esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Internal Leakage Minimization Based on Sealing Technology
### Variable Gap Sealing
#### 2.3.2 Variable Gap Sealing Based on Different Material Properties  
Heterogeneous materials have superior physical, chemical, and mechanical properties and are widely used in various industries. The loading surface can deform at the position of the heterogeneous body due to the difference between the deformation of the matrix and the heterogeneous body.  
[PERSON] et al. [29] designed a new type of heterogeneous ring structure based on the principle of equivalent inclusions, shown in Figure 8. The radial deformation was produced due to the difference in the matrix and heterogeneous ring materials, which was used to improve the sealing performance of the gap seal hydraulic cylinder. The mathematical model of the elastic deformation caused by the ring-shaped heterogeneous material was  
Figure 7: Schematic diagram of the variable gap sealing: (**a**) lip structure; (**b**) cavity structure.  
established, and the heterogeneous ring model was numerically simulated. The results show that the soft ring structure can cause convex deformation on the surface of the piston, and the hard ring structure can cause concave deformation on the surface of the piston. Therefore, the gap in the hydraulic cylinder can be effectively reduced by reasonably setting the structure, distribution parameters, and material properties of the heterogeneous ring.  
[PERSON] et al. [30] proposed a variable gap sealing method for hydraulic cylinders based on a magnetic shape memory alloy (MSMA). This is a kind of smart material and will deform under a magnetic field, as shown in Figure 9. The amount of deformation is related to the size of the magnetic field. In their study, a plurality of annular grooves was designed on the outer surface according to the length of the piston, and several MSMA auxiliary support blocks were continuously arranged in the annular groove, thereby constructing a few gapped seal rings. When changing the coil current, the size of the magnetic field can be changed. Therefore, the gap is controllable and variable to achieve the purpose of reducing leakage.  
The above two methods change the sealing gap of hydraulic cylinders through the difference in deformation of heterogeneous materials or the flexibility of smart materials, which are still in the experimental stage and difficult to implement for application.
# On-Demand Scheduling of Command and Responses for Low-Power Multihop Wireless Networks +
## 6 Discussion and Future Work
### Packet Fragmentation  
Because SCoRe piggybacks timeslot information into existing packets, extra room is obviously necessary. As \(N_{\text{dslot}}\) is aggregated at each hop, the overhead for \(N_{\text{dslot}}\) gathering is fixed and small (2 bytes in our prototype). However, for \(N_{\text{dslot}}\) assignment, the scheduling information embedded by each node recursively into command packets is proportional to the number of its children. Because link protocol (e.g., IEEE 802.15.4) has a limit on payload size (e.g., 127 bytes), packet fragmentation technique (e.g., 6 LoWPAN) may be necessary to overcome this limitation if we have a larger network.
## 5 Simulation
The comparison between the observation error of the finite-time disturbance observer and that of the disturbance observer is depicted in Figure 8. From Figure 8, we can see that the observation errors \(\tilde{d}_{u}\), \(\tilde{d}_{r}\) of the disturbance observer converge to 0 in about 25 s and 27 s, respectively, and the observation errors \(\tilde{d}_{u}\), \(\tilde{d}_{r}\) of the finite-time disturbance observer converge to 0 in about 15 s and 10 s, respectively, which are shorter than the convergence times of the observation errors of the disturbance observer. The surge force \(\tau_{u}\) and the yaw moment \(\tau_{r}\) of the underactuated MSV under input saturation are shown in Figures 9 and 10, respectively. Under the finite-time input saturation compensator, the surge force and yaw moment of the underactuated MSV are all within the ranges of the input saturation constraint. Consequently, from the simulation results, it can be concluded that the proposed finite-time path following control scheme is more efficient and provides a better dynamic performance. Figure 4: Desired path and actual path of the underactuated MSV. Figure 5: The surge velocity, sway velocity and yaw angle velocity of the underactuated MSV. Figure 6: Along-track error of the underactuated MSV by different guidance laws. Figure 7: Cross-track error of the underactuated MSV by different guidance laws. Figure 8: The estimation errors by different disturbance observers. Figure 9: The surge force \(\tau_{u}\) under the input saturation.
## 2 Materials and Methods  
The determination of Polychlorinated Biphenyls (PCBs) in water was carried out according to MU 1792-77 (Guidelines for the determination of organochlorine pesticides and polychlorinated biphenyls in their combined presence in environmental objects and biomaterial) using gas chromatograph \(\diamonds\)Chromos GH-1000\(\diamonds\); electron capture detector (ECD) and capillary column 220 C, evaporator temperature 240 \(\diamonds\)C, detector temperature  
Figure 5: Long-term dynamic of the average annual runoff of the Ural River (Source: [21]). 300 \({}^{\circ}\)C, gas consumption carrier (nitrogen <<ultrapure>>)--38 mL/min. As a standard, we used the GSO of the composition of a Sovol solution in hexane, which is a mixture of PCB-52, PCB-101, PCB-138, PCB-153 and the sum of tetra-, penta, and hexachlorobiphenyls. The content of petroleum products both in water and fish was determined in the <<Scientific and Analytical Center <<Biomedpreparate>> (company \"Laborfarma\", Almaty, Kazakhstan) by the GLC method on a gas chromatograph Hewlett Packard 6890 (USA) with a flame ionization detector (FID). The separation of hydrocarbons was performed on a capillary column DB-608, 30 m \(\times\) 0.53 mm with a film thickness of 0.83 \(\upmu\)m. The quantitative determination of the total content of petroleum hydrocarbons in water samples was performed using FID calibration with a solution of a standard oil sample in the concentration range from 1 to 10 mg/mL [26], in fish samples a solution of a standard sample C\({}_{10}\)-C\({}_{40}\) from the company FlukaLot 0001452805, Almaty, Kazakhstan was used. Heavy metals were determined by the method of atomic absorption with atomization in a flame using the mercury-hydride prefix TIAS 100 on the atomic absorption spectrometer analyst 300 from the Perkin Elmer. The determination of organochlorine pesticides (Dichlordiphenyltrichlorethan or DDT and Hexachlorocyclohexane or HCH) both in water and in fish muscles was performed on a gas chromatograph using the same method used to determine PCBs, as indicated above, taking into account different chromatography conditions for PCBs and organochlorine pesticides. To assess the contaminants level in water, the MPC for fisheries management reservoirs was used [27; 28; 29; 30]. Other existing MPCs used are [31] presented in Table 1.  
Comparing the MPC standards in force in different countries and according to the recommendation of the WHO, it is possible to conclude the following. The WHO, USEPA (USA) and EU standards are designed to assess the quality of potable water and their values are quite close to each other in a number of elements. Comparing them with the standards for potable water in the CIS, the latter are more stringent for metals such as cadmium, copper and zinc for manganese and lead, on the contrary, and for chromium (VI)--almost equivalent.
## 3 Method
### Traditional Hybrid Retrieval Fusion Methods  
Building upon the overview of AttenFlow's objectives, we first examine the existing approaches in hybrid retrieval systems to understand the motivation behind our RCCF innovation. Traditional hybrid retrieval systems typically combine sparse retrieval methods (e.g., BM25) with dense retrieval approaches (e.g., neural embeddings) to leverage the complementary strengths of both paradigms [11; 12]. Sparse retrieval excels at exact term matching and handles out-of-vocabulary terms effectively, while dense retrieval captures semantic similarity and contextual relationships [35; 36].  
**Linear Weighted Combination:** The most straightforward fusion approach combines retrieval scores using fixed weights:  
\[\text{Score}_{\text{final}}(d)=\alpha\cdot\text{Score}_{\text{sparse}}(d)+(1- \alpha)\cdot\text{Score}_{\text{dense}}(d) \tag{1}\]  
where \(\alpha\in[0,1]\) is a hyperparameter that balances the contribution of sparse and dense retrievers. However, this method requires careful score normalization due to the different scales of sparse and dense scores, and the fixed weight \(\alpha\) may not be optimal for diverse query types [13].  
**Reciprocal Rank Fusion (RRF):** RRF has emerged as a popular rank-based fusion method that avoids score normalization by focusing solely on document rankings [14]:  
\[\text{RRF}_{\text{score}}(d)=\sum_{r\in R}\frac{1}{k+\text{rank}_{r}(d)} \tag{2}\]  
where \(R\) is the set of retrieval systems, \(\text{rank}_{r}(d)\) is the rank of document \(d\) in retriever \(r\), and \(k\) is a constant (typically 60) that controls the impact of lower-ranked documents. While RRF demonstrates strong empirical performance and robustness, it completely discards score information and treats all retrievers equally regardless of their relative strengths for specific queries [37].  
**Limitations of Traditional Methods:** These existing fusion approaches suffer from several key limitations that motivated our development of RCCF. First, they ignore the consensus level among retrievers, missing valuable reliability signals that could indicate result quality. Second, they apply fixed fusion strategies regardless of retriever agreement or disagreement, failing to adapt to varying query characteristics. Third, they lack confidence estimation for the final results, providing no indication of result reliability. Finally, they cannot adaptively adjust to different query characteristics or retriever performance variations [38; 39]. These limitations highlight the need for a more sophisticated fusion approach that can dynamically assess and incorporate retriever consensus.
# Traffic-Based Heuristics for Regenerator Placement in Translucent Dynamic Optical Networks
## 4 Proposed Regenerator Placement Heuristics
### Blocking Probability Estimation  
Consider a node \(n\) with \(r(n)\) regenerators installed and the estimate of its offered load given by \(\Gamma_{n}\) (\(\Gamma_{n}\) as a place holder for either \(\alpha_{n}\) or \(\beta_{n}\)). Node \(n\) can be seen as a pool of \(r(n)\) servers under an offered load \(\Gamma_{n}\). In such a case, the probability that node \(n\) has no available regenerator on the arrival of a request can be evaluated as: \(BP(n)=B(r(n),\Gamma_{n})\), in which \(B(s,a)\) is the Erlang B formula, derived using the Markov chain theory [31], that returns the blocking probability (BP) of a system with \(s\) servers under an offered load \(a\).  
To choose which node the regenerator shall be installed on, it is important to quantify the impact that the addition of a regenerator in each network node causes on the network path request blocking probability. As known in Markov theory, the blocking probability of a node \(n\), \(BP(n)\), is the long-term proportion of time that the node spends without an available regenerator. Therefore, the number of requests that require a regenerator at node \(n\) and are blocked is proportional to \(BP(n)\) multiplied by the total requested traffic. We may then think about quantifying the portion of the overall network BP that is caused by blocking imposed on the paths that use node \(n\) as a regeneration point.  
Let us assume that each path \(\pi_{ij}\) that may use a regenerator in node \(n\) experiences approximately a blocking probability given by \(BP(n)\). Using \(E_{ij}(n)\) as a place holder for either \(G_{ij}(n)\) or \(F_{ij}(n)\), it is possible to estimate the amount of the overall network BP only due to the blocking caused by node \(n\) as  
\[BP_{N}(n)=\frac{\sum_{i=1}^{N}\sum_{j=1}^{N}B(r(n),\Gamma_{n})\cdot E_{ij}(n)} {\sum_{i=1}^{N}\sum_{j=1}^{N}\omega_{ij}} \tag{6}\]  
or  
\[BP_{N}(n)=(\Gamma_{n}/L)\cdot B(r(n),\Gamma_{n}). \tag{7}\]  
Thus, one may evaluate the network BP reduction caused by the inclusion of a single regenerator in the node \(n\) using  
\[\Delta BP_{N}(n)=\frac{\Gamma_{n}}{L}([B(r(n),\Gamma_{n})-B(r(n)+1,\Gamma_{n })]). \tag{8}\]  
The deviation shown in Equation (8) is used to decide which node receives a new regenerator at each algorithm iteration.  
We provide in Appendix A a proposal to evaluate (8) considering a flexgrid/multiple rates demands scenario that occurs in elastic optical networks (EONs).
# 3L-YOLO: A Lightweight Low-Light Object Detection Algorithm
## 2 Methods
### 3L-YOLO Model
#### 2.2.4 MPDIoU Loss  
YOLOv8 uses CloU to calculate the overall overlap between the predicted and ground truth bounding boxes. However, CloU loss is difficult to optimize when the predicted and ground truth boxes have the same aspect ratio but different sizes. To improve the positioning accuracy in low-light target detection, MPDIoU loss [46] was employed. Unlike traditional IoU, MPDIoU not only measures the overlap between the predicted and ground truth boxes but also considers the center point distance and the deviation in width and height. This approach minimizes the distance between the top-left and bottom-right corners of the predicted and ground truth bounding boxes, thereby enhancing the accuracy and efficiency of bounding box regression. The expression of the MPDIoU loss is as follows:  
\[\begin{split} L_{MPDIoU}&=1-MPDIoU\\ &=1-\left(\frac{A\cap B}{A\cup B}-\frac{d_{1}^{2}}{w^{2}+h^{2}}- \frac{d_{2}^{2}}{w^{2}+h^{2}}\right)\end{split} \tag{7}\]  
where A and B are predicted and ground truth bounding boxes, respectively. w and h represent the image width and height. \(d_{1}\) is the distance between the upper left corners of the predicted box and the ground truth box, while \(d_{2}\) is the distance between the lower right corners of these boxes. \(d_{1}\) and \(d_{2}\) are calculated as follows:  
\[\begin{split} d_{1}^{2}&=\left(x_{1}^{gt}-x_{1}^{ prd}\right)^{2}+\left(y_{1}^{gt}-y_{1}^{prd}\right)^{2}\\ d_{2}^{2}&=\left(x_{2}^{gt}-x_{2}^{prd}\right)^{2}+ \left(y_{2}^{gt}-y_{2}^{prd}\right)^{2}\end{split} \tag{8}\]  
where \(\left(x_{1}^{prd},y_{1}^{prd}\right)\) and \(\left(x_{2}^{prd},y_{2}^{prd}\right)\) are the coordinates of the upper left and lower right corners of the predicted box. \(\left(x_{1}^{gt},y_{1}^{gt}\right)\) and \(\left(x_{2}^{gt},y_{2}^{gt}\right)\) are the coordinates of the upper left and lower right corners of the ground truth box.
# Evaluation and Prediction of Higher Education System Based on AHP-TOPSIS and LSTM Neural Network
## 2 Related Work
Apple also used LSTM to optimize the Siri application in 2017. In the field of mechanical fault diagnosis and prediction, [PERSON] et al. [17] used the advantages of LSTM to design a method for detecting weak signals of marine clutter with strong applicability and high accuracy, which had a 30% improvement in detection performance. In the field of computer vision, [PERSON] et al. [18] fused two parallel LSTMs with image attributes and visual information in a hidden state and multiple steps to form a parallel fusion LSTM (pLSTM) structure. In turn, this structure makes the attributes and visual information complementary or enhanced, thus generating subtitles more accurately. Their test results are better than some of the most advanced image-captioning methods. In addition, [PERSON] et al. [19] proposed a graph-based long short-term memory (GLSTM) model based on graphs to predict PM2.5 by introducing a sub-matrix in the memory cell of LSTM. Compared with other models, their proposed model achieves better prediction results. Moreover, some researchers combined evaluation models with prediction models, which enlightens us to be creative in modeling. For example, [PERSON] et al. [20] used machine learning and error analysis methods to provide a new evaluation method for the launch safety of propellant charging and established its corresponding predictability to significantly improve its practicability. In addition, [PERSON] et al. [21] proposed a data evaluation method and application process based on a heating professional mechanism and actual operating data. Then, they set up different environments to examine their prediction model and compared the prediction results with evaluation criteria and professional mechanisms. It was indicated that the accuracy of their prediction model is greatly improved and applicable to guide the energy-saving operations of heating substations. These articles have emphasized the influence of the environment on the parameters of LSTM and the importance of controlled experiments. As such, we also adopt actual different policies to control the parameters.
# Testing the Frequency Response Control Function of Level-Dependent Earplugs
## 2 Materials and Methods
### Implementation of Filters in the Sound Processor  
The developed earplugs consisted of a sound processor, a BAR (Balanced Armature Receiver) loudspeaker and a MEMS (microelectromechanical system) microphone. In order to implement the frequency response control function in the sound processor, digital bandpass filters with center frequencies of 125, 250, 500, 1000, 2000, 4000 and 8000 Hz were implemented. These frequencies were selected due to the fact that hearing protectors were selected using the octave band method using data in the octave frequency bands with the center frequencies given above [4]. This range completely covers the speech range of 170-4000 Hz [17]; therefore, switching on all filters will eliminate the effects of attenuation, allowing speech to be heard in its entire range. In addition, the proposed filtration range also includes the range in which auditory danger signals are designed. In accordance with the requirements of the EN ISO 7731 standard [18], warning signal frequencies should be such that spectral components are present in the range of 500-2500 Hz, and it is recommended that at least two dominant spectral components are present in the range of 500-1500 Hz. In practice, there are also signaling devices that emit a signal with frequencies above the specified range, but not higher than 4 kHz [19].  
When designing the filters, the guidelines presented in the EN 61260-1 standard were used [20] concerning the properties of octave and partial-octave bandpass filters. The design was also based on experience in designing filters for the level-dependent system proposed for use in earmuffs [21]. This article describes the process of designing the octave filters, including determining, among other things, the exact lower and upper octave band frequencies of individual digital filters and the minimum and maximum values of the frequency characteristics of the class 2 octave filter. The rest of this article presents the work conducted to verify the correct operation of digital filters and to check the operation of the frequency response control function of level-dependent earplugs.
# Adaptive Clutter Intelligent Suppression Method Based on Deep Reinforcement Learning
## 2 Signal Model  
Assuming that the radar transmitter transmits an LFM signal, the radar pulse width (PW) is \(T_{p}\), the pulse repetition interval (PRI) is \(T_{r}\), the frequency modulation (FM) bandwidth is \(B\), the LFM rate is defined as \(\mu=B/T_{p}\), and the carrier frequency is \(f_{c}\), then the radar transmits the signal in the \(n\)th transmit cycle as the following Equation (1):  
\[S_{n}(t)=\text{rect}\bigg{[}\frac{t-nT_{r}}{T_{p}}\bigg{]}\exp\bigg{[}\text{j} 2\pi(f_{c}(t-nT_{r})+\frac{1}{2}\mu(t-nT_{r})^{2})\bigg{]} \tag{1}\]  
where \(\text{rect}\bigg{[}\frac{t-nT_{r}}{T_{p}}\bigg{]}=1,|t|\leq\frac{T_{p}}{2}+nT _{r}\). At the moment \(t=0\), the distance of the target relative to the radar is \(R_{0}\), and the speed of uniform motion is \(v\). At this time, the echo delay of the target can be expressed as \(\tau=2(R_{0}-vt_{n})/c\), \(t_{n}=nT_{r}\), \(c\) is the speed of light, and the echo model of a single target in the \(n\)th cycle can be obtained as  
\[x_{nr}(t)=\text{rect}(\frac{t-nT_{r}-\tau}{T_{p}})\exp[\text{j}2\pi(f_{c}(t-nT _{r}-\tau)+\frac{1}{2}\mu(t-nT_{r}-\tau)^{2})]+c(t)+n(t) \tag{2}\]  
where \(c(t)\) is the clutter, and \(n(t)\) is the Gaussian white noise generated by the receiver. Based on Equation (2), the fast- and slow-time-dimensional echo matrix of the echo signal is established. Fast-time-dimensional pulse compression of the matrix can be obtained as  
\[\mathbf{X_{c}}=\begin{bmatrix}x_{1}(1)\exp(\text{j}f_{d}T_{r})&x_{1}(2)\exp( \text{j}f_{d}T_{r})&\cdots&x_{1}(L)\exp(\text{j}f_{d}T_{r})\\ x_{2}(1)\exp(\text{j}2f_{d}T_{r})&x_{2}(2)\exp(\text{j}2f_{d}T_{r})&\cdots&x_{ 2}(L)\exp(\text{j}2f_{d}T_{r})\\ \vdots&\vdots&\vdots&\vdots\\ x_{N}(1)\exp(\text{j}Nf_{d}T_{r})&x_{N}(2)\exp(\text{j}Nf_{d}T_{r})&\cdots&x_{ N}(L)\exp(\text{j}Nf_{d}T_{r})\end{bmatrix} \tag{3}\]  
where \(\mathbf{X_{c}}\) is the echo data matrix of \(N\times L\), \(N\) is the number of pulse accumulations, \(L\) is the number of range cells, \(\text{j}\) is the imaginary unit, and \(f_{\text{d}}\) is the Doppler frequency of the echo signal.
## 1 Introduction
[22] (i.e., \(f_{yung}^{esc}=NIR_{v}/i_{o}\cdot\omega_{\lambda}\)), fraction of absorbed photosynthetically active radiation (fAPAR) was used to replace the canopy interceptance for remote sensing applications in this method. In addition, the normalized difference vegetation index (NDVI) was used in this study to assess the reflectance of pure vegetation (\(NIR_{v}\approx NIR_{T}\)-NDVI) to solve the \"black soil problem\" [24]. [PERSON] et al. [14] proposed to directly derive the canopy interceptance from remotely sensed data at hand (leaf area index (LAI) and clumping index (CI) satellite products) using the method of [25] (i.e., \(i_{o}=1-\exp(-G(\theta)\cdot LAI\cdot CI/COS(\theta))\)). However, these estimation methods were developed for homogeneous vegetation which tend to the theoretical and have been mainly validated using 1-D radiative transfer models. Their performance for the estimation of SIF escape probability has not yet been tested and evaluated for sparse vegetation canopies which typify large areas of the boreal forest [26], most such forests are discontinuous in structure and contain overstory and understory layers with varying species and phenologies which depend on factors such as site fertility [27]. In this study, we investigate whether the estimation methods of far-red SIF escape probability based on SIT holds for discontinuous forest canopies using the model-based assessment strategy. To achieve this, we used a 3-D RT model, DART, to simulate the TOC SIF and per-leaf emitted SIF (i.e., total emitted SIF) based on the 3-D radiative budget simulation results. After that, the escape probability of SIF in the forest area was directly calculated based on the 3-D simulation results. Subsequently, the performance of estimation methods for escape probability based on SIT was analyzed under different vegetation conditions, such as different leaf optical properties, canopy structure, and background optical properties.
## 6 Future Directions
### Dedicated Beamlines for PEFC Materials Development  
At present, PEFC studies rely on multiple beamlines at SPring-8, each optimized for specific techniques, such as XAFS, HAXPES, XRD, PDF, and SAXS. Although this approach has yielded valuable insights, scheduling constraints and competition for beam time have limited the pace of the research. Establishing a dedicated beamline tailored specifically to PEFC materials would integrate multiple measurement techniques, streamline scheduling, and facilitate continuous, targeted experimentation. Such infrastructure would significantly accelerate PEFC research and bolster the field's ability to adapt rapidly to emerging scientific priorities.