esa-sceva/satcom-chunk-collection huggingface dataset:
# Fault-Resilient Manufacturing Scheduling with Deep Learning and Constraint Solvers
## 7 Case Study and Results
### Results and Discussion
#### 7.4.2 Fault-Present Environment--EDD Scheduling Results  
In the fault-present, three machines exhibit high fault probabilities during specific time periods, as shown in Table 6.  
Under EDD scheduling, several jobs encountered faults requiring rework, as shown in Figure 6:  
* Job_10 (Task A): Failed on Machine_A1 during time unit 5
* Job_02 (Task A): Failed on Machine_A1 during time unit 6
* Job_07 (Task B): Failed on Machine_B2 during time unit 9
* Job_12 (Task C): Failed on Machine_C1 during time units 7-8  
These failures triggered a cascade of scheduling disruptions. The rework requirements for Job_10 and Job_02 on Machine_A1 caused significant delays, forcing subsequent tasks to be postponed. This ripple effect particularly impacted Machine Group B's operations, where the delayed Task B operations created resource conflicts and further scheduling complications. The situation was exacerbated by additional failures in Machine_B2 and Machine_C1, leading to multiple rework cycles that disrupted the original schedule's efficiency. These cascading effects resulted in reduced machine utilization across all groups, with particularly significant impacts on Group B (42.9%) and Group C (57.7%), while also causing several jobs to miss their due dates.  
\begin{table}
\begin{tabular}{l l c} \hline \hline
**Scenario** & **Description** & **Number of Faulty Machines** \\ \hline Fault-free & No machine faults & 0 \\ Fault-present & Faults in Machine A1 (5–6 time units), Machine & 3 \\  & B2 (8–9 time units) and C1 (7–9 time units) & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Scenario descriptions  
Figure 5: Scheduling results in fault-free environment.
# Efficacy Improvement of Anomaly Detection by Using Intelligence Sharing Scheme
## 5 Experimental Evaluation and Discussion
### Dataset Description  
The dataset used for experimental evaluation consists of network communication of client networks that employ Cisco CTA-IDS. Data was gathered over the first week of January 2018. There were in total 622 networks, but only 310 of these networks were chosen at random to produce a sizeable dataset that simulates a realistic use-case while still being reasonably comfortable to work with (541 GB of raw data). Important characteristics of the dataset are shown in Table 2. The dataset entries were labeled by an expert at Cisco as being either legitimate or malicious. These labels are available both at the level of individual flows as well as the level of individual hostnames, and used as ground truth in all experiments. Networks are sorted independently for each line in order to make the lines monotonic. There are 310 companies in total, out of which 143 contain at least one malicious HTTP flow and 97 contain at least one malicious HTTP(S) flow; 6 networks contain zero HTTP flows and 16 contain zero HTTP(S) flows.  
Companies vary wildly in the number of flows. To illustrate, 50% of the biggest companies serve 99.0% of the total number of flows. There is a need to pay attention at the time of result analysis. Detailed visualization of the company's identification ID is illustrated in Figure 5.  
In order to test the deployment scenario described in Section 4.3, the dataset must be split chronologically into two parts. The first part is used to create and test a global reputation table while the second part is used to evaluate the possible improvements of per-flow anomaly scores using a reputation table. Coupled with the fact that there must be a warm-up period in which the anomaly detectors are learning the characteristics of network traffic and do not provide useful output, the following splits were achieved under the 7 days of data:  
* The first day is discarded as a warm-up period.
* The next three days are used to compute the global reputation tables.
* The last three days are used to evaluate the improvements of anomaly score using the global reputation tables.
# RNN-Based Sequence to Sequence Decoder for Run-Length Limited Codes in Visible Light Communication
## 2 Related Background
### RLL Codes  
Since a poor UAV working environment, such as one with strong ambient illumination [6], increases the interference in the VLC link, there is a pressing need to ensure reliable data communications. In UAVs with VLC systems, RLL codes are usually used to ensure reliable data transmission and flicker-free perception. RLL encoder receives random binary data and divides them into length-\(K\) frames, and then maps each frame into length-\(N\) frames. Additionally, RLL codes can guarantee equal numbers of 1 s and 0 s in every RLL-coded frame for dimming adjustments and flicker-free perception. Conventional RLL decoder relies on table look-up decoding. It maps the hard decision bits \(\vartheta\) into a source word to recover the transmitted data. If the RLL decoder is not able to find the corresponding codeword in the codebook due to erroneous detection, the decoder will select the codeword with the minimum Hamming distance to \(\vartheta\). Three main types of RLL codes, namely, Manchester codes, 4B6B codes, and 8B10B codes, are commonly adopted in VLC systems. In 4B6B codes, there are 16 codewords, and each length-4 source word is mapped to a length-6 codeword, which is shown in Table 1[4].  
This paper establishes an RNN-based decoder to replace the \"detector\" and the \"RLL decoder\" in Figure 1, so that \(\hat{\mathbf{x}}\) is as close as possible to \(\mathbf{x}\). In our work, we ignore the effect of ECCs and consider 4B6B codes as an example. It should be emphasized that our work is also applicable to other RLL codes.
# Multiscale and Failure Analysis of Periodic Lattice Structures
###### Abstract  
A full-cycle, multiscale analysis technique was developed for periodic lattice structures with geometric repetition, aiming for more efficient modeling to predict their failure loads. The full-cycle analysis includes both upscaling and downscaling procedures. The objective of the upscaling procedure is to obtain the effective material properties of the lattice structures such that the lattice structures can be analyzed as continuum models. The continuum models are analyzed to determine the structures' displacements or buckling failure loads. Then, the downscaling process is applied to the continuum models to determine the stresses in actual lattice members, which were applied to the stress and stress gradient based failure criterion to predict failure. Example problems were presented to demonstrate the accuracy and reliability of the proposed multiscale analysis technique. The results from the multiscale analysis were compared to those of the discrete finite element analysis without any homogenization. Furthermore, physical experiments were also conducted to determine the failure loads. Then, multiscale analysis was undertaken in conjunction with the failure criterion, based on both stress and stress gradient conditions, to compare the predicted failure loads to the experimental data.  
Keywords:lattice structure; multiscale modeling; homogenization; failure criterion +
Footnote †: 2020: _Applied sciences_
## 2 Related Works
### Hyperparameter Optimization  
Apart from parameters dependent on data or model, hyperparameter is dependent on machine learning model. Learning rate for deep learning or max depth for decision tree are prominent examples. It is very important to find optimal value for each hyperparameter since hyperparameters significantly affects the performance of model. Manual search of hyperparameters usually requires intuition of skillful researchers, luck and consumes too much time. There exists two famous systematic methodology called grid search and random search [8]. Grid Search searches hyperparameter values in predefined regular intervals and decide values of highest performance, even though human actions are also required. Such uniform and global nature of grid search is a better than manual search however, also requires much time in case of combination for hyperparameters increase. Random search, a random sampling approach of grid search shows increased speed of hyperparameter search. However, these two search methods does repetitive unnecessary search since they does not utilize prior knowledge of hyperparameter search. Bayesian Optimization is another useful method with systematic use of prior knowledge [9]. Bayesian Optimization assumes an objective function \(f\) of input value x, where objective function \(f\) is a blackbox and requires time to compute. Searching \(x^{*}\) for \(f\) with small number of input values as small as possible is main purpose. Surrogate Model and Acquisition Function are also required. Surrogate Model does probabilistic estimation for unknown objective function based on known input values and function values pair of \((x_{1},f(x_{1})),...,(x_{t},f(x_{t}))\). Probability models for surrogate model are Gaussian Processes (GP), Tree-structured Parzen Estimators (TPE), Deep Neural Networks and so on. Acquisition Function recommends next useful input value of \(x_{t+1}\) based on current estimations of objective function in order to search optimal input value of \(x^{*}\). Two strategies are famous: exploration and exploitation. Exploration recommends points with high standard deviation since \(x^{+}\) may exist in uncertain region. Exploitation recommends points around the point with highest function value. Two strategies are equally important in order to search optimal input value, \(x^{+}\). However, calibrating ratio of two strategy is very important since these two strategies show trade off. Expected Improvement (EI) is the most frequently used as acquisition function since EI contains both of exploration and exploitation. Probability of improvement (PI) is a probability of a certain input value \(x\) which function value is bigger than \(f(x^{+})=max_{i}f(x_{i})\) among current estimations of \(f(x_{1}),...,f(x_{t})\). Then EI calculates the effect of \(x\) considering PI and magnitude between \(f(x)\) and \(f(x^{+})\). EI can consider Probability of Improvement (PI), and another strategies such as Upper Confidence Bound (UCB), Entropy Search (ES), and so on are exist.
# Fast Format-Aware Fuzzing for Structured Input Applications
## 3 Methodology
### Indirect Dependency Recognizer
#### 3.3.2. Deps Detection  
\"Deps\" means dependencies here. _Deps detection_ performs byte-by-byte analysis on the specified suspensive field to recognize indirect dependencies in each byte of it.  
As Algorithm 3 shows, in the stage of _Deps detection_, all the bytes within the specified fragment will be examined for what dependencies exist. The dependencies we obtained are recorded into _deps_ with the information such as the index of the byte, the affected _cmp_ instruction, and the type of affected operand. Finally, we output the dependency array and finish.  
```
Input: initial input: \(T\), offset range of suspensive field: \(range\), dependency array: \(deps[]\) Output: Dependency array: \(deps[]\)
1\([start,end]\gets range\)
2 for\(byte\_index\in[start,end]\)do
3\(FlipByte(T,byte\_index)\)
4\(run\_target(T)\)
5\(FlipByte(T,byte\_index)\)
6 for\(cmp(id,v0,v1)\in(cmp\_map[]-skip\_cmp[])\)do
7 ifcmp.v0 changedthen
8\(MarkDeps(deps,cmp.id,byte\_index,V0\_DEPS)\)
9 end if
10 ifcmp.v1 changedthen
11\(MarkDeps(deps,cmp.id,byte\_index,V1\_DEPS)\)
12 end if
13
14 end for
15
16 end for
17 return \(deps\)
```  
**Algorithm 3**Deps detection algorithm.  
The time complexity analysis of the _Selective Flip_ strategy and the comparative analysis with WEIZZ we place in Appendix C.
## 4 Simulation Results
### Performance Evaluation
Furthermore, the proposed CDM model demonstrated a processing speed of 50 images reconstructed per second, regardless of the modulation schemes, when using the CIFAR  
Figure 6: SSIM performance of the conditional diffusion model across different modulation schemes. CDM-LV: conditional diffusion model with learnable variance, CDM-FV: conditional diffusion with fixed variance, DDPM: denoising diffusion probabilistic model, Rx Data: distorted image data at the receiver. 10 dataset. This reconstructed rate indicates the model's practical applicability in real-time scenarios with present-day computational capability, making it suitable for dynamic environments where timely image restoration is essential. In Figure 8, the visual representations of the reconstructed images are illustrated over different modulation schemes. In the case of an SNR of 0 dB across all modulation schemes, significant distortion was confirmed. It can be visually confirmed that the images were greatly reconstructed through the proposed CDM compared to the received OFDM-based images. Overall, these results demonstrate that CDM is highly effective in ensuring image fidelity, particularly in environments characterized by varying levels of distortion. Figure 8: Comparison of reconstructed images for various modulation schemes under different SNR levels (0 dB to 15 dB). Each subfigure corresponds to a specific modulation scheme: (**a**) BPSK, (**b**) QPSK, (**c**) 8 PSK, (**d**) 16 QAM, (**e**) 32 QAM, and (**f**) 64 QAM. The first row of images represents the original images \(x_{0}\), the second row depicts the received images \(y\) at the receiver, and the third row illustrates the reconstructed images \(\hat{x}_{0}\) resulting from the proposed scheme. Figure 7: Change in learnable variance with respect to the number of diffusion time steps. LV (\(\Sigma_{\theta}(x_{t},y,t)\)): learnable variance, \(\widetilde{\sigma_{t}}\): variance of the conditional reverse process, \(\sigma_{t}\): variance of the conventional diffusion process.
# A Vision of 6 th Generation of Fixed Networks (F6G): Challenges and Proposed Directions
## 5 Optical Switching: Key Challenges and Proposed Directions
### Need for More Sophisticated Switches to Support the F6G Ecosystem
In general, the optical switches can be categorized based on their supported services, as defined in Section 2, into (a) ultra-fast, dedicated to Time-Sensitive Networks (TSN), and (b) ultra-high capacity, dedicated to use-cases that require switching very high-volume traffic. In the next sub-sections, we discuss the challenges and potential solutions for these two discrete cases of switching.
# The Future for End-Stage Kidney Disease Treatment: Implantable Bioartificial Kidney Challenge
## 1 Introduction  
End-Stage Kidney Disease (ESKD) represents the final, irreversible stage of chronic kidney disease, where the kidneys lose their ability to sustain the essential life-supporting functions due to significant and permanent damage. At this stage, kidney replacement therapies become essential for survival. The primary modalities of treatment include dialysis and kidney transplantation. Dialysis, which can be performed as either hemodialysis or peritoneal dialysis, involves mechanically removing waste products and excess fluid from the blood when the kidneys can no longer perform these tasks. Hemodialysis is typically performed in a clinic setting several times a week (usually three times per week), while peritoneal dialysis can be performed at home but requires daily treatment. Kidney transplantation, on the other hand, involves the surgical replacement of the kidney functionwith a healthy kidney from a donor, living or deceased. Transplantation generally offers better quality of life and survival rates compared to dialysis but requires immunosuppressive medications to prevent organ rejection. Each of these therapies has its indications, advantages, and limitations, and the choice of therapy is individualized based on patient-specific factors including the underlying cause of ESKD, patient's overall health, lifestyle considerations, and personal preferences. This article reviews the state of the art of existing technologies for replacing the kidney function through the use of wearable and implanted artificial organs by analyzing their current limitations and possible future developments.
# Real-Time Identification of Look-Alike Medical Vials Using Mixed Reality-Enabled Deep Learning
## 4 Proposed Deep Learning Model
### Backbone Enhancement  
To improve feature extraction, the original CSPBlock in YOLOv4-Tiny was replaced with a ResBlock-based structure Figure 4. While CSPBlocks are effective for general object detection, they introduce an information bottleneck that can degrade performance when identifying small objects. By contrast, ResBlocks preserve more detailed features while maintaining computational efficiency.  
Figure 4: Architecture of the Proposed Lightweight YOLOV4 and comparison of original CSPBlock and modified ResBlock.This figure presents the architecture of the proposed Lightweight YOLOv4 model, showcasing the integration of CSPBlock and ResBlock. The architecture is divided into three  
Figure 3: Sample dataset images showing visually similar vials. This figure displays a set of images from the dataset, highlighting the look-alike vials commonly encountered in medical environments. The vials, which appear similar in size and shape, are annotated with labels to indicate potential confusion. Each vial is marked with the annotation “LOOK ALIKE?”, suggesting the challenge of visually distinguishing these vials. This image demonstrates the complexity of the task and the importance of accurate detection for preventing errors in vial identification, which is addressed by the proposed system.  
primary sections: the backbone, the neck, and the head. The backbone consists of a series of convolutional layers, including a ResBlock, which is designed to extract robust features for detecting small objects like medical vials. The CSPBlock is used in the initial stages of the network to improve feature extraction efficiency, while the ResBlock (modified in this model) focuses on enhancing small object detection and improving model robustness by incorporating direct concatenation and skip connections. These modifications are key to optimizing performance in small object detection in real-time Mixed Reality (MR) applications. The SPP (Spatial Pyramid Pooling) module further improves the model's ability to detect objects at multiple scales, ensuring more accurate vial identification. The architecture also includes YOLO heads at three different scales (13 \(\times\) 13, 26 \(\times\) 26, and 52 \(\times\) 52), enabling the detection of objects of varying sizes. This proposed lightweight design ensures high accuracy and efficiency in detection, especially in real-time applications in devices with limited computational resources.  
A ResBlock consists of two \(3\times 3\) convolutional layers followed by a \(1\times 1\) convolutional layer, with a direct residual connection, mathematically represented as \(y\) in Equation (4):  
\[y=x+F(x,W), \tag{4}\]  
where \(x\) represents the input, \(F(x,W)\) is the transformation function applied to \(x\) using weight parameters \(W\), and \(y\) is the output feature map. This structure allows for an improved gradient flow, preventing feature degradation and ensuring the effective learning of vial-specific features.
## 4 Discussion
### Divisions of the Types of Voids and Future Research  
(1) Divisions of the types of voids  
Based on the explorations of the void fractions and void shapes, the overburden of the stope was vertically divided into a collapse zone, a fracture zone, and a bending subsidence zone, and the coal seam was horizontally divided into a natural accumulation zone and a re-compaction zone. The voids in the goaf were widely distributed in an accumulation zone. The mining influence line bounded the void's distribution area, namely, class I and class II, as shown in Figure 13.  
(2) Future research  
The above analysis shows that many voids existed behind the working face after mining. Voids are a resource that can be used to treat solid wastes such as gangue. In recent years, many scholars have proposed methods for gangue treatment to achieve green mining and improve coal quality [33; 34; 35]. The fluidization filling method for gangue has been put forward and was inspired by the above research. The technical concept of this method is to separate coal gangue underground, crush the washed and excavated gangue underground, and mix it with water to prepare fluidized gangue. The fluidized gangue is pumped to the working face through a filling pump and a pipeline, and it is inserted into the gap in the goaf using pipelines. Under the condition that the production efficiency of the working face is not affected, the underground gangue treatment capacity can be maximized. The overall concept of gangue fluid filling is shown in Figure 14. This technology can reduce the cost of gangue treatment without affecting normal production. At the same time, it can reduce the ecological damage and pollution of the ground environment and prevent spontaneous combustion near the goaf and the stop line.  
Figure 13: Void distribution in the goaf.
## 1 Introduction
According to our comparative analysis results with the state-of-the-art SGX-based DSSE schemes, Bunker-B [8] and SGX-SE1 [11], the search time of the proposed scheme is approximately \(27\times\) faster than that of Bunker-B, while providing the same security level. Compared with SGX-SE1, the proposed scheme achieves a higher level of security while minimizing performance degradation. Contributions: Our contributions are summarized as follows:  
* We conduct a comprehensive analysis of the existing Type-II backward private schemes, and discover information leakage that falls outside the purview of the existing backward privacy notions. We then demonstrate how those leakages are exploited to extract the deletion history in Type-II schemes. * We demonstrate how our findings on information leakages exacerbate the security of known Type-II backward private schemes by exploiting the Vo et al. DSSE scheme [11] and Sun et al. DSSE scheme [14]. * We design a novel forward and Type-II backward private DSSE scheme based on SGX, which hides the information leakage of deletion history with high efficiency. * We conduct a comparative analysis of our scheme with the state-of-the-art SGX-based DSSE schemes, Bunker-B [8] and SGX-SE1 [11], in both synthetic and real-world Enron [15] datasets. According to the analysis, our scheme shows higher efficiency in search latency with negligible utility loss under the same security level (cf. Bunker-B) while showing similar efficiency with a higher security level (cf. SGX-SE1).