esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Influence of Passing (Low) Beam Intensity on Detection Distances
### Test Set-Up  
The test set-up is located on an empty airstrip with a 1.2 km long straight road. On this road, two human-shaped dummies are placed at a distance of 5 m from the center of each driving lane. Both dummies are coated in a matte black paint with a measured reflectance of \(\rho=5\%\). During each test run, only one of the dummies is shown to the test subjects.  
The test vehicle (passenger vehicle) is equipped with modified headlamps that allow pulse-width modulation (PWM) dimming of the passing beam in two stable states. Before the test, both light distributions are measured using a goniophotometer and compared to each other. One passing beam set-up corresponds to a maximum of 21,000 cd (full output), and the other reaches 10,000 cd (50% output). The rest of the light distribution is scaled proportionally.  
Both the test vehicle as well as the detection dummies are equipped with GPS sensors, continuously registering their corresponding positions at 5 Hz. The test vehicle is equipped with a detection button. While driving along the straight road at \(v_{\mathrm{Test}}=50\) km h\({}^{-1}\) (with activated cruise control), the test subjects are then asked to press the button as soon as they are certain that they are able to detect the pedestrian alongside the road. When the button is activated, the current GPS position of the test vehicle is recorded, and the distance to the test dummy's corresponding GPS position can be calculated. This allows measurement of the detection distance to the target.  
The complete set-up is shown schematically in Figure 1, where the start of the test is marked by \(A\), and the end of the runway is marked by \(B\). The dashed gray line indicates the route test subjects took to return to their starting position.  
To allow proper adaptation to each headlight distribution, the switch between both set-ups is always done at point \(B\), so that the subjects can adapt to the new intensity on their way to the starting point (\(A\)). All test runs are started after dark, when the measured horizontal illuminance on the road is below 0.2 lx.
# Lightweight Physical Layer Aided Key Agreement and Authentication for the Internet of Things +
## 3 Physical Layer Aided Authentication and Key Agreement (PL-AKA)
### Physical Layer Challenge-Response Authentication (PHY-CRAM)
#### 3.1.3 Physical Layer Verification  
In the conventional PHY-CRAM [8], it is decided whether a received signal is transmitted from a legitimate IoT device or an intrusion device in the physical layer verification step. To this end, two hypotheses are considered: \(\mathcal{H}_{1}\) is the alternative hypothesis that the received signal is transmitted by the legitimate IoT device with a legitimate secret key denoted by \(\mathbf{\kappa}_{AB}\), and \(\mathcal{H}_{0}\) is the null hypothesis that the received signal is transmitted by the intrusion device with an arbitrary secret key denoted by \(\mathbf{\kappa}_{E}\). In [8], the test statistics of \(\zeta=|\mathbf{\kappa}_{AB}\mathbf{\mathbf{Y}}_{R}^{\mathrm{T}}|\) are used for binary hypothesis testing. Here, \(\mathbf{\mathbf{Y}}_{R}=[Y_{R}(f_{1}),Y_{R}(f_{2}),\cdots,Y_{R}(f_{L})]\) is a received vector in the response stage. In this paper, based on the test statistics of \(\zeta\), a novel cross-layer authentication strategy integrating the PHY-CRAM scheme to the cryptography-based authentication is proposed, for which details are provided in the following subsection.
# Deep Learning for Short-Term Load Forecasting--Industrial Consumer Case Study
###### Abstract  
In the current trend of consumption, electricity consumption will become a very high cost for the end-users. Consumers acquire energy from suppliers who use short, medium, and long-term forecasts to place bids in the power market. This study offers a detailed analysis of relevant literature and proposes a deep learning methodology for forecasting industrial electric usage for the next 24 h. The hourly load curves forecasted are from a large furniture factory. The hourly data for one year is split into training (80%) and testing (20%). The algorithms use the previous two weeks of hourly consumption and exogenous variables as input in the deep neural networks. The best results prove that deep recurrent neural networks can retain long-term dependencies in high volatility time series. Gated recurrent units (GRU) obtained the lowest mean absolute percentage error of 4.82% for the testing period. The GRU improves the forecast by 6.23% compared to the second-best algorithm implemented, a combination of GRU and Long short-term memory (LSTM). From a practical perspective, deep learning methods can automate the forecasting processes and optimize the operation of power systems.  
machine learning; deep learning; short-term forecasting; industrial electricity load
## 4 Results and Discussion
### Result of Syntheses  
This review formulated eight research questions that were established to assess and synthesize collected data. The formulated research questions gave insight into the aspects mostly investigated by the researchers, with fault diagnosis, energy management, health index, lifecycle management, and energy management emerging as key components of the examination by most researchers. The research questions also examined the publication sources and discovered that conferences were preferred over journals, most likely due to the importance of immediately sharing research findings in this rapidly developing sector. The regional distribution of research was also assessed, revealing that interest in transformer condition monitoring via IoT technology is broad, with India leading the way, followed by China and Pakistan. This study also looked at IoT clouds, microcontrollers, communication channels, and software tools used in this area of research. The Arduino IDE was the most popular software because it allows for easily programming Arduino boards and third-party microcontrollers. Wi-Fi was one of the most common communication protocols, while the most preferred IoT clouds and microcontrollers were Things Speak and Arduino Uno, respectively. Data synthesis findings and results are further discussed in detail under Research Questions.
## 4 Discussion
The S-N curves revealed that fatigue responses at 5, 25, and 50 Hz increase as the applied frequencies vary accordingly and that data scattering has a minor impact among the explored frequencies but is nevertheless present (0.3038 at 5 Hz, 0.5763 at 25 Hz, 0.4687 at 50 Hz, and 0.7691 at 20 kHz); (v) to approach loading frequency studies, is quite unfeasible to test specimens at every single excitation frequency. Thus, a regression/interpolation model should be applied for extracting design values from the experimental range. To understand the strength distribution, values at N = 2 \(\times\) 10\({}^{6}\) were selected for three probability levels (P = 10, 50, 90%), and fatigue strengths were well-approximated using a power law model. The analyses carried out in this paper have important implications for components and structures that are commonly subject to a broad frequency spectrum, as in the case of aerospace components during their operative conditions. Neglecting frequency rate effects could lead to earlier failures if components spend a significant number of cycles subjected to low-frequency loads, as demonstrated in this work. At the same time, attention should be paid when extracting material properties from ultrasonic testing. Indeed, in the case of fatigue strength, the extrapolation of such quantities for lower numbers of cycles could be risky, as fatigue strength can be overestimated. Practically, the design of components should be addressed to carefully incorporate the effects of multiple frequencies within the analysis environment, going beyond material property extraction using a single harmonic approach. In this way, possible earlier failures could be properly anticipated to develop reliable components with extended capabilities for working under a broad spectrum. Additionally, this concept is also important to implement rational maintenance inspections.
## 3 Experimental Results and Analysis
### About the Datasets and Evaluation Indexes  
RSICD: This is a large-sized RS image subtitle dataset created by [PERSON] et al. [35], including 10,921 RS images classified into \"Airport\", \"Industrial Zone\", and 28 other categories, each of which had a size of 224 \(\times\) 224, matched with five statements of descriptive texts.  
UCM-Captions: This dataset was created by [PERSON] et al. [36] based on [37], and includes 2100 RS images classified into 21 categories of scenes, each of which had a size of 256 \(\times\) 256 and was also matched with five statements of descriptive texts.  
Sydney-Captions: This dataset was also created by [PERSON] et al. [36], based on [38], and included 613 RS images of size 500 \(\times\) 500 classified into seven categories of scenes, each of which was matched with five statements of descriptive texts.  
On the basis of the above, given the large sizes of these datasets and the wide variety of scenes, RSICD can evaluate the performance of cross-modal retrieval models with greater effectiveness, and it has become favored for tasks in this field [18].  
Evaluation indexes: This paper uses three indexes to evaluate the effectiveness of the model. The first index is Recall at K (R@K, K = 1, 5, and 10), which is common in the field of image retrieval and denotes the percentage of positive examples occurring among the first K images. The second index is mR, proposed in [39] by [PERSON] et al., which represents the mean of six recall rates. The third index is R@SUM, proposed in [7] by [PERSON] et al., which represents the sum of six recall rates.  
It should also be noted that all the above datasets were used in the same way: 80% of the images and subtitles were used for training, 10% for verification, and 10% for testing.
# Analyzing LLAMA3 Performance on Classification Task Using LoRA and QLoRA Techniques
## 3 Experiments and Results
### Constant \(\alpha\)  
In this experimental setup, we keep \(\alpha\) constant at \((2\times r)\) and analyze the impact of varying the value of the hyperparameter \(r\) on the model's performance. An increase in the value of \(r\) implies the injection of more trainable parameters. Additionally, as we have kept the value of \(\alpha\) constant, an increase in the value of \(r\) decreases the scaling factor (\((\alpha/r)\)), resulting in more consideration of pretrained weights and less focus on trained or adapted parameters. As shown in Figure 5, for the all-linear layer adaptation, the performances of the LoRA and QLoRA models remain pretty steady as long as the value of \(r\) is \(\leq\)64. However, the performance of LoRA degrades as the value of \(r\) exceeds 64. Similarly, the performance degrades as the value of \(r\) exceeds 128, \((r>128)\), at the QLoRA setting. When the value of \(r\) jumps from 64 to 256 and then to 512, the performances of all the metrics significantly drop. Therefore, we recommend using a model with a low number of trainable parameters (at \(r=8\) and \(r=16\)), as these yield the optimal results for both LoRA and QLoRA techniques, as compared to higher values of \(r(r>16)\).  
Figure 5: LLaMA3—linear layer adaptation, \((r=1,2,4,8,16,32,64,128,256,\) and 512).  
In the case of the attention-layer adaptation, as shown in Figure 6, both the LoRA and QLoRA models maintain steady performances as long as the value of \(r\) is \(\leq\)256. However, the performances dip significantly when \(r\) exceeds 256. Also, after comparing Figures 5 and 6, it can be observed that the performances that LoRA and QLoRA achieved by adapting to all-linear layers are also attainable by adjusting only the attention layers (for \(r=16\)).  
According to this analysis, we recommend using QLoRA with a lower value of \(r\leq 16\). In both experiments (1 and 2), LoRA and QLoRA significantly improved the performance of the LLaMA3-8B base model. Additionally, both experiments demonstrated how LoRA's performance is attainable using QLoRA and with lower GPU memory requirements but at the expense of more time required to train the model. Additionally, instead of adapting to all the linear layers, QLoRA can attain the same performance by adjusting only the attention layers, which require far fewer trainable parameters.
## 2 Materials and Methods
### Theoretical Considerations
#### 2.2.4 Determining the IFD for Emission Classes G3E, G2B and G1B  
The following notations are made: \(K_{MG}\) is the slope of the phase modulator, the phase deviation and \(\Delta\varphi=K_{MG}\cdot\mathbf{\chi_{m}}\) is expressed as product between the phase modulator slope \(K_{MG}\) and the modulation signal amplitude \(X_{m}\), and \(f(t)\) is the normalized modulation signal with zero mean value (the same quantities as in Section 2.2.2). Thus, the phase-modulated signals can be expressed as in Euation (15):  
\[s_{MG}(t)=A_{p}\cos\big{[}\omega_{p}t+\varphi(t)\big{]}=A_{p}\cos\big{[} \omega_{p}t+K_{MG}\mathbf{\chi}(t)\big{]}=A_{p}\cos\big{[}\omega_{p}t+\Delta \varphi\cdot f(t)\big{]}= \tag{15}\]  
Because the actual information is contained only in the received signal phase \(\varphi(t)\), before demodulation the signal is passed through an amplitude limiter. Therefore, at the demodulator input the limited signal power \(P_{SMG}\) is expressed with Equation (16):  
\[P_{S_{MG}}=\overline{s_{MG}^{2}(t)}=A_{p}^{2}/2, \tag{16}\]  
and the noise power is the same as in relationship (3).  
The shape of the noise-affected signal can be described by Equation (17):  
\[s_{MG}(t)=A_{p}\cos\bigl{[}\omega_{p}t+\varphi(t)+\varphi_{z}(t)\bigr{]}=A_{p}\cos \bigl{[}\omega_{p}t+\Phi\bigr{]}, \tag{17}\]  
where \(\phi_{z}(t)\) is the noise component in the total phase of the signal \(\Phi\). The demodulated signal will be proportional to \(\Phi=\varphi(t)+\varphi_{z}(t)\). Thus, the signal and noise power, respectively, will depend on the phase modulator slope \(K_{MG}\) and/or the phase demodulator slope \(K_{DG}\), as seen in Equations (18) and (19):  
\[P_{SoutG3E}=K_{DG}^{2}\cdot K_{MG}^{2}\overline{f^{2}(t)}, \tag{18}\]  
\[P_{ZoutG3E}=\frac{K_{DG}^{2}}{A_{p}^{2}}\cdot\frac{1}{2\pi}\int\limits_{\omega _{c}-\pi B}^{\omega_{c}+\pi B}N_{0}d\omega=2\frac{K_{DG}^{2}}{A_{p}^{2}}N_{0}B. \tag{19}\]  
The result for G3E and G2E is an IFD expressed by Equation (20):  
\[\rho_{G3E}=\frac{P_{Sout}/P_{Zout}}{P_{Simp}/P_{Zinp}}=\frac{K_{DG}^{2}K_{MG} ^{2}\overline{f^{2}(t)}}{2\Bigl{(}K_{DG}^{2}/A_{p}^{2}\Bigr{)}N_{0}B}\cdot \frac{2N_{0}B}{A_{p}^{2}}=K_{MG}^{2}\cdot\overline{f^{2}(t)}, \tag{20}\]
## 3 Results
### Effectiveness Outcomes between Baseline and Ending Treatment Assessments
The UEMS related to the treated UE was significantly greater upon ending the treatment than at baseline for the control group (15.07 (6.49) versus 13.53 (5.11), p1 = 0.039) and the intervention group (15.60 (5.12) versus 13.13 (5.24), p1 < 0.01) (Figure 4a). In the two groups, the magnitude of the improvement was high, with a large effect (\(n^{2}=0.656\) for the intervention group and \(\eta^{2}=0.255\) for the control group) (Table 2). When we focus the analysis on the cases in which the treated arm was the dominant UE, we can observe the loss of statistical significance, maintaining only that which was detected in the intervention group between the assessments at baseline and upon ending the treatment (Figure 4b). The results related to ROM were expressed as a percentage related to a reference pattern formed by neurologically healthy subjects. No statistically significant differences were found between the assessments at baseline and at the ending in both groups, with the exception of elbow joint range in the control group (106.20 (33.38) versus 148.96 (60.32), p1 < 0.01) (Table 3). However, with regard to the UE dexterity measured by kinematic indices, significant improvements were observed in both groups. The scores obtained in the smoothness index S1 and in the area of the hexagon describing all of them improved significantly (example in Figure 3). However, the comparison between the baseline and ending conditions for the smoothness S1 index was greater in the intervention group, with greater effect on the observed difference (\(\eta^{2}=0.438\)) than in the control group (\(\eta^{2}=0.281\)). The improvement in the smoothness S1 was statistically significant in the control group (p1 < 0.05) and in the intervention group (p1 < 0.01) (Table 3, Figure 5a). No statistically significant differences were found in the improvements for the coordination index in the two groups analyzed. However, the magnitude of this improvement was high, with a large effect for the intervention group (\(\eta^{2}=0.241\)) (Table 3). Figure 4: (**a**) Upper extremity motor score (UEMS) of the upper limb treated for both groups when the total sample is analyzed (control group (\(n=16\)) and intervention group (\(n=15\))); (**b**) upper extremity motor score (UEMS) of the upper limb treated for both groups when the treated arm was the dominant arm (control group (\(n=12\)) and intervention group (\(n=9\))). Dark grey represented the control group. \({}^{*}p<0.05\), \({}^{**}p<0.01\). The subjects' perception of UE use improved progressively in successive assessments using the CUE scale. The graphical results can be seen in Figure 5b, which shows a
## 4 Highly Dynamic Simulations and Results
### Simulation of Inertial Calculation
#### 4.2.1 Norm Analysis Simulation  
In order to prove the correctness of the norm analysis method above, the first 10 ms of the acceleration of the X-43 are picked up; these 10 ms give the greatest dynamic movement in the whole trajectory. The initial velocity of the vehicle is set as (2146.4 m/s, 0, 0). Ten Monte Carlo simulations were conducted and the root-mean-square errors (RMSEs) of three velocity errors were compared with the calculated velocity error from Equation (28). A histogram of the RMSE is shown in Figure 3, and the detailed statistical analysis of Figure 3 is shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Movement State** & **Time (s)** & \
\begin{tabular}{c} **Forward Acceleration** \\ **(m/s/s)** \\ \end{tabular} & **Pitch Rate (\({}^{\circ}\)/s)** &
\begin{tabular}{c} **Final Velocity** \\ **(m/s)** \\ \end{tabular} \\ \hline Accelerative running & 0–20 & 4.00 & 0 & 80.000 \\ Accelerative taking-off & 20–35 & 1.00 & 1 & 95.000 \\ Accelerative climbing & 35–75 & 2.00 & 0 & 175.00 \\ Steady climbing & 75–100 & 0 & 0 & 175.00 \\ Change to level flight & 100–115 & 0 & \(-\)1 & 175.00 \\ Steady level flight & 115–145 & 0 & 0 & 175.00 \\ Separation from carrier & 145–150 & 0.68 & 0 & 178.40 \\ Ignition of the rocket & 150–153 & 10.0 & 0 & 208.40 \\ Accelerative head-up & 153–163 & 19.0 & 1 & 398.40 \\ Accelerative climbing & 163–250 & 19.0 & 0 & 2051.4 \\ Change to level flight & 250–260 & 0 & \(-\)1 & 2051.4 \\ Quick popup of X-43 & 260–265 & 19.0 & 0 & 2146.4 \\ High acceleration of X-43 & 265–270 & 300 & 0 & 3646.4 \\ Steady level flight & 270–300 & 0 & 0 & 3646.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the X-43 hypersonic aircraft’s dynamic trajectory.  
Figure 2: 3D trajectory of the X-43 hypersonic aircraft.  
From Figure 3 and Table 3, the velocity error calculated by Equation (28) matches well with the RMSE of the velocity errors from the Monte Carlo simulation. Hence, the proposed norm analysis method is correct and the calculated velocity error can be trusted to work in the loop stability analysis. Further, the calculation times of the norm analysis method and the Monte Carlo method were compared by Matlab platform as performed on a PC equipped with an I7-7700 CPU; the result shows that the norm analysis method only takes 0.01 s to get the velocity error while the Monte Carlo method needs 0.42 s. So, from this part, we can see that the proposed norm analysis method is more concise, efficient, quantitative, and precise.
## 5 Results and Discussion
### Number of MLP Layers  
In this subsection, we explore the influence of choosing the appropriate number of MLP layers. The number of MLP layers has a strong correlation with the number of model parameters and the degree of model sparsity.  
According to our experiments, in Table 3, choosing a moderate number of MLP layers (four layers) brings the best results. It is obvious that more MLP layers mean more parameters and a stronger ability to learn from complex data distributions. When the number of MLP layers is small, although LGNet can still bring obvious improvements compared to the original structure, its advantages have not been fully stimulated. When there are too many MLP layers (such as 16), the data allocated to each MLP layer is proportionally reduced, which may cause the MLP layers to not be fully trained. For example, if there are 1000 training samples in the dataset, each MLP layer of LGNet-4 MLP can be allocated 250 training samples on average, while each MLP layer of LGNet-16 MLP may only be allocated 62.5 training samples on average. It can cause models with too many MLP layers to suffer from overfitting due to the limited allocated data. The degradation of the model performance of LGNet-16 MLP in Table 3 confirms our conclusion.
# IoT-Enabled Precision Agriculture: Developing an Ecosystem for Optimized Crop Management
## 4 Experiments and Simulation
### Experiment Mobility Model for Both Scenarios
The RWP mobility model distributes the nodes randomly within predefined simulation areas after the start of the simulation. At the beginning of the simulation run, every mobile node remained in its location for a certain random period (i.e., a pause time), and this was uniformly chosen between 0-60 s. Once this time expires, it moves to a random location within a simulation area and at a speed uniformly distributed between minspeed and maxspeed. The mobile node then moves to the newly selected destination location at the selected speed. The node pauses for the specified pause time before repeating the same process when it arrives. The node chooses a new speed and destination combination and then moves again after the completion of the pause time. The simulation setting for the speed of the mobile nodes is uniformly selected between 1 and 2 m/s. This model is used in several simulation studies and is closer to simulating a horse's movement. The impact of mobility on performance can be significant in wireless sensor networks for mobile applications such as horse monitoring. As the nodes move, the network topology changes and the routing paths may need to be recalculated. This can lead to increased network overhead, decreased packet delivery ratio, and increased power consumption. Additionally, the moving nodes may experience varying levels of signal strength and interference, leading to further challenges in maintaining stable communication. Thus, it is essential to consider the impact of mobility on the overall system performance in wireless sensor networks for mobile applications.