esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of AQM-Based RLC Buffer Management on the eNB Scheduling Algorithm in LTE Network +
## 3 Background
### Radio Resource Management  
Radio Resource Management (RRM) is an eNB application level function that ensures the efficient use of available radio resources. Radio resources are allocated into the time/frequency domain (see Figure 3). In the frequency domain, the total bandwidth is divided into sub channels of 180 kHz, each one with 12 consecutive and equally spaced OFDM sub-carriers. In the time domain, instead, they are distributed every Transmission Time Interval (TTI), each one lasting 1 ms. The time is split into frames, each one composed of 10 consecutive TTIs. Furthermore, each TTI is made of two time slots with length 0.5 ms, corresponding to 7 Orthogonal Frequency Division Multiplexing (OFDM) symbols in the default configuration with a short cyclic prefix. A time/frequency radio resource spanning over two time slots in the time domain and over one sub-channel in the frequency domain is called Resource Block (RB) and corresponds to the smallest radio resource unit that can be assigned to an UE for data transmission. As the sub-channel size is fixed, the number of RBs varies according to the system bandwidth configuration (e.g., 25 and 50 RBs for system bandwidths of 5 and 10 MHz, respectively). Readers interested in RRM can refer to [37].  
Figure 3: Time–Frequency radio resource grid.  
Figure 2: LTE network architecture.
## 5 Limitations of the Study  
There were some limitations to the study. Although our novel probe placement procedure managed to eliminate the influence of the measurement errors caused by the probe location, attention should be paid to the fact that we did not eliminate the orientation between muscle fiber direction and the probe. The shear modulus does not necessary assume isotropy. In a transverse isotropic medium, there are two shear moduli: \(\mu\_\)perpendicular and \(\mu\_\)parallel, which are measured depending on the orientation of the muscle fibers in relation to the shear wave propagation and the shear wave deflection. For in vivo materials such as muscle, the mechanical properties are not isotropic, resulting in the hardness varying with different directions of muscle fibers and probe. The influence of muscle fiber direction has been reported previously [14], and future studies should take this issue into consideration. In this study, we reported the muscle hardness in terms of shear modulus within the region of interest for TA, RF and GM under both RST and MRL conditions. Due to the limitation of the probe size and the nature of the in vivo materials, it should be noted that the localized hardness cannot represent the whole muscle. Mapping the muscle hardness along the whole muscle area could be considered as a potential future research direction.
## 4 Experiment
### Generation of Position Fingerprints
#### 4.1.2 Measurement of Fingerprints  
In this study, RSSI values were measured evenly across 73 locations for 3 min each. 51 of these locations were used as training and reference, and the remaining 22 locations were used as locating points. The distribution of reference and test fingerprints is shown in Figure 8b.
# Diffusion Model as a Base for Cold Item Recommendation
## 3 Cold Item Recommender with a Diffusion Model Base
### Preliminary: Diffusion Model
Therefore, instead of using \(x_{0}\), we train and adopt a \(x_{0}\)-predictor which predicts \(x_{0}\) from \(x_{t}\) and \(t\) by using neural networks. In addition, because we want to control DM's denoising (generation) direction, the predictor also takes the generation condition \(c\) as an input. As a result, \(x_{0}\) in Equation (6) is replaced by \(x_{0}\)-predictor \(\hat{x}_{\theta}(x_{t},t,c)\) and generates a new equation, Equation (8). The final denoising sample follows Equation (9). \[\mu_{\theta}(x_{t},t,c)=\frac{\sqrt{\bar{\alpha}_{t}}(1-\bar{x}_{t-1})x_{t}+ \sqrt{\bar{\alpha}_{t-1}}(1-\alpha_{t})\hat{x}_{\theta}(x_{t},t,c)}{1-\bar{ \alpha}_{t}} \tag{8}\]  
\[p_{\theta}(x_{t-1}|x_{t},c)=\mathcal{N}(x_{t-1};\mu_{\theta}(x_{t},t,c),\sigma _{q}^{2}(t)\text{I}) \tag{9}\]  
**Training:** DM training is equivalent to training \(\hat{x}_{\theta}(x_{t},t,c)\). Because the goal of \(\hat{x}_{\theta}(x_{t},t,c)\) is to predict \(x_{0}\), the objective function is designed to minimize the prediction error. We follow [PERSON] et al. 's proposal [27], Equation (10), which not only reduces computational cost but also assigns greater importance to predictions made from noisier inputs, i.e., the more challenging cases. \[L_{d}=\mathbb{E}_{t,x_{0},c}\parallel x_{0}-\hat{x}_{\theta}(x_{t},t,c)\parallel \tag{10}\]where \(\|\cdot\|\) represents the vector norm. Algorithm 1 shows the DM training details. For a given data point \(x_{0}\) and its condition \(c\), an arbitrary timestep \(t\) is uniformly sampled between \(1\) and \(T\), and the gradient of the objective function is calculated for the predictor training. ```
0: Dataset \(X\) with assigned conditions to its elements, maximum timestep T
0: Trained model parameter \(\theta\)
1: Initialize \(\theta\) with random values
2:while\(\theta\) is not converged do
3: Randomly sample a batch \(X^{\prime}\subset X\)
4:for all\((x_{0},c)\in X^{\prime}\) do
5: Sample \(t\sim U(1,T)\)
6: Sample \(x_{t}\sim q(x_{t}|x_{0})\) (Equation (3))
7: Calculate loss \(L_{d}\) using Equation (10)
8: Update \(\theta\) using gradient descent with \(\
abla_{\theta}L_{d}\)
9:endfor
10:endwhile
```  
**Algorithm 1** DM Train  
**Inference:** Algorithm 2 shows DM's inference procedure details. DM takes a given pure Gaussian noise and condition pair and performs denoising for each timestep to generate the data satisfying the condition. The reparameterization trick [57] is adopted for sampling. ```
0: Condition \(c\), maximum timestep T
1:\(x_{T}\sim\mathcal{N}(0,\mathrm{I})\)
2:for\(t\gets T\) to \(1\)do
3:\(\epsilon\sim\mathcal{N}(0,\mathrm{I})\)
4:if\(t==1\)then
5:\(\epsilon=0\)
6:endif
7:\(x_{t-1}=\mu_{\theta}(x_{t},t,c)+\sigma_{q}(t)\epsilon\) (\(\mu_{\theta}(\cdot)\): Equation (8), \(\sigma_{q}(t)\): The square root of Equation (7))
8:endfor
9:return\(x_{0}\)
```  
**Algorithm 2** DM Inference
# Tensor-Based Approaches for Nonlinear and Multilinear Systems Modeling and Identification
## 5 Tensor-Based Approaches for Nonlinear Systems  
The use of tensor-based approaches for nonlinear systems has proved advantageous in three areas: (i) parametric complexity reduction in order to get efficient and computationally fast parameters estimation algorithms, (ii) generating new representations of nonlinear systems thanks to tensor decompositions, and (iii) structural identification of systems which can be represented as combinations of dynamical linear systems with static nonlinear blocks. In Section 5.1, we first describe polynomial models. Then, in Section 5.2, we introduce standard discrete-time Volterra models. To reduce the parametric complexity of such models, in Section 5.3, we present a tensor approach which consists in using a PARAFAC decomposition of Volterra kernels. The expansion of Volterra kernels on orthogonal bases is considered in Section 5.4. Finally, some links between block-oriented models (Hammerstein, Wiener, and Wiener-Hammerstein) and tensor representations via their associated Volterra kernels are established in Section 5.5
## 3 Circuit Level Design
### Top Level Design  
In the design of the channels, it is necessary to define parameters such as inductance, capacitance, duty cycle, and the operating frequency of the system. The values of these will determine the final behavior of the converter and help achieve the specified design targets. First, it is necessary to define the desired voltage conversion. As mentioned previously, the design is aimed at a varied range of applications. Once the input and output voltages are defined in the design specifications, the duty cycle (\(D\)) can be determined using (5), where \(V_{o}\) corresponds to the output voltage, and \(V_{dc}\) is the input voltage.  
\[D=1-\frac{V_{o}}{V_{dc}} \tag{5}\]  
The inductance value \(L_{o1,2,3}\) for the channels is calculated by (6), where \(I_{o}=3I_{L}\) is the average output current, \(I_{L}\) is the average output current per channel, \(r_{I}\) is the maximum expected current ripple, and \(f_{sw}\) is the switching frequency. The output capacitance  
Figure 3: Gate driver topology.  
is determined by (7), with \(r_{V}\) denoting the maximum expected voltage ripple, \(R_{o}^{eq}=3R_{o}\) denoting the equivalent load resistance, and \(R_{o}\) denoting the output load resistor.  
\[L_{o1}=L_{o2}=L_{o3} =\frac{V_{dc}(1-D)D}{n_{c}f_{sw}l_{r}l_{r}} \tag{6}\] \[C_{o} =\frac{r_{I}}{8n_{c}^{2}f_{sw}r_{V}R_{o}^{eq}} \tag{7}\]  
The LC filter and design parameters used in these equations are derived from the nominal design specifications [44]. However, it is important to note that values such as voltage and current ripples depend on the specific applications for which the converter is being designed, and other parameters, such as the selected dead time. These values should be adjusted accordingly to ensure the target performance in the intended application. The ripple parameters allow for a design margin, enabling trade-offs with the operating frequency of the system, which in turn affects the size of the passive components used in the converter.  
The control signals are managed externally using pulse width modulation (PWM), which offers several advantages, including precise control of the output voltage. For the three-channel system, the phase shift between each channel should be \(360^{\circ}/n_{c}\), with \(n_{c}\) being the number of channels [45]. Additionally, the rise times, fall times, and propagation delays of the gate drivers must be taken into account in the switching scheme to ensure that the transistors are activated at the correct times as per the design.
## 3 Proposed Models
### Log Parsing and Preprocessing  
#### 3.2.1 Log Parsing  
Log parsing aims to fetch variables from unstructured logs and save invariant variables as log templates. Before log anomaly detection, it is necessary to parse the unstructured and free-text log so that the obtained log can be passed to the anomaly detection model. Drain is characterized by high parsing accuracy and efficiency. Therefore, logs parsed by Drain meet the requirements of the proposed model. This study uses the Drain method to parse all log data. As shown in Figure 2, Drain builds a fixed-depth parsing tree. The log is preprocessed by a simple regular expression according to domain knowledge. The log group is first distinguished by the length of the log message (the number of tokens), after which the tokens in front of the log are identified. Then, token similarity is calculated to find the best match, and finally, the parsing tree is updated.  
Figure 1: LTAnomaly framework. Drain is used for log parsing, and semantic relationships and log component values are captured from the log sequence to generate a log feature matrix. Transformer is combined with LSTM for anomaly detection. Note: \(\oplus\) means a connection. \({}^{*}\) denotes a variable.  
Figure 3 shows that the log message is converted to a log template. For example, the log \"081109 203615 148 INFO dfs. DataNode$PacketResponder: PacketResponder 1 for block blk_38865049064139660 terminating\" is parsed by Drain into the log template \"PacketResponder <*> for block <*> terminating\", where \"<*>\" is the wildcard character. The log template is composed of fixed text strings.  
Figure 3: Example of log parsing. Note: * denotes a variable.  
Figure 2: The log parsing process for Drain. Note: * denotes a variable.
# Boundary-Aware Hashing for Hamming Space Retrieval
## 5 Conclusions  
We propose a new boundary-aware hash (BAH) for efficient Hamming space retrieval, consisting of a boundary-aware contrastive loss and a quantization loss. The core idea  
Figure 8: Sensitivity study for BAH using 16 bits and 64 bits on the four datasets: (**a**) the R@\(H\leq 2\) curves w.r.t \(\alpha\) for different datasets, (**b**) the P@\(H\leq 2\) curves w.r.t \(\alpha\) for different datasets, and (**c**) the MAP@\(H\leq 2\) curves w.r.t \(\alpha\) for different datasets.  
Figure 7: Comparison of R@\(H\leq 2\), P@\(H\leq 2\), and MAP@\(H\leq 2\) in BAH and competitors on training ImageNet dataset with noisy labels. (**a**) R@\(H\leq 2\). (**b**) P@\(H\leq 2\). (**c**) MAP@\(H\leq 2\).  
of boundary-aware contrastive loss is to discriminatively penalize the data inside and outside the Hamming ball by introducing boundary information (i.e., Hamming radius). It can effectively push dissimilar pairs from the Hamming ball and pull similar pairs into it. Moreover, it effectively solves the problem of imbalanced data without up-weighting similar pairs or other optimization strategies. Extensive experiments on four benchmark datasets have shown that the proposed BAH outperforms the state-of-the-art baselines.  
In the future, to perform Hamming space retrieval more effectively, we will further study the loss based on boundary-aware probability. The Hamming space retrieval hashing method is designed more efficiently by comparing and analyzing the ability to use boundary information between metric and probability. In addition, we apply the boundary-aware contrastive loss to cross-modal Hamming space retrieval to further verify the effectiveness of BAH.  
Conceptualization, W.H. and G.S.; Data curation, W.H. and Y.C.; Formal analysis, W.H., Y.C. and G.S.; Funding acquisition, L.W., G.S. and M.J.; Investigation, G.S. and M.J.; Methodology, W.H.; Project administration, G.S. and M.J.; Software, W.H. and Y.C.; Supervision, W.H., L.W., G.S. and M.J.; Validation, Y.C.; Visualization, W.H. and Y.C.; Writing--original draft, W.H.; Writing--review & editing, L.W., G.S. and M.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Beijing Postdoctoral Research Foundation: Q6042001202101.  
Not applicable.  
Data Availability Statement: Data sharing is not applicable to this paper as no new data were created or analyzed in this study.  
The authors declare no conflict of interest.
## 4 Analysis and Discussion
### Benefits of Using the Proposed Approach to Classify Land Use  
Accurate land use classification is of tremendous importance in scientific research and agriculture with the use of remote sensing data as an increasingly common practice for this purpose. Based on a CNN and taking complete advantage of the convolution in feature extraction, the design of the CNN architecture adapting to the features of the remote-sensing images is the key in land use classification by this method.  
We have taken the feature difference between the edge pixel and the inner pixel in the white wheat planting area into full consideration, this significantly improve the extraction accuracy of the edge pixel. Compared with earlier research, the model presented in this paper has the following advantages.  
Firstly, two types of kernels were used in the convolution of the model, which allowed the full utilization of the strength of the convolution in the extraction of spatial semantics and made appropriate use of the rich information contained in the pixels of the remote sensing images, thus achieving a more accurate segmentation.  
Secondly, pooling layers were not used in the model. Although the speed of the feature aggregation was consequently reduced, the information of the exact location to which an eigenvalue corresponds was retained, thereby effectively mitigating the loss in the accuracy due to the positioning error of the deconvolution and improving the overall effect of the segmentation.  
The model presented in this paper provides a solution for the edge extraction problem or the segmentation of the winter wheat plantation using GF-2 images. It has an important role to play and enhances the efficiency of the agricultural survey. This model has been utilized by the Department of Agriculture and the Meteorological Bureau of Shandong Province, China.
## 1 Introduction  
Cone-beam computed tomography (CBCT) was developed due to the need for a diagnostic tool that produces high-resolution, three-dimensional scans of the dentomaxillofacial region with lower radiation doses than medical CT [1, 2, 3]. CBCT has become an accepted diagnostic tool in daily endodontic practice [2, 3]. Numerous studies have addressed the use and efficacy of CBCT for various clinical indications in endodontics including: surgical was non-surgical treatment [4]; pre-operative versus intra- [5] and post-operative [4] evaluations; and the evaluation of anterior versus posterior teeth [6]. However, these studies present substantial inconsistencies in terms of study design, results, and conclusions [4, 5, 6]. Evidence-based dentistry (EBD) is an approach to oral healthcare that incorporates the best available clinical evidence to help practitioners make decisions for an individual patient's treatment needs and preferences [3, 7, 8, 9, 10]. Thus, a comprehensive review of the diagnostic efficacy and risk of bias assessment of CBCT imaging in endodontics literature, using evidence-based dentistry principles, would be of great use [3,8,9].  
A previous literature review of the diagnostic efficacy of CBCT imaging in endodontics [3] identified 485 possibly relevant articles. On closer examination, only five articles met the review's inclusion criteria for assessing the effects of CBCT on decision making, and only one article assessed CBCT's effect on treatment outcomes [3]. The authors concluded that the probable benefits of CBCT imaging for endodontic patients are not yet clear and that current knowledge is mainly limited to assessments based on low levels of efficacy [3]. Comparable results were also reported by [PERSON] et al. in 2015 [11], who assessed the diagnostic efficacy of CBCT for periapical lesions, and concluded that the efficacy of the technique has only been assessed in studies with low diagnostic efficacy [11].  
Since 2015, additional articles have been published regarding the indications of CBCT in endodontics [12, 13, 14, 15]. Thus, an updated systematic review of the available literature regarding the diagnostic efficacy of CBCT in endodontics and its effects on decision making and treatment outcomes is timely [7,8,11,16,17]. In addition, the previous reviews [3,11] concentrated on ranking the reviewed articles according to levels of diagnostic efficacy; however, the efficacy and methodological quality of CBCT in the context of specific clinical indications is as yet unclear [4]. To the best of our knowledge, a comprehensive analysis of the methodological quality and characteristics of the studies in the literature, and especially those that support a high level of CBCT diagnostic efficacy, has not yet been conducted.  
The aim of this study was to assess the studies with a high level of diagnostic efficacy and their risk of bias by a systematic review of the literature.
## 4 Monitoring Result of Asphalt Concrete in the Test
### Responses Induced by the Train Load  
Figure 7 shows the measured accumulative strains at the bottom of the asphalt concrete substructure and the earth pressure on the surface of the roadbed in the forced vibration tests. The results are positive, indicating that the bottom of the asphalt concrete layer is in the tensile state and the surface of the roadbed is in the compressive state under the action of the cyclic train load. Meanwhile, the tensile strains and the earth pressure near the expansion joint of the base plate are larger than those near the middle position of the base plate. More specifically, for the asphalt concrete slice 1 (near the expansion joint), the maximum earth pressure on the surface of the roadbed is 40 kPa, 55 kPa, and 76 kPa in test-1, test-2, and test-3, respectively. The maximum tensile strains at the bottom of the asphalt concrete layer are 29 \(\upmu\upvarepsilon\), 47 \(\upmu\upvarepsilon\), and 85 \(\upmu\upvarepsilon\) in test-1, test-2, and test-3, respectively. Meanwhile, for slice 2 (near the middle position), the maximum earth pressure is 30 kPa, 50 kPa, and 64 kPa and the maximum tensile strains are 23 \(\upmu\upvarepsilon\), 35 \(\upmu\upvarepsilon\), and 57 \(\upmu\upvarepsilon\) in test-1, test-2, and test-3, respectively.  
As is known to all, under normal conditions, the vibration of the base plate and the vertical pressure should be more obvious near the vibration exciter. However, the expansion joints are weak points in the whole ballastless track system. It may result in different vibration characteristics of the base plate near the expansion joint, which may lead to greater vertical earth pressure in this place. However, the vibration mechanism is unclear currently. Further studies about this are required based on the relevant vibration theory.  
Figure 6: Arrangement of sensors in the test (unit: mm).  
Besides, with the increase in the vibration frequency and the amplitude of the vibration force (actually, the train speed and axle load), the strain and the earth pressure increase, and the growth rate near the expansion joint is mostly larger than that near the middle of the base plate.  
Figure 7: Measured accumulative strains and the earth pressure in the forced vibration tests: (**a**) maximum value of the earth pressure; (**b**) average value of the strain; and (**c**) maximum value of the strain.
## 3 Proposed Scheme  
In this section, we first describe the data collection and preprocessing process. Next, we present an MAML-based few-shot ASC process. Finally, the learning process of the proposed scheme is presented in detail. Figure 1 and Algorithm 1 show the overall architecture of TAPT and the meta-training process of TAPT, respectively.  
```
0: Original waveform data \(D_{in}\), learning rates \(\alpha\), \(\eta\) Output: Pretrained parameters \(\theta\) from a ASC model \(f_{\theta}\)
1:\(D_{in^{\prime}}\gets STFT(\mathit{Padding}_{1,\sec}(\mathit{Sampling}_{16 \mathrm{kHz}}(D_{in})))\)
2: Construct a set of meta-training tasks \(S_{task}=\{T_{1},\,\ldots,\,T_{N}\}\) (\(T_{i}\): \(m\)-way \(k\)-shot task) from the meta-training set \(M_{train}\)
3: Randomly initialize \(\theta\), \(\varpi_{\gamma}\), \(\varpi_{\beta}\)
4: Let \(\theta=\left\{\theta_{j}^{j}\right\}^{j=1..1}\) where \(j\) is the layer index and \(l\) is the number of layers of a network
5:while meta-learning epochs do
6: Sample a batch from task \(T_{i}\)
7:for each task \(T_{i}\)do
8: Sample data samples \((D_{T_{i}}\), \(D^{\prime}_{T_{i}})\) from \(T_{i}\)
9: Compute \(\
abla_{\theta}\mathcal{L}_{T_{i}}^{D_{i}}(f_{\theta})\)
10: Generate layer-wise transformation parameters \((\gamma,\ \beta)\)
11:\(\left\{\gamma^{j}\right\}^{j=1..1}=g_{\varpi_{\theta}}(\
abla_{\theta} \mathcal{L}_{T_{i}}^{D_{i}}(f_{\theta})\)
12:\(\left\{\beta^{j}\right\}^{j=1..1}=g_{\varpi_{\beta}}(\
abla_{\theta}\mathcal{ L}_{T_{i}}^{D_{i}}(f_{\theta})\)
13: Compute transformed initial parameters: \(\overline{\theta}_{i}^{j}=\gamma_{i}^{j}\theta^{j}+\beta_{i}^{j}\)
14: Initialize \(\theta_{i}^{\prime}=\left\{\overline{\theta}_{i}^{j}\right\}^{j=1..1}\)
15:for number of inner-loop updates do
16: Compute \(\mathcal{L}_{T_{i}}^{D_{i}}\left(f_{\theta_{i}^{\prime}}\right)\)
17: Perform gradient descent to compute transformed parameters: \(\theta_{t}=\theta_{t}-\alpha\
abla_{\varphi_{i}}\mathcal{L}_{T_{i}}^{D_{T_{i}} }(f_{\varphi_{i}})\)
18:endfor
19: Compute \(\mathcal{L}_{T_{i}}^{D_{T_{i}}}\left(f_{\theta_{i}^{\prime}}\right)\)
20:endfor
21: Perform gradient descent to update weights: \(\left(\theta,\varpi_{\gamma},\varpi_{\beta}\right)\leftarrow\left(\theta, \varpi_{\gamma},\varpi_{\beta}\right)-\eta\
abla_{(\theta,\varpi_{\gamma}, \varpi_{\beta})}\sum\limits_{i=1}^{N}\mathcal{L}_{T_{i}}^{D^{\prime}_{T_{i}}} \left(f_{\theta_{i}^{\prime}}\right)\)
22:endwhile
```  
**Algorithm 1** Meta-training process of TAPT  
Figure 1: Overall structure of MAML-based ASC.