esa-sceva/satcom-chunk-collection huggingface dataset:
# A Microservices-Based Control Plane for Time-Sensitive Networking
## 4 Tests and Discussion of Results
### Performance Analysis  
We now describe performance tests to evaluate the resource usage of our design. First, it is important to note that, at the system's core, there is a microservice with an ILP model that incorporates the constraints and defines the objective function of the TSN scheduling problem. This microservice can use any ILP solver, including those shown in Table 1. We compared GLPK and Gurobi in terms of the time they took to offer a solution for a given number of streams in a defined topology. Secondly, it is important to consider another condition that can affect the performance of our system: this is the hardware of the machine in which the ILP Calculator microservice runs. We ran our tests over three different platforms:  
1. A laptop with a four-core Intel Core i7-7820 HK processor with a maximum frequency of 3.9 GHz and 16 GiB of RAM.
2. An Elastic Compute Cloud (EC2) instance on Amazon Web Services (AWSs) of type T3.small with a two-core Intel Xeon Scalable processor with a maximum frequency of 3.1 GHz and 2 GiB of RAM [32].
3. An EC2 instance on AWSs of type M5 Zn.large with a four-core Intel Xeon Scalable processor with a maximum frequency of 4.5 GHz and 16 GiB of RAM [33].  
According to the literature, the actual performance one will get from using multiple cores in an ILP problem depends on several factors, such as the nature of the optimization problem, the size of the model, the availability of memory resources, and the specific system configurations [34]. In this test we determined which is the option that provides a solution to the problem in the shortest time. Finally, with the results of the three previous experiments, we selected the best ILP model solver and compared the two platforms with the best performance to identify what characteristics the platform running the microservice should have, both in terms of hardware resources and solver.  
We now describe the characteristics of the experiments. The topology is shown in Figure 13, where all links are 1 Gbps. Regarding flows, a custom microservice created the inputs of the Jetconf microservice, generating specifications sent through the RabbitMQ queue. All streams had a deadline of 500 ms. The periods of the streams varied between 125 ms, 250 ms, and 500 ms with uniform probability. The streams could be composed of one to three frames with the same probability. The number of streams went from fifteen to twenty-five streams per problem, with jumps of five streams between each test. We repeated each test 100 times for each number of streams.
# Brake Light Detection Algorithm for Predictive Braking
## 3 Methods
### Algorithm
The purpose of defining the threshold ranges and sending only (\(L\), \(a\), \(b\)) within those thresholds is to help the random forest algorithm to respond to those colors that are present in brake lights while they are lit. Figure 4: Scatter plot represents individual pixel values in LAB, HSV, and BGR colorspaces. Red triangles represent the brake light color values when lit, and gray circles represent full image value distribution. Figure 3: Workflow for the algorithm. In Stage (iii), the random forest algorithm (Figure 5) must classify whether the brake lights are on or off. The random forest algorithm is an ensemble of decision trees which have been trained on the image dataset. A hundred decision trees were included in the forest in the conducted tests. The image preprocessed earlier is resized into a pixel size of 30 \(\times\) 30. The 30 \(\times\) 30 image is then transformed into an input vector including all the three variables (\(L\), \(a\), \(b\)) with a length of 2700 elements. This input vector is fed into the random forest classifier. The classification was chosen to be positive if the confidence value returned by the random forest surpassed 0.6. A confidence level of 0.6 was chosen as a suitable value, after practically evaluating the algorithm performance on videos captured from traffic. The 0.6 confidence level increases the robustness of the classification, while still upholding reliable classification in actual braking scenarios. This is somewhat higher than the intuitively selected confidence level of 0.5. Figure 6 shows the mask visualizing the relative importance of the individual variables, based on the weights learned by the random forest algorithm (the color white being important, black being less so). From these weights, one can observe the process through which the algorithm chooses to report a positive. It searches for high pixel values in the learned areas in each channel. In the presented application of brake light detection, \(L\) gives an overall light, \(a\) gives red, and \(b\) gives yellow. It should be noted that the mask in the figure is a visualization of the training result brought back to image format; the random forest algorithm has the mask information in its own input vector format. In the algorithm, the lights in triangular formation are amplified, whereas a light in a random location would be excluded or attenuated. This approach allowed attenuation of light sources, such as sunlight, scattered light, or oncoming vehicle headlights. The mask can also be viewed as a probability distribution indicating the degree to which each pixel value affects the prediction whether the brake lights are on or off. \begin{table}
\begin{tabular}{c c} \hline \hline
**Lower Threshold** & **Upper Threshold** \\ \hline \(77<L<147\) & \(180<L<255\) \\ \(169<a<224\) & \(98<a<161\) \\ \(161<b<210\) & \(140<b<241\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Threshold values for the L*a*b colorspace. Figure 5: Workflow for the random forest classifier.
# LaRaCog: A Protocol for Cognitive Radio-Based LoRa Network
## 4 LoRaWAN Protocol
### LoRaWAN Key Features
#### 4.2.1 Spreading Factors (SF)  
LoRa is a Chirp-Spreading Spectrum (CSS) technology that offers good resistance to interference. The long communication distance is obtained at the expense of the transfer rate. The LoRa technology is defined by three main parameters: spreading factor (SF), bandwidth (BW), and carrier frequency [45].  
SF can be summarized as the duration of the chirp. LoRa operates with SF from SF7 (the shortest time on air) to SF12 (the longest on air). Each level up in SF doubles the time on air to transmit the same amount of data. With the same bandwidth, longer time on air obviously results in less data transmitted per unit of time [9; 46].  
However, longer SFs are usually used when the EDs are far away from the GW. Although they reduce the data rate, they ensure a low probability of packet rejection. The SF configuration feature can be efficiently exploited by the GW in LoRaCog to ensure parallel functioning with LoRaWAN. When the GW sends the channel parameters using short SF, only the nearby EDs will be covered. Consequently, these EDs operate according to LoRaCog, while the faraway EDs will not be able to receive the GW's message, and thus, they will remain performing the transmission as per LoRaWAN. Indeed, the value of the SF may be calibrated by the network to achieve the target number of the EDs to operate over the licensed spectrum.  
Figure 3: Class A operation.  
Figure 2: LoRaWANProtocol Stack [44].
## 3 Results and Discussion
### Adsorption Isotherms

Table 2: Parameters used in Langmuir, Freundlich and Tempkin equations. Figure 5: MB removal percentage corresponding to different C\({}_{0}\) values of MB solutions by (**a**) raw hydrocarbons and (**b**) modified hydrocarbons. Removal efficiency by activated carbons was always greater than 99% and thus not reported. the physicochemical nature of the sorption process with a multilayer coverage of active sites on the adsorbent surface [51]. Results show an R\({}^{2}\) equal to 0.8201, 0.9525 and 0.8823 for samples 190 MHC, 220 MHC and 250 MHC, respectively. The equilibrium data were also fitted to the Freundlich equation. Data show good correlation coefficients, in particular for the modified hydrochars; however, they were lower than Langmuir (for HC) and Tempkin (for MHC) values meaning that the MB adsorption does not follow Freundlich model closely.
## 3 Results and Discussion
### Fouling/Scaling Analysis  
The normalized flux (flux divided with the corresponding driving force to flatten the effect of small temperature variations) obtained by using virgin and used (after and before cleaning) membranes has been shown in Figure 10. The mean values of normalized flux before and after cleaning was calculated to be 0.00036 LMH/Pa and 0.00052 LMH/Pa, where the latter is similar to the flux measured for the virgin membrane. These observations clearly indicate the buildup of scaling at the membrane surface during MCr tests. However, the recovery of flux after cleaning indicates that the scaling built-up during the test was reversible.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Experiment** & **Al** & **Fe** & **Mg** & **Mn** & **Na** & **P** \\ \hline
**47 \({}^{\circ}\)C-21.6 L/h** & nd & nd & 0.00042 & 0.0021 & 28.47 & nd \\
**56 \({}^{\circ}\)C-21.6 L/h** & 0.065 & nd & 0.0233 & 0.0032 & 32.84 & 0.0053 \\
**38 \({}^{\circ}\)C-21.6 L/h** & 0.012 & 0.00005 & 0.016 & 0.0023 & 26.21 & 0.0048 \\
**47 \({}^{\circ}\)C-30.7 L/h** & 0.0032 & 0.0008 & 0.015 & 0.0026 & 30.53 & 0.0028 \\
**47 \({}^{\circ}\)C-14.8 L/h** & 0.146 & 0.0027 & 0.045 & 0.0059 & 23.32 & nd \\ \hline \hline \end{tabular}
\end{table}
Table 5: Content [%] of cations in crystalline phases. Not detected (nd).  
Figure 9: XRD spectra of the crystalline phases precipitated in the MD and MCr experiments. XRD of pure Na2 SO4 was also made (blue curve).
## 1 Introduction
### Residential Sector in Jordan  
The building sector plays a pivotal role in global energy consumption, accounting for approximately 40% of energy demand in Europe and Australia and around 39% in the United States [5]. In Jordan, the impact of residential buildings is particularly significant, contributing to 21.5% of final energy consumption and 48% of total electricity consumption [6; 7]. With projections indicating a sustained increase in energy demand, the need for effective interventions in the residential sector is crucial. These interventions are key to achieving significant economic, social, and environmental benefits. Figure 1 illustrates the electrical energy consumption by sector in Jordan for the year 2021 [7].  
Urbanization, population growth, and rising living standards are primary drivers of the increasing energy demands in Jordan's residential sector [8]. The urban population in Jordan reached 93.2% in 2021, with over 90% of Jordanians residing in urban areas, notably in Amman, which houses more than 43% of the country's population [9]. This rapid urbanization, largely driven by migration from rural areas seeking better opportunities and services, has a profound impact on energy consumption. In particular, the urban subsector is responsible for 84% of residential energy consumption in Jordan [6].  
Furthermore, the high proportion of time spent indoors by individuals, often exceeding 90%, emphasizes the significant role of buildings in overall energy consumption patterns [10]. Space heating and cooling alone account for 61% of household energy use and approximately 14% of global energy demand [11]. The building sector is, thus, a leading contributor to global energy consumption. Predictions suggest that energy consumption for space heating and cooling could account for about 37% of total global energy demand by 2050 [12]. Therefore, optimizing building envelopes and implementing more efficient HVAC systems are vital strategies for ensuring indoor thermal comfort while reducing energy consumption for these purposes. Regarding building envelopes, ref. [13] critically reviewed climate-adaptive building envelopes and biomimetic solutions, analyzing various adaptive and biomimetic envelope examples.
# A Recursive Optimization Approach for Buffer Allocation in Large Production Lines
## 6 Numerical Experiments
### Forty-Machine Lines  
As in the previous two experiments, one hundred 40-machine production lines were randomly generated and the desired production rate \(P^{*}\) was set to 0.8. Table 8 compares the performance of the gradient, RBD, and ROD algorithms. The gradient and RBD algorithms always obtain the best result \(N^{best}\) in the 100 problems, whereas the ROD algorithm obtains \(N^{best}\) in 76 problems. Although the ROD algorithm does not obtain \(N^{best}\) in the remaining 24 problems, the maximum deviation is just 1 buffer unit, with a maximum percentage error of only 0.08%.  
The deviation of the ROD algorithm is due to the non-uniqueness of the solutions to the decomposed sub-problems. Note that the optimal solution to an integer-valued BAP is essentially an approximation of the optimal solution to the corresponding real-valued BAP. Thus, an integer-valued BAP may have multiple optimal solutions. Consider the pseudocode of the ROD algorithm in Table 6. Due to non-uniqueness, the solutions of the sub-lines \(L_{1}\) and \(L_{2}\), i.e., \((x_{1},...,x_{K-s-1})\) and \((y_{s+1},...,y_{K-1})\), may not be accurate lower bounds of the respective buffer capacities in the original line \(L\). One way to avoid this problem is to reduce \(x_{i}\), \(i=1,...,K-s-1\) and \(y_{i}\), \(i=s+1,...,K-1\) by one buffer unit. For example, consider the production line described in Table 9. The minimum total buffer capacities obtained by the gradient, RBD, and ROD algorithms are 1489, 1489, and 1490, respectively, with corresponding computation times of 2849.7 s, 119.8 s, and 11.4 s. If the ROD algorithm is modified as described above, it leads to the same solution as the gradient and RBD algorithm, but the computation time increases from 11.4 s to 182.7 s. We feel that the modification is unnecessary, because the ROD algorithm is accurate enough. The average percentage error for the 100 BAPs is only 0.02%.  
Figure 8 compares the computation times of the three algorithms. The average time for optimizing the 100 lines by the gradient algorithm is 2961.7 s, while the average computation times using the RBD and ROD algorithms are 134.7 s and 15.8 s, respectively. Once again, the recursive algorithms show a great advantage over the gradient algorithm.
## 6 Results and Discussion
### Results in the Segmentation of the Panels Surface  
A robust segmentation algorithm of the surface of the panels is crucial. This way, all of the thermal defects found in the images are located inside the surface of the panels. Segmentation performance is commonly evaluated with respect to Gold Standard (GS) manual segmentation performed by a human expert. Furthermore, a combination of segmentations by multiple \((n)\) experts is usually employed to attenuate intra-subject variability when performing the manual segmentation of \((m)\) images and obtain a truthful GS. In this sense, different strategies have been proposed to combine the segmentations. In this case, the Simultaneous Truth and Performance Level Estimation (STAPLE) approach is used [31]. When evaluating the performance of segmentation algorithms with respect to GS, a contingency table considering True Positive \((TP)\), True Negative \((TN)\), False Negative \((FN)\), and False Positive \((FP)\) is commonly used, where positive and negative refer to pixels belonging to panels and background, as in accord with the GS segmentation, respectively. Based on this table, Accuracy \((Acc)\), Sensitivity \((Se)\), and Specificity \((Sp)\) are the most frequently adopted measures, where \(Acc\) is the proportion of true results, both \(TP\) and \(TN\), among the total number of examined cases \((m)\). \(Se\), also referred as \(TP\) rate, measures the proportion of positives, both \(TP\) and \(FN\), which are correctly identified. \(Sp\) measures the proportion of negatives, both \(TN\) and \(FP\), which are correctly identified. A formal definition of the aforementioned measures used for evaluating the results in the segmentation of the surface of the panels can be seen in (7). For the evaluation of the segmentation algorithm of the panels surface, \(n=3\), \(m=20\).  
\[Accuracy(Acc)(\%) :(TP+TN)/(TP+TN+FP+FN) \tag{7}\] \[Sensitivity(Se) =TP/(TP+FN)\] \[Specificity(Sp) =TN/(TN+FP)\]  
Based on the values included in Table 2, the obtained results are: \(Acc=98.46\%\), \(Se=0.983\), and \(Sp=0.986\), showing that the proposed algorithm for the PV panels surface detection is adequate for filtering the thermal defects inside the PV panels.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metric** & **Value** \\ \hline True Positive (TP) & 109,226 \\ True Negative (TN) & 218,454 \\ False Positive (FP) & 3200 \\ False Negative (FN) & 1920 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Contingency table for the segmentation of the panels surface.
## 3 Methodology  
The methodology used in this study is based on the execution of four steps, regardless of the ML method to be used.  
* Step 01: Data collection and measurement (selection);
* Step 02: Data pre-processing;
* Step 03: Model execution (transformation/mining);
* Step 04: Validation of the results (interpretation/knowledge).  
Figure 2 presents the execution flow to obtain knowledge through data and metrics collection, preprocessing, execution of the ML model, and final validation of the results.  
This research used the collaborative environment, \"Colab\" (Collaboratory), a product of Google Research, based on the open-source project Jupyter. The sections used have access to a processor with two cores, 12 Gbytes of RAM, and an L3 cache of 40-50 Mbytes. Furthermore, Google Colab is a free cloud service hosted by Google to encourage machine learning and artificial intelligence research. This environment is widely used to run Python code with machine learning libraries and tools.  
Figure 2: Get knowledge cycle.  
In each of the four stages, the libraries NumPy, Pandas, Seaborn, and Scikit-learn were used to manipulate and analyse the data, generate calculations and statistical plot graphs, and apply the practice of ML methods. Specifically, NumPy is the foundational package for scientific computing in Python, and Pandas provides tools for data analysis and manipulation [37; 38]. Seaborn is used for plotting statistics, and Scikit-learn is a machine learning library that supports supervised and unsupervised learning [39; 40]. These are some of the main Python libraries [41].
# Mobility Management Based on Beam-Level Measurement Report in 5G Massive MIMO Cellular Networks
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
5G NR & Fifth-generation new radio \\ AAS & Active antenna array system \\ BM & Beam management \\ BS & Beam switching \\ BSF & Beam switching failure \\ CSI-RS & Channel state information reference signal report \\ FSM & Finite-state-machine \\ gNB & Next-generation-Node-B \\ HO & Handover \\ HOF & Handover failure \\ IA & Initial access \\ IBH & Inter-beam-handover \\ LOS & Line-of-sight \\ MMIMO(massive MIMO) & Massive multiple-input-multiple-output \\ MR & Measurement-report \\ NLOS & Non-line-of-sight \\ PUCCH & Physical-uplink-control-channel \\ PUSCH & Physical-uplink-shared-channel \\ QoS & Quality-of-service \\ RLF & Radio Link failure \\ RSRP & Reference-signal-received-power \\ Rx & Receiver \\ SINR & Signal-to-interference-and-noise-ratio \\ SLS & System-level-simulation \\ SSB & Synchronization-symbol-burst \\ TRP & Tx-Rx beam-pair \\ TTT & Time-to-trigger \\ Tx & Transmitter \\ UDN & Ultra-dense-networks \\ ULA & Uniform-linear-array \\ \end{tabular}
## 2 Methodology
### Optimization
#### 2.3.1 Genetic Algorithm-Swarm (GA-Swarm)  
GA-Swarm is a novel technique developed in this research by incorporating the'swarm' behaviour of PSO within GA. GA-PSO hybrid techniques have been used previously [34; 35; 36]. However, these techniques either incorporate mutation within PSO to ensure the technique can escape local optima or apply the GA 'fitness' function to swarm particles in order to divide them and then apply crossover and mutation functions on the 'better' half of the population.  
In GA-Swarm, as shown in Figure 2, PSO is incorporated into the GA algorithm by ensuring that after crossover, the offspring'swarm' towards the best solution in order to ensure a more efficient search within the solution space.  
The offspring is given a bias toward the best solution if a random number is less than a predefined probability, pSwarm, which is usually equal to 0.2.  
\[child_{j}=(1.0-sSwarm)*child_{j}+sSwarm*gBest_{j} \tag{45}\]  
For the strength of bias, sSwarm is taken to be equal to 0.2.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Objective Function** & **Description** \\ \hline GLOW & Gross Lift-Off Weight (total mass of LV including payload) \\ \(l_{sp}\) & Specific Impulse \\ \(T/W\) & Thrust-to-Weight ratio \\ TWISP & Simultaneously maximize \(l_{sp}\) and \(T/W\) \\ \hline Design Variable & Description \\ \hline \(F\) & Thrust \\ \(p_{c}\) & Combustion Chamber Pressure \\ \(\Delta t\) & Burn time \\ \hline Constraints & Description \\ \hline \(\Delta V\) & Change in velocity \\ \(H\) & Required altitude \\ \hline \hline \end{tabular}
\end{table}
Table 2: OF and Variable names and descriptions.
# Thermal Failure Propagation in Lithium-Ion Battery Modules with Various Shapes
## 3 Results and Discussion
### Square Module  
Figure 20 plots the typical surface temperature curves of batteries in square modules and the domino effect in the thermal failure propagation is further displayed in Figure 21. It exhibits three phases of failure during the propagation in square modules. The two batteries close to the heater failed at first, followed by the three batteries in the middle of the module, and the three batteries at the corner failed last. For the module with discharging treatment, the thermal failure appeared at 391 s, 612 s, and 735 s, respectively, and for the module without discharging treatment, the time to thermal failure  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **With Discharging** & **Time to the** & **Time to the** & **Propagation** & \multirow{2}{*}{**Mass Loss (g)**} \\  & **Treatment** & **First Phase (s)** & **Second Phase (s)** & **Speed (mm/s)** & \\ \hline
1 & Yes & 412 & 504 & 0.071 & 60.6 \\
2 & No & 470 & 587 & 0.061 & 56.5 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The detailed information of hexagon modules.  
Figure 19: The typical curves of mass loss for hexagon modules.  
Figure 18: The domino effect of thermal failure propagation in hexagon module.  
was 412 s, 636 s, and 863 s, respectively. In the square module, it had a propagation distance of 50.9 mm. Hence, the two modules had a propagation speed of 0.069 mm/s and 0.059 mm/s, respectively.  
Finally, the mass loss process of square modules during tests was shown in Figure 22. Consistent to the findings above, the square module with discharging treatment exhibited an earlier mass loss and it lost a mass of 84.0 g, which was relatively larger than that of another module, 83.0 g. The detailed information is seen in Table 7.  
Figure 20: The typical surface temperature curves of batteries in square modules: (**a**) with discharging treatment; (**b**) without discharging treatment.