esa-sceva/satcom-chunk-collection huggingface dataset:
# Zero-TCE: Zero Reference Tri-Curve Enhancement for Low-Light Images
## 1 Introduction
These curves are then utilized to adjust the dynamic range of the input image at the pixel level to obtain an enhanced image. These three curves are meticulously designed to boost image brightness while avoiding overexposure, preserving the highlight regions of the original image, and enhancing image contrast without excessive enhancement, making the image appear more realistic. More importantly, the proposed network is lightweight, enabling more robust and precise dynamic range adjustment. Firstly, we design two modules based on DCE-Net, the TCE-L and TCE-C modules, which output two curves that are specifically for enhancing image brightness and contrast, respectively. Additionally, we develop a multi-scale feature fusion (MFF) module that integrates multi-scale features from both the original and preliminarily enhanced images, taking into account the brightness distribution characteristics of low-light images, to obtain the optimal enhanced image. The key advantage of the proposed algorithm lies in its zero-reference nature, meaning no paired or unpaired data are required during training. This is achieved by adopting a set of non-reference loss functions, including spatial consistency loss, exposure control loss, color constancy loss, and illumination smoothness loss, to ensure image quality and avoid issues such as artifacts and color distortion. Furthermore, we incorporate the structural similarity index measure loss to preserve accurate image textures. These loss functions are designed to capture high-level features of images, rather than just pixel-level differences, thereby helping to improve the model's ability to adapt to new lighting conditions. The contributions of this study are as follows:  
1. We propose Zero-TCE, a zero-reference dual-path network for low-light image enhancement based on multi-scale deep curve estimation. This network is independent of paired and unpaired training data, thereby mitigating the risk of overfitting. We design three image-specific curves that collectively enable brightness enhancement, contrast improvement, and multi-scale feature fusion for low-light images. 3. We indirectly assess enhancement quality through task-specific non-reference loss functions, which preserve accurate image textures, enhance dark details, and avoid issues such as artifacts and color distortions. The structure of the remainder of this paper is outlined as follows: Section 2 delves into related work, providing an overview of the current approach in the field. Section 3 presents a comprehensive description of the proposed Zero-TCE method, elucidating its intricacies in detail. In Section 4, we present and discuss the experimental results obtained through the application of Zero-TCE. Lastly, Section 5 concludes the paper by summarizing our findings and outlining potential directions for future research.
# Multipole Multi-Layered Magnetorheological Brake with Intermediate Slots
## 3 Mathematical Models
### Magneto-Stress Model  
The next step is creating an analytical model of the MR brake to find the total torque generated by the brake. The total torque generated by the MR brake contains field torque, viscous torque, and friction torque. Field torque depends on the magnetic field strength, which can be estimated by the curve fitting method. Viscous torque depends on speed and brake structure, and frictional torque is a fixed value. This mathematical model is prepared based on the Bingham-plastic model as given in Equation (19).  
\[\tau=\tau_{yd}+\eta\frac{\omega r}{g} \tag{19}\]  
where \(r\) is the radius of the rotor disc, \(\tau_{yd}\) is yield shear stress due to the magnetic field, \(\eta\) is the viscosity of MR fluid, \(\omega\) is the angular velocity, and \(g\) is MR layer thickness.  
The torque generated by the designed multi-layer MR brake can be calculated through area integration (Equation (20)). Specifically, this involves summing the contributions from each layer and integrating the area of the MR fluid.  
\[T=\sum_{n=1}^{4}\tau_{mm}{}^{2}*\left(\int_{0}^{z}\int_{0}^{2\pi}\tau_{n}\text{ d}\theta\text{d}Z\right) \tag{20}\]  
Substituting Equation (19) in (20):  
\[T_{mm}=\sum_{n=1}^{4}\tau_{mm}{}^{2}*\left(\int_{0}^{z}\int_{0}^{2\pi}\tau_{n} \text{d}\theta\text{d}Z\right)+\frac{2\pi\eta\eta\text{w}Z}{g}{\sum_{n=1}^{4} \left(\tau_{mm}{}^{3}\right)} \tag{21}\]  
where T\({}_{mm}\) is active torque in MR layers, \(r_{mr}\) is the radius of the MR layer, \(n\) is the layer number, \(Z\) is the axial width, and w is the speed. The Lord MRF-140 CG magnetorheological fluid was utilized in this investigation.  
Equation (23) gives the total torque generated by the MPML MR brake.  
\[T=T_{mm}+Frictional\ torque \tag{22}\]  
\[T=\sum_{n=1}^{4}\tau_{mm}{}^{2}*\left(\int_{0}^{z}\int_{0}^{2\pi}\tau_{yn} \text{d}\theta\text{d}Z\right)+\frac{2\pi\eta\text{w}Z}{g}{\sum_{n=1}^{4} \left(\tau_{mm}{}^{3}\right)}+\text{T}_{fr} \tag{23}\]  
where \(T_{fr}\) is the frictional torque. Yield stress \(\left(\tau_{y}\right)\) of the MR fluid can be found by using a curve-fitting method from experimental studies [21],  
\[\left(\tau_{y}\right)=k5*\text{H}_{MR}{}^{5}+k4*\text{H}_{MR}{}^{4}+k3*\text{H} _{MR}{}^{3}+k2*\text{H}_{MR}{}^{2}+k1*\text{H}_{MR}{}^{1}+k0\]  
The values of k1-k5 are the experimental curve-fitting constants, and those are as follows: \([-2\times 10^{-10}\quad 9\times 10^{-8}\quad-2\times 10^{-05}\quad 0.0002 \quad 0.5324\quad 1.0303]\)
## 4 Results and Discussion
### Land/Water Discrimination from Geometry-Based Features  
As mentioned earlier, the labeling of water points using geometry-based features depends on the returns from the benthic layer. Consequently, the combination of C1/C2 and C3 was required. The water points were labeled as such if the _PD_ of a LiDAR point in C3 was higher than the _PD_ in C1  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Point Feature**} & \multirow{2}{*}{**Channel**} & \multicolumn{2}{c}{**Completeness (\%)**} & \multicolumn{2}{c}{**Correctness (\%)**} & \multirow{2}{*}{**Overall Accuracy (\%)**} \\ \cline{3-3} \cline{5-7}  & & **Land** & & **Water** & & **Land** & **Water** \\ \hline \multirow{3}{*}{_ICOV_} & C1 & 51.6 & 93.5 & **97.9** & 25.0 & 57.8 \\  & C2 & 38.8 & **93.6** & 96.6 & 24.6 & 48.4 \\  & C3 & **76.7** & 54.0 & 73.6 & **58.0** & **68.2** \\ \hline \multirow{3}{*}{_ID_} & C1 & 72.3 & 60.9 & **91.5** & 27.5 & 70.6 \\  & C2 & **76.3** & 57.5 & 89.4 & 34.2 & **73.0** \\ \cline{1-1}  & C3 & 51.7 & **88.7** & 88.5 & **52.3** & 65.5 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Accuracy measures of labeled points based on _ICOV_ and _ID_. The best results are highlighted in bold.  
Figure 6: Labeled LiDAR points based on _ICOV_ and _ID_.  
or C2. For _NOR_ feature, a LiDAR point with double returns in C3 and a single return in either C1 or C2, was labeled as a water point. Figure 7 shows the labeled LiDAR points based on _PD_ and _NOR_. The completeness, correctness, and overall accuracy of the labeled points are provided in Table 7.  
The _PD_ feature shows lower overall accuracy, and most land points were misclassified as water. This is mainly due to the land area reflecting more returns in C3 than in C1 and C2 because the land objects exhibited various characteristics at different wavelengths. Therefore, the condition that the _PD_ in C3 is higher than the _PD_ in C1 or C2 was not achieved. The _NOR_ feature demonstrated promising results. However, this is attributed to the presence of benthic returns from C3. Despite the relatively high overall classification accuracies obtained using _NOR_, there are misclassified points in water bodies. This is mainly due to single returns from water surfaces or benthic layers in C3 and, hence, the necessary condition for labeling water points was not achieved.
## 2 EO Satellite and In Situ Observations for Earth Surface  
This section reviews the satellites and in situ platforms that have dedicated sensors for Earth surface monitoring and that have been used to inform model development. Both surface dedicated and surface sensitive instruments are considered.
## 4 Experimental Validation and Result Analysis
### Processing of Experimental Data
#### 4.1.1 Result of Experimental Data Denoising  
According to the data signal processing method described in Section 2.1, the dataset Bearing1-3 is as an example, and denoising processing is applied to the bearing dataset. As shown in Figure 8, it presents a comparison of the vibration signals before and after denoising. As shown in Table 3, we use RMSE (Root Mean Squared Error), RMS (Root Mean Square), and MAE (Mean Absolute Error) to evaluate the denoising performance, and compared with the classical Particle Swarm Optimization-based Variational Mode Decomposition denoising algorithm (PSO-VMD).  
The formula for RMSE is [5]:  
\[RMSE=\sqrt{\frac{1}{n_{o}}\sum\limits_{o=1}^{n_{o}}{(z_{o}-\hat{z}_{o})^{2}}} \tag{44}\]  
where \(z_{o}\) represents the original vibration signal, \(\hat{z}_{o}\) denotes the signal after denoising treatment, and \(n\) is the number of samples.  
The formula for RMS is [8]:  
\[RMS=\sqrt{\frac{1}{n_{o}}\sum\limits_{o=1}^{n_{o}}{\hat{z}_{o}^{2}}} \tag{45}\]  
\begin{table}
\begin{tabular}{c c c} \hline
**Conditions** & **Training Set** & **Testing Set** \\ \hline Condition 1 & Bearing 1-1–Bearing 1-2 & Bearing 1-3 \\ Condition 2 & Bearing 2-1–Bearing 2-2 & Bearing 2-6 \\ Condition 3 & Bearing 3-1–Bearing 3-2 & Bearing 3-5 \\ \hline \end{tabular}
\end{table}
Table 2: Bearing dataset.  
Figure 8: Denoising result.  
The formula for MAE is [8]:  
\[MAE=\frac{1}{n_{o}}\sum_{o=1}^{n_{o}}\left|z_{o}-\hat{z}_{o}\right| \tag{46}\]  
It can be seen that the RMSE, RMS, and MAE values obtained using the wavelet threshold denoising algorithm are 0.032, 0.079, and 0.000012, respectively, which are all smaller than those of the comparative algorithm PSO-VMD. Therefore, the wavelet threshold algorithm exhibits stronger denoising capability. Through denoising processing, the bearing signal becomes purer, with improved signal quality, thus providing an accurate data foundation for subsequent feature extraction, failure threshold detection, and life prediction of bearing signals.
Sensors_**2015**, _15_, 5914-5934; doi:10.3390/s150305914  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Proactive and Reactive Transmission Power Control for Energy-Efficient On-Body Communications**  
**[PERSON] 1,*, [PERSON] 2 and [PERSON] 2**  
\({}^{1}\) Facultad de Minas, Departamento de Energia Electrica y Automatica, Gaunal, Universidad Nacional de Colombia, Medellin, Carrera 80 No.65-223, Nucleo Robledo, Medellin, 4-72, Colombia  
\({}^{2}\) Department of Computer Architecture and Automation, Complutense University of Madrid,  
C/ Professor [PERSON], Madrid s/n. 28040, Spain; E-Mails: [EMAIL_ADDRESS] (J.R.); [EMAIL_ADDRESS] (J.L.A.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +57-301-768-5706.  
Academic Editor: [PERSON]  
_Received: 5 January 2015 / Accepted: 5 March 2015 / Published: 11 March 2015_  
**Abstract:** In wireless body sensor network (WBSNs), the human body has an important effect on the performance of the communication due to the temporal variations caused and the attenuation and fluctuation of the path loss. This fact suggests that the transmission power must adapt to the current state of the link in a way that it ensures a balance between energy consumption and packet loss. In this paper, we validate our two transmission power level policies (reactive and predictive approaches) using the Castalia simulator. The integration of our experimental measurements in the simulator allows us to easily evaluate complex scenarios, avoiding the difficulties associated with a practical realization. Our results show that both schemes perform satisfactorily, providing overall energy savings of 24% and 22% for a case of study, as compared to the maximum transmission power mode.  
**Keywords:** wireless body sensor networks (WBSNs); transmission power control (TPC); received signal strength indication (RSSI); human body communication
# The Microwave Temperature and Humidity Profiler: Description and Preliminary Results
## 5 Discussion  
Passive millimeter-wave sounding has been a longstanding approach to observing temperature and humidity profiles. However, state-of-the-art systems are bandwidth-limited and provide relatively coarse profiles that are not sufficient to address the challenging resolution requirements demanded by the PBL science community. Information content analyses have demonstrated the capabilities of hyperspectral approaches to increase the density of overlapping weighting functions thereby increasing retrieval resolution [28; 29]. A similar effect to the increased overlapping weighting function can be achieved by viewing the same Earth target from multiple view angles. This high-density measurement together with the multangular information, which provides independent atmospheric information, might lead to a PBL estimation with a finer vertical resolution of around ~350 m, according to simulations using radiosondes. To support MTHP measurements being capable of eventually retrieving PBL information, we have computed a simulation of two spectra, one computed with an inversion layer and another one computed without an inversion layer using the frequency and angular resolution of MTHP. The simulation is provided in Figure 15. As can be seen, small differences in the bottom part of the atmosphere can be detected by the lowest part of the spectrum up to 0.45 K for the simulated case.  
Figure 15: Simulation of a MTHP-like spectrogram with two profiles, one with an inversion layer and another one without the inversion layer. (**a**) The temperature profile used and (**b**) the difference between the profile with inversion layer and the profile without inversion layer.
# Influence of Open Differential Design on the Mass Reduction Function
## 5 Optimization of Differential Transmission
### Analysis of Sensitivity of Parameter Change on Response Objective  
The determination of local sensitivities in Ansys Workbench is carried out by the partial derivative of the output response with respect to the target design variable. The rate of change of the output response when the design variable changes is measured by this partial derivative. Depending on whether the output response increases or decreases with an increase in the design variable, the local sensitivity can be either positive or negative.  
Local sensitivities are critical in design optimization because they allow us to determine which design factors have the greatest impact on the output response. By changing the  
\begin{table}
\begin{tabular}{c c c} \hline
**Responses** & \(R^{2}\) & **RMSE** \\ \hline Overall mass & 0.99532 & 0.03193 \\ Mass of the ring gear & 0.99589 & 0.00149 \\ Mass of the drive shaft & 0.99988 & 0.00700 \\ \hline \end{tabular}
\end{table}
Table 11: Accuracy of the RS model for response variables.  
Figure 16: Values of the numerical model versus predicted values: (**a**) mass of the ring gear; (**b**) mass of the drive shaft; (**c**) overall mass of the optimized design elements.  
design variables with the highest sensitivity, the response of the output can be improved while reducing the number of design iterations required. Local sensitivity indices of the influence of design parameters on the output mass parameters are shown in Figure 17.  
It can be seen that all noticeable local sensitivities have a negative value, indicating that an increase in the value of the design variable leads to a decrease in the value of the output mass parameter. Looking at the overall mass parameter, it can be seen that an increase in all input parameters has an influence on the decrease in overall mass. The most influential parameter is the inner radius of the inner radius of the drive shaft (\(r\)), followed by the overhang height of the ring gear (\(h\)), while the horizontal and vertical chamfers of the ring gear (\(b\), \(a\)) have a significantly smaller influence on the overall mass. These two parameters have a significant influence on the change in the mass of the ring gear, but the overhang height of the ring gear (\(h\)) has a significantly higher local sensitivity, indicating that the output response is very sensitive to changes in this parameter. The mass of the drive shaft is influenced exclusively by the inner radius of the drive shaft (\(r\)).
## 6 Trusted Routing Operation in AODV
### Initiation of a Secure VANET  
Let, we have a simple VANET having three vehicles \((V_{1},V,V_{3})\) moving in a forward direction. \(V_{1}\) has one neighbor and node \(V\) has two neighbor i.e., \(V_{1}\) and \(V_{3}\). At beginning each node has no entry in neighbor routing table, so the opinion metric is \(u=(0,0,0,1)\).  
Now \(V_{1}\) want to discover a route to node \(V_{3}\) the process of node \(V_{1}\), \(V\) and \(V_{3}\) can be describe as  
(1) \(V_{1}\) broadcast RREQ requesting route path to node \(V_{3}\) and waits for RREP in time \(t\) from \(V\).  
(2) \(V\) receive RREQ packet after that node \(V\) will check route to \(V_{3}\) by checking opinion \(\omega^{V}_{V_{1}}\) and \(\omega^{V}_{V_{3}}\). As it is network initial stage so uncertainty will be high and currently no route to \(V_{3}\).  
* Node \(V\) authenticates both the neighbors to verify certificate if \(V_{1}\) passes, the successful event is increased by 1 and the new opinion \(\omega^{V}_{V_{1}}=(0.33,0,0,0.67)\) is made. \(V\) also authenticates \(V_{3}\) and revises the same process. If node \(V_{1}\) fails the authentication, then new opinion will be \(\omega^{V}_{V_{1}}=(0,0.33,0,0.67)\). \(V\) will not forward the packet till the expiry time.
* If \(V_{3}\) has also been authorized and \(V\)'s route table will be updated and \(V\) will re-broadcast the RREQ to \(V_{3}\) after \(V_{3}\) passed the authentication, \(V\) will forward the RREQ. If \(V_{3}\) fails authentication process then opinion \(\omega^{V}_{V_{3}}\) will be re-calculated accordingly.  
(3) Node \(V_{3}\) will also check \(\omega^{V_{3}}_{V}\) and node \(V\)'s trustworthiness if \(V\) passes authentication, \(V_{3}\) will generate an RREP packet to \(V\) and update its route table. If not, \(V_{3}\) will drop the RREQ packet.
## 2 System Model
### Expected Irradiance Estimation  
The next target is to estimate the expected value of the instantaneous, normalized irradiance, \(I_{j}\). Remembering the possible limits within which irradiance can be detected, the RV expectation formula is adapted as follows [39]:  
\[E\big{[}I_{j}\big{]}=\int\limits_{I_{j,\min}}^{B}I_{j}f_{I_{j}}\big{(}I_{j} \big{)}\,dI_{j}. \tag{8}\]  
Figure 3: (**a**) Symmetric TJ PDF curves as a function of time \(T\) for various TJ strength parameter values. The corresponding timeslot of 100 Mbps is 10 ns, the edges of which are at \(T_{1}=+5\) ns and \(T_{2}=-5\) ns. (**b**) Time-jittered pulses for two random jitters, \(\Delta T\), and opposite chirp parameters. In any case, the pulse amplitude, received at instant \(T=0\), is much lower than the corresponding maximum pulse amplitude, as detected at \(z=0.75\,z_{c2}\).  
In order to solve the integral above, \(y=T_{pw}\sqrt{\ln\left(B/I_{j}\right)}\) is used as the RV transformation, yielding the following expression:  
\[E\big{[}I_{j}\big{]}=\frac{2B}{\sqrt{2\pi}\sigma_{T}Z}\int\limits_{0}^{y_{0}} \exp{\left[-y^{2}\left(\frac{1}{T_{pw}^{2}}+\frac{1}{2\sigma_{T}^{2}}\right) \right]}dy, \tag{9}\]  
where \(y_{0}=T_{sl}/2\). Finally, after some mathematical computations, we derive the following expression for the expected irradiance:  
\[E\big{[}I_{j}\big{]}=\frac{T_{pw}B}{Z\Xi}\Bigg{\{}erf\Bigg{[}\frac{\Xi T_{sl}} {2T_{pw}\sqrt{2}\sigma_{T}}\Bigg{]}\Bigg{\}}, \tag{10}\]  
where \(\Xi=\sqrt{2\sigma_{T}^{2}+T_{pw}^{2}}\). Illustrations of the expected irradiance as a function of the propagation distance can be found in Figure 4, for various values of TJ strength parameter.
## 4 The Proposed QoS-Aware Routing
### Dynamic Packet Classifier  
The service differentiation paradigm used in this paper is as follows. We define four classes of data packets - ordinary data packets (OP), reliability-driven data packets (RP), delay-driven data packets (DP) and most critical data packets (CP). The CP packets are given the highest priority due to their stringent delay and reliability constraints, e.g., they may carry electroencephalogram (EEG) and electrocardiogram (ECG) monitoring information during a critical situation such as a surgery. The next higher priority is given to DP packets which should be delivered within a predefined deadline, but may tolerate reasonable packet loss, e.g., video streaming. The RP packets should be delivered without loss, but do not need to be immediate or within a hard deadline, such as vital signal monitoring, respiration monitoring and PH-level monitoring. The lowest priority is given to the OP packets that are corresponding to regular measurements of patient physiological parameters, like body temperature, heartbeat, _etc._, that typically indicate normal values.  
Figure 2: Data-centric multiobjective QoS-aware routing architecture.  
As shown in Figure 2, on reception of data packets either from application layer or from neighbor nodes, the dynamic packet classifier (DPC) of a node assigns them to one of the proper aforementioned categories and feeds them into the respective module in first-come-first-serve (FCFS) manner.
## 3 Results and Discussion
### Volatile Compounds Analysis of Set Yoghurt  
The concentrations of volatile compounds in the set yoghurt samples are presented in Table 1. Generally, a total of 10 compounds including one ketone, four acids, and five alkanes were successfully identified using solid phase microextraction and GC-MS. Acetoin plays an important role in yoghurt aroma and could induce the production of a butter-like flavour [2]. However, there was no significant difference in the content of acetoin among the set yoghurt samples with and without the addition of RE, which indicated that RE had no impact on the butter-like flavour of the set yoghurt. On the contrary, four acid compounds showed an upward trend with increasing RE concentrations. Butanoic, hexanoic, n-decanoic, and octanoic acids were reported to endow set yoghurt with acidic and cheesy characteristics [52]. However, another report suggested that the longer-chain molecules of n-decanoic acid could result in a rancid characteristic [53]. Fortunately, n-decanoic acid could be considered negligible due to its low content in the set yoghurt with RE. These results indicated that the addition of RE might be beneficial in improving the acidic and cheesy properties of set yoghurt. The majority of alkanes have high flavour thresholds; a higher concentration of alkanes could favour fermented products such as set yoghurt with a fruity flavour [54; 55]. As shown in Table 1, three alkanes, decamethyl cyclopentasiloxane, dodecamethyl cyclohexasiloxane, and tetradecamethyl cycloheptasiloxane, showed an upward trend with increasing RE concentrations. Another report also suggested that alkanes correlated with the improvement of the set yoghurt's taste [54]. Consequently, the addition of RE possessed the potential to enhance the flavour of yoghurt through the improvement in bacteria metabolism or its interactions with milk components.  
Figure 7: Pearson correlation analysis of 12 tested physicochemical characteristics with significant differences in the set yoghurt after 24 h of low-temperature storage. (**a**) Control samples; (**b**) set yoghurt with 0.05% RE; (**c**) set yoghurt with 0.10% RE; (**d**) set yoghurt with 0.15% RE. * and ** indicate significance at the \(p<0.05\) and 0.01 levels, respectively.