esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Azimuth, Elevation and Delay Estimation for Single Base Station Localization in 3D IIoT +
## 3 A Three Step MUSIC-Taylor-Based Localization Approach
### Step 2, Taylor-Series Based Enhancement  
Consider one of the points estimated in the previous MUSIC-based step, denoted as \(\mathbf{\tilde{p}}_{0}=(\tilde{\theta}_{0},\tilde{\phi}_{0},\tilde{\tau}_{0})\). Let \(\mathbf{\Delta}_{0}=(\Delta\theta_{0},\Delta\phi_{0},\Delta\tau_{0})\) represent the difference between this initial point and the true values, given by \(\mathbf{\Delta}_{0}=(\theta-\tilde{\theta}_{0},\phi-\tilde{\phi}_{0},\tau-\tau_{0})\). The three-dimensionalfirst-order Taylor-series expansion of the steering function around this initial point can be expressed as:  
\[s(\theta,\phi,\tau)\ \approx\ s(\tilde{\mathbf{p}}_{0},\mathbf{\Delta}_{0})=s(\tilde{\mathbf{p}}_{0 })+s^{\prime}_{\theta}(\tilde{\mathbf{p}}_{0})\Delta\theta_{0}+s^{\prime}_{\phi}( \tilde{\mathbf{p}}_{0})\Delta\phi_{0}+s^{\prime}_{\tau}(\tilde{\mathbf{p}}_{0})\Delta \tau_{0}, \tag{12}\]  
where \(s^{\prime}_{\theta}\), \(s^{\prime}_{\phi}\) and \(s^{\prime}_{\tau}\) are the first derivative with respect to \(\theta\), \(\phi\) and \(\tau\), respectively. By substituting Equations (6), (11) and (12) into (10), the object function can be rewritten as a function of \(\tilde{\mathbf{p}}_{0}\) and \(\mathbf{\Delta}_{0}\),  
\[(\tilde{\mathbf{\theta}},\tilde{\mathbf{\phi}},\tilde{\mathbf{\tau}})=\operatorname*{arg \,min}_{\mathbf{\Delta}_{0}}S(\tilde{\mathbf{p}}_{0},\mathbf{\Delta}_{0})^{\mathrm{H}}\mathbf{V }\mathbf{V}^{\mathrm{H}}S(\tilde{\mathbf{p}}_{0},\mathbf{\Delta}_{0}). \tag{13}\]  
If \(\mathbf{S}\) is continuous, the minimization can be achieved by taking the first derivatives of (12) with respect to \(\mathbf{\Delta}\). To simplify the notation, we will omit the estimated point \(\mathbf{p}_{0}\) as variables, and also omit the subscript '\(0\)' in the following equations. Let \(F(\mathbf{\Delta})\) represent the object function in Equation (13), \(\mathbf{A}\) denote the derivative \(\frac{\partial s^{\prime}_{\theta}(\tilde{\mathbf{p}}_{0})}{\partial\Delta\theta}\), \(\mathbf{E}\) denote the derivative \(\frac{\partial s^{\prime}_{\theta}(\tilde{\mathbf{p}}_{0})}{\partial\Delta\theta}\), and \(\mathbf{T}\) denote the derivative \(\frac{\partial s^{\prime}_{\theta}(\tilde{\mathbf{p}}_{0})}{\partial\Delta\tau}\).
# Reliability and Maintenance Analysis of Unmanned Aerial Vehicles +
## 6 The UAV as a Complex Maintenance System
### The Thresholds of Preventive Maintenance  
Considering the above calculations, when we want to evaluate the occurrence of the preventive maintenance intervals, we need to consider the confidence interval in terms of knowledge of the subsystems.  
Reconsidering the confidence interval and the \(n\) cycle, the second part of the expression (9) becomes:  
\[Xi(nt)+Xi(nt)\ \alpha<H_{i} \tag{16}\]  
Obviously, the real problem happens when the threshold \(H_{i}\) is exceeded:  
\[Xi(nt)+Xi(nt)\ \alpha<H_{i} \tag{17}\]  
so:  
\[Xi(nt)\ (1+\alpha)<H_{i} \tag{18}\]  
And the _new sof failure_ limit is:  
\[Xi(nt)\ <\frac{H_{i}}{1+\alpha} \tag{19}\]  
Therefore, it is necessary to keep the confidence interval as narrow as possible.  
Knowledge of the subsystems is therefore essential for evaluation: we risk calculating the total reliability of the system without evaluating the total accuracy that, in the worst case, would lead to a wrong assessment of preventive maintenance or an incorrect evaluation of preventive maintenance. Obviously, this is an undesirable situation.  
The condition for the threshold \(C_{i}\) is:  
\[C_{i}\ \leq\ Xi(nt)-Xi(nt)\ \alpha \tag{20a}\]  
so:  
\[C_{i}\ \leq\ Xi(nt)\ (1-\alpha) \tag{20b}\]  
and:  
\[\frac{C_{i}}{1-\alpha}\ \leq\ Xi(nt) \tag{21}\]  
Now, the first member of the (9) becomes:  
\[\frac{C_{i}}{1-\alpha}\ \leq\ Xi(nt)\ <\frac{H_{i}}{1+\alpha} \tag{22}\]  
Now we can objectively quantify the level of accuracy needed to define the preventive maintenance intervals.  
It is still useful to specify the function from a graphical point of view. Now consider the upper zone of Figure 4a in detail (see Figure 7):  
From Figure 7a, the correlation between confidence and useful interval for preventive maintenance is evident (in Figure 7b the detail is enlarged and the confidence intervals evidenced): the lower the confidence, the higher the probability that corrective maintenance is necessary.
## 3 System Model
### Problem Establishment  
The flying trajectory of the UAV and the transmission power of the MD are collaboratively optimized to maximize the user's calculation efficiency, in order to make the best use of the available computing resources. As a result, the UAV-assisted MEC system's computing efficiency maximization problem can be stated as:  
\[P:\max_{\mathsf{s}(n),\rho(n)}\eta \tag{11}\] \[C1:0\leqslant p_{i}(n)\leqslant p_{i,max},\forall i,n,\] \[C2:s_{i}(n)\leqslant 1,\forall i,n,\] \[C3:\parallel\mathsf{q}(n+1)-\mathsf{q}(n)\parallel\leqslant V_{ max}\tau,\forall n,\] \[C4:\mathsf{q}(1)=\mathsf{q}_{1},\] \[C5:0<x_{i}(n)<L,0<y_{i}(n)<W,\ \forall i,\] \[C6:0<x(n)<L,0<y(n)<W.\]  
In the optimization problem \(P\), \(\eta\) is the total computational efficiency of the MDs. \(C1\) denotes the transmission power restriction, and all MDs' transmission powers are constrained to not go over the maximum transmission power. \(C2\) represents the user association constraint, indicating that the task is performed on the UAV. \(C3\) denotes thatthe UAV's maximum flying speed cannot be exceeded. In \(C4\), The UAV starts from the same place as in \(\mathrm{q}_{I}\), which is its initial position. \(C5\) and \(C6\) represent the user, and the UAV will not leave the area, respectively.
###### Abstract  
This paper studies the problem of generating cooperative feasible paths for formation rendezvous of unmanned aerial vehicles (UAVs). Cooperative path-planning for multi-UAV formation rendezvous is mostly a complicated multi-objective optimization problem with many coupled constraints. In order to satisfy the kinematic constraints, i.e., the maximum curvature constraint and the requirement of continuous curvature of the UAV path, the Pythagorean hodograph (PH) curve is adopted as the parameterized path because of its curvature continuity and rational intrinsic properties. Inspired by the co-evolutionary theory, a distributed cooperative particle swarm optimization (DCPSO) algorithm with an elite keeping strategy is proposed to generate a flyable and safe path for each UAV. This proposed algorithm can meet the kinematic constraints of UAVs and the cooperation requirements among UAVs. Meanwhile, the optimal or sub-optimal paths can be obtained. Finally, numerical simulations in 2-D and 3-D environments are conducted to demonstrate the feasibility and stability of the proposed algorithm. Simulation results show that the paths generated by the proposed DCPSO can not only meet the kinematic constraints of UAVs and safety requirements, but also achieve the simultaneous arrival and collision avoidance between UAVs for formation rendezvous. Compared with the cooperative co-evolutionary genetic algorithm (CCGA), the proposed DCPSO has better stability and a higher searching success rate.  
unmanned aerial vehicle (UAV); path-planning; formation rendezvous; Pythagorean hodograph (PH); distributed algorithms; cooperative particle swarm optimization (CPSO); cooperative co-evolutionary algorithm (CCGA) +
Footnote †: journal: Applied  
1
## 2 Materials and Methods  
### Experimental Equipment  
The gas diffusion was determined using an experimental system that was specifically designed for this project to contain the gas mixtures and measure their concentration over time, using UV-Vis spectroscopy technology. The experimental equipment is shown in Figure 2.  
The designed system consisted of a 60 L tank, a DH-2000-S-DUV-TTL light source (Ocean Insight, Orlando, FL, USA), a Maya2000 Pro spectrophotometer (Ocean Insight), a pressure indicator (WIKA, Klingenberg, Bayern, Deutschland), and a feeding system composed of polytetrafluoroethylene (PTFE) tubes to introduce the gas mixtures in the tank. The tank had similar dimensions to an actual medium voltage switchgear, but was slightly narrower, 0.8 \(\times\) 0.5 \(\times\) 0.15 m, which favors vertical gas diffusion.  
The light source emits a continuous UV-Vis spectrum, between 190-750 nm, which is split into the four fibers that end in four lenses that are located at four different heights of the tank, as the interest is focused on the diffusion that happens vertically. The measurement lines are located at 0.13, 0.35, 0.57, and 0.78 m; for simplicity reasons, the lines are referred to as Bottom (B), Medium Bottom (MB), Medium Top (MT), and Top (T), respectively.  
The emitted light passes through the tank and is collected in four other lenses and fibers that merge and terminate in the spectrophotometer, which provides the corresponding ultraviolet absorbance spectra. A shutter is installed in each line, so the signal of each line is measured individually.  
Figure 2: (**a**) Gas chamber and (**b**) scheme of the experimental equipment used for the gas diffusion study and its components. T (Top), MT (Medium Top), MB (Medium Bottom), and B (Bottom) correspond to the different heights of the measurement lines.
###### Abstract  
In this paper, a combined approach of Principal Component Analysis (PCA)-based Extreme Learning Machine (ELM) for boiler output forecasting in a thermal power plant is presented. The input used for this prediction model is taken from the boiler unit of the Yermarus Thermal Power Station (YTPS), India. Calculation of the accurate electrical output of a boiler in an operating system requires the knowledge of hundreds of operating parameters. The dimensionality of the input dataset is reduced by applying principal component analysis using IBM@SPSS Software. In the process of principal component analysis, a dataset of 232 parameters is standardized into 16 principal components. The total dataset collected is divided into training and testing datasets. The extreme learning machine is designed for various activation functions and the number of neurons. Sigmoid and hyperbolic tangent activation functions are studied here. Its generalization performance is examined in terms of the Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square (RMSE), and Mean Absolute Percentage Error (MAPE). ELM and PCA-ELM are compared. In both the ELM and PCA-ELM models, when the extreme learning machine was designed with a sigmoid activation function with 100 nodes in the hidden layer, RMSE was 5.026 and 4.730, respectively. Therefore, the developed combined approach of PCA-ELM proved as a promising technique in forecasting with reduced errors and reduced time.  
Keywords:extreme learning machine (ELM); principal component analysis (PCA); boiler output forecasting; activation function; hidden neurons; sigmoid function +
Footnote †: journal: _sciences_
## 3 Materials and Methods
### Traffic Measurements
#### 3.3.1 Game \(A\) (MMORPG)  
The game consists of tens of EC2 virtual machines (VMs). Each VM hosts a custom TCP server software ([[https://medium.com/@dpinoagustin/custom-tcp-application-with-golang-427998c8a75c](https://medium.com/@dpinoagustin/custom-tcp-application-with-golang-427998c8a75c)]([https://medium.com/@dpinoagustin/custom-tcp-application-with-golang-427998c8a75c](https://medium.com/@dpinoagustin/custom-tcp-application-with-golang-427998c8a75c)) (accessed on 23 January 2024)). Each server is assigned a particular space in the shared virtual space (e.g., one might correspond to a village, while another might host a monster dungeon and/or a battlefield). Therefore, the client directly communicates with a particular server depending on the player's location. This means that certain  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Game** & **Protocol** & **Description** \\ \hline \(A\) & TCP & Application messages are in Protocol Buffers [26] on TCP. \\ \hline \(B\) & HTTP/1.1 and /2 &
\begin{tabular}{l} Designed to use RESTful API. \\ Application messages are in JSON over HTTPS (using TCP). \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Network protocols and message formats.  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **Game _A_** & **Game _B_** \\ \hline Time span & 2 h (10 July 2023 UTC) & 2 days (5–6 August 2023 UTC) \\ \hline Client-to-server messages & 19,010,841 & 335,145,778 \\ \hline Server-to-client messages & 16,917,007 & 335,145,778 \\ \hline Observed sessions & 408 & 53,749,009 \\ \hline Session duration(s) (avg/med/stdev) 1 & 1819.1/1305.2/2359.4 & 1563.7/629.4/2382.1 \\ \hline Msgs per session (avg/med/stdev) & 49,540.8/15,429/86,622.8 & 448.2/210/478.9 \\ \hline Bit rates (KBps) (avg/med/stdev) & 240.2/237.3/30.8 & 14,219.9/13,977.2/3648.0 \\ \hline Log size in total & 5 GB & 150 GB \\ \hline \hline \end{tabular}  
* Due to the potential session terminations of a mobile application, we measured the time interval between the first and last message in a session instead.  
\end{table}
Table 3: Key aspects about traffic measurements.  
measurements can be influenced by spatial aspects because players may show different behavioral patterns in different (virtual-world) locations.  
In order to minimize such a spatial impact, we collected log messages from a server that is in charge of both a village and a monster dungeon. Each log entry records an application-level Protocol Buffers message as well as a timestamp, a message direction, a message type, a player session, and a message length. The measurement spans two hours during the high season, allowing us to capture traffic behavior during the period of peak activity. As previously mentioned, Game \(A\) offers an auto-hunting feature, which allows the player's character to engage in game activities even when the player is not actively controlling them. Consequently, this feature leads to longer session durations and an increased number of messages per session, as reflected in Table 3. Below is a part of the log message given:
# Cross-Subject Classification of Effectiveness in Performing Cognitive Tasks Using Resting-State EEG
## 3 Results
### Group Differences and Cross-Subject Group Classification
The SHAP feature values indicate the following differences in PSD of resting-state EEG and their distribution over the scalp, which played a substantial role in the classification performance: theta1 band in the left temporal lobe; theta2 in the temporal and left prefrontal cortex; alpha1 in the left junction of central and temporal areas, above the right prefrontal and right parietal regions; alpha2 band in the left temporal lobe; beta1 band in the prefrontal region; left frontal area, central parietal area, and dorsolateral frontal areas; beta2 in the bilateral junctions of the frontal and temporal regions, and above the right frontal lobe; and beta3 predominantly in the bilateral anterior frontal regions, central areas, and right dorsolateral cortex. Figure 3: Classification of resting-state EEG belonging to the most successful participants with advanced math skills (SAM) versus resting-state EEG belonging to the most successful participants with basic math skills (SBM): (**a**) Mean Shapley Additive Explanations (SHAP) values for power spectral density (PSD) feature contributions and their locations in the sensor space for six frequency bands; the color bar reflects that the more intensive yellow-white color corresponds to greater contribution of this feature in the classification accuracy; (**b**) receiver operating characteristics area under curve (ROC-AUC) illustrates the probability of true positive rate against false positive rate in recognition of two classes for cross-subject classification of SAM versus SBM subject pairs restrings-state EEG (six different folds are reflected in different colors and the bold blue line corresponds to mean ROC-AUC based on PSD). The dashed red line corresponds to the chance level to distinguish SAM versus SBM subject EEG, the gray area shows 1 standard deviation of ROC probability.
## 4 Impacts of HTST and UHT on Food Quality
### Basic Physicochemical Characteristics
#### 4.2.1 Total Soluble Solids  
The UHT technique, because of the very fast processing time, does not significantly affect the components of the TSS. Generally, many studies confirm the lack of significant changes in TSS after preservation with this method [22; 35; 36; 37; 39; 41; 45; 52; 53; 54], as well as during storage [27; 41; 42; 45; 53]. However, different observations are found by [PERSON] et al. [63]. The significant decrease in TSS they noted was caused by the design of the equipment. The authors assume that because of the need to constantly supply water to the apparatus, the test sample could be diluted and the TSS decreased. Decreases in TSS were also demonstrated by other studies [12; 17; 42]. Moreover, no significant effects of UHT on the contents of sucrose, glucose and fructose in purple sweet potato nectar were revealed [35]. In another study, there was no effect on the sucrose content, but significant decreases in glucose, fructose and total sugars were observed [45].  
Thermal processing of peach juice (72 \({}^{\circ}\)C/15 s) did not result in a significant change in TSS following HTST treatment compared to raw juice immediately after processing, as well as during 28 days of storage [31]. Also, other studies have shown no significant effects of HTST on the TSS of juices, nectars and beverages from fruits and vegetables immediately after preservation [5; 22; 30; 32; 50; 64; 65], as well as during storage [5; 50; 65; 66]. [PERSON] et al. [67], examining three blackberry-soy-flaxseed-based beverages, also confirmed the lack of significant effects of HTST preservation (71.1 \({}^{\circ}\)C/3 s) on TSS. [PERSON] et al. [46] indicated a 9.5% increase in the TSS in blended orange and carrot juice. In lime juice, a decrease of more than 8% in TSS (85 \({}^{\circ}\)C/30 s and 95 \({}^{\circ}\)C/30 s) was observed [63].
## 2 Model and Methodology
### Scenario Definition  
A total of 18 working scenarios have been defined; they are the result of a three-step combination of various assumptions, as described below.  
The first step is related to the two different geographical areas analyzed--here Florence and Bruxelles--and affects the following input variables:  
* Average daily travel \(avd_{FI}\) and \(avd_{BXL}\).
* Publicly accessible stalls \(PP_{FI}\) and \(PP_{BXL}\).
* Private residential parking \(PR_{FI}\) and \(PR_{BXL}\).  
The second step considers the three different xEV fleet forecast scenarios calculated for each geographical area, here defined as low, medium and high; it affects the following inputs variables:  
* BEV circulating fleet on y-th year \(\left(BEV_{FI}\right)_{y}\) and \(\left(BEV_{BXL}\right)_{y}\).
* PHEV circulating fleet on y-th year \(\left(PHEV_{FI}\right)_{y}\) and \(\left(PHEV_{BXL}\right)_{y}\).  
The final step is related to user behavior modeling and specifically to the charging events probability distribution over the estimated range of the vehicle \(p\).
# Development of Traffic Scheduling Based on TSN in Smart Substation Devices
## 4 Experiment and Data Analysis  
### Experiment  
In order to verify the TSN characteristics of the end-side device of the smart substation proposed in this paper, two different industrial controllers were used to simulate the interval layer device and the process layer device, respectively. The interval layer device serves the purpose of sending packets, whereas the process layer device is responsible for receiving packets. These simulations imitated the transmission process of the differential protection commands, and the TSN performance of the end-side device of the smart substation was verified through the measurement of the latency of the time-sensitive flow containing the differential protection commands.  
The experimental scheme is depicted in Figure 5, involving both devices running the Linux-rt 5.3 real-time operating system, which is also equipped with ETF Qdisc and MQPRIO Qdisc. On the interval layer device, a periodic real-time packet-sending application is running with a sending period of 1 ms, a message length of 200 bytes, and a VLAN priority of 5. The I210 NIC is employed to send the message out at regular intervals according to the process illustrated in Figure 3. The two devices are connected through two MOXA TSN switches and one HIRSCHMANN TSN switch. The TSN switches turn on the Qbv scheduling function with a cycle period of 1 ms. Its GCL table is shown in Table 3, and the gating row from left to right indicates the switching status of queues Q7-Q0, respectively, where \"1\" signifies \"on\" and \"0\" signifies \"off\". Meanwhile, the process layer device uses the Tcpdump tool to capture the corresponding message and record its reception time.  
The offset of the message sending time TxTime is set to 200 us on the interval layer device. This coincides with the moment when the TSN switch opens the corresponding time slot. This arrangement ensures that the message achieves the shortest sojourn time when it passes through the TSN switch. Additionally, the process layer device uses the I210 NIC to capture the hardware time slot of the message, known as the receive time ArriveTime. This allows for the calculation of the delay of the time-sensitive stream using the following formula:  
\[\text{delay}=\text{ArriveTime}-\text{Txtime} \tag{1}\]  
The MOXA TSN switch supports IEEE 802.1 AS, and both devices run linuxptp to complete clock synchronization across all devices. After completing the clock synchronization, two sets of comparative experiments were conducted. In one set, the interval layer device activated the ETF Qdisc and the timed sending function of I210, whereas in the other set, these features were deactivated. Each group sent a total of 600,000 packets.  
Figure 5: Test of timing and sending function of TSN end device.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Time Slot Entry** & **Slot Length** & **Gate Status** \\ \hline T0 & 0–200 us & 1101111 \\ T1 & 200–600 us & 00100000 \\ T2 & 600–1000 us & 1101111 \\ \hline \hline \end{tabular}
\end{table}
Table 3: GCL table of TSN switch.
## 4 Experiments
### Performance Measurement
#### 4.5.1 Evaluation Metrics  
To evaluate the performance of our sentiment analysis models, we used standard metrics such as Accuracy (ACC), Precision, Recall, and the F-measure. These metrics provide a comprehensive assessment of the model's effectiveness in predicting sentiment across both positive and negative classes.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Context Window** & **Max Output** **Tokens** & **Training Data** & **Trained Tokens** & **Learning Rate** & **Batch Size** & **Epochs** \\ \hline GPT-4o-mini-2024-07-18 & 128,000 tokens & 16,384 tokens & Up to October 2023 & 3,909,592 & \(1.8\times 10^{-4}\) & 12 & 3 \\ GPT-3.5-turbo-0125 & 16,385 tokens & 4096 tokens & Up to September 2021 & 4,617,049 & \(2\times 10^{-4}\) & 10 & 3 \\ Llama-2-7b-hf & 8192 tokens & 4096 tokens & Up to December 2022 & - & \(1\times 10^{-4}\) & 4 & 5 \\ Llama-3-8B-Instruct & 8192 tokens & 4096 tokens & Up to December 2023 & - & \(1\times 10^{-4}\) & 4 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of fine-tuned models and hyper-parametersThe mathematical formulation for Accuracy is  
\[\text{Accuracy (ACC)}=\frac{TP+TN}{TP+TN+FP+FN} \tag{15}\]  
where  
* \(TP\) represents True Positives.
* \(TN\) represents True Negatives.
* \(FP\) represents False Positives.
* \(FN\) represents False Negatives.  
Precision is calculated as  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{16}\]  
Recall is defined as  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{17}\]  
The F-measure (F1-score) is calculated as  
\[\text{F-measure}=2\times\frac{\text{Precision}\cdot\text{Recall}}{\text{ Precision}+\text{Recall}} \tag{18}\]