esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Evaluations
### Experimental Results
#### 4.4.1 Comparative Experiment
The proposed backbone network can comprehensively detect diseased areas, excels in detecting subtle and irregular lesions that other backbones often overlook, and shows excellent attention to fine-grained features, outperforming other backbone network models. Overall, the heatmap confirms the enhanced detection capabilities of the proposed backbone network, consistent with its excellent quantitative metrics (Table 3). These results highlight its suitability for real-time, accurate, and lightweight agricultural disease detection tasks. In addition, in order to fully verify the generalization ability and practical application value of the model, we specially selected the public tea disease dataset provided by [PERSON] et al. [43], which is highly representative and widely recognized because it covers typical disease samples in a variety of real scenarios. We used this dataset to conduct independent tests, focusing on evaluating the performance of the model on unseen data, including the classification accuracy and detection efficiency of different disease types. The specific experimental data and results are shown in Table 4 and Figure 10. This process cannot only comprehensively test the robustness and stability of the model in complex scenarios, but also provide more authoritative and credible results support for the research results, laying a solid foundation for subsequent practical promotion and application. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Precision** & **Recall** & **mAP50** & **mAP50:95** & **FLOPs** & **FPS** & **Params** & **Size** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(G)** & **(F/S)** & **(M)** & **(M)** \\ \hline RT-DETR-R18 & 88.20 & 82.30 & 85.90 & 61.80 & 57.00 & 221.30 & 19.88 & 40.20 \\ Ours & 88.90 & 81.70 & 85.40 & 58.70 & 28.30 & 335.60 & 10.30 & 20.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of independent testing on public datasets. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Backbone**} & **Precision** & **Recall** & **mAP50** & **mAP50:95** & **FLOPs** & **FPS** & **Params** & **Size** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(G)** & **(F/S)** & **(M)** & **(M)** \\ \hline RT-DETR-R18 & 89.00 & 82.50 & 86.40 & 62.40 & 57.00 & 237.40 & 19.88 & 40.50 \\ ConvNextV2 & 88.70 & 78.70 & 84.30 & 57.30 & 33.00 & 198.40 & 12.30 & 25.30 \\ LSKNet & 87.70 & 81.20 & 85.80 & 60.70 & 38.70 & 154.80 & 12.77 & 26.00 \\ EfficientViT & 88.50 & 80.30 & 85.10 & 60.00 & 27.20 & 288.10 & 10.71 & 22.80 \\ RepViT & 86.50 & 79.30 & 83.10 & 58.00 & 35.30 & 234.30 & 13.31 & 27.70 \\ EfficientFormerV2 & 87.90 & 82.50 & 85.60 & 60.40 & 29.50 & 185.40 & 11.80 & 75.80 \\ UniRepLKNet & 87.20 & 79.50 & 83.40 & 55.90 & 33.40 & 197.40 & 12.71 & 26.80 \\ Ours & 88.90 & 80.30 & 85.60 & 59.90 & 28.50 & 350.70 & 10.81 & 21.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of detection metrics across various network backbones.
## Abbreviations  
The following abbreviations are used in this manuscript:  
AP Access point  
AUDR Average user data rate  
CDF Cumulative distribution function  
FL Fuzzy logic  
FOV Field of vision  
FTPA Fractional transmit power allocation  
GRPA Gain ratio power allocation  
ISD Inter-site distance  
ITU-R International telecommunications union-radio  
LED Light-emitting diode  
LiFi Light fidelity  
LOS Line of sight  
Mbps Megabits per second  
MF Membership function  
NOMA Non-orthogonal multiple access  
OFDMA Orthogonal frequency division multiple access  
OMA Orthogonal multiple access  
OOK On-off keying  
OWC Optical wireless communication  
PDF Probability density function  
PLS Power law strategy  
QoS Quality of service  
SIC Successive interference cancellation  
SINR Signal interference plus noise ratio  
UDN Ultra dense network  
VLC Visible light communication  
WiFi Wireless fidelity
# On the Fairness of Internet Congestion Control over WiFi with Deep Reinforcement Learning
## Appendix A Detailed Calculations
### Bbr  
During this period, the delivery rate remains unchanged but RTT increases. Moreover, BBR limits the data pacing rate, also known as the sending rate, and inflight data to one BDP to control the congestion [47; 48], where  
\[BDP=Btlbw.RTprop\]  
where \(RTT_{prop}\) refers to Round Trip Propagation Time.  
BBR considers the most recent estimated delivery rate and RTT to obtain its transmission capability. In BBR, the current Btlbw is the maximum delivery rate of the last 10 RTT, and the current \(RTT_{prop}\) is the maximum delay calculated in the past 10 s [47; 48]. BBR adjusts the pacing and CWND rate as follows, as mentioned in [47]:  
\[pacing\_rate=pacing\_gain.Btlbw\] (A9)  
\[CWND=cwnd\_gain.BDP\] (A10)  
Using a fixed-point approach along the lines of [18], we obtain the equilibrium congestion window \(W^{\star}\) as  
\[W^{\star}=\frac{\alpha\tau^{\star}}{\tau^{\star}-2\tau_{m}}\] (A11)  
\(\tau_{m}\) is the minimum RTT.  
We consider cases to explain the causes of unfairness and starvation.  
Case i: Considering two BBR flows with different RTTs, \(\tau_{1}\) and \(\tau_{2}\), and experiencing the same loss, \(p\), we can evaluate how the congestion windows \(W_{1}^{\star}\) and \(W_{2}^{\star}\) evolve as  
\[W_{1}^{\star}=\frac{\alpha\tau_{1}}{\tau_{1}-2\tau_{m}};\ W_{2}^{\star}=\frac {\alpha\tau_{2}}{\tau_{2}-2\tau_{m}}\]  
\[\frac{W_{1}^{\star}}{W_{2}^{\star}}=\frac{\tau_{1}}{\tau_{2}}\Big{(}\frac{\tau _{2}-2\tau_{m}}{\tau_{1}-2\tau_{m}}\Big{)}\] (A12)  
If \(\tau_{1}=2\tau_{2}\), then  
\[\frac{W_{1}^{\star}}{W_{2}^{\star}}=\frac{2\tau_{2}}{\tau_{2}}\Big{(}\frac{ \tau_{2}-2\tau_{m}}{2\tau_{2}-2\tau_{m}}\Big{)}\]  
Case ii: Considering BBR flows with the same RTTs, \(\tau\), and experiencing different losses, \(p_{1}\) and \(p_{2}\), we can evaluate how the congestion windows \(W_{1}^{\star}\) and \(W_{2}^{\star}\) evolve:  
\[W_{1}^{\star}=\frac{\alpha\tau_{1}}{\tau_{1}-2\tau_{m}};\ W_{2}^{\star}=\frac {\alpha\tau_{2}}{\tau_{2}-2\tau_{m}}\]\[\frac{W_{1}^{\star}}{W_{2}^{\star}}=\frac{\tau-2\tau_{m}(1-p_{2})}{\tau-2\tau_{m} (1-p_{1})} \tag{113}\]  
If \(p_{1}=2p_{2}\), then \(\frac{W_{1}^{\star}}{W_{2}^{\star}}=\frac{1-p_{2}}{1-2p_{2}}\).  
As we know, **Starvation** is an extreme case of unfairness; the two cases discussed above will have the following consequences:  
Case i: If \(p_{1}=np_{2}\), where \(n=1,\ldots,20\)\(\frac{W_{1}^{\star}}{W_{2}^{\star}}=\frac{1-p_{2}}{1-np_{2}}\)  
Case ii: If \(\tau_{1}=n\tau_{2}\), where \(n=1,\ldots,20\)\(\frac{W_{1}^{\star}}{W_{2}^{\star}}=\frac{3n}{5n-2}\)
## 2 Synthesis and Characterization of MIP
### Polymerization Parameter in MIP Synthesis
#### 2.1.5 Initiator  
Heat, light, and chemical/electrochemical reactions are utilized to initiate free-radical polymerization in the presence of templates. Nonetheless, the investigation system may allow drivers to choose between the two. Photochemical or thermally triggered initiators, for example, would not be triggered if the template was photochemically or thermally unstable. When hydrogen bonding influences complexation, lower polymerization temperatures are preferred, and photochemically active initiators may be used in these cases due to their efficiency at low temperatures ([PERSON] et al., 2000).  
MIPs has a presence of specific recognition regions in the polymer net formed through template-tailored synthesis hence, MIPs are characterized by their selectivity and specificity. There are three stages to this synthetic process. First, the pre-polymerization structure or complex is formed from functional monomers and the templated molecule in the presence of an appropriate solvent. It is a crucial stage, since it determines the formation of the specific recognition regions responsible for the imprinted materials selectivity. In the case of MIPs, this step can be completed either by the covalent or non-covalent approaches, which differ in the type of interactions formed between the template and monomers. The covalent approach is executed via the formation of chemical bonds and, as a result, a new pre-polymerization compound is obtained. In the non-covalently formed pre-polymerizationcomplex, weak molecular interactions. The following step of the imprinting process comprises of a polymerization reaction, usually in the presence of a cross-linker, to fix the pre-polymerization structure and form a polymeric matrix with the specific recognition regions and the template inside of them. In the last stage, the template is removed from the polymer by physicochemical processes, such as hydrolysis reaction (in the covalent approach) or desorption (in the non-covalent approach), yielding a highly cross-linked polymeric matrix with three-dimensional specific recognition regions complementary in term of the molecular volume and geometry of chemical functionalities to the imprinted entity. Those regions are able to adsorb/desorb the template molecule [54].
# Loosely Coupled GNSS and UWB with INS Integration for Indoor/Outdoor Pedestrian Navigation +
## 4 Test Setup
### Geofencing  
Having the UWB position estimation expressed in a geographic reference system allows a data-fusion algorithm to be developed which easily combines the GNSS observations and provides coordinate consistency between outdoor and indoor positioning module. Another major advantage of working with georeferencing measurements is that the observations are related to a well-defined space with well-known boundaries. This means that the estimated position, and also the raw measurements, are not related to a virtual local reference frame any more but are connected with a geographic zone and therefore acquire spatial awareness. This condition allows a geofencing approach to be used in the proposed estimation framework which consist in defining a virtual static perimeter which boundaries are expressed in geographic coordinates. As tracked mobile objects move across the geofence, an algorithm switch may be triggered. In our work, two polygons were defined, one corresponding to the georeferenced area of the indoor laboratory and another corresponding to an adjacent indoor corridor. The trigger between two estimation algorithms is performed only when that used moves from the outdoor environment to one of the two geofence.
# Lightweight Transformer Network for Ship HRRP Target Recognition
## 2 Proposed Model
### Adaptive Gradient Clipping  
In this paper, an adaptive gradient clipping algorithm is used in the process of optimization. [PERSON] et al. (2018) proposed a simple method to adaptively select the gradient clipping threshold based on the numerical history of the gradient descent process observed during the training process, resulting in smoother loss function curves and more robust recognition performance.  
Suppose the objective function is \(f(\mathbf{X};\theta)\), where \(\mathbf{X}\) is the input data and \(\theta\) is a parameter, and given a learning rate \(\lambda\), the parameter \(\theta\) is updated iteratively from \(t-1\) to \(t\) with gradient descent as defined below.  
\[\theta_{t}=\theta_{t-1}-\lambda h_{c}\
abla_{\theta}f(\mathbf{X},\theta_{t-1}) \tag{14}\]  
\[h_{c}=\min\biggl{\{}\frac{\eta_{c}}{\parallel\
abla_{\theta}f(X,\theta_{t-1}) \parallel},1\biggr{\}} \tag{15}\]  
where \(\eta_{c}\) is a clipping hyperparameter that is set according to the model. In clip by norm, the entire gradient is scaled if the norm of the gradient exceeds a threshold.  
The function of the adaptive gradient clipping algorithm is to let \(\eta_{c}\) follow the model training process dynamically by setting a new hyperparameter \(p\), representing percentile cut-off for setting \(\eta_{c}(t)\) to cut the gradient parametrization. In a training process, \(\mathbf{X_{t}}\in D\), \(D\) represents the training dataset and \(t\) is one of the training batches. Supposing that \(G_{h}\) stores the gradient norm of each batch and the initial \(G_{h}(0)=[\;]\).  
\[g_{h}(t)=\
abla_{\theta}f(\mathbf{X_{t}};\theta_{t-1}) \tag{16}\]  
The gradient norm is added to \(G_{h}\) for each batch computed, i.e., during the training of the \(t\)th batch, there are:  
\[G_{h}(t)=[g_{h}(0),g_{h}(1),\dots,g_{h}(t-1),g_{h}(t)] \tag{17}\]  
Calculate the gradient norm of the \(p\)th percentile value in the current gradient vector.  
\[\eta_{c}(t)=p\times G_{h}(t) \tag{18}\]  
The gradient cropped \(\theta_{t}\) is obtained by bringing \(\eta_{c}(t)\) into (15).
## 4 Materials and Methods
### RNA Preparation and Quantitative RT-PCR  
Two mL aliquots were taken from the previously prepared cultures for RNA extraction. Cell pellets were resuspended in 100 \(\mathrm{\SIUnitSymbolMicro\mathrm{g}}\) L Tris-EDTA (TE) buffer with 1 mg mL\({}^{-1}\) lysozyme and incubated at 37 \({}^{\circ}\)C for 10 min. Total RNA was isolated using the NucleoSpin RNA kit protocol 5.2 (Macherey-Nagel, Duren, Germany) according to the instructions of the manufacturer. RNA quality and quantity were assessed using a NanoDrop spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA), and all RNA samples were found to have a 260/280 nm absorbance ratio of 2.17 \(\pm\) 0.02. RNA samples were stored at \(-\)80 \({}^{\circ}\)C. A total of 700 ng of the single RNA preparations were converted to cDNA using the Quantitect reverse transcription kit (Qiagen, Hilden, Germany) following the instructions of the manufacturer.  
Primers for PCR amplification of cDNA are listed in Table 1. A Bio-Rad (Munich, Germany) thermal cycler c1000 Touch CFX96-Real-Time system was used for the quantification of the cDNA. Triplicate PCR reactions were performed using the Luna universal qPCR master mix (New England Biolabs, Frankfurt am Main, Germany). Eight microliters of 10-fold dilution of cDNA and 0.5 \(\mathrm{\SIUnitSymbolMicro\mathrm{g}}\) mL of primers (10 \(\mathrm{\SIUnitSymbolMicro\mathrm{g}}\)), with a final concentration of 250 nM used in the total volume of 20 \(\mathrm{\SIUnitSymbolMicro\mathrm{g}}\) mL. qPCR cycling conditions were adjusted to 95 \({}^{\circ}\)C, 1 min for the initial denaturation, followed by 40 cycles of 95 \({}^{\circ}\)C, 15 s, 60 \({}^{\circ}\)C, 30 s, 72 \({}^{\circ}\)C and 30 s with data acquisition. A melt curve was run at the end of the 40 cycles using a temperature range from 60 to 95 \({}^{\circ}\)C to confirm the presence of a unique PCR reaction product. To check for residual contaminating genomic DNA, negative controls without reverse transcriptase were analyzed in the same way. The amount of signal in the controls was usually close to the non-template control. Reaction efficiencies were calculated for each amplicon. Quantitative cycle (Cq) values were generated using the Bio-Rad CFX Maestro software 2.3 (Biorad CFX, Germany) and used to quantify the stability of the reference gene using the 2\({}^{-\Delta\mathrm{Cl}}\) method [59]. The Cq values were also used to quantify the relative gene expression to the reference gene _rpoS_ by the 2\({}^{-\Delta\Delta\mathrm{Cl}}\) method [59].
# Application of Collision Warning Algorithm Alarm in Fishing Vessel's Waterway
## 4 Application in Fishing Vessel's Waterway Based on Vpass Data
### Case Study of the Collision Warning Algorithm Application Based on Vpass Data
Similarly, when ship B came closer to ship D at 318.1 s to generate the second main alarm at 341.2 s, it created a risky situation for ship A, which approached in the same direction initially, generating the alarm continuously for 96 s. When the risk criteria were removed, the alarm was turned off and generated again at 452.2 s until 471.4 s when the risk was removed. As shown in Figure 9b, the alarms were generated when the developed algorithm and the DCPA and TCPA criteria were applied. However, when only DCPA and TCPA criteria were applied, the alarms were turned on and off repeatedly when the first alarm was generated at 171 s and was turned off at \(\sim\) 266.8 s. Similar to the real ship test or in the other cases when only \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Time (s)** & **Target Ship** & **Distance (m)** & **DCPA (m)** & **TCPA (s)** & **Risk** & **Alarm** \\ \hline
171.6 & Ship D & 935.74 & 27.27 & 101.01 & 7.0 & first Main \\
172.9 & Ship D & 923.76 & 27.09 & 99.72 & 7.0 & second Main \\
266.8 & Ship D & 135.12 & 135.10 & 0.58 & 5.0 & second Main \\
318.1 & Ship D & 58.76 & 27.63 & 59.93 & 7.0 & second Main \\
341.2 & Ship A & 85.38 & 26.37 & 38.55 & 7.0 & second Main \\
437.2 & Ship A & 26.25 & 17.65 & 566.25 & 5.0 & second Main \\
452.2 & Ship A & 17.35 & 12.95 & 149.64 & 7.0 & second Main \\
471.4 & Ship A & 5.56 & 5.40 & 7.04 & 7.0 & second Main \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Analysis of the situation of alarm generation on ship B in case 2. Figure 9: (a) Diagrammatic representation of the track of the alarms generated from the results of case 2 and (b) graphical representation of the alarms generated on ship B depending on the time (from the top, the alarms based only on DCPA and TCPA criteria, alarms based only on the PARK model risk value criterion, and alarms based on the algorithm). the PARK model risk value criterion was applied, alarms were generated at a significant distance from one another, which could reduce the ship operator's confidence. Comparison of the Waters That Generated Alarms and the Risk Waters Based on the Maritime Risk Assessment Tools  
To validate whether an alarm occurs in dangerous seas using the collision warning algorithm, alarm-prone and high-risk water areas were compared. A high-risk water evaluation was conducted using the environmental stress (ES) model and the International Association of Marine Aids to Navigation and Lighthouse Authorities (IALA) Waterway Risk Assessment Program MkII (IWRAP). Since no model has been developed to assess the risk of small vessels, the ES model, which is commonly used in Korea and Japan, and the IWRAP were used.
# Walking Gait Phase Detection Based on Acceleration Signals Using LSTM-DNN Algorithm
However, the plantar pressure sensor needs to be accurately mounted at the designated location [16], which requires subjects with different shoe sizes to wear the appropriate insole. This will undoubtedly increase the cost of the study. In this case, as the level of sensor technology increases, the use of inertial sensors to detect gait phase is becoming more widespread. The use of inertial sensors is also due to the fact that the acceleration signal exhibits typical waveform characteristics in the gait cycle [12], with which we can detect specific phases. In this study, we utilized the acceleration sensor to detect the gait phase and achieve a better recognition effect. Previous studies have positioned inertial sensors on the instep [17], thigh [18], and calf [19]. In view of previous studies and to further improve the effectiveness of gait event monitoring, this paper considers the position of the instep, lower leg, and thigh because the classifier has better classification performance at the lower extremity position [20]. When the speed sensor is installed in these three body parts, it can continuously output different periodic signals, which is the basis for the gait phase division by the acceleration signal. The study presented here features a gait phase detection system that utilizes inertial motion data from three acceleration sensors to detect two gait phases by means of four detection algorithms. Based on the comparison between the performance of the deep learning algorithm and the machine learning method, the input values of the two types are derived from the acceleration data of three different body parts, revealing which algorithm is more suitable for the gait phase detection system.
## 3 Experiment and Discussion
### Accuracy Evaluation of Measurement System  
Since the actual morphology of a tested workpiece in the DED process cannot be directly and simultaneously measured using other proven technology to obtain a ground truth for the comparison analysis of our developed system, we have compared the measurement results of standard parts with the proposed method before we test the system on the site. Two balls of the gauge were measured separately. The results are shown in Figure 10. The comparison between the measurement results and the ground truth of this standard specimen is shown in Table 1. Figure 10a and d, respectively, shows the deformed fringe images by two cameras when ball A and ball B are measured separately. Figure 10b,e show their height distribution. Figure 10c,f are the standard deviation distribution between the measuring results and their fitting sphere in Geomagic software, which is 0.0484 mm and 0.0466 mm, respectively.  
According to the above accuracy evaluation results, the measurement error of the developed measurement system is within 0.05 mm, which can meet the requirements of 3D shape measurement in the manufacturing process of metal parts.  
Figure 9: Spectral distribution of the additive manufacturing process of 30 CrMnSiNi2A steel under the laser power of 1200 W, (**a**) high intensity distribution of the visible wavelength, (**b**) rapidly increasing spectral intensity at the initial stage of laser additive manufacturing, (**c**) stable spectral dynamic distribution in the stable stage of laser additive manufacturing, the intermediate interval is the state when the laser is shut down.