esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of Yunnan Pine Shoot Beetle Stress Using UAV-Based Thermal Imagery and LiDAR
## 5 Conclusions  
This study confirmed the feasibility of using TIR imagery for monitoring infestations of bark beetles in Yunnan pines using combined UAV-based LiDAR data at the individual tree and plot scales. The correlation between SDR and temperature was greatly affected by low LAIs. After the data of low LAI were removed, an obvious correlation between SDR and temperature was found to exist at the single plant and plot scales. Determination coefficients of 0.7890 and 0.5532 were calculated, respectively, which confirmed the feasibility of using TIR to estimate SDRs. In this study, the detection accuracy of SDR in infected forest still had room for improvement if a three-dimensional radiative transfer (RT) model was to be used, such as RAPID, DART, and LESS. These RT models can provide more detailed data for reducing the influence of environment parameters and furthering the application of multi-source remote sensing. In further research, the scale will be expanded to pixel scale using a combination of thermal infrared and optical satellite images to improve the accuracy of monitoring large-scale regional forest infestations.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON], [PERSON] and [PERSON] conducted the field experiments; [PERSON] analyzed the data; [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (41571332), the Science and Technology Project of Henan Province (212102310024), the Talents Training Program of the Henan Academy of Sciences (220401010), and the Special Project for Team Building of the Henan Academy of Sciences (200501007).  
Not applicable.  
Written informed consent has been obtained from the patient(s) to publish this paper.  
The authors declare no conflict of interest.
## Materials and Methods
### HS-SPME GC-MS Analysis of VOCs  
A qualitative and quantitative evaluation of VOCs produced by _B. methylotropicus_ BCN2 and _B. thuringiensis_ BCN10 composition was done using HeadSpace solid phase microextraction (HS-SPME) coupled with gas chromatography-tandem mass spectrometry analysis (GC-MS) according to the method previously described by [PERSON] et al. (2015) with modifications. SPME fiber (2 cm--50/30 mm DVB/CAR/PDMS, Supelco Inc, Bellefonte, PA, USA) was preconditioned according to manufacturer's recommendations. The strain BCN2 or BCN10 was inoculated into 40 mL of LB medium in a 100 mL vial for 12 h at 37 \({}^{\circ}\)C and the vials were put inside the Ziploc bags with the fresh fruit, where the placement was the same as described in Section 4.3. The LB medium without inoculation was the control. The SPME fiber was inserted into the vial, fixed at about 1 cm above the surface of the fermentation liquid, and exposed to headspace in 50 \({}^{\circ}\)C hot water for 30 min to extract the VOCs. Trapped compounds were then thermally desorbed from the fiber for 2 min in the GC injection port at 250 \({}^{\circ}\)C in the split-less injection mode. For peak separation and detection, a Bruker GC 451 gas chromatograph equipped with a HP-5 fused silica capillary column (30 m by 0.25 mm inside diameter; 0.25 mm film thickness, J&W Scientific Inc., Folsom, CA, USA). The transfer line was heated at 250 \({}^{\circ}\)C, the ion source at 220 \({}^{\circ}\)C and carrier gas (He) flow rate was 1 mL/min. The mass spectrometer was operated in electron impact mode at 70 eV, scanning the range of 35/500 m/z in a full scan acquisition mode. The GC oven temperature was set at 40 \({}^{\circ}\)C for 4 min and then programmedto rise from 40 to 90 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min, from 90 to 160 \({}^{\circ}\)C at 5 \({}^{\circ}\)C/min and from 160 to 280 \({}^{\circ}\)C at 40 \({}^{\circ}\)C min [47]. GC-MS took cyclohexanone as the internal standard substance and made a quantitative test.  
The identification of VOCs was confirmed by retention time and mass spectra comparison with identical standard compounds. The mass spectra of VOCs were compared with those in the NIST/EPA/NIH Mass Spectrometry Library with respect to the spectra in the NIST 11 MS databases. Blank sample analysis was performed under the same conditions in order to exclude interfering substances. All measurements were made with three replicates.
## 3 Methodology
### Applied Example  
This section describes the various calculations that were used to forecast DDoS attacks based on the historical data provided. There is a proper explanation of the functions conducted by the suggested hybrid model.  
The ultimate BiSLTM outcome is provided as input, and the SoftMax function is used to determine the probability of each tag: \"\(t1\)\", \"\(t2\)\", etc. Equation (6) was used to compute net input:  
_For DDoS normal/attack-1 (target variable 1 class label) = \"\(t1\)\"_  
The net input of the first class (\(t_{1}\)) of DDoS attacks is calculated as follows:  
\[t_{1}=l_{1}\times w_{2}+l_{2}\times w_{2}+b\]  
\[t_{1}=0.2\times 2.2+0.5\times 1.8+0.8\]  
\[t_{1}=0.44+0.9+0.8=2.14\]  
The net input of the second classes of DDoS attacks is calculated in the same way:  
\[t_{2}=0.921\]  
The softmax activation function was used using Equation (7) to compute the probability of each label (\(t_{1}\),\(t_{2}\)):  
\[softmax\left(t_{1}\right)=\frac{exp^{t_{1}}}{exp^{t_{1}}+exp^{t_{2}}} \tag{7}\]  
\[softmax\left(t_{1}\right)=\frac{exp^{2.14}}{exp^{2.14}+exp^{0.921}}\]  
\[softmax\left(t_{1}\right)=\frac{8.499}{11.01}=0.77\]  
The SoftMax functions for the other DDoS attack/normal classes were derived in the same way:  
\[softmax\left(t_{2}\right)=2.512/11.011=0.23\]  
The T1 DDoS traffic(normal) had the highest probability, according to this calculation. As a result, the projected DDoS attack decision was \"A\" based on the presented historical traffic data (Figure 3).  
Algorithm 1 shows the pseudocode processes of the suggested model for predicting DDoS attacks.  
Figure 3: Classification using the softmax function.  
```
1. Input: CICLDDoS2019 (after preprocessing) labeled dataset D as csv file.
2. Split into train (Strain, NRrain)-test (Stest, NStest) using Scikit learn.
3. Build the vocabulary to map integer to CICLDDoS2019
4. Transform each CICDDoS2019 data stream into sequence of integers.
5. Procedure CNN+ BiLSTM model (Strain, NStrain)
6# Initialization of Sequential function Model=Sequential()
7. using Embedding Layer to map integers to low dimensional vectors Model.add(Embedding())
8#Dropout Layer for preventing overfitting Model.add(Dropout(0.5))
9#-Applying Bi-LSTM layer for context information extraction Model.add (Bidirectional (LSTM()))
10#Prediction of DDoS Attacks using Softmax function Model.add(Dense(3, activation='softmax')).
11#Compilation Function
```  
**Algorithm 1** Pseudocode of the proposed DDoS attack prediction model.
# A Physical-Layer Watermarking Scheme Based on 5G NR
## 2 Watermark System Model on 5G NR
### Performance Analysis for Bit Error Rate
#### 2.3.2 Bit Error Rate of the Watermark Bit  
In this paper, the watermark is embedded into the phase of DMRS, and the DMRS is a QPSK signal. According to the reference [24], when the watermark is embedded into the QPSK signal in the way of phase rotation, the bit error rate of the watermark can be expressed as:  
\[P_{e}(\eta)=Q\Bigg{(}\sin\theta\sqrt{\frac{2\eta E_{s}}{n_{0}}}\Bigg{)}+Q \Bigg{(}\cos\theta\sqrt{\frac{2\eta E_{s}}{n_{0}}}\Bigg{)}-2Q\Bigg{(}sin\theta \sqrt{\frac{2\eta E_{s}}{n_{0}}}\Bigg{)}Q\Bigg{(}\cos\theta\sqrt{\frac{2\eta E _{s}}{n_{0}}}\Bigg{)} \tag{17}\]  
where \(\eta=|\mu|^{2}\), \(\mu\) is the attenuation coefficient, \(E_{s}\) is the average symbol energy, \(n_{0}\) is the power spectral density of noise, and \(\theta\) is the radian of phase rotation. For the Gaussian channel, \(\eta\) is the constant 1, so the bit error rate of the watermark bit in the Gaussian channel can be expressed as:  
\[P_{e}=Q\Bigg{(}\sin\theta\sqrt{\frac{2E_{s}}{n_{0}}}\Bigg{)}+Q\Bigg{(}\cos \theta\sqrt{\frac{2E_{s}}{n_{0}}}\Bigg{)}-2Q\Bigg{(}sin\theta\sqrt{\frac{2E_{ s}}{n_{0}}}\Bigg{)}Q\Bigg{(}\cos\theta\sqrt{\frac{2E_{s}}{n_{0}}}\Bigg{)} \tag{18}\]  
Substituting (18) to (16), we obtain the average bit error rate of the watermark bit.
###### Abstract
In reference [41], considering the varying importance of target nodes, a sensor node deployment strategy based on k-coverage was designed using the characteristics of the Reuleaux triangle. In [42], under the premise of ensuring network connectivity, a node deployment algorithm with high coverage was proposed, but it overlooked the issue of deployment cost. The reference [43; 44; 45; 46; 47] conducts a survey on the sensor deployment problem of wireless sensor networks from different perspectives and application scenarios. In summary, although there has been extensive research on node deployment in wireless sensor networks, several important aspects have been relatively overlooked, including network connectivity, deployment costs, the selection of homogeneous or heterogeneous sensors, and the choice of sensing models. Compared with the state-of-the-art work, there are some limitations in the above papers, such as uncertainty in the sensor detection range and multi-objective optimization strategy. The probabilistic perception model, a statistical-based approach, effectively captures the complexity and uncertainty inherent in real-world sensing processes. This paper makes the following key contributions to the field of sensor deployment problems in wireless sensor networks:  
1. This paper addresses key practical challenges, including uncertain sensor node counts, varying target importance, location-specific deployment costs, and environmental obstacles. 2. This paper innovatively incorporates four key factors, deployment cost, sensor power consumption, connectivity, and sensing probability, by assigning appropriate weights to each, thereby optimizing sensor network deployment for practical applications. 3. The deployment process fully accounts for both small and large obstacles, as these areas are unsuitable for sensor placement. This paper explores obstacle scenarios to ensure compatibility with diverse deployment environments. 4. Due to uncertainty in determining the required number of sensors, this paper proposes a sensor network deployment strategy using a particle swarm-genetic hybrid algorithm. The process is divided into two phases: the first phase determines the optimal sensor count, and the second phase develops the optimal deployment strategy for that count. This approach effectively optimizes deployment objectives, ensuring compliance with sensing requirements and connectivity constraints, even when the exact sensor count is unknown. The remainder of this article is organized as follows, Section 2 provides the system models and an overview of the sensing model in the IIoT situation. The sensor deployment strategy based on the particle swarm/genetic hybrid algorithm is created in Section 3. The simulation results and performance analysis are discussed in Section 4. In Section 5, the conclusions are given.
## 1 Introduction  
Jaw defects may occur due to various causes such as tumors, trauma, and diseases caused by infection such as osteomyelitis. Primary squamous cell carcinoma (SCC) of the mandible is one reason for mandibular resection. Generally, when carcinoma of the mandible invades the skin, the involved soft tissue and mandible are removed and reconstructed using a reconstruction plate and soft tissue flap or complex reconstruction using hard and soft tissue free flaps.  
Reconstruction of the jaw requires not only recovery of the contour but also rehabilitation of dentition for chewing and speaking. Therefore, reconstruction of bone and soft tissue through osteocutaneous free flap transfer is regarded as the best treatment to restore function using implants or implant-supported dentures [1].  
Hard and soft tissue free flaps used in reconstruction of the jaw include the radial forearm, scapula, fibula, and deep circumflex iliac artery (DCIA) flap [2]. The surgeon chooses the donor site based on various factors such as the size of the hard tissue and soft tissue required for reconstruction and the physical condition of the patient. The DCIA flap has been used with the fibula free flap for the reconstruction of jaw defects because it has the advantages of reproducing the natural shape of the arch, having sufficient width and height, and providing bicortical bone containing good-quality cancellous bone. In particular, it is useful for the restoration of partial defects in the mandible, such as segmental mandibularctomy and hemi-mandibulectomy [3]. Another important factor to consider when choosing donor sites is donor site morbidity. The DCIA is known to have few major complications; however,various minor problems have been reported. These complications include gait disturbance, hernia, and fracture. In particular, large bony defects in the ilium area during restoration of a wide bone defect cause weakening of the supporting tissue, which causes complications such as gait disturbance, iliac fracture, and hernia [3]. To reduce this complication, the monocortical DCIA flap can be used, or titanium mesh can be applied to the iliac crest bone defect. However, these methods have limitations when used to reduce hip contour reproduction and gait disturbance [4].  
The introduction of Computer-Aided Design (CAD) and Computer-Aided Manufacturing (CAM) systems has led to several advantages for both clinicians and patients, and commercial software can be reliably used to simulate and perform procedures [5].  
The purpose of this study was to introduce a case involving reconstruction of the donor site using a patient-specific implant applied after reconstruction of a mandibular defect after segmental mandibular due to primary squamous cell carcinoma in the mandible.
## Results
### Comparison of PInueological Transition Dates (PTDs) Derived from VIs and GPP  
The comparison of PTDs derived from different VIs and GPP is shown in Figures 6 and 7. The PTDs derived from VIs for the Green-up period (UD, SOS\({}_{\text{trs}}\), and POS1), with the exception of CamNIRv, are systematically delayed from the ones derived from GPP (Figure 6). By contrast, the PTDs extracted from the VIs have a good agreement with the PTDs extracted from the GPP in the Dry-down period (POS2, EOS\({}_{\text{trs}}\), RD). The difference between PTDs extracted from VIs and GPP ranges within 10 days in the Dry-down period. By contrast, their difference ranges from 8-25 days for the Green-up period, with the only exception of CamNIRv and GPP, which show a smaller difference (Figure 7 panel UD and EOS\({}_{\text{trs}}\)).  
Specifically, the PTDs derived from CamNIRv at the Green-up period and the ones derived from CamNDVI at the Dry-down period have no significant difference compared to the ones derived from GPP, respectively (Table 4). The PTDs extracted from CamNIRv and GCC have smaller differences (9.4 and 7.0 days, respectively) with the ones derived from GPP in the Green-up period, while they have larger difference with the PTDs derived from GPP compared to CamNDVI (difference of 4.6 days) and CamRVI (difference of 5.0 days) in the Dry-down period (Tables 4 and A2).  
The results show that POS1 derived from VIs have a large difference with the POS1 derived from GPP especially for CamNDVI and CamRVI (>20 days). All the POS2 derived from VIs have no statistically significant difference with the ones extracted from GPP, with the difference between VIs and GPP ranging within 5 days (Figure 7).  
Figure 6: Comparison of phenological transition dates (PTDs) extracted from vegetation indexes (VIs): PhenoCam-based green chromatic coordinate (GCC), normalized difference vegetation index (CamNDVI), near-infrared reflectance of vegetation index (CamNIRv), ratio vegetation index (CamRVI), and gross primary productivity (GPP). Please see the definition of each PTD in the Table 2.
# Charge-Line Dual-FET High-Repetition-Rate Pulsed Laser Driver
## 4 Test Results  
### Preliminary Tests of the System  
During preliminary tests of the electrical parameters of the presented driver, a resistive load R\({}_{\mathrm{L}}\) = 33 \(\Omega\) was used. The Figure 7a shows different settings of the FPGA timing signal leading to different intervals obtained at the dual-FET driver. The maximum pulse width is approximately 2 ns, which is in a good agreement with the design assumptions. The measured current-pulse rise times t\({}_{\mathrm{r}}\) 10%-90% \(<\) 140 ps and fall times t\({}_{\mathrm{r}}\) 10%-90% \(<\) 430 ps are sufficient to generate sub-nanosecond laser pulses. The falling edge negative current undershoot and spike is noticeable in Figure 7a. To compensate this effect, the increase of the matching resistor R\({}_{\mathrm{M}}\) value might be considered.  
Figure 7b shows the pulse peak current regulation via +HV voltage adjustment. In the test load configuration (R\({}_{\mathrm{L}}\) = 33 \(\Omega\)), the maximum peak current of 0.96 A was obtained.  
Operating with higher repetition rates leads to the peak power amplitude compression. In Figure 8, a less than 2.5 dB drop of the initial current amplitude is observed for f\({}_{\mathrm{rep\,max}}\) = 20 MHz. The obtained  
Figure 6: PCBs of the pulsed laser driver system: (**a**) Dual-FET output stage; (**b**) Timing circuit (Field-Programmable Gate Array (FPGA)) PCB–ZTEX evaluation board with the Spartan 6 device.  
f\({}_{\text{repmax}}\) frequency at 1.25 A pulse current is high, when compared to professional solutions available on the market [14].
## 3 Materials, Methods, and Findings
### RAG with the LSTM Hybrid Model
#### TF-IDF Nave Bayes Model  
The combination of TF-IDF and Naive Bayes gives effective results with a low computational cost in text classification tasks. While TF-IDF extracts meaningful features from texts, the Naive Bayes model uses these features for classification purposes. The alpha value for the Laplace smoothing is set to 1.0. Figure 17 shows the complexity matrix of the TF-IDF Naive Bayes model.  
The model correctly classified 92 instances in the \"no verbal violence\" class and incorrectly labelled 10 instances as \"verbal violence present\" (false positive). In the \"verbal violence present\" class, the model made 104 correct predictions and misclassified 4 instances as \"no verbal violence\" (false negative). These results show that the overall accuracy of the model is 93.33%. In the \"verbal violence present\" class, the sensitivity rate is 96.30%, indicating that the model detects the positive class with a high accuracy. The false positive rate is 9.80% and the false negative rate is 3.70%, which shows that the model has a balanced performance in both classes.
## 3 Results
### Static Balance (COP)
#### 3.5.2 Eyes-Closed  
Under the eyes-closed condition, the experimental group showed a significant reduction in sway area, from 23.93 \(\pm\) 35.14 mm\({}^{2}\) to 2.13 \(\pm\) 2.41 mm\({}^{2}\) (\(p\) < 0.05), whereas the control group showed a non-significant decrease from 18.67 \(\pm\) 43.43 mm\({}^{2}\) to 1.53 \(\pm\) 3.02 mm\({}^{2}\) (\(p\) > 0.05). However, none of the variables exhibited significant group \(\times\) time interactions.  
Sway distance declined from 6.28 \(\pm\) 1.96 cm to 4.22 \(\pm\) 1.83 cm in the experimental group and from 5.64 \(\pm\) 1.47 cm to 4.84 \(\pm\) 1.29 cm in the control group. A significant group \(\times\) time interaction was found (F = 5.613, \(p\) = 0.025), indicating a greater reduction in the experimental group.  
Sway velocity was reduced from 0.24 \(\pm\) 0.15 cm/s to 0.16 \(\pm\) 0.06 cm/s in the experimental group and from 0.19 \(\pm\) 0.05 cm/s to 0.17 \(\pm\) 0.04 cm/s in the control group. The group \(\times\) time interaction was not significant (F = 2.185, \(p\) = 0.150) (Figure 5). As no significant interaction effects were found for sway area or sway velocity under eyes-closed conditions, these results should be interpreted with caution. The reduction in visual input may have limited the differentiating impact of the trunk stabilization intervention.
## 2 Materials and Methods  
### Design  
A cross-sectional study was conducted to characterise the motor pattern of BP exercise based on barbell kinematic parameters and to analyse the differences in these parameters and other mechanical variables among different intensity intervals. The data were recorded through a direct-computation 1 RM (One-Repetition Maximum) protocol. The One-Repetition Maximum (1 RM) represents the maximum load an individual can lift in a single repetition with proper technique [31]. After determining the 1 RM load, all sets' loads were normalised (%RM) based on that 1 RM. BP sets were grouped into intervals of intensity (load): interval 1 (55 to 75% 1 RM), interval 2 (>75 to 85% 1 RM) and interval 3 (> 85 to 100% 1 RM). These intervals were selected considering the following: (1) [PERSON] and [PERSON] [31] (p. 127) indicate that, in the Box Power Cleans exercise, execution velocities are statistically similar for loads <75% 1 RM. To avoid the possible replication of this phenomenon, loads under 75% 1 RM were classified as light loads (interval 1). This interval spans from 75% to 55% 1 RM, as 55% 1 RM was the lowest recorded load. (2) [PERSON] and [PERSON] [31] (p. 276) also report that the NSCA recommends training with loads between 70% and 85% 1 RM for advanced athletes, so loads above 75% and below 85% 1 RM were classified as interval 2. (3) Loads greater than 85% 1 RM were considered high (sub-maximal and maximal) and classified as interval 3.  
The kinematics of the BP were recorded for each lift during the 1 RM protocol at each applied load intensity. The following parameters were computed for the eccentric and concentric phases under different load conditions: (1) maximum velocity (VMAX); (2) mean velocity (VMEAN); (3) maximum acceleration (AMAX); (4) mean acceleration (AMEAN); (5) maximum mechanical power (PMAX); (6) mean mechanical power (PMEAN); and (7) mechanical work (W).