esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
Physical modeling (using an artificial thunderstorm cell) of the possible influence of upward streamer discharges from the rod or elongated model elements of a power transmission line monitoring systems on their operation in the electric field of a thunderstorm cloud and/or lightning produced many results. Firstly, upward streamer discharges that formed on the monitoring systems model elements (i.e., sensors, and receiving and transmitting devices) can affect the functioning of these systems due to the fact that in the spectrum of their currents, there are frequencies of up to tens of megahertz, which is close to the operating frequencies of power transmission line monitoring systems. Secondly, upward streamer discharges on the model elements of power transmission lines can also induce signals in the neighboring rod or elongated elements of the monitoring systems. These signals are dangerous for the monitoring systems' operation, since the discharge spectrum contains frequencies of tens to hundreds of megahertz (up to units of gigahertz).  
It was found that the parameters of the current pulse of upward streamer discharges, the signals that they induce in the neighboring elements, and their spectral characteristics depend on the electric field distribution near the model rod or elongated element (electric field amplification factor). The highest frequencies in the spectrum of the current pulse of the streamer corona flash are typical for the model elements with a high electric field amplification coefficient of >25. The highest frequencies in the spectrum of the signal induced in the neighboring element by the upward streamer discharges are typical for model elements with an amplification coefficient of 10-25. Thus, both the frequency ranges in the spectrum of the current pulse of upward streamer discharges formed from the model elements of sensors and receiving-transmitting devices of the power transmission line monitoring system and the frequency ranges in the spectrum of signals induced by close upward streamer discharges on these elements may appear close to the operating frequency ranges of analog-to-digital converters of sensors and/or devices for transmitting data in monitoring systems. This can lead to failures in monitoring systems operation, false alarms, incorrect transmission of information, and, as a result, create significant risks for the functionality of these systems during thunderstorms.  
Project administration and methodology, A.T.; software, L.C. and O.B.; visualization, T.K.; investigation, A.O. and N.L.; writing--original draft preparation, V.V.; funding acquisition, D.K. All authors have read and agreed to the published version of the manuscript.  
This study conducted by Moscow Power Engineering Institute was financially supported by the Ministry of Science and Higher Education of the Russian Federation (project No. FSWF-2020-0019).  
Not applicable.  
The authors declare no conflict of interest.
## 5 Experiments  
### Experimental Setup  
#### 5.1.1 Models and Environment  
Our proposed methods, QFeM and QFeP, aim to mitigate the quantization bottleneck, which is discussed in Section 3.3, caused by the activation spikes, especially in the GLU variants. To validate the proposed efficiency methods, we tested publicly released LLMs that were implemented with GLU according to their paper and source code. We recognize that recent LLMs, including LLAMA-2-[7B, 13B, 70B][47], LLaMA-3-[7B, 70B][48], Mistral-7B[49], Mistral-8x7B[6], SOLAR-10.7B[50], and Gemma-7B[51], utilize the GLU architecture. LLMs with the original FFN are not covered, as they suffer from the existing outliers rather than activation spikes. All models were sourced from the Hugging Face model repository (available at: [[https://huggingface.co/models](https://huggingface.co/models)]([https://huggingface.co/models](https://huggingface.co/models)), accessed on 17 April 2025). We used Python 3.10, PyTorch 2.2, and Transformers 4.38.1 as our primary software environment and employed the lm-eval-harness library for LLM evaluation. Experiments were conducted on a single NVIDIA RTX 4090 and a single NVIDIA A100 GPU, where smaller models were evaluated on the NVIDIA RTX 4090, and larger models were evaluated on the NVIDIA A100 based on their respective sizes.
# Autonomic Network Management and Cross-Layer Optimization in Software Defined Radio Environments
###### Abstract
In order to confirm the applicability and efficiency of our approach, we implement the main functionalities of the framework in a real-world SDR testbed. Specifically, the feasibility and the effectiveness of the proposed autonomic, cross-layer, resource control mechanism for CRNs are demonstrated and quantitatively evaluated in realistic conditions by exploiting the capabilities of the ORCA project infrastructure [4]. In particular, we provide a realization of our ANMO-SDR framework in SDR based CRNs built in two different testbeds, namely IRIS and ORBIT of the ORCA federation. We present experimental results demonstrating the feasibility of ANMO-SDR based on specific performance indicators in actual testing topologies consisting of Universal Software Radio Peripheral (USRP) devices. ORCA provides a realistic testbed environment for setting up, evaluating and analyzing the practical and operational aspects of the proposed resource management of SUs of an SDR-enabled CRN, especially under variations of the wireless environment and the network (i.e., primary user activity). The rest of this paper is organized as follows. In Section 2 we provide a brief overview of relevant background, while in Section 3 we introduce and explain the proposed framework. In Section 4 we specify the proposed framework in a practical implementation for resource allocation in CRNs, while in Section 5 we provide results from experiments with the practical implementation in testbeds of the ORCA project. Finally, Section 6 concludes the paper and provides directions for future research.
###### Abstract  
Harmful algal blooms (HABs) often cause great harm to fishery production and the safety of human lives. Therefore, the detection and prediction of HABs has become an important issue. Machine learning has been increasingly used to predict HABs at home and abroad. However, few of them can capture the sudden change of Chl-a in advance and handle the long-term dependencies appropriately. In order to address these challenges, the Long Short-Term Memory (LSTM) based spatial-temporal attentions model for Chlorophyll-a (Chl-a) concentration prediction is proposed, a model which can capture the correlation between various factors and Chl-a adaptively and catch dynamic temporal information from previous time intervals for making predictions. The model can also capture the stage of Chl-a when values soar as red tide breaks out in advance. Due to the instability of the current Chl-a concentration prediction model, the model is also applied to make a prediction about the forecast reliability, to have a basic understanding of the range and fluctuation of model errors and provide a reference to describe the range of marine disasters. The data used in the experiment is retrieved from Fujian Marine Forecasts Station from 2009 to 2011 and is combined into 8-dimension data. Results show that the proposed approach performs better than other Chl-a prediction algorithms (such as Attention LSTM and Seq2 seq and back propagation). The result of error prediction also reveals that the error forecast method possesses established advantages for red tides prevention and control.  
long short-term memory (LSTM); attention; harmful algal blooms (HABs); Chlorophyll-a (Chl-a); spatial; temporal; error forecast +
Footnote †: journal: Future Internet
# Development of a Robotic Airboat for Online Water Quality Monitoring in Lakes
## 7 Discussion
The Piripau weir showed an average of 5.93, indicating a more acidic character, in relation to the other two lagoons analyzed. The low pH of the effluent (\(<7.0\)) might have occurred due to the organic overload and low oxygen conditions, or because of nitrification, when the alkalinity of the lake (buffer capacity) was low [33]. According to [PERSON] [34], bacteria, for example, do not support pH above 9.5 or below 4.0, thus, the optimum pH range is between 6.5 and 7.5. * Temperature--Measurement of the heat intensity that influences the solubility of gases in water. In general, as the temperature increases from 0 to 30 \({}^{\circ}\)C, variables such as viscosity, surface tension, compressibility, specific heat, ionization constant, and latent heat of vaporization decrease, whereas thermal conductivity and vapor pressure increase [31]. In the Cajazeiras Lagoon and the Piripau Weir, averages of the ambient temperature of 30.93 \({}^{\circ}\)C and 33.89 \({}^{\circ}\)C were recorded to be above 30 \({}^{\circ}\)C, for both. For the averages of the water temperature, these values were below 30 \({}^{\circ}\)C.
* Oxidation-Reduction Potential--is the representation of changes in the oxidation state of ions or nutrients, related to the solubility of metals and the availability of these nutrients to the aquatic communities [35]. It is worth noting that Resolution CONAMA 357 did not establish minimum or maximum standards for a potential redox of the waters.
# Multi-Scale Aggregation Residual Channel Attention Fusion Network for Single Image Deraining
## 2 Related Work
### Single Image Deraining Methods
[35] proposed the half-instance normalization block with two subnetworks for training to improve image recovery. [PERSON] et al. [36] proposed an effective pyramid feature decoupling network (i.e., PFDN) for single image deraining, which could accomplish image deraining and detail recovery with the corresponding features. [PERSON] et al. [11] proposed a multi-stage encoder-decoder network architecture that used supervised attention to pre-weight local features and lateral connections with the aim of preventing information loss. [PERSON] et al. [12] built a full Bayesian generative model for rainy images where the rain layer is parameterized as a generator with the input being some latent variables representing the physical structural rain factors. To solve this model, they employed the variational inference framework to approximate the expected statistical distribution of a rainy image in a data-driven manner. [PERSON] et al. [13] proposed a progressive coupled network (PCNet) to well separate rain streaks while preserving rain-free details. To this end, they investigated the blending correlations between them and particularly devised a novel coupled representation module (CRM) to learn the joint features and the blending correlations. [PERSON] et al. [14] proposed a multi-stage deep neural network for image deraining, which was equipped with the refined attention in attention (RAiA) module. Specifically, RAiA extends the original attention in attention by exploiting the joint dependencies of channel and spatial information to generate the dynamic allocated weights. Although the aforementioned methods solve some problems, the problems of blurring and smoothing of image details resulting in an inability to distinguish between rain streaks and texture features remain unsolved in the images. In short, the details of texture are sometimes blurry and not clearly visible in the images. The effect of removing rain streaks is not good. To develop a robust streak removal and improved detail blurring method, the present study proposes a multi-scale aggregation residual channel attention fusion network for single image deraining.
## 3 Performance Analysis of Balance Actuator
### Balancing Accuracy  
During the balancing procedure, the balance actuator could progressively decrease the unbalance vibration of the detected rotor system through the mass distribution. The minimum amount of mass change could be used to express the balancing accuracy of the balance actuator, and smaller amounts indicated a higher balancing accuracy. The minimum amount of mass change can be expressed by  
\[M_{\text{min}}=\rho u_{\text{max}}st \tag{17}\]  
where \(u_{max}\) is the maximum balancing velocity, \(s\) is the section of the connecting tube, and \(t\) is the minimum action time. The minimum action time \(t\) was mainly determined by the delay time of the controller, the response time of the solenoid valves, and the delay time in the process of gas transmission. The experimental results show that the minimum action time was 0.05 s.  
From the linearity analysis of the balance capacity [24], the change of balance capacity and the transfer mass were linearly related, and the linear coefficient \(\text{dU}/\text{dm}\) for the actuator in this paper was about 25 (g.mm)/g. Therefore, the minimum amount of balance capacity can be obtained by  
\[\Delta U_{\text{min}}=\frac{dU}{dm}M_{\text{min}} \tag{18}\]  
The results in Figure 5 show that, when the kinematic viscosity of the balance liquid was 100 cSt and 300 cSt, the maximum balancing velocity \(u_{max}\) was 3.0 m/s and 1.0 m/s, respectively. Substituting these parameters into Equation (18), the minimum amount of balance capacity \(\Delta U_{min}\) was about 7 g.mm and 2.4 g.mm.  
According to GB/T 9239.1-2006, the permissible residual imbalance \(U_{per}\) is represented as  
\[U_{per}=1000m\cdot e_{per}\approx 9549.3\frac{mG}{n} \tag{19}\]  
where \(G\) is the required balance level, \(m\) is the effective mass of the rotor system, and \(n\) is the working speed. For the actuator used in this experiment, the total mass of the rotor system was about 16.5 kg, and its permissible residual imbalance with different balance levels is shown in Table 2.  
According to the criterion \(\Delta U_{min}<2U_{per}\), the final balancing accuracy of the experimental setup was G0.4, with the kinematic viscosity of balance liquid higher than 100 cSt.
## 4 Discussion
### SOA Formation Study  
The SOA yields determined in this research for the Cl reaction of T2M2B range between 0.26% and 1.65% under NO\({}_{x}\)-free conditions. Under high NO\({}_{x}\) conditions and using ammonium sulfate as seed, [PERSON] et al. reported a much higher SOA yield (39.1%) for the corresponding OH reaction [18]. The difference between the two studies may be due to the formation of less volatile products in the OH reaction than those obtained in the Cl reaction that contribute to the SOA formation. However, there are studies that show that SOA yields in the Cl reactions are comparable to the formed in other oxidation scenarios (OH or O\({}_{3}\) reaction, photolysis, etc.), such as a study reported for some monoterpenes [34]. Therefore, the low values determined in this research compared to the OH reaction [18] can be attributed to the different conditions that were used and that may change the mechanism of SOA formation and affecting the SOA yield. First, the effect of NO\({}_{x}\) in \(Y_{\text{SOA}}\) seems to be very complex as there are multiple studies that show contradictory results, as summarized by [PERSON] et al. [18], so it is hard to state clearly if the observed difference can be due to this factor. Secondly, the presence of ammonium sulfate seeds enhances \(Y_{\text{SOA}}\)[35] and would explain the difference observed between the OH reaction studied by [PERSON] et al. [18] and the results presented in this study. Moreover, there are other factors that can affect the SOA yields, such as the temperature and the relative humidity. Temperature in this research was slightly higher than the temperature set by [PERSON] et al. [18] (the difference is less than 10 K), and at higher temperatures the semi-volatile species are more present in the gas phase, but it is hard to know if this can explain the lower SOA yields observed in this study. Finally, the relative humidity in both studies is similar, since no additional H\({}_{2}\)O was added in any of them, so this factor should not be the source of the difference observed.
## 3 Results
### Optimization Results
#### 3.2.2 Optimization with Genetic Algorithm  
Objective Function \(J_{\rm 2A}\)  
As the second scenario included parameters associated with the nozzle only so hook thicknesses, position and leaning and nozzle outer band shell thickness. As a goal defined maximization of the natural frequency of the first system mode. The aim of the study is to verify if the same effect of frequency shift can be obtained excluding shell parameters on the casing. A history of the change in natural frequency of the first system mode is presented in Figure 19.  
The algorithm converged after 1471 evaluations (15 iterations) to propose three candidates (the top three solutions) reported in detail in Table 6. The maximum number of iterations were not reached since the convergence stability percentage criterion was fulfilled before.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **Baseline** & **Candidate 1** & **Candidate 2** & **Candidate 3** \\ \hline Shell\_1\_THCK (in) & & & 0.100 & \\ Shell\_2\_THCK (in) & & & 0.100 & \\ Shell\_3\_THCK (in) & & & 0.100 & \\ FWD Hook THCK (in) & 0.150 & 0.198 & 0.200 & 0.198 \\ AFT Hook THCK (in) & 0.090 & 0.118 & 0.119 & 0.118 \\ FWD Hook Lean (degree) & 90 & 80.637 & 82.204 & 81.153 \\ AFT Hook Lean (degree) & 90 & 80.064 & 80.060 & 80.253 \\ Nozzle Shell THCK (in) & 0.060 & 0.076 & 0.086 & 0.087 \\ FWD Hook Pos (in) & 0 & 0.038 & 0.038 & 0.040 \\ AFT Hook Pos (in) & 0 & \(-\)0.049 & \(-\)0.047 & \(-\)0.049 \\
1 st System mode Frequency (Hz) & 118.0 & 128.6 & 128.6 & 128.6 \\ Geometry Surface Area (in\({}^{2}\)) & 18.965 & 19.049 & 19.061 & 19.062 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Optimization results—Objective function \(J_{\rm 2A}\).  
Figure 19: Convergence history for first mode natural frequency.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **Baseline** & **Candidate 1** & **Candidate 2** & **Candidate 3** \\ \hline Shell\_1\_THCK (in) & 0.100 & 0.105 & 0.106 & 0.105 \\ Shell\_2\_THCK (in) & 0.100 & 0.169 & 0.169 & 0.169 \\ Shell\_3\_THCK (in) & 0.100 & 0.114 & 0.115 & 0.114 \\ FWD Hook THCK (in) & 0.150 & 0.164 & 0.163 & 0.162 \\ AFT Hook THCK (in) & 0.090 & 0.101 & 0.102 & 0.107 \\ FWD Hook Lean & 90 & 85.007 & 84.742 & 87.618 \\ (degree) & 90 & 80.949 & 82.309 & 82.062 \\ Nozzle Shell THCK (in) & 0.060 & 0.067 & 0.067 & 0.068 \\ FWD Hook Pos (in) & 0 & 0.013 & 0.013 & 0.014 \\ AFT Hook Pos (in) & 0 & \(-\)0.046 & \(-\)0.045 & \(-\)0.042 \\ Frequency (Hz) & 118.0 & 140.3 & 140.1 & 140.0 \\ Geometry Surface Area (in\({}^{2}\)) & 18.965 & 19.109 & 19.112 & 19.113 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Optimization results—Objective function \(J_{\rm 1}\).
# Multi-Line Prefetch Covert Channel with Huge Pages
## 4 The Design of a Multi-Line Prefetch Covert Channel with Huge Pages
### The Workflow for Multi-Line Encoding and Decoding
```
1:Shared Variables: volatileintreceiver_done_flag = 0\(\triangleright\) Synchronization flag CACHE_BLOCK_SIZE = 64\(\triangleright\) L1 cache line is 64 bytes
2:procedureSender: RUNning in core 1
3:while*receiver_done_flag == 0 do
4:USLEPP(0.1)\(\triangleright\) Poll with light sleep to reduce CPU usage
5:endwhile
6:*receiver_done_flag \(\leftarrow\) 0
7:start_t \(\leftarrow\) cc_sync()
8:lines_to_access \(\leftarrow\) secret_message * N\(\triangleright\) N is bucket size
9:whilerdtscp() \(-\)start_t \(<\) config.interval do\(\triangleright\) config.interval as a communication frame: within that frame, one message is sent. 10:for\(i=0\) to lines_to_access \(-\) 1 do
11:Access memory at config.addr + i * CACHE_BLOCK_SIZE
12:endfor
13:endwhile
14:endprocedure
15:
16:procedureReceiver: RUNning in core 2
17:while*receiver_done_flag == 1 do
18:USLEPP(0.1)
19:endwhile
20:start_t \(\leftarrow\) cc_sync()
21:whilerdtscp() \(-\)start_t \(<\) config.interval do
22:t1 \(\leftarrow\) rdtscp()
23:for\(j=0\) to 511 do\(\triangleright\) Prefetch all L1 cache lines
24:Prefetch memory at config.addr + j * CACHE_BLOCK_SIZE
25:endfor
26:t2 \(\leftarrow\) rdtscp()
27:total_time \(\leftarrow\) t2 - t1
28:Decode message:
29:iftotal_time \(>\) T1 then
30:decoded_message \(\leftarrow\) 1
31:elseiftotal_time \(>\) T2 then
32:decoded_message \(\leftarrow\) 2
33:\(\vdots\)
34:else
35:decoded_message \(\leftarrow\) n
36:endif
37:endwhile
38:receiver_done_flag \(\leftarrow\) 1\(\triangleright\) Allow sender to proceed
39:endprocedure
```  
**Algorithm 2** Covert channel communication via prefetch-based encodingAn Alternative Encoding Approach:  
In addition to the shared-memory read-only configuration, we explored a second encoding approach where both the sender and the receiver were granted write permissions to the shared memory. In this configuration, the receiver observes longer latencies during PREFETCHW operations due to state transitions from I (invalid) to M (modified), instead of the S to M transition in the read-only setup. This change occurs because the sender writes to the shared memory, transitioning cache lines to the I state from the receiver's perspective. When the receiver executes PREFETCHW, the coherence protocol must perform additional operations to bring the line back into the M state, resulting in a higher timing overhead. While this alternative provides a slightly higher decoding accuracy due to the more pronounced timing gap between accessed and unaccessed lines, it results in a lower throughput because of the increased latency in the decoding phase. Therefore, the choice between these two configurations--read-only versus writable shared memory--represents a trade-off between accuracy and the transmission speed.
# Predictive Modeling of Signal Degradation in Urban VANETs Using Artificial Neural Networks
## 1 Introduction
### Problem Statement  
The central challenge revolves around ensuring reliable data packet transmission paths, especially for emergency vehicles traversing intricate urban landscapes. Buildings and other infrastructural elements can significantly weaken signals, resulting in transmission errors. While traditional models offer some solutions, they may not fully capture the complexities of urban environments. This underscores the need for an advanced solution capable of accurately predicting transmission errors, thus facilitating proactive measures to maintain seamless communication.