esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
All the methods mentioned above, without case 0\(\leftrightarrow\)n being considered, may lead to inaccurate or insufficient dynamic model updating results because some modes, especially those in experimental modal data, are ignored. In addition, constant CMPs obtained by pre-analysis may not work when the correlated or uncorrelated modes (natural frequencies or mode shapes) change dramatically over iterations, especially for complex structures with a wide range of uncertain parameters, complicated boundary conditions, and significant structural simplification. A more general and accurate updating method is needed. According to the one-to-one symbiotic relationship between natural frequencies and mode shapes [26], it is feasible to pair and screen out the CMPs before each iteration without manual pre-analysis to exploit analytical and experimental modal data fully. Moreover, since the model updating, in essence, is the dynamic inverse problem with ill-posed multiple solutions, the optimization algorithm should have the ability to search for the global optimum solution [27; 28]. The evolutionary algorithm, including genetic algorithm [29], evolutionary programming [30], and evolutionary strategy [31], has two central characters: population search strategy and information exchange among individuals in the population [32; 33; 34]. These two characters can solve the problems of the global optimal solution searching and correlated mode screening before each iteration in dynamic model updating. The correlated mode auto-pairing and adaptive evolution screening (CMPES) method is proposed to solve the mentioned problems. A correlated mode auto-pairing strategy is proposed to determine the one-to-one CMPs adaptively before each iteration without pre-analysis and is suitable for all the 1\(\leftrightarrow\)1, 1\(\leftrightarrow\)n and 0\(\leftrightarrow\)n cases in dynamic model updating. The objective function constructed by correlated and penalized subitems of the initial generation can be determined based on this strategy. Then, the population evolutionary screening mechanism is employed to screen the CMPs further and search the global minimum. Meanwhile, the adaptive switch from penalized to correlated subitem during iteration can ensure that all the potential of analytical or experimental modes can be fully exploited. The outline of this paper is as follows. In Section 2, the correlated mode pairing and screening mechanism and an objective function of optimization problem stemming from dynamic model updating are introduced briefly. Then, the CMPES method is proposed in Section 3. In Section 4, a thin plate with small holes, an F-shaped structure, and an intermediate case (IMC) of a gas turbine were presented to validate the accuracy, effectiveness, and engineering application potential of the proposed method. Section 5 is the conclusion.
## 4 Multifrontal Approach
### Out-of-Core Working (Hsl_ma77)  
As the problem size grows, the computed factors and working space required by direct solvers generally increases significantly. This can lead to there being insufficient physical memory to store the data, particularly when the linear systems arise from discretizations of three-dimensional problems. In such cases, it may be possible to use a direct solver that is able to hold its data structures on disk, that is, an out-of-core solver. HSL_MA77 is designed as an out-of-core multifrontal solver.  
The multifrontal method needs data structures for the original matrix \(A\), the frontal matrix \(F\), the stack of generated elements, and the matrix factor \(L\). As already observed, computed columns of \(L\) are not needed again until the solve phase, so an out-of-core method writes the columns of \(L\) to disk as they are computed. If \(A\), the stack and \(F\) are held in main memory and only the factor written to disk, the minimum possible input/output for an out-of-core method is performed: it writes the factor data to disk once and reads it once for the forward elimination and once for the back substitution. However, for very large problems, it may be necessary to hold further data on disk. HSL_MA77 is designed to also allow the stack and the original matrix data to be stored on disk, leaving only \(F\) to be held in main memory.  
Separate dense kernels were designed for use within HSL_MA77[2, 38]. These employ block algorithms and, as in our other sparse solvers, the computation is performed using BLAS routines. A key feature that is important when attempting to minimise memory requirements, but does lead to complications in the coding and some copying overheads, is that storage for only the lower triangular part of \(F\) is required.  
Writing the data to disk is done efficiently within HSL_MA77 through the use of a purpose-written system for virtual memory management. By exploiting information known by the programmer about when data will next be required, it is possible to significantly outperform the operating system's virtual memory manager. The virtual memory management system used by HSL_MA77 is described by [PERSON] and [PERSON] [39] (see [2] for further details).  
HSL_MA77 was designed primarily as a serial code. However, an option is offered to exploit node-level parallelism. The requirement to use multiple stacks when exploiting tree-level parallelism, and the lack of appropriate locking within the virtual memory routines means that the exploitation of tree-level parallelism within HSL_MA77 would be difficult.  
We note that HSL_MA77 allows the matrix \(A\) to be input both by square symmetric elements and by rows. Furthermore, the elements or rows may be entered one at a time, on separate calls to an input routine. This form of input reduces main memory requirements by avoiding the need to assemble \(A\) and is particularly convenient for many large-scale engineering applications that employ a finite-element approach. All the other solvers in this study require \(A\) to be input by rows in a single call.
# BWLM: A Balanced Weight Learning Mechanism for Long-Tailed Image Recognition
## 4 Experiments
### Implementation  
We train the models by using a long-tailed training set and evaluate them by using a balanced test set. Following [34], we divide the training set into three types based on the sample size of each category to further report accuracy: Many (greater than 100), Medium (Between 20 and 100), and Few (less than 20).  
We use the PyTorch toolbox [61] to train our models. We use ResNet32 [8] on CIFAR100-LT and ResNeXt50 [62] on ImageNet-LT. The models are trained with a batch size of 64 on a single NVIDIA GeForce RTX 3090 GPU for 320 epochs on CIFAR100-LT and 500 epochs on ImageNet-LT. The momentum of the SGD optimizer is 0.9. According to the findings of [31], when the data is imbalanced, loss and validation errors gradually increase after the learning rate drops. We use a cosine learning rate regulator [63] to gradually decay the learning rate from 0.01 to 0.
## 3 Methodology
### Pre-Processing of Sentinel-1 SAR Data  
The Sentinel-1 ground range-detected (GRD) data acquired in IW modes, with VV and VH backscatter intensity, were divided into \(0.5^{\circ}\times 0.5^{\circ}\) tiles. This grid system was referenced in the SRTM \(1^{\circ}\times 1^{\circ}\) data organization system. First, each \(1^{\circ}\) grid was subdivided into four \(0.5^{\circ}\) grids to improve the efficiency of pre-processing and post-analysis. Then, on each \(0.5^{\circ}\) grid, the Sentinel application platform (SNAP) software provided by ESA was used for border noise removal, thermal noise removal, and radiometric calibration. Then, the image pixels were multi-looked to 30 m square and resampled with a bilinear approach to match the Landsat Transverse Mercator (UTM) coordinate system. The recently released STRM 1 arc-second (-30 m) digital elevation model (DEM) for terrain correction was used. The step of \"terrain flattening\" was omitted because the study area  
Figure 2: Flowchart showing the processes of our algorithm.  
was in a plains area and the terrain was flat; therefore, it did not affect the results of water extraction. On the other hand, adding this step would slow down the overall preprocessing process. This was in contradiction to the efficiency that we sought for the proposed method. The pre-processed images are shown in Figure 3.
# TSCH Multiflow Scheduling with QoS Guarantees:
## 1 Introduction
### TSCH Schedulers and Protocols  
To assign the operating states of each node to the corresponding cells, it is first necessary to carry out the TSCH scheduling process. This decides what action the node will perform in each timeslot. The node will be able to receive, transmit, or sleep. The nodes covered by the scheduler must be correctly synchronised so that the transmission and reception timeslots match. Because there is no standard for this scheduling, multiple TSCH schedulers have been developed, which can be classified into autonomous, distributed and centralised. In addition, SDN is considered a more specialised centralised approach which separates the control plane and the data plane to optimise the overall performance.  
The differences between each of the approaches can be seen in Figure 2, using the same topology. In Figure 2a autonomous scheduler, the nodes only exchange RPL messages over established wireless links, and each node reuses the obtained RPL information to do the scheduling. In distributed schedulers Figure 2b, in addition to the RPL information, the nodes exchange negotiation packets to request or accept cells in the TSCH schedule. In centralised Figure 2c,d a single device is used to create the scheduling, therefore each device must send control traffic with topology information. However, the simple centralised Figure 2d, maintains the operation of the devices and protocols as in Figure 2a,b. In Figure 2c, the devices and protocols are simpler since they do not have to make any decisions because the control plane is completely centralised. This section briefly describes the main features of the existing TSCH schedulers, as well as the specific performance of some of them which will be used in a comparative analysis.
## 5 Performance Analysis  
### Data Security  
Under the characteristics of ABE, if the user (subscriber) does not meet the attributes set by the data owner (publisher), the user cannot obtain the corresponding private keys. The user meets the conditions and transmits the attribute data to the authority, and the authority then calculates the result related to the data user sent back. After receiving it, the user can correctly resolve the secret number. The plain text is ciphered by the secret number on the elliptic curve, and the secret number is generated by the random chaos signal. It is hard to find out any information in the ciphertext. With LSSS, if you want to get the correct secret number, you must meet the attributes to calculate it. Based on the characteristics of elliptic curves, the difficulty of ECDLP is a well-known problem. Finally, after chaos synchronization, the information about the attributes will not be transmitted and will only be about the synchronization parameters of the chaos system. Due to the chaos system, which is sensitive to initial values, if there is not the same chaos system, the signal will diverge. Then, the eavesdropper cannot get the correct secret number. Therefore, the entire transmitted data is encrypted by it, and it is extremely difficult to identify the plain text.
# Spatiotemporal Traffic Prediction Using Hierarchical Bayesian Modeling
## 3 Methodology and Data
### Hierarchical Bayesian Modeling
#### 3.1.2 Autoregressive (AR) Model  
The independent AR model is proposed by [PERSON] et al. (2018). Let \(Y_{l}=Y_{I(s_{ij};t)},\ldots,Y^{\prime}_{I(s_{n};t)}\) be the vector of observations and \(N_{l}=N_{I(s_{ij};t)},\ldots,N^{\prime}_{I(s_{n};t)}\) be the true square root of the bus count in \(l\) and \(t\) that represent the short time component, and the long time component at sites \((s_{l},s_{j})\). We define the hierarchy of the AR model in Equations (20) and (21):  
\[Y_{I(s_{ij};t)}=f(x_{I})_{(s_{ij};t)}+\epsilon_{I(s_{ij};t)} \tag{20}\]  
\[f(x_{I})_{(s_{ij};t)}=\rho N_{It-1}+\chi_{I(s;t)}\beta+\eta_{(s_{ij};t)} \tag{21}\]  
\(\epsilon_{I(s_{ij};t)}\) is a random error that we assume to be independently normally-distributed that follows \(\epsilon_{i}\sim N(0,\sigma^{2})\). We assign a prior distribution as shown in Equation (21), where \(\rho\) denotes the unknown parameter for temporal correlation with \(0<\rho<1\). The \(\rho\) parameter is used to reduce the computations in the GP model.  
We continue to assume that \(\chi_{I(s;t)}\) represents the matrix of the covariates between time and space, and \(\beta\) represents the vector of regression coefficients. The spatiotemporal random effects error \(\eta_{(s_{ij};t_{0})}\) is distributed as stationary parameter with zero mean\({}^{\prime\prime}\). Let \(\theta=(\rho;\beta;\sigma_{\eta}^{2},\sigma_{\varepsilon}^{2};\dot{\varphi};v)\) denote all the parameters of the AR model, and we integrate the posterior predictive distribution for \(Y_{I(s_{ij};t)}\) over the model parameters with respect to the joint posterior distribution as follows:  
\[\pi(Y(s^{\prime},t)|y)=\int\pi(Y(s^{\prime},t)|N_{I}(s^{\prime},t),\sigma_{ \varepsilon}^{2},y)\times\pi(N_{l}(s^{\prime},t)|\theta)\times\pi(\theta,y^{ \ast}|y)\times\pi(\theta|y)dN_{l}(s^{\prime},t)dy^{\ast}d\theta \tag{22}\]
# Reflection Characteristics Measurements of Indoor Wireless Link in D-Band
###### Abstract
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Year** & **Materials** & **Dielectric Properties** & **Equipment** & **Frequency** & **Results** \\ \hline
1966 & Plexiglass & Permittivity & Fabry-Perot resonator & 143 GHz & 2.60 [25] \\ \hline
2005 & Plaster,glass and wood & Absorption coefficient and refractive index & THz time-domain spectroscopy & 70–350 GHz & Figures to show the measured parameters VS frequency [17]. \\ \hline
2007 & Ingrain wallpaper and two plaster samples & Surface roughness & Commercially available equipment for optical 3D micro- and nanometrology & 100–1000 GHz & 0.13 mm, 0.05 mm and 0.15 mm, respectively [20]. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Reference Review of Measuring Dielectric Properties of Materials. The studies in Table 1 provide potential solutions for indoor wireless propagation in mm-Wave and THz range. However, to the best of our knowledge, there are few studies on the reflection properties of interior building materials in the whole D-band (110-170 GHz). What's more, the measurements of these properties in Table 2 are complex and time-consuming in reflection modeling due to the lack of devices with a high-frequency band and high resolution. Therefore, in this paper, we demonstrate the reflection measurements of the parallel polarized wave under the multiple incident angles and five kinds of materials in the whole D-band, and proposed a method based on the theoretical reflection model to estimate two dielectric properties of materials by minimum mean square error (MMSE) criterion. This method can also prove the feasibility and robustness of our experiment so that the D-band indoor reflection characteristics can be explored. The paper's structure is as follows. The principles of reflection measurements, theoretical reflection model, and MMSE criterion are introduced in Section 2. Section 3 illustrates the procedure of our method. Section 4 presents the experimental setup of the reflection measurements and the method of data processing. In Section 5, the measured results are compared with the values theoretically calculated by the model. Additionally, in this section some characteristics, such as reflection coefficients, relative permittivity, and surface roughness of materials, are analyzed and discussed. Conclusions wrap up this paper in Section 6.
## 4 Dataset Acquisition
### Validation Metrics  
An essential component of this process is the validation of the results produced by the proposed methodology. To ensure the accuracy and relevance of the generated reports, we used validation metrics commonly employed in the literature (Section 2). Among these, the Bilingual Evaluation Understudy (BLEU), Metric for Evaluation of Translation with Explicit ORdering (METEOR), and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) stand out.  
BLEU is a metric that evaluates the quality of texts generated by the model by comparing them to one or more reference texts, considering up to four-word n-grams (BLEU-1 to BLEU-4). This metric is widely used in machine translation and text generation by computational models [57]. in Equations (2)-(4).  
\[\text{BLEU}=\text{BP}\times\exp\left(\sum_{n=1}^{N}w_{n}\cdot\log(p_{n})\right), \tag{2}\]  
where BP is the brevity penalty factor, \(N\) is the maximum number of considered n-grams, \(w_{n}\) is the weight assigned to each n-gram, \(p_{n}\) is the precision of n-grams (Equation (3)), and BP is the brevity penalty factor (Equation (4)).  
\[p_{n}=\frac{\sum_{\text{candidate}}\sum_{\text{matching n-grams count of matching n-grams}}}{\sum_{\text{candidate}}\sum_{\text{possible n-grams count of possible n-grams}}}. \tag{3}\]\[BP=\begin{cases}1&\text{if }c>r\\ e^{(1-\frac{r}{c})}&\text{if }c\leq r,\end{cases} \tag{4}\]  
where \(c\) is the length of the candidate text and \(r\) is the average length of the references.  
METEOR, on the other hand, goes beyond n-grams and takes into account synonyms and morphological variations, providing a more sensitive evaluation of the linguistic and contextual variations in texts generated by models [58]. We can see the representation of the METEOR formula in Equation (5).  
\[\text{METEOR}=(1-\lambda)\cdot\text{precision}+\lambda\cdot\text{recall}\cdot( 1-\beta\cdot\text{penalty}), \tag{5}\]  
where  
\[\text{precision}=\frac{\text{matched unigrams}}{\text{total candidate}},\] \[\text{recall}=\frac{\text{matched unigrams}}{\text{total reference}},\] \[\text{penalty}=\gamma\cdot\left(\frac{\text{total candidate}}{\text{total reference}}\right)^{\delta},\]  
where \(\lambda\) is the balancing parameter between precision and recall,  
\(\beta\) is the parameter controlling the impact of the penalty,  
\(\gamma\) is the penalty decay parameter, and  
\(\delta\) is the penalty growth parameter.  
ROUGE is a metric that measures the overlap of units such as n-grams, words, and bigrams between the generated text and the reference text. It is particularly useful for evaluating the quality of automatically generated summaries by models [59]. We can observe the ROUGE equation in Equation (6).  
\[\text{ROUGE-L}=\frac{\sum_{\text{documents}}\text{LCS}(\text{candidate}, \text{reference})}{\sum_{\text{documents}}\text{word count in reference}}, \tag{6}\]  
where LCS (candidate, reference) is the length of the longest common subsequence, documents is the set of reference documents, and word count in reference is the total number of words in the references.
## 2 Materials and Methods
### Experimental Set-Up  
Thirteen males (age: 27 \(\pm\) 3.7 years, height: 1.81 \(\pm\) 0.06 m, mass: 82.7 \(\pm\) 6.2 kg) were selected to participate in the present study. The inclusion criteria for being selected were that participants were experienced runners (20-40 km per week) and free from injuries during testing. All participants gave informed written consent before testing. A single testing session, comprising nine trials of treadmill running (1-degree inclination) for 40 s (per trial), was completed by each participant. Treadmill running speeds started at 10 km/h and increased at 1 km/h increments to 18 km/h. Each trial was captured using an 18-camera motion capture system (Vicon, Oxford, UK). Participants wore a standard-issue vest containing a GPS device (STATSports Apex, Newry, Northern Ireland, UK) with an embedded high-frequency triaxial accelerometer and were provided with standardized running shoes (Puma Anzarun, Herzogenaurach, Germany).  
The standard issue vest positioned the GPS device around the posterior aspect of the thoracic spine. The embedded triaxial accelerometer sampling frequency was set to 100 Hz (standard manufacturer setting). The positioning of the device orientated the accelerometer axes to the following planes of motion: \(y\)-axis (vertical), \(z\)-axis (anterior/posterior) and \(x\)-axis (medial/lateral). Fifty-four infrared markers (14 mm) were attached to the participants, corresponding to the modified Istituto Ortopedico Rizzoli (IOR) marker set with five additional clusters attached to the left thigh, right thigh, left shank, right shank, and the posterior aspect of the GPS device [33; 34; 35]. Eighteen optical cameras (VICON MXT40, Oxford, UK) recorded the coordinate data of the infrared markers at 100 Hz [36].  
Raw accelerometer and marker coordinate data were transferred from the respective software (STATSport APEX version-2.0 and Vicon Nexus version-2.12) to Visual 3D software version-2022 (C-Motion Inc., Germantown, MD, USA). Synchronization of accelerometer and motion capture data was conducted by an assistant \"tapping\" the GPS device at the beginning of each running trial. The frame of the tap was established in each data set, and then ten consecutive gait cycles were selected for analysis following a 20-s ramp period.
## 3 PMP-MPC Energy Management Strategy for HEAGV
### SOC Reference Trajectory Planning  
If the SOC reference trajectory is appropriate, the optimized performance will be significantly improved in the MPC framework of EMS [31]. SOC decreases roughly linearly with increasing hybrid electric vehicle driving distance. However, the driving cycle of HEAGV includes both air and ground phases. HEAGV has a higher power requirement in flight mode than in ground phases which can lead to a large amount of electrical energy being consumed. If SOC has a linear downward trend, increasing the driving distance may exceed the SOC minimum. Therefore, the SOC reference trajectory of HEAGV is different from that of hybrid vehicles. The high power requirement of HEAGV while in flight mode leads to a large amount of electrical energy being consumed. The battery is discharged, and the SOC is on a decreasing trend. The SOC tends to increase when the HEAGV is in ground mode when the battery is changed to ensure proper travel. Therefore, this paper proposes a fast planning method for global SOC reference trajectory based on traffic information. The ITS system is used to obtain the average speed information of different road sections in the driving route and to segment the different modes of HEAGV. SOC reference trajectory is divided into two types according to the HEAGV working mode. Different formulae calculate the change of SOC of different segments. The SOC reference trajectory segments of HEAGV in flight and ground modes are expressed as follows.  
\[SOC_{ref}(k+h_{p})=SOC(k)-\frac{L(k+h_{p})}{L_{0}-L(k)}(SOC(k)-SOC_{f}) \tag{16}\]  
\[SOC_{ref}(k+h_{p})=SOC(k)+\frac{L(k+h_{p})}{L_{0}-L(k)}(SOC(k)-SOC_{f}) \tag{17}\]  
where \(k\) is the current time step, \(h_{p}\) is the size of preview horizon, \(v\) is the actual vehicular speed, \(v_{p}\) is the predicted speed in the preview horizon, \(L(k)\) and \(L(k+h_{p})\) indicate the distance traveled up to \(SOC(k)\), and \(SOC(k+h_{p})\) refers to the initial and final values SOC boundary in a preview horizon.  
The SOC reference trajectory is shown in Figure 7. In the first stage, the SOC curve decreases when the HEAGV is in flight mode. In the second stage, the SOC curve rises and HEAGV is charging in ground mode to prepare for the next flight. In the third stage, the SOC curve is charging in ground mode to prepare for the next flight.  
stage, HEAGV switches to flight mode, consuming a large amount of power, and the SOC curve decreases.