esa-sceva/satcom-chunk-collection huggingface dataset:
# Dark-YOLO: A Low-Light Object Detection Algorithm Integrating Multiple Attention Mechanisms
###### Abstract  
Object detection in low-light environments is often hampered by unfavorable factors such as low brightness, low contrast, and noise, which lead to issues like missed detections and false positives. To address these challenges, this paper proposes a low-light object detection algorithm named Dark-YOLO, which dynamically extracts features. First, an adaptive image enhancement module is introduced to restore image information and enrich feature details. Second, the spatial feature pyramid module is improved by incorporating cross-overlapping average pooling and max pooling to extract salient features while retaining global and local information. Then, a dynamic feature extraction module is designed, which combines partial convolution with a parameter-free attention mechanism, allowing the model to flexibly capture critical and effective information from the image. Finally, a dimension reciprocal attention module is introduced to ensure the model can comprehensively consider various features within the image. Experimental results show that the proposed model achieves an mAP@50 of 71.3% and an mAP@50-95 of 44.2% on the real-world low-light dataset ExDark, demonstrating that Dark-YOLO effectively detects objects under low-light conditions. Furthermore, facial recognition in dark environments is a particularly challenging task. Dark-YOLO demonstrates outstanding performance on the DarkFace dataset, achieving an mAP@50 of 49.1% and an mAP@50-95 of 21.9%, further validating its effectiveness for face detection under complex low-light conditions.  
low-light object detection; image enhancement; partial convolution; overlapping pooling; multi-attention mechanism; lightweight Article
# Sensitivity Analysis of an Implanted Antenna within Surrounding Biological Environment
## 1 Introduction
Such full wave computational approaches give reliable results about the resulting efficiency of the transmission link, taking into account inhomogeneous materials and fine details of the implanted system. Nevertheless, any modification in the problem needs to run again the 3D simulation and in case of parametric or sensitivity analysis this may lead to heavy computational burden. In such a situation, the introduction of stochastic tools allows researchers to deal with the variability of all the parameters describing the electromagnetic problem. Such approaches were shown to be very efficient in the framework of the determination of specific rate absorption (SAR) in biological tissues due to mobile phones at microwaves frequencies [17; 18; 19] and more recently to electromagnetic compatibility problems [20]. This paper shows that a surrogate model based on polynomial chaos expansion (PCE) provides a powerful tool for optimizing the performances of the transmission link and perform a sensitivity analysis at low cost. In particular, such PCE gives straightforward results regarding Sobol's indices highlighting the most significant geometrical parameters in the study.
## 2 Data and Methods
### Data and Pre-Processing
#### 2.2.5 Land Use Data  
The MODIS MCD12Q1 Collection 5 (land-cover product) was used. This yearly global land cover data has a spatial resolution of 500m. We downloaded the land cover map during 2001-2020 from the MODIS official website ([[https://www.earthdata.nasa.gov/eosdis/daacs/lpdaac](https://www.earthdata.nasa.gov/eosdis/daacs/lpdaac)]([https://www.earthdata.nasa.gov/eosdis/daacs/lpdaac](https://www.earthdata.nasa.gov/eosdis/daacs/lpdaac)) (accessed on 2 June 2021)). We adopted the classification scheme of the International Geosphere-Biosphere Programme (IGBP), including 11 natural vegetation types, three developed and mosaic land types, and three non-vegetated land types. The land cover in the Songnen Plain was summarized into seven types, including deciduous broadleaf forest, mixed forest, grassland, cropland, wetland, water body, and other types [31].
## 4 Results and Discussion
### Implementation Details  
In this research, all models are run on an Intel Corei7-1165G7 CPU with 16 GB DDR4 memory, and CY22 Intel i7-1165G7, Integrated Intel Iris Xe graphics utility. With a batch size of 32, we employ adaptive moment estimation as the optimization for backpropagation. The learning rate has been set at 0.0001 and will be dropped by half if the test accuracy does not improve after 20 epochs. The early stop method is used to end training intelligently. All of the designs presented in this section are tested using 5-fold cross-validation. The entire data set is separated into five non-intersecting subsets. One of the five parts is used as the validation set and the rest as the training set each time. The end result is a mean accuracy of five fold.
## 3 Results
### Validation  
The proposed methodology was validated with the International Society for Photogrammetry and Remote Sensing (ISPRS) benchmark dataset, specifically the third area of the data captured over Vaihingen, Germany [46; 47]. Building outlines and LiDAR data, where building points were classified manually and the density is 4 pts/m\({}^{2}\), were used from the benchmark as input data. The output building models of the proposed algorithm were  
Figure 10: Illustrations of shaping several cases of buildings of various layouts with roofs without height jumps, where for the buildings (**left**) segments of points (**middle**) were obtained from the input point cloud for the reconstruction of buildings (**right**).  
compared by height difference with the ground truth data from the benchmark, as shown in Figure 11. Ground truth data is presented as building roof faces, given as 3D polylines. The height difference was estimated over a regular grid with 10 cm resolution. Statistics for the comparison is given in Table 2, where RMSE (Root Mean Square Error) was estimated over height differences, completeness specifies the proportion of the building area covered by geometrically valid building models, and \(e_{0.5}\) designates the proportion of the building area, where the height difference is lower than 0.5 m.  
The majority of the difference manifested over areas, where height jumps are present. Some difference can be observed over parts of roofs with small elements (e.g., chimneys or small dormers) or parts of the dataset, where the point cloud is too sparse.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metric** & **Value** \\ \hline RMSE [m] & 1.31 \\ \hline Completeness [\%] & 98.9 \\ \hline \(e_{0.5}\) [\%] & 79.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Validation of the obtained results from the comparison with ISPRS benchmark data.  
Figure 11: Height difference comparison of the output of the proposed algorithm with the ground truth data of the ISPRS benchmark.
# A 28 nm Bulk CMOS Fully Digital BPSK Demodulator for US-Powered IMDs Downlink Communications
## 4 The Proposed Fully Digital BPSK Demodulator
### Non-Overlapping Phases Generator  
The non-overlapping phases generator is a well-known circuit in digital design literature [37; 38]. The solution used for the demodulator, shown in Figure 8, generates the two counter-phase non-overlapped signals,\(V_{P1}\) and \(V_{P2}\), starting the input signals, \(V_{AC1}\) and \(V_{AC2}\). As can be seen in Figure 6, only the signal \(V_{P2}\) is used for the successive circuital blocks and, for this reason, it is the only one buffered.  
The amount of separation, \(\Delta\varphi\), is set by the introduced delay cells in the like-latch connected NAND gates. The dimensions of the CMOS inverters are set as follows: the widths \(W_{n}=0.32\,\upmu\)m and, being \(\alpha=\sqrt{\mu_{n}/\mu_{p}}\sim 3\), for symmetry reasons, \(W_{p}=\alpha W_{n}=0.96\,\upmu\)m. As for the NAND gates, \(N_{1A,1B}\), their dimensions are found using the equivalent inverter rule. Indeed, the widths of the NMOS transistor were chosen as \(W_{n,N_{1A,1B}}=0.64\,\upmu\)m and for the PMOS transistor, \(W_{p,N_{1A,1B}}=1.92\,\upmu\)m. The lengths were chosen as \(L_{n,p}=6L_{min}=0.9\,\upmu\)m in order to reduce the overall power consumption of the block. As claimed above, an output CMOS buffer was added to increase the driving capability for the modulation driving/enabling phase \(V_{P2}\). A 25-fF capacitor, implemented by using the M1-M3 MOM option, was added to reduce output glitches. The measured phase separation was \(\Delta\varphi\sim 4\,\mathrm{ns}\), whose value is made evident in the zoomed portion of Figure 9.
# Towards Sentiment Analysis for Romanian Twitter Content
## 3 Data Processing Methodology
### Hyperparameter Optimization  
When applying each of the abovementioned learning algorithms, we need to tune them with proper parameters selected so as to minimize the generalization error [64; 65]. Various approaches could be considered such as exhaustive grid search, random search [65], Bayesian optimization [66] or evolutionary optimization (EO) [67].  
Given the large number of parameters to optimize, and noticing the vast literature accompanying the metaheuristic design of DNNs [36] or recent applications of DL where parameters were selected with the help of genetic algorithms [68; 69; 70], or suggestions that EO could outperform Bayesian optimization [71], we decided to employ a classical genetic algorithm for hyperparameter search.  
We used Sklearn-genetic-opt library [72] for implementing genetic algorithm-based hyperparameter optimization in relation with our selected algorithms. Sklearn-genetic-opt makes usage of the Deep framework ([[https://github.com/deap/deap](https://github.com/deap/deap)]([https://github.com/deap/deap](https://github.com/deap/deap)), accessed on 5 September 2022) [73], which supplies many evolutionary algorithms needed for solving optimization problems.  
The GA was designed as following. Given a number \(N\) of parameters to optimize for some specific learning method, a chromosome is a vector \((p_{1},p_{2},\ldots,p_{N})\) of valuesselected for each parameter. A population consists of 20 individuals which is evolved over 40 generations with a crossover probability of 0.8 and mutation probability of 0.1. Individuals are selected for the next generation with a standard elitist tournament of size 3. Internally, each individual is evaluated using the accuracy as fitness function, computed with 3-fold cross-validation.  
In the case of DNN, we considered among the parameters the following: the network capacity (the number of hidden layers and the number of units per layer), the activation function, the regularization function, the drop-out rate  
Since both CNN and LSTM need the embedding weight parameter which is 2D tensor, we modified the source code of Sklearn-genetic-opt, in order to transmit the multidimensional parameters directly to Deap.  
The Appendix A presents in full the parameters considered for evolutionary optimization, for all classifiers.  
In general, convergence is seen after 15-20 generations, thus evolving the populations over 40 generations is more than enough to guarantee a good parameter selection.  
For the BERT-based classifier, as learning just one model is very time consuming, we omitted to perform the evolutionary optimization procedure. Instead of cross-validation, we took 10% of the training set for validation and we let the learning to optimize the loss function for several epochs. We noticed that the model rapidly overfits, thus, we stop the learning after two epochs.
# Choice of Screws for Fixation of Mandibular Condyle Fractures Guided by Anthropometric Data
## 2 Materials and Methods  
The approval of the bioethics committee at the Medical University of Lodz, numbers RNN/125/15/KE and RNN/738/12/KB, was obtained for the study. All computed tomography images were acquired after anonymization [11] from the Maxillofacial Surgery Clinic Database: 66 cone-beam computed tomography (CBCT) images, Carestream CS 9300 3D (Carestream Dental LLC, Atlanta, GA, USA), and 184 fan-beam computed tomography (FBCT) volumetric scanner (Aquilion ONE, Toshiba, Otawara, Japan) images. CT scans of healthy Caucasian patients aged 8-88 (average 44.56 \(\pm\) 18.12) constituted the inclusion criterion for the study.  
Study images of patients suffering from degenerative changes in the TMJ region (e.g., ankylosis) and modeling changes of the mandible (tumor growth, dysplasia) were excluded.  
CT scans of post-traumatic patients in the region of the mandible, after ORIF and after resection of mandible, were also excluded from the study. The exclusion criteria also included low quality of the tomography and numerous artifacts in the images.  
Using histogram analysis, bone segmentation was performed using global thresholding defined for the CBCT and FBCT by the individual histogram analysis according to the Baillard and Barillot protocol [12]. DICOM axial images were transformed into three-dimensional bone models. Each model contained two condylar process results in the 500 obtained CT-based 3D models. The subsequently obtained models were subjected to measurements. Segmentation, model preparation, and measurements were performed using Mimics 17.0 software (Materialise, Leuven, Belgium).  
For all models, the auxiliary \"base line\" was determined based on the algorithm described by [PERSON] [13] (Figure 1). All of the above measurements were applied to the models perpendicularly to the base line.  
Auxiliary lines: neck_basal (between the lowest point in the semilunar notch and the most backward point at its height) and neck_top (between the most forward and most backward point at the level of the condylar head reference line described by [PERSON]) [13];  
* Width_neck_basal--width between the most mesial and distal point at the level of neck_basal;
* Width_neck_top--width between the most mesial and distal point at the level of neck_top;
* Width_head--the widest measurement of the head of the mandible;
* Thickness_sigmoid_notch--width measured 1 mm below the sigmoid notch.  
Statistical analysis was performed in Statgraphics Centurion 18 (Statgraphics Technologies Inc., The Plains, VA, USA).
# Greenhouse Ventilation Equipment Monitoring for Edge Computing
## 2 Methods
### Extracting Regions of Interest Based on Tracking
Considering the relative stability of target aspect ratios in consecutive video frames captured by cameras, we utilize the CIOU metric. An enhanced version of IOU, CIOU not only accounts for the aspect ratios and centroid distances of bounding boxes but also circumvents tracking loss scenarios which are typical when IOU values approach zero. As Figure 7 demonstrates, in instances in which consecutive frames lack target overlap, resulting in an IOU of zero, CIOU effectively measures the centroids' proximity and shape resemblance, thereby facilitating more accurate matching. The CIOU formula is expressed as follows:  
\[IOU=\frac{A\cap B}{A\cup B} \tag{1}\]  
\[CIOU=IOU-\frac{\rho^{2}(A,B)}{c^{2}}-\alpha v \tag{2}\]  
\[\alpha=\frac{v}{(1-IOU)+v} \tag{3}\]  
\[v=\frac{4}{\pi^{2}}\bigg{(}\tan^{-1}\frac{w^{A}}{h^{A}}-\tan^{-1}\frac{w^{B}} {h^{B}}\bigg{)}^{2} \tag{4}\]  
\(A\) and \(B\) symbolize the target bounding boxes across consecutive frames, \(\rho(A,B)\) represents the Euclidean distance between their centroids, and \(c\) denotes the diagonal length of their minimal enclosing area. _CIOU_ integrates _IOU_ with two penalization terms: the first, \(\frac{\rho^{2}(A,B)}{c^{2}}\), quantifies the centroids' distance (0 when coinciding; 1 at a maximum distance); the second, \(\alpha v\), evaluates the bounding boxes' shape similarity. Among them, the \(v\) value increases with widening aspect ratio disparities, and \(\alpha\), a balancing factor, amplifies in conjunction with rising _IOU_ values, keeping \(v\) constant. Figure 7: (**a**) CIOU schematic diagram; (**b**) comparison of IoU and CIOU in different overlapping situations.
## 6 Results
### Elbow Estimation
#### 6.3.2 Elbow Radius Estimation  
Figure 22a shows the estimation result of the elbow radius. The red line shows the designed radius of the elbow, which is 762 mm. We can see from the result that the robot underestimates the elbow radius. The mean estimated radius is 549.27, which is 28% smaller than the designed radius. This issue comes from the distortion of the point cloud in the elbow due to the multipath problem. Figure 23 shows an example of the point cloud near the elbow plane that is used for tangent estimation. Around the area for tangent estimation, the point cloud tends to be distorted toward the inner direction and has a  
Figure 21: Elbow direction estimation result.  
Figure 20: Detected pipe types over navigation distance.  
much higher random error compared to other areas. As a result, the estimated tangent is much steeper than the expected tangent from the elbow model. It leads to smaller elbow radius estimation.  
On the other hand, according to the result in Figure 22a, the standard deviation is 27.06 mm and is about 3.5% of the radius, which is a small value. It indicates that the distortion in Figure 23 is relatively consistent. Therefore, the bias in elbow radius estimation can be corrected experimentally. Figure 22b shows the elbow radius estimation result after bias correction with the design radius. However, the results of elbows with different radii need to be collected and tested to get the common bias correction value.
## 2 Frequencies and Mode Shapes of the Double-Beam System
### Parametric Studies
#### 2.3.4 Beam Stiffness Ratio  
The effect of the beam stiffness ratio (\(s_{\rm k}\)) on the frequency ratios (\(\omega_{0,i},\ i=1,\ 2\)) in the 1 st basic mode for different boundary conditions is shown in Figure 9. Both \(\omega_{0,1}\) and \(\omega_{0,2}\) increase with \(s_{\rm k}\). The influence of boundary conditions on \(\omega_{0,1}\) is big when \(s_{\rm k}\) is large. Figure 10 shows the effect of \(s_{\rm k}\) on the mode amplitude ratios (\(\phi_{0,i},\ i=1,\ 2\)) for different boundary conditions in the 1 st basic mode. Both \(\phi_{0,1}\) and \(\phi_{0,2}\) decrease with \(s_{\rm m}\) and the decreasing rates are large when \(s_{\rm m}\) is small. \(\phi_{0,1}\) is larger than \(1\) when \(s_{\rm m}\) is smaller than \(1\) and \(\phi_{0,1}\) is smaller than \(1\) when \(s_{\rm m}\) is larger than \(1\). \(\phi_{0,2}\) is larger than \(-1\) when \(s_{\rm m}\) is smaller than \(1\) and \(\phi_{0,2}\) is smaller than \(-1\) when \(s_{\rm m}\) is larger than \(1\). \(\phi_{0,1}\) and \(\phi_{0,2}\) are closer to \(1\) for looser boundary conditions, which means that the modes of two beams are closer to each other for looser boundary conditions.
## 3 Optically Pumped Probe Design
### Probe Front End  
The cesium optically pumped probe is a high-sensitivity magnetometer that utilizes the properties of cesium atoms to measure the magnetic field strength [18]. Our design adopts a self-oscillating cesium optically pumped probe structure. The probe model is shown in Figure 3, and the core components include a cesium atomic lamp, optical components, cesium absorption chamber, and high-precision photodetector module. This probe uses a cesium atomic lamp as the core light source to polarize cesium atoms.
## 3 Results and Discussion
### SAS1 and SAS2 Experiments
The bottom of the vessel was free of co-solvent for both configurations of the SAS process (see Figure 3) and a similar dry \"body-mass\" as in experiments 5 and 6 was observed. The SEM images (Figure 6) verify the presence of silica particles and CAP/Q co-precipitates. Furthermore, a variation between SAS1 and SAS2 processes was not observed, with stick-shaped particles obtained for both SAS1 and SAS2, usually identified as Q particles [30; 31], NPs and MPs corresponding to CAP, and larger spheres corresponding to silica. In these experiments, the amount of co-precipitate with Q was lower than in experiments 5-6 with MNG, and this change was due to the experimental conditions used, which favored a higher precipitation of MNG than CAP, as described previously [31; 34]. In order to confirm the presence of quercetin and mangiferin in the silica co-precipitates, 5 mg of each sample were dissolved in 50 mL of acetone/DMSO for MNG co-precipitates and 50 mL of ethanol for Q co-precipitates (Table 2). The contents in these solutions were measured by UV-VIS spectrophotometry at a wavelength of 365 nm using a previously obtained calibration curve. \begin{table}
\begin{tabular}{c l l l} \hline \hline
**Exp.** & **Quantity (mg)** & **Process** & **Co-Precipitate** \\ \hline
5 & 1.58 \(\pm\) 0.05 & SAS2 & CAP/MNG \\
6 & 2.36 \(\pm\) 0.06 & SAS1 & CAP/MNG \\
7 & 0.302 \(\pm\) 0.002 & SAS1 & CAP/Q \\
8 & 0.45 \(\pm\) 0.01 & SAS2 & CAP/Q \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantity of antioxidant in 5 mg of sample. Figure 5: SEM images for deposition experiments 5 and 6. Figure 6: SEM images for deposition experiments 7 and 8. It was observed that in experiments 7 and 8--corresponding to the use of quercetin--the concentration was significantly lower than in experiments 5 and 6, where the concentration of mangiferin was six times higher than quercetin for the SAS1 process and three times higher for the SAS2 process. The fact that the amount of MNG was higher than Q is not unexpected since the operating conditions imposed to achieve the \"body-mass\" are suitable for MNG according to previous studies carried out on CAP/MNG co-precipitation.