esa-sceva/satcom-chunk-collection huggingface dataset:
# The Role of Renewable Energy Sources and Industry 4.0 Focus for Africa: A Review
## 3 Renewable Energy
### Wind Energy  
Wind energy uses three (usually) blades attached to a central hub, which is connected to an AC electrical generator (usually at 11 kV), which then catches passing wind, which forces the edges to rotate the hub and generate electricity. In South Africa, hub heights are generally 90-100 m above the ground, with blades approximately 50 m in length, which gives a swept diameter of roughly 80 m.  
Depending on the manufacturer, wind turbine sizes range between 1 MW to 3 MW. They are very effective in coastal regions that experience relatively sustainable wind and less than the average thunderstorms to preserve the blades. The blades are made of laminated polycarbonates and are delivered to the site entirely constructed.  
An inherent issue with wind energy comes into play due to the wind required to spin the turbine. The airflow must be as close to laminar as possible to create the required pressure gradient. The placement of turbines needs to be carefully considered over a large area to derive the maximum benefit. Another issue in the same vein is the erratic nature of atmospheric wind. Turbines can only generate if there is wind, and analysis of generation patterns of wind farms in South Africa has demonstrated this quite effectively. Measures are therefore required to maintain output despite the intermittence of wind availability. One advantage is that wind energy is available 24 h per day, unlike solar energy, which is restricted to daytime operation. However, the sporadic nature of wind energy's availability means an energy storage mechanism is required for a given facility to provide continuous power.
# A Location Privacy Preservation Method Based on Dummy Locations in Internet of Vehicles
## 5 Performance Analysis
### Security Analysis
#### 5.1.2 Inference Attack  
The LBS server and RSUs have global information, such as information matrix \(Q\)(r, \(q\), \(e\)), road information \(\mathbf{R}\) and \(k\) locations in the service query, and so on. Based on this information, the LBS server or the RSU can act as an active attacker to launch reasoning attack and acquire some sensitive information of users.  
Suppose \(p_{G}\)(event) be the probability that an attacker successfully guesses that event is true. The proposed method should satisfy (9) to resist inference attack.  
\[p_{G}(\mathbf{r}_{i,j}\in\mathcal{B}\Big{|}\mathcal{B}\cap\mathcal{G}^{\prime\prime }\
eq\varnothing)=p_{G}(\mathbf{r}_{i,j^{\prime}}\in\mathcal{B}\Big{|}\mathcal{B} \cap\mathcal{G}^{\prime\prime}\
eq\varnothing),\mathbf{r}_{i,j}\in\mathcal{G}^{ \prime\prime},\mathbf{r}_{i^{\prime},j^{\prime}}\in\mathcal{G}^{\prime\prime},\ \mathbf{r}_{i,j}\
eq\mathbf{r}_{i^{\prime},j^{\prime}}, \tag{9}\]  
where set \(\mathcal{B}\) consists of the locations obtained by an attacker.  
For any dummy location \(\mathbf{r}_{i,j}\) generated by RR-DLS algorithm, the probability of \(\mathbf{r}_{i,j}\) being guessed as the real location is  
\[p_{G}(\mathbf{r}_{i,j}\in\mathcal{B}\Big{|}\mathcal{B}\cap\mathcal{G}^{\prime\prime }\
eq\varnothing)=\frac{p_{G}(\mathbf{r}_{i,j}\in\mathcal{B},\ \mathcal{B}\cap\mathcal{G}^{\prime\prime}\
eq\varnothing)}{p_{G}(\mathcal{B} \cap\mathcal{G}^{\prime\prime}\
eq\varnothing)}=\frac{p_{i,j}}{p_{G}(\mathcal{B} \cap\mathcal{G}^{\prime\prime}\
eq\varnothing)},\ \mathbf{r}_{i,j}\in\mathcal{G}^{\prime\prime}. \tag{10}\]  
Substituting (10) into (9), we have  
\[p_{i,j}\simeq p_{i^{\prime},j^{\prime}}\,\mathbf{r}_{i,j}\in\mathcal{G}^{\prime \prime},\mathbf{r}_{i^{\prime},j^{\prime}}\in\mathcal{G}^{\prime\prime},\ \mathbf{r}_{i,j}\
eq\mathbf{r}_{i^{\prime},j^{\prime}}. \tag{11}\]  
The proposed dummy location selection algorithm under road restriction selects locations with the same or similar probability of service requests and service semantics. Hence, the proposed RR-DLS method satisfies the condition in (11), which means that the method can effectively resist inference attack.
# Spam Email Detection Using Long Short-Term Memory and Gated Recurrent Unit
###### Abstract  
In today's business environment, emails are essential across all sectors, including finance and academia. There are two main types of emails: ham (legitimate) and spam (unsolicited). Spam wastes consumers' time and resources and poses risks to sensitive data, with volumes doubling daily. Current spam identification methods, such as Blocklist approaches and content-based techniques, have limitations, highlighting the need for more effective solutions. These constraints call for detailed and more accurate approaches, such as machine learning (ML) and deep learning (DL), for realistic detection of new scams. Emphasis has since been placed on the possibility that ML and DL technologies are present in detecting email spam. In this work, we have succeeded in developing a hybrid deep learning model, where Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU) are applied distinctly to identify spam email. Despite the fact that the other models have been applied independently (CNNs, LSTM, GRU, or ensemble machine learning classifier) in previous studies, the given research has provided a contribution to the existing body of literature since it has managed to combine the advantage of LSTM in capturing the long-term dependency and the effectiveness of GRU in terms of computational efficiency. In this hybridization, we have addressed key issues such as the vanishing gradient problem and outrageous resource consumption that are usually encountered in applying standalone deep learning. Moreover, our proposed model is superior regarding the detection accuracy (90%) and AUC (98.99%). Though Transformer-based models are significantly lighter and can be used in real-time applications, they require extensive computation resources. The proposed work presents a substantive and scalable foundation to spam detection that is technically and practically dissimilar to the familiar approaches due to the powerful pre-processing steps, including particular stop-word removal, TF-IDF vectorization, and model testing on large, real-world size dataset (Enron-Spam). Additionally, delays in the feature comparison technique within the model minimize false positives and false negatives.  
spam email; LSTM; gated recurrent unit; deep learning; kNN; naive Bayes
# Applications of Mueller Matrix Polarimetry to Biological and Agricultural Diagnostics: A Review
## 4 Applications of MMP to Biology and Agriculture
### Milk Composition Analysis and Food Quality Control  
Among the needs of agriculture, milk quality control is of great importance. Information on the quantitative content of milk components (fat, proteins, lactose, somatic cells, progesterone, amino acids, etc.) is the basis for assessing milk quality and monitoring the nutritional balance and clinical condition of cows. In particular, the content of fat and protein is considered the main criterion for determining the market value of milk. Rapid analysis of milk composition is necessary for a prompt response to deviations in the parameters of the physiological state of animals and for timely correction of food rations if milk yield decreases. Additionally, milk analysis is an important part of the medical diagnostics which concern human milk for newborns [39]. Rapid milk testing can be performed by optical methods [35; 36; 37; 38; 39], among which MMP-based ones [35; 36] are capable of multicomponent analysis. Using a setup which is actually an MM scatterometer, an empirical dependence of the MM elements of diluted milk on the amount of fat particles was obtained [35]. By solving the inverse problem for MM measurements, the distributions of fat and casein particles are retrieved; thereby, both fat and casein content in milk can be determined simultaneously from MM [36]. The ability of MMP to authenticate olive oil by linear dichroism and optical rotation of the circular birefringence retrieved from the MM of oil samples was revealed [41]. Changes in the depolarization of fresh and aged meat samples over time are clearly visible on MM polarimetric images and can serve as quantitative indicators for the non-invasive assessment of meat freshness [42].
# Joint Iterative Decoding Design of Cooperative Downlink SCMA Systems
## 6 Simulation Results
### BER Evolution for AWGN Channel  
In this subsection, we investigated the BERs of the proposed downlink cooperative SCMA system under an AWGN channel. We first compared the BER performance of the analyzed and MPA detector for the proposed cooperative SCMA system with \(K=4\) and \(K=5\), as shown in Figure 4. The theoretical average BER was computed according to (27) and the steps of the MPA were discussed in Section 3. As can be seen from Figure 4, it is straightforward that the analytical bounds are almost overlapped with the simulated BERs for both \(K=4\) and \(K=5\) in the moderate and high-SNR regions.  
In the next stage of simulations, the BERs of the proposed ideal cooperative SCMA system under an AWGN channel and different iterations of the MPA detector for \(K=4\) and \(K=5\) are depicted in Figure 5 and Figure 6, respectively. It is observed that the BER performance of the cooperative SCMA system is better than that of the non-cooperative one. This is because the location of users in the second group is far from the BS, and the strength of the signal received by these users is relatively weak. Moreover, the cooperative SCMA system suffers from large decreases in BER performance when the number of iterations for the MPA detector is not large enough. Simulations demonstrated that six iterations can be used to arrive at the intended results.  
Figure 4: Comparison of the BERs of the analyzed and MPA detector for the proposed cooperative SCMA system under an AWGN channel with \(K=4\) and \(K=5\).  
Figure 5: The BER performance of the proposed ideal cooperative SCMA system under an AWGN channel and different iterations of the MPA detector for \(K=4\).  
We also evaluated the BER performance of the proposed non-ideal cooperative SCMA system under an AWGN channel and different SNRs of the source-relay (S-R) link for \(K=4\) and \(K=5\) in Figure 7 and Figure 8, respectively. This is practical since the decoding of the SCMA codewords in the relay is often imperfect, i.e., the decoded bits suffer from a certain amount of errors. We claim that there is no detection error when the SNR of the S-R link is high. Observe from both Figures 7 and 8 that the destination user achieves better BERs when the SNR of the S-R link becomes higher, which in return provides us with guidance for how to approach the design of the relay's location.  
Figure 6: The BER performance of the proposed ideal cooperative SCMA system under an AWGN channel and different iterations of the MPA detector for \(K=5\).  
Figure 7: The BER performance of the proposed non-ideal cooperative SCMA system under an AWGN channel and different SNRs of the S-R link for \(K=4\).
# Breaking Alert Fatigue: AI-Assisted SIEM Framework for Effective Incident Response
## 4 Methodology
### Filtering Problem  
The filtering problem involves identifying critical alerts from a given set of alerts. Recall the three event types defined in Section 2.1: normal, escalation, and emergency. Emergencies are relatively rare in environments controlled by an intrusion response team, and using such events as direct targets for learning may be overly aggressive. Thus, we propose to use escalations as positive samples for the target classification task. As a result, the classifier imitates the process of the intrusion response team by selecting escalations from the outputs of IDSs.  
The following two reasons further explain the rationale behind this choice. First, selecting escalations from normal events typically only requires the information contained in the alert, whereas determining if an escalation is an emergency typically requires evidence beyond that provided in the alert. Second, the daily number of escalations reflects the throughput of the intrusion response team; thus, the predicted number of alerts and vigilance levels for ML methods targeting escalations should be reasonably appropriate.  
The following definition formally describes the filtering problem as a binary classification task.  
**Definition 1** (Filtering Problem).: _Let \(\{\mathbf{x}_{i},y_{i}|i=1,\ldots,N\}\) denote a labeled alert set, where \(\mathbf{x}_{i}\in\mathbb{D}\) is a \(d\)-dimensional vector obtained from the one-hot encoding representation of an alert, where \(y_{i}=1\) if the alert is an escalation; otherwise, \(y_{i}=0\). The filtering problem is solved by a decision function \(f(\mathbf{x}_{i};\theta)\) with a loss function \(\mathcal{L}(f(\cdot;\theta),\mathbf{x}_{i},y_{i})\). Specifying the functional form of \(f\) and \(\mathcal{L}\) completely specifies the classification algorithm. \(\theta=\arg\min_{\theta}\mathcal{L}(\theta)\) defines the classifier. A sample \(\mathbf{x}\) is predicted to be positive (a critical alert) if \(f(\mathbf{x};\theta)>0\); otherwise, it is classified as negative (a noncritical alert)._
## 4 GPS Application for Garbage Trucks Modeled as a Travelling Salesman Problem
### Our Approach
This comparison demonstrates that GVNS produces results that, in all cases, are near-optimal and significantly superior to the NN algorithm, which is widely used by most GIS/GPS software. As a result of these better routing solutions, the total amount of fuel is drastically reduced. Thus, our method is environmentally friendly, since fuel consumption has a direct impact on the environment.
## 2 Results
### Transcriptome Analysis in P. notoginseng  
Illumina Hiseq sequencing technology was employed to analyze the transcriptome of the 18 samples (six tissues, namely, flower, leaf, stem, rhizome, root, and fibril; three biological replicates for each part) and to clarify the mechanism of saponin synthesis in _P. notoginseng_. A total of 120.70 Gb of data were obtained, and the average output of each sample was 6.71 Gb of data. After filtering was performed, 805 M clean reads with an average size of 150 bp were used for the _de novo_ assembly. Overall, 1,325,735 contigs with an average size of 846 bp were obtained, 158,551 unigenes were assembled, and the percentage of GC was 41.05% with an average contig size of 1274 bp and an N50 contig size of 2012 bp (Table 1 and Supplementary Table S2).  
Figure 1: HPLC chromatograms and contents of saponins in _P. notoginseng_. **(A)** HPLC chromatogram profiles. 1. Notoginseng R1; 2. Ginsenoside Rg1; 3. Ginsenoside Re; 4. Ginsenoside Rb1; 5. Ginsenoside Rc; 6. Ginsenoside Rb2; 7. Ginsenoside Rd. **(B)** Contents of the seven saponins. NG: notoginsenoside; G: ginseng; PTS: Total amount of protopanaxatriol saponins (NG-R1, G-Rg1, and G-Re); PDS: Total amount of protopanaxadiol saponins (G-Rb1, Rc, Rb2, and Rd); Total: Total amount of protopanaxatriol and protopanaxadiol saponins. Different letters represent the significant differences between the five tissues at \(p\) < 0.05.
## 3 RUL Prediction Methodology
### Envelope Analysis for Bearing Fault Detection  
#### 3.1.1 Signal Pre-Processing: High-Pass Filtering  
Signal processing and feature extraction play a crucial role in the reliability of data-driven RUL prediction. In vibration-based condition monitoring, the acceleration signals associated with the localized bearing faults tend to exhibit a cyclic pattern of high-frequency impulse responses occurring at resonance frequencies. These responses are induced by the series of quasi-periodic impacts generated by the moving components passing the fault [26], thereby dominating the high-frequency range. To mitigate the influence of low-frequency components, the high-pass filter is applied to the vibration acceleration signal, with the cut-off frequency at \(1\) kHz.
# Real Cyclic Load-Bearing Test of a Ceramic-Reinforced Slab
## 4 Analysis of Results
### Cyclic Response  
As mentioned before, six different cycles of loading-unloading were carried out on six consecutive days. The processes of loading, as well as unloading, were totally manual and smooth, placing the pallets slowly on the tested slab area.  
From the curve obtained during the second cycle, we observed a significant change in the initial stiffness until 2.00 kN/m\({}^{2}\), as well as a smaller remaining deflection (almost nonexistent, less than 0.2 mm). This phenomenon may be explained by the relocation of the re-bars which occurred during the first cycle after reaching higher levels of load than usual. As has been mentioned, under a high level of loading, plastic phenomena take place. Micro-crushing of concrete and ceramics results in two different consequences: the first is the appearance of remaining strains after unloading; the second is typical plastic hardening due to geometrical relocation.  
This hardening effect is clearly seen when reloading in the second and further cycles up to 2.00 kN/m\({}^{2}\), where the stiffness of the section is much higher than that of the original one. After a certain loading (2.00 kN/m\({}^{2}\)), the slab behaves in exactly the same way as the original one until about 6.00 kN/m\({}^{2}\) of load, where micro-crushing tends to appear again. This hardening behavior informs of the proximity of collapse, since it is a clear signal of creep between reinforcements and concrete after micro-crushing (see Figure 12). However, the high rigidity of the material type (controlled by inclined steel re-bars) makes the solution really rigid for loading and unloading. This behavior is shear-based, with quite a short span and derived from having inclined bars.  
Figure 11: Obtained diagram of the loading and unloading process (Cycle 1).  
From the curves which were obtained in the following cycles up to the sixth, no loss of ultimate loading capacity was observed. At each cycle, the load reaches a value of 7.50 kN/m\({}^{2}\), although each time there is a slight reduction of stiffness. After the second cycle, the pattern of loading and unloading is almost the same for all cycles, except for the first loading steps during the last two cycles, where plastic hardening plays a significant role (see Figure 13. This phenomenon goes hand in hand with a clear reduction of remaining strains; both effects are clear signals of a certain damage process within the slab when subjected repeatedly to high levels of loading and unloading in consecutive cycles.  
All these results lead to the conclusion that this type of slab is very rigid due to its shear-based response and dependence on reinforcements. This rigidity does not decrease significantly when repeating cycles up to high rates of loading.
# Analytical Computation of the Spatial Resolution in GNSS-R and Experimental Validation at L1 and L5
## 2 Methodology
### Review of Some Basic Principles of Electromagnetism
#### 2.1.4 Principle of Superposition  
Once the electric fields diffracted by the semi-plane (the white area in the plane z = 0 in Figure 5) are computed, the electric fields diffracted by the complementary semi-plane (the light-gray area in the plane z = 0 in Figure 5), when it is not opaque, can be computed using Babinet's principle, and then the total electric field arriving at the receiver can be computed as the sum of both.
## 2 Algorithm Principle
### MP Denoising Algorithm  
For the power-line harmonics and random noise in the MRS signal, we propose an MP denoising algorithm based on an oscillating atomic library. The specific implementation steps include the following:  
(1) Construction of an over-complete atomic library  
An oscillatory atomic library was constructed according to Equation (5). To satisfy the over-completeness of the atomic library, an atom in the atomic library was required to be redundant, that is, the number of atoms \(K\) was much larger than the signal length \(N\).  
(2) Removal of power-line harmonics  
Let \(\rho=0\); then, the \(p\) value corresponding to each power-line harmonic point was calculated according to Equation (8), and the search range and search step length corresponding to the \(p\) value of each power-line harmonics point were set. Then, the atom \(g_{\gamma 0}\) with the highest degree of similarity to the signal to be decomposed was searched for in the over-complete atomic library, such that  
\[s=<s,g_{\gamma 0}>g_{\gamma 0}+R^{1}s \tag{10}\]  
where \(<s,g_{\gamma 0}>\) is the inner product of the signal to be decomposed \(s\), the atom \(g_{\gamma 0}\), \(<s,g_{\gamma 0}>g_{\gamma 0}\) represents the projection of \(s\) in the direction \(g_{\gamma 0}\), and \(R^{1}s\) is the remaining part of the signal to be decomposed, and is called the residual error. Evidently, \(g_{\gamma 0}\) and \(R^{1}s\) are orthogonal, that is,  
\[\|s\|^{2}=|<s,g_{\gamma 0}>|^{2}+\|R^{1}s\|^{2} \tag{11}\]  
If the energy of residual signal \(R^{1}s\) is to be minimized, then \(g_{\gamma 0}\in D\) should be selected in the atomic library to maximize the value of \(|<s,g_{\gamma 0}>|\) and achieve the best search.  
Next, a repeated iteration was performed to realize sparse decomposition of noisy data, and the best atom found was linearly combined to complete the reconstruction of the current power-line harmonic. The reconstruction method was  
\[f_{filter}=\sum_{k=0}^{M-1}<R^{k}s,g_{rk}>g_{rk} \tag{12}\]  
where \(M\) represents the number of atoms selected. Finally, the reconstructed power-line harmonic was subtracted from the MRS signal, and the search range for the value of \(p\) was updated. Step (2) was looped until all power-line harmonics were removed.  
(3) Extraction of MRS signals  
Let \(\rho>0\); then, the discretization parameters \(p\) and \(r\) corresponding to the Larmor frequency \(f_{l}\) can be calculated according to Equation (8), and their search ranges and search step sizes can be determined. After this, the atom that best matched the MRS signal was determined according to Equation (10). Finally, the MRS signal was directly reconstructed according to the best atom found using Equation (12).