esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Area, Data, and Methodology
### Data and Methodology  
#### 2.2.1 MODIS LST  
The moderate resolution imaging spectroradiometer aboard two polar-orbiting sun-synchronous satellites, Terra and Aqua (10:30 am/pm and 1:30 am/pm local solar time, respectively), from the NASA Earth Observing System, provide climate modeling grid (CMG) temperature and emissivity values at 0.05 \(\times\) 0.05 degrees (5600 m \(\times\) 5600 m at the equator) latitude/longitude, which have been recognized and well used for land surface temperature estimates for various land regions globally, available from February 2000 onwards. The Level 3 MODIS (MOD11C1, collection 6.1) monthly LST data products for 23 years (from March 2000 to February 2023) were used for the present study.
## 3 Results
### Calculation of Green Vegetation Fraction
Prior to 2000, \(N_{s}\) was more variable and averaged 0.12; whereas, afterwards, \(N_{s}\) was stable and around 0.1. Decreases in \(N_{c,v}\) were seen for evergreen needleleaf, shrublands, and grasslands, while increases were seen for deciduous broadleaf and mixed forests. Similar but less apparent jumps occur in other products as well at this same time including a noticeable increase in \(N_{s}\) for NDVIg from 0.05 to 0.08 and decreases in the \(N_{c,v}\) of many land cover classes for STAR. Also, the interannual values of \(N_{c,v}\) are much more stable for NDVIg than the other three products. It's very challenging to identify the true variability of vegetation, the use of \(N_{c,v}\) and \(N_{s}\) each year helps to remove some of the spurious variability (jump) but a more rigorous approach is still needed to identify the true variability in the future. In comparison to the original NDVI relationships between products shown in Figure 1, nearly all of the derived GVF relationships are much closer to one-to-one (Figure 7). On the other hand, all correlations between products were reduced slightly. This slight reduction is also evident in the time-series of spatial correlations for GVF in Figure 3. Similar to Figure 7, the time series of the spatial regression slope in GVF is much closer to 1 than that for NDVI itself (Figure 4). Furthermore, the GVF relationship between VIP and NDVIg is much more stable with time (than that of NDVI)and the relationship between VIP and NDVIIg is slightly more stable, suggesting a correcting influence of GVF on relationships between products each year. Table 2 shows how the interannual mean and range in MGVF vary across the six datasets over all pixels for each IGBP land cover class. In comparison to \(N_{p,max}\) which was discussed earlier, most land cover classes have a higher average MGVF. The largest increases were seen over the closed shrublands, savannah, and grassland classes. More importantly the MGVF across different datasets for each given class are much more uniform than \(N_{p,max}\), showing how well GVF normalizes the values. The interannual variation expressed through the range in Table 2 suggests reduced variation in MGVF for most classes in comparison to \(N_{p,max}\). The results from [PERSON] _et al._[14] compare very similarly to Table 2. The largest differences are in classes where there wasn't much data in this study such as savannah, closed shrublands, and wetlands which tend to have lower mean MGVF than this study. The ranges are also similar in most classes except larger ranges in open shrublands and barren classes. While the large range in this study is not surprising, it's uncertain why the global study of [PERSON] _et al._[14] has reduced barren interannual variability. One possible reason is that areas of barren land throughout North America are fairly  
Figure 7: Same as Figure 1 but for final GVF datasets derived from the NDVI. isolated and hence may include some very sparse open shrubland areas, whereas other areas on Earth such as Africa and Asia have large barren deserts that only change at the margins.
## 5 Conclusions  
The most critical issue in the present age of network communication is network interference. The increasing frequency of cyber-attacks seriously endangers networks. Numerous studies were performed to identify how to prevent network interference and safeguard privacy and network security. In this study, new and enhanced DL techniques were developed to detect cyber-attacks. Min-max normalization was utilized to normalize the data. Due to the imbalance of training data, several minority attacks occurred. Therefore, to avoid these issues, synthetic minority oversampling techniques (SMOTE) were developed. By utilizing the Hybrid Genetic Fire Hawk Optimizer (HGFHO), feature selection was executed to select optimal features. Finally, the residual dense-assisted multi-attention transformer (Op-ReDMAT) model was employed to classify the selected features. Performance analysis was implemented to show the efficiency of the proposed model. The experimental results showed that the UNSW-NB15 dataset attained a higher precision, accuracy, F1-score, error rate, and recall of 97.2%, 98.82%, 97.8%, 2.58, and 98.5%, respectively. On the other hand, the CICIDS2017 dataset attained higher precision, accuracy, F1-score, and recall of 98.6%, 99.12%, 98.8%, and 98.2% respectively. To provide the cyber-security community with automated security services, the researchers aim to expand the cyber-security datasets and create a data-driven IDS.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the author.  
The author would like to thank the Deanship of Scientific Research at Shaqra University for supporting this work.  
The author declares no conflict of interest.
## 3 Experimental Verification
### Experimental Testing
#### 3.2.2 Loading Process  
The static loading test was performed after the completion of the post-tensioning process. The vertical load was generated by hydraulic jacks, and it was transferred through two horizontal I-steel spreading beams to the segmental beam uniformly, as shown in Figure 7. The beam was tested for two cycles under the selected applied load of 20 kN. In the first cycle, the applied load was increased progressively to 20 kN and then decreased to approximately 5 kN before starting the second cycle. In the second cycle, the applied load was reduced to 0 kN. A load of 20 kN was selected here for analysis to ensure that no joint opening and damage occurred and that the beam deformed linearly under the applied load. A more detailed loading process is provided in [PERSON] et al. [22]. The applied load was monitored by load cells, and the deflection of the beam in the experimental tests was obtained using four linear variable differential transformers (LVDTs). The locations of these installed LVDTs are shown in Figure 8.  
All LVDTs were placed at the joints between different segments of the beam as shown in Figure 8. LVDT2 and LVDT3 were located on the top surface of the beam between the second and third segments, whereas LVDT1 and LVDT4 were placed at the center of the top surface between the first and second segments, and the second and third segments, respectively.
## 2 Compatibility between Indoor Radon Exposure and Buildings' Energy Efficiency Improvement
### Indoor Radon Management  
The amount of radon that reaches the terrestrial surface depends on factors related to the uranium content in the bedrock, soil permeability, mineral porosity, and the potential existence of failures or cracks in the soil substrate. Outdoor radon concentrations are on average about 10 Bq/m\({}^{3}\), representing no harm to human health. On the other hand, radon penetration from foundation soils and rocks to the building's environment determines an increase of indoor concentration due to the atmospheric pressure difference between outdoor and indoor air. Usually, the indoor air pressure is lower thus facilitating the gas entry, mainly through the cracks in the floors and walls, or in the plumbing, or even through the construction materials joints, etc., [13].  
Indoor radon concentration varies both on a daily and seasonal basis, therefore it does not show a constant value throughout the day or the year. Radon concentration tends to be higher at night than during the daytime and reaches a higher value in winter when compared to other seasons [2]. In the summer, natural ventilation by windows opening causes air movement and consequently lowers indoor radon concentrations. On the opposite, in winter buildings are airtight and the heating systems are generally on, so heat movement contributes to air pressure differences that generate a suction effect of the radon present in the soil.  
Usually, the most effective way to improve indoor air quality is to eliminate individual sources of pollution or to reduce their emissions. In many cases, source control is a more cost-effective approach to protect IAQ, because for increasing the ventilation rate it is only necessary to enlarge the number of air renovations. This approach is adequate for lowering indoor air pollutants' concentration; however, it can drastically raise energy consumption for heating and compromise energy efficiency.
## 3 Results and Discussion
### Spectroscopic Characterization
#### 3.3.3 Electron Temperature
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Balmer Series** & \(\lambda_{pq}\) & \(E_{p}\) **(eV)** & \(g_{p}\) & \(A_{pq}\) **(10\({}^{8}\) s\({}^{-1}\))** & **Transition (\(p\rightarrow\vartheta\))** \\ \hline
**H\({}_{\alpha}\)** & 656.28 & 12.088 & 18 & 0.44100 & 3 \(\rightarrow\) 2 \\ \hline
**H\({}_{\beta}\)** & 486.13 & 12.749 & 32 & 0.08419 & 4 \(\rightarrow\) 2 \\ \hline
**H\({}_{\gamma}\)** & 434.05 & 13.055 & 50 & 0.02530 & 5 \(\rightarrow\) 2 \\ \hline
**H\({}_{\delta}\)** & 410.17 & 13.221 & 72 & 0.00973 & 6 \(\rightarrow\) 2 \\ \hline
**H\({}_{\epsilon}\)** & 397.01 & 13.321 & 98 & 0.00439 & 7 \(\rightarrow\) 2 \\ \hline \end{tabular}
\end{table} 
Table 4: Parameters used in the calculation of electron temperature. Figure 12: Modified Boltzmann plot of hydrogen lines vs. energy of the departure level \(p\) for applied voltage of 14 kV, rotation frequency of the power electrode of 18,000 rpm, and the gas flowrate of 1 sLm. were linearly distributed. Therefore, by ignoring the H\({}_{\alpha}\) line in the fitting procedure, the electron temperature was determined from the calculation of the line slope (14,400 K for these conditions). Figure 13 shows the variations of electron temperature as a function of applied voltage, rotation frequency of the power electrode, and the gas flow rate. It can be observed that the electron temperatures had a similar behavior to the gas temperatures with the changes of the experimental parameters. The temperature of electrons increased with the increase of applied voltage while their temperature decreased by increasing the rotation frequency of the power electrode and the gas flow rate. This shows the transfer of energy between electrons and heavy particles. For all studied conditions, the rotational temperature was much lower than the electron one, due to no equilibrium condition in this plasma. It means that this discharge was not in equilibrium and was a 2-temperatures plasma, where the electron and gas temperatures are different. As mentioned, the electric field directly gives the energy to the electrons, which transfer this energy to heavy particles by elastic and inelastic collisions. If the equilibrium between particles is not reached, the translational energy of heavy particles is lower than one of the electrons: \(T_{e}>T_{g}\)[25,26]. This difference of temperatures of both species justifies the high values of LPG conversion and H\({}_{2}\) formation. The energy of electrons is efficiently used in this conversion instead of the heating of heavy particles.
# A Game-Theoretic Rent-Seeking Framework for Improving Multipath TCP Performance +
## 3 Modeling Framework
### Equilibrium Properties  
We now discuss the application of our new utility function from Equation (6) to the solution of our dynamic system defined in Equation (4).  
Let \((\mathbf{\lambda}^{\star},\mathbf{p}^{\star})\) be the equilibrium of the aforementioned dynamical system and let \(\mathbf{\lambda}^{\star}_{(-\mathrm{i})}\) be the action vectors of all of the MPTCP subflows, except for the one along path \(i\). Now, by using KKT, since \(U_{i}(\lambda)\) is concave for each \(\lambda_{i}\), there exists a Lagrange multiplier \(L_{i}(\mathbf{\lambda}^{\star}_{(-\mathrm{i})})\) such that \(\mathbf{\lambda}^{\star}_{i}\) maximizes the Lagrangian:  
\[Q_{i}(\lambda_{i}) = U_{i}(\lambda,\mathbf{x}_{(-\mathrm{i})})-L_{i}(\mathbf{x}_{(-\mathrm{i })})\Big{(}B-\sum_{j=1}^{K}\lambda_{j}\Big{)};\] (8) and \[L_{i}(\mathbf{x}_{(-\mathrm{i})})\Big{(}B-\sum_{j=1}^{K}\lambda_{j} \Big{)}=0. \tag{9}\]  
Equation (9) is obtained by using the complementary property. In addition, observe in Equation (8) that the Lagrangian \(Q_{i}(.)\) replaces the utility \(U_{i}(.)\).  
The Lagrange multipliers are interpreted as _pseudo-prices_ of the paths. If a price is set on path \(i\) while other players are at equilibrium, then the subflow on path \(i\) pays \(\lambda_{i}L_{i}(\mathbf{\lambda}^{\star}_{(-\mathrm{i})})\) for its use of the bandwidth. In this case, \(\mathbf{\lambda}^{\star}\) is an equilibrium of our non-linear dynamical system with the capacity constraints. However, the pricing is not tractable, since it may vary from one subflow to another (for similar use of the network resources). In addition, it depends on the equilibrium selected.  
Therefore, our main objective is to find a _constant_ Lagrange multiplier \(L\) that is independent of \(i\). Furthermore, \(L\) should be independent of the actions taken by any of the MPTCP subflows, along with an associated equilibrium \(\mathbf{\lambda^{\star}}\) for our new relaxed game. If we can find such a constant Lagrange multiplier \(L\), the trajectory \((\mathbf{\lambda}(t),\mathbf{p}(t))\) generated by our cooperative MPTCP algorithm converges to a unique and globally stable _normalized_ equilibrium \((\mathbf{\lambda^{\star}},\mathbf{p^{\star}})\). Moreover, our interest is to establish the existence and uniqueness of the _normalized_ equilibrium. Therefore, we investigate the _diagonally strict concave utility_ functions of our dynamical system (given by Equation (1)), since it is known that these lead to a unique global equilibrium (Theorem 4 in [18]).
## 2 Search Method  
To select papers that describe studies that focused on the use of wearables to predict the presence and severity of depressive symptoms, we conducted a search in PubMed (from the time of inception to June 2023) using the following terms: \"wearables\", \"depression\", \"predictive algorithms\", and variations of these terms. In addition, the reference lists of selected papers were checked for relevant publications. We chose PubMed in particular because the articles found in this database likely reflect best what clinical research professionals will encounter when looking to research the applications of wearables in healthcare. We included original research studies. Review studies were considered not eligible. We considered studies written in English only. Given the scoping nature of this review, the list of included studies is not comprehensive but instead represents the range of methodologies and applications of wearables in this field of research. As such, studies were included if they met each of the following three criteria: (1) using a predictive algorithm to (2) detect or predict depressive symptoms according to clinical standards using (3) data collected using a wearable device. Furthermore, wearables were selected to be wrist-worn and feasible to be used in day-to-day life. Studies using smartphone data were not excluded as long as wrist-worn wearable data were used as well. The reasoning behind this was that the requirement for smartphones would not significantly hinder practical implementation due to the wide availability of smartphones to the general public. Any algorithm training method was acceptable as long as the reported aim was to detect or predict symptoms of depression using wearable data. This includes algorithms detecting current depression or symptom severity, as well as the prediction of future symptom severity or onset of depression. In-person clinical interviews with a mental health care professional (i.e., a psychiatrist or resident) constituted the current gold standard for diagnosis and were therefore preferred as ground truth. In order to maintain our focus on clinical depression, studies that instead used screeners to determine true mood were only included if classifications were made using the clinical cut-off scores.
# Effect of Transducer Burn-In on Subjective and Objective Parameters of Loudspeakers
## 2 Burn-In Process  
The burn-in procedure was conducted in accordance with the PN-EN 60268-5:2021 [4] and AES75-2023 [5] standards. The loudspeaker systems under testing were subjected to a broadband signal known as \"Music Noise\" [5], applied at a voltage corresponding to half of the maximum input voltage \(U_{MAX}\), as determined by Equation (1). The burn-in process lasted for a duration of three hours.  
\[U_{MAX}=\sqrt{P_{MAX}.Z} \tag{1}\]  
The AES75-2023 standard defines the \"Music Noise\" signal as a new benchmark that yields results more representative of actual music playback compared to traditional program-type signals.  
In the article \"Loudspeaker Break-In: Fact or Fiction?\" published in Audioholics, [PERSON] notes that, \"Depending on the number of mechanical factors relating to its design, construction, and materials composition, a net post-burn-in compliance increase on the order of 5-10% would not be unusual\" [6]. He emphasizes that mechanical components of the loudspeaker, such as the upper and lower suspensions, undergo changes in compliance as a result of operational stress. His research suggests that the burn-in process for a transducer may take only a few seconds and could be integrated into the manufacturing workflow. The article presents models of the transducer before and after burn-in, highlighting observed differences. The amplitude discrepancy was limited to just 0.09 dB in favor of the burned-in transducer model. Differences were also noted in the peak values of the impedance magnitude characteristics.  
In his publication \"The Audio Expert: Everything You Need to Know About Audio\" (Focal Press) [7], [PERSON] addresses common audiophile myths and acknowledges that over time, relaxation phenomena may occur in the materials used in transducer construction. However, he concludes that such changes are unlikely to result in perceptible auditory differences.
# A Unified PUF and Crypto Core Exploiting the Metastability in Latches
## 3 Physical Unclonable Function
### Implementation  
Figure 7 shows the architecture of the implemented PUF. The PUF exploits the metastability in NOR latches. The PUF architecture is designed to be unified with a crypto core, changing the operational mode of the latches between PUF and memory with the signal _mode_. Therefore, the _counter's_ value can be generated in the PUF mode or stored in the crypto core mode. The PUF implementation consists of two branches of latches, and the 32-bit challenge selects which branch is used for the PUF output response. Each branch consists of 32 PUF latches. The implemented latches present an unbalanced response in all bits due to the lack of calibration and implementation strategies to improve the mismatch. The length of the challenge and response of the PUF implementation is 32 bits. In addition, the unified PUF generates an overhead of one cycle in the crypto core function.  
Figure 8 illustrates the techniques applied to reduce the noise and improve the process variation to obtain unbalanced latches in the implementation. The latches are constructed with NOR cells using the LUT-6 primitive of the FPGA. In addition, the \(INIT\) value of the NOR function in the LUT differs in each latch. Therefore, the mismatch of the implemented latch increases in the PUF application. Additionally, a placement blockage is implemented in the slices of the latch, reducing the noise introduced by external circuits into the latch. On the other hand, the static entropy source implemented in the latch must operate as an elementary bit memory. The entropy source is implemented in one of the internal stages of a ChaCha20 crypto core as described in Section 4. Finally, the PUF-ChaCha20 is connected to a system on chip (SoC) based on an RISC-V for testing the implementation [40].  
Figure 7: PUF block diagram implementation.
# Data Augmentation Methods for Enhancing Robustness in Text Classification Tasks
## 2 Related Work
### Text Data Augmentation  
There has been a proliferation of research on data augmentation methods for deep learning models in recent years, intending to improve performance metrics such as the accuracy and F1-score. Examples of such methods include EDA [10], which employs a combination of random synonym replacement, random word deletion, random word position swap, and random synonym insertion, and AEDA [11], which randomly inserts punctuation marks into the original text. Other approaches include Data Boost [12], which utilizes a large-scale pre-trained language model with reinforcement learning optimization, and LAMBDA [13], which also uses a large-scale pre-trained model as a generator, fine-tuning it and training an additional classifier using identical datasets. The augmented data are then filtered using the classifier to obtain a high-quality dataset. UDA [14] applies data augmentation methods from supervised learning to semi-supervised learning, and Contextual Augmentation [15] employs a label-conditional language model to predict paradigmatic relation-based word replacement. In [16], dependency trees break sentences into smaller pieces and change the order of words for data augmentation in low-resource languages. [17] develops methods to find suitable text modification operations for data augmentation automatically.  
These methods are generally designed to improve the model's performance rather than robustness and often involve replacing words with similar words. While these methods effectively improve the performance, they are limited in their ability to generate text with diverse semantics and lexical variety, which can result in a lack of robustness.
## 3 System Performance  
In this section, the end-to-end SNDR, the exact and asymptotic closed-form expressions for the OP and the throughput of the considered network with HIs for AF and DF protocols are obtained, respectively. Especially, the opportunistic terrestrial relay selection scheme is applied to the network to get better system performance.