esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Three-Dimensional Modelling by the SfM-MVS Approach
### Images Acquisition in SfM and MVS Approach  
SfM and MVS approaches have become very popular since they can provide fully automated 3D modelling for arbitrary images without any prior knowledge or onsite measurements. Indeed, starting from a collection of stereoscopic images, it is possible to build a 3D point cloud. Figure 8 shows an example of a 3D reconstruction where, for each captured image, location and file name are reported.  
The standard error \(\sigma_{X,Y,Z}\) of the X, Y, and Z object coordinates of a generic 3D point may be evaluated by the following relation [78]:  
\[\sigma_{X,Y,Z}=\frac{q\,Z}{c\sqrt{k}}\sigma_{p_{\xi}}, \tag{7}\]  
where  
\(q\)--design factor expressing the strength of the camera network (generally between 0.4 and 2);  
\(c\)--focal length;  
\(k\)--number of images used to determine the same point;  
\(\sigma_{p_{\xi}}\)--measurement precision of image coordinates.  
In order to obtain a high-quality three-dimensional model, the base-to-depth ratio (B/D) must be increased during image acquisition, and a set of images with converging rather than parallel optical axes must be achieved [78].  
In general, the SfM-based approach offers enormous redundancy to compensate for the potentially modest loss of geometric strength. Consequently, in UAV photogrammetry, in order to create a robust and reliable network, it might be useful to create parallel flight planes at different heights [79]. For dealing with SfM problems in computer vision, projective geometry is a fundamental tool, especially in multiple-view geometry. For this reason, before explaining the SfM and MVS process, a brief introduction to epipolar geometry is illustrated.
## 2 Materials and Methods
### Participants  
The participants were recruited from dementia networks of Mental Healthcare (GGzE) in Eindhoven, the Netherlands, between November 2018 and July 2019. Potential participants were made aware of the possibility of participating in this study by their personal GGzE care professionals. They either contacted the researcher themselves if they wanted to participate, or their care professional contacted the researcher. If the potential participants had shown interest, they were invited by the investigator of this study. She provided them with both written and oral information about the study aim, risks, benefits, privacy and rights of participants. If they still wanted to participate one week after the first contact, a home visit meeting took place between the participants and the investigator. Instructions were provided, questions could be asked, informed consent was signed and baseline measurements were taken.  
A total of four participants with a diagnosis of dementia were recruited. Three of them were included in the study based on the criteria below, and one participant was excluded because criteria '(iv)' and '(v)' were not met.  
In order to be eligible to participate in this study, a person with dementia had to meet all of the following inclusion criteria:  
1. Diagnosed with any type of dementia; in terms of DSM-5 [40] neurocognitive disorder (ii) Age > 65 years
2. Living at home with an informal caregiver
3. A score of 1 or higher on a minimum of 3 items of the Katz Index ('limited help needed')
4. A score of 1 or higher on a minimum of 3 items of the Lawton IADL ('limited help needed')  
Exclusion criteria for this study were:
5. Ocular/visual problems, e.g., ophthalmic abnormality that greatly impedes light perception, e.g., dense bilateral cataracts
6. Pre-existing (severe) psychiatric problems (e.g., bipolar disorder, psychosis)
7. Incapacitated according to an objective expert
8. Bedridden
9. Palliative treatment or terminally ill  
The main characteristics of each of the three participants (persons with dementia) are listed in Table 1.  
Figure 1: Schematic representation of the research design.
## 3 Elicitors and Their Classifications  
Elicitors protect plants against biotic and abiotic stresses by mimicking the plant's metabolic responses triggered by pathogen infection [79; 80; 81]. Elicitors do not kill pathogens but cause plants to stimulate their innate resistance to subsequent attacks. This response of plants against subsequent attacks has been termed differently by several researchers [80; 82; 83]. These include induced systemic resistance (ISR) or systemic acquired resistance (SAR), which involves the accumulation of phytoalexins and pathogen-related proteins and the thickening of plant cell walls [5; 83]. According to [PERSON] et al. [82] and [PERSON] et al. [84], SAR is associated with pathogens, while ISR is related to beneficial microorganisms. Compared to conventional agrochemicals, elicitors are eco-friendly and non-toxic. Moreover, a low concentration is sufficient to enhance the synthesis of secondary metabolites and protection against a wide array of pathogens [85].  
Elicitors are classified differently based on their origin (plants or microorganisms), molecular structure (general or specific), and nature but are generally grouped under biotic and abiotic [5; 7; 81; 83]. All elicitors originating from microorganisms are biotic, and depending on the stress type, they can be classified either as endogenous or exogenous. Physical and chemical elicitors are abiotic in nature and have no biological origin. [PERSON] and [PERSON] [5] further categorized elicitors based on their structures as general and specific elicitors. While general elicitors can induce defense in host and non-host plants, specific elicitors can only trigger defense in host plants. Precisely for specific elicitors, the pathogen and host plant have complementary pairs of genes. Thus, the infectious gene present in the pathogen triggers resistance only in a host plant with the corresponding complementary gene [5]. That means the absence of the complementary gene in the plant will result in disease. However, the relative nature of general elicitors is restricted and not recognized by all plants. Plant hormones such as jasmonic acid and salicylic acid are also considered elicitors [80]. The various classifications of elicitors are shown in Figure 1.
## 1 Article
###### Abstract  
Subspace clustering methods based on the low-rank and sparse model are effective strategies for high-dimensional data clustering. However, most existing low-rank and sparse methods with self-expression can only deal with linear structure data effectively, but they cannot handle data with complex nonlinear structure well. Although kernel subspace clustering methods can efficiently deal with nonlinear structure data, some similarity information between samples may be lost when the original data are reconstructed in the kernel space. Moreover, these kernel subspace clustering methods may not obtain an affinity matrix with an optimal block diagonal structure. In this paper, we propose a novel subspace clustering method termed kernel block diagonal representation subspace clustering with similarity preservation (KBDSP). KBDSP contains three contributions: (1) an affinity matrix with block diagonal structure is generated by introducing a block diagonal representation term; (2) a similarity-preserving regularizer is constructed and embedded into our model by minimizing the discrepancy between inner products of original data and inner products of reconstructed data in the kernel space, which better preserve the similarity information between original data; (3) the KBDSP model is proposed by integrating the block diagonal representation term and similarity-preserving regularizer into the kernel self-expressing frame. The optimization of our proposed model is solved efficiently by utilizing the alternating direction method of multipliers (ADMM). Experimental results on nine datasets demonstrate the effectiveness of the proposed method.  
high-dimensional data clustering; subspace clustering; kernel methods; block diagonal representation; similarity preservation
## 5 Discussion
### Favorable Areas of Caprocks  
The sealing ability of the main structural units was analyzed and favorable areas of the caprock were identified, with the actual drilling exploration as a constraint (Figure 12).  
The thickness map shows that the gypsum-salt rocks in the Awati Sag are thick, deeply buried, widely distributed and continuous. Meanwhile, tectonic movements in this area are mainly characterized by overall uplift and subsidence, and faults are not developed. Therefore, it can be inferred that this area is a favorable area for caprocks. Bachu Uplift has developed gypsum-salt rocks, with the greatest thickness in the northern region. However, wells ST 1 and F 1, located in the northern part of the Bachu Uplift, have poor preservation conditions, due to the development of multiple faults which penetrate the caprock. In the Tazhong uplift, The Middle Cambrian gypsum-salt rocks are mostly developed in the northern and southern regions, and the oil- and gas-exploration breakthrough in the well  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Well Name**} & \multirow{2}{*}{**Lithology**} & \multirow{2}{*}{**Cumulative**} & \multirow{2}{*}{**Single-Layer**} & \multirow{2}{*}{**Weight of**} & \multirow{2}{*}{**Fracture**} & \multirow{2}{*}{**Continuity**} & \multirow{2}{*}{**Diagenesis**} & \multirow{2}{*}{**Couprchesive**} & \multirow{2}{*}{**Couprchesive**} \\  & & & & **Thickness** & & **Formation** & & & & Weight \\ \hline H 4 & 3.5 & 3.5 & 3.5 & 2.6 & 3 & 2.5 & 4 & 3.24 & Very good \\ TC 1 & 1 & 1 & 2 & 1 & 3 & 1 & 4 & 1.75 & Fair \\ F 1 & 3.2 & 3.5 & 2.8 & 2 & 0.1 & 2 & 4 & 2.5 & Poor \\ BD 4 & 3.5 & 3.5 & 3 & 4 & 3 & 3 & 4 & 3.43 & Very good \\ K 2 & 3.5 & 2.5 & 2.5 & 2.5 & 3 & 3 & 4 & 2.98 & Good \\ T 1 & 3.8 & 2.3 & 2.9 & 3 & 3 & 3 & 4 & 3.14 & Very good \\ H 6 & 2 & 2 & 2 & 1 & 3 & 1 & 4 & 2.1 & Good \\ BT5 & 3.6 & 2.5 & 2.2 & 2.5 & 3 & 3.2 & 4 & 2.97 & Good \\ MB 1 & 3.5 & 2.5 & 2.8 & 2.2 & 3 & 3 & 4 & 2.98 & Good \\ ZS 1 & 2.5 & 2 & 2 & 1 & 3 & 2 & 4 & 2.3 & Good \\ ZS 5 & 3 & 1.8 & 1.8 & 1 & 3 & 2 & 4 & 2.34 & Good \\ ST 1 & 3 & 2.8 & 2.2 & 2.3 & 0.5 & 1 & 0.5 & 1.92 & Poor \\ Z 4 & 3.5 & 3.8 & 3.8 & 2.8 & 3 & 3 & 4 & 3.41 & Very good \\ LT 1 & 3 & 2.5 & 4 & 2 & 4 & 3 & 4 & 3.18 & Very good \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comprehensive evaluation results of sealing ability of Middle Cambrian caprock in the Tarm Basin.  
Figure 12: Distribution of favorable areas of the Middle Cambrian caprock in the Tarm Basin.  
ZS 1 has confirmed that the caprock in this area has good sealing properties. In the Tabei Uplift, the Middle Cambrian gypsum-salt rocks are not developed. The well LT 1 has revealed that its lithology is gypsiferous dolomite and limestone, and its single layer can reach 47.2 m. Under certain depth conditions, gypsiferous dolomite also has good sealing ability. Therefore, this is a favorable area for the development of caprocks.
## 3 Results and Discussion
### Water Activity, Synresis, Viscosity and Particle Sizes
However, a viscosity decrease was observed for all samples during cold storage, which can be attributed to proteins and carbohydrates hydrolysis. It should be pointed out that on day 28, the viscosity of the WFG2 sample (1501.24 \(\pm\) 0.06 Pa\(\cdot\)s) was approximately 45-fold higher than the viscosity of whey (33.52 \(\pm\) 0.10 Pa\(\cdot\)s) (\(p<0.05\)). The largest particle size during storage was noted for the WMP sample, followed by the W sample. As can be seen in Table 3, the addition of FG caused a significant decrease in particle size (\(p<0.05\)). The enhanced viscosity of the samples can be linked to FG's \"weak gel-like\" properties and the ability to form networks [23; 60]. Moreover, it was reported that FG plays the role of a thickening agent, which is able to increase the viscosity and decrease the particle size of emulsions [26]. Electrostatic interactions produce profound effects on biopolymer interactions betweenthe charged species [60]. In the case of a protein-anionic polysaccharide biopolymer system, electrostatic interactions can be attractive or repulsive. When the pH of the solution is greater than the pI of the protein, repulsive interactions will dominate, since both biopolymers have negative charges. However, attractive electrostatic interactions occur when the pKa of the anionic polysaccharide is less than the pH of the solution, and the pH, in turn, is less than the pI of the protein [60]. As reported, whey protein's isoelectric point is approximately at pH = 5.2, and below this value, it will be positively charged, whereas polysaccharide (FG) is negatively charged [27; 64]. In an acidic environment, FG possesses a negative charge [27]. Thus, presumably, FG and whey proteins interacted and formed complexes, resulting in enhanced textural properties, syneresis and decreased hydrodynamic properties.
# Cooperative Jamming-Based Physical-Layer Group Secret and Private Key Generation
## 3 Group Secret and Private Key Generation Scheme
### Key Agreement  
After the channel measurement stage, there is still no common randomness among the group members. Slepian-Wolf coding [25] is further employed in the key agreement stage, enabling group members to generate SKs and PKs. The key agreement is based on a two-step public discussion:  
1. The first step: The first step involves the relay utilizing Slepian-Wolf coding to transmit specific bits of helper data derived from its observations over the public channel. This process allows [PERSON] and [PERSON] to independently recover the relay's observations \(\tilde{Y}_{R}^{(1)}\) and \(\tilde{Y}_{R}^{(2)}\), respectively, and subsequently obtain estimates of the reference channel \(G_{1}\).
2. The second step: The second step involves [PERSON] employing Slepian-Wolf coding to send particular bits of helper data based on her observations through the public channel. This allows [PERSON] and [PERSON] to recover [PERSON]'s observation \(\tilde{Y}_{A}\) by leveraging the correlated estimates from the reference channel \(G_{1}\). Subsequently, the legitimate members establish a group SK or PK.  
In the following subsections, the details of the aforementioned two public discussion steps are provided.
# Data Imputation Based on Retrieval-Augmented Generation
## 4 Experimental Verification
### Evaluation of Re-Ranker Effectiveness
#### 4.3.2 Baseline Model  
**Re-ranker Baseline:**We compare our re-ranker with several baseline methods, selecting representative re-ranker models for evaluation. The experiments were conducted using the same datasets and evaluation metrics. Through these comparisons, we gain a more comprehensive understanding of the performance of our re-ranker and its position relative to the state-of-the-art methods. The baselines are introduced as follows:  
* monoBERT [104]: This is a model designed for query-based paragraph re-ranking, utilizing the BERT model. This process involves concatenating the query with the paragraph text, then using the [CLS] vector to compute the re-ranking score. To compare monoBERT with our re-ranker, we use the provided parameters to initialize it and fine-tune it on our training data.
* RoBERTa-LCE: This introduces a new loss called Local Contrastive Estimation (LCE) to train a more robust re-ranker. We fine-tune the RoBERTa-base model using the LCE loss on our training data for a fair comparison.
* GPT-3.5 with List: Recent studies have highlighted the exceptional performance of LLMs in relevance re-ranking tasks. Thus, we use GPT-3.5 as a re-ranker employing the list method. Specifically, GPT-3.5 is fed a set of retrieved tuples and an incomplete tuple, instructing the model to generate a re-ranked list based on relevance to the incomplete tuple. To manage input limitations, we adopt a sliding window strategy with a window size of 30 and a step size of 14.
## 2 Design of the State Vector
### Error of the State Vector  
Let \(\mathbf{\tilde{X}}\) denote the expectation of \(\mathbf{X}\), and let \(\mathbf{\widetilde{X}}\) denote the error between the expectation, and the measurement, written as \(\mathbf{\widetilde{X}}=\mathbf{X}-\mathbf{\hat{X}}\). \(\mathbf{\widetilde{X}}\) with 28 elements, is described as follows:  
\[\mathbf{\widetilde{X}}=\Big{\{}\Delta\mathbf{p}_{ic}{}^{T}\Delta\mathbf{v}_{c}{}^{T}\Delta \mathbf{v}_{i}{}^{T}\delta\mathbf{\theta}_{ic}{}^{T}\delta\mathbf{\theta}_{i}{}^{T}\Delta \mathbf{\omega}_{c}{}^{T}\Delta\mathbf{\omega}_{i}{}^{T}\Delta\mathbf{b}_{ai}{}^{T}\Delta \mathbf{b}_{ai}{}^{T}\Delta\lambda\Big{\}}^{T}, \tag{8}\]  
With the small-angle assumption, when the rotation angle corresponding to a unit quaternion \(\overline{\mathbf{q}}\) is very small, the error of \(\overline{\mathbf{q}}\) is written as \(\delta\overline{\mathbf{q}}=\left[q_{0},\delta\mathbf{q}{}^{T}\right]^{T}\approx\left[ 1,\frac{1}{2}\delta\mathbf{\theta}^{T}\right]^{T}\)[23]. Because the algorithm operates at a high update rate, the high-order terms that yieldnegligible computational results are disregarded, such as \(\delta\mathbf{q}\cdot\Delta\mathbf{\omega}\), \(\delta\mathbf{q}\cdot\delta\mathbf{q}\), and \(\delta\mathbf{q}\cdot\mathbf{n}\). Then, the description of the derivative \(\widetilde{\mathbf{X}}\) can be inspected. According to Equation (2), the derivative of the coupled translation error is as follows:  
\[\Delta\dot{\mathbf{p}}_{ic}=\mu_{c}\Delta\mathbf{v}_{i}+\lambda(1-\mu_{c})\mathbf{R}_{c}^{ i}\Delta\mathbf{v}_{c}, \tag{9}\]  
The expectation of the linear acceleration of the IMU is \(\hat{\mathbf{a}}_{i}=\mathbf{a}_{mi}-\hat{\mathbf{b}}_{ai}\), and \(\Delta\mathbf{b}_{ai}=\mathbf{b}_{ai}-\hat{\mathbf{b}}_{ai}\). According to Appendix A, the rotation can be reformed as \(\mathbf{R}_{i}=\mathbf{R}(\overline{\mathbf{q}}_{i})\approx\hat{\mathbf{R}}_{i}(\mathbf{I}_{3}+ \lfloor\delta\mathbf{\theta}_{i}\times\rfloor)\) under the small angle assumption. After neglecting the high-order terms, the error of the linear velocity of the IMU is written as follows:  
\[ \begin{array}{l}\Delta\dot{\mathbf{v}}_{i}=\mathbf{a}_{i}-\hat{\mathbf{a}}_{i}=\mathbf{R}_{ i}(\mathbf{a}_{mi}-\mathbf{b}_{ai}-\mathbf{n}_{\mathbf{a}_{mi}})-\mathbf{g}-\hat{\mathbf{R}}_{i}(\mathbf{a}_{mi}- \hat{\mathbf{b}}_{ai})+\mathbf{g}\\ \approx-\hat{\mathbf{R}}_{i}\lfloor\hat{\mathbf{a}}_{i}\times\rfloor\delta\mathbf{\theta} _{i}-\hat{\mathbf{R}}_{i}\Delta\mathbf{b}_{ai}-\mathbf{n}_{\mathbf{a}_{mi}}\\ \end{array} , \tag{10}\]  
For general quaternions and angular velocities, \(\overline{\mathbf{q}}=\hat{\mathbf{q}}\otimes\delta\overline{\mathbf{q}}\) and \(\overline{\mathbf{\omega}}=\hat{\mathbf{\omega}}+\Delta\overline{\mathbf{\omega}}\). By subjecting \(\overline{\mathbf{q}}_{ic}{}^{*}=(\overline{\mathbf{q}}_{c})^{1-\mu_{\omega}{}^{*}} \otimes(\overline{\mathbf{q}}_{i})^{\mu_{\omega}{}^{*}}\) and \((\overline{\mathbf{q}}_{i}^{\mu_{\omega}{}})^{*}\otimes\overline{\mathbf{q}}_{i}^{\mu_ {\omega}{}^{*}}=1\) to Equation (5), the error of the coupled rotation can be deducted as follows:  
\[
## 4 Results
### Assimilation of Sentinel-1 Derived SSM
In addition, irrigation events are not properly detected in March when the crop cover is fully developed. This mainly originated from issues related to the retrieved SSM product at this time. The backscattering mechanism is characterized by a significant contribution of the interaction between the soil and the canopy that the simple water cloud model used to retrieved SSM was not able to simulate correctly [17] leading to poor SSM quality. On average, ~50% of the irrigation events can be detected in a range of 4 to 5 days around the actual irrigation date with a limited Pbias of ~4% and only one false event detected over the season (Table 2).
# Cloud-Based Internet-of-Things System for Long-Term Bridge Bearing Monitoring Using Computer Vision
## 2 Proposed Approach
### Software Development  
In addition to hardware development, an effective software application framework is essential for controlling stable event-triggered sensing operations and managing data transfers. Figure 4 illustrates the flowchart of the hardware and software application framework designed for the proposed system. When the sensor is triggered by the timer, the MCU sets the latch to hold the power, initialize, and read the battery voltage (Vbat) first. When the battery level is lower than 3.6 V, which is 50% of the total capacity, the MCU sets the RTC alarm to preset hours and sets the sensor to inactive mode for charging. If the battery level is above 3.6 V, the MCU initializes the LTE modem and communicates with the server to receive configuration parameters such as time interval and time for sleep when the battery is low. Once these commands are received, the MCU sets the RTC schedule alarm values via the I2C bus. The MCU then activates the USB camera to capture an image. After the image is taken, the sensor sends the (1) image, (2) battery voltage, (3) time when sensing started, and (4) sensor ID to the server before the Raspberry Pi powers down.  
Figure 4: Flowchart of operation of hardware and software.  
Figure 3: Front & Back View of Designed PCB.
# Estimation of Impact Loads Transmitted to Vibro-Ripper Housing Using Transfer Path Analysis
###### Abstract  
This study aimed to estimate impact loads delivered to vibro-ripper housing through link modules, together with loads transmitted in various directions. Housing vibration resulting from impact loads generated during the operating condition and frequency response functions were assessed by vibration and modal experiments, respectively. Vibration data and transfer functions were applied in a transfer path analysis (TPA) model to analyze the quantified impact loads transmitted through the key components of the vibro-ripper to its housing. Impact loads derived by TPA for different housing parts were compared with those in the tooth derived from load-cell measurements, validating the TPA method. As a result of the verification, the impact load calculated by the TPA method was 193.7 kN, whereas that from the striking force measured by the load cell was 220 kN, a difference of 12.3%. The results of this study may be important input values for numerical analysis in equipment design and can be used as key data for structural safety evaluation and optimization. In summary, this paper introduces the vibration-based TPA method and considers its applicability to construction machinery exposed to impact vibration and loads.  
vibro-ripper; impact load; vibration experiment; transfer path analysis; load estimation Article
# A Routing Algorithm Based on Real-Time Information Traffic in Sparse Environment for VANETs
## 5 Simulation and Results  
In this section, we evaluate the RTEIT protocol proposed in this paper through NS-3 simulator. SUMO provides tracefile as the simulation mobile scenario. GPSR is the most classic location-based routing, and its results are used as our comparison group. MM-GPSR is an improvement of GPSR, which improves the link quality by limiting the range of \(Q\). In our proposed scheme, link quality is considered and redundant paths are reduced by new parameters. The results of RTEIT are compared with GPSR, MOPR, and MM-GPSR.