esa-sceva/satcom-chunk-collection huggingface dataset:
# Time-Dependency in the Corrosion Process of Reinforced Concrete Subjected to a Chloride Solution
## 4 Results and Discussion
### Electrical Parameters in Galvanostatic Pulse Technique
After depassivation, the steel roughness increased, and ionic movement became more active because the passive film on the steel surface was destroyed, increasing the capacitance and reducing the polarisation resistance [13]. When the polarisation resistance decreased significantly, the capacitance also decreased and then increased. This phenomenon was not observed for the 0.6 OPC-1, for which considerable corrosion was found to proceed. Provided that the capacitance indicates the steel conditions, such as the presence of a passive film and corrosion products, depassivation can be explained. The capacitance trend indicated the destruction of the passive film and the formation of corrosion products. The passive film comprising Fe\({}_{3}\)O\({}_{4}\), which is a main product with a 20 dielectric constant, was destroyed and transformed by \(\gamma\)-FeOOH, with a dielectric constant of 2.6 [30]. Finally, \(\alpha\)-FeOOH was formed as a corrosion product, which is more porous and has a higher dielectric constant of 11 [30]. The steady increase in capacitance accounted for the change in the corrosion product phase. Both the capacitance and polarisation resistance of the steel in the 0.6 OPC-1 were lower than those in the 0.6 OPC samples at 186 days. The results indicate that magnetite was significantly transformed into \(\gamma\)-FeOOH, which is relatively more corrosive. The time constant, the required time to charge a capacitor by approximately 63% of the difference from an initial value or to discharge a capacitor by approximately 36.8% [32], is an indicator of the electrochemical condition of steel embedded in concrete. If the electrical circuit comprises a resistor parallel to a capacitor, the time constant is equal to the product of the resistance and capacitance, which is independent of geometrical factors. Thus, the steel condition can be described using a time constant. The time constant is given with respect to the corrosion potential for all the samples, as shown in Figure 7. The figure shows that the time constant in the active state, corresponding to less than 20 s, was lower than that in the passive state, corresponding to more than 70 s, consistent with previously reported data [33]. This indicates that less adherent/porous corrosion products on the steel surface readily charge/discharge electrons during the polarisation process, leading to a rapid response in the time constant. The long time constant in the passive state can explain the difficulty in achieving a steady state when using the DC polarisation technique owing to the low capacitance and diffusion process. In addition, the values were independent of time, as shown in Figure 7. Hence, the time constant is useful for determining the steel condition; however, it is suggested as a qualitative rather than a quantitative indicator.
# Potential of Sentinel-1 Radar Data for the Assessment of Soil and Cereal Cover Parameters
## 2 Study Site and Database
### Database
#### 2.2.2 Ground Measurements
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Date**} & \multicolumn{2}{c}{**Radar Image**} & \multicolumn{2}{c}{**Soil Moisture**} & \multirow{2}{*}{**Hrms (cm)**} & \multirow{2}{*}{**LAI (m\({}^{2}\)/m\({}^{2}\))**} & \multirow{2}{*}{**H (cm)**} \\  & **(Polarisation, Angle)** & & & & & \\ \hline
6 December 2015 & VV/VH, 39–40\({}^{\circ}\) & [5.2–26.49] & [0.56–2.93] & & \\
3 February 2016 & VV/VH, 39–40\({}^{\circ}\) & [5.2–25] & - & [0.64–4.16] & [16.38–64.37] \\
28 February 2016 & VV/VH, 39–40\({}^{\circ}\) & [4.48–28.05] & - & [1.28–5] & [28.72–95.10] \\
15 April 2016 & VV/VH, 39–40\({}^{\circ}\) & [10.81–23.31] & [0.62–32.34] & [0.03–4.25] & [56.3–112] \\
9 May 2016 & VV/VH, 39–40\({}^{\circ}\) & [9.2–23.02] & - & [0.001–0.03] & [76.8–110.7] \\
23 December 2016 & VV/VH, 39–40\({}^{\circ}\) & [23.14–41.18] & [0.72–4.55] & - & - \\
16 January 2017 & VV/VH, 39–40\({}^{\circ}\) & [11.96–33.19] & - & [0.05–4.23] & [8.34–25.63] \\
21 February 2017 & VV/VH, 39–40\({}^{\circ}\) & [8.97–30.64] & [1.08–3.78] & [0.28–5] & [11.57–62.57] \\
18 March 2017 & VV/VH, 39–40\({}^{\circ}\) & [8.12–31.58] & - & [0.58–5] & [11.8–84.07] \\
23 April 2017 & VV/VH, 39–40\({}^{\circ}\) & [6.36–34.46] & - & [0.09–1.6] & [56.93–99.07] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Radar and ground measurement campaigns during 2015–2017. Radar image configurations; Root mean square surface height (Hrms); leaf area index (LAI); vegetation height (H). Figure 3: Daily precipitation and mean surface moisture over the studied site during the measurement campaigns. were made using a 1 m long pin profiler, with a separation of 2 cm between successive needles. For each test plot, five parallel and five perpendicular profiles were compared in order to take the possible effects of directional roughness retrieval into account. The roughness profiles are considered to be stationary and ergodic. Two statistical parameters are then estimated: the root mean square surface height (Hrms) and the correlation length (L) [34]. These were computed from the correlation functions of the heights of the surface profiles. We considered two roughness measurement campaigns during each agricultural year, which was sufficient to account for most variations in soil roughness (Table 2). The soil roughness can vary over a wide range of values, mainly in autumn, prior to the sowing of cereals. After sowing, the soil is no longer tilled and is affected only by very small temporal variations in roughness. During all measurement campaigns, Hrms ranged between 0.56 and 4.55 cm, and the correlation length varied between 1.96 and 13.14 cm.
crucial for the effective functioning of IoT applications. Current IoT architectures employ a diverse range of communication technologies, encompassing short-range solutions tailored for localized environments like homes and industrial settings as well as long-range solutions capable of covering vast geographic areas [9; 10]. The introduction of 5G technology marks a pivotal moment in the IoT landscape, addressing the evolving connectivity needs of IoT applications. The standardization of 4G-IoT systems, such as Long-Term Evolution Machine type communication (LTE-M) and NarrowBand Internet of Things (NB-IoT), continued to evolve in 5G standards [11; 12].  
These advancements collectively contribute to a transformative 5G-IoT ecosystem, unlocking a vast spectrum of possibilities for the future of connected devices and services [13; 14; 15; 16]. The IoT is rapidly evolving, paving the way for innovative scenarios and expanding the range of existing IoT applications, laying the foundation for future 6G-IoT systems [17; 18]. However, despite the progress made, there are still unique challenges that need to be addressed, particularly in the research area of the Internet of Remote Things (IoRT).
## 7 Conclusions  
In this paper, we consider the problem of transmitting the event report to the mobile actor in wireless sensor and actor networks. By exploiting the trail information left by the mobile actor, we propose a trail-based search strategy (TS) that consists of search phase and chase phase. The search phase is successful when either the actor or its footprints are discovered, then the packets are forwarded along the trail until it is received by the actor in the chase phase. The redundancy reduction scheme based on TS (TS-R) is proposed to reduce the transmission redundancy by using local trail information. The theoretical analysis derives the condition to guarantee the trail connectivity, and proves that TS can reduce search delay and energy consumption by comparing with ERS. Simulation results verify the efficiency of the TS strategy.  
TS is a reasonable solution to handle actor mobility in WSANs. It scales well in the scenarios with multiple actors. Moreover, It has better performance than ERS since it increases the success rate of search attempts by taking advantages of footprint information. Compared with location services, TS performs better in the event-driven applications where the data transmission is initiated only when the event occurs. It is an interesting issue to combine the trail-based mechanism with location service which will be studied in future works.  
Figure 14: The energy consumption over the number of actors.  
Figure 15: The search delay over the number of actors.  
**Acknowledgments:** This work is partially supported by NSF of China (61673116, 61304260, 61622307, 61703105, 61703106), NSF of Fujian Province of China (2017)01500, 2014J05072), NSF of Zhejiang Province of China (LY18F030013).  
**Author Contributions:** [PERSON] and [PERSON] designed the protocol and wrote the paper, [PERSON] executed the experiments and analyzed the data, [PERSON] designed the protocol and provided theoretical analysis, [PERSON] contributed analysis tools and wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction
### State of the Art
The results of some experimental trials [34] with TW and BDL systems in nuclear power plants [35], windowless laboratories [36], hospital rooms [37; 38], and ICUs [39; 31] support the improvement in the regulation of melatonin and cortisol levels. However, while it is necessary to continue studying the impact of lighting in environments with high demands for visual performance [40; 41], such as those involving rotating shift workers and occupants with high vulnerability, such as those in an ICU [39], this requires a balance between the needs of the healthcare workers to remain alert due to rotating shifts, the enhancement of their chromatic discretization capacity in regards to visual recognition [42], and the improvement of patients' sleep [1]. However, electric lighting must necessarily serve as a complementary system to natural light, since the rational utilization of natural light allows for a significant reduction in energy consumption associated with the use of these electric lighting systems [33; 43; 44]. Moreover, it promotes the improvement of both patient well-being and circadian entrainment [1]. The use of natural light in these spaces through proper window design should therefore be a priority in the architectural process. To optimize this design, there are multiple lighting metrics, both static [45; 46; 47] and dynamic. Two of these dynamic metrics stand out for their simplicity and reliability: daylight autonomy (DA) [48], which allows for the determination of the percentage of hours of annual use in which electric lighting is not necessary because required illuminance levels are met by natural light, and circadian stimulus autonomy (\(CSA\)) [1; 33], which allows for the assessment of the suppression of the circadian stimulus over the year due to perceived light. In both cases, these dynamic metrics incorporate the spatial, climatic, and usage conditions of the evaluated spaces into their assessment. Using lighting metrics, various previous studies have focused on optimizing window design in different building types [2; 49], such as offices [41; 50; 51; 52], educational centers [33; 53; 54; 55], residential buildings [56; 57; 58; 59], and even hospital settings [1; 60; 61; 62]. However, in the case of hospital environments, particularly critical spaces like the ICU, there is often a lack of simultaneous consideration of lighting criteria for promoting circadian entrainment and improving work and visual performance. This is why the present study can serve as a guide for window design for ICU environments, applicable in both the design phase and for renovations of existing buildings.
## 2 Ultra-Wide Band Sensor Networks Signal Model  
We consider binary time hopping code division pulse position modulation (PPM) impulse radio ultra-wide band wireless sensor networks with \(N_{u}\) users [48], where the transmitted UWB wireless signal with PPM from the \(q\)-th user can be written as [36]  
\[s_{[q]}(t)=\sqrt{\frac{E_{[q]}}{N_{f}}}\sum_{i=0}^{\infty}\Xi_{[q]}\left( \left\lfloor\frac{i}{N_{f}}\right\rfloor\right)d_{[q]}(i)\;\;p_{\text{tx}} \Big{(}t-iT_{f}-c_{[q]}^{\text{TH}}(i)T_{c}-\varsigma_{[q]}\Big{)}, \tag{1}\]  
where \(E_{[q]}\) denotes the average energy per transmitted symbol of the \(q\)-th user; each symbol consists of \(N_{f}\) basic pulses \(p_{\text{tx}}(t)\) with an ultrashort time duration \(T_{p}\); the frame period is \(T_{f}\) seconds; the number of time slots per frame is \(N=T_{f}/T_{c}\); the chip period is \(T_{c}\); commonly, the chip period is longer than the impulse duration; \(\Xi_{[q]}\left(\left\lfloor i/N_{f}\right\rfloor\right)\) denotes the information symbol transmitted by the \(q\)-th ultra-wide band sensor; the index of the symbol is \(\left\lfloor i/N_{f}\right\rfloor\) (\(\left\lfloor z\right\rfloor\) denotes the integer part of \(z\)); \(d_{[q]}(i)\) is binary random variables taking values \(\pm 1\) with equal probability; and \(c_{[q]}^{\text{TH}}(i)\) is the time hopping code of the \(q\)-th user, where \(c_{[q]}^{\text{TH}}(i)\in\{0,1,...,N_{h}-1\}\); the timing jitter of the \(q\)-th user \(\varsigma_{[q]}\sim\mathcal{U}(0,T_{c})\) with \(\mathcal{U}(a,b)\) denoting the uniform distribution on the interval \([a,b]\).  
We choose the impulse wave \(p_{\text{tx}}(t)\) to be the second derivative of a Gaussian monocycle, which can be expressed as:  
\[p_{\text{tx}}\left(t\right)=\sqrt{\frac{1}{\sqrt{\pi}\tau_{p}}}\left[1-4\pi \left(\frac{t}{\tau_{p}}\right)^{2}\right]\exp\left[-2\pi\left(\frac{t}{\tau_ {p}}\right)^{2}\right], \tag{2}\]  
where \(\tau_{p}\) is the normalization duration factor controlling the 3-dB bandwidth.
# Multi-Satellite Task Parallelism via Priority-Aware Decomposition and Dynamic Resource Mapping
## 3 Problem Description and Formulation
### Problem Definition
Then, the observation task execution time is calculated as follows:  
\[\varphi_{m}^{p}(t)=\sum_{s\in S}\omega_{s}^{p}\cdot r_{s}^{p}(t), \tag{10}\]  
where \(\omega_{s}^{p}\) is the priority of the observation task, and \(r_{s}^{p}(t)\) is the execution time for observation tasks. The compression task execution time is calculated as follows:  
\[\varphi_{m}^{b}(t)=\sum_{s\in S}\frac{r_{s}^{b}(t)}{\
u}, \tag{11}\]  
where \(r_{s}^{b}(t)\) is the execution time for compression tasks, and \(\
u\) is the efficiency parameter for computational resources. Our goal is to minimize the total execution time of tasks in the multi-satellite cooperative computing system. Inputs include resource characteristics \(\mathbf{R}(T_{ij})\) and task requirements \(\mathbf{M}\), with the optimal task allocation strategy \(\mathbf{X}\) being the output. The objective function is\[\min\sum_{m=1}^{M}\Big{(}\varphi_{m}^{p}(t)+\varphi_{m}^{b}(t)\Big{)}, \tag{12}\]  
where \(\varphi_{m}^{p}(t)\) denotes the observation time, and \(\varphi_{m}^{b}(t)\) denotes the time for data processing and compression. This combinatorial optimization problem involves complex task decomposition and resource mapping.
# Nemesis: Neural Mean Teacher Learning-Based Emotion-Centric Speaker
## 3 Results
### Comparison with the State-of-the-Art
#### 3.3.2 Emotion-Centric Image Captioning Task  
We compared our proposed neural speaker to the best-performing emotion-centric image captioning models introduced by [19] on the ArtEmis dataset. These neural speakers include a captioning model inspired by meshed-memory transformer (\(M^{2}\)) [42] architecture, and an LSTM-based model inspired by \"Show, Attend and Tell\" (SAT) [9]. In addition, this comparison included the emotionally grounded variations of these models (i.e., \(M^{2}\)-EG and SATEG).  
As can be observed in Table 6, our proposed model outperforms both the emotionally grounded and standard variations of \(M^{2}\) and SAT speakers with respect to all incorporated metrics. This improvement is more notable in models after the SCST fine-tuning stage. The only exception is the EGNemesis, and the reason for this degradation has been elaborated on previously. As an example, Figure 5 shows some generated utterances from SATEG and EGNemesis models, where EGNemesis appears to generate more abstract, diverse, emotionally rich, and human-like captions.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Metric** & **SAT** & **SATEG** & \(M^{2}\) & \(M^{2}\)**-_EG_ & **Nemesis** & \(\text{Nemesis}_{SCST}\) & \(\text{EGNemesis}\) & \(\text{EGNemesis}_{SCST}\) \\ \hline BLEU-1 & 0.536 & 0.520 & 0.507 & 0.511 & 0.539 & 0.711 & 0.479 & 0.700 \\ BLEU-2 & 0.290 & 0.280 & 0.282 & 0.282 & 0.311 & 0.406 & 0.260 & 0.403 \\ BLEU-3 & 0.155 & 0.146 & 0.159 & 0.154 & 0.178 & 0.211 & 0.141 & 0.241 \\ BLEU-4 & 0.087 & 0.079 & 0.095 & 0.090 & 0.106 & 0.113 & 0.080 & 0.115 \\ METEOR & 0.142 & 0.134 & 0.140 & 0.137 & 0.141 & 0.166 & 0.129 & 0.165 \\ ROUGE-L & 0.297 & 0.294 & 0.280 & 0.286 & 0.294 & 0.341 & 0.262 & 0.336 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of state-of-the-art results and Nemesis after both cross-entropy training and SCST fine-tuning.  
Figure 5: A comparison between the examples of generated captions by EGNemesis and SATEG models along with the emotional class extracted by the image-to-emotion classifier, which has been utilized in the emotional grounding process. It can be observed that the generated utterances by EGNemesis appear to be more abstract, human-like, and emotionally rich.
# Experimental Demonstration of MmWave Vehicle-to-Vehicle Communications Using IEEE 802.11 ad
## 5 Experimental Result
### Coverage and Beam Measurement
Figure 8: Measured SNR of beam angle \(\pm 30^{\circ}\). **Observation 3**. : IEEE 802.11 ad operating on 60 GHz demonstrates a pathloss exponent of approximately 2.1 at LOS. Figure 9 shows the varying receive SNR values in the moving vehicle. In contrast to the previous measurement with fixed nodes, we obtained the received SNR samples from a slowly moving vehicle against a static transmitting vehicle. In this case, we observed that the SNR decreased exponentially corresponding to the distance; the SNR decreased by approximately 18 dB when the vehicle crossed the 20 m distance. By applying the pathloss equation (Equation (2)), \(\alpha*10 log(20/d_{0})=18\). Thus, the pathloss exponent was calculated as about 2.2 with the \(d_{0}=3\), which is the distance between vehicles when the SNR is 10 dB. This measured exponent value was close to to the value of one of the LOS models described in [56]. **Observation 4**. : The receiver SNR fluctuates during the communication between moving vehicles.
# AE-DTI: An Efficient Darknet Traffic Identification Method Based on Autoencoder Improvement
## 1 Introduction  
The darknet refers to certain cyberspaces not included by ordinary search engines [1], also known as the deep web. On the darknet, users can anonymously access and share various information and content, including illegal and dangerous content such as drugs, weapons, terrorist propaganda, and more. Therefore, the darknet has also become the focus of governments and law enforcement agencies of various countries, and the traffic identification of the darknet has become an essential means of monitoring and combating crimes on the darknet [2].  
Traffic identification refers to identifying specific network traffic types by analyzing and processing network data packets [3]. There are two main existing traffic identification methods [4]. The machine learning-based method: this method builds a traffic identification model by training with known normal traffic and dark network traffic; the model can learn the characteristics and patterns of different types of traffic and is used to identify unknown traffic. The deep learning-based method: This method employs deep neural networks to learn high-level features in network traffic. Deep learning methods can automatically learn and represent complex features, so it is very suitable for processing large-scale network traffic data.  
However, the existing darknet traffic identification relies on all traffic characteristics, which requires a long computing time and a large amount of system resources, resulting in low identification efficiency. To this end, this paper proposes an autoencoder-based darknet traffic identification method (AE-DTI). First, AE-DTI maps the feature values to pixels of a two-dimensional grayscale image after deduplication and denoising of the darknet traffic dataset. Then, AE-DTI designs a new feature selection algorithm (AE-FS) to downscale the grayscale graph, and AE-FS trains a feature scoring network, which globally scores all the features based on the reconstruction error to select the features with scores greater than or equal to a set threshold value. Finally, AE-DTI uses a one-dimensional convolutional neural network with a dropout layer to identify darknet traffic on the basis of alleviating overfitting.
# Adjustment Algorithm for Free Station Control Network of Ultra-Large Deepwater Jacket
## 3 Results and Discussion  
### An Overview of the Control Network  
A high-precision control network was laid around the construction site's periphery to ensure the construction quality of an ultra-large jacket. Two observation phases were carried out using a Leica TS50 total station to adopt the free station method. Each station observed all of the visible control points for six observation sets, simultaneously observing the slope distance, horizontal direction, and vertical angle and measuring the dry and wet temperatures and atmospheric pressure at the site to impose ranging meteorological corrections.
###### Abstract  
Aiming to address the problems of difficulty in selecting characteristic quantities and the reliance on manual experience in the diagnosis of transformer core loosening faults, a diagnosis method for transformer core looseness based on the Gram angle field (GAF), residual network (ResNet), and multi-head attention mechanism (MA) is proposed. This method automatically learns effective fault features directly from GAF images without the need for manual feature extraction. Firstly, the vibration signal is denoised using ensemble empirical mode decomposition (EEMD), and the one-dimensional temporal signal is converted into a two-dimensional image using Gram angle field to generate an image dataset. Subsequently, the image set is input into ResNet to train the model, and the output of ResNet is weighted and summed using a multi-head attention module to obtain the deep feature representation of the image signal. Finally, the classification probabilities of different iron-core loosening states of the transformer are output through fully connected layers and Softmax layers. The experimental results show that the diagnostic model proposed in this paper has an accuracy of 99.52% in identifying loose iron cores in transformers, and can effectively identify loose iron cores in different positions. It is suitable for the identification and diagnosis of loose iron cores in transformers. Compared with traditional methods, this method has better fault classification performance and noise resistance.  
transformer vibration; loose iron core; Gram angle field; multi-head attention mechanism; fault diagnosis +
Footnote †: journal: applied sciences