esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
We evaluate the performance of a CNN-BiLSTM architecture alongside machine learning models (k-nearest neighbors (KNNs), support vector machines (SVMs), multilayer perceptron (MLP), logistic regression (LR), random forest (RF), and decision trees (DTs)) in identifying normal networks and detecting attacks, particularly denial of service (DoS)attacks within an IDS. The evaluation utilizes well-established datasets NSL-KDD and UNSW-NB15, assessing metrics such as accuracy, precision, recall, F1-score, and false alarm rate to determine the optimal model for IDS applications. To the best of our knowledge, no comparison has been made between using lightweight machine learning models and deep learning models. The remainder of this paper is structured as follows: Section 2 discusses related works in the field of intrusion detection, highlighting recent developments and methodologies. Section 3 presents a detailed overview of some recent intrusion detection schemes for IoT applications, as well as the methodologies employed. Section 4 presents the experimental results of both deep learning and machine learning models on the evaluated datasets. Section 5 discusses the implications of the results and compares them with state-of-the-art studies in the domain. Finally, Section 6 provides the conclusion of this study, summarizing the key findings and outlining avenues for future research in intrusion detection and IoT security.
# Acceleration Techniques for Teeth Movements in Extractive Orthodontic Therapy
## 4 Discussion
### Micro-Osteo-Perforations (MOPs)  
[PERSON] et al. (2022) compared the effects of photobiomodulation (PBM) and micro-osteo perforations (MOPs) on the speed of canine retraction in a study group of 30 patients [31]. The extraction of the premolars was followed by a waiting period of 3 months to allow trabecular bone formation. Three vertical MOPs (approximately 19-gauge diameter) were performed distal to the canine root using Propel contra-angle perforation screws in the MOPs group. In the PBM group, the canine was stimulated using a Gallium Aluminium Arsenide (GaAlAs) semiconductor diode laser for 10 s at ten different points of the canine root. The retraction rate was approximately 1.1-fold higher in patients treated with MOPs than in comparison patients [31].  
[PERSON] et al. (2018) analysed accelerated canine distalisation using MOPs (Figure 4) [37]. After 28 days, the movement was assessed by analysing the digital models of the two arches using the canine and second premolar at three locations as retrievals. MOPs increased OTM over twice as much as the C Side [37].  
In the study by [PERSON] et al. (2018), the retraction was activated immediately after the MOPs were executed. MOPs were carried out using a manual screwdriver (Propel device) and drilling screws. Each patient received three perforations (1.5 mm in diameter and 2-3 mm deep) in the extraction space at an equal distance from the canine and second premolar at the level of the bony cortical performed every 28 days until the space was closed entirely. When MOP patients were compared to the control individuals, there was an increase in OTM. Therefore, the authors suggest its use after carefully evaluating the risk-benefits, as there is an increase in costs and initial discomfort during the procedure [29]. [PERSON] et al. (2020) compared the extent of canine distalisation in patients undergoing cortical perforations without flaps or MOPs.  
Patients were divided into an intervention group and a CGr. In one session, three MOPs (diameter 1.5 mm, depth 2.5 mm) were executed distal to the canine on both sides using the Propel device at the buccal cortical level. The mesial movement of the molar associated with canine retraction was also investigated using the median palatine line and the most prominent part of the palatine wrinkles as references.  
The orthodontic movement was accelerated 2-3 times in the MOPs group compared to the CGr [45]. Compared to the other articles used in this section of the discussion, [PERSON]'s work did not compare the improvement in the speed of canine retraction with a CGr treated only with fixed therapy. Still, it compared with a group exposed to PBM [31]. It can be deduced, however, that MOPs are a valid aid in increasing the speed of tooth retraction, although particular attention must be paid to reducing pain when performing this procedure.  
MOPs have shown promise in accelerating orthodontic movement [8,69,70]. These are simple and minimally invasive procedures; however, they may cause temporary discomfort, swelling, or slight pain at the treated site.
# Formation Mechanism and Response Strategies for Urban Waterlogging: A Comprehensive Review
## 2 Research Methodology
### Phase 2: Methodological Framework  
The analysis was organized into three interconnected stages to systematically map the research landscape.  
1. Literature Analysis  
The initial stage focused on categorizing the selected literature to identify overarching trends. This included examining the temporal distribution of publications to track evolving research priorities, geographical patterns in study locations, and methodological approaches such as experimental, numerical, or AI-driven techniques [15]. Key themes like theoretical frameworks, drainage system optimization, and climate change impacts were also highlighted.
2. Descriptive Analysis  
Based on the initial categorization, a detailed overview of the literature was provided at this stage. Through statistical methods, we obtained the distribution of articles in terms of year of publication and research methodology. It can be seen that the selected articles were mainly distributed in the last decade and various research methods were involved.
3. Scientometric Analysis  
To visualize the scientific landscape, we used VOSviewer software (version 1.6.20) to perform the scientometric analysis [19]. The software created a distance-based visual representation of the network based on the proximity of any two nodes. In the scientometric analysis, key areas of urban flooding research, AI approaches in flooding research, and applications of AI in flooding research and mitigation were identified using this software.
# Nonparaxial Propagation Properties of Specially Correlated Radially Polarized Beams in Free Space
## 2 Nonparaxial Propagation Theory of an SCRP Beam
Recalling the integral formulae with different \(n\)[69]:  
\[\int_{-\infty}^{\infty}x^{n}\exp\Bigl{(}-bx^{2}+2 cx\Bigr{)}dx=n!\sqrt{\frac{ \pi}{b}}\left(\frac{c}{b}\right)^{n}\exp\left(\frac{c^{2}}{b}\right)\sum_{u=0 }^{[n/2]}\frac{1}{u!(n-2u)!}\biggl{(}\frac{b}{4c^{2}}\biggr{)}^{u}, \tag{15}\]  
By substituting Equations (2)-(4) into Equations (11)-(13), replacing \(R_{j}\) in the exponential term of Equations (11)-(13) by Equation (14), and that in the denominator term by \(r_{j}\), we obtain (after tedious integral calculations and operations over \(x_{10}\), \(y_{10}\), \(x_{20}\), \(y_{20}\)) the following expressions for the elements of the CSD matrix of the nonparaxial SCRP field in the half space \(z>0\):  
\[W_{\alpha\alpha}(x_{1},y_{1},x_{2},y_{2},z)=z^{2}\Delta\biggl{[}\frac{O}{2a_{3 }}-C+\frac{k^{2}{\alpha_{2}}^{2}}{4N^{2}r_{2}^{2}\delta_{0}^{2}}+\frac{iCkb_{ \alpha}\alpha_{2}}{2 MNr_{2}\delta_{0}^{2}}+\frac{Ok^{2}{b_{\alpha}}^{2}}{4M^{ 2}}\biggr{]}\quad,\qquad(\alpha,\beta=x,y), \tag{16}\]  
\[W_{xy}(x_{1},y_{1},x_{2},y_{2},z)=-\frac{kz^{2}}{\delta_{0}^{2}}\Delta\biggl{[} \frac{ix_{2}}{2 Nr_{2}}-\frac{Cb_{x}}{2M}\biggr{]}\times\biggl{[}\frac{iy_{2}} {2 Nr_{2}}-\frac{Cb_{y}}{2M}\biggr{]}, \tag{17}\]  
\[W_{yx}(x_{1},y_{1},x_{2},y_{2},z)=W_{xy}^{*}(x_{2},y_{2},x_{1},y_{1},z), \tag{18}\]  
\[ \begin{array}{ll}W_{az}(x_{1},y_{1},x_{2},y_{2},z)&=-z\Delta\{A_{a0}+A_{a1}Q_{ a1}+A_{a2}Q_{a2}+A_{a3}Q_{a3}\\ &-\biggl{(}\frac{ika_{2}}{2 Nr_{2}\delta_{0}^{2}}-\frac{Q_{a1}}{\delta_{0}^{2}} +\frac{Q_{a1}}{2N\delta_{0}^{2}}\biggr{)}\left(B_{\beta 0}+B_{\beta 1}Q_{\beta 1 }+B_{\beta 2}Q_{\beta 2}\right)\},\end{array}  \tag{19}\]  
\[W_{z\alpha}(x_{1},y_{1},x_{2},y_{2},z)=W_{z\alpha}^{*}(x_{2},y_{2},x_{1},y_{1},z), \tag{20}\]  
\[ \begin{array}{ll}W_{zz}(x_{1},y_{1},x_{2},y_{2},z)&=\Delta[W_{zxx}(x_{1},y_{1},x_{2},y_{2},z)\ +W_{zxy}(x_{1},y_{1},x_{2},y_{2},z)\\ &+W_{2 yx}(x_{1},y_{1},x_{2},y_{2},z)\ +\ W_{zyy}(x_{1},y_{1},x_{2},y_{2},z)\bigr{]}, \end{array}  \tag{21}\]  
where  
\[ \begin{array}{ll}W_{zz\alpha}(x_{1},y_{1},x_{2},y_{2},z)&=A_{a0}\alpha_{1}+(A_ {a1}\alpha_{1}-A_{a0})Q_{a1}+(A_{a2}\alpha_{1}-A_{a1})Q_{a2}\\ &+(A_{a3}\alpha_{1}-A_{a2})Q_{a3}-A_{a3}Q_{a4},\end{array}  \tag{22}\]\[ \begin{array}{ll}W_{2\alpha\beta}(x_{1},y_{1},x_{2},y_{2},z)&=\frac{1}{\delta_{0} ^{2}}\times\left(\frac{C}{2M}-\frac{C{{}^{2}b_{h}}^{2}}{4M^{2}}-C_{A1}-\frac{C_{ \beta 2}kb_{h}}{2M}\right)\\ &\times\left(D_{\beta 0}+\frac{D_{\beta 1}kb_{\beta}}{2M}+\frac{D_{\beta 2}}{2M}+ \frac{D_{\beta 2}k^{2}b_{\beta}}{4M^{2}}\right),\end{array}  \tag{23}\]  
with  
\[
## 6 Implementation and Application of the PORTAL-DOORS System
### Development Roadmap for the PORTAL-DOORS System  
Current plans envision following a PORTAL-DOORS Project roadmap with iterative software development for the PORTAL-DOORS System with these milestones:  
* Version 0.5: Implementation as an AJAXified web application with back-end database and front-end web browser client for partial PORTAL server functionality and partial DOORS server functionality. Version 0.5.4 was the last 0.5.* version published on 3/29/2009.
* Version 0.6: Implementation as RESTful web services with both ASP.net based clients enhanced with user-friendly graphical user interfaces and editors for managing (entering and updating) data records at PORTAL-DOORS servers on Microsoft Windows platforms. The current version 0.6.4 is operational as a RESTful web service for user access and an AJAXified web application for agent access.
* Version 0.7: Completion and revision of lexical PORTAL functionality including interoperability with terminology tools.
* Version 0.8: Completion and revision of semantic DOORS functionality including interoperability with ontology tools.
* Version 0.9: Implementation as RESTful web services with JAVA based servers and clients for Linux and Mac OS X platforms.
* Version 1.0: Official release of PORTAL-DOORS System models and schemas for an authoritative server at a single site for all platforms.
* Version 2.0: Multi-site functionality (including security) for distributed interacting authoritative servers.
* Version 3.0: Multi-site functionality (including provenance) for distributed interacting non-authoritative servers operating with request forwarding and response caching amongst the distributed servers.
## 4 The Third-Order Beam Theory
### Equations of Equilibrium  
The equations of equilibrium of the RBT are derived using the principle of minimum total potential energy. We introduce the following stress resultants:  
\[(N_{xx},M_{xx},P_{xx}) =\int_{A}(1,z,z^{3})\sigma_{xx}\,dA,\ \ (N_{xz},P_{xz})=\int_{A}(1,z^{2}) \sigma_{xz}\,dA, \tag{75a}\] \[(P_{xy},R_{xy}) =\int_{A}(1,z^{2})\mathcal{M}_{xy}\,dA,\ \ Q_{yz}=\int_{A}z \mathcal{M}_{yz}\,dA,\] (75b) \[\bar{M}_{xx}=M_{xx}-\alpha\,P_{xx},\ \ \bar{N}_{xz}=N_{xz}-\beta P_{xz},\ \ \beta=3 \alpha\ =\frac{4}{h^{2}}. \tag{75c}\]  
The equations of equilibrium of the RBT are:  
\[-\frac{dN_{xx}}{dx}=f \tag{76}\] \[-\frac{d}{dx}\bigg{(}N_{xx}\frac{dw}{dx}\bigg{)}-\frac{d\bar{N}_{ xz}}{dx}-\alpha\,\frac{d^{2}P_{xx}}{dx^{2}}\] \[-\frac{1}{2}\frac{d^{2}P_{xy}}{dx^{2}}-\frac{1}{2}\beta\frac{d^{ 2}R_{xy}}{dx^{2}}+\beta\,\frac{dQ_{yz}}{dx}=q\] (77) \[-\frac{d\bar{M}_{xx}}{dx}+\bar{N}_{xz}-\frac{1}{2}\frac{dP_{xy}} {dx}+\frac{1}{2}\beta\frac{dR_{xy}}{dx}-\beta\,Q_{yz}=0 \tag{78}\]  
The duality pairs (the first element of the pair denotes a generalized displacement while the second element denotes a generalized force):  
\[(u,N_{xx}),\ \ (w,V_{\rm eff}),\ \ \bigg{(}-\frac{dw}{dx},M_{\rm eff} \bigg{)},\ \ (\phi_{x},\bar{M}_{xx}), \tag{79}\]  
where  
\[V_{\rm eff} =\bar{N}_{xz}+\alpha\,\frac{dP_{xx}}{dx}+N_{xx}\frac{dw}{dx}+ \frac{1}{2}\frac{dP_{xy}}{dx}+\frac{1}{2}\beta\frac{dR_{xy}}{dx}-\beta\,Q_{yz}, \tag{80}\] \[M_{\rm eff} =\frac{1}{2}\,P_{xy}+\frac{1}{2}\,\beta R_{xy},\ \ \ \ \bar{M}_{xx}=\bar{M}_{xx}+\frac{1}{2}\,P_{xy}-\beta\frac{1}{2}R_{xy}. \tag{81}\]  
Thus, there are four boundary conditions at each boundary point. Requiring \(\partial w/\partial x\) as well as \(\phi_{x}\) to vanish at a support necessarily implies that the shear force, when shear stress is computed using the constitutive relation \(\sigma_{xz}=G\gamma_{xz}\), is zero. However, the effective shear force \(V_{\rm eff}\) is not zero.  
One of the challenges of higher-order theories is the ability to specify boundary conditions that involve higher-order stress resultants. In most cases, one does not know the known values of the higher-order stress resultants. Therefore, whenever the lower-order stress resultant is specified, we assume that the corresponding higher-order stress resultant is known to be zero. For example, if \(M_{xx}\) is specified at a point, we assume that \(\bar{M}_{xx}=M_{xx}\) (implying that \(P_{xx}=0\) there).
## 4 SMS-Based Telemetry Data Transmission Protocol
### Implementation  
The protocol discussed in overview here was implemented as a theoretical application. While initial code has been generated to allow for protocol messages to be created, there is no real world implementation at this time. However, the next step in this research is to create a fully working, point to point implementation of the protocol, using real-world GSM modems and appropriately written software. This will include the creation of classes/objects to implement the protocol structure and encoding, and manage the transmission and reception of protocol messages of all different types. The ability for a user to encode and decode a message, and then read a message in human readable form when it is received will also be developed. An end-to-end implementation of the protocol could act as both trainer and simulator, allowing protocol users to develop application ideas and test them out in real world protocol messages, performing actual transmissions and checking all aspects of the data they wish to send.  
The second development of this research will be the implementation of a system to make use of the protocol in a real world application. The system will be an implementation of a biometric data monitoring application with remotely monitored users transmitting data to a central base station. The various hardware devices for such a system exist right now, and for this research, we would undertake the definition of a new application for the component hardware, combining all of the disparate parts into a fully integrated working telemetry system using this protocol for its data transmission. Such a project would require the integration of medical sensor hardware with GSM compatible communications devices, and the implementation of remote and medical centre base station software and hardware to offer the appropriate levels of functionality to each type of user of the system. Finally, in terms of ongoing development, it is reasonable to believe that different versions of the protocol could evolve over time, to cater for specific needs and applications. The protocol discussed here, defined as version 1, is intended to be the foundation upon which a variety of applications can be built. This protocol is generic enough that it does not place unnecessary constraints on the application using it. However, it must be considered that, for some specific applications, variations to the protocol may be required to best address the business, control or data requirements of that application.
## 6 Future Research Directions
### Energy Management  
Various ground-based equipment can be recharged by power stations in 6G networks, while other sensors and infrastructure in the air, sea, and space cannot. Furthermore, 6G networks must be used to link a huge number of flexible smart low-power devices. As a result, advanced energy management strategies are critical in 6G networks. AI techniques have the potential to aid these infrastructures and devices in improving their energy management strategy and prolonging their useful life by intelligently managing the amount of power they use or through energy harvesting. As a result, power management is an important yet complex topic for 6G networks [36].
# Large Scale Implementations for Twitter Sentiment Classification
## 4 Sentiment Analysis Classification Framework
### Feature Description
#### 4.1.1 Word and N-Gram Features  
Each word in a tweet is treated as a binary feature. Respectively, a sequence of 2-5 consecutive words in a sentence is regarded as a binary n-gram feature. If \(f\) is a word or n-gram feature, then  
\[w_{f}=\frac{N_{f}}{count(f)} \tag{1}\]  
is the weight of \(f\) in the feature vector, \(N_{f}\) is the number of times \(f\) appears in the tweet and \(count(f)\) declares the count of \(f\) in the Twitter corpus. Consequently, rare words and n-grams have a higher weight than common words and have a greater effect on the classification task. Moreover, if we encounter sequences of two or more punctuation symbols inside a tweet, we consider them as word features. Unlike what authors propose in [7], we do not include the substituted meta-words for URLs, references and hashtags (URL, REF and TAG respectively) as word features (see and Section 4). Additionally, the common word RT, which means \"retweet\", does not constitute a feature. The reason for omission of these words from the feature list lies in the fact that they appear in the majority of tweets inside the dataset. Therefore, their contribution as features is negligible, whilst they lead to a great computation burden during the classification task.
## 2 Optical Characterization of MCM  
The imaging performance of MCM was analyzed by using a USAF 1951 resolution test target. The USAF 1951 resolution test (MIL-STD-150A) is a standardized method for verifying microscopic resolution [31, 32]. Figure 2a,b exhibit the captured negative and positive USAF 1951 targets through the bottom illumination. The MCM shows an imaging area of 6090 \(\upmu\)m \(\times\) 5950 \(\upmu\)m and a magnification of 2.15\(\times\). In order to clearly observe detailed patterns in the center area of the USAF 1951 target, the captured images were magnified through digital zoom. As a result of analyzing the intensity profiles of the target patterns in the magnified images, the MCM clearly observes group 7 element 1, which means that the resolving power of MCM achieves 3.91 \(\upmu\)m, as shown in Figure 2c,d.  
The modulation transfer function (MTF) of the captured images was analyzed in a transmission and reflection light microscopy mode through the slanted-edge method (ISO 12233) for detailed comparisons, as shown in Figure 3a. The slanted-edge method offers MTF values corresponding to the spatial frequency by analyzing the pixels in a user-defined region of an image [33]. The experimental results show that the spatial frequency corresponding to MTF 50 of the reflection light mode using the top light exhibits 135 lp/mm in the negative target, which is a slightly higher value compared to the transmission light mode of 123 lp/mm. As shown in Figure 3b, the MTF values were also analyzed by using the analysis method of thru-focus MTF according to the target distances to observe the imaging depth of field. The images were captured by changing the position of the object through the \(z\)-axis stage, and the MTF was calculated at the spatial frequency of 50 lp/mm. As a result, the images captured at distances of about \(\pm\)15 \(\upmu\)m show the MTF above 0.2. This means that the depth of field of the MCM objective lens is 30 \(\upmu\)m.  
Figure 2: Imaging characterization of MCM. The captured (**a**) negative and (**b**) positive USAF 1951 resolution target images through the MCM. The central area was magnified with digital zoom. The corresponding normalized intensity profiles of captured (**c**) negative and (**d**) positive USAF 1951 target images.
# Harnessing Spatial-Frequency Information for Enhanced Image Restoration
## 3 Methodology
### Multi-Scale Pooling Attention (MPA)  
The proposed MPA utilizes global average pooling on the input features along specific directions, such as the vertical and horizontal axes, to address the high complexity of spatial attention. The resulting squeezed features are then fed into several strip convolutions, encompassing both small and large receptive fields, to aggregate local and long-range information:  
\[\left[x_{\text{h}},\ x_{\text{w}}\right]=\left[\text{GAP}_{\text{h}}(x),\ \text{GAP}_{\text{w}}(x)\right], \tag{9}\]  
\[A_{\text{h}}=\sum_{i=0}^{3}\text{DWConv}_{1\times k_{i}}(x_{\text{h}}),\ A_{ \text{w}}=\sum_{i=0}^{3}\text{DWConv}_{k_{i}\times 1}(x_{\text{w}}), \tag{10}\]  
where \(\text{GAP}_{\text{h}}\) and \(\text{GAP}_{\text{w}}\) denote global average pooling in the h- and w-axis directions, respectively. \(A_{\text{h}}\in\mathbb{R}^{1\times W\times C}\) and \(A_{\text{w}}\in\mathbb{R}^{H\times 1\times C}\) represent the information aggregated from features squeezed in each direction. Then, we compute the attention map \(A\in\mathbb{R}^{\text{H\times W\times C}}\) by calculating the dot product between the information aggregated for each axis. After performing pointwise convolution for information exchange between channels, the attention values are projected into the range (\(-1\), \(1\)) using the Tanh function, which helps suppress irrelevant information while enhancing useful information. This process can be expressed as follows:  
\[A=\text{Tanh}\big{(}\text{PWConv}(A_{\text{h}}A_{\text{w}})\big{)},\quad\text{ Out}=A\otimes x. \tag{11}\]  
In Equation (11), PWConv refers to the pointwise convolution, and Out represents the final output of the MPA.