esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
As a result of the increase in the number of smart buildings and advances in technology, energy consumption in buildings has become increasingly important. The estimation of energy consumption in buildings is critical for energy efficiency. Accurate estimation of photovoltaic (PV) solar power plant efficiency is crucial for optimizing the performance of renewable energy applications. In this study, advanced machine learning regression models such as XGBoost, CatBoost, LightGBM, AdaBoost and Histogram-Based Gradient Boosting are used to predict PV efficiency based on ten internal features (Open Circuit Voltage (Voc), Short Circuit Current (Isc), Maximum Power (Pmpp), Solar Irradiation Spread (SIS), Maximum Voltage (Vmpp), Maximum Current (Impp), Fill Factor (FF), Parallel Resistance (Rp), Series Resistance (Rs), and Module Temperature (Tm)) of PV module measurements from the Utrecht University Photovoltaic Outdoor Test Facility. As a result, CatBoost outperformed the others, achieving the lowest prediction error MSE of 0.002 and the highest R\({}^{2}\) value of 0.90. To interpret the model's predictions, we applied Explainable Artificial Intelligence techniques, in particular SHAP and LIME, which identify key features affecting efficiency and increase model transparency. The integration of these methods provides valuable insights for PV solar power plant design and optimization.  
Photovoltaic Solar power plant efficiency; XGBoost; CatBoost; AdaBoost; SHAP; LIME 2020 IEEE/RSJ International Conference on Intelligent Systems (ISIS)
## 2 Tabular Method for Reducing UGR Calculations, for Every Point on the Room Midline
### Method Explanation  
When an observer is located at a point with a certain distance from the walls, it is rational to have an estimation for UGR at that point. Reducing the number of calculations of Equation (1) for that point would be desirable for reducing UGR calculation time and energy.  
A UGR table is a table as illustrated in Supplementary Table S1. As mentioned before, it is designed for obtaining UGR in the middle of each wall in a room. In the proposed method, when a point on a room midline is considered, for obtaining UGR for observer at that point, it is assumed that the space behind the observer is removed and UGR is obtained from the UGR table for a new room with space equal to the remaining space of the main room (The new room dimensions in the observer's field of view are smaller, but in other directions, dimensions of the main and new rooms are equal) in front of the observer (Figure 3). If the exact corresponding dimensions do not exist in the table, the related UGR can be interpolated or extrapolated.  
In this research, the Y direction is assumed to be the direction of the observer's movement, and the X direction is assumed to be perpendicular to the direction of the observer's movement.  
As previously indicated, for a sample luminaire, the UGR table is illustrated in the Supplementary File as Table S1. If it is assumed the room has dimensions of 8 m \(\times\) 8 m, then, based on a luminaire height of H = 2 m from the observers' eye, the length and width dimensions of the room will be 4H \(\times\) 4H. Reflection factors for the ceiling, walls, and floors are 70, 50, and 20, respectively. Viewing direction is at right angles to the lamp axis. Therefore, for the observer position at the wall midpoint, UGR will be 21.2; for the observer position in the center of the room with the same viewing direction, UGR will be 18.9. The latter number is obtained based on the proposed method and corresponds to a room with dimensions of 8 m \(\times\) 4 m (4H \(\times\) 2H), while in reality, the dimensions of the room have not been changed.  
Figure 2: Illustrating the points UGR table was originally designed for (black arrows), and points for which the UGR table can be used (red lines).
## 3 High-Voltage Battery and Air Cooling System Modeling
### Equivalent Circuit Model  
Following a common approach from literature, the battery pack was modeled with an equivalent circuit [34]. To this end, [PERSON]'s theorem was applied to the pack configuration illustrated in Table 1 and constituted by A123 cells. In particular, the following equations hold:  
\[OCV_{bat}=N_{s}OCV_{cell} \tag{12}\]  
\[R_{bat}=\frac{N_{s}}{N_{p}}R_{cell} \tag{13}\]  
where \(N_{s}\) and \(N_{p}\) are respectively the number of cells in series per branch and the number of branches in parallel, \(OCV_{cell}\) and \(OCV_{bat}\) represent open circuit voltages of one single cell and of the high-voltage battery pack, whereas \(R_{cell}\) and \(R_{bat}\) are the internal resistances of the cell and pack, respectively.  
Similarly, cell and battery pack capacities \(C_{cell}\) and \(C_{bat}\) were linked as in Equation (14):  
\[C_{bat}=N_{p}C_{cell}. \tag{14}\]  
Battery current \(I_{bat}\) can be computed by means of Equation (15) by applying the power conservation law to the battery equivalent circuit model and by introducing battery power \(P_{bat}\), which is detailed in Equations (22)-(25) in Section 4:  
\[I_{bat}=\frac{OCV_{bat}-\sqrt{OCV^{2}_{bat}-4P_{bat}R_{bat}}}{2R_{bat}}. \tag{15}\]  
The instantaneous battery C-rate \(c\) could then be evaluated using Equation (16):  
\[c=\frac{|I_{bat}|}{C_{bat}}. \tag{16}\]  
Battery state-of-charge \(SOC\) could consequently be evaluated by knowing its value at the beginning of the driving mission (\(SOC_{0}\)) and using the current integration method:  
\[SOC=SOC_{0}-\int_{0}^{t}\frac{I_{bat}}{C_{bat}}dt. \tag{17}\]  
It should be noted that cell parameters are not constant, but rather their values change during HEV operation. With respect to \(OCV_{cell}\), its value varies with \(SOC\), whereas \(R_{cell}\) depends on more factors, namely \(SOC\), C-rate, battery temperature, and charging/discharging conditions [14]. \(OCV_{cell}\) is sensitive to temperature variation as well, but its variation normally has less impact.
# Improving the RST-OIL Algorithm for Oil Spill Detection under Severe Sun Glint Conditions
## Materials and Methods
### Rst-Oil
Since TIR data are at 1 km of spatial resolution, a downscaling procedure has been implemented by a C++ code developed ad hoc and applied to the cloud mask to co-locate it on the corresponding NIR imagery at a spatial resolution of 250 m.
# The Development of an Optimal Control Strategy for a Series Hydraulic Hybrid Vehicle
## 2 System Configuration and Modeling
### Driving Cycles  
A common method that is used to describe the operating condition of a vehicle is a velocity _versus_ time profile, also known as a drive cycle. These profiles are used in association with certain test procedures to validate the vehicle's performance, both for government regulation and for internal evaluation by manufacturers.  
To evaluate the effectiveness of different sizes of hydraulic components and the performance of the system, two typical driving cycles, Japan 1015 and HWFET, were used as velocity reference trajectories. The respective speed profiles are presented in Figure 3a,b. The Japan 1015 cycle is used in Japan to test emissions and fuel economy for light-duty vehicles. This driving test has the typical stop-and-go patterns that are found in urban driving conditions and is an attractive application for HHVs. The HWFET, a chassis dynamometer-driving schedule that was developed by the United States Environmental Protection Agency, is used to determine the fuel economy of light-duty vehicles under highway conditions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Parameter** & **Value** \\ \hline \(m\) & Vehicle mass (Gross Weight) & 3490 kg \\ \(A_{\mathrm{f}}\) & Front area & 2.5 m\({}^{2}\) \\ \(\mathrm{C_{d}}\) & Drag coefficient & 0.3 \\ \(\mathrm{\rho}\) & Air density & 1.2 kg. m\({}^{-3}\) \\ \(i_{\mathrm{df}}\) & Differential ratio & 4.875 \\ \(f_{\mathrm{r}}\) & Tire rolling resistance & 0.008 \\ \(r_{\mathrm{w}}\) & Tire radius & 0.312 m \\ \(\mathrm{\theta}\) & Road grade & 0\% \\ \(P_{\mathrm{e,max}}\) & Maximum output power of the engine & 61 kW \\ \(\omega_{\mathrm{e,max}}\) & Maximum engine speed & 4700 rpm \\ \(\omega_{\mathrm{e,min}}\) & Minimum engine speed & 800 rpm \\ \(J_{\mathrm{e}}\) & Engine inertia & 0.12 kg. m\({}^{2}\) \\ \(J_{\mathrm{P1}}\) & Pump inertia & 0.02 kg. m\({}^{2}\) \\ \(D_{1}\) & Maximum displacement of the pump & 55 cm\({}^{3}\). rev\({}^{-1}\) \\ \(D_{2}\) & Maximum displacement of the & 75 cm\({}^{3}\). rev\({}^{-1}\) \\ \(V_{\mathrm{a}}\) & Accumulator volume & 68 L \\ \(V_{\mathrm{h}}\) & High-pressure hose volume & 1.85 L \\ \(p_{\mathrm{a,max}}\) & Maximum working pressure of the & 350 bar \\  & accumulator & 120 bar \\ \(p_{\mathrm{pr}}\) & Pre-charge pressure of the accumulator & 120 bar \\ \hline \hline \end{tabular}
\end{table}
Table 1: System Parameters.  
Figure 2: The Simulink Model of SHHV.
## 4 Experiments
Due to the time-sensitive nature of many Metasploit modules, the correlation between the test data and the training data, despite utilizing 95% of the data for training, was not strong. Consequently, the model had not learned all the knowledge and could not accurately answer all the questions in the test set; therefore, the practical value of the model trained in the experiment is not greater than that of the model trained on the full dataset. Therefore, we fine-tuned the entire dataset on the Llama model and experimented with several queries. Figure 5 illustrates the enhanced performance in responding to specific Metasploit module inquiries post-fine-tuning, accurately providing detailed information about the respective modules. From a human intuitive perspective, the improvement is quite pronounced, and the model maintained both conciseness and accuracy across similar questions. Inevitably, the limitation of fine-tuned models lies in their inability to acquire dynamically updated knowledge, representing a constraint of the refinement process. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**BLEU**} & \multirow{2}{*}{**METEOR**} & \multicolumn{6}{c}{**ROUGE**} \\ \cline{4-7}  & & & & **Rouge1** & & **Rouge2** & **RougeLS** \\ \hline \multirow{2}{*}{
\begin{tabular}{l} Gamma \\ Finetuned \\ \end{tabular} } & 9 B & 0.04 & 0.25 & 0.21 & 0.09 & 0.16 & 0.18 \\  & 9 B & 0.08 & 0.34 & 0.31 & 0.19 & 0.26 & 0.27 \\  & 9 B & 0.13 & 0.40 & 0.41 & 0.27 & 0.35 & 0.36 \\  & 9 B & 0.47 & 0.58 & 0.63 & 0.48 & 0.58 & 0.58 \\  & 8 B & 0.07 & 0.29 & 0.22 & 0.12 & 0.18 & 0.19 \\  & 8 B & 0.41 & 0.50 & 0.58 & 0.42 & 0.52 & 0.52 \\  & 7 B & 0.07 & 0.31 & 0.26 & 0.13 & 0.20 & 0.21 \\  & 7 B & 0.38 & 0.46 & 0.54 & 0.39 & 0.49 & 0.49 \\  & 7 B & 0.05 & 0.19 & 0.21 & 0.10 & 0.17 & 0.18 \\  & 7 B & 0.41 & 0.53 & 0.59 & 0.42 & 0.53 & 0.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance on natural language generation evaluation metrics including BLEU, METEOR, and ROUGE. It is evident that across all experiments, the models demonstrated enhanced performance in penetration testing knowledge Q&A after fine-tuning.
## 3 Results and Discussion
### Interfacial Tension Measurements (IFT)
#### 3.3.4 Effect of CaCl\({}_{2}\)/MgCl\({}_{2}\) on IFT  
Figure 6 shows the DIFT of DCB/DSB at different concentrations of CaCl\({}_{2}\)/MgCl\({}_{2}\). It can be observed that the addition of Ca\({}^{2+}\) and Mg\({}^{2+}\) also showed a synergistic effect on the reduction in IFT. For DCB, when the concentration of Ca\({}^{2+}\) ranges from 0.01% to 0.5%, the surfactant can reach an ultra-low interfacial tension level and the minimum value (7.9 \(\times\) 10\({}^{-4}\) mN/m) is reached when the concentration of Ca\({}^{2+}\) is 0.2%. For DSB, when the Ca\({}^{2+}\) concentration is 0.01-1%, the surfactant can reach an ultra-low interfacial tension level and a minimum value (7.2 \(\times\) 10\({}^{-4}\) mN/m) is reached.  
The addition of Mg\({}^{2+}\) has a more significant effect on IFT and also has a synergistic effect. For DCB, when the concentration of Mg\({}^{2+}\) ranges from 0.01% to 0.2%, the surfactant can reach an ultra-low interfacial tension level and the minimum value (6.5 \(\times\) 10\({}^{-4}\) mN/m) is reached when the concentration of Mg\({}^{2+}\) is 0.2%. For DSB, when the concentration of Mg\({}^{2+}\) ranges from 0.01% to 0.2%, the surfactant can reach an ultra-low interfacial tension level and the minimum value (1.1 \(\times\) 10\({}^{-3}\) mN/m) is reached when the concentration of Mg\({}^{2+}\) is 0.01%. Compared with Ca\({}^{2+}\), the surfactant showed lower tolerance to Mg\({}^{2+}\). The reason is that the bonding ability of Mg\({}^{2+}\) is stronger than that of Ca\({}^{2+}\) and more easily binds with surfactant ionic groups [32], and the molecular hydrophobicity is enhanced, which leads to a transfer from the oil-water interface to the oil phase under the condition of low solution concentration, resulting in the increase in IFT.  
DSB has higher tolerance to divalent cations than DCB (Ca\({}^{2+}\): DCB tolerance range is 0.01-0.5%, DSB tolerance range is 0.01-1%; Mg\({}^{2+}\): DCB tolerance range is 0.01-0.2%, DSB tolerance range is 0.01-0.2%). Compared with the carboxyl group, the hydroxypropyl sulfonyl is a strong acid group, and the inner salt structure formed by the quaternary ammonium group in the molecule is more stable and not sensitive to the external electrolyte ions, so it has a higher tolerance. In conclusion, the effect of cations on IFT is as follows: Na\({}^{+}\) < Ca\({}^{2+}\) < Mg\({}^{2+}\); it is thought that the binding degree between an ion head group and counterion increases with a rise in ionic polarizability and valence state and decreases with a rise in hydration radius.  
Figure 5: Effects of sodium chloride on IFT between the optimum concentrations of surfactants and Xinjiang crude oil at 40 \({}^{\circ}\)C for DCB (**a**) and DSB (**b**).
# A Survey on Datasets for Emotion Recognition from Vision: Limitations and In-the-Wild Applicability
## 2 Perceived Emotion in Humans  
Although emotion recognition is a multidisciplinary field of study, it is currently highly dependent on the advances made from research on cognitive psychology. With the rise of this field in the 1950s, theories based on cognitive approaches have gained significant appeal by connecting sentiment and behavior [26]. Current research also proposes a significant amount of sub-fields that study specific behaviors which we can correlate with emotion, such as kinesics [27], haptics (or tactile) communication [28], and proxemics [29]. Undoubtedly, the advances in these fields will directly contribute to new, exciting approaches to emotion recognition using deep learning. Furthermore, given how emotion recognition is a cognitive task, we can develop models that could try to imitate at some level the way that humans perceive emotion.  
However, there are already many discoveries and discussions in the literature. [PERSON], for instance, in his work _The expression of the emotions in man and animals_[30], studied how our emotions influence our body movements. This publication is not new but instead goes back one and a half centuries. Other significant researchers, such as [PERSON] [31] and [PERSON] [4; 32], have also contributed profoundly to the perception of this field. Therefore, we hypothesize that the limiting factor for the advance of this technology is not related to a lack of computational power or the availability of state-of-the-art deep learning models; but the lack of quality data that researchers could use to train such models. Designing and generating a dataset is expensive, and this factor could be even more impactful for more specific tasks, which is the case for emotion recognition and general fields that need to create descriptions for concepts that are natural and innate to humans.
## 1 Introduction
### Related Works
In [31], the focus is on minimizing transmission costs in an IoT framework with a hybrid access point (HAP) and multiple sensor nodes, employing a Stackelberg game extended to a dynamic game for efficient power control. Power control for energy efficiency in a wireless network is addressed in [32] with numerous transmitters, modeling it as a dynamic game based on available energy and channel conditions. Ref. [33] examines uplink power control using the non-orthogonal multiple access (NOMA) protocol in scenarios with many users, establishing an equilibrium strategy via MFG theory. Power management in densely populated small-scale cellular networks is investigated in [34], integrating MFG theory to ensure unique mean field equilibrium and network stability. Finally, ref. [35] focuses on optimizing quality of service (QoS) in cellular networks through decentralized optimization using nonlinear MFG control theory.
## 2 6G Key Enabling Technologies
### Cell-Free Architectural Approaches  
In conventional wireless networks, the traditional concept of base station (BS) MS has been adopted in the vast majority of network deployments. In this case, the cellular topology is divided into a finite number of BSs, where each BS can cover a certain geographical area according to its technical specifications. However, the interconnection of IoT devices on one hand, and the need for ubiquitous connectivity with ultra-high data rates and low latency on the other, necessitate more flexible approaches regarding connectivity and resource allocation. Therefore, over the last decade, the concept of CF networks has emerged. To this end, various access points (APs) are deployed in dense geographical areas, and an MS can be served by multiple APs. Several APs are connected to a common central processing unit (CPU), which manages connectivity issues. Hence, the handover and associated signaling burden can then be reduced. However, fronthaul capacity can be severely affected in cases where each AP simultaneously serves all MSs under consideration. In practical CF deployments, there can be multiple CPUs, where each one serves a particular subset of APs to equally divide the computational burden. Moreover, MSs are not simultaneously served by all APs but rather only from the ones with good signal reception, which also minimizes interference and hardware complexity [22].  
Figure 2: The concept of federated learning.  
In general, the concept of CF systems is also combined with mMIMO technology. To this end, distributed MIMO (dMIMO) antennas are located per AP, which can provide connectivity in harsh propagation conditions. CF-mMIMO can be considered as a potential physical-layer technology for future wireless networks since it can benefit from all the advantages of distributed antenna systems (DASs) and network MIMOs, such as macro-diversity gain, high channel capacity, link reliability, and, in general, more degrees of freedom in the wireless link [38]. An overview of CF technology is also depicted in Figure 3. As can be observed, an arbitrary MS is served by two APs for improved signal reception. This set of serving APs changes dynamically, as the MS changes position in the wireless orientation. This feature can be particularly beneficial for highly demanding applications in terms of coverage, transmission rates, and latency, such as autonomous driving. To this end, dMIMO configurations are deployed per AP that facilitate high signal diversity.
# LoRaWAN Network for Fire Monitoring in Rural Environments
## 4 System Description  
This section details the different elements our system is composed. Firstly, the network architecture developed to collect data from the environment is presented. Our system is comprised of an Arduino node with a LoRa Shield and several sensors. Finally, a LoRa Gateway is in charge of collecting data and sending it to our server to display it in a web interface.
## 4 Experimental Results and Analysis  
### Model Training  
The Windows 10 Pro operating system was used to build the PyTorch deep learning framework on a hardware device based on an Intel(R) Xeon(R) CPU E3-1230 v3@ 3.30 GHz (12 GB of running memory) and an NVIDIA GeForce GTX 1080 Ti GPU, and using the Python programming language to implement the bamboo shoot detection model training process. First, we used LabelImg software to annotate the data set and create the smallest possible rectangular box for each moso bamboo shoot, and the accuracy of the annotation was closely related to the accuracy of the model. The generated xml file can be converted into a text format (txt) file containing the sample image path and the coordinate information of the region by using a format conversion program to prepare for model training. Then, the improved object detection model (named Bamboo-YOLOv4) was trained for moso bamboo shoot detection and the results were compared. Finally, the trained model was deployed to the server for field trials and validation. After several experiments, the parameters were set as follows: Mosaic (data enhancement) = False, Cosine_lr (learning rate of cosine annealing decay) = True, and label_smoothing (label smoothing) = 0.005, Epoch = 2500. The improved model training loss curve is shown in Figure 7. When the loss function converges, the model has completed training.
# Joint Deployment Optimization of Parallelized SFCs and BVNFs in Multi-Access Edge Computing
## 5 Performance Evaluation
### The Impact of the Number of Edge Servers on Different Algorithms  
In this section, we compare our proposed algorithm with two other algorithms. Figure 8a shows that the average delay decreases with the number of edge servers. When the number of edge servers increases, the critical VNF can be deployed in nearby edge servers.  
Figure 8: (a) Throughput when the number of edge servers changes; (b) the number of VNF instances  
and VNF reliability of all accepted user requests when the number of edge servers changes; **(c)** the average reliability provided by one VNF instance, all active VNF instances and all VNF instances of all accepted user requests when the number of edge servers changes; **(d)** throughput when the number of edge servers changes.  
Figure 8b,c show that the average VNF reliability rises as the number of edge servers increases. Since the number of edge servers increases, algorithms can select edge servers with shorter communication distances to deploy parallel VNFs. Thus, the number of feasible sets and the reliability enhancement increase. For SRPD, the decrease in delay leads to a decrease in the number of parallel VNFs, thus enhancing reliability.  
Figure 8d shows that the throughput increases with the number of edge servers. When the number of edge servers increases, the resource capacity of the MEC network increases and can process more user requests.