esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Index Assessing the Viability of PAR Application Projects Used to Validate PAR Models
###### Abstract
However, none of them takes into account the probabilities of exceedance that are usually employed to assess the viability of solar projects. These probabilities show the probability of exceeding a certain irradiance value [42]. For instance, P90 indicates an exceedance probability of 90%, which means that this irradiance value is exceeded 90% of the time. In other words, this value corresponds to percentile 10 of the variable. In this regard, three exceedance probability scenarios are recommended in [43] (P50, P90, or P99). In this work, we propose a validation index for models, based on these probabilities, so that those models that show a better value of this index will be more representative of the degree of viability that can be expected for a future project which uses _PAR_. As mentioned, the usual validation indices estimate the closeness of the model to the observed data point by point, so the model can be used to estimate the _PAR_-specific value, which can be used, to estimate outcomes based on this variable, such as the biomass productivity. Thus, a good model according to these indices must also be representative of the degree of viability of the project, but it is possible that a bad model according to these indices turns out to adequately represent that viability. Thus, the objectives of this work are:  
1. To present a methodology to develop _PAR_ models based on satellite data provided by a Moderate-Resolution Imaging Spectroradiometer (MODIS). The models' development utilized data from each cluster to be specific for each region. Subsequently, a site adaptation technique was applied to the best models to test if there was an improvement in the results. 2. To introduce a statistical index to assess the goodness of the developed models. This index is defined in Equation (12) (_PAR_ could be replaced by any other modeled variable) and allows the estimation of the goodness of a model. Utilizing the defined Exceedance Probability Index (EPI), we can make a joint comparison of all those exceedance probabilities corresponding to modeled and measured values.
# Application of Ultrafiltration for Recovery of Bioactive Phenolic Compounds from Rose Wastewater
## 2 Materials and Methods
### Methods
#### 2.2.3 The Determination of the Key Parameters of the Ultrafiltration Process  
* The permeate flux (_J_, L/(m2h)) was determined using the following formula: \[J=V/(A\times t),\] (1) where \(V\) is the volume of the collected permeate, L; \(A\) is the area of the membrane, m2; and \(t\) is the duration (time) of ultrafiltration, h.
* The volume reduction ratio (VRR) was determined by the following formula: \[VRR=\frac{V_{F}}{V_{R}},\] (2) where \(V_{F}\) is the feed volume of rose wastewater, dm3; and \(V_{R}\) is the volume of the collected retenate, dm3.
* Energy demand (_E_, kWh/m3) was calculated as follows: \[E=\frac{W_{pump}}{J\times A},\] (3) where \(W_{\text{pump}}\) is the power required by the pump, kWh; \(J\) is the permeate flux, calculated by Equation (1) and expressed in m3/(m2h); and \(A\) is the area of the membrane, m2.  
* The power required by the pump (\(W_{\text{pump}}\), kWh) was determined as follows: \[W_{pump}=\frac{p*Q_{feed}}{\eta},\] (4) where \(p\) is the pressure at the entry of the ultrafiltration module, Pa; \(Q_{\text{feed}}\) is volumetric flow rate, m\({}^{3}\)/s; and \(\eta\) = 0.7 is the pump's efficiency according to [29].
* Rejection (\(R\), %) and concentration factor (CF) were determined by the following equations: \[R=\left(1-\frac{C_{P}}{C_{R}}\right)\times 100\%\,\text{and}\] (5) \[CF=\frac{C_{R}}{C_{F}}\] (6) where C\({}_{P}\), C\({}_{R}\), and C\({}_{F}\) (%) are the contents of the phenolic compounds in the permeate, reentate, and feed rose wastewater, respectively.
# An Intelligent Vision System for Detecting Defects in Micro-Armatures for Smartphones
## 3 Methodology
### Deep Convolutional Neural Networks (DCNNs)  
Recently, DCNNs has shown great detection power in computer vision, which has been widely used in various applications, e.g., classification [PERSON] et al. (2016), image segmentation [PERSON] et al. (2016), object tracking He et al. (2016), and so on. A classic DCNNs architecture consists of several layers of convolutional, activation, and pooling layers, followed by fully connected layers at the end. A simplified version of DCNNs can be composed of the following five parts:  
1. Input: The input of DCNNs is usually a batch of 3-channel color image matrices with fixed size, which depends on the network structure you are using.
2. Conv: The convolutional layers perform feature extraction and feature mapping by employing a set of fixed-size filters sliding on local receptive fields after receiving feature maps. The filter sizes are usually odd, such as \(3\times 3\) or \(5\times 5\). The weight-sharing scheme is applied in the convolution operations.
3. Activation: Since convolution is a linear operation, it is necessary to use the activation layer to nonlinearly map the output of the convolutional layer, thereby increasing the expression ability of the model. Nowadays, rectified linear units (ReLU) have become the most widely used activation function because they can effectively prevent the gradient from disappearing and accelerate the convergence speed during the training process [PERSON] et al. (2016). The mathematical transformation between each input value x and its output y can be formulated as \[y=\left\{\begin{array}{ll}x,&x>0\\ 0,&x\leq 0\end{array}\right.\] (5)
4. Pool: The pooling layer performs a form of non-linear down-sampling to compress the input feature map, which makes the feature map smaller in spatial size and reduces the complexity of the network computation. The most common pooling operation is max pooling, which outputs the max value from the neighborhood of the input feature map.
5. FC: Each node of the fully connected
## 1 Introduction  
The commercial use of photovoltaic modules dates back to the 1970s and 1980s of the 20 th century. Ease of use, maintenance and installation led to mass integration in the early 21 st century. The PV systems are classified into grid-connected and off-grid applications according to the type of grid connection [1]. Nowadays, due to self-sufficiency, the off-grid system is a very common way of connection, as it presents a great energy potential in underdeveloped countries or in countries without a well-established electricity network. To facilitate the digital transformation of energy infrastructure, it is also necessary to encourage the development and introduction of smart grids [2, 3, 4] and microgrids [5]. A crucial factor for PV system integration is also the assessment of energy potential [6] at a specific location, which ranges from 650 to 1500 kWh/kW\({}_{\text{P}}\) for the area of Europe.  
Electricity production of PV systems depends on parameters, such as solar radiation, operating temperature of the PV module and air mass factor. Nevertheless, it is extremely important to assess the performance of the PV system in partial shading conditions [7]. The operating temperature of the PV module mainly affects the electrical parameters of the PV cell, such as saturation current, photon current, shunt and series resistance, diode ideality factor, short-circuit current, open-circuit voltage and consequently, the output power of the PV module [8]. An increase in temperature of the PV module by 1 \({}^{\circ}\)C results in a 0.45% drop in the output power [9] or a 0.22% drop in efficiency of the PV module [10]. The temperature under standard test conditions (STC) is defined as the operating temperature of the PV module, but only in the PV cell layer. It is very difficult or almost impossible to measure the temperature of the PV cell layer in various outdoor conditions. Therefore, various models have been developed in order to determine the operating temperature of PV cells with help of meteorological and experimental data [11; 12; 13; 14; 15]. In addition to meteorological data, operating temperature of the PV module is also influenced by the type of materials and operation PowerPoint at which the PV module operates. The operating temperature of the PV cell is higher than the temperature of the backside or backsheet layer. However, the measurements of the operating temperature of the PV cell are almost impossible to perform under outdoor conditions. Therefore, the temperature sensors and all other measuring equipment should be calibrated in an estimated time to avoid some unnecessary errors. The most common equipment used for meteorological measurements is the platinum resistance thermometer (temperature sensor), anemometer (wind speed and direction sensor) and pyranometer (solar irradiance). Pyranometer [16] and anemometer [17] are some of the most sensible sensors that need to be calibrated in the estimated time (approx. 1-2 years). Otherwise, they can cause measurement uncertainties that increase with age.
## 6 Conclusions and Future Work  
This paper presents a novel global localization system, based on measuring the cyclic variations in light intensity resulting from the movement of the celestial bodies. In this case, the rotation of the earth and the resulting sunrises and sunsets are taken into account.  
Thus, an algorithm is proposed to find the geographic coordinates for an outdoor object which measures the light intensity throughout a day and is able to identify accurately the sunrise and sunset times.  
The conclusion obtained from the results are that the accuracy of the system is relatively high taking into account the devices which are required to build the system. Also, it is denoted that the total accuracy mainly depends on the precision when identifying sunrises and sunsets, and not on the celestial model employed.  
Compared to current global localization methods, SGPS can not be applied to tasks requiring a high accuracy, such as search and rescue, navigation, _etc_. However, the accuracy should be sufficient for certain data gathering applications such as water monitoring and climate change monitoring.  
Future research includes improving the accuracy using other magnitudes such light spectrum and improving robustness towards impurities in the water. Moreover, artificial intelligence techniques may be employed to better identify the sunrise and sunset times. Another improvement is to study ways of counteracting the systematic error in longitude due to the usage of the Spencer EqT and latitude due to the equinoxes.
# A Review of Ku-Band GaN HEMT Power Amplifiers Development
## 2 Ku-Band GaN HEMT HPA Technology
### Other Additional Design Techniques  
Ku-band GaN HEMT power amplifiers have been extensively studied to achieve high output power as a replacement for TWTAs. However, several research groups have conducted and published studies aimed at improving performance beyond just output power.  
To enhance linearity, some researchers have embedded linearizers within the MMIC or designed separate linearizer modules in front of the MIC [23; 47]. In [23], a linearizer composed of a simple diode and the inductance of a microstrip line was placed between the buffer and the power amplifier, resulting in an increase of 5 dB in linear output power with third-order intermodulation distortion (IMD3) levels below \(-25\) dBc. In [47], a linearizer using two diodes, a bandpass filter (BPF), and microstrip lines was designed for the front end of the entire system, including the MIC power amplifier, improving amplitude modulation to amplitude modulation (AMAM) and amplitude modulation to phase modulation (AMPM) by 2 dB and 5 degrees, respectively.  
In addition to linearity, a Doherty power amplifier was employed in [50] to increase backoff efficiency for telecommunication systems, achieving drain efficiencies exceeding 28% at 6 dB backoff power. In [25], a control circuit for load modulation was integrated into a balanced power amplifier design, resulting in 10-16 W output power and a high power-added efficiency (PAE) of 25-40% across the 6-18 GHz band, including Ku-band.  
With the expansion of Ku-band satellite communication and UAV communication services, further research is anticipated to improve the linearity and efficiency of Ku-band GaN HEMT power amplifiers while maintaining high output power.
## 4 Conclusions  
In this paper, we have reported on the design and performance of the IOCore multimode micro-transceiver and its viability for deployment in large-scale integration or co-packaged assemblies in 5G+ and other high-temperature, space-constrained environments. One important feature of the second-generation IOCore micro-transceiver is the integrated quantum dot laser and laser redundancy circuit, which enable operation in hightemperature environments with substantially improved reliability. The relaxed assembly constraints of a multimode interface give rise to a reduced-cost solution at the expense of restricting the device-to-device optical link distance to under 500 m. This trade-off allows multimode silicon photonics micro-transceivers to satisfy very short-reach, low-cost, temperature-resilient requirements in diverse applications. In addition, the IOCore micro-transceiver has been evaluated under single- and dual-phase immersion cooling environments, with a negligible change in performance compared to operation in a standard air-cooled environment as reported in [19].  
Conceptualization, K.K., K.N., F.C. and L.G.; methodology, K.K., K.N., L.G. and F.C.; validation, K.N., Y.H., K.Y., T.M., S.K. and M.K.; formal analysis, K.K., M.K. and S.K.; investigation, K.N., Y.H., K.Y., T.M., S.K., M.K., L.G. and F.C.; writing--original draft preparation, R.P., K.K., L.G. and F.C.; writing--review and editing, L.O., S.A.S. and R.P.; project administration, K.K.; supervision, K.K.; funding acquisition, K.K. All authors have read and agreed to the published version of the manuscript.  
This paper incorporates results obtained from the project JFNP20017, commissioned by the New Energy and Industrial Technology Development Organization (NEDO) in Japan.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to confidentiality restrictions.  
The authors declare no conflict of interest.
# Study on Drone Handover Methods Suitable for Multipath Interference Due to Obstacles
## 2 Related Work
### Conventional Methods and Issues  
Related studies have not adequately discussed the problems associated with handovers, such as the communication environment with the connection destination based on the location where the drone has moved and the conditions for switching the connection destination. In other words, it is important to devise a handover method that is suitable for drones because drones move faster than conventional handovers on the ground, which assume tablets and cellular terminals. In addition, handover technology to switch connection destinations while drones are flying has not yet been studied. In conventional handover, if handover is performed using only simple received power, the received power will be determined to be high, including the interference power due to multipath, so it is difficult to select the optimal handover trigger and the optimal AP. Therefore, multipath interference due to building reflections and diffraction is not included. The handover is performed when the received power from the base station to which the drone is connected decreases, as shown in Figure 6, but the signal is reflected by multipath due to buildings and other factors, resulting in interference waves. Even if the power received from the AP is high, SINR becomes low and communication quality deteriorates. Therefore, it is necessary to make a handover decision by considering the SINR, which changes continuously based on the flight, rather than simply checking the received power. Based on these considerations, it is also necessary to include the requirement for drones to fly in environments that include buildings, etc. It is necessary to construct a new evaluation method that includes multipath interference in 3D space and time series by flight, which is different from the conventional evaluation method for ground communication.  
The conventional connection switchover trigger in a handover uses the sequence shown in Figure 7 if the authentication procedure is omitted. If the STA, which is a mobile station, continuously receives beacons, it recognizes that it is in the communication area under the AP and maintains the connection. When the beacon signal cannot be received an arbitrary number of times, the STA decides to perform a handover and connects to a new AP based on the information of a beacon signal with high reception power that has been searched for in advance. In the conventional method, a handover is not performed even if the received power is low, unless the beacon signal is not received. In other words, even if the signal from the current AP weakens and the communication speed decreases, a connection cannot be established with an AP with better conditions. One of the objectives of this study is to solve these problems.
## 5 Discussion  
### Comparison of Non-Time Series Models and Time Series Models  
To determine a more suitable earthquake prediction model for the AETA data, this paper compares the effects of three time series models and five non-time series models on the validation set, as shown in Figure 10. The first five are non-time series prediction models, namely LightGBM, NN, SVM, GBDT, RF, and the last three are time series prediction models, namely LSTM, GRU, and CNN+GRU.  
The input of the time series prediction model is two-dimensional time series feature data, which completely contains the information of AETA raw signals to the maximum extent. In contrast, the input data of the non-time series prediction model is compressed, and no longer retains the time series information of the raw data. Therefore, it is difficult for the model to learn the changes of the time dimension.  
According to the bar chart, it is obvious that the overall effect of the three time series prediction models is significantly better than that of the five non-time series prediction models in four indicators. Moreover, the LSTM model has the best performance among these prediction models. The LSTM prediction effects of each station are shown in Appendix B. Prediction effects of the LSTM model for magnitude and epicenter of each station are shown in Appendix C.
# Collaborative Caching for Implementing a Location-Privacy Aware LBS on a MANET
## 5 Data Security and Privacy Considerations  
The LBS scheme we designed is distributed and has a dual purpose: protecting location privacy as well as increasing user consciousness on possible privacy infringement. This method ensures the security of private details while at the same time educating people about how their actions with the system can affect their privacy. In this part, we will discuss those important components of the scheme that need to be analyzed or considered so that effectiveness reliability and user friendliness are achieved. Our aim is to let users know more about their loss of privacy, making them have knowledge-based sharing decisions concerning data while appreciating features of our system that preserve anonymity.  
In our model, MANET users are considered honest but curious, while LBS providers are viewed as potentially malicious. Mobile nodes face significant limitations in compromising user privacy due to the following:  
* Limited storage and processing capacity;
* Restricted communication with users in other cells;
* Need to frequently erase cache for resource management.  
These constraints make extensive user profiling difficult within the MANET. In contrast, centralized LBS providers can collect and analyze large amounts of data over time, posing greater risks to user privacy through potential data exploitation or vulnerability to hacking.  
The queries considered in our scheme are range queries with respect to public places (e.g., restaurants, ATMs, banks, hotels, parks, hospitals). Although a single query might not reveal much information about an user, the collection of data over time could potentially reveal the user's lifestyle.  
Our technique is intended to protect privacy at the application layer. To protect users' privacy, other techniques can add other protection layers, such as MAC address spoofing, to prevent tracking or identification of the user's device. The communication between users and the LBS server is protected by cryptographic techniques (e.g., TLS, transport layer), and sensitive data are stored in encrypted form. Our application layer privacy approach is orthogonal to other privacy techniques, such as VPNs (network layer), and so on.
## 3 Test Data Analysis
### Adsorption Isotherms-Modeling Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Initial Concentration (\(\mu\)g mL\({}^{-1}\))**} & \multirow{2}{*}{**Temp (K)**} & \multicolumn{4}{c}{**Film Diffusion Model**} & \multicolumn{4}{c}{**Weber-Morris Model**} & \multicolumn{4}{c}{**Dumwald-Wagner**} \\ \cline{3-10}  & & \(R\) & & \(\mathbf{K_{\textit{int}}}\) & & & & & & \\ \multirow{4}{*}{50} & 303 & 0.0372 & 0.98 & 0.511 & 3.73 & 0.99 & 0.050 & 0.032 & 0.97 & 0.057 \\  & 313 & 0.0397 & 0.99 & 0.682 & 3.88 & 0.99 & 0.045 & 0.035 & 0.98 & 0.045 \\  & 323 & 0.0437 & 0.99 & 0.111 & 5.12 & 0.97 & 0.273 & 0.038 & 0.98 & 0.178 \\ \hline \multirow{4}{*}{100} & 303 & 0.0055 & 0.99 & 5.158 & 3.08 & 0.98 & 0.056 & 0.032 & 0.99 & 0.201 \\  & 313 & 0.0078 & 0.99 & 2.191 & 4.49 & 0.99 & 0.078 & 0.005 & 0.99 & 0.030 \\  & 323 & 0.0060 & 0.99 & 5.121 & 3.42 & 0.97 & 0.117 & 0.004 & 0.98 & 0.192 \\ \hline \multirow{4}{*}{150} & 303 & 0.0066 & 0.90 & 4.282 & 2.67 & 0.94 & 0.086 & 0.005 & 0.90 & 2.156 \\  & 313 & 0.0171 & 0.99 & 0.762 & 9.95 & 0.99 & 0.204 & 0.012 & 0.99 & 0.387 \\ \cline{1-1}  & 323 & 0.0202 & 0.96 & 0.477 & 11.09 & 0.96 & 1.314 & 0.023 & 0.97 & 0.907 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Determined boundaries for the dissemination models. Figure 7: Energy information fitting to format the film dissemination starting with BG focus: (**a**) 50 \(\mu\)g/mL and (**b**) 100 \(\mu\)g/mL and (**c**) 150 \(\mu\)g/mL. Furthermore, the values of \(R^{\prime}\) infers the fast adsorption of a solute onto the outside of the particles, leaving a flimsy film. The phenomena is retarded to the process of diffusion, which affects the rate of adsorption. This step confirms that the diffusion phenomenon limits the adsorption process.