esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Bird Species Recognition from Images with Feature Enhancement and Contrastive Learning
## 2 Materials and Methods
### The Overview of Our Proposed Method  
An overview of our proposed method is presented in Figure 1, which consists of consists of three key components: feature extraction, feature enhancement, and softmax function. The feature extraction component utilizes a multi-feature fusion module to extract informative features from input bird images. This allows the model to distinguish between various bird species based on these extracted features. The feature enhancement component improves the performance of the bird classifier by enhancing the robustness and accuracy of the extracted features, thereby facilitating better differentiation between different bird species. The softmax function is used to classify bird features and output the probability of each bird species. By working together, these three modules form a comprehensive bird classification system for automated image identification.  
We further propose a novel approach using contrastive learning and Siamese networks to enhance bird species classification performance. A Siamese network consists of three identical sub-networks with shared weights and architecture that process input data in parallel. Given three input images, the network generates embedding vectors for each image. We then leverage the triplet loss function [28] to optimize the Siamese network. This ensures that images from the same class have embedding vectors closer together in the embedding space, while those from different classes are farther apart. This process helps our model learn more distinctive feature representations, ultimately leading to improved classification accuracy.  
To evaluate our proposed approach, we input the test dataset into our classifier model. The model then classifies the images and assigns class labels to each image. Finally, we employ various performance metrics to assess the accuracy and effectiveness of our method in classifying bird species.
## Results
### Fourier Transforms Infrared Spectroscopy (FTIR)  
Figure 2 shows the FTIR spectra characteristic of the cellulose powders and fabricated CA fibers in the region of 4000-500 cm\({}^{-1}\). The cellulose spectra show peaks at 3650-3000 cm\({}^{-1}\) (O-H hydroxyl group stretching vibration), 2900-2800 cm\({}^{-1}\) (-CH\({}_{2}\)-alkyl stretching vibration), 1645 cm\({}^{-1}\) (C=O stretching), and 1020 (C-O stretching). Furthermore, the crystalline structural alteration of cellulose from cellulose I to cellulose II were studied with the investigation of the peaks of 897, 1107, 1161, and 1430 cm\({}^{-1}\) absorption bands, which were assigned to group C\({}_{1}\) frequency, ring asymmetric stretching, C-O-C asymmetric stretching, and CH\({}_{2}\) symmetric bending vibration mainly in cellulose I, respectively [40].  
\begin{table}
\begin{tabular}{c c c c} \hline
**Sample** & **Mn (g-mol\({}^{-1}\))** & **Mw (g-mol\({}^{-1}\))** & **Mz (g-mol\({}^{-1}\))** & **PDI** \\ \hline C\({}_{\rm c}\) & 61,760 & 163,500 & 404,220 & 2.647 \\ C\({}_{\rm s}\) & 78,700 & 565,630 & 1,942,200 & 7.187 \\ \hline \end{tabular}
\end{table}
Table 1: The result of size exclusion chromatography of cellulose powders C\({}_{\rm c}\) and C\({}_{\rm s}\). C\({}_{\rm s}\) has a higher molecular weight and polymer dispersity index (PDI).  
Figure 2: The Fourier transform infrared spectra of cellulose powders (C\({}_{\rm s}\), (a), red and C\({}_{\rm c}\), (b), black) and wet-spun CA fibers (F\({}_{\rm c}\), (c), blue and F\({}_{\rm s}\), (d) green).  
Moreover, the 893 cm\({}^{-1}\) absorption band (\(\beta\)-glucosidaseilic linkages between the sugar units) also confirms that the crystalline cellulose I can be almost negligible in the regenerated cellulose fibers since the 893 cm\({}^{-1}\) is assigned to group C1 frequency in cellulose II [41; 42]. Besides, the typical vibrations in amorphous cellulose at 1260 and 1460 cm\({}^{-1}\) are similar to those in cellulose in the hydrated melts.
# Research on Safe Following Distance on an Expressway Based on Braking Process Analysis
## Modeling
### Establishment of a Mathematical Model of Minimum Safe Distance  
#### 3.2.1 Analysis of Safe Distance  
If the front car and the following car do not collide, from the perspective of driving speed, the ideal situation is that the final speeds of car A and car B are equal; that is, \(V^{\prime}{}_{B}=V^{\prime}{}_{A}\) (including a requirement that the final speed of car A and car B is zero). Otherwise, the two cars will collide (if \(V^{\prime}{}_{B}>V^{\prime}{}_{A}\)) or the calculated result is not the critical distance (\(V^{\prime}{}_{B}<V^{\prime}{}_{A}\)). In addition, if the front car and the following car do not collide, it can be seen in Figure 1 that the driving distance must align with the following formula:  
\[D_{A}+D\geq D_{B}+d \tag{8}\]  
Therefore, the distance \"\(D\)\" between the front car and the following car before braking must align with the following formula:  
\[D\geq D_{B}+d-D_{A} \tag{9}\]  
Therefore, in order to avoid collision between car A and car B, the critical safe distance \(D\)* must align with the following formula:  
\[D*=D_{B}+d-D_{A} \tag{10}\]  
According to the above analysis, it can be seen that the required distance between car A and car B in the longitudinal arrangement varies with speed, braking performance, the value of \"\(d\)\" and other factors. In practice, \"\(d\)\" can be viewed as a certain value. Therefore, when the driving speed and braking performance are different for car A and car B, the required safe following distance will also be different.
## 2 Materials and Methods
### OVA-Induced Asthmatic Mice  
Specific pathogen-free (SPF) BALB/c female mice (six weeks of age) were purchased from Orientbio Inc. (Seongnam, Korea). Animals were maintained in environmentally controlled SPF conditions and were provided with a standard diet and water ad libitum. All experimental procedures were approved by the Animal Experimental Ethics Committee of Chungnam National University (Daejeon, Korea). Asthma is induced by ovalbumin (OVA; Sigma-Aldrich, St. Louis, MO, USA). Mice were randomly divided into five groups (five animals per group): NC (normal control group), OVA (OVA-induction group), Dex (dexamethasone: OVA-induction + Dex at 1 mg/kg), JGTW-200 (OVA-induction + JGTW at 200 mg/kg), and JGTW-400 (OVA-induction + JGTW at 400 mg/kg). Dex (Sigma-Aldrich) was used as a positive control for relieving symptoms of allergic responses. BALB/c mice were sensitized on days 1, 8, and 15 via an intraperitoneal injection of OVA (50 \(\upmu\)g) emulsified with 4 mg of Imject(tm) album adjuvant (Thermo Fisher Scientific Inc., Waltham, MA, USA). Two weeks after the last sensitization, the mice were injected intranasally with OVA solution (1 mg/mL) on days 28, 31, 33. JGTW groups was administered by gavage to mice at doses of 200 or 400 mg/kg once daily from days 24 to 33. The NC, OVA, or Dex groups were administered distilled water or Dex orally, respectively. Two days after the last exposure, mice were sacrificed by intraperitoneal injection of pentobarbital, and tracheostomy was conducted to get bronchoalveolar lavage fluid (BALF). The total inflammatory cells in BALF were evaluated by counting in at least 4 squares of a hemocytometer (C-Chip, NanoEnTek, Seoul, Korea). Plasma obtained by cardiac puncture was used for subsequent analysis.
## 2 Method Analysis
### Centralized Beamformer (MVDR Algorithm)
At last, the beamformer output is given as follows:  
\[Z=\mathbf{W}^{H}\mathbf{Y}=\!\!\left(\frac{\mathbf{R}_{nm}^{-1}\mathbf{H}}{ \mathbf{H}^{H}\mathbf{R}_{mi}^{-1}\mathbf{H}}\right)^{\!H}\!\mathbf{Y}=\frac{ \sum_{i=1}^{N_{d}}H_{i}^{H}\sigma_{i}^{-2}Y_{i}}{\sum_{i=1}^{N_{d}}H_{i}^{H} \sigma_{i}^{-2}H_{i}}, \tag{14}\]  
where \(H_{i}\) and \(Y_{i}\) are the ATF and the collected signal of node \(i\), respectively. This is the result of the MVDR beamforming algorithm with FC. In the centralized beamformer, the FC was required to collect the information of all sensor positions. Besides, each node had to report its \(Y_{i}\) to the FC. Hence, when the node number of the WASN increased, the centralized process lacked robustness and scalability.
## 3 System Overview
### Automatic Forklift  
The robot for the designed SLAM algorithm was a manned electric forklift (model A30) produced by the HANGCHA company, as shown in Figure 2. The forklift was mounted on six pneumatic tires: four wheels at the front drove the machine, with every two fixed together, and two small wheels in the rear steered it. The total mass of the forklift was 4850 kg; the capacity of the lift was 1000 kg; and its velocity was limited up to 5 km/h for both empty and full load operations. The length, width, and height of the forklift were \(3700\times 1260\times 2100\) mm.  
Figure 2: The forklift equipped with basic sensors in our proposed system. (**a**) Two monocular cameras triggered by the same signal source are used to capture pictures of the warehouse, and the NAV350 sensor is used to measure the global position of the forklift in the warehouse. (**b**) The image captured by left the monocular camera; (**c**) the image captured by the right monocular camera.  
Our SLAM algorithm was based on stereo pair images captured by two monocular cameras mounted in the forward facing direction on top of the lift. The two cameras were triggered by the same signal for picture synchronization. The ground truth came from the SICK NAV350 2D laser scanner navigation sensor attached at the top of the forklift. The forklift could operate in automatic mode or manual mode, which could be switched at any time. In order to obtain automatic navigation of the forklift, we designed a control system that controlled the steering wheel, accelerator signal, and lift signal with a computer and was equipped with the necessary sensors.
## 4 Discussion
In contrast, non-PSM patients had a BCR-free survival rate of 93.8%, which was statistically significant [31]. Only a small number of studies with inconsistent definitions of BCR compared biochemical recurrence-free survival in the BNP versus the non-BNP group of patients. [PERSON] et al. reported that rates of BCR-free survival for BNP and non-BNP groups were the same after considering pathological stage, grade, preoperative PSA, and status of margins. However, BCR in this study was defined as an elevation of PSA above 0.1 ng/mL [19]. A study conducted by [PERSON] et al. with a follow-up period of 7 years reported that disease-free survival (DFS) is not modified depending on whether the patients underwent RP and BNP, or RP alone. The same study using univariate and multivariate analyses of PSM at BNP showed no significant importance considering DFS. [18]. According to [PERSON] et al., almost all BNP patients (90%) had a PSA lower than 0.2. On the other hand, at 12.5 years of follow-up, 80% of the patients without BNP had a PSA of less than 0.2 mcg/L [23]. In our study, at a median follow-up of 48 months (IQR, 44.6-51.4), the overall BCR rate was 22,4%, which is in accordance with the current literature [19; 28]. Comparing patients with BNP versus non-BNP, there was no statistically notable dissimilarity in BCR incidence (21.2% vs. 23.5%, \(p=0.713\)) and BCR-free survival (49.2 vs. 46.7 months, \(p=0.643\)). In the Cox regression analysis, the only statistically significant predictor of BCR was PSM (\(p<0.001\)). PSM at the base of the prostate were not significantly more associated with BCR compared to other PSM localizations (\(p=1.00\)). Our results indicate that BNP is not associated with worse cancer control. A small number of patients was a major limitation of our study, which could affect the conclusion due to the restricted statistical potential and a relatively short follow-up period. Since the study design involved the selection of patients according to the method of preservation of the bladder neck in relation to the presence of intravesical growth of the medial lobe of the prostate, another limitation of this study is selection bias. Another limitation of our study is the fact that multiparametric MRI was not applied in all patients, as a result of the limited availability of this procedure. A randomized clinical trial among patients who do not have intravesical growth of the medial lobe of the prostate would be a better study design. The advantages of our study were prospective, single-surgeon study design, and single pathologist evaluation. On the other hand, the learning curve of the surgeon during the time of study has to be considered. Additionally, we reported BCR-free survival in the BNP and non-BNP groups.
## 3 Results
### Energy Dissipation Distribution  
EDD (Energy dissipation distribution) is the magnitude of energy dissipated by the each step. This distribution is useful in a sense that it presents individual energy/head dissipating behavior of the steps during the flow. In the literature [7; 8; 9], the authors have mostly stressed the total energy dissipation. Our study presents EDD in the form of a new variable named dissipation ratio. Energy dissipation distribution is the head or energy dissipated by each step at the steady state flow condition. In general, energy dissipation should be different at different steps due to difference in velocities. The flow velocity also changes when water flows from crest of the model to the bottom; therefore, different values of EDD for each flow rate are expected.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Riser Type** & **Q = 0.027** & **Q = 0.042** & **Q = 0.059** & **Q = 0.068** & **Q = 0.0208** \\ \hline Vertical & 59.17\% & 52.58\% & 43.81\% & 37.93\% & 61.42\% \\ \hline
30\({}^{\circ}\) & 59.91\% & 52.53\% & 42.08\% & 36.28\% & 64.61\% \\ \hline
60\({}^{\circ}\) & 59.99\% & 52.33\% & 40.12\% & 36.11\% & 65.48\% \\ \hline
90\({}^{\circ}\) & 59.63\% & 51.4\% & 42.27\% & 34.8\% & 65.12\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Total Energy Dissipation.  
Figure 14: Graphical view of total energy dissipation.  
Dissipation ratio for a particular step = 100 \(\times\) (head at the riser/head at the tread)/(head at the riser). Energy dissipation distribution across different steps is presented below.  
From Figure 15a,b, it is evident that dissipation increases at the lower steps. Moreover, for the curved risers, this increase in dissipation is shifted to other steps. It can be seen in that for a 90-degree curved riser for each unit discharge, increase in energy dissipation is smooth. Ideally, a stepped spillway dissipating equal energy at each step is the best spillway as it causes less cavitation and is thus less prone to damage. A regular behavior in the nature of EDD is missing. Figure 16a,b show that energy dissipation increases while going down along the steps. Moreover, in contrast to this, for other flow rates, this shifted to higher steps in the curved risers.  
Figure 16: (**a**).EDD for the Q = 0.042 m\({}^{3}\)/s. (**b**). EDD for the Q = 0.059 m\({}^{3}\)/s. (**c**). EDD for the Q = 0.068 m\({}^{3}\)/s.  
Figure 15: (**a**). EDD for the Q = 0.0208 m\({}^{3}\)/s. (**b**). EDD for the Q = 0.027 m\({}^{3}\)/s.
# Comparison of L1 and L5 Bands GNSS Signals Acquisition
## 3 Assessment of Processing Time and Memory Requirements
### Acquisition of a Pilot Channel with Assistance  
This section presents the serial and semi-parallel implementation when \(N_{\rm SCB}\) delays of the secondary code should be tested instead of \(N_{\rm S}\), with \(1\leq N_{\rm SCB}<N_{\rm S}\) (for the case \(N_{\rm SCB}=1\), only the serial implementation is considered). Note that since only the number of delays is reduced, the processing time will be reduced, but the amount of memory will not change.
## Materials and Methods
### Data  
#### 2.2.1 Data from Remote Sensing Observations  
* Precipitation Data  
The precipitation data used in this study were obtained from the Climate Hazards Group Infrared Precipitation with Station (CHIRPS) dataset (version 2.0) [44], with 0.05\({}^{\circ}\) spatial resolution satellite imagery, and were converted into monthly temporal resolution. Developed by Earth Resource Observation and Science Center and the United States Geological Survey (USGS), CHIRPS is a 30+ year quasi-global rainfall dataset incorporating satellite and station data and to create gridded precipitation time series for hydrology monitoring and correlational and trend studies.  
* Surface Soil Moisture Data  
The surface soil moisture data were obtained from the National Aeronautics and Space Administration (NASA)-United States Department of Agriculture (USDA) Global Soil Moisture Dataset [45; 46; 47; 48], with 0.25\({}^{\circ}\) spatial resolution and monthly temporal resolution. This dataset is generated through the data assimilation approach using a modified two-layer Palmer model using a 1-D ensemble Kalman filter (EnKF) to integrate the satellite-derived soil moisture observations. This dataset includes products of soil moisture profile (%), surface and subsurface soil moisture, as well as their standardized anomalies values computed using a 31 day moving window.  
* Water Storage Data  
The changes in groundwater storage are represented by the variations in the water equivalent height (in cm) observed from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-on mission monthly gravitational anomalies datasets [49; 50; 51] in 1\({}^{\circ}\) spatial resolution. GRACE is a twin satellite setup that measures global changes in water anomalies. These anomalies depict the changes in the total water column represented by water equivalent height, which includes surface, sub-surface, and groundwater components.  
* Vegetation Indices Data  
The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard of Terra satellite provides three vegetation index products: evapotranspiration (ET) [52], NDVI [53], Leaf Area Index (LAI) and fraction of absorbed photosynthetically active radiation (FPAR) [54]. All the products are regridded and converted into monthly data over 1 km spatial resolution. The calculation of MODIS ET is affected by other variables, including albedo, biomes type and LAI [52]. LAI is defined as half the total leaf area per unit ground area [55]. FPAR is defined as the fraction of the incoming solar radiation that is absorbed by a photosynthetic organism in the photosynthetically active radiation spectral region. The MODIS products are proved to effectively capture the dominant seasonal LAI and FPAR patterns in many arid and semi-arid ecosystems [56].
## 2 Establishment of Disturbance Model  
### Establishment of Disturbance Model of FSS  
The 3D model of FSS is shown in Figure 1a. It consists of 36 support cylinders, which are evenly distributed on the support floor with a diameter of 1250 mm. As shown in Figure 1b, the FSS is divided into three sectors on average, and each sector contains 12 support cylinders. The upper and lower chambers of support cylinders in each sector are connected through hydraulic lines, which ensures that the performance parameters of the upper and lower chambers in the same sector are the same. The support rigidity of the support cylinder can be adjusted by changing the pressure value of the upper and lower chambers, and the support height of support cylinder can be adjusted by changing the pressure difference of the upper and lower chambers. Therefore, the attitude of the supported optical mirror can be finely adjusted by changing the support height and support rigidity of three sectors of FSS. During the processing of the optical mirror, the change of ambient temperature and the air contained in the working medium will affect the support pressure, support rigidity, and support height of FSS. To study the influence level of each influencing factor and facilitate the error analysis of each influencing link, the disturbance model of FSS for the ambient temperature and working medium was established.  
Because of the independence of the three working sectors of the FSS, the working pressure and air content of the working medium in the sectors are not identical and do not interfere with each other. The disturbance model of the flexible support cylinder can also be divided into three parts:  
\[\mathbf{D}=\left[\begin{array}{c}\mathbf{D}_{1}\\ \mathbf{D}_{2}\\ \mathbf{D}_{3}\end{array}\right]=\left[\begin{array}{c}\begin{array}{c}\sum \limits_{i=1}^{12}(a_{if}(P,F,\Delta\mathbf{T}_{i})+b_{i}f(P,F,\Delta\mathbf{V}_{i})+ \mathbf{M}_{i})\\ \sum\limits_{i=1}^{12}(a_{if}(P,F,\Delta\mathbf{T}_{j})+b_{j}f(P,F,\Delta\mathbf{V}_{j} )+\mathbf{M}_{j})\\ \sum\limits_{k=1}^{12}(a_{k}f(P,F,\Delta\mathbf{T}_{k})+b_{k}f(P,F,\Delta\mathbf{V}_{k })+\mathbf{M}_{k})\end{array}\right] \tag{1}\]  
where \(\mathbf{D}_{1}\) is the disturbance of sector 1, \(\mathbf{D}_{2}\) is the disturbance of sector 2, \(\mathbf{D}_{3}\) is the disturbance of sector 3, \(\mathbf{T}\) is the disturbance of ambient temperature, \(\mathbf{V}\) is the disturbance of working medium, \(a\), \(b\) are disturbance coefficients, \(\mathbf{M}\) is the disturbance caused by the manufacturing error of the parts, \(P\) is the working pressure, and \(F\) is the force at the support point.
## 6 Discussion and Recommendations
### Recommendations  
Based on the results and the discussion presented above, we provide the following insights and recommendations to researchers and practitioners deploying low-power wireless communication systems using the SUN-OFDM physical layer in real-world environments:  
1. Thanks to the small effect of packet length in the PDR with respect to the SIR, SUN-OFDM allows for using larger packets (i.e., packet bundling) to increase the transmission efficiency (i.e., more effective data with the same packet headers) without sacrificing robustness. In fact, SUN-OFDM allows payloads of up to 2047 bytes, effectively allowing to transmit full IPv6 packets without fragmentation or allowing to group up to sixteen 127-bytes frames in a single 2047-bytes frame.
2. Despite the fact that SUN-OFDM transceivers consume a higher amount of energy compared to state-of-the-art IEEE 802.15.4 transceivers due to the additional circuitry required to operate (scrambler, convolutional encoder, puncture, interleaver, Viterbi decoder), the higher level of robustness against interference provided by SUN-OFDM allows for using higher data rates (up to 800 kbps) to reduce the average energy consumption of the transmitter and the receiver devices.
3. As the preamble of a SUN-OFDM packet is transmitted using the lowest MCS option of the current configuration, and includes information regarding the MCS option used to transmit the packet payload, this allows the transmitter to switch between different MCS options of the same SUN-OFDM configuration without any changes in the receiver configuration. It is hence possible for the transmitter to use an aggressive modulation for an initial packet transmission, and use a more robust modulation when re-transmitting. Similarly, acknowledgement frames can be transmitted using the most robust modulation to increase the probability they are received.
4. The use of SUN-OFDM for deployments with a high device density and/or high interference levels is advisable, as it provides higher spectral efficiency, while maintaining a similar level of robustness against interference with respect to OQPSK-DSSS. If interference is of concern, choosing OFDM1-MCS1 over OQPSK-DSSS translates into a 4x capacity increase while providing an average advantage of 9 dB against the same interference. In contrast, if interference is not a concern, choosing OFDM4-MCS5 offers a 26x capacity increase, while maintaining a similar level of protection against the same type of interference.