esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Approximation of the IRM Hit Ratio for FIFO  
Because the analytical FIFO solution of Equations (2) and (3) needs an extended number representation for large caches and the LRU solution of Equations (11)-(13) is not scalable, simpler approximations have been proposed. The FIFO hit ratio approximation refersto the time spans that an object \(O_{k}\) spends inside the cache (\(T_{InCache}\)) and outside (\(T_{Exten,k}\)), which are measured by the number of requests in both time spans. For independent requests, the external phase \(T_{Exten,k}\) is geometrically distributed with mean \(\overline{T}_{Exten,k}=1/p_{k}\) until the next request to \(O_{k}\).  
When an object enters the cache, it starts from the top FIFO position and steps down by one place per cache miss. We assume a constant cache miss probability \(1-h_{IRM}^{FIFO}\), independent of the current cache content. Next, the mean number of requests that an object spends in a FIFO cache is the same for all objects: \(\overline{T}_{InCache}\approx M(1-h_{IRM}^{FIFO})\). Finally, we obtain the hit ratio per object \(O_{k}\): \(h_{k}^{FIFO}=p_{k}\overline{T}_{InCache}/(\overline{T}_{InCache}+\overline{T} _{Exten,k})\). In total,  
\[h_{IRM}^{FIFO}=\sum_{k=1}^{N}h_{k}^{FIFO}=\sum_{k=1}^{N}p_{k}\frac{\overline{T }_{InCache}}{\overline{T}_{InCache}+\overline{T}_{Exten,k}}=\sum_{k=1}^{N} \frac{p_{k}\overline{T}_{InCache}}{\overline{T}_{InCache}+1/p_{k}} \tag{4}\]  
This leads to the following iterative computation scheme for \(\overline{T}_{InCache}\):  
\[\overline{T}_{InCache}=M/(1-h_{IRM}^{FIFO})=M/(\sum_{k=1}^{N}p_{k}-\frac{p_{ k}\overline{T}_{InCache}}{\overline{T}_{InCache}+1/p_{k}})=M/\sum_{k=1}^{N} \frac{1}{\overline{T}_{InCache}+1/p_{k}} \tag{5}\]  
Starting from \(h_{IRM}^{FIFO}=0\Rightarrow\overline{T}_{InCache}=M\), \(\overline{T}_{InCache}\) is monotonously increasing in each iteration step of Equation (5). There is a unique solution, and the iteration is converging towards this solution. The computation effort of Equation (5) is \(O(N)\) per iteration round.  
The FIFO (RANDOM, CpR) hit ratio approximation of Equation (5) is equivalent to the approach by [PERSON] and [PERSON] [25]. Based on a slightly more restrictive assumption of Poisson request processes per object, [PERSON] et al. [37] provide an alternative derivation by using queueing theory and [PERSON] et al. [10] by using time-to-live caching.
# The POSEIDON Ocean Observing System: Technological Development and Challenges
## 3 Challenges during Operations
### Environmental Forcing, Fouling, Corrosion  
Over the last decades, oceanographic equipment and instrumentation following the state-of-the-art technological trends has improved radically and has become more efficient. Limitations in energy, transmission and data storage are no longer the most important obstacle for the long-term operation of marine observatories. Biofouling drastically affects the performance of the equipment exposed to the sea and it is one of the main limiting factors in the duration of the deployment, consequently increasing the operational cost. All the system modules are eventually affected, and the results include poor data or equipment failure. Biofouling will gradually grow on the sensors as well, and it will affect the quality of the data. If the sensor is deployed in the coastal or euphotic zone, it may face very short  
Figure 10: In panel (**a**), the E1M3A buoy displays severe damage caused by a failed powerline, the buoy electronics and power container were destroyed by galvanic corrosion. (**b**) Inductive cable failure resulting in the loss of underwater telemetry. (**c**) A CTD recovered after 12 months of operation with external sensor cabling failures. (**d**) Failure of the screws in the head of an ADCP attached in the surface buoy, the result was the destruction of the sensor.  
term biofouling effects. Techniques to prevent biofouling are implemented on instruments, especially for optical, membrane sensors and other sensors for which the interface between the seawater and the sensing element must remain intact [17]. These techniques can be active, e.g., mechanical wipers for optical sensors, or passive, such as the use of copper covers and special coatings. Although the existing antifouling methodologies have been able to extend deployment times, they are not ideal and new technologies must become effective following the rapid advancements in instrumentation [18]. Biofouling has always been present since the early beginning of the POSEIDON buoy operations (Figure 11), affecting the sensor performance and data [19].  
Similar to biofouling, although does not affect directly the data quality, corrosion creates limitations to the equipment lifetime. The different metal pieces of observatory equipment in contact with seawater also show different corrosion rates. This factor must be considered throughout the design of the field system since a poor choice of materials could accelerate corrosion rates. Coatings combined with cathodic protection are the most common tools used for the protection of materials in offshore environments.
## 5 Conclusions  
In this paper, the channel characteristics have been simulated from the proposed model and compared with the measured results for validation. Time-varying Rician k-factors were used for channel modeling to derive the channel statistics. The model was able to capture the impact of structural parameters of the cutting and viaduct environments on the channel statistics. At mmWave frequency, the LCR of the measured and simulated environments is observed to be very close to 1 at 0 dB in both environments. Since a non-stationary channel model was considered, the stationary interval in the two environments was investigated. Therefore, the proposed 3D non-stationary MIMO channel model approximates well the measured results and can be applied for mmWave band channels in viaduct and cutting environments. Further more,the MIMO can be configured to massive MIMO with different planes.  
Conceptualization, E.A. and Y.W.M.; methodology, E.A.; software, E.A.; validation, Y.W.M.; formal analysis, Y.W.M.; investigation, E.A.; resources, E.A.; writing original draft preparation, E.A.; writing, review and editing, Y.W.M. and E.A.; visualization, Y.W.M.; supervision, Y.W.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 4 Experimental Results and Analysis
### UNSW-NB15 Results and Analysis
When compared to CBC-IDS, CBF-IDS exhibits significant improvements in precision, recall, and F1 score, with increases of 1.76%, 72.16%, and 35.15%, respectively. Furthermore, despite having lower precision than CBA-IDS, CBF-IDS shows significant improvements in recall and F1 score for Shellcode, with increases of 69.23% and 28.32%, respectively. Figure 8b illustrates that CBF-IDS attains a precision of 56.00%, a recall of 40.00%, and an F1 score of 46.67% on the minority class Worms. In Figure 8b, it can be observed that while CBF-IDS shows relatively lower precision on the minority class Worms, it achieves excellent performance in terms of recall and F1 score. When compared to CBC-IDS, CBF-IDS achieves significant improvements of 179.92% and 91.35% in recall and F1 score, respectively. Additionally, in comparison to CBA-IDS, CBF-IDS exhibits noteworthy improvements of 100% and 43.34% in recall and F1 score, respectively. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{3}{c}{**CBC-IDS**} & \multicolumn{3}{c}{**CBA-IDS**} & \multicolumn{3}{c}{**CBF-IDS**} \\ \cline{2-10}  & **Precision** & **Recall** & **F1 Score** & **Precision** & **Recall** & **F1 Score** & **Precision** & **Recall** & **F1 Score** \\ \hline Analysis & 81.25 & 4.86 & 9.17 & 96.43 & 5.05 & 9.59 & 79.55 & 6.54 & **12.09** \\ Backdoor & 76.92 & 6.44 & 11.88 & 89.66 & 5.58 & 10.51 & 88.37 & 8.15 & **14.93** \\ DoS & 38.55 & 0.98 & 1.91 & 51.47 & 2.14 & 4.11 & 57.62 & 4.74 & **8.76** \\ Exploits & 59.45 & 93.58 & 72.71 & 58.57 & 95.37 & 72.57 & 60.28 & 93.70 & **73.37** \\ Fuzzers & 66.14 & 52.77 & 58.71 & 71.16 & 48.44 & 57.64 & 71.34 & 51.29 & **59.68** \\ Generic & 99.81 & 97.11 & 98.44 & 99.76 & 97.17 & 98.45 & 99.69 & 97.46 & **98.56** \\ Normal & 88.98 & 93.42 & 91.15 & 89.26 & 94.19 & 91.66 & 89.09 & 94.52 & **91.73** \\ Reconnaissance & 86.35 & 71.19 & 78.04 & 90.55 & 72.98 & 80.82 & 92.70 & 74.48 & **82.60** \\ Shellcode & 58.08 & 38.08 & 46.00 & 64.64 & 38.74 & 48.45 & 59.10 & 65.56 & **62.17** \\ Worms & 83.33 & 14.29 & 24.39 & 87.50 & 20.00 & 32.56 & 56.00 & 40.00 & **46.67** \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Performance of each class for UNSW-NB15. Figure 8: Performance of the CNN-BiLSTM model using different loss functions on the minority class for UNSW-NB15: (**a**) Performance for the minority class Shellcode; (**b**) performance for the minority class Worms.
# Recovering Texture with a Denoising-Process-Aware LMMSE Filter
## 3 Recovering Texture via Statistical Analysis  
In this section, we introduce our denoising method that recovers the texture lost by denoising a noisy observation. In this paper, we define a structure (or cartoon) component \(\mathbf{s}=f(\mathbf{y})\in\mathbb{R}^{m}\), where \(f(\cdot):\mathbb{R}^{m}\rightarrow\mathbb{R}^{m}\) is a map corresponding to the denoising process of WNNM. Then, in contrast to previous work [8; 19; 20; 21], we define a texture component \(\mathbf{t}\) as the difference between the original image \(\mathbf{x}\) and the structure \(\mathbf{s}\) (i.e., \(\mathbf{t}=\mathbf{x}-\mathbf{s}\)). Thus, we obtain our extended observation model, i.e.,  
\[\mathbf{y}=\mathbf{s}+\mathbf{t}+\mathbf{n}. \tag{5}\]  
Please note that the above equation is equivalent to Equation (1) since \(\mathbf{x}=\mathbf{s}+\mathbf{t}\). Our goal is to estimate the texture component \(\mathbf{t}\) that has been lost via WNNM.  
First, we denoise the observation \(\mathbf{y}\) with WNNM. Following WNNM, we estimate each texture patch matrix \(\mathbf{T}_{i}\) from the corresponding structure patch matrix \(\mathbf{S}_{i}\), which is obtained in the final iteration of the WNNM procedure, and the corresponding observation patch matrix \(\mathbf{Y}_{i}\). Because we use an LMMSE filter to estimate \(\mathbf{T}_{i}\), we need the statistical information on the relationship between the texture and noise; however, this information cannot be calculated directly because \(\mathbf{t}\) and \(\mathbf{n}\) are not observable. Instead, we estimate this information using [PERSON]'s lemma and several assumptions, which are described below, and then reconstruct the estimated texture patch matrices to obtain the estimated texture component \(\hat{\mathbf{t}}\). Finally, we obtain an estimate of the original image, \(\hat{\mathbf{x}}\), by adding the estimated texture component \(\hat{\mathbf{t}}\) to the denoised image \(\mathbf{s}\).
# Visual Clue Guidance and Consistency Matching Framework for Multimodal Named Entity Recognition
## 4 Experiments
### Baselines  
To comprehensively evaluate the effectiveness of our proposed visual clue guidance and consistency matching framework (GMF), we selected representative text-based and multimodal NER models for comparison. Text-based NER methods include BiLSTM-CRF [29], a classical NER model employing a bidirectional LSTM structure and an CRF layer; HBiLSTM-CRF [30], a variant of CNN-BiLSTM-CRF that replaces the CNN layer with LSTM for character-level word representations; BERT [19], serving as a multilayer bidirectional Transformer encoder followed by a softmax decoder; and BERT-CRF, similar to BERT but using a CRF decoder for label prediction; and T-NER [3; 31], a NER system designed specifically for tweets, utilizing a range of effective features, including dictionaries, context, and positive word features. Multimodal NER methods include GVATT-HBiLSTM-CRF [4], which combines HBiLSTM-CRF with an attention mechanism to obtain representations merging text and visual information, and UMT-BERT-CRF [7], a leading multimodal NER model comprising a multimodal interaction module and an auxiliary module for pure text entity span detection. Additionally, we include other state-of-the-art multimodal models for comparison: MAF [32], which calculates similarity scores between text and images and uses the score to determine the proportion of visual information to retain; UMGF [1], which proposes a unified multimodal graph fusion method to capture fine-grained semantic correspondences between semantic units of different modalities; BFCL [33], which leverages a Transformer-based bottleneck fusion mechanism to reduce noise propagation in the visual modality; and DebiasCL [34], which utilizes hard sample mining and debiased contrastive loss to alleviate biases in both the quantity and entity types, enabling global learning for aligning text and image feature spaces. Lastly, GMF, the model proposed in this  
Figure 7: GMF test results on the development set and loss on the training set.  
study, aims to enhance the MNER performance by integrating visual clue guidance and consistency matching.
# Smart Grid Security: A PUF-Based Authentication and Key Agreement Protocol
## 7 Conclusions and Future Work  
When considering the phases of grid modernization, key requirements such as data security, reliability, and accuracy are crucial for realizing the grid's full potential [44]. A breach in any of these aspects can jeopardize the entire system. This concern is even more critical in emerging new grids, such as water and gas grids, often fully working on battery, therefore operating in resource-limited environments with constraints on processing power, memory, and energy consumption.  
Initially, we examined the security of various research protocols using different models, highlighting their vulnerabilities and potential solutions. Subsequently, to address these issues, we introduced a PUF-based protocol for smart grids (SG) named PPSG, utilizing PUF and ECC methods. Our formal security analysis of PPSG within the RoR model demonstrated that it offers robust security against adversaries constrained by polynomial time. Furthermore, our cost analysis revealed that PPSG stands out as one of the most lightweight protocols compared to recent related work.  
To validate PPSG's performance in a real-world scenario, we simulated a smart meter and connected it to various electrical components representing smart home devices. The results showcased PPSG's superiority in terms of computation and communication costs, as well as energy consumption, making it a promising choice for smart grid applications.  
Finally, although PUF-based protocols offer an interesting approach to SMs security they have certain limitations, such as vulnerability to machine learning attacks, environmental variability, and aging. In this paper, we aimed to tackle the issue of machine learning attacks by developing an efficient protocol in terms of energy use and computation speed. This area of research is a call for researchers to explore further options to overcome additional challenges. For instance, they could introduce features like temperature compensation to lessen the effect of environmental changes on PUF responses. Another approach could involve continuous monitoring to spot any alterations in PUF behavior over time. These strategies offer potential ways to enhance the overall performance of PUFs and contribute to enhancing the reliability, security, and longevity of PUF functions in smart meters.  
[PERSON], [PERSON], [PERSON] and [PERSON] contributed equally to this work. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** The data presented in this study are available in this article.  
**Acknowledgments:** [PERSON] was supported by Shahid Rajaee Teacher Training University under grant number 4899.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 7: Energy consumption of PPSG versus related protocols [15; 16; 17; 18; 19; 20; 21; 23; 25; 32].
## 2 Measurement System Set-Up  
The measurement set-up for the common-mode noise emitting from the power cables is shown in Figure 1. A desktop PC and a progressive cathode-ray tube (CRT) monitor were either put on a wooden table with a height of 0.8 m or on the floor. A liquid crystal monitor (LCD) was also examined as a monitor, but no difference between CRT and LCD was observed for the noise measurement. This work used a CRT monitor because its resolution can be set flexibly in analog, unlike an LCD, whose resolution is restricted digitally by the number of image cells. The power cables of the PC and the monitor were independently connected to outlets on the wall of a laboratory, which was on the second floor in a ferroc concrete building. An analog Video Graphics Array (VGA) cable carrying analog components RGBHV (red, green, blue, horizontal synchronization, vertical synchronization) was used as a monitor signal cable connected between the PC and the monitor. The coaxial and the parallel type of power cables were connected in series for measuring both common-mode and normal-mode currents. Cable sheaths, shielding, and ferrite cores of these power cables and signal cables were removed in order to measure a normal-mode current and to facilitate measurement of small noise current. A split-core active current probe (A6302, Tektronix, US) with an amplifier (AM503, Tektronix, US) whose 3 dB bandwidth was at 50 MHz was used for detecting the current on the cables. The output from the current probe was measured by using a spectrum analyzer or a digital oscilloscope. A total 3 dB bandwidth of this measurement system was confirmed to be 50 MHz. The serial data of the noise current derived from the oscilloscope was then used for the signal processing. A specification of the monitor signal was set mainly at a resolution of \(800\times 600\) pixels and a refresh rate of 60 Hz, where a period of vertical synchronizing signal was 16 ms (60 Hz), a period of horizontal synchronizing signal was 26 \(\upmu\)s (38 kHz), and a dot clock frequency was 40 MHz (25 ns). Another resolution of \(1280\times 1024\) pixels was also tested to examine the performance of the signal processing method proposed in this work.  
Figure 1: Measurement set-up for noise on cables.
## 3 Materials and Methods
### YOLOv8  
The YOLO (You Only Look Once) algorithm, first proposed by [PERSON] et al. [36], has made significant strides in real-time object detection due to its unique design and efficient operation. Unlike traditional methods, YOLO adopts an end-to-end structure and treats object detection as a regression problem, allowing a single neural network to detect multiple objects in an image simultaneously. This approach enables YOLO to achieve high accuracy while maintaining excellent real-time performance, making it widely applicable in various scenarios.  
As an improved version, YOLOv8 introduces an anchorless design that directly predicts the center point of a target without relying on a preset anchor offset. In previous YOLO models, anchor boxes were used to predict the position and size of targets, requiring manual configuration and adjustment based on training data to match the target object's dimensions. This approach posed several challenges: it demanded extensive hyperparameter tuning, which complicated the model's training process, and the predefined anchor boxes often did not align well with the target distributions in customized datasets, leading to reduced generalization ability. The anchor-free design of YOLOv8 addresses these limitations by simplifying the model structure. By eliminating anchor boxes, YOLOv8 reduces the number of prediction frames needed, enhances computational efficiency, and speeds up the non-maximal suppression (NMS) process. These improvements collectively reduce the overall processing time and make the model more effective and efficient for real-time applications. Additionally, YOLOv8 is not just an algorithm but part of a broader framework called Ultralytics, released on GitHub. This framework supports a variety of image recognition tasks, including detection, segmentation, and pose estimation, and it offers greater flexibility and ease of use for different applications.  
Despite the subsequent release of YOLOv10, which introduces a consistent dual allocation strategy to enable NMS-free training and further enhance real-time detection, its performance in the task of recognizing tuna is significantly lower compared to YOLOv8. The performance results of the recently released YOLO family of algorithms, evaluated using instance segmentation, are presented in the Section 4. Although YOLOv8 demonstrates slightly better recognition accuracy compared to other YOLO versions, it still suffers from a limited ability to learn detailed textures and low inference efficiency when recognizing tuna in complex scenes at a fine-grained level.
## 3 Results
### The Role of Face Masks and Different Noise Conditions on Listening Difficulty  
With this analysis, the researchers aimed to assess the extent to which the presence of different types of face masks affects listening difficulty for children under several competitive conditions of SNRs. As with the analysis presented in Section 3.1, Figure 4 and Table 5 report the outcomes of the listening tests in terms of listening difficulty mean values, which may vary between the less difficult (score 1) and the most difficult (score 5), and standard errors across participants. They are shown separately for each experimental condition related to the type of mask and noise.  
HoH listeners experienced a higher listening difficulty compared to the CG participants (\(p<0.001\)). Furthermore, in relation to the type of masks, Masks 1 and 2 were the most competitive with respect to the no-mask condition, as they lead to higher listening difficulty values (\(p<0.001\)), whereas Mask 3 did not differ significantly with respect to the no-mask condition (\(p>0.05\)).  
Considering the effect of noise, the no-noise condition was less challenging compared to SNR of +10 dB (\(p<0.001\)), SNR of +5 dB (\(p<0.001\)), and SNR of 0 dB (\(p<0.001\)). Furthermore, listening difficulty values in the no-noise condition were not statistically different between EG and CG participants.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **No Mask** & **Mask 1** & **Mask 2** & **Mask 3** \\ \hline \multicolumn{5}{c}{_Experimental group (EG)_} \\ \hline No noise & 0.95 (0.01) & 0.97 (0.01) & 0.98 (0.01) & 0.96 (0.01) \\ +10 dB SNR & 0.91 (0.01) & 0.83 (0.02) & 0.87 (0.02) & 0.90 (0.01) \\ +5 dB SNR & 0.81 (0.02) & 0.76 (0.02) & 0.77 (0.02) & 0.87 (0.02) \\ 0 dB SNR & 0.64 (0.02) & 0.51 (0.02) & 0.47 (0.02) & 0.55 (0.02) \\ \hline \multicolumn{5}{c}{_Control group (CG)_} \\ \hline No noise & 0.98 (0.02) & 1.00 (0.00) & 0.95 (0.03) & 0.98 (0.02) \\ +10 dB SNR & 0.95 (0.03) & 0.88 (0.05) & 0.97 (0.02) & 0.94 (0.03) \\ +5 dB SNR & 0.97 (0.02) & 0.87 (0.04) & 0.87 (0.05) & 0.98 (0.02) \\ 0 dB SNR & 0.83 (0.05) & 0.86 (0.04) & 0.81 (0.05) & 0.83 (0.05) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mean values of speech intelligibility scores and related standard errors in brackets.  
Figure 3: Mean speech intelligibility scores and related standard errors (error bars). Black circles (\(\bullet\)) show the Experimental Group (EG) results; empty circles (\(\odot\)) show the Control Group (CG) results.