esa-sceva/satcom-chunk-collection huggingface dataset:
# Harnessing the Cloud: A Novel Approach to Smart Solar Plant Monitoring
## 2 Performance Parameters  
Increasing solar plant efficiency through monitoring has been a challenge and is under the intense scrutiny of researchers. Factors affecting a solar plant's efficiency can be broadly characterized as either meteorological or electro-physical parameters [11], as presented in Table 1. For accurate performance evaluation, it is imperative to monitor these parameters at both panel and system levels [12]. These sensed parameters need to be analyzed and correlated for predictive diagnostics at each solar module level [4; 12; 13; 14]. The overall output of any given solar plant is proportionally dependent on each individual solar panel's output. In this context, the IEC61724 \"Photovoltaic System Performance Monitoring\" standard has been devised for solar plant performance evaluations [15; 16]. The standard classifies solar plant monitoring systems into three classes, as listed in the details below [15]:  
Figure 1: This figure shows the overall overview of the paper. This paper spans a comparative analysis of the proposed methods in the literature, IoT architectural paradigms, primary technologies, our novel cloud-based system, and a discussion on AI-based techniques.  
* Class A demands for higher accuracy and precision and is devised for utility-scale solar plants. The sampling rate interval is set to 1 min frequency. The accuracy of sensed electrical parameters should be better than 2% of the reading. Ambient temperature readings should be within \(\pm\)2 \({}^{\circ}\)C of actual ambient temperature. Variance in sensed irradiance should not be greater than 8% (ranging from 100 W/m\({}^{2}\) to 1500 W/m\({}^{2}\)) [15]. The uncertainty in wind speed should be 0.5 m/s for wind speeds less than 5 m/s and it should be less than 10% for speeds greater than 5 m/s [16].
* Class B systems have medium accuracy and precision. Class B is implemented in large commercial solar plant systems. The required sampling interval is 1 min, with the recording interval set at 15 min [15].
* Class C systems have basic accuracy and precision is mainly used in smaller commercial and residential installations. In this class, the maximum recording interval can be 60 min with a sampling interval of 1 min [15].  
A timestamp is associated with each sampling record and is synchronized using GPS or network time protocol (NTP)-based time. Communication technology employed should be able to transmit sensed data anywhere irrespective of the solar plant's installed location, with transmission delivery speed in accordance with the sampling interval. For the aforementioned reasons, wireless technologies are preferred for decentralized systems [17].
# FONDUE: A Framework for Node Disambiguation and Deduplication Using Network Embeddings +
## 3 Methods
### Fondue-Nda
#### 3.3.1. A First-Order Approximation for Computational Tractability
Let \(\mathbf{F}_{1}^{1}\in\mathbb{R}^{d\times|\Gamma(i)|}\) be a matrix of which the columns correspond to the gradient vectors \(\
abla_{\mathbf{x}_{i}}\mathcal{O}^{p}(\boldsymbol{A}_{ij}=1,\mathbf{x}_{i}, \mathbf{x}_{j})\) (one column for each \(j\in\Gamma(i)\)), and let \(\mathbf{F}_{i}^{0}\in\mathbb{R}^{d\times|\Gamma(i)|}\) be the matrix of which the columns correspond to the gradient vectors \(\
abla_{\mathbf{x}_{i}}\mathcal{O}^{p}(\boldsymbol{A}_{ij}=0,\mathbf{x}_{i}, \mathbf{x}_{j})\) (also one column for each \(j\in\Gamma(i)\)). Moreover, let \(\mathbf{b}_{i}\in\{1,-1\}^{|\Gamma_{i}|}\) be a vector with a dimension corresponding to each of the neighbors \(j\in\Gamma(i)\) of \(i\), with value equal to \(1\) if that neighbor is a neighbor of \(i^{\prime}\) and equal to \(-1\) if it is a neighbor of \(i^{\prime\prime}\) after splitting \(i\). Then the gradient Equation (4) can be written more concisely and transparently as follows:  
\[\mathcal{O}(\mathcal{G},\mathbf{X}) =\sum_{j:\{i,j\}\in V\times V}\mathcal{O}^{p}(A_{ij},\mathbf{x}_ {i},\mathbf{x}_{j})\]4The aim of FONDUE-NDA is to identify node splits for which the embedding quality improvement is maximal. As argued above, we propose to approximately quantify this by means of a first order approximation, by considering the two-norm squared of this gradient, namely by maximizing \(\|\
abla_{\mathbf{\delta}_{i}}\mathcal{O}(\hat{\mathbf{G}}_{si},\hat{\mathbf{X}}_{si})\|^{2}\). Denoting \(\mathbf{M}_{i}=(\mathbf{F}_{i}^{1}-\mathbf{F}_{i}^{0})^{\top}(\mathbf{F}_{i}^{1}-\mathbf{F}_{i}^{0})\) (and recognizing that \(\mathbf{b}_{i}^{\top}\mathbf{b}_{i}=|\Gamma(i)|\) is independent of \(\mathbf{b}_{i}\)), FONDUE-NDA can thus be formalized in the following compact form:  
\[\mathcal{O}(\mathcal{G},\mathbf{X}) =\sum_{j:\{i,j\}\in V\times V}\mathcal{O}^{p}(A_{ij},\mathbf{x}_ {i},\mathbf{x}_{j})\]5  
Note that \(\mathbf{M}_{i}\succeq\mathbf{0}\) for all nodes and all splits, such that this is an instance of Boolean quadratic maximization problem [25; 26]. This problem is NP-hard, thus it requires further approximations to ensure tractability in practice.
## 2 Motivation and Related Works  
Various platforms can be used for the HAPs depending on the telecommunication mission timescales. A stratosphere airship will be the likely choice for missions with timescales of months or years, while for missions over several days or weeks, an unmanned aerial vehicle (UAV) may be more suitable or a manned plane for a mission with a timescale of hours. For example, the high altitude airship (HAA) of Lockheed Martin, which is a kind of stratosphere airship, has a planned mission time of one month [9]. Solar energy is considerably appealing to provide communication services over a timescale of months or years, particularly if it is assumed that, for either buoyancy or aerodynamic lift in the thin atmosphere, the HAP will contain large surfaces suitable for collection [10]. At the Equator, the solar power flux can reach up to 1300 \(\mathrm{W/m^{2}}\), which is quite adequate for an HAP energy source, even if solar cell efficiencies of \(10\%\)-\(15\%\) are assumed. However, the problem remains that energy has to be stored for overnight use. At higher latitudes or during winter months, the available power for overnight use will not be sufficient. Adding batteries, such as lithium-ion at about 110 W\(\cdot\)h/kg, will result in a very large, weighty and expensive HAP [11]. Battery power is generally considered for emergency communications for small-sized UAVs with timescales of days or weeks. Therefore, the HAP is not only a power-limited system, but it is also in some cases an energy-limited system, in contrast with the satellite platform. Since the HAPs are battery-powered units, energy-efficient transmission over the IHS network is important, which also includes the battery-operated remote sensor devices. Since there is a clear requirement to operate under energy-constrained situations, the power should be used more efficiently through careful network optimization and energy-efficient transmission schemes.  
In recent years, emergency communication networks and energy-efficient transmissions have undergone extensive studies within WSNs. We briefly sum the related works up into three topics: the emergency network architectures, energy-efficient transmissions and integrated HAP/satellite networks, which are discussed further in the following subsections.
## 1 Introduction
Using HELLO and transmission control, the OLSR is a proactive link state routing protocol enhanced for mobile ad-hoc networks that determines and extends the information throughout the MANET [19]. Using shortest-path forwarding, each node in the network determines the next hop destination of other nodes. Since the OLSR's primary function is to store and update its routes, it can provide a route instantaneously whenever it is needed [20]. The OLSR protocol has been used in several recent studies to transmit videos more efficiently and with better results than with other conventional protocols. Combining deep-learning techniques can result in higher performance and further increase the efficiency of the OLSR. For video transmissions, the proposed study therefore used the OLSR protocol with deep learning.
## 1 Introduction
In their research, they conducted degradation tests on fuel cells, developed degradation models based on constant degradation rates, and validated them using actual operational data from buses. However, the specific sources of the coefficients used in the models were not explained, including their relationship with temperature, humidity, and operating conditions. Another approach to fuel cell degradation modeling is based on the establishment of equations derived from physical principles. Due to their foundation in physical phenomena, models built upon these equations have higher accuracy compared to empirical methods and are more widely applicable across different fuel cell models. However, current research in this area mainly focuses on catalyst degradation [21] or membrane degradation [21]. When considering parameters such as current density, load variations, temperature, humidity, etc., the computational cost increases, making it challenging to apply these models in practical applications. In this study, a hybrid semi-empirical and semi-physical model was developed by combining the principles of both the physical and empirical approaches. This degradation model utilizes the degradation rate of the fuel cell measured under reference conditions as a baseline and scales it using a physical model. Similar methods can be found in the research conducted by [PERSON] et al. [22], where they also accounted for the effects of current density, temperature, and relative humidity to correct significant deviations in the empirical model's degradation rate. However, their results showed a linear trend in predicting the fuel cell degradation rate. Although it exhibited low error when applied to the fuel cell hybrid electric vehicle discussed in the paper, it cannot guarantee the same level of accuracy when replacing the fuel cell, as the scaling coefficients were obtained via fitting using artificial intelligence algorithms rather than derived from physical equations.
## 2 Experimental Facility and Procedure
### Radiation Noise Measurement System  
As shown in Figure 1, the experimental apparatuses include a soundproof room, water circulation system, circuit control system, data acquisition and storage system. During the operation, to absorb the external noise and reduce the influence of the surrounding environment and motor operation on the measurement results, the motor and centrifugal pump were insulated in a soundproof room; the exterior walls of the soundproof room, along with the motor, were clad with soundproof cotton. In addition, the internal walls of the soundproof room were protected by soundproof cotton to reduce the noise reflection.  
The AWA14423L type microphone (Hangzhou Aihua Instruments Co., Ltd., Hangzhou, China) was used to measure the radiation noise. The AWA6290M+ type two channel signal analyzer was used for the radiation noise signal acquisition and analysis, moreover, 1/3 Octave analysis method was adopted. Further details regarding the measurement characteristics of instruments used in the system are listed in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Inlet diameter, mm & 80 \\ Impeller diameter, mm & 250 \\ Outlet diameter, mm & 50 \\ Rated flow rate, m\({}^{3}\)/h & 50 \\ Design head, m & 80 \\ Rated rotational speed, rpm & 2900 \\ Blade number & 6 \\ Blade-passing frequency, Hz & 290 \\ Shaft-passing frequency, Hz & 48.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geometric and performance parameters of the test pump.  
Figure 1: Layout and instrumentation of measurement system. 1: soundproof room; 2: valve; 3: flow meter; 4: radiation noise monitoring point; 5: computer; 6: two channel signal analyzer; 7: pressure recorder; 8: pressure monitoring point; 9: pump; 10: motor; 11: acoustic enclosure; 12: water tank; 13: frequency converter; and 14: bracket.  
Additionally, during the operation, the flow valve installed downstream on the outlet pipe was kept fully opened and unchanged. The pump was driven by the YVF2180L-2 type three-phase asynchronous motor and the rotational speed was regulated by the Y0300G3 type frequency converter, then the radiation noise frequency characteristics were studied under various rotational speeds. According to the similarity law, the relationship between rotational speed and flow rate is defined as,  
\[\frac{Q_{1}}{Q_{2}}=\frac{n_{1}}{n_{2}} \tag{1}\]  
where \(Q\) and \(n\) represent the flow rate and rotational speed, while the subscript 1 and 2 represent two different operation conditions. To ensure the safety of the running system, seven different rotational speeds that are less than or equal to rated rotational speed, i.e., varying from 1700 to 2900 rpm in increments of 200 rpm were considered. Table 3 shows the rotational speeds and corresponding flow rates.
## 6 Validation within an Industrial Setting
### Evaluate the Effectiveness of the Controls
#### 6.5.3 Penetration Test Result  
The penetration tests identified two different types of vulnerabilities. Along with the test result, the penetration service provider also included recommendations on how to mitigate the vulnerabilities. Below is the list of vulnerabilities, along with mitigation recommendations which were identified during the penetration testing:  
* Potential denial of service points: During testing, there were four potential DoS points found. These are requests that timeout within 10 s due to malformed data inside the payload. These can be run multiple times in multiple threads, driving up the usage and putting stress and strain on the service. Recommendation: It was advised that the API endpoints backend code should handle potential malformed data gracefully by input validation. Additionally, a proper HTTP response is needed if an API endpoint failed to process a request, so that the user can retry a request later. Action: Added input validation to validate the input data stream. Additionally, an error response code was also added to notify the user that API endpoints were unable to process the malformed input data.
* Security misconfiguration--Stack traces enabled: During testing, it was discovered that stack traces were enabled for some API endpoints. Recommendation: It was advised to turn off the stack trace for all endpoints and use a code review process to detect this coding error during development. Action: Stack trace was disabled for all the endpoints and the exception was written into a log file for auditing.  
After making the necessary changes in the codebase to address the issues found during the penetration testing, the update was shared with the penetration service provider.  
A retest of the updated application was conducted, and it was unable to reproduce these vulnerabilities.
# Jumping Locomotion Strategies: From Animals to Bioinspired Robots
## 4 Aerial Righting
### Aerial Righting in Jumping Animals
#### 4.1.1 Aerial Righting by Way of Aerodynamic Torque  
**Aerial righting in animals with wings:** Animals capable of flapping flight show excellent ability in aerial righting through asymmetric motion, both in excursion and velocity, between their left and right wing. This strategy is adopted by a lot of birds [219; 220; 221] and insects [222; 223; 224; 225; 226]. The tail movement in birds and the abdomen movement in insects help maneuver their flight [227; 228; 229; 230]. In addition to flapping, a lot of animals developed gliding flight using wings [231; 232], patagial membranes [233; 234; 235], or the extended body skeletons [236; 237]. During gliding, the ability tosubstantially alter the aerodynamic force in order to reorient the body is the key for the transition between leaping and gliding and allows those arboreal animals to travel long distances between perches with lower risk of damage [235]. Nonequilibrium glides are demonstrated in a lot of gliding animals and are used to maneuver their gliding trajectories and landing postures [238; 239; 240; 241; 242].  
**Aerial righting in wingless animals:** The active controllability over the aerodynamic forces using legs or body parts are demonstrated in some wingless insects. In the descending phase of ants, high speed videos show that they can maneuver targeted landing by using bilaterally asymmetric motions of the hind legs to affect body rotations around the vertical axis [243; 244]. After being released upside-down, wingless nymphal stick insects can actively modulate limb position. The associated aerodynamic torques is used for dorsoventral reorientation and body posture stabilization [245].
## 3 Results and Discussion
### Subjective Evaluation
Finally, Mish performs in a precise way with the general shape compared to Leaky ReLU but still being very diffuse. The details of the interiors are more defined, but there are no details in the triglyphs and lines of the columns. The shadows are coherent. The background is well represented. It shows much sensibility to the light saturation, which means that the facades with better contrast show a better reconstruction. By comparing all the activation functions in total, the following statements are made. Among the four possibilities, Leaky ReLU is the worst. It shows many problems with straight lines, and it generates many artifacts. ReLU seems to obtain more definition, with less diffuse elements. It also shows better definition in small details, but Mish seems to have a better performance with the problem of element background. The reconstructions performed by ReLU and Swish are very similar in terms of sharpness and interior elements, but it seems that ReLU is a little bit better. This can be seen in a smaller number of transparencies and better-defined shadows. ReLU makes more detailed reconstructions, and Swish makes more defined ones. All the activation functions have problems with the light saturation (the perspectives with more contrast have a better reconstruction). Summarizing, the subjective evaluation considers the performance of the activation functions in the following order: ReLU, Swish, Mish, and Leaky ReLU. This subjective evaluation suggests a general consideration: the predictive nature of generative AI could be a possible reason for the defects shown in generated images by the activation functions: transparencies, some lack of definition in the general shapes, low sharpness in the architectonical edges, blurriness, low level of detail, and the overlap between some elements of both images, due to contrast. The materiality and concrete situation of the built architectural elements have a univocal condition, which the predictive nature of artificial intelligence does not always seem to be able to restore faithfully, except by increasingly successful approximations.
## 3 Proposed Scheme: MC-TIRP
### Trust Evaluation Component
#### 3.2.3 Q-Learning Based Trust Evaluation  
Q-learning is a form of model-free RL. RL is formulated using the Markov decision process (MDP) and is defined as a quintuple (S,A,E,T,R), where S is the set of states of the system, A is the set of actions performed by the agent that affects the system, E is the set of external events that the agent has no control over, and T is the transition that connects each state, action, and event. R is the reward that the agent receives for taking action [3; 21]. A trust value is updated using the Q-learning technique, in which the agent learns an action-value function, labeled Q (state, action), that describes the value of performing the action in state \(s\). Q-learning is implemented using Equation (10) [22; 23]. Initially, all Q tables are set to 0, and to update the Q value, each node evaluates the trust of neighboring nodes.  
\[Q(s,a)=(1-\alpha)\times Q(s,a)+\big{(}\alpha\ \times\ \big{[}Reward+\mu\times MAX \ Q\big{(}s^{\prime},\ a^{\prime}\big{)}\big{]}\big{)}. \tag{10}\]  
We define the state \(s\) as the trust status of each node and update it with rewards according to the calculated local and global trust values. In addition, to reflect the trust state of nodes and networks, the alpha ratio \(\alpha\) is set to 0.5, and the decay factor \(\mu\) is set to 0.9. In particular, if a random action is frequently selected, the network performance may be degraded, so epsilon is set to lower the random probability. The Q value is calculated based on the trust value for each  
via trust evaluation and derives Q-values for each path based on local and global trust. The source node then checks the Q-values of all paths and executes an algorithm to transmit mission-critical data. Notice that mission-critical data A and B are transmitted over the route with the max Q-value (Path 3). C, D, and E data of relatively low importance are randomly transmitted among paths that satisfy each network requirement. As a result, mission-critical data is transmitted through a path that guarantees reliability and QoS while satisfying the requirements of each data.
# Blur Kernel Estimation by Structure Sparse Prior
## 3 Method
### Optimization Procedure
#### 3.3.3 \(\alpha\) Update  
The \(\alpha\) sub-problem is given by:  
\[\hat{\alpha}=\underset{\alpha}{argmin}\;||x-D\alpha||_{2}^{2}+\lambda_{2}||d \odot\alpha||_{2}^{2}. \tag{19}\]  
Inspired by [PERSON] _et al._[25], the solution of the sub-problem is  
\[\hat{\alpha_{i}}=(C_{i}+\lambda diag(d))\setminus 1, \tag{20}\]  
\[\alpha_{i}=\hat{\alpha_{i}}/1^{T}\hat{\alpha_{i}},\]  
where \(C_{i}=(D-1x_{i}^{T})(D-1x_{i}^{T})^{T}\) denotes the data covariance matrix.  
Details about the procedure of blur kernel estimation can be found in Algorithm 1. To start, we initialize the sparse representation \(\alpha\) as that recovered from \(y\) with respect to \(D\), and the latentsharp image \(x\) as \(D\alpha\). Then, we fix two variables, namely \(x\) and \(\alpha\), and update \(k\). Similarly, \(x\) and \(\alpha\) can be updated, respectively. We repeat the operation until obtaining a stable result of \(k\).  
```
Input: blurred image \(y\), dictionary \(D\), kernel size Output: blur kernel \(k\) Initialization: \(k,x,\alpha\); Loop over coarse-to-fine levels: for\(t\)=1: 20 do Blur kernel estimation: update the \(\hat{k}\) from Equation (12) Latent image estimation: for\(i\)=1: 10 do  update the \(\hat{x}\) from Equation (16)  update the \(\hat{u}_{l}\) from Equation (17) Sparse representation: update the \(\hat{\alpha}\) from Equation (20) Return: blur kernel \(k\)
```  
**Algorithm 1**Blur kernel estimation by structure sparse prior.
## 4 Discussion
### Influence of Modification of the Use Profile  
The model is simulated with two modifications of the use profile. The simulated use case is Paris (-average Europe), 30% shading losses, direct charge in the main battery, charge with the grid every five days. The first modification is the addition of a roundtrip Saturday afternoon: driving 5 p.m.-6 p.m. and 7 p.m.-8 p.m. In this case, the yearly distance covered by the VIPV is 614 km instead of 612 km with only a home/work roundtrip. The influence is about 0.3%, which is not significant. The second modification presented in Figure 14 is the addition of a long distance trip (600 km) during Saturday of holiday periods and two rapid charges during these days. The yearly distance covered by the VIPV is 608 km, which is 1.1% lower than the considered use profile in this paper.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**PV Surface** & & **1.44 m\({}^{2}\)** & **2 m\({}^{2}\)** & **Evolution** \\ \hline PV peak power (kW) & & 0.312 & 0.433 & 39\% \\ \hline \multirow{2}{*}{PV useful energy per year (kWh)} & Min & 46 & 87 & 47\% \\  & Max & 227 & 326 & 30\% \\ \hline \multirow{2}{*}{Distance covered by VIPV per year (km)} & Min & 293 & 554 & 47\% \\  & Max & 1444 & 2078 & 31\% \\ \hline \multirow{2}{*}{Distance covered by VIPV} & Min & 3.3 & 5.4 & 39\% \\ \cline{1-1}  & Max & 14 & 20.9 & 33\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of the values for two surfaces of a solar roof for a passenger car.  
Figure 14: Profiles for long distance trip, direct charge in HV battery, Paris, shading 30%.