esa-sceva/satcom-chunk-collection huggingface dataset:
# Network Lifetime Improvement through Energy-Efficient Hybrid Routing Protocol for IoT Applications
###### Abstract
According to some researchers, the network lifetime can be considered the time elapsed until the first sensor node loses all its energy in the network. In order to optimize the network lifetime, researchers have been working in the direction of optimizing various parameters, namely hop count, path reliability, energy consumption, and so forth. The authors of this paper attempted to improve the network lifetime through the optimization of the routing protocol, hop count, and reliable path. In the proposed approach, the authors used factors such as residual energy, hop count, and the reliable path to the sink in order to maximize the network lifetime. The performance of the proposed approach was validated with various metrics, such as packet delivery, throughput, energy consumption, and more. The following are the main contributions of the current research:  
* The integration of the GA and PSO algorithms for optimal routing to transfer data from the CHs to the BS. The authors of this paper propose a PSOGA-based routing protocol that works towards the optimization of the network lifetime of WSNs. The proposed protocol determines a score based on the residual energy, buffer, hop count, and reliable path. The evaluated score is used to determine the next hop node in the path. * The authors redefined network lifetime using three important aspects of a network, namely the number of alive nodes, sink connectivity, and successful packet delivery. Further, a performance evaluation framework was employed based on the redefined network lifetime to evaluate the performance of routing protocols. * The proposed approach was implemented and tested for different scenarios with varying grid sizes and node densities. * The competence of the proposed approach was established by deploying the BS at the edges of the grid. The paper is structured as follows: Section 2 presents related works; Section 3 discusses the proposed PSOGA model; Section 4 discusses the proposed hybrid PSOGA model; Section 5 presents the network setup; Section 6 presents a comparison between the results of the proposed PSOGA model with the PSO and LEACH protocols; Section 7 concludes the paper with suggestions for possible avenues of future research.
# Applications of Raman Microscopy/Spectroscopy-Based Techniques to Plant Disease Diagnosis
## 7 \"Raman Shifts\" in Cell Biology  
\"Raman shifts\" are shifts between the energy of the incident photon of laser light and the energy of the photon which has interacted with the molecule under analysis, allowing for the exact assessment of vibrations in the normal functional groups of biochemical compounds, such as proteins, lipids or nucleic acids [46; 61]. This makes it possible to study their conformation and changes in a non-invasive way. The distinctive features of the method include its chemical selectivity, biological molecule sensitivity, noninvasive manner and applicability to conducting in situ analyses of living single cells [53; 60]. So, the energy of the scattered photon is shifted by the energy of the vibrational mode, resulting in a characteristic Raman shift, which is a fundamental concept in Raman spectroscopy, a technique used to analyze the molecular structure of materials. In summary, the \"Raman shift\" (i) is a unique \"fingerprint\" for each molecule; (ii) provides information about the molecular structure, including the arrangement of atoms, bonds and functional groups;(iii) helps assign specific vibrational modes to specific molecular structures, allowing for a deeper understanding of the molecular dynamics; (iv) is sensitive to subtle changes in the molecular structure, making it an effective tool for detecting small changes in molecular conformation or interactions; (v) can be used to monitor changes in molecular structure in real time, making it useful for applications such as process control and monitoring; (vi) can be used to quantify the concentration of specific molecules or species, allowing for accurate chemical analysis; (vii) can been used to study biological processes, such as protein-ligand interactions and enzyme activity [37].
# Numerical Modeling of Crack Growth under Mixed-Mode Loading
###### Abstract  
The aim of this paper is to simulate the propagation of linear elastic crack in 3D structures using the latest innovation developed using Ansys software, which is the Separating Morphing and Adaptive Remeshing Technology (SMART), in order to enable automatic remeshing during a simulation of fracture behaviors. The ANSYS Mechanical APDL 19.2 (Ansys, Inc., Canonsburg, PA, USA), is used by employing a special mechanism in ANSYS, which is the smart crack growth method, to accurately predict the crack propagation paths and associated stress intensity factors. For accurate prediction of the mixed-mode stress intensity factors (SIFs), the interaction integral technique has been employed. This approach is used for the prediction of the mixed-mode SIFs in the three-point bending beam, which has six different configurations: three configurations with holes, and the other three without holes involving the linear elastic fracture mechanics (LEFM) assumption. The results indicated that the growth of the crack was attracted to the hole and changes its trajectory to reach the hole or floats by the hole and grows when the hole is missing. For verification, the data available in the open literature on experimental crack path trajectories and stress intensity factors were compared with computational study results, and very good agreement was found.  
crack growth; stress intensity factors; mixed-mode; failure analysis; LEFM; ANSYS
## 4 Features and Issues of Existing Applications
### Visual Representation
Examples include Siemens Tecnomati  
Figure 10: Visual representation of existing applications. Dassault Delmia Human. Such software supports the building of a simulation model for workplace production processes and human motion. These models can evaluate performance measures or ergonomic indices by performing simulation experiments. Significantly, the motion of a human worker can be simulated by providing the data collected from a workplace, which has led several researchers to use human motion simulation software as a visualization tool [8; 17; 27; 40; 41; 42]. Despite their high prices, commercial human motion simulation software provides novel ways to visualize and analyze human motion. Furthermore, open-source simulators such as Gazebo can also be used to provide visual representations [35]. Sometimes, DT applications adopt the MoCap system, which is supplied as a package that includes both hardware devices and software tools for processing the collected data. In this case, the visual representations of human workers can be obtained using hardware vendor-provided software.
## 4 Discussion
### Recommendations for Clinical Practice  
This review supports the use of PBMT as an adjunct in Fibromyalgiagia treatment. Most studies employed devices using specific wavelengths between 630-850 nm, particularly 660 nm (red) and 808-850 nm (NIR-I), known for their effective tissue penetration. Single-wavelength systems, especially in the 800-850 nm range, showed the most consistent results. Although NIR-II wavelengths allow deeper penetration, they were not used in the reviewed studies and cannot yet be recommended.  
Furthermore, both localized and whole-body PBMT applications yielded positive outcomes. Localized treatments typically targeted tender points, such as the trapezius or cervical region, while whole-body devices, like NovoTHOR(r) beds, provided broader systemic effects. The latter approach appeared to produce more sustained improvements in pain and quality of life, likely due to its influence on nociceptive pathways and autonomic regulation. Consequently, whole-body PBMT may be preferred, when available, particularly for patients with widespread symptomatology.  
Moreover, standard treatment protocols generally involved two to three sessions per week over a period of 4 to 12 weeks, delivering doses between 4 and 60 joules per site, or following manufacturer-specific guidelines. Given PBMT's favorable safety profile and non-invasive nature, its combination with aerobic or resistance exercise is encouraged, as this may enhance clinical outcomes related to pain modulation, physical function, and psychological well-being. Such combinations align with contemporary recommendations for multimodal management of Fibromyalgiagia syndrome.  
In order to ensure the effectiveness and individualization of care, clinicians should monitor patient progress using standardized assessment tools. Instruments such as the VAS, the FIQ, or the SF-36 are valuable for tracking changes in pain intensity, physical capacity, and health-related quality of life, and for guiding adjustments to treatment parameters as needed. Lastly, patient education plays a pivotal role in adherence and engagement. Explaining the underlying mechanisms of PBMT, such as enhanced mitochondrial activity, increased ATP production, and reduced oxidative stress, can help demystify the intervention, reinforce its legitimacy, and foster patient commitment throughout the course of therapy.
# Hierarchical Modeling for Medical Visual Question Answering with Cross-Attention Fusion
## 3 Methodology  
Figure 2 illustrates the overview of our proposed hierarchical medical visual question answering model. First, we clarify that each medical image corresponds to a complete medical report. The medical image and a question extracted from the report form each sample, where the question is categorized into three hierarchical levels based on granularity. The method primarily leverages the varying granularity levels of medical questions to prompt the medical image, then hierarchically inputs the questions from the current sample into distinct answer decoders for prediction. This enhances the visual reference space, guiding the model to focus on specific image regions for more accurate answers.  
To achieve this goal, we first encode the image from an image-question pair using an image encoder to obtain \(V_{I}\). The hierarchical prompts corresponding to the medical questions of each image are then encoded by a text encoder. These prompts--\(p_{1}\), \(p_{2}\), or \(p_{3}\), depending on the question's hierarchical level--generate prompt embeddings \(V_{p}\). The image encoding \(V_{I}\) and prompt encoding \(V_{p}\) are fed into an attention-based alignment module for preliminary fusion, producing image-prompt features \(F_{p}\) to strengthen the visual reference space. The medical question from the sample is input into the same text encoder to obtain question features \(V_{q}\). Finally, the question features \(V_{q}\) and image-prompt features \(F_{p}\) are processed by hierarchical cross-attention answer decoders for multi-modal fusion and final answer prediction. The subsequent sections provide detailed descriptions of each module.  
Figure 2: Overview of HiCA-VQA architecture. It comprises: (1) Hierarchical prompting module that generates prompts for questions at different levels. (2) Image encoder. (3) Text encoder. (4) An Alignment Module is responsible for aligning image and prompt features. (5) Hierarchical Answer Decoders that fuse multi-modal features for final prediction.
# Improved Anti-Collision Algorithm for the Application on Intelligent Warehouse
## 4 Performance Analysis
### Total Number of Slots  
Supposing that there are n tags in the system, and each tag has \(k\) bits. Because GMQT uses a three-bit grouping strategy, the search tree is an 8-ary tree. The root is in level 0 and the highest level of a tree is \(\lceil k/3\rceil-1\) for a perfect 8-ary tree. Since the GMQT algorithm can accurately identify the group number and the collision bit, there is no idle slot, and the total number of slots N\({}_{cy}\) = N\({}_{col}\)+N\({}_{iden}\); here, N\({}_{col}\) denotes the number of collision slot, and N\({}_{iden}\) denotes the number of identification slots, so N\({}_{iden}\) = n:  
\[\text{N}_{col}=\sum_{H=0}^{\lceil k/3\rceil-1}\sum_{j=1}^{2^{3H}}P_{col}(H). \tag{1}\]  
Because there is no idle slot, the probability of collision slots can be expressed as:  
\[P_{col}=1-P_{iden}. \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Slot**} & \multicolumn{2}{c}{**QUERY**} & \multicolumn{2}{c}{**Bit String**} & \multicolumn{2}{c}{**Stack**} \\ \cline{2-9}  & **Prefix** & **Group** & & **Status** & **id** & **Group Mapping** & **PS** & **GS** \\ \hline
1 & \(\varepsilon\) & & Tag A,B,C,D,E,F & Collision &?????????? &???? & 00? & 00,01,11 \\
2 & \(\varepsilon\) & 00 & Tag A,B & Collision & 000??0?0 & 0000,0001 & 01,11 \\
3 & \(\varepsilon\) & 01 & Tag C,D,E & Collision &???????? & 0000,0001,001,110 & 11 \\
4 & \(\varepsilon\) & 11 & Tag F & Identify F & 01001001 & 0000,0001,001,110 & \\
5 & 0000 & Tag B & Identify B & 1000 & 0100 & 0001,001,110 & \\
6 & 0001 & Tag A & Identify A & 0010 & 0010 & 001,110 & \\
7 & 001 & Tag C,E & Collision &?00?? & 070? & 110,001000,001100 & \\
8 & 110 & Tag D & Identify D & 11111 & 0001 & 001000,001100 & \\
9 & 001000 & Tag E & Identify E & 00 & 001100 & \\
10 & 001100 & Tag C & Identify C & 11 & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: The identification procedure of six tags.  
Let \(\text{P}_{iden}(H)\) denote the probability that a node in level H is selected as an identification slot. \(\text{P}_{iden}(H)\) corresponds to the probability that only one tag ID has the same prefix of (\(k\)\(-\)\(3H\)) bits and (\(n\)\(-\)\(i\)) tag IDs have different prefixes. Thus, \(\text{P}_{iden}(H)\) can be derived as:  
\[P_{iden}(H)=\binom{n}{1}\times\frac{\binom{2^{k-3H}}{1}}{\binom{2^{k}}{1}} \times\frac{\binom{2^{k}-2^{k-3H}}{n-1}}{\binom{2^{k-1}}{n-1}}. \tag{3}\]  
In addition to the slot of the query prefix, the group number needs to be queried separately, so the total number of slots can be expressed as:  
\[G(n)=N(n)+N(group), \tag{4}\]  
where \(N(group)\) represents the number of slots for querying group number. After each command, the reader can infer the possible groups or prefixes directly according to the collision bits. Tags are divided into four groups at most, so we can get inequalities as follows:  
\[0\leq N(group)\leq 4N(n). \tag{5}\]
## 5 Discussion  
The obtained results indicate that compatibility between 5G and FS depends on the protection criteria that have been chosen. Traditionally, \(I/N\) criteria is used in many studies, and may be considered as a conservative approach. At the same, the \(I/N\) criteria is pretty stringent and does not always reflect the real-case scenario. Given that fixed service point-to-point stations have fixed locations, and their antenna patterns are pointed towards each other's main lobes, it is possible to calculate the wanted signal with high precision. Therefore, using \(C/I\) criteria allows us to consider a more realistic case. \(C/I\) criteria largely depends on the modulation order and coding rate used [22]. In this study, \(C/I\) criteria was used for the highest order modulation (64 QAM) that is supported by FS, in the 6425-7125 MHz frequency band. This means that for the lower modulation orders, \(C/I\) criteria would be lower than 30 dB and, thus, for lower modulation orders, the FS system would have an even larger \(C/I\) margin.  
It should be kept in mind that for cross-border scenarios, it is very rare when an FS station is oriented with its main lobe towards the border, a side-lobe interference scenario is much more common. At the same time, main lobe scenarios may occur as well and taking into account that the typical link length of FS stations is 38 km for the 60 m height antennas, and 10 km for the 20 m height antennas, it can be concluded that protection distances based on \(C/I\) are enough to provide compatibility in cross-border scenarios. Figure 12 below illustrates why protection distances lower than link length provide compatibility between FS and 5G.  
Figure 11: \(C/I\) curves for adjacent channel interference when 5G network and FS receivers are located in the same area.  
We would also like to note, that using territorial or frequency separations are not the only ways to achieve compatibility between 5G and FS in cross-border scenarios. Many other techniques can be applied, for example, the administrations may agree on sectorial restrictions of 5G BS; in particular, azimuths and elevation angles of 5G antennas may be restricted in the direction of the neighboring administration.  
At the national level, 5G operators may agree with an FS operator to change its FS station to the station that operates in another frequency range; such substitution can be compensated by the involved 5G operator and is a very common way to solve compatibility issues at the national level. Therefore, regulators are not limited to the approaches proposed in this paper, although other solutions require the analysis of a concrete case.
# Improving Bundle Routing in a Space DTN by Approximating the Transmission Time of the Reliable LTP
## 2 Related Work  
The Licklider transmission protocol (LTP) can serve as a convergence layer adapter that can carry BP data bundles. The LTP supports both reliable and best-effort transmissions. In the former case, reliability is achieved through bulk retransmissions of lost segments, which helps to reduce at minimum the number of control message exchanges and avoid the long propagation delay penalty of space channels [6]. Several studies have analyzed the impact of protocol parameters, such as the segment length [7,8] and several enhance ments that target the reliability of LTP signaling [9] and the application of Reed-Solomon codes [10]. Prior studies have looked into the block transmission time [11; 12; 13]. In a prior work, an analysis of the LTP's stochastic behavior yielded an accurate modeling of the reliable transmission time of LTP blocks and the approximation used in this paper [5].  
The routing problem as an optimization objective is related to the socially optimal and selfish routing discussion concerning partial optimality [14], and a related analysis was presented by [PERSON] et al. [15] by using sheaves for a DTN model. With CGR [16; 17] being a routing standard for DTNs, significant efforts have been devoted to studying this protocol's performance, mainly through experimentation with the reference ION-DTN implementation [11; 18; 19] and the high-rate DTN [20]. CGR attempts to minimize the delivery time of bundles at their final destinations, but extensions have been explored to consider the reliability of the contacts [21] and the verification of the energy capability of the nodes [22]. It has been argued that CGR may suffer from scalability issues, and an alternative approach have been explored [23]. To avoid tying the proposed method to a specific routing algorithm, conclusions are first reached through a protocol and implementation-agnostic method by analyzing the optimal performance with the selected estimation methods for the bundle transmission time. Then, the results are verified while considering CGR as the bundle-routing method.
## Appendix A
Then, the integral along \(R^{\prime}\) splits as follows: \[\int\limits_{0}^{R}H_{0}^{(2)}(kR)J_{0}(kR^{\prime})R^{\prime}dR^ {\prime}+\] \[\int\limits_{R}^{a}H_{0}^{(2)}(kR^{\prime})J_{0}(kR)R^{\prime}dR^ {\prime}\] Using again the formula 5.52.1 in [35], the first integral gives: \[\frac{R}{k}H_{0}^{(2)}(kR)J_{1}(kR),\] and the second integral results in \[J_{0}(kR)\bigg{[}\frac{a}{k}H_{1}^{(2)}(ka)-\frac{R}{k}H_{1}^{(2)}(kR)\bigg{]}.\] Finally, summing up and making use of the Wronskian [34], we obtain: \[b_{n}(\mathbf{r})=-j\frac{4}{k^{2}}+\frac{2\pi a}{k}J_{0}(k|\mathbf{r}- \mathbf{r}_{n}|)H_{1}^{(2)}(ka).\] (A8)We now return to the computation of (A4). \[ \begin{split}\mathbb{A}_{e}[\mathbb{A}_{i}[fE_{i}]]&= \sigma\frac{k^{4}\zeta^{2}}{64}\sum\limits_{n}H_{0}^{(2)}(k|\mathbf{r}_{o}- \mathbf{r}_{n}|)\cdot\\ &\iint\limits_{\Omega}H_{0}^{(2)}(k|\mathbf{r}_{o}-\mathbf{r}|)b _{n}(\mathbf{r})\ d\mathbf{r}.\end{split} \] (A9)  
The coefficients \(b_{n}(\mathbf{r})\) assume the expression (A6), when they are calculated outside the \(n^{th}\) bar, and (A8), when they are calculated inside the \(n^{th}\) bar. So, it is convenient to split the integration over the investigation domain \(\Omega\) as follows:  
\[\iint_{\Omega}=\iint_{n^{th}bar}+\iint_{\Omega-n^{th}bar}.\]  
Then:  
\[ \begin{split}\iint\limits_{n^{th}bar}H_{0}^{(2)}(k|\mathbf{r}_{o} -\mathbf{r}|)b_{n}(\mathbf{r})\ d\mathbf{r}=H_{0}^{(2)}(k|\mathbf{r}_{o}- \mathbf{r}_{n}|)\cdot\\ \cdot\int\limits_{0}^{2\pi}\int\limits_{0}^{a}\biggl{[}-j\frac{4 }{k^{2}}+\frac{2\pi a}{k}J_{0}(kR)H_{1}^{(2)}(ka)\biggr{]}RdRd\theta=\\ =H_{0}^{(2)}(k|\mathbf{r}_{o}-\mathbf{r}_{n}|)\biggl{[}-j\frac{4 \pi a^{2}}{k^{2}}+\biggl{(}\frac{2\pi a}{k}\biggr{)}^{2}H_{1}^{(2)}(ka)J_{1}( ka)\biggr{]},\end{split} \] (A10)  
Again, using the addition theorem applied to the Hankel function  
\[ \begin{split} H_{0}^{(2)}(k|\mathbf{r}_{o}-\mathbf{r}|)=H_{0}^{( 2)}(k|\mathbf{r}_{o}-\mathbf{r}_{n}-\mathbf{R}|)\\ \iint\limits_{\Omega-n^{th}bar}H_{0}^{(2)}(k|\mathbf{r}_{o}- \mathbf{r}|)b_{n}(\mathbf{r})\ d\mathbf{r}=\frac{2\pi a}{k}J_{1}(ka)\cdot\\ \cdot H_{0}^{(2)}(k|\mathbf{r}_{o}-\mathbf{r}_{n}|)\int\limits_{0} ^{2\pi}\int\limits_{a}^{R_{n}(\theta)}H_{0}^{(2)}(kR)J_{0}(kR)RdRd\theta.\end{split} \] (A11)  
where the terms of the index \(m\
eq 0\), whose contribution to the integral is negligible, have been neglected. In (A11), \(R_{n}(\theta)\) represents the distance between the center of the \(n\)-th bar and the point on the dielectric border at angle \(\theta\), as depicted in Figure 2. The related integral can be calculated in closed form [35; 36] as follows:  
\[ \begin{split}\int\limits_{a}^{R_{n}}H_{0}^{(2)}(kR)J_{0}(kR)RdR= \\ =\frac{R_{n}^{2}}{2}\Bigl{[}J_{0}(kR_{n})H_{0}^{(2)}(kR_{n})+J_{1 }(kR_{n})H_{1}^{(2)}(kR_{n})\Bigr{]}+\\ -\frac{a^{2}}{2}\Bigl{[}J_{0}(ka)H_{0}^{(2)}(ka)+J_{1}(ka)H_{1}^{ (2)}(ka)\Bigr{]}.\end{split} \] (A12)  
where for the sake of brevity the dependence of \(R_{n}\) on \(\theta\) was implied.