\overline{f}[t]\) and \(\overline{f}[t\ +\ 1]\) denote the noisy frames of the \(t\)-th frame and the \((t+1)\)-th frame respectively, \(n[t]\) and \(n[t+1]\) denote noise terms respectively. The SSIM value between noisy frames can be calculated as follows,  
\[\overline{s}[t]\ =\text{SSIM}\Big{(}\overline{f}[t],\overline{f}[t+1]\Big{)}=s[t ]+\delta(n[t],n[t+1]) \tag{9}\]  
where \(\delta(n[t],n[t+1])\) is the interference caused by \(n[t]\) and \(n[t+1]\). According to Equation (9), when a forged frame is attacked by noise, the SSIM value between noisy frames would involve not only the SSIM value of the original frame but also the interference terms. So when a forged video is mixed with too much noise, the periodicity of the SSIM would be influenced, thereby degrading the detection performance.  
According to the central limit theorem [14], \(e[t]\) and \(n[t]\) can be modeled as Gaussian noise with zero mean whether the video frame is attacked by lossy compression or external noise or not. Because there naturally exists noises in video sequences, the noises would inevitably be introduced while repeating frames. Therefore, by detecting the noise variation in the temporal domain, the fake traces can be can detected. Compared with the residual detection and similarity detection, the advantage of noise detecting is that the forgery evidence directly comes from noise term, which avoids noise interference and, therefore, can improve the detection performance.
# Unmanned Aerial Traffic Management System Architecture for U-Space In-Flight Services
## 2 Background
### U-Space
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Phase**} & \multirow{2}{*}{**Service**} & **Overall** & **Covered in Our** \\  & & **Implementation** & **UTM System** \\  & & **Level** & \\ \hline \multirow{4}{*}{U1 Foundation Services} & E-registration & 19\% & \\ \cline{2-4}  & E-identification & 17\% & \\ \cline{2-4}  & Pre-tactical geofencing & 23\% & \\ \hline \multirow{4}{*}{U2 Initial Services} & Tactical geofencing & 13\% & \\ \cline{2-4}  & Flight planning management & 6\% & \\ \cline{2-4}  & Weather information & 3\% & \\ \cline{2-4}  & Tracking & 4\% & ✓ \\ \cline{2-4}  & Monitoring & 5\% & ✓ \\ \cline{2-4}  & Drone aeronautical information management & 18\% & \\ \cline{2-4}  & Procedural interface with ATC & 20\% & \\ \cline{2-4}  & Emergency management & 9\% & ✓ \\ \cline{2-4}  & Strategic deconfiction & 6\% & \\ \hline \multirow{4}{*}{U3 Advanced Services} & Dynamic geofencing & 5\% & \\ \cline{2-4}  & Collaborative interface with ATC & 8\% & \\ \cline{1-1} \cline{2-4}  & Tactical deconfiction & 0\% & ✓ \\ \cline{1-1} \cline{2-4}  & Dynamic capacity management & 4\% & \\ \hline U4 Full Services & To be defined & 0\% & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The U-space services for each development phase, together with their current implementation level. Our system focuses on in-flight services to handle unexpected events during the flight phase of the UAS. existing. In this paper, we focus on in-flight functionalities to develop a UTM system, although our architecture is general enough to cover all kinds of services. In particular, we integrate those services related to the management of unexpected events while the UAS are flying, namely tracking, monitoring, emergency management, and tactical deconfliction. These services belong to the U2 and U3 implementation phases, which are scheduled to be developed between 2021 and 2029.
## 3 Methodology
### Hybridizing ML and GO Algorithms  
In the RBFNN model, the number of clusters in the hidden layer, representing the RBF kernel clusters, is the key hyperparameter. This is determined through trial-and-error analysis to minimize the RMSE of the FV predictions. The chosen number of clusters defines RBFNN decision variables, including output layer weights, kernel center positions, and kernel \(\sigma\) values. Figure 2 illustrates the GO algorithm's process for identifying the optimal hyperparameter values for the RBFNN predictor.  
Figure 2: Applying the GO algorithm to determine optimal hyperparameter values for the RBFNN predictor.  
The MELM model's development often relies on random weight and bias specification, resulting in inconsistent prediction accuracy across multiple runs. To achieve reliable and consistent accuracy, time-consuming and repetitive trials are necessary to determine the optimal configuration. This trial-and-error approach is inefficient. By integrating MELM with optimization algorithms, the model tuning and configuration process can be significantly accelerated. Figure 3 outlines the MELM-GO implementation sequence, which involves a two-stage optimization process.  
The GO algorithm (Figure 3) evaluates the optimal MELM structure using estimated ranges, calculating the RMSE for each target hyperparameter. Through iterative adjustments over ten model runs, the algorithm selects the model structure that generates the lowest RMSE. Subsequently, the optimal weights and biases are determined via a second optimization stage, where the number of decision variables corresponds to the total number of weights and biases in the optimal MELM structure. Initial values are assigned to the population, and their RMSE values guide adjustments to weights and biases until the termination condition is reached, yielding the best-performing weights and biases for the optimal MELM model structure. This two-stage optimization strategy replaces random assignment, reducing computational time and significantly enhancing the FV prediction performance of the MELM-GO model. Appendix B explains the primary assessment metrics used to assess the FV prediction performance of the HML models. These are relative error (RE), average relative error (ARE), root-mean-square error (RMSE), relative root-mean-square error (RRMSE), coefficient of determination (R\({}^{2}\)), performance index (PI), and an index based on the percentage of predictions that fall within the error range of \(\pm\)20% (a20-index).
# Remote Image Capture System to Improve Aerial Supervision for Precision Irrigation in Agriculture
## 2 Materials and Methods
### Description of the Remote Image Capture System (RICS)
#### 2.1.2 Image Capture Module of the RICS Node  
There are many commercially available camera modules that are highly compatible with systems based on the Arduino platform (Arduino, Ivrea, Italy). These capture modules have different specifications in terms of image resolution, connection type, output format, connectivity,  
Figure 4: Sample view and diagram of the RICS box. (**a**) Assembly of the prototype RICS node. (**b**) The same prototype with the box closed, showing the two photovoltaic modules and the position of the antenna. (**c**) Diagram of the elements in a stacked mount: XBee 868 LP, a battery charger/photovoltaic module controller, A20 module and the Li-ion battery in the top of the enclosure.  
etc. To maintain compatibility with the ZigBee communication system, an important feature is to use a camera where the transmission of image information is in UART (Universal Asynchronous Receiver-Transmitter) serial format, and controllable with AT-type commands, a system that shares the Digi XBee communication platform used in this development.  
Related to the size of the camera module, there are solutions for the Arduino and tiny devices such as ArduCam (ArduCam, China), although it cannot be connected directly to the Arduino and need several connections. On the other hand, the A20/A6C module has the same camera (OV7670) but it is smaller and needs just two connections for the Arduino, RX/TX. Besides, this camera module can be configured with different resolutions (VGA, Quarter-VGA and Quarter-QVGA) and allows configuring different parameters such as flash mode, night vision, image quality, image rotation, exposure, brightness, white balance and contrast. The cost of the module is also an important aspect to consider, since other solutions that have been considered offer a smaller size, better resolution and better GPRS, but are much more expensive.  
The transmission system and the coding of control instructions in the A20/A6C system make it possible to avoid using a software to save images from the camera. This would require a coordinating module to implement the reception and compression of the images, which would be a hard bottleneck in the development of the system. Another interesting feature of this A20/A6C module is that it has WiFi and GPRS communication, which uses the same communication/control AT protocol to allow connecting the system to a WiFi or GPRS data network for sending information.
# Intelligibility and Listening Effort of Spanish Oesophageal Speech +
## 3 Experiment 1: Preliminary Word Error Rate and Listening Effort Measurements
### Materials and Methods
#### 3.1.4 Automatic Speech Recognition  
We collected ASR WER scores with a Kaldi-based [40] Spanish ASR system. It is implemented with the s5 recipe for the Wall Street Journal database. We used 13 Mel-Frequency Cepstral Coefficients (MFCCs) as acoustic features and a mean and variance normalization (CMVN) to mitigate the effects of the channel. The details of the training procedures are described in [41].  
The training material for the ASR system was healthy laryngeal speech, as described in [42]. Some modifications were made to the ASR system to adapt it to the requirements of our experiment. As the sentences we used contained many low-frequency words, as much as 23% of the words were out of vocabulary (OOV) words in the lexicon of the original ASR system, which contained \(37,632\) entries. Therefore, we created a new lexicon with the 701 words present in the 100 sentences of the original corpus. Together with this reduced lexicon, a unigram language model with equally probable words was used. The acoustic models were unchanged. A unigram language model is a very simple language model and only considers probabilities of single words. A more sophisticated language model would consider the probabilities of single words and also combinations of words (word pairs, word triplets, etc.), which can result in better ASR performance.  
Although the final WER numbers obtained with this simple ASR are not comparable to a sophisticated ASR system, the procedure serves our purpose of evaluating the intelligibility, comparing the performance of healthy and oesophageal speakers, and establishing a baseline reference for other parallel research in the field (such as evaluating the improvements of speech modification algorithms [43]).
# Efficient Management of Power Losses from Renewable Sources Using Removable E.V. Batteries
## Materials and Methods
### Research Hypothesis  
The large-scale deployment of renewable energy sources will decentralize and increase electricity production. As a result, by 2050, approximately 80% of electricity will come from renewable energy sources, with electricity providing half of the E.U.'s final energy demand (which implies approximately 40% of all energy produced in 2050). Production will increase up to 2.5 times above current levels to achieve the European Commission's [54] emission reduction target. As a result of these estimates, we have established the first  
Figure 1: Integrated electricity storage and supply system. Source: developed by authors.  
research hypothesis that verifies the feasibility of the European Union's objective based on past trends.  
**Hypothesis 1 (H1).**_Renewable energy production (SREt), at the E.U. level, will account for 40% of total energy consumed in 2050 (all sectors), which means 80% of the total electricity consumed in 2050._  
Electricity from renewable sources is produced at non-standard hours when consumption is reduced. Therefore, a significant part cannot be consumed and used, despite network optimization and resizing. Therefore, a storage solution is needed to replace classic solutions (inverters, storage in conventional batteries), as fossil fuel energy production (which can be programmed, thus ensuring the balance of the energy system at present) will decrease considerably. Given [PERSON] et al. [9] allegations, we have taken into account (as a working hypothesis) that a percentage of 30% of all electricity produced from renewable sources (SRTe) cannot be used and consumed at the time of production and must be stored, defining the variable (SRTen). Based on this assumption, we formulated the second hypothesis, which aims to establish whether the amount of electricity that cannot be used and consumed at the time of production (SRTen) can be stored in removable modular batteries, for which there will be demand until the level of 2050.  
**Hypothesis 2 (H2).**_At the level of 2050, there will be demand for removable modular batteries (used in electric vehicles) that can take the amount of unsolicited electricity on the market at the time of production (SRTen), ensuring its storage and mobility._  
To test the hypothesis, we will determine the number of electric vehicles powered by potential renewable energy obtained by storing quantities that cannot be used and consumed at the time of production (SEV). Then we will compare this number of potential electric vehicles with the number of vehicles estimated based on the historical trend for 2050 (estimates based on SV). Furthermore, we assume that all vehicles will be electric by 2050, according to the E.U. Transport White Paper [1]. Finally, we will consider an average consumption of 0.2 Kwh/km (consumption calculated according to [55]) and an average annual distance traveled of 12,000 km/vehicle (average length assumed based on [56]).
# An Experience Transfer Approach for the Initial Data of Iterative Learning Control
## 5 ILC of Amplitude Adjusting
### Simulation Test  
For the experience transform of longitudinal shift amplitude adjusting, a control system of robot manipulator is taken as example. Suppose that the moment of inertia ratio between the load and robot arms is 0.8:0.2. The old arm is replaced by a light one with the 50% moment of inertia, and the load and the driving motor remain unchanged. The ILC with experience transform pre-set its initial iteration learning data of the new system according to Equation (12). In addition, a certain random measurement error is taken into account in the simulation.  
For the experience transform of proportional variable amplitude adjusting, take the similar example of robot manipulator. In this example, the driving power of the manipulator is supposed to increase 20%. The ILC experience transformation of the original system is carried according to Equation (13). The simulation results of the two kinds of ILC with amplitude adjusting are shown in Table 3 and Figure 8.  
According to the comparison results in the figure, the iteration turns required for the same accuracy, say 0.1, are 7, 5 and 20, respectively. The learning efficiency improves 65% and 75%. The approach based on ILC of experience transform shows better initial deviation and convergence turns.
# Transmission Power Control in Wireless Sensor Networks Using Fuzzy Adaptive Data Rate
###### Abstract  
As the technology of Internet of Things (IoT) becomes popular, the number of sensor nodes also increases. The network coverage, extensibility, and reliability are also the key points of technical development. To address the challenge of environmental restriction and deployment cost, most sensor nodes are powered by batteries. Therefore, the low-power consumption becomes an important issue because of the finite value of battery capacity. In addition, significant interference occurs in the environment, thereby complicating reliable wireless communication. This study proposes a fuzzy-based adaptive data rate for the transmission power control in wireless sensor networks to balance the communication quality and power consumption. The error count and error interval perform the inputs of a fuzzy system and the corresponding fuzzy system output is guard that is utilized for limiting the upper bounds of data rate and transmission power. The long-term experimental results are introduced to demonstrate that the control algorithm can overcome environmental interference and obtain low-power performance. The sensor nodes have reliable communication under an ultra-low-power consumption. The experimental results show that the total power consumption of the proposed approach has been improved 73% compared with the system without executing the algorithm and also indicate the Packet Error Rate (PER) is close to 1%. Therefore, the proposed method is suitable for the battery supply IoT system.  
Internet of Things (IoT); wireless sensor network (WSN); adaptive rate control; transmission power control; fuzzy controller +
Footnote †: journal: Sensors
## 4 Test Results and Analyses
### Bond Properties of Single Steel Fiber
#### 4.3.1 Bond Strength
Considering the safety coefficient of 1.10 for the tensile strength of steel fiber less than 800 MPa as that of normal-strength steel bar [38], the \(u_{\text{sf}}\) of 90.9% would keep sufficient reliability to avoid the fracture of steel fiber. The \(u_{\text{sf}}\) of HF4, IF1, MF, and LF are higher than 90.9%, which corresponds to the fibers broken in the tests. Only the CF has \(u_{\text{sf}}\) of 46.2% with the cracking of mortar. Figure 13 presents the strength ratios \(u_{\text{de}}\) and \(u_{\text{res}}\) of different deformed steel fibers. All \(u_{\text{de}}\) for hooked-end and crimped steel fibers are less than 60%. Those of indentation, milling, and large-end steel fibers are higher than 60%. This indicates that the chemical adhesion accounts for a large proportion in indentation, large-end, and milling steel fibers. The \(u_{\text{res}}\) of hooked-end steel fibers are higher than 70%, while those of CF, CF2, and IF2 are less than 50%. This corresponds to the CF, CF2, and IF2 with failure mode of mortar cracking at point P due to the mechanical anchorages and the physical friction along steel fibers obviously decreasing.