esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Evaluation
### Financial Cost Issues  
The total cost of each of the discussed sensor nodes, after adding EUR 30 for the LoRa32u4 development board and its antenna, EUR 10 required for the power conversion circuits, EUR 5 for the solar panel, EUR 10 for the 3.5 mm plugs, EUR 3 for the multiplexer/demultiplexer, EUR 5 for the Li-Ion battery and its socket, EUR 8 needed for a good-quality plastic enclosure box, and roughly EUR 10 for the rest of the tinkering components was around EUR 80.  
The sink/gateway node required EUR 40 for a Raspberry Pi Model 3 B+, EUR 25 for the LoRa32u4 board, EUR 5 for the RTC module, EUR 5 for a plastic enclosure box, and EUR 25 for the power-supplying part including the batteries, resulting in a total cost for this node type close to EUR 100. A Raspberry Zero W could also have been used as well to reduce the cost by EUR 20.
# Wavelet-Based Analysis of Motor Current Signals for Detecting Obstacles in Train Doors
## 5 Experimental Results
### Hard-Object Scenario  
Similarly, hard-object detection was tested by placing a hard object between the doors approximately 45 cm above the bottom of the door. The current, norm, and peak profiles initially resembled those in the no-obstacle case, until the hard object was detected. As shown in Figure 16a, at sample 303, the motor current surged to 3.25 A, resulting in increases in the W9 and peak amplitudes to 58.1 and 13.3, respectively. These observations satisfied the hard-object detection condition in the algorithm, causing the door-closing program to halt.  
Additionally, Figure 16b shows the motor current and force signals, with the measured force on the hard object reaching 182.3 N, closely aligning with the simulation results. These findings demonstrate the system's reliability in detecting and identifying the hard obstacles effectively.
# Integrating Environmental Data for Mental Health Monitoring: A Data-Driven IoT-Based Approach
## 4 Results and Experiment
### Class Imbalance and Model Performance
#### 4.3.3 Long Short-Term Memory  
Figure 5 shows the strengths and weaknesses of the LSTM model:  
* **Strengths:** LSTM achieves comparable results to SVM and LR for the \"Very Low\" class, with 113,329 correct predictions. It also performs the best among the three models for the \"Very High\" class, achieving 7246 correct predictions.
* **Weaknesses:** The LSTM model fails to predict any instances of the \"Low\", \"Medium\", and \"High\" classes, misclassifying them all as \"Very Low\". This suggests significant overfitting to dominant class patterns, likely due to insufficient representation of minority classes in the training data.  
Figure 5: Confusion Matrix for Long Short-Term Memory (LSTM): The model excels in predicting “Very High” but fails entirely for “Low”, “Medium”, and “High” classes.
## 3 Results
### Annotation of Transcripts and Differentially Expressed Genes (DEGs)  
To investigate the global transcriptomic profile related to light quality responses in basil leaves, RNA-Seq analysis was carried out. A total of nine cDNA libraries were constructed from the W, B, and R groups. The cDNA sequencing generated 4.39-6.52 million raw reads and 4.24-6.32 million clean reads for each library. Approximately 97.97% of Q20, 94.18% of Q30, and 49.69% of GC contents were obtained for each library after filtering, which indicated that the RNA-Seq data were of high quality. Subsequently, 202,967 unigenes were produced by de novo transcriptome assembly, with a mean length of 1104 bp, an N50 length of 1623 bp, and an N90 length of 500 bp. Functional annotation analysis revealed that unigenes could be successfully annotated in at least one of the seven databases, including the COG, GO, KEGG, Swiss-Prot, NR, NT, and Pfam databases. Moreover, species distribution analysis showed that 42.19% of the unigenes were homologous with the sequence of _Sesamum indicum_, and 22.03%, 13.22%, and 2.91% of the unigenes were homologous with the sequences of _Handraanthus impetiginus_, _Erythranthe guttata_, and _Olea europaea_ var. _sylvestris_, respectively. FPKM values were used to estimate the gene expression levels. Pearson's correlation coefficients and PCA analysis showed high reproducibility among the biological replicates (Figure S4), suggesting that these sequencing data were reliable for differential gene expression.
## 3 Result Analysis and Discussion
### Signal-to-Noise Ratio (SNR)  
The SNR is a metric that quantifies the ratio of signal power to noise power. In Flip-Filtered OFDM, the unipolar signal is produced by transmitting the positive part in the first subframe of the F-OFDM signal, and the reversed negative component is in the second subframe. Let us assume \({}^{+}\mu(n)\) and \({}^{-}\mu(n)\) are the noise elements of the channel inserted into the two F-OFDM subframes, respectively. Subsequently, the two F-OFDM-receiving subframes are expressed as:  
\[{}^{+}y(n)=^{+}x(n)+^{+}\mu(n) \tag{20}\]  
\[{}^{-}y(n)=-^{-}x(n)+^{-}\mu(n) \tag{21}\]  
Then, the transmitted signal power \(\sigma_{x,Flip}^{2}\) is defined as:  
\[\sigma_{(x,\text{Flip})}^{2}=\frac{1}{2N}\sum_{n=0}^{N-1}\left|{}^{+}x(n)\right| ^{2}+\frac{1}{2N}\sum_{n=N}^{2N-1}\left|{}^{-}x(n)\right|^{2} \tag{22}\]  
In a similar procedure, the power noise at the receiver, \(\sigma_{\mu,Flip}^{2}\), can be given by:  
\[\sigma_{(\mu,\text{Flip})}^{2}=\frac{1}{2N}\sum_{n=0}^{N-1}\left|{}^{+}\mu(n )\right|^{2}+\frac{1}{2N}\sum_{n=N}^{2N-1}\left|{}^{-}\mu(n)\right|^{2} \tag{23}\]  
In conventional Flip-F-OFDM, the dipolar signal is retrieved at the receiver by combining the two parts of the transmitted signal. Thus, the power of the bipolar signal for the \(N\)-points at the receiver is defined as:  
\[P_{(\mathbb{S},\text{Flip})}=\frac{1}{N}\sum_{n=0}^{N-1}\left|{}^{+}x(n) \right|^{2}+\frac{1}{N}\sum_{n=0}^{N-1}\left|{}^{-}x(n)\right|^{2} \tag{24}\]  
\[P_{(\mathbb{S},\text{Flip})}=2\sigma_{(\mathbb{S},\text{Flip})}^{2} \tag{25}\]  
Similarly, the noise power can be expressed as:  
\[P_{(\mu,\text{Flip})}=\frac{1}{N}\sum_{n=0}^{N-1}\left|{}^{+}\mu(n)\right|^{2 }+\frac{1}{N}\sum_{n=0}^{N-1}\left|{}^{-}\mu(n)\right|^{2} \tag{26}\]  
\[P_{(\mu,\text{Flip})}=2\sigma_{(\mu,\text{Flip})}^{2} \tag{27}\]  
Accordingly, the SNR of the conventional Flip-F-OFDM at the receiver is presented as:  
\[\text{SNR}_{\text{Flip-OFDM}}=\frac{\sigma_{(\mathbb{S},\text{Flip})}^{2}}{ \sigma_{(\mu,\text{Flip})}^{2}}=\frac{2\sigma_{\mathbb{S}}^{2}}{2\sigma_{\mu }^{2}}=\frac{\sigma_{\mathbb{S}}^{2}}{\sigma_{\mu}^{2}} \tag{28}\]  
Combining real and imaginary components in the proposed Flip-F-OFDM further doubles both the signal power and noise power to restore the complex transmitted signal. As a result, the following expression can be used to show the SNR at the receiver of the proposed Flip-F-OFDM:  
\[\text{SNR}_{\text{Proposed Flip-FOFDM}}=\frac{4\sigma_{\mathbb{S}}^{2}}{4\sigma_ {\mu}^{2}}=\text{SNR}_{\text{Flip-FOFDM}} \tag{29}\]
## 5 Experiment
### Performance Evaluation  
#### 5.4.1 Anomaly Detection Results  
We compared the detection performance of MST-VAE with ten baseline methods on the five real-world datasets. Because the randomness in the training process may affect the detection results, we executed five repeated experiments for each method and obtain the highest score. The performance of different methods are presented in Table 3. It can be observed that most of the methods achieved the highest performance on the SMD because this dataset mainly contains severe anomalies. Among eleven methods, LSTM-NDT shows the lowest performance due to the difficulty of capturing external factors of prediction-based methods, as discussed in Section 1. Without considering the temporal information in the time series data, the classical unsupervised methods fail to detect context and pattern anomalies and their performance results degrade in ASD. Among the five datasets, only the WADI dataset has the range values of normal data shifted in the test set. These range values are unseen in the training data, causing the performance of most methods to drop dramatically (0.4275 in USAD). However, the results demonstrate that MST-VAE (0.9479) and Interfusion (0.9103) can still adapt to unseen data.  
Figure 7: Illustration of adjusted \(F1\) with a threshold of 0.5.  
Interfusion is the first study that explores both temporal dependency and dependency across multiple metrics, which helps the detection results stable among different datasets. Moreover, MST-VAE implicitly learns dependency between metrics using Conv1 Ds and extracts complex temporal information based on the multi-scale module. For that reason, MST-VAE has demonstrated an improvement in detection performance compared to Interfusion, of 1.63% on average.
# Trade-Off Analysis of Hardware Architectures for Channel-Quality Classification Models
## 2 Related Work
[30] presented an accelerator architecture for matrix computing method with changeable dimensions using SVM; this was achieved following the next steps: (i) the matrix data are stored as an external memory; (ii) the problem of the limited resources due to the matrix dimension is solved by the data mover of programmable logic, which transmits data to the processing element with the control of processing system; (iii) the transmission adopts a synchronous data mechanism; (iv) the architecture of the processing element is optimized to improve the data transmission performance and the effective utilization ratio of the floating-point unit; (v) multiple processing elements are implemented on the programmable logic; and (vi) multiple matrices are processed in parallel. [PERSON] et al. [31] proposed a strategy based on an SDVN framework to schedule the start-sending time of each transmission. The aim was to maximize the packet delivery ratio (PDR) while meeting QoS requirements of transmission delay, avoiding issues of time-slot allocation and synchronization. For this purpose, they developed an algorithm called TSGS focused on finding the optimal sending time for each connection with higher PDR while meeting QoS requirements for transmission delay. Their results show that they could effectively improve the PDR by at least 15%, enhance the collision-avoidance performance by almost 40%, and reduce the MMP ratio by about 3% compared with the random transmission. We have described several applications that use different ML algorithms above, demonstrating the wide variety of fields that need intelligent systems to fulfill specific necessities. However, to the best of our knowledge, the classification of low and high CQI for offering noise detection for SDR using the eurecom elasticmon 5G 2019 has not been approached.
# A Novel Method for Assessing Regional Tendon Stiffness and Its Significance
## 3 Results
### Effect of Laterally Displaced Patellar Position on the Shear Elastic Modulus of Patellar Tendon  
Table 3 lists the shear modulus when the patella was in its resting position and taped in the laterally displaced position. In the female subjects, the tendon shear modulus significantly decreased by 4% \(\pm\) 39% (\(p\) = 0.03) in the lateral but significantly increased by 13% \(\pm\) 37% (\(p\) = 0.001) in medial half. In male subjects, a significant increase of 17% \(\pm\) 32% (\(p\) = 0.036) was found in the medial half of the patellar tendon. No significant change was detected in the lateral half (\(p\) = 0.177).
## 3 Results  
In this section, we evaluate the feasibility and performance of the proposed antenna calibration method. We adopt real GNSS observations collected with high-grade antennas and then artificially add simulated PCCs to the original observations to simulate the cases of low-cost antennas. The advantage of using simulated PCCs is that we can accurately evaluate the accuracy of the calibration results by comparing them with the phase patterns used to simulate the PCCs. In addition, we can flexibly test a variety of different phase patterns.
## 5 State of the Art
### Military  
The use of multi-UAV in the military field represents a real advantage, such as lowering the loss of both soldiers' lives and the military arsenal [2]. In October 2016, the US Department of Defense ([[https://www.defense.gov/News/Releases/Release/Article/1044](https://www.defense.gov/News/Releases/Release/Article/1044)]([https://www.defense.gov/News/Releases/Release/Article/1044](https://www.defense.gov/News/Releases/Release/Article/1044)) 811/department-of-defense-announces-successful-micro-drone-demonstration/, accessed on 15 March 2023) successfully tested one of the greatest deployments of 103 small UAVs in California. The micro-UAVs flew together under an adaptive formation and each drone collaborated with the others by using one distributed brain to achieve their mission.  
In the military area, swarm UAVs are generally used for attack, defense [3], and intelligence missions such as surveillance and reconnaissance [2]. The authors in [2,3] have shown that the swarm UAV attack is effective for offensive missions because it allows them to completely invade the enemy's defenses with a high probability of hitting several targets. This is the tactic of \"quality by quantity\". It consists of attacking simultaneously a target according to a defined formation. The most used formation is the simultaneous attack in a dispersed way. For example, if a swarm composed of 5 UAVs attacks a target simultaneously, the probability of shooting down one UAV is \(\frac{1}{5}\), which is 20%. This probability evolves according to the scalability of the UAVs in the swarm.  
Although UAVs show a good ability in attack missions, they can also be used for defensive or counter-offensive purposes against other enemy UAVs. [PERSON] [3] showed that the use of numerous miniature decoy UAVs could disrupt the mission of the attacking swarm by eliminating some enemy UAVs. This will have a considerable impact on the cooperation and coordination mechanism of the attacking swarm. It should be noted that the number of UAVs per swarm is very decisive regarding who would win.
## 2 Methodology  
### Support Vector Regression (SVR)  
SVR, pioneered by [PERSON] and [PERSON] [48], is an algorithm that works based on machine learning. This algorithm is extensively used in numerous fields such as blasting, tunneling, landslides, and rockburst [49; 50; 51; 52; 53; 54; 55]. It is broadly used to solve many regression- and classification-related problems. SVR generally uses a mapping of data with an _f(x)_ function for the aim of transforming a low-dimensional non-linear dataset into a high-dimensional linear problem in feature space. The SVR for regression can be defined as follows.  
Assume a training dataset \(\text{T}=\{(x_{1},y_{1}),(x_{2},y_{2}),\ldots,(x_{k},y_{k})\ \}\), where \(x_{i}\) and \(y_{i}\) stand for the input and output, respectively, then \(x_{i}\in R^{n}\) and then \(y_{i}\in R\), and \(k\) signifies the training observations. The SVM model for the regression problem can be calculated as follows [48]:  
\[f(x)\ =a.\delta(x)\ +\ b \tag{1}\]  
where \(a.\delta(x)\) stands for the kernel function. Table 2 presents a list of some types of kernel functions for regression. These functions can be effectively used to transform a dataset from a low to a higher dimension for SVR.  
Then, the Lagrangian can be solved as follows:  
\[\max\sum\limits_{i=1}^{k}y_{i}(\xi_{i}-\varepsilon_{i})-\eta(\xi_{i}- \varepsilon_{i})-\frac{1}{2}\sum\limits_{i=1}^{k}\sum\limits_{j=1}^{k}(\xi_{i }-\varepsilon_{i})\big{(}\xi_{j}-\varepsilon_{i}\big{)}\delta\big{(}x_{i},x_{ j}\big{)} \tag{2}\]  
where  
\[\left\{\begin{array}{c}\sum\limits_{i=1}^{k}(\xi_{i}-\varepsilon_{i})=0\\ 0\leq\varepsilon_{i},\xi_{i}\leq C,\ i=1,2,\ldots k\end{array}\right. \tag{3}\]  
where \(C\) represents the penalty factor (cost) of a kernel function. For the dataset to be mapped, Equation (2) needs to meet the conditions of Karush-Kuhn-Tucker [56] as:  
\[\left\{\begin{array}{c}\varepsilon_{i}(f(x_{i})-y_{i}-\eta-\omega_{i})=0\\ \xi_{i}(y_{i}-f(x_{i})-\varepsilon-\hat{\omega_{i}})=0\\ \varepsilon_{i}\xi_{i}=0;\ \omega_{i}\hat{\omega_{i}}=0\\ (C-\varepsilon_{i})\omega_{i}=0;\ (C-\hat{\varepsilon_{i}})\hat{\omega_{i}}=0 \end{array}\right. \tag{4}\]  
The SVM for regression can be finally solved as follows:  
\[f(x)\ =\sum\limits_{i=1}^{k}(\xi_{i}-\varepsilon_{i}).\delta\big{(}x_{i},x_{ j})\ +\ b \tag{5}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Kernel** & **Function** & **Parameter** \\ \hline Linear & X,Y & - \\ Polynomial & (gX,Y + c) d & g, c, d \\ Radius basis function (RBF) & \(\exp{(-g|X-Y|^{2})}\) & g \\ Sigmoid & tanh (gX,Y + c) & g, c \\ \hline \hline \end{tabular}
\end{table}
Table 2: Types of kernel used in this research.  
\[b\ =y_{i}+\eta-\sum_{i=1}^{k}(\varepsilon_{i}-\varepsilon_{i}).\delta\big{(}x_{i},x_ {j}\big{)}\ +\ b \tag{6}\]
# Efficient Re-Parameterization Residual Attention Network for Nonhomogeneous Image Dehazing
## 4 Results
### Results of Ablation Studies
#### 4.2.2 Effect of Two Novel Loss Functions  
Secondly, the effectiveness of the two newly proposed loss functions is also verified by the ablation study, as listed in the bottom rows of Table 2. In the S3 setting, only the \(L_{1}\) loss function is used during training. On top of that, the \(L_{\text{CA}}\) loss function is added to the S4 setting, which regulates the network to recover the saturation and brightness. It can be seen that S4 enjoys a moderate PSNR gain, while suffering a slight loss in terms of the SSIM. Since \(L_{\text{CA}}\) is calculated in a pixel-wise way, it does not focus on the recovery of the structural information, which is evaluated by the SSIM. Finally, in the RRA-Net setting, both the \(L_{\text{CA}}\) and \(L_{\text{Laplace}}\) loss functions are used during training. With the help of \(L_{\text{Laplace}}\), which is designed to protect high-frequency features, both the PSNR and SSIM metrics are lifted substantially from 19.21 dB and 0.746 to 19.813 dB and 0.765, respectively, as shown in the last row of Table 2. Through this ablation study, we find that using a combination of \(L_{\text{CA}}\) and \(L_{\text{Laplace}}\) can boost the dehazing performance effectively since these two loss functions are designed from different perspectives.
## 3 Results and Discussions
### Surface Current Density  
The EM waves striking the resonant surface induce currents on the surface. The magnitude of these induced surface currents helps us to analyze the resonance mechanism and the influence of each element on FSS performance. The surface current distribution of the FSS unit cell for the TE polarized wave at a normal incidence is presented in Figure 5. In Figure 5, the strong current concentration present at the modified corners (SRRs) of the outer element and the weak induced current on the inner structure verify that the CMSL-FSS element resonates at the 7.9 GHz, X-band uplink frequency. At the same time, Figure 5 indicates that the MJCL provides an upper stopband centered at the 11.9 GHz downlink frequency in the Ku-band.
## 1 Introduction
The information collection and management [14], demand prediction [15], system modeling [16; 17], and intelligent control [18] provide sufficient conditions for the implementation of vehicle departure intelligent control strategies. In this study, we firstly composed and analyzed the vehicle departure characteristics of the parking lot exit, and innovatively introduced the concept of cooperative control of ramp control into parking lot exit control. Then, we proposed timing and inductive control strategies for leaving parking lots in coordination with the ground-level dynamic and underground static traffic. Specifically, timing control and inductive control of exit vehicles are achieved through intelligent control of the exit gate levers of parking lots. Finally, the effectiveness of the control strategy was verified based on the Simulation of Urban Mobility (SUMO) simulation with the example of the Jinding super-large underground parking lot in Shanghai, China, which is composed of nine rectangular grids. The contributions and originality of this study are summarized as follows. First, we propose novel control strategies for vehicle departure using the concept of cooperative control of ramp control, which significantly eases the congestion of the regional road network. Second, we simulated the application in a representative parking lot in the Jinding area to carry out experiments and provide a reference for real-world applications. Third, the proposed control strategies can be further cooperatively used with signal control at intersections to improve the safety and efficiency of regional traffic.
## 3 Results and Discussion
### L. monocytogenes Behavior in DCH during Storage after HPP: Growth/No Growth  
The results of challenge test showed that _L. monocytogenes_ was able to grow in DCH with an \(a_{w}\) of 0.96 regardless of the HPP level applied, whereas growth was not observed in DCH with a lower \(a_{w}\) (Figure 4). Contrary to _Salmonella_, the output of the gamma approach showed that temperature was not the main limiting factor for the pathogen's growth (\(\gamma_{T}\) = 0.055, Table S2), as _L. monocytogenes_ is a psychrotrophic microorganism able to grow at temperatures slightly below 0 \({}^{\circ}\)C [25]. The quantification of the individual effects of the intrinsic (\(a_{w}\), pH, and lactic acid concentration) and extrinsic (storage temperature) factors and their interactions on the _L. monocytogenes_ behavior through the gamma concept showed that, within the experimental domain of the present study, the growth/no-growth boundary predictions for this pathogen depended on the \(a_{w}\), with the DCH with an \(a_{w}\) value of 0.95 being the predicted boundary value for _L. monocytogenes_ growth (Table 2). A value of 0.92 is considered the minimum growth limit for _L. monocytogenes_ when no other stressing factors are present [25]. However, when a low \(a_{w}\) is combined with other factors such as low temperature, its bacteriostatic effect is enhanced and the minimum growth limit decreases. In this respect, the microbiological criteria for foodstuffs [2] established that foods with an \(a_{w}\)\(<\) 0.92 are automatically considered unable to support the growth of the pathogen. According to this regulation, other categories of products can also belong to this category, subject to scientific justification. The growth/no-growth model predicted growth in DCH with an \(a_{w}\) = 0.95 with a very small \(\Gamma\) value (\(\Gamma\) = 3.14 \(\times\) 10\({}^{-3}\)) and no growth of _L. monocytogenes_ was observed when it was pressurized at 450 or 750 MPa (Trials 52-55, Table 2), indicating that the growth/no-growth boundary of the pathogen in pressurized DCH was also limited by other factors not considered in the calculation of the \(\Gamma\) factor, which could also include the potential injury caused by HPP on _L. monocytogenes_ cells. Therefore, the present study provides scientific evidence to justify that the pressurizedDCH with an \(a_{w}>0.92\), up 0.95, can belong to the category not supporting the growth of _L. monocytogenes_. Figure 4: _L. monocytogenes_ survival and inactivation during the storage at 7 \({}^{\circ}\)C of pressurized dry-cured ham (DCH) with an \(a_{w}\) (**a**) 0.870–0.915, (**b**) 0.919–0.920, (**c**) 0.940, and (**d**) 0.950. Symbols correspond to observed counts and lines to the fit of the Weibull model (Equation (2)) to data. Empty symbols correspond to replicates where the pathogen was not detected.