esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Differential Quadrature Time Element Method
## 2 Basic Rules and Formulations
### DQ Rod and Beam Elements
#### 2.4.1 C\({}^{0}\) DQ Rod Element  
Assume the longitudinal displacement function of a uniform rod element is  
\[u(x)=\sum_{i=1}^{M}l_{i}(x)u_{i} \tag{18}\]  
where \(l_{i}\) stands for Lagrange polynomials, \(u_{i}=u(x_{i})\) is the displacement at the Guass-Lobatto quadrature point, and \(x_{i}\) is the coordinate of the Guass-Lobatto quadrature point. According to DQ rule and Gauss-Lobatto quadrature rule, the total potential energy functional and kinetic energy coefficient functional are  
\[\begin{array}{rl}\prod&=\frac{1}{2}\int_{0}^{L}ES\left(\frac{\partial u}{ \partial x}\right)^{2}\mathrm{d}x-\int_{0}^{L}fu\mathrm{d}x\\ &=\frac{1}{2}\sum\limits_{i=1}^{m}C_{i}^{*}ES\left(\sum\limits_{j=1}^{m}A_{ij} ^{(1)}u_{j}\right)^{2}-\sum\limits_{i=1}^{m}C_{i}^{*}f_{i}u_{i}\\ &=\frac{1}{2}\mathbf{u}^{\mathrm{T}}\mathbf{Ku}-\mathbf{u}^{\mathrm{T}}\mathbf{F}\\ \\ T_{0}=\frac{1}{2}\int_{0}^{L}\rho S\mathbf{u}^{2}\mathrm{d}x=\frac{1}{2}\rho S \sum\limits_{i=1}^{m}C_{i}^{*}u_{i}^{2}=\frac{1}{2}\mathbf{u}^{\mathrm{T}}\mathbf{Mu} \end{array} \tag{19}\]  
where \(E\) and \(\rho\) are the Young's modulus and mass density, respectively; \(S\) and \(L\) are the across section area and length of rod element, respectively; and \(f_{i}\) is the distributed force. The mass matrix \(\mathbf{M}\), stiffness matrix \(\mathbf{K}\), generalized load vector \(\mathbf{F}\) and other variables are given by  
\[\mathbf{K}=ES\mathbf{A}_{S}^{\mathrm{T}}\mathbf{C}_{S}^{*}\mathbf{A}_{s}\mathbf{M}=\rho S\mathbf{C}_{S }^{*}\mathbf{F}=\mathbf{C}_{s}^{*}\mathbf{f} \tag{21}\]  
\[\mathbf{u}^{\mathrm{T}}=\left[u_{1}\;u_{2}\;\cdots\;u_{m}\right] \tag{22}\]  
\[\mathbf{f}^{\mathrm{T}}=\left[f(x_{1}),\;f(x_{2}),\;\cdots\;f(x_{m})\right] \tag{23}\]  
\[\mathbf{C}_{s}^{*}=\mathrm{diag}[C_{1}^{*}\;C_{2}^{*}\;\cdots\;C_{m}^{*}] \tag{24}\]  
where the matrix \(\mathbf{A}_{s}\) is the first order weighting coefficient matrix with respect to space, and \(\mathbf{C}_{s}^{*}\) is the Gauss-Lobatto quadrature coefficient diagonal matrix.
## 3 Results
### Impacts of DSPPs on Preadipocyte and Adipocyte Viability  
We examined cell viability at various times during the cell growth to investigate whether DSPPs hinder the proliferation of preadipocytes.  
According to Figure 3A,B, after 24, 48, and 72 h of incubation, DSPPs (25, 50, and 100 ug/mL) decreased the total number of viable preadipocytes in the pre-confluent and post-confluent phases in comparison to the controls (\(p<\) 0.05). Growth-arrested post-confluent 3T3-L1 cells completed two cycles of mitotic clonal enlargement following stimulation with MDI differentiation media before escaping the cell sequence and communicating adipocyte-specific genes [23]. Intriguingly, at a dose of 100 ug/mL after 72 h, DSPPs reduced MDI-stimulated proliferation in a dosage-dependent manner, leading to approximately 25% less adipocytes. These findings show that DSPPs have an inhibitory impact on cell growth. Moreover, mature adipocytes (D\({}_{8}\)) were subjected to a cell viability experiment to test whether DSPPs have any harmful effects. DSPPs were administered to fully differentiated adipocytes for 24 and 48 h when they were not in the proliferation stage. Treatment with DSPPs at doses of 25, 50, and 100 ug/mL decreased (\(p<\) 0.05) the number of viable cells (Figure 3C). Measuring apoptosis in 3T3-L1 fibroblasts and adipocytes treated with DSPPs, e.g., by investigating the cleavage of caspases/PARP and TUNEL staining, is currently being investigated.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
# Lymphedema Surgical Treatment Using BioBridgeTM: A Preliminary Experience
## 4 Discussion
A second liposuction procedure was used to remove any fibro-adipose tissue that was still present. BioBridgeTM implantation was used in cases with persistent volume increase to reduce fluid buildup and further improve lymphatic outflow. The authors observed that BioBridgeTM positioning helped further reduce the edema and size of the affected limbs, as later-stage LE is characterized by nonfunctional lymphatic channels that run the length of the diseased leg, and these channels were addressed via BioBridgeTM implantation. A retrospective study by [PERSON] et al. included patients who underwent venolymphatic anastomoses and/or vascularized lymph node transfer, followed, or not, by a delayed implant of collagen scaffolds to obtain volume reduction of the affected upper or lower limb. The authors observed a significantly greater edema reduction in the BioBridgeTM cohort, with a major edema reduction in the lymph-node-transfer patient group. They also found the presence of new lymphatic collectors at indocyanine green fluorescence angiography and a decreased dermal backflow at scintigraphy [34]. The possible mechanism for this is that collagen scaffolds promoted vascular-endothelial-factor release, lymphatic cell migration, and lymphatic network organization along the direction of the lymphatic flow [27]. This phenomenon is more evident when a lymph node transplantation is performed thanks to the local release of growth factors. This article describes a preliminary experience, including only three cases, but the use of MR lymphography helped in the confirmation of lymphangiogenesis at follow-up imaging. As already highlighted in a recent literature review, the experiences reported in the literature of the adjunction of nanofibrillar collagen scaffolds included a small number of patients and a high inhomogeneity of patient features, LE stages, surgical techniques, and additional procedures (liposuction) [14]. Moreover, the studies presented in the literature reported multi-step surgical procedures, with BioBridgeTM positioning after lymph-node transplantation or venolymphatic anastomosis +/ - liposuction, whereas we opted for a one-shot surgical technique including nano fibrillar collagen scaffold placement. Large case series are needed to validate our results and the routine use of BioBridgeTM in the clinical setting and to establish a possible role in LE prevention.
# Risk Assessment Method of Solar Smart Grid Network Security Based on TimesNet Model
## 1 Introduction  
Distributed energy such as wind power and solar are rapidly developing. As a vital component of the new power system, the network security issues of solar smart grid are increasingly receiving attention. Smart grid refers to the intelligent transformation of traditional power grids through advanced sensing and measurement technology, information and communication technology, computer technology, and control methods, to improve the efficiency, sustainability, and reliability of power distribution [1, 2]. However, this integration also brings new Network Security Risks (NSRs), including network layer attacks, network physical layer attacks, and coordinated attacks [3]. Especially in the context of widespread deployment of distributed energy resources such as solar energy, NSR evaluation is particularly important. Since the communication dependency and architectural diversity of these resources have expanded the threat surface and exacerbated the network security situation of the power system [4]. The NSR analysis of smart grids is gradually shifting from a physical security analysis focused on engineering faults to a comprehensivesecurity risk analysis that considers both information networks and physical systems [5]. The integration of distributed energy sources such as solar energy has brought flexibility and sustainability to smart grids, but it has also introduced new security issues such as increased data complexity and expanded attack surfaces. However, the current NSR analysis methods for smart grids are relatively disconnected between information security research and system operation research, lacking comprehensive consideration of the security issues of information physical fusion systems. In real-world scenarios, electricity theft in solar smart grids not only involves physical tampering of power equipment, but may also be achieved through network attacks to tamper with data or remotely control metering devices, posing a threat to the network security and stable operation of smart grids. In this context, this study proposes a Solar Smart Grid (SSG)-NSR evaluation method, which includes two parts: a TimesNet-based Smart Grid Theft Behavior Detection (SGTBD) model and a Bidirectional Long Short-Term Memory (Bi-LSTM)-based Smart Grid Intrusion Detection (SGID) model. The purpose of the research is to effectively detect electricity theft behavior in the physical network of the smart grid and intrusion attacks in the information network, provide real and reliable basis for the NSR evaluation work of relevant personnel, and thus improve the reliability of SSG power supply. The innovation lies in using Deep Belief Network (DBN) for data dimensionality reduction, and inputting the reduced data into Bi-LSTM for intrusion detection to improve the detection accuracy.
## 3 Results and Discussion
### Thermal Conductivity Test  
#### 3.2.1 Admixture Ratios  
Figure 10 illustrates the variation in thermal conductivity of carbonized and solidified loess as the CL content increased, showing a notable reduction of 16.7%. The thermal conductivity was tested using one-way analysis of variance (ANOVA) to determine whether there were significant differences among the different CL dosage groups. The F-statistic was 5.37, and the \(p\)-value was 0.0101 (<0.05), indicating that there was a statistically significant difference in the average thermal conductivity values among the different CL dosage groups. This reduction can be analyzed from two key perspectives: the constitutive properties of the materials used, and the evolution of the soil's microstructure. From a thermal conductivity standpoint, the thermal properties of individual components play a crucial role. Under normal temperature conditions (20 \({}^{\circ}\)C), the thermal conductivity of soil particles in porous media typically ranges from 1 W/(m\(\cdot\)K) to 5 W/(m\(\cdot\)K), whereas water and air exhibit much lower thermal conductivities of 0.6 W/(m\(\cdot\)K) and 0.026 W/(m\(\cdot\)K),  
Figure 9: Variations in degree of carbonation with (**a**) carbonation duration for MgO solidified loess and (**b**) carbonation duration for CL-MgO solidified loess with different CL dosages.  
respectively [45]. CL, a natural organic polymer, has an even lower thermal conductivity, typically ranging between 0.1 W/(m\(\cdot\)K) and 0.5 W/(m\(\cdot\)K), making it significantly less thermally conductive than soil particles. Similarly, MgO, with a thermal conductivity of 0.04-0.07 W/(m\(\cdot\)K) [46], is classified as an effective insulating material. Consequently, the introduction of these components inherently lowers the thermal conductivity of loess, due to their substantially lower heat transfer capacities. From a microstructural evolution perspective, the addition of CL and MgO induces significant changes in the internal structure of loess. The carbonation of MgO produces carbonate compounds that, together with CL, create a pore-filling effect, modifying the soil's microstructure. This process results in the formation of an irregular pore network within the soil, which disrupts heat transfer pathways. The combination of this altered microstructure with the inherent insulating properties of MgO and lignocellulosic further reduces the thermal conductivity of carbonized and solidified loess, thereby improving its insulating performance.
## 4 Analysis Results
### Stress Transfer Function  
Figure 13 shows the hot spot position for the fatigue damage evaluation. To evaluate a hot spot position, such as the hatch corner along the length of the hull (which is a typical fatigue damage evaluation position in a container ship), the intersection of the longitudinal bulkhead and the transverse bulkhead was divided. The stress of the free edge at the hatch corner was used to calculate the fatigue damage.  
Figures 14 and 15 show the results of the head sea, the bow-quartering sea, and the \(L/4\) and near \(3L/4\) locations, which are weak points of fatigue strength torsional moments. Here, \(L\) is the ship length, and the applied ship speed is 15 knots. As the expression of load transfer function, the stress transfer functions are also expressed as the rigid body component and the flexible body component. There are three stress transfer functions for a bow-quartering sea (heading: 120\({}^{\circ}\)) and a head sea (heading: 180\({}^{\circ}\)). The meaning of each stress transfer function is as follows.  
1. Rigid body: The stress response calculated by applying only low-order modes (from first mode to sixth mode) of the eigenvector (currently used in the final design stage);
2. 1D beam model: The stress response calculated by applying the theory of stress calculation from Section 2.3 using the hull girder loads (fluid-structure interaction analysis was carried out and used to calculate the stress response using hull girder moments such as VBM, HBM, and TM);
3. 3D global model: The stress response calculated by applying the theory of hydroelastic analysis from Section 2.2 (fluid-structure interaction analysis was carried out and used to directly calculate the stress response at the hot spots).  
First, in view of the similarity of the responses of (b) and (c), it can be seen that the fatigue strength of the hatch corner of the container can be evaluated using only the hull girder loads. The responses of (b) and (c) regarding the springing effect tended to be slightly larger than or similar to those of (a), with only rigid body responses. In addition, the springing phenomenon occurred at a lower frequency in the head sea than in the bow sea, owing to the effect of the wave encounter frequency based on the ship's speed. As the hatch corner bracket exhibited a strong relationship with the torsional moment, both the  
Figure 13: Hot spot points for fatigue analysis.  
low-frequency hull motion response by wave loading, and the high-frequency springing response by resonance, were greater in the bow-quartering sea than in the head sea.
# Value of Information Based Data Retrieval in UWSNs
## 2 Related Work
### AUV Path Planning  
Path planning is a diverse subject and there is a lot of literature on it specifically in the artificial intelligence community [51; 52]. In terms of route discovery, a path planner is an algorithm that provides a sequence of steps which give a valid route between two points on a map (usually represented by a graph or a grid). Among the sub-goals or constraints of path planners are to find the most optimal route in terms of shortest distance, minimal time, optimal fuel expenditure, and so on. They essentially convert a set of high-level goal specifications into a sequence of low-level instructions, thus breaking down the problem solution into simpler steps.  
Depending upon the nature of the environment, path planners can be static or dynamic based on how the information is being updated. Path planners, therefore, can also be classified as pre-computing algorithms or reactive algorithms [52]. If the path is planned before the mobile agentstarts the course then the algorithm is pre-computing, while if the plan is updated during the course, in reaction to changes in objectives, constraints or new obstacles, then the algorithm is deemed reactive. Maps represented by graphs are usually solved by planners that are variants of [PERSON]'s Algorithm. One of the well-known variants is \(A^{*}\) path planning which uses admissible heuristics to accelerate the path planning. The path planned in [12] can be seen as a two-tiered approach; the higher level algorithms, such LPP or GPP, determine the sequence of node visitation on the map, while \(A^{*}\) provides the detailed sequence of steps required to travel from a source to a destination node.  
AUVs are autonomous devices and hence require path planning techniques to help them navigate beneath seas and oceans. In terms of navigation, they have certain issues that hamper them more as compared to dry-land autonomous agents, which include communication limitations, limited sensing, and power issues. AUV path planning has been around for a while now. One of the earlier path planning techniques used for AUVs is case-based reasoning [53]. One of the first efforts to use \(A*\) for path planning for AUVs is in [54]. \(FM^{*}\) is developed in [55] which gives a continuous path based on a discrete representation of the environment and also takes water currents into account. The path planning approach in [56] uses methods based on potential field strategies. This approach has been further improved on in [57]. Genetic Algorithms [58; 59; 60] and evolutionary algorithms [61] have also been employed for AUV path planning. Path planning for data mules has also been explored from the perspective of sensor networks in [44; 45]. We have proposed path planning techniques for AUVs in UWSNs [12; 49; 50].
## 2 Materials and Methods
### Air Pollutants Measurement  
To obtain data on the total suspended particle (TSP) and PAH, we used a high-volume air sampler (120 SL, Kimoto Electric Co., Ltd., Osaka, Japan) equipped with quartz fiber by filters (PALLFLEX Filter, Tokyo Dylec Corp.) at a flow rate of 1000 L/m\({}^{3}\), located at Kanazawa University, Ishikawa Prefecture, Japan. After 24 h of collection, the filter was replaced by a blank filter. The collected filter was kept for at least 24 h in a desicator to keep it dry and was then stored at \(-\)30 \({}^{\circ}\)C before extraction. Approximately 50 cm\({}^{2}\) of the filter was cut from the quartz fiber filter and thoroughly cut into small pieces (approximately 1 \(\times\) 1 cm\({}^{2}\)) and loaded into a glass flask. An aliquot of the PAH internal standards (Pyrd-d10 and BaP-d12) was added to the flask. A total of 80 mL of ethanol:benzene (\(v/v\) 1:4) was used for PAH extraction by sonication. The extract was washed with 5% NaOH, 20% H\({}_{2}\)SO\({}_{4}\) and ultrapure water. The organic phase was evaporated to 100 \(\upmu\)L under a gentle nitrogen flow, then reconstituted with 900 \(\upmu\)L of acetonitrile (ACN) and filtered into vials. High-performance liquid chromatography (HPLC), which coupled with a fluorescent detector and was utilized with an Intersil ODS-P16 column (250 \(\times\) 4.6 mm i.d., 5 \(\upmu\)m), was used to calculate the concentration of each PAH, including fluoranthene (FIf), pyrene (Pyr), benzo(a)anthracene (BaA), chrysene (Chr), benzo(_o_)fluoranthene (BBF), benzo(_o_)fluoranthene (BkF), benzo(_a_)pyrene (BaP), dibenz(_a_)anthracene (DahA), benzo(_o_)perylene (BghiP) and indeno[1,2,3-\(\alpha\)d]perylene (IcdP). The temperature of the HPLC was set at 20 \({}^{\circ}\)C and the flow rate was 1 mL/min. The limit of detection (LOD) and limit of quantitation (LOQ) of each PAH by HPLC are shown in Table S1, and all undetectable PAHs concentrations were regarded as 0. The sum of these PAHs was used as the total PAH concentration. To make the concentration data obtained more reliable, we used recovery and reproductivity tests. The recovery test was conducted by spiking the internal standard mixture with known concentrations on blank and sample filters, then extracting and analyzing using the method described above. The recovery rate was 110-120%. The reproductivity test was performed by analyzing the same samples using the same method at two facilities at Kanazawa University. The reproductivity range was 90-115%. Data on PM\({}_{2.5}\), NO\({}_{2}\), and SO\({}_{2}\) were obtained from the Atmospheric Environmental Regional Observation System: AEROS provided by the Ministry of the Environment Government of Japan [24].
# A Novel Bi-Dual Inference Approach for Detecting Six-Element Emotions
## 3 Bi-Dual Inference for Detecting Emotions
### Bi-Dual Inference Construction
\[f_{dual}(x)=\underset{y^{\prime}\in Y}{\arg\min}\Big{\{}\alpha \ell_{f}\big{(}x,y^{\prime}\big{)}+(1-\alpha)\ell_{g}\big{(}x,y^{\prime}\big{)} \Big{\}} \tag{10}\]4 Bi-Dual Inference model. For any \(x\in X\), the proxies \(f_{i}\) collaborate to generate a corresponding \(y^{\prime}\in Y\) using the function \(y^{\prime}=F_{q}(x)\). They then work together to reconstruct the original input \(x^{\prime}\in X\) using the function \(x^{\prime}=G_{v}(y^{\prime})\). The reconstruction error between \(y^{\prime}\) and \(y^{\prime}=F_{q}(x^{\prime})\) is also taken into account. The resulting dual learning loss is defined by Equation (22). \[ \begin{split}&\ell_{dual}\big{(}M_{x},M_{y},F_{q},G_{v}\big{)} \\ &=\frac{1}{|M_{x}|}\sum_{x\in M_{x}}\Delta x\big{(}x,G_{v}\big{(}F _{q}(x)\big{)}\big{)}+\frac{1}{|M_{y}|}\sum_{y\in M_{y}}\Delta y\big{(}y,F_{q} (G_{v}(y))\big{)}.\end{split}  \tag{22}\]  
\(|M_{x}|\) represents the number of samples in the dataset \(D_{x}\), while \(|M_{y}|\) represents the number of samples in the dataset \(D_{y}\). \(F_{q}\) denotes the set of multiple original models, and \(G_{v}\) represents the set of multiple pairwise models. The error \(\Delta x(x,x^{\prime})\) measures the discrepancy between the original input \(x\) and the reconstructed input \(x^{\prime}\) obtained from multiple primal models. This error serves as the feedback error necessary for updating the optimization model. Similarly, \(\Delta y(y,y^{\prime})\) represents the feedback error between the original output \(y\) and the reconstructed output \(y^{\prime}\) obtained from multiple dual models. To enhance the model's performance, we incorporate \(N\) model pairs \((f,g)\) for joint training. When \(N=1\), the model reverts to a standard pairwise inference learning model. In summary, we implement the model using the Bi-dual inference algorithm (Algorithm 1). The algorithm begins by computing multiple \(f\) and \(g\) using Equations (10) and (11). During this computation process, the Lagrange parameters \(\lambda_{xy}\) and \(\lambda_{yx}\), as well as the trained and optimized parameters \(\theta_{xy}\) and \(\theta_{xy}\), are adjusted. After completing the first for-loop, the resulting multiple \(f\) and \(g\) are then used in Equations (20) and (21) for training, yielding the set \(F_{q}\) of multiple primal models and the set \(G_{v}\) of dual models. Finally, all parameters are fed into Equation (22) to calculate the loss function of the model. Figure 5: Partial dual task frame diagram.