esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Interest Context Network for Click-Through Rate
## 4 Experiments and Analysis
### Comparing Models  
In order to verify the performance of the model in this paper, the following classic and widely used models are used for comparison.  
* **Base Model**[14]: The base model proposed by [PERSON] et al. in the DIN paper, the Embedding and MLP methods, is the basic framework of deep networks for most CTR models.
* **PNN**[13]: An improved version of the base model. The PNN model introduces a product layer after the embedding layer, through which the interaction of higher-order features is captured.
* **DeepFM**[10]: The prototype of the DeepFM model is the Wide and Deep [11] model, which is mainly composed of two parts: (1) the wide part composed of the FM, which handles the artificially designed cross-product features, and (2) the deep model part, which performs deep learning; the nonlinear relationship between the features is automatically extracted, that is, the fully connected part mentioned above.
* **xDeepFM**[12]: With the same idea as the DCN [36] model, xDeepFM is an improved version of the Wide and Deep model. The difference is that DCN replaces the logistic regression part of the Wide and Deep model with a cross-layer, which effectively alleviates the problem of gradient disappearance. xDeepFM adds the compressed sensing layer (CIN) module to the Wide and Deep model, and compresses the three-dimensional matrix obtained by the interaction of the features through CIN, so as to perform feature interaction.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datasets** & **Features** & **Numbers** & **Total Samples** \\ \hline \multirow{6}{*}{Taobao} & Users & 376 & \multirow{6}{*}{11,198} \\  & Items & 9066 & \\ \cline{1-1}  & Categories & 1248 & \\ \cline{1-1}  & Behavior Types & 4 & \\ \cline{1-1}  & Timestamps & 11,198 & \\ \hline \multirow{6}{*}{Amazon} & Users & 88,462 & \multirow{6}{*}{91,206} \\  & Items & 8510 & \\ \cline{1-1}  & Scores & 5 & \\ \cline{1-1}  & Timestamps & 91,206 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic statistics for datasets.  
* **DIN**[14]: Based on the base model, a local activation unit is added to set the weight of the user's historical behavior, so as to explore the similarity between the user's historical behavior and the items to be recommended.
* **DICN**: This is the new model proposed in this paper, as introduced in Section 3. Two local activation units explore the similarity of user historical behavior, items to be recommended and contextual features.
# On Site Investigation and Health Monitoring of a Historic Tower in Mantua, Italy
## 4 Continuous Dynamic Monitoring of the _Gabbia_ Tower
### Temperature Effects on Natural Frequencies: Dynamic Regression and ARX Models
It is worth mentioning that: (a) in the framework of a dynamic monitoring project, different input candidates might be considered in the dynamic regression model (1) and (2), such as temperature, humidity, wind speed, traffic loads on bridges, _etc._ (see e.g., [24; 25; 26]); (b) in order to properly describe the influence of changing environmental conditions, the data used to estimate the parameters of the regression models should be collected over a significant period of time, denoted as the training period (or reference period), and including a statistically representative sample of temperature conditions. Once DR or ARX models have been established for the identified frequencies, the impact of changing environment on each modal frequency should be eliminated (or at least minimized) by defining the following quantities:  
1. The residual error vectors \(\varepsilon_{i}=\mathbf{y}_{i}-\mathbf{X}_{i}\mathbf{b}_{i}\) (\(i=1,\,2,\,\ldots\,,\,M\) where \(M\) is the number of automatically identified modal frequencies); and
2. The cleaned observation vectors \(\mathbf{y}_{i}{}^{*}=\mathbf{1}\,\mathbf{u}_{i}+\varepsilon_{i}\) (\(i=1,\,2,\,\ldots\,,\,M\) where \(\mathbf{1}\) is the unit vector and \(\mathbf{u}_{i}\) is the mean value of the identified \(i\)-th frequency data in the reference period). Hence, both the residual error and the cleaned observation vectors might be used to identify the occurrence of abnormal structural changes or damages not observed during the training period.
## Enforcing Security Mechanisms in the IP-Based Internet of Things: An Algorithmic Overview  
**[PERSON] *, [PERSON] and [PERSON]**  
Department of Information Engineering, University of Parma, Parco Area delle Scienze 181/A, 43124, Parma, Italy; E-Mails: [EMAIL_ADDRESS] (G.F.); [EMAIL_ADDRESS] (L.V.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +39-0521-905741; Fax: +39-0521-905758.  
_Received: 6 December 2012; in revised form: 8 February 2013 / Accepted: 7 March 2013 / Published: 2 April 2013_  
**Abstract:** The Internet of Things (IoT) refers to the Internet-like structure of billions of interconnected constrained devices, denoted as \"smart objects\". Smart objects have limited capabilities, in terms of computational power and memory, and might be battery-powered devices, thus raising the need to adopt particularly energy efficient technologies. Among the most notable challenges that building interconnected smart objects brings about, there are standardization and interoperability. The use of IP has been foreseen as the standard for interoperability for smart objects. As billions of smart objects are expected to come to life and IPv4 addresses have eventually reached depletion, IPv6 has been identified as a candidate for smart-object communication. The deployment of the IoT raises many security issues coming from (i) the very nature of smart objects, e.g., the adoption of lightweight cryptographic algorithms, in terms of processing and memory requirements; and (ii) the use of standard protocols, e.g., the need to minimize the amount of data exchanged between nodes. This paper provides a detailed overview of the security challenges related to the deployment of smart objects. Security protocols at network, transport, and application layers are discussed, together with lightweight cryptographic algorithms proposed to be used instead of conventional and demanding ones, in terms of computational resources. Security aspects, such as key distribution and security bootstrapping, and application scenarios, such as secure data aggregation and service authorization, are also discussed.  
**Keywords:** security; lightweight cryptography; Internet of Things (IoT); smart objects; secure communication protocols; secure data aggregation
# QoE Modeling on Split Features with Distributed Deep Learning
## 5 Distributed Learning Approaches on Split Feature Scenarios  
We study two distributed learning techniques as detailed in this Section, _DEL_ and _vFL_. In this experiment setup, we formulate both problems as binary classifications. The labels that are ranging between 11 and 97 are quantized into two classes: the values that are less than the median of the MOS scores (62) are considered to be the \"poor-or-worse\" class 0, and the labels that are more than the median of the MOS scores are considered to be the \"good-or-better\" class 1. Different from the state-of-the-art [36] definition of this split, we do not put a buffer zone between the two classes as was done for instance in the E-model, in order not to decrease the sample size in the dataset further.  
In the experiments under this Section, the performance evaluation of the corresponding scenarios are performed via at least 10 iterations. Due to the binary classification formulation of the estimation models, evaluation metrics such as _precision_, _recall_, and _F1-score_ are considered. _Precision_ is a measure that calculates the ratio between the number of true \"good-or-better\" estimations and the total number of \"good-or-better\" estimations, i.e., the number of true positives divided by the sum of numbers of true and false positives. _Recall_ is the ratio of true \"good-or-better\" estimations to the number of actual \"good-or-better\" values, i.e., the number of true positives divided by the sum of the number of true positives and the number of false negatives. The harmonic mean of precision and recall yields the _F1-score_ in decimal points ranging between 0 and 1. A higher F1-score indicates a better-performing model.  
We use boxplots, 95% confidence intervals and the _[PERSON] post hoc test_[37] to quantify the pair-wise statistical significant difference between the scenarios. We evaluate the F1-score model accuracy on the test sets, after the training phase is completed. The improvement percentages of accuracy values, \(x\), are computed as given in Equation (1).  
\[\frac{x_{\text{improved}}-x}{x}*100 \tag{1}\]  
For example, if the mean F1-score of all experiment iterations of scenario A is 0.80; and if the mean F1-score of all experiment iterations of scenario B is 0.896; and if the two distributions are statistically significantly different according to _[PERSON] post hoc test_ (\(p\)-value less than 0.05), then we consider a statistical significant 12% improvement in F1-score in Scenario B as opposed to Scenario A.  
Figure 4: SHAP values of input features.
## 1 Introduction
[13] used the mode superposition method to calculate the transmission loss of a diesel exhaust muffler under the acoustic-structure coupling effect and found that the average transmission loss decreased by 7.4%. [PERSON] et al. [14] derived the finite element equations for a muffler system under the acoustic-structure coupling effect and theoretically analyzed the relationships between the coupling system and its structural and acoustic modes. [PERSON] et al. [15] conducted a comparative analysis of the radiated noise from two improved straight-through perforated tube mufflers, taking into account the acoustic cavity structure coupling, by employing the direct boundary element method. [PERSON] et al. [16] selected a flexible-walled expansion muffler as the research object. A sound-structure coupled analytical model was established and verified for this muffler. The silencing performances of rigid-walled and flexible-walled mufflers were then analyzed comparatively. While the studies mentioned above offer valuable references for investigating the acoustic performance of mufflers under acoustic-structure coupling, they have limitations when it comes to analyzing the acoustic attenuation performance of split-stream rushing exhaust mufflers under such effects. As a result, their findings cannot be directly applied to engineering practices. Accurate design and optimization of the muffler can only be achieved through a thorough understanding of its acoustic-structure coupling effects. The working principle of the split-stream rushing muffler is illustrated in Figure 1[5]. When airflow enters through the inlet, it is directed by the conical surface at point A into the ring cavity. From there, the airflow is divided into two groups and passes through the rushing slots at points B, C, and D, E. These two rushing airflows, moving at equal velocities but in opposite directions, converge and collide at the center of the inner cavity and effectively reduce the airflow velocity. Finally, the combined airflow exits through the tailpipe. This design achieves noise reduction and lowers exhaust resistance by decreasing the airflow velocity within the muffler. This research focuses on a split-stream rushing exhaust muffler as its research object and employs COMSOL simulation software to perform relevant analyses of the structural modes, acoustic cavity modes, and coupled modes of the muffler. Furthermore, the impact of the acoustic-structural coupling effect on its acoustic performance was investigated using the acoustic finite element method, and the findings were validated experimentally. Based on this, a single-factor optimization study was performed on the structural parameters of this muffler. This investigation aimed to elucidate the patterns of influence of individual factors on transmission loss and to identify a muffler structural parameter configuration that yields better acoustic performance.
## 5 System Simulation
### Energy Calculations
Finally, we calculate the network life-time in terms of the first node dead (FND) and the residual energy after multiple rounds of data exchanged. As we couldn't find exact energy calculations of compute one byte of PBC authentication, we consider it to be x% of the energy to receive one byte  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Cumulative Number of Bytes Up to each Pass**} \\ \hline  & & **Pass** & **2** & **8** & **14** & **20** \\ \hline \multirow{4}{*}{
\begin{tabular}{c} **CENTERA**  
(as it is higher). We vary x between 10%, 50%, and 100%. Note that it is well known that the energy to compute is much lower than the energy to transmit/receive a byte, thus our chosen values of x are all high assumptions to check the worst case of the energy life-time. We consider that each sensor node has a small battery of 15,000 J and perform the life analysis as follows. Table 11 shows the network life-time calculations while varying the value of x between 10%, 50% and 100%. The results are the average energy consumption in the first pass and the average in the following passes in mJ. Also it shows that the first node dies after over 203,073 passes when x = 10% and over 83,482 passes even when considering the energy to compute one byte equal that to receive one byte. Figure 12 shows the average residual energy in every node starting with a full 15,000 J battery at multiple of ten passes. It shows the low energy consumption of CENTERA, since even when considering the energy to compute one byte equals that to receive a byte, the average residual energy after 1,000,000 passes is still 13,497 J which is around 90% of the battery charge; whereas the battery is still over 93.5% assuming x = 10%. Figure 13, shows the residual energy for the most active node, or closest to the BS. It can be seen that even in the most unfair assumption of x = 100%, the first dead node is seen at over 80,000 passes. Also, the figure shows that assuming x = 10%, the residual energy in the worst case is a bit over 50%. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**X**} & \multicolumn{2}{c}{**10\%**} & \multicolumn{2}{c}{**50\%**} & \multicolumn{2}{c}{**100\%**} \\ \cline{2-7}  & **First Pass** & **Av** & **First Pass** & **Av** & **First Pass** & **Av** \\ \hline Energy Node Av. (mJ) & 33.16 & 9.61 & 36.79 & 11.83 & 41.34 & 14.59 \\ Energy Worst Case (mJ) & 47.79 & 73.86 & 87.83 & 120.89 & 137.88 & 179.68 \\ \hline Av. Node Dead (Passes) & 1,560,206.07 & 1,268,336.34 & 1,027,959.17 \\ FND (Passes) & 203,073.02 & 124,076.20 & 83,482.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Network Life-time Calculations. Figure 12: Average node residual energy in J.
## 5 Understanding Decentralised Identity Systems
### Security and Privacy Considerations  
DIMSs are designed to promote user privacy and prevent centralised authorities from controlling people's identities. These systems must be extremely secure, employing strict standards to preserve data and assure the accuracy of identity-related actions. Advanced encryption ensures the security of user information. Some decentralised models also take a \"privacy-by-design\" approach, utilising technologies such as ZKPs and selective disclosure. This enables users to verify their identities without disclosing their actual details [51; 55].  
Although safe to some extent, the DIMS also has several weaknesses. In a DIMS, users are the owners of their identity, which is secured through encryption. The owners are responsible for keeping their private keys. If the key is lost, there is no central authority to assist in revoking or recovering the key [58; 59].  
Figure 7: Sequence diagram of DIMS.
# Tectonic Subsidence and Its Response to Geological Evolution in the Xisha Area, South China Sea
## 5 Discussion
### Tectonic Subsidence of Pliocene (5.5-1.9 Ma) to Present  
Fifth PhaseTransitional Phase from the Pliocene (5.5-1.9 Ma) to Present  
The slow sedimentary rate also responded to the slow tectonic subsidence, as shown in Figure 8. We should be cautious in interpreting the tectonic subsidence as a result of the activity of regional geological events, such as the volcanism, the dextral movement of the Red River Fault Zone and the transgression of the continental shelf from west to east [4; 5; 9]. Theoretically, when the nature of the Red River Fault Zone changed from sinistral to dextral movement, the extrusive component could have disappeared and shifted to an extensive component according to stress-field analysis, as shown in Figure 10B, which would lead to regional stress to absolute release and further cause rapid tectonic subsidence. Indeed, this rapid tectonic subsidence took place and was accompanied by diapirs and high rate of sedimentation in the Yinggehai Basin, the Beibuwan Basin, and the northern Qiongdongnan Basin [8; 17; 38; 48; 53; 54].  
However, this rapid subsidence did not prospectively occur in the Xisha area, but slow subsidence did, perhaps due to volcanism in this period. The volcanism was fierce in the Xisha area, different from the weak volcanism to the west of study area, constrained by seismic stratigraphy and well data since 5.5 Ma [24; 55; 56]. Unlike the magmatism in the rifted stage, which overflowed along the major faults, the vertical intrusive magma appeared to uplift the overburden or extrude peripheral strata and lead them to rise after the late Miocene [5; 57]. This vertical intrusion, associated with the Hainan Mantle Plume, probably hampered the regional tectonic subsidence. The statistics of drill cores also revealed that most of the volcanoes were primally active in the Pliocene and gradually weakened from 1.9 Ma [58], which should be responsible for the slow rise in subsidence rate in the Pleistocene.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Age** & **Stages** & **Scales (km2)** \\ \hline \multirow{4}{*}{Middle Miocene} & \multirow{2}{*}{thriving} & 80,500 \\ \cline{3-3}  & & 63,500 \\ \cline{2-3}  & & 40,300 \\ \cline{2-3}  & & 25,500 \\ \hline Late Miocene & \multirow{2}{*}{drowning} & 5450 \\ \cline{2-3}  & & 870 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The statistical area of reef and carbonate platform in each stage, modified from Yang at al. [36].
## 3 Results and Discussion
### Error Analysis  
Figure 11 shows the results of the error analysis for the validation conditions. The horizontal axis of the figure is the crank angle, the vertical axis is the error value, the black horizontal line indicates 10% error line, and the colored dashes represent the specific error between the predicted and actual values for all samples in different validation operating conditions. From Figure 11a,b, it can be seen that the error between the predicted and actual values for all samples in the validation condition 1, 2, and 4 is less than 10%, and the prediction error in the range of 0\(\sim\)180\({}^{\circ}\) CA ATDC is relatively larger. There are more samples with prediction errors greater than 10% in condition 5 and 6. After counting, the percentage of samples with all prediction errors below 10% in the validation set is 94%.  
Figure 9: Regression analysis of validation set.  
Figure 10: Comparison between predicted value and actual value of in-cylinder pressure in validation conditions.
# Contour Mission Flight Planning of UAV for Photogrammetric in Hillside Areas
## 3 Simulation Test  
To verify the differential effectiveness of the contour-type flight planning method proposed in this study in mountainous areas, we will discuss it through two simulation zones. Simulation Case 1 is a general slope in Nanhua District, and Simulation Case 2 is a hillside terrain in Maolin District where landslides occur. Two regions with different surface changes were selected. The existing DEMs were used to simulate the planning and to compare and analyze the differences. General users commonly use a GSD of 4 cm and an overlap rate of 80% for planning. The calculated flying height is 200 m, the flight route spacing N is 140 m, and the elevation difference threshold (D) is 34 m.
# A Conceptual Design of Deployable Antenna Mechanisms
## 2 Materials and Methods  
### A Conceptual Design of the Deployable Antenna Mechanism  
Figure 1a shows sequential images of when the proposed deployable antenna mechanism moves from the stowed to the deployed stages. As depicted in Figure 1b,c, the deployable antenna mechanism hierarchically consists of (1) a 6-bar linkage mechanism that connects these two mechanisms, resulting in the deployment, (2) a single pantograph mechanism that forms the lower ring, and (3) a V-folding mechanism that forms the upper ring.
# Rectification for Stitched Images with Deformable Meshes and Residual Networks
## 3 Materials and Methods
### Loss Function
#### 3.3.1 Local Loss  
The content loss term in the RIS-DMRN consists of two components: content loss and mesh loss. The content loss term, represented by Equation (2), involves the comparison between the predicted mesh (\(m\)) applied to the input irregular image (\(I_{p}\)) and the warped version of the irregular image (\(D\left(I_{p},m\right)\)) using the bending operation. Additionally, the content loss incorporates the difference between the predicted mesh and the ground truth mesh (\(T\)). The function \(C\), denoting the \"conv4\" convolutional layer in the width recognition network, plays a role in shaping the content loss term. This formulation aims to ensure that  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Block Type = B(3,3)** & **Output** \\ \hline conv1 & \(\left[3\times 3,16\right]\) & \(32\times 32\) \\ conv2 & \(\left[3\times 3,\,16\times k\right]\times\text{N}\) & \(32\times 32\) \\  & \(\left[3\times 3,\,16\times k\right]\) & \\ conv3 & \(\left[3\times 3,\,16\times k\right]\times\text{N}\) & \(16\times 16\) \\  & \(\left[3\times 3,\,16\times k\right]\times\text{N}\) & \(8\times 8\) \\ conv4 & \(\left[3\times 3,\,16\times k\right]\times\text{N}\) & \(1\times 1\) \\ avgpool & \(\left[8\times 8\right]\) & \(1\times 1\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Schematic structure of width residual neural network. In the table, “k” represents the multiplier for the convolutional kernels in the original module, “N” indicates the number of residual modules in that layer, and “B (3,3)” signifies each residual module consisting of two 3 \(\times\) 3 convolutional layers. The image undergoes processing through a mean pooling layer, resulting in the final output image.  
the rectified image aligns closely with both the original irregular content and the ground truth mesh structure.  
\[l_{content}=\left\|T-D(I_{p},m)\right\|_{2}+\left\|C(T)-C(D(I_{p},m))\right\|_{2} \tag{2}\]  
For the mesh loss term in RIS-DMRN, the formula can be expressed as Equation (3):  
\[l_{mesh}=\sum_{i,j}\left\|W(I_{p},m_{i,j})-W(I_{p},T_{i,j})\right\| \tag{3}\]  
where \(l_{mesh}\) represents the mesh loss term, \(i\) and \(j\) are indices within the mesh, \(m_{i,j}\) is the predicted mesh by the model, \(T_{i,j}\) is the ground truth label mesh, and \(W\) is the mesh generation function. This loss term aims to encourage the model to better learn and preserve the mesh structure of the image by comparing the differences between the predicted mesh by the model and the true label mesh.