esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Sensing Platform for IoT-Based Precision Agriculture
## 4 Results
### Data Validation
#### 4.1.3 Aerial Spectrometer Data Validation Using Ground-Based Data--Experiment 3  
In this experiment, the impact of UAV movement and vibration on aerial spectrometer data is analysed. The spectrometer sensor used in this research reads 18 channels of the spectrum, ranging from ultraviolet to infrared. In order to determine whether or not all aerial channels have the same pattern compared with their corresponding ground-based channel, correlation coefficient analysis between each channel is conducted.  
Figure 10 shows that the correlation coefficients of all channels are relatively similar and the value is slightly lower in the G, H, and I channels. Therefore, since all channels show a similar linear relationship pattern, we focus on three E, H, and O channels to perform the data analysis. Channel E represents the green colour, which can be used to determine plant health; channel H represents the yellow colour, which can be used to identify plant disease and discolouration, and channel O represents near-infrared (NIR), which is used in PA to determine the plant stress level. Figures 11 and 12 show the comparison and distances between aerial green, yellow, and NIR channel data with corresponding ground-based channel data during the 13 aerial data collection cycles.  
As can be noticed from Figures 11 and 12, the aerial spectrometer data have significant fluctuations compared with ground-based data, particularly during afternoon cycles. In order to determine the possibility of using the aerial channel to predict the ground truth data, linear regression analysis on the three channels is performed, which is shown in Table 5.  
As the correlation coefficient and linear regression analysis show, the relationship between the aerial and ground-based spectrometer is not strong. This impacts the validity of aerial spectrometer data; however, since the environmental observation context variable might impact the spectrometer reading, data calibration might improve the validity of aerial spectrometer data. The calibration of such data is discussed in the next section.
###### Abstract  
Drones integrated with 5G New Radio (NR) base stations have emerged as a promising solution for efficient victim search and localization in emergency zones where cellular networks are disrupted by natural disasters. Traditional approaches relying solely on uplink Sounding Reference Signal (SRS) for localization face limitations due to User Equipment (UE) power constraints. To overcome this, our paper introduces BWSAR, a novel three-stage Search-and-Rescue (SAR) methodology leveraging 5G-NR beam sweeping technologies. BWSAR utilizes downlink Synchronization Signal Block (SSB) for coarse-grained direction estimation, guiding the drone towards potential victim locations. Subsequently, finer-grained beam sweeping with Positioning Reference Signal (PRS) is employed within the identified direction, enabling precise three-dimensional UE coordinate estimation. Furthermore, we propose a trajectory optimization algorithm to expedite the drone's navigation to emergency areas. Simulation results underscore BWSAR's efficacy in reducing positioning errors and completing SAR missions swiftly, within minutes.  
drone/UAV; 5G NR; positioning; beam sweep; search-and-rescue Article
# Tuberculosis Vaccines: An Update of Recent and Ongoing Clinical Trials
## 3 TB Novel Vaccines
### TB Subunit Vaccines
#### 3.1.4 H4:IC31  
H4:IC31, another candidate vaccine, is composed of two active components: H4 antigen (fusion protein of _M. tuberculosis_ antigen 85B and TB10.4), and an immunological adjuvant called IC31(r), which is a combination of the antimicrobial peptide KLK and oligodeoxyynucleotide (ODN) 1a (ODN1a) [(12; 16)]. Previous phase I clinical trials have proven an acceptable safety profile with immunogenicity, capable of triggering multifunctional CD4+ T cell responses in previously BCG-vaccinated healthy individuals [(16)]. These same trials provided the optimal antigen-adjuvant dose combinations [(16)]. The previously mentioned clinical trial (ClinicalTrials.gov: NCT02378207) also investigated the safety and immunogenicity of H4:IC31 in healthy adolescents. In the H4:IC31 vaccine group, 24 participants were administered 15 mcg H4/500 nmol and IC31 IM as 0.5 mL at days 0 and 56. 91.6% of these participants reported AEs (compared with 83.3% in the H56/IC31 group), with the most common being pain at the injection site and a headache. No SAEs were reported in this group. However, the response rate at day 168 for both Ag85B and TB10.4 was the highest, registering 21.7% and 8.7%, respectively, compared to 9.5% Ag85B and 0% TB10.4 in the H56:IC31 group. A phase II clinical trial assessing the safety, immunogenicity, and prevention of TB with the vaccine in a similar population group, in comparison to BCG revaccination, was also carried out (ClinicalTrials.gov: NCT02075203). The 989 healthy adolescents were enrolled, of which 330 made up the H4:IC31 cohort. This group received two doses of 15 mcg H4/500 nmol IC31 each on days 0 and 56. The results indicate that 35.76% of participants reported AEs in this cohort, compared with 99.7% of participants reporting AEs in the BCG revaccination group (\(n=330\)). The H4:IC31 cohort also showed a higher percentage of participants with an immune response compared to the BCG revaccination group. At day 70, 81.8% of participants showed an immune response measured by a 13-colour intracellular cytokine staining assay performed on peripheral blood mononuclear cells to assess CD4+ T cells expressing IFN-\(\gamma\), TNF, IL-2, IL-17, IL-22, CD107a, and/or CD154, alone or in combination, in response to stimulation with peptide pools representing the entire amino acid sequence of the TB mycobacterial antigens, Ag85B and TB10.4, and BCG antigens, compared with 16% in the BCG revaccination group. Although not available yet, results have been submitted for another phase I/II clinical trial to assess the safety and immunogenicity of H4: IC31 in BCG-primed infants (ClinicalTrials.gov: NCT01861730).
## 2 Methodology  
### Architecture Overview  
The encoder-decoder structure is the mainstream network structure for semantic segmentation, and UNet is a classic and effective encoder-decoder network structure, where the encoder plays the role of feature extraction and downsampling to reduce the resolution of the image, and the decoder is used for feature recovery and the feature fusion and segmentation of the image. In this paper, based on the UNet network architecture, VGG16 is selected as the encoder. The DHAM is introduced to replace the skip connections, and the decoder part is entirely replaced with the RGSM to enhance the network's attention to defects and improve the feature fusion capability, thereby enhancing the segmentation accuracy for defects. The improved network model slightly increases the number of parameters and slightly reduces the segmentation speed, but they remain within acceptable limits. Compared to other models, it still maintains several advantages.  
Figure 1 illustrates the improved UNet network architecture proposed in this paper. The blue arrows represent the 3 \(\times\) 3 convolutional layer and the ReLU activation function, which are used for feature extraction in the encoder. The pink arrows represent the 2 \(\times\) 2 max pooling layer, which reduces the image's dimensions. The orange arrows represent the DHAM, which enhances the attention to defects under interference from background similarity. The yellow arrows represent the upsampling layer, which increases the image's dimensions for feature fusion. The green arrows represent the RGSM, which enhances the feature fusion capabilities of the decoder. The purple arrows represent the 1 \(\times\) 1 convolutional layer, which alters the output image dimensions to produce the final segmentation result. This network architecture first employs VGG16 as the encoder, replaces ordinary  
skip connections with the DHAM, and then uses multiple RGSMs as the decoder, with a \(1\times 1\) convolutional layer to output the final result of segmentation.
# Collaborative Indoor Positioning by Localization Comparison at an Encounter Position
## 2 Related Work
### Localization with Indoor Maps  
In order to reduce the localization error in IMUs or other approaches, there exist methods to utilize indoor maps [18; 19]. These methods perform localization by combining indoor maps and map matching algorithms with a particle filter [7; 20; 21]. Map matching localizes the user's position on the indoor map. Precisely, the methods predict the user's walking trajectory on the indoor map by a particle filter using IMU data. The particles hold parameters such as position, orientation, and scale, which are successively updated by the velocity vectors obtained from the IMU. Each particle has a weight, representing confidence in the user's location. Then, the position on the indoor map is calculated by the weighted average over the positions of the particles. Generally, the weight needs to be small if particles pass through the obstacles in the indoor map, such as a wall in the floor plan. [PERSON] et al. [20] calculated the overlap of obstacles with the trajectory. Then, the weight was decided according to the degree of the overlap.  
The methods in [7; 18; 19; 20; 21] use indoor maps and map matching to reduce error accumulation. However, the localization fails if the error accumulation is too large and the indoor map is distorted. The proposed collaborative method solves this problem using the information from the other device.
# Road Crack Detection by Combining Dynamic Snake Convolution and Attention Mechanism
## 1 Introduction
[26] proposed a CrackCLF network based on adversarial networks, embedding closed-loop feedback into the neural network to address the issue of the model's inability to automatically adapt to environmental changes and effectively extract and detect wave cracks. The application prospects of road disease detection based on deep learning are promising, yet there are still several problems: (1) the dataset is limited, leading to low efficiency and difficulty in detection, (2) the detection accuracy for small cracks is insufficient, and (3) the detection of crack integrity is unsatisfactory. To address the aforementioned issues, this paper combined several public datasets to train the neural network and expand the dataset by the data augmentation method. Inspired by the U-Net model, we combined U-Net with dynamic serpentine convolution and the attention mechanism to improve the detection accuracy of discontinuous cracks.
## 3 Operational Requirements
### Technical Specifications
#### 3.4.7 Autonomy  
The battery is the main component that powers all the robot's systems, from the traction motors to the processing and perception systems. An efficient battery design ensures that the robot can operate for extended periods without the need for frequent recharging, which is essential for productivity at harvest. Lithium-ion batteries are a common choice due to their high energy density, allowing a large amount of energy to be stored in a relatively small and lightweight package. These batteries also have a long lifespan and are capable of withstanding numerous charge and discharge cycles without significant degradation [72]. Fruit-harvesting robots are designed to be energy efficient, minimizing energy consumption without compromising performance. This includes the use of high-efficiency electric motors and the optimization of control algorithms that adjust energy use according to operational needs.  
The integration of solar charging is a viable option to extend the autonomy of fruit-harvesting robots, especially in agricultural fields with high sun exposure. Solar panels can provide additional power, reducing reliance on frequent recharges and increasing operation time in the field. Robots equipped with solar charging systems can operate in a hybrid mode, using solar power during the day and switching to battery power at night or in low-light conditions. This maximizes operating time and reduces the need for external refills [73].
# A Privacy-Preserving Protocol for Utility-Based Routing in DTNs
## 4 System Design of PPUR
### Generation of Encounter Record Information for Anonymous Nodes  
When forwarding messages, nodes need to calculate the utility value according to the number and time of node encounters. So, it is necessary to generate encounter record information for anonymous nodes. Assuming that two nodes \(N_{i}\) and \(N_{j}\) encounter, the encounter record information \(\mathrm{ET}_{ij}\) will be generated as shown in Figure 2.  
In the encounter record information, \(\mathrm{ET}_{ij}\), \(\mathit{Pid}_{i}\), and \(\mathit{Pid}_{j}\) are the nodes' pseudo-IDs; \(t_{ij}^{1}\)... \(t_{ij}^{n-1}\) and \(t_{ij}^{n}\) are the encounter time samples of nodes \(N_{i}\) and \(N_{j}\) (other information such as encounter location can be included when needed); \(\mathit{SigPid}_{i}\) and \(\mathit{SigPidj}\) are the digital signatures of nodes \(N_{i}\) and \(N_{j}\); \(E_{\mathit{SKi}}\) is the private key of node \(N_{i}\); and \(H\) is the hash function used to generate the summary.  
Figure 2: Encounter record information.  
Nodes \(N_{i}\) and \(N_{j}\) generate the session key Key\({}_{ij}=\hat{e}\left(sk_{i}\), \(H_{1}(Pid_{j})\right)=\hat{e}\left(H_{1}(Pid_{i})\), \(sk_{j}\right)=\hat{e}\left(H_{1}(Pid_{i})\), \(H_{1}(Pid_{j})\right)^{s}\). Encounter record information is encrypted by the session key Key\({}_{ij}\) to generate the encrypted encounter record \(\text{ET}^{\prime}{}_{ij}=E_{\text{Key}_{ij}}\) (ET) to ensure message confidentiality.
## 3 Method
### Pre-Trained Model for the Second-Class Entities
#### 3.2.3 CRF Model  
The bidirectional long short term memory (Bi-LSTM) is effective at handling long-distance text information in the named entity recognition task, but it struggles with the dependency between adjacent tags. Bi-LSTM's drawbacks can be remedied by conditional random field (CRF), which can obtain an ideal prediction sequence through the relationship between adjacent labels. CRF reasoning layer is thus added following the Bi-LSTM network layer.  
Conditional random field is a distribution model of conditional probability \(P(Z\mid Y)\), which is used to predict another set of output sequence Z given a set of input sequence Y.  
In a given training set Y and the corresponding marking sequence Z, K characteristic functions \(f_{k}(y,z)\) needs to learn the model parameter \(v_{k}\) of linear CRF and conditional probability \(P_{v}(z\mid y)\). \(z_{v}(y)\) is the generalization factor. The conditional probability \(P_{v}(z\mid y)\) and model parameter \(v_{k}\) satisfies the relationship shown in Equation (8):  
\[P_{v}(z\mid y)=P(z\mid y)=\frac{1}{z_{v}(y)}exp\Big{(}\sum_{k=1}^{K}v_{k}f_{k} (y,z)\Big{)}=\frac{exp\Big{(}\sum_{k=1}^{K}v_{k}f_{k}(y,z)\Big{)}}{\sum_{z}exp \Big{(}\sum_{k=1}^{K}v_{k}f_{k}(y,z)\Big{)}} \tag{8}\]  
According to Equation (8), given the conditional probability \(P(z\mid y)\) of the conditional random field and an observation sequence \(y=(y_{0},y_{1},\dots,y_{n})\), the sequence y satisfying the maximum of \(P(z\mid y)\) can be found. Sentence level sequence features can be fully utilized by conditional random field (CRF) models to combine contextual information. Some limitations are applied to the label prediction of the sequence label results in order to ensure the reliability of the output label. This means that the label result cannot contain the beginning of a person entity (B-Person) followed by the inside of a chemical entity (I-Chemical). The output results may be in the wrong sequence if we simply use the bidirectional long short term memory (Bi-LSTM) layer since the Bi-LSTM model can only learn character-level features and lacks feature analysis at the complete sentence level. In other words, B-person may be followed by I-chemical. Similar issues can be avoided by adding a CRF reasoning layer after the Bi-LSTM network layer. Additionally, during data training, the CRF layer can automatically learn these constraints.
## 2 Modeling and Analysis of State Transitions for Energy-Efficient Femto BS
### Performance Analysis
In the idle state, exit from idle is caused by any of the following three events:  
* Data session arrival (\(T_{21}\));  
The mean residence time in the active (data) state is as follows, which is a busy period of the \(M/M/\infty\) model:  
\[\overline{t_{1}} = \frac{e^{\lambda_{s}/\mu_{s}}-1}{\lambda_{s}} \tag{20}\]  
The mean residence time in the idle state is derived as:  
\[\overline{t_{2}} = \mathrm{E}[t_{2}]=\mathrm{E}[\min\{T_{21},T_{23},T_{24}\}]\] \[= \int_{0}^{\infty}\mathrm{Pr}(\min\{T_{21},T_{23},T_{24}\}>t)dt\] \[\pi_{2} = \frac{1}{Den}\]0 \[\pi_{2} = \frac{1}{Den}\]1 \[\pi_{2} = \frac{1}{Den}\]2  
The mean residence time in the active (signaling) state is derived as:  
\[\pi_{2} = \frac{1}{Den}\]3  
The mean residence time in the sleep entering state is derived as:  
\[\pi_{2} = \frac{1}{Den}\]4  
The mean residence time in the idle sleep state is derived as:  
\[\pi_{2} = \frac{1}{Den}\]5  
The mean residence time in the waking up state is derived as:  
\[\pi_{2} = \frac{1}{Den}\]6  
Based on the values of \(\pi_{k}\) and \(\overline{t_{k}}\) obtained from Equations (4)-(9) and Equations (20)-(25), respectively, we finally derive the steady-state probability of each femto BS state using Equation (1). The steady-state probability of each state is derived as follows:  
\[\pi_{2} = \frac{1}{Den}\]7 (26) \[\pi_{2} = \frac{1}{Den}\]8  
where \(\psi_{k}\) is the power consumption of a femto BS in state \(k\). Although transition into the sleep state can save energy, additional delay is introduced for session initiation, since MSs must wake up femto BSs and obtain new IP address using DHCP [21]. Therefore, there is tradeoff between energy consumption and delay, and thus, the effect of delay introduced by turning off the radio interface of BSs should be analyzed. In this paper, cumulative average delay per unit hour in the proposed state model is obtained as:  
\[\pi_{2} = \frac{1}{Den}\]9  
where \(D_{on}\) and \(D_{off}\) are the delay needed to initiate a new data session in the idle and sleep states, respectively. Since there is tradeoff between energy consumption and cumulative average delay, we define a cost as a weighted sum of energy consumption and cumulative average delay in order to analyze the tradeoff as follows:  
\[\pi_{3} = \frac{P_{23}}{Den}\]0  
where \(w_{energy}\) and \(w_{delay}\) are weight factors for energy consumption and cumulative average delay, respectively. The approach of using a weighted sum is used widely in the literature in order to compare the effect of factors that have different units. Finally, the low-power radio signaling load is obtained as:  
\[\pi_{3} = \frac{P_{23}}{Den}\]1
## 2 Materials and Methods  
### Description of the Study Area  
The study area was selected as the Rajmahal Hills, situated in the eastern portion of India (the easternmost part of the Chotanagpur Plateau) (Figure 1). Rajmahal Hills is prolonged across the four districts (i.e., Sahibganj, Pakur, Godda, and Dumka) of the Jharkhand state. Rajmahal Hills was formed due to volcanic eruption in the Mesozoic era of the Jurassic period, ~0.1 billion years ago. The Rajmahal Hills series is extended over ~2600 km\({}^{2}\), predominately enclosed with vegetation (~2000-2200 km\({}^{2}\) area). The major vegetation types of Rajmahal Hills belong to the deciduous broadleaf forest (DBF) followed by savannas (SV), natural vegetation (NV), woody savannas (WSV), and mixed forest (MF) [27; 40]. In the study regions, broadly three seasons--winter: October to February; monsoon: June to September; and summer: March to May--are recognized as having a significant impact on the vegetation growth cycle [40]. Typically, the summertime and wintertime air temperatures range from 10 to 20 \({}^{\circ}\)C, respectively. Over 75% of the rainfall occurred during the monsoon season, with an average annual rainfall of 1500 to 1700 mm [40].  
The large extent of vegetation coverage has various socio-ecological importance for the regions, including local climate governance. However, vegetation coverage over the Rajmahal Hills has been remarkably disturbed in recent decades due to large-scale stone quarrying/mining activities and other human interventions (timber cutting, settlement encroachments, construction works, etc.) [27]. Rajmahal Hills is highly enriched in mineral resources, such as coal reserves, kaolin, bentonite, and plant fossils of the Jurassic period [45]. The abundant availability of mineral resources seriously threatened the vegetation cover and land use dynamics due to mining activities over the Rajmahal Hills.
## 1 Introduction
The derived analytical results show the importance of damper placement regarding the multiple instable modes and multiple damper positions within the BHA. Due to the large number of variable parameters such as the critical mode (frequency and mode shape), the position of the damper and the design of the damper, analytical solutions with regard to the effective damping range of the damper are necessary for an effective design and optimization of dampers in drill string dynamics. Therefore, as in [22], analytical and semi-analytical solutions regarding the effective damping range of the dampers must be determined. For nonlinear dampers, various methods to approximate nonlinearities exist. The harmonic balance method [23; 24; 25] is one possibility to approximate nonlinear parts of a differential equation that has been used for drill string dynamic [22]. Especially for tuned systems like nonlinear tuned mass dampers, methods like the Multiple Scales (MS) [26] or Lindstedt-Poincare (LP) [27] are suitable approximation options. Figure 1: Nonlinear torque characteristic at the bit cf. [8]. Multiple Scales Lindstedt-Poincare (MSLP) is the rather new combination of MS and LP to solve nonlinear systems [28]. In the following, the MSLP method is used to determine suitable analytical solutions for various nonlinear tuned damper concepts to reduce high-frequency torsional vibrations within the drill string. First, a suitable finite element (FE) model of a drill string is developed and reduced to one modal degree of freedom representing a critical torsional mode. This minimal model is extended by two tuned nonlinear damper concepts. Similar to a tuned mass damper, the basic structure of both nonlinear dampers consists of an inertia mass that is connected by a linear spring and linear damper to the structure. One nonlinear damper has an additional cubic nonlinear stiffness, and the other, a cubic nonlinear stiffness and a friction contact. Second, the resulting nonlinear models are solved using MSLP. It is shown that in this specific case MSLP is more accurate than other methods, like the MS method. Third, the parameter influences are determined, and the analytical solutions are transferred to a self-excited drill string vibration to realize a robust and optimized design. Finally, the analytical results are compared with time domain simulations of self-excited drill string vibrations using a reduced FE drill string model. Furthermore, influences and design specifications for drill string vibrations are discussed.