esa-sceva/satcom-chunk-collection huggingface dataset:
# Lightweight Micro-Expression Recognition on Composite Database
## 3 Methodology
### Proposed Model  
The proposed model (Figure 4) is based on EfficientNet-B0 model, the lightest variant of EfficientNet. However, EfficientNet-B0 is still comparatively bigger than the other shallower networks proposed for MER--bearing in mind that these networks require pre-extracted OF features [9; 10; 32]. Therefore, this work proposed a truncated version of EfficientNet-B0, called Efficient-ME with a balance of model complexity (number of parameters) and feature extraction capability. The goal is to demonstrate that a lightweight network under a million parameters with low input complexity can perform better than the other shallow MER models.  
This network keeps the first 5 blocks of EfficientNet-B0 and drops the last 2 blocks. After the 5 th block of EfficientNet-B0, a global average pooling layer and a dropout layer is added before being fed to a dense layer with 3 nodes for classification. Global average  
Figure 4: Efficient-ME network architecture.  
Figure 3: Face alignment and cropping. From left to right: original image, face aligned image, cropped image.  
pooling layer was added to generate the output feature map to be used in the dense classification layer. A dropout rate of 0.5 is utilized to prevent overfitting [16].  
The input dimension is maintained at 224 \(\times\) 224 with 3 channels. The input images were not transformed to grayscale nor normalized as the first few layers in the Keras's implementation of EfficientNet already take care of this. The dense classification layer uses Softmax activation function together with L1 and L2 kernel regularizers with both regularizing factors set to 0.01 to reduce the kernel weights and prevent overfitting [35]. With the proposed setup, the total number of parameters in Efficient-ME is 867k which is under the target of 1 million parameters.  
The justification for truncating EfficientNet-B0 until the 5 th block stems from the observation in [36] that a variety of MBConv layers in a network gives better results than using a single type of layers only. Therefore, truncating up to the 5 th block will maintain the pair of MBConv6 with 3x3 convolution and MBConv6 with 5x5 convolution configuration (Block 2 and Block 3 pair as well as Block 4 and Block 5 pair, respectively). Another motivation for choosing Block 5 as the cut-off point is the target model size of under 1 million that is achievable by truncating at the 5 th block. It is also important that the last convolution layer can extract sufficient features for accurate classification. Truncating the network shallower will result in fewer features and details to be captured by the model.  
The optimizer used is Adam optimizer with multi-class focal loss (FL). The parameters' configuration for the FL loss function is \(\gamma=2\)[37] and \(\alpha\) that represents the classes' weights as [0.27, 0.35 and 0.38] for class Negative, Positive and Surprise, respectively. Adam optimizer and the \(\alpha\) values are selected by using grid search cross-validation.
# Securing IPv6 Neighbor Discovery Address Resolution with Voucher-Based Addressing
## 5 Discussion
### Summary  
There is no tangible velocity towards the adoption of a robust security solution for Neighbor Discovery's issues. It appears these security weaknesses have long been covered and patched with a cocktail of willful ignorance and idle hands. SEND and CGA have proven over the course of decades to be undesirable and too complicated or esoteric for most practical applications. Network monitoring solutions require consistent involvement and interference by administrators who ultimately \"just want it to work\" so they can have peace of mind. Other proposals to fix NDP's weaknesses have either languished in obscurity, are too complex, or fail to focus specifically on their practicalities and acknowledge their own shortcomings.  
The real fix to these weaknesses is to create and subsequently adopt effective NDP security solutions which provide a trinity of attributes: simplicity, privacy, and flexibility. A solution lacking one or more of these three properties will not be preserved in the long term without painstaking effort, as time has already proven. Voucher-Based Addressing seeks to establish itself as a proposal touting these attributes and aiming to solve a specific problem rather than ambitiously trying to remediate all the complex issues of the protocol at once.  
In summary, VBAs bind input link-layer addresses to sets of privacy-focused, deterministic output addresses that can be reconstructed by verifying neighbors to confirm a reported binding. Address generation and verification are constrained to some initial conditions set by a shared voucher on the local network. The generated addresses appear outwardly random to off-link nodes and are easily rotatable to other seemingly random values, yet on-link they provide all the information required for voucher-aware neighbors to verify them. The neighbor consensus of voucher parameters acts as an enforceable handshake during the address resolution process. This process is not actively falsifiable without introducing obvious network disruptions, thus preventing threat actors from subversively intercepting or modifying traffic between neighbors in on-path attacks.  
VBA is a simple, unique, transparent, privacy-conscious, and flexible standard for use in Neighbor Discovery Protocol transactions. The proposal presented in this research maintains a high level of abstraction and does not beget very much concrete and practical evidence of its practicality. There is still, however, a very pressing need for a low-configuration, low-complexity technology to fill the security void left by the absence of SEND and CGA adoption in the modern enterprise.
# An Automatic Geometric Registration Method for Multi Temporal 3D Models
###### Abstract
The basis of geometric registration between models is to obtain the spatial coordinate information of the ground point of different multi-temporal 3D models. The conventional method can obtain the spatial position of corresponding points by manual measurement, and then estimate the geometric transformation model parameters of the two-period 3D model based on the position difference. Due to the influence of human factors, the efficiency of this method is too low and the reliability of the results is insufficient. So, it is necessary to propose a method that can automatically match the corresponding points of the multi-temporal 3D models and calculate their positions with high precision. There are inevitably incorrect corresponding points after spatial-feature-point extraction, which are considered as outliers. The conventional method of eliminating outliers based on two-view geometry verification is not effective, so the robust and high-precision solution of the relative geometric transformation parameters of the models is another problem. In this paper, a method for automatic geometric registration of multi-temporal 3D models is presented. In detail, it consists of three parts. Firstly, the spatial-feature points are extracted from the 3D model, and the initial matching between the spatial-feature points of different time models is completed. Secondly, based on the corresponding spatial positions of the initial spatial-feature points, the relative geometric transformation model parameters of the multi-period 3D model are calculated. Finally, all the vertex positions of the registered 3D model are transformed based on the calculated model parameters, so as to keep them unified with the spatial reference of the baseline model. The rest of the paper is organized as follow. Section 2 briefly introduces the study area and the study-data-acquisition process. Section 3 introduces the proposed methodology and key technologies used in this study. Section 4 describes the experimental objectives, experimental process and results, and provides a detailed analysis based on the experimental results. Section 5 offers the conclusions of the study.
###### Abstract  
The influence of solar activity on the ionosphere, a critical area of investigation due to its relevance to the Sun-Earth relationship, has been extensively examined through various methodologies. The ability of solar events to induce disturbances in both the ionosphere and the geomagnetic field is widely acknowledged. This specific study focused on sporadic incidents resulting from X-class solar flares that occurred between 2021 and 2022. Utilizing a methodology that involved analyzing data at 5 Hz intervals using wavelet algorithms, the data from the GNSS stations of the National Autonomous University of Mexico (UNAM) were investigated. The primary emphasis was on deducing the Total Electron Content (TEC) within the ionosphere. Subsequently, this parameter for each satellite during instances of solar flares was analyzed. The approach uncovered disruptions in the ionosphere triggered by solar flares, even in cases where events transpired at the periphery of the solar disk and were of magnitudes smaller than X2.  
Keywords:ionosphere; GNSS; wavelets; solar flare; total electron content +
Footnote †: journal: remote sensing
## 2 Methodology Architecture  
Let us consider a time series vector N(t) as the sample space of t variables, such that t is not null, belongs to the set of real numbers, and is acquired from sensors as time series. The first step is to train patterns of seismic waves that can be compared with N(t) in real-time to characterize the seismic event. Earthquake, the most important special case in refractive seismology, be E(t) and is given by the characteristic piecewise random function:  
\[\text{E(t) }=\left\{\begin{array}{l|l}W1(t1)\\ W2(t2)\\ W3(t3)\end{array}\right|\ \begin{array}{l|l}1\,s\,<\,t1\,<\,17\,s\\ 4.5\,s\,<\,t2\,<\,50\,s\\ 23\,s\,<\,t3\,<\,100\,s\end{array}\right\} \tag{1}\]  
In Equation (1), \(W1\), \(W2\), and \(W3\) are the three seismic wave types that are time-stamped. \(W1\) is the p-wave, \(W2\) is the S-wave, and \(W3\) is the surface wave. t1, t2, and t3 are the arrival times of \(W1\), \(W2\), and \(W3\) respectively.  
A standard or characteristic earthquake seismograph pattern is exhibited in Figure 1. First, P-waves arrive, second, S-waves, and then surface waves (Rayleigh (R) or Love (L) waves). The sequential procedure for early warning and detection of an earthquake needs the realization of sequential events in the fashion of occurrence called SWEDA. The steps of SWEDA are given as:  
1. Gradient map auto-calibration (GMAC)
2. Micro-seism or seismic noise filtration (SNF)
3. Peak detection sequence (PDS)
4. Autoregressive pattern mapping sequence (APMS)
5. Scaling coefficients array generation sequence (SCAGS)
6. STA/LTA arrays windows sequence (SLAWS)
7. Earthquake probabilistic sequence (EPS)
8. Seismic features extraction (SFE)
9. ADC scaling/range and communication bus configuration SWEDA was programmed and tested on three-layered devices for constrained application testing:  
1. MATLAB for Desktop PC Workstation.
2. MATLAB Coder for Raspberry Pi.
3. Python 2.7 for Single Board Computer (Raspberry Pi).  
Figure 1: Earthquake seismograph and wave arrivals [3].  
The sequence of operations was as characteristic as the seismic wave patterns or geo-seismic faults that will be characterized and detected by the algorithm. First, the placement map of sensors was created for assigning all current positions equal to zero so that whatever the type of plain and surface is the initial state vector in the time domain as time series. The noise filtering, thresholding, peak-detection, event capturing, or recording algorithm triggering, as well as auto-scaling for events using computed windows and seismic event extraction, was automated. In the case of high-resolution needs and high data rates, the algorithm re-configures itself at t\({}_{1}\) > t\({}_{0}\), i.e., t\({}_{0}\) is the time at which the first sample set was processed in SWEDA and t\({}_{1}\) is the time at which the second sample set was processed by SWEDA. The entire process is summarized in Figure 2.
# Joint Resource Allocation for Multiuser Opportunistic Beamforming Systems with OFDM-NOMA
## 4 Problem Solution
### A Joint Iterative Algorithm  
To take power allocation, subcarrier scheduling and user pairing into consideration simultaneously, a joint iterative algorithm is proposed, named by Algorithm 2.  
```
1:Input:\(N_{c}\), \(U\), \(P_{\text{total}}\) and \(\left|{}_{n_{c},k_{n_{c}}}\right|^{2}\).
2:Output: Power allocation result \(P_{1,n_{c}}\), \(P_{2,n_{c}}\), subcarrier mapping \(\vartheta\) and user pairing \(k_{n_{c}}\).
3:Initialize: \(P_{1,n_{c}}=P_{\text{total}}\), \(P_{2,n_{c}}=0\), \(n_{c}=0\) and \(i=1\);
4:while\(n_{c}\in[0,N_{c}-1]\)do
5:while\(i\in[1,U\cdot(U-1)]\)do
6: Schedule subcarriers and users based on (31);
7: Allocate power to the paired users according to (25);
8: Calculate \(E_{\text{total}}\) with power allocation, subcarrier scheduling and user pairing result;
9: Record \(E_{\text{total}}\), power allocation \(P_{1,n_{c}}\), \(P_{2,n_{c}}\), subcarrier scheduling \(\vartheta\) and user pairing \(k_{n_{c}}\);
10:\(i=i+1\);
11:endwhile
12:\(i=1\);
13:\(n_{c}=n_{c}+1\);
14:endwhile
15:Search the maximum \(E_{total}\) and corresponding power allocation \(P_{1,n_{c}}\), \(P_{2,n_{c}}\), subcarrier scheduling \(\vartheta\) and user pairing \(k_{n_{c}}\).
```  
**Algorithm 2**A joint iterative algorithm.  
Algorithm 2 mainly contains three steps, as:  
Step 1: Initialization: Set the number of waiting users \(U\), total transmit power \(P_{\text{total}}\), the number of subcarriers \(N_{c}\) and the equivalent channels \(\left|{}_{n_{c},k_{n_{c}}}\right|^{2}\).  
Step 2: Three-layer iterations: A three-layer iterative algorithm is user to find the optimal solution.  
(1) Outer-layer: \(n_{c}\) is from 0 to \((N_{c}-1)\), which decides the subcarrier scheduling.  
(2) Adjacent inner-layer: Search \(i\) from 1 to \(U\cdot(U-1)\) for user pairing.  
3. Inner-layer: According to the given subcarrier scheduling and user pairing scheme, we allocate \(P_{1,n_{c}}\) and \(P_{2,n_{c}}\) to the selected users, according to (25). Calculation: Evaluate the SE of OBON system \(E_{\text{total}}\) with (16). Record processing: Store the SE \(E_{\text{total}}\), corresponding \(P_{1,n_{c}}\), \(P_{2,n_{c}}\), \(\theta\) and \(k_{n_{c}}\). Step 3: Output: Output the maximum \(E_{\text{total}}\), corresponding \(P_{1,n_{c}}\), \(P_{2,n_{c}}\), \(\theta\) and \(k_{n_{c}}\). All the parameters, which include power allocation result \(P_{1,n_{c}}\), \(P_{2,n_{c}}\), subcarrier scheduling \(k_{n_{c}}\) and user pairing \(\theta\) scheme, are decided through the proposed joint iterative algorithm. Then the signals of users are superimposed and transmitted together.
# Rheology, Hydration, and Microstructure of Portland Cement Pastes Produced with Ground Acai Fibers
## 3 Results and Discussion
### Rheological Tests
Furthermore, the zeta potential results (Figure 2c) showed that the chemical treatment decreased its absolute value from \(-\)32.2 to \(-\)25.7 mV, indicating a higher agglomeration trend (and consequently worse dispersion) for the treated fibers, which can negatively impact the rheological properties of the cementitious composite. Finally, the rougher surface of the chemically treated fibers (Figure 3e) tends to increase the friction between the particles during flow, which can also hinder the rheological properties of the fresh mix. Furthermore, dry fibers (both in natura and treated) led to further increases in yield stress and viscosity than the respective saturated fibers for the same incorporation level. This can be justified by the water absorption of the dry fibers: since both yield stress and viscosity are directly related to the inter-particle distance, the partial removal of mixing water caused by absorption increases the friction and probability of collision between particles. This behavior had already been reported in the literat  
Figure 5: Rheological properties of the pastes: (**a**) yield stress; (**b**) viscosity. Note: IN = in natura, \(S\) = saturated; T = treated; QTZ = quartz. materials. [PERSON] et al. [52] incorporated dry and pre-saturated SAP in ultra-high performance concrete, observing that the incorporation of pre-saturated SAP had a marginal effect on the yield stress and viscosity of concrete while the incorporation of dry SAP significantly affected it. [PERSON] et al. [48] investigated the use of four types of SAP in cement mortar, finding that the water uptake by SAP was the major factor that governed the rheological properties of fresh mortar. Finally, the incorporation of quartz powder also increased the yield stress and viscosity of the paste. These results are in agreement with those previously reported by [PERSON] et al. [53] and [PERSON] et al. [54] for quartz filler incorporation, and [PERSON] for other mineral fillers [55].
## 4 Classification of Energy Efficient MAC Protocols for UWSNs
### Frequency Domain
#### 4.1.2 Code Division Multiple Access (CDMA)-Based MAC Protocol  
CDMA allows numerous nodes in UWSNs to work within the same frequency band at almost the same time, and observing that signals from different nodes are identified using pseudo noise (PN) codes, which are essential to transport messages of different nodes in UWSNs [81]. The disturbances in communicating nodes are eliminated at the receiver's end that use dissemination procedures in order to receive the right message. CDMA technology allows high data transmission packets to be sent from one node to another at the same time. In [82] the authors have established a CDMA-based underwater MAC approach that allows for a periodic sleep mode which enables the communicating system to consume less energy.
# Communication-Traffic-Assisted Mining and Exploitation of Buffer Overflow Vulnerabilities in ADASs
## 3 Traffic-Assisted BOF Vulnerability Mining and Exploitation Method in ADASs
### Validation of BOF Vulnerability  
If a BOF vulnerability is discovered, attempting to construct a POC program is possible. When validating a BOF vulnerability, the most vital aspect is constructing an appropriate payload for testing purposes. The payload is a block of data that contain adequate bytes to attempt to overwrite or modify the target application's buffer. Consideration is necessary when building the payload, particularly regarding the number of bytes to be used: it needs to be large enough to cover the buffer's end while small enough to fit legally in the buffer. A too-small payload will be inadequate to control the program's behavior, and if the payload is too large, it may prevent the program from crashing.  
To verify the existence of a BOF vulnerability, we need to create one or a series of data packets. These packets correspond to the buffer's length or size based on the length requested in the program's pseudo-code and the actual buffer's size. We then use tools or write a program to send these packets to the target application program from our computer. The target application program typically has a BOF vulnerability if it crashes after receiving the payload. If the payload fails to cause the application to crash, we must adjust it and send it again until the vulnerability is discovered.
# Requirements for Crafting Virtual Network Packet Captures
## 3 Requirements
### Performance  
The necessary speed of the generation process depends on the intended purpose. If the generation process injects the packets into a real network in real time, the performance of the process should touch 10 GBit/s [22]. A limiting factor of a generation process is the deployed hardware, but the design of the software heavily affects the overall performance. In addition to this, the memory allocation of the creation process should be suitable for the created capture file.
# Responses to the Impact of Drought on Carbon and Water Use Efficiency in Inner Mongolia
## 2 Study Area and Methods
### Methodology
#### 2.3.4 Correlation Analysis  
To further investigate the impact of drought on carbon and water use efficiency, the correlation coefficients of the drought severity index (DSI) with CUE and WUE were calculated on an image-by-image basis using Pearson's correlation coefficients. Correlation analysis can reveal the closeness of the inter-relationship between two or more variables. This method can test the strength and directional distribution of the correlations between the factors of the study variables, is simple and easy to use, and has been widely used in various subject areas [34; 35]. It is calculated as  
\[R=\frac{\sum\limits_{i=0}^{n}\left(x_{i}-\overline{x}\right)(y-\overline{y})}{ \sqrt{\sum\limits_{i=0}^{n}\left(x_{i}-\overline{x}\right)^{2}}\sqrt{\sum \limits_{i=0}^{n}\left(y-\overline{y}\right)^{2}}} \tag{8}\]  
where R is the correlation coefficient, with value range [-1, 1]; \(x_{i}\) indicates the CUE and WUE values in year \(i\); \(\overline{x}\) indicates the average value of CUE and WUE in all years; \(y_{i}\) indicates the annual average value of DSI in year \(i\); and \(\overline{y}\) indicates the average value of DSI in all years.
## 3 Results
### Validating the Accuracy
#### 3.1.1 One-Dimensional Magnetic Model  
Figure 2 illustrates the presence of a magnetic layer embedded within a conductive half-space with a resistivity of 200 \(\Omega\cdot\text{m}\), while the relative permeability of the magnetic layer is 2. The first layer has a thickness of 200 m, whereas the magnetic layer has a thickness of 100 m. Comparisons of the apparent resistivities at three different frequencies are displayed in Table 1. It can be observed that the results obtained through the \(\mathbf{A}-\phi\) method exhibit excellent agreement with the analytical solutions [24], as indicated by relative errors below 0.3%.