esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on the Remaining Life Prediction Method of Rolling Bearings Based on Multi-Feature Fusion
## 3 Prediction of RUL Based on the Multi-Feature Fusion Method  
The precise RUL of a bearing in real industrial production processes is uncertain at any particular point. Therefore, analyzing the historical operating data of existing bearings is crucial for industrial safety as it allows for an accurate projection of the lifespan of other bearings. To this end, a RUL prediction approach is proposed that utilizes multi-feature fusion, which consists of four main parts: multi-sensor data fusion processing, parallel TCN-LSTM-Transformer feature extractor construction, parallel multi-scale attention mechanism design, offline model training, and online bearing RUL prediction. The structure of the lifetime prediction network, which utilizes multi-feature fusion, is illustrated in Figure 5. Firstly, the multi-sensor data is normalized, and the sensor data is combined in each channel to produce an optimal fusion of data from several sensors; Secondly, a parallel multi-branch feature learning network was constructed using TCN, LSTM, and Transformer, where TCN extracts the local features of the data, LSTM captures the long-term dependencies present in the sequential data, and Transformer extracts the global feature representation of the bearing data. Meanwhile, a parallel multi-scale attention mechanism that captures both local and global dependencies is designed to go a step further in capturing the global and local contextual information of the sequence in order to accomplish adaptive weighted fusion of the output features from the three feature extractors. Next, the shallow features obtained by the parallel feature extractor are residually connected to the deeper features through the attention process, which enhances the efficiency of utilizing both the preceding and subsequent feature information. Finally, the fused features output RUL predictions through the fully connected layer.
# Precoding for RIS-Assisted Multi-User MIMO-DQSM Transmission Systems
## 2 System Model  
The RIS-MU-DQSM system model is presented in Figure 1. We assume that there is not a direct link between the transmitter (Tx) and receivers (Rx), which can be due to unfavorable propagation conditions or blockings by obstacles [24; 25]. All communication passes through the RIS. The BS is equipped with \(N_{t}\) Tx antennas, while the destination is composed of \(K\) users or MS, each one equipped with \(N_{r}\) Rx antennas. The RIS uses \(N_{s}\) reflective surfaces. Thus, the end-to-end configuration can be considered as a \((K\times N_{r})\times N_{s}\times N_{t}\) RIS-assisted MU-MIMO-DQSM transmission system, hereinafter referred to as RIS-MU-DQSM.  
The channel matrix between the BS and the RIS is defined as \(\mathbf{G}\in\mathbb{C}^{N_{s}\times N_{t}}\). The channel matrix between the RIS and the \(k\)-th MS is defined as \(\mathbf{H}_{k}\in\mathbb{C}^{N_{r}\times N_{t}}\). The channel model is defined for two scenarios. First, a quasi-static Rayleigh fading channel is considered where its elements are assumed to be independent and identically distributed (i.i.d.) complex Gaussian random variables with mean zero and variance one, \(\mathcal{CN}(0,1)\). Additionally, a more realistic channel model where the spatial correlation between the RIS mirrors is considered. We assume that perfect CSI is available at the BS and all MS or users. It is worth noting that obtaining CSI can be a challenging task [26]. As has been proposed in previous works, this task can be carried out using control links between the BS and the users when the line-of-sight (LoS) path is absent in this channel [27; 28]. However, the design of CSI dissemination strategies is beyond the scope of this work.
## 1 Introduction
NTK, while offering a mathematical framework for extending context windows, can suffer from practical issues such as out-of-bounds rotation angles, leading to suboptimal extrapolation performance. YaRN attempts to mitigate some of these issues by partitioning the NTK approach, but it introduces additional complexity and necessitates fine-tuning of hyperparameters for each specific model. To address these limitations, we propose Segmental Base Adjustment for RoPE (SBA-RoPE), a novel technique aimed at expanding the context window of pre-trained LLMs by strategically adjusting the base values used in RoPE. By selectively extrapolating high-frequency dimensions and interpolating those with maximum angles less than \(2\pi\), we treat length extrapolation as a prediction-stage Out-Of-Distribution (OOD) problem. RoPE allocates different angles to different dimensions, with some high-frequency dimensions having fully learned all angles within 0 to \(2\pi\). Extrapolating these dimensions does not degrade performance, as these angles have been thoroughly trained during pre-training. However, some low-frequency dimensions have only learned partial angles within 0 to \(2\pi\), making them unable to extrapolate on longer texts and only able to interpolate. Thus, due to the periodic nature of trigonometric functions, even with high-frequency dimensions extrapolated, the thoroughly trained angles within 0 to \(2\pi\) during pre-training do not cause a perplexity explosion problem. Meanwhile, by selecting interpolation for low-frequency dimensions, OOD is avoided for these dimensions. This process is illustrated in Figure 1. SBA-RoPE facilitates an efficient extension of the context window with minimal fine-tuning. This method not only preserves the model's performance for tasks within the original context window, but also enhances its adaptability to tasks that demand longer contexts. Our contributions are as follows:  
* We introduce SBA-RoPE, a novel method for extending the context window of LLMs by segmentally adjusting the base of Rotary Position Embeddings, requiring only minimal fine-tuning steps. * For tasks within the original context window, our method minimally impacts model performance, showing minimal degradation compared to the original Pythia-2.8B model based on the GPT-NeoX architecture. * For tasks in the extended context window, our method achieves comparable or superior performance on pasaskey and perplexity tasks, indicating our model's ability to generalize to longer lengths without sacrificing performance.
# Phishing Attacks Survey: Types, Vectors, and Technical Approaches
## 3 Phishing Methods and Techniques
### Phishing Technical Approaches
#### 3.3.21 Session Fixation  
This type of attack is conducted when the session ID used for the users' authentication process is not protected. The hacker makes use of packet sniffing tools to determine the session ID and session key. With this information, the hacker is able to masquerade as the certified authority and perform spoofing attacks. Examples of packet sniffing tools are Wireshark Packet Sniffer and SmartSniff.  
Another method in which this is achieved is when an attacker has gained access to a user's account credentials. Various techniques can be employed to achieve this, ranging from phishing to using spyware and cookie poisoning. When an account has been successfully hacked, an attack can be used to obtain a user's personal information and any corporate data he may hold, in addition to jeopardizing his cloud computing services. Thus, an unauthorized user is able to gain access to a legitimate user's account.  
Users can detect if they have been hijacked by checking if any threats have been made toward their cloud computing account. When a threat is detected, hardware and software used to combat the issue should be installed because these protect data from attackers. Emails should always be double-checked and verified before clicking on any embedded links, particularly if the emails appear to be important for work or requests to reset passwords.
# Performance Analysis of Dual-Hop Hybrid RF-UOWC NOMA Systems
## 4 Ergodic Capacity Analysis
### Ergodic Capacity \(EC_{x_{2}}\)  
The CDF \(F_{\gamma_{b}}(\gamma)\) is given as  
\[\begin{split} F_{\gamma_{b}}(\gamma)&=1-pr(\gamma_{K }^{2}>\gamma,\gamma_{D2}^{2}>\gamma)\\ &\overset{(e)}{=}1-\Pr(|h_{w}|^{2}>\frac{\gamma}{\rho_{s}d^{-v}(a_ {2}-\eta a_{1}\gamma)})\times pr(|h_{2}|^{2}>\frac{\gamma}{\rho_{R}(a_{2}-\eta a _{1}\gamma)}),\end{split} \tag{24}\]  
where \((e)\) stems from the independence of the channels gain and \(0<\gamma<\frac{\rho_{2}}{a_{1}\eta}\). Then\[EC_{x_{2}}=\frac{1}{2\ell n2}\int\limits_{\gamma=0}^{a_{2}/qa_{1}}\frac{1}{1+ \gamma}(1-F_{[h_{w}]^{2}}(\frac{\gamma}{\rho_{s}d^{-v}(a_{2}-\eta a_{1}\gamma)}))( 1-F_{[h_{2}]^{2}}(\frac{\gamma}{\rho_{R}(a_{2}-\eta a_{1}\gamma)}))d\gamma, \tag{25}\]  
then applying variable transformation of \(\tau_{b}=\gamma/(a_{2}-\eta a_{1}\gamma)\) and using the Rayleigh CDF (3) and the tight approximated EGG CDF at high SNR (11), we can write  
\[\begin{split} EC_{x_{2}}&=\frac{1}{2\ell n2}\int \limits_{\gamma=0}^{\infty}\frac{e^{-\phi_{1}\tau_{b}}\Big{(}1-\phi_{2}\tau_{ b}\Big{(}1-\phi_{2}\tau_{b}\Big{)}-\phi_{3}\tau_{b}\Big{)}}{(1+\eta a_{1} \tau_{b})(1+(a_{2}+\eta a_{1})\tau_{b})}d\tau_{b}\\ &=\frac{1}{2\ell n2}(I_{1}-\phi_{2}I_{2}-\phi_{3}I_{3}),\end{split} \tag{26}\]  
where  
\[\begin{split} J_{M}&=\int\limits_{\gamma=0}^{ \infty}\frac{\tau_{b}^{Y_{M}}e^{-\phi_{1}\tau_{b}}d\tau_{b}}{(1+\eta a_{1}\tau_ {b})(1+(a_{2}+\eta a_{1})\tau_{b})}\\ &=\frac{1}{a_{2}}\int\limits_{\gamma=0}^{\infty}\frac{\tau_{b}^{ Y_{M}}e^{-\phi_{1}\tau_{b}}d\tau_{b}}{(\frac{1}{(a_{2}+\eta a_{1})}+\tau_{b})}- \frac{1}{a_{2}}\int\limits_{\gamma=0}^{\infty}\frac{\tau_{b}^{Y_{M}}e^{-\phi_ {1}\tau_{b}}d\tau_{b}}{(\frac{1}{\eta a_{1}}+\tau_{b})},\end{split} \tag{27}\]  
where \(M\in\{1,2,3\}\) and \(Y_{M}\) is the \(M\)th element in the vector \(Y=[0,(1/r),(ac/r)]\). Using [23] (Equation 3.383.10), \(J_{M}\) can be expressed as  
\[J_{M}=\frac{\Gamma(Y_{M}+1)}{a_{2}}[((\frac{1}{a_{2}+\eta a_{1}})^{Y_{M}}e^{ \frac{\phi_{1}}{(a_{2}+\eta a_{1})}}\Gamma(-Y_{M},\frac{\phi_{1}}{(a_{2}+\eta a _{1})}))-((\frac{1}{\eta a_{1}})^{Y_{M}}e^{\frac{\phi_{1}}{\eta a_{1}}}\Gamma( -Y_{M},\frac{\phi_{1}}{\eta a_{1}}))]. \tag{28}\]  
By Substituting (28) into (26), a closed-form expression of \(EC_{x_{2}}\) is obtained.
## 6 Worst-Case Latency Analysis of the FWOS Model
### Worst-Case End-to-End Latency for \(Q_{k}\) Traffic  
The network calculus (NC) approach considers the upper bound of the arrival curve and the service curve's lower bound to compute the upper bound end-to-end latency. For every node \(h\) in the selected networking path, the upper arrival curve \(\alpha_{k}^{h,u}(t)\) and the lower service curve \(\beta_{k}^{h,l}(t)\) have to be defined to calculate the maximum experienced latency in each link. The upper arrival curve at the source node can be found using Equation (3). After that, in every node \(h\) from the selected path, the aggregate upper arrival curve at the ingress port for \(Q_{k}^{h}\) traffic can be determined using the ingress arrival curve at the previous node, as follows:  
\[\alpha_{k}^{h,u}(t)=\alpha_{k}^{h-1,u}\Big{(}t+WCD_{k}^{h-1}\Big{)} \tag{36}\]  
where \(WCD_{k}^{h-1}\) is the maximum latency experienced by \(Q_{k}^{h-1}\) traffic in \(h-1\). This latency equals the largest horizontal distance between the upper arrival curve at the ingress port of node \((h-1)\) and the lower service curve of the related resource, as follows [13]:  
\[WCD_{k}^{h-1}=H\Big{(}\alpha_{k}^{h-1,u}(t),\ \beta_{k}^{h-1,l}(t)\Big{)} \tag{37}\]  
Then, we can calculate the upper bound of the end-to-end latency as the summation of the maximum latencies experienced in all nodes through the selected path, as  
\[WCD_{k}^{total}=\sum_{h=1}^{N}WCD_{k}^{h} \tag{38}\]  
where \(N\) is the number of nodes that the \(k\)-th-priority traffic passed through. According to all mathematical analysis presented in Section 6, the FWOS algorithm can be summarized as follows:
## 4 Results and Discussion
### Analysis of Excavation Sequence
#### 4.1.3 Determination of Excavation Sequence  
From the above data, it was clear that, during the construction of a small clear-distance, double-arch tunnel under an asymmetrical load, different excavation sequences had a greater impact on the horizontal deformation than on the vertical deformation. In the asymmetrical terrain, the deeply buried side of the tunnel was under greater pressure from the surrounding rock. Therefore, it was important to consider reducing the load on the deeply buried side of the support structure first. If the deeply buried side of the tunnel was excavated first, the later tunnel would cause a secondary disturbance to the support structure already erected on the deeply buried side, increasing the force on the deeply buried side. Conversely, if the shallow side of the tunnel was excavated first, the support structure on the deeply buried side would not be disturbed, which would be more beneficial to the safety of the tunnel. This was also consistent with the findings of [PERSON] et al. [27].  
The numerical simulation results showed that there was little difference between the two excavation sequences \"1\(\rightarrow\)2\(\rightarrow\)3\(\rightarrow\)4\" and \"3\(\rightarrow\)4\(\rightarrow\)1\(\rightarrow\)2\". This indicated that the force state of the support structure was more likely to be influenced by the excavation sequence of the double-arch tunnel itself, rather than the excavation sequence of the left line and  
Figure 15: The maximum value of the principal stress in the middle partition wall under different excavation sequences: (**a**) the maximum value of the tensile principal stress on the shallowly buried side; (**b**) the maximum value of compressive principal stress on the shallowly buried side; (**c**) the maximum value of the tensile principal stress on the deeply buried side; (**d**) the maximum value of the compressive principal stress on the deeply buried side.  
the right line. Therefore, the key to the construction sequence was that \"Tunnel 2\" should be excavated after \"Tunnel 1\", and that \"Tunnel 4\" should be excavated after \"Tunnel 3\". Based on the engineering geological conditions and numerical simulation results, the excavation sequence of either \"1\(\rightarrow\)2\(\rightarrow\)3\(\rightarrow\)4\" or \"3\(\rightarrow\)4\(\rightarrow\)1\(\rightarrow\)2\" was recommended.
# Novel Scoring for Energy-Efficient Routing in Multi-Sensored Networks
###### Abstract  
The seamless operation of inter-connected smart devices in Internet of Things (IoT) wireless sensor networks (WSNs) requires consistently available end-to-end routes. However, the sensor nodes that rely on a very limited power source tend to cause disconnection in multi-hop routes due to power shortages in the WSNs, which eventually results in the inefficiency of the overall IoT network. In addition, the density of the available sensor nodes affects the existence of feasible routes and the level of path multiplicity in the WSNs. Therefore, an efficient routing mechanism is expected to extend the lifetime of the WSNs by adaptively selecting the best routes for the data transfer between interconnected IoT devices. In this work, we propose a novel routing mechanism to balance the energy consumption among all the nodes and elongate the WSN lifetime, which introduces a score value assigned to each node along a path as the combination of evaluation metrics. Specifically, the scoring scheme considers the information of the node density at a certain area and the node energy levels in order to represent the importance of individual nodes in the routes. Furthermore, our routing mechanism allows for incorporating non-cooperative nodes. The simulation results show that the proposed work gives comparatively better results than some other experimented protocols.  
IoT WSN; routing; load balancing; energy efficiency +
Footnote †: journal: Sensors  
0
Footnote 0: Copyright** © 2022 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 2 Geological Settings  
The Nanjing region extends east-west in the midwest segment of Cathaysia Block (Figure 1a). XHLOF is located in the middle section of the Nanjing tin-tungsten polymetallic metallogenic belt and the southwestern margin of the Qitianling pluton (Figure 1b). In  
Figure 1: Distribution map of granite intrusions and major tungsten–tin deposits in the Nanling region. (**a**) Tectonic map of South China; (**b**) Geological map of Nanling region.  
addition, XHLOF is also located at the intersection between the NE-trending Chenzhou-Linwu deep-seated fault zone and the S-N-trending Leiyang-Linwu fault zone [20].  
The structure of the XHLOF is very complex due to the strong activity and superposition of multi-stage structures. The overall tectonic framework is a tectonic-magmatic dome that is dominated by the NS-trending Tongtianmiao dome structure, with NS-trending faults and NE-trending complex linear folds developing on both sides [20]. The Cambrian epimetamorphic rock is in the core of the dome, and the Devonian, Carboniferous, and Triassic strata are on the sides. Faults are developed in the dome which is dominated by the NE-trending and NW-trending faults. These two kinds of faults were mainly formed in the Yanshanian period and are closely related to the magmatism and hydrothermal mineralization in the XHLOF [28]. The ore-bearing strata of the XHLOF are mainly the Cambrian (C), the Middle Devonian Taiomajian Formation (D\({}_{2}\)_t_), the Middle the Devonian Qiziqiao Formation (D\({}_{2}\)_q_), the Upper Devonian Shetianqiao Formation (D\({}_{3}\)_s_), and the Upper Devonian Xikuangshan Formation (D\({}_{3}\)_x_) [21]. Stratiform-like cassiterite-sulfide orebody often occurs in the unconformity between the Cambrian strata and the Tiaomajian Formation, and this phenomenon exists in both the Xianghualing tin deposit and the Laohuya lead-zinc-tin deposit [28]. The magmatic activity in the XHLOF is relatively intense, and the Laiziling and Jianfengling plutons located in the Cambrian-Devonian strata are the main intrusions exposed on the surface and the main granite related to mineralization [20]. The main lithology of the Laiziling and Jianfengling plutons are biotite granite, and they have obvious vertical lithofacies zoning [29]. In addition, some felsic dykes are exposed on the surface.  
In the Laohuya lead-zinc-tin deposit, which is located in the northern part of the XHLOF, no magmatic rocks were found on the surface of the mining area or in the tunnels underground, and the concealed granite (LG) was found by drill hole ZK3001 (Figure 2, modified from [30]). The lithofacies of LG gradually transitioned from the medium-grained granite (Figure 3a) in the deeper part to the fine-grained granite (Figure 3b) in the shallower part with no obvious cross-cutting relationship.  
Figure 2: Geological sketch map of Laohuya mining area.
# Environment Perception with Chameleon-Inspired Active Vision Based on Shifty Behavior for WMRs
## 6 Experiments
### Target Tracking Experiment of WMRs on Soft-Rough Sand  
In this experiment, the robot was located on soft-rough sand to search and track a red target in front of it, as shown in Figure 15.  
Figure 14: Tracking data curve of WMRs on rigid, flat ground.  
Figure 15: Target tracking process of WMRs on soft-rough sand.  
Target search stage: first, the robot was initialized with \((\phi_{b},\varphi_{l},\varphi_{r},\theta_{l},\theta_{r})=(0,0,0,40,40)\), and the camera entered the short-focus mode. Then, at the \(11\)th second and \(15\)th second, the robot performed a rapid scan of the environment using the chameleon-inspired binocular negative-correlation-motion target search model.  
\[(\phi_{b},\varphi_{l},\varphi_{r},\theta_{l},\theta_{r})=\left\{ \begin{array}{l}(20,36,-36,40,40),q=1\\ (-20,36,-36,40,40),q=2\end{array}\right. \tag{13}\]  
Then, the four acquired images were compared and analyzed using the selective attention algorithm, and it was determined that when the neck and right camera were rotated by \((\phi_{b},\varphi_{r},\theta_{r})=(20,-36,40)\), respectively, the area located at the coordinate \((248,49)\) of the image had the highest saliency. The horizontal and vertical orientation of the target was derived by the PTZ rotation equation as \((-28.6^{\circ},29.7)\). The image processing during the target search phase is shown in Figure 16.  
Target alignment stage: first, in the \(23\)rd second, the robot body and vision system rotated by \((\phi_{b},\varphi_{l},\varphi_{r},\theta_{l},\theta_{r})=(0,0,0,29.7,29.7), \ \omega=-28.6^{\circ}\), respectively, aligning with the target, and the cameras moved together to realize stereo vision. Then, in the \(40\)th second, the camera zoomed into the long-focus mode to obtain high-resolution pictures of the target to achieve precise target alignment.  
Target tracking stage: starting from the \(40\)th second, the robot started to track the target. The experimentally collected rotation angles of each axis of the vision system and the target tracking error are shown in Figure 17. The tracking error curve showed that although the tracking error fluctuated due to vibration problems with the robot itself, it always fluctuated around the \(0\) axis and was basically between plus and minus \(15\) pixels, indicating that it could achieve the tracking of the target.  
Figure 16: Image processing during target search stage: (**a**) saliency map; (**b**) superpixel segmentation process.  
Figure 17: Tracking data curve of WMRs on soft-rough sand.  
The experiments showed that this chameleon-inspired active-vision environment-perception strategy based on a shifty-behavior mode for mobile robots could achieve the rapid search and tracking of the target, and in this process, the coordinated movement of various parts could realize a sequence of actions being executed rhythmically, which achieved the reproduction of the visual behavior of chameleons in the vision system of the mobile robot with satisfactory results.