esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Comparison and Impact Analysis of Pouring Schemes for UHPC Layer on Bridge Deck
### Analysis of the Impact of Unbalanced Weight Pouring Scheme I and Bridge Deck
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Scheme Type**} & \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Sub Scheme Name**} & \multicolumn{2}{c}{**Maximum Stress**} & \multirow{2}{*}{**Longitudinal**} & \multirow{2}{*}{**Location Description**} \\  & & & & & \\ Value (MPa) & & & & & \\ \hline Original design & 0 & Full hall support & 6.2 & \(-169\) & Near the auxiliary pier \\ \hline \multirow{5}{*}{Unbalanced pouring} & 1 & Single interval & 13.5 & \(-168\) & Near the auxiliary pier \\ \cline{1-1} \cline{2-5}  & 2 & Double spacing & 13.5 & \(-168\) & Near the auxiliary pier \\ \cline{1-1} \cline{2-5}  & 3 & Single-amplitude symmetry & 13.7 & \(-168\) & Near the auxiliary pier \\ \cline{1-1} \cline{2-5}  & 4 & Double-amplitude symmetry & 14 & \(-168\) & Near the auxiliary pier \\ \cline{1-1} \cline{2-5}  & 5 & Single progressive & 13.5 & \(-168\) & Near the auxiliary pier \\ \cline{1-1} \cline{2-5}  & 6 & Double-amplitude progressive & 13.5 & \(-168\) & Near the auxiliary pier \\ \cline{1-1} \cline{2-5}  & 7 & Single-frame first tower end & \(13\) & \(-168\) & Near the auxiliary pier \\ \cline{1-1} \cline{2-5}  & 8 & Double-width first tower end & \(13\) & \(-168\) & Near the auxiliary pier \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Unbalanced pouring Scheme I—UHPC bridge deck tensile stress during the completion stage. \begin{table}
\begin{tabular}{c} Single-frame first \\ then edge end \\ \end{tabular} & \(-24.785\) & \(-81\) & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._additional deformation of the bridge deck caused by each sub-plan during the construction phase is about \(-24\) mm, which is significantly different from the original design plan. (2) During the completion stage of the bridge, the tensile stress of the UHPC bridge deck in each sub-scheme is 13-14 MPa, which is significantly higher than the original design scheme's 6.2 MPa. Compared to the construction stage, the stress increases by about 11 MPa, with the higher stress located near the auxiliary pier; although UHPC has strong crack resistance (usually with a tensile strength of 10-25 MPa), the large tensile stress of the completed bridge is unfavorable for the later bearing capacity of the bridge. (3) The deformations and structural stresses of sub-schemes 1 to 8 during the construction and bridge completion stages are relatively similar, indicating that the pouring sequence of blocks and frames has no significant impact on the pouring of UHPC concrete. The obvious influencing factor is the overall pouring direction, which is 1 when pouring from the edge to the tower end first (sub-schemes 1 to 6), or 2 pouring from the tower end to the edge first (sub-schemes 7 to 8). Overall, pouring from the tower end to the edge first is beneficial for reducing the tensile stress values of UHPC bridge decks during the construction and completion stages, but the improvement is not significant.
## 4 Experiments and Results
### Feature Construction
The denotation of the features is based on the channel from where it originates (e.g., C0108) and the feature index (1 out of 100, H00 denotes the first and H99 the last of the features). Thus, the denotation C0108:H01 stands for the feature with index-1 based on bit depth, which is in fact the drill string acceleration. Figure 10 shows a portion of such features generated within a 42-h window. The base channels in that case were sampled with a sampling frequency of 0.1 Hz equivalent to a sampling interval of 10 s. In all charts, the bit depth is assigned to the right axis and drawn as a black line. In Figure 10a, the bit velocity is drawn as a yellow line within a range of about \(\pm 0.8\) m/s. The bulk of the positive bit velocities on the left (3:20 to 6:10) is due to the trip-in of the drill string into the borehole. The middle part of the chart indicates the actual \begin{table}
\begin{tabular}{c c c c} \hline \hline
**ID** & **Symbol** & **Unit** & **Description** \\ \hline C0108 & mdBit & m & Total (measured) depth of bit \\ C0110 & mdHole & m & Total (measured) depth of hole \\ C0112 & posBlock & m & Block position \\ C0113 & ropAv & m/s & Drill rate \\ C0114 & hkidAv & kg & Hookload, measured at surface \\ C0116 & wobAv & kg & Weight on bit, measured at surface \\ C0118 & tqAv & J & Rotary torque, measured at surface \\ C0120 & rpmAv & rad/s & Rotary speed, measured at surface \\ C0121 & presPumpAv & Pa & Pump pressure, measured at surface \\ C0130 & flowInAv & m3/s & Mud flow into the hole \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Base channels. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**ID** & **Symbol** & **Unit** & **Description** \\ \hline D0101 & n.a. & m & mdHole \(-\) mdBit \\ D0201 & n.a. & m & mdHole \(+\) posBlock \\ D0301 & n.a. & m & mdBit \(+\) posBlock \\ E0101 & n.a. & W & tqAv \(*\) rpmAv \\ E0201 & n.a. & W & pressPumpAv \(*\) flowInAv \\ E0301 & n.a. & W & ropAv \(*\) wobAv \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Extended base channels. drilling operation starting at 6:10 and ending the next day at 9:20. Over this period, the average bit velocity is about 16 m/h (equivalent to 5 mm/s) and thus not really perceptible in the chart. The large spikes in that range (e.g., at 14:00) indicate so-called ream and wash operations applied for cleaning the borehole. The right part of the chart shows, again, a period of large bit velocities due to a trip-out operation when the drill string is removed from the borehole. In Figure 10b, the bit acceleration is drawn against the time and the chart is similar to that above; a period of large acceleration values occurs during a trip-in and trip-out. Some acceleration spikes during drilling are caused by the ream and wash operations. Figure 10c shows the pump power (E0201, cp. Table 5), the product of the pump pressure (C0121), and flow rate (C0130). It is obvious that the most pump power is required for drilling; for the trip-in and trip-out it is almost zero.
# Edge Server Deployment Strategy Based on Queueing Search Meta-Heuristic Algorithm
## 6 Discussion
### Factors Influencing Performance  
Several factors impact the effectiveness of QSA.  
User distribution and density: When users are densely distributed, the performance gap between QSA and other algorithms becomes more evident.  
Base station placement: A higher number of base stations generally reduces hop counts, but QSA consistently finds better deployment solutions.  
Parameter settings: The population size and iteration count significantly affect QSA's execution time and optimization ability. A carefully tuned parameter set can improve efficiency without sacrificing accuracy.
## 1 Introduction  
Percutaneous transluminal angioplasty (PTA) of femoro-popiletal artery stenosis has become a routine treatment option for symptomatic patients requiring revascularization, especially when their clinical characteristics or surgical risk do not favor bypass surgery [1]. Moreover, tremendous technological advancements such as the widespread adoption of drug-eluting devices have addressed one of the main issues of such a procedure, i.e., PTA durability [2; 3]. Indeed, even in settings characterized by a high atherosclerotic burden (long and calcified stenosis, chronic total occlusions, and multilevel disease), the one-year primary patency rates reported are the most satisfactory, being in the range of 80-90% across different studies and different devices used (drug-coated balloons, drug-eluting stents, biodegradable stents, covered stents, etc.) [4; 5; 6; 7].  
However, the degree of residual stenosis and the presence of dissections occurring after angioplasty are two procedural factors that may affect the long-term result of PTA regardless of the device used and of the recanalization technique adopted [8; 9]. Therefore, every relevant residual stenosis or dissection should be properly fixed at the time of the index procedure to reduce the risk of target lesion failure at follow-up. However, in the case of residual \"near 50%\" stenoses and/or long dissections, the decision of whether or not to optimize the PTA result might be more challenging. This is because the decision is based on angiography, which as a bi-dimensional imaging technique that does not provide any functional information [10; 11].  
To overcome these issues, new and more sophisticated imaging modalities such as optical coherence tomography (OCT), intravascular ultrasound (IVUS), or CO\({}_{2}\) angiography imaging have been used in small cohorts of patients [12]. Compared to such advanced and expensive techniques, the intra operative assessment of femoro-popliteal artery PTA by Doppler ultrasound (DUS) may represent a cheaper and more widely available alternative imaging modality. To the best of our knowledge, DUS has not been systematically adopted for such a purpose before now.  
The aim of this study was to evaluate whether an intraoperative Doppler ultrasound (DUS) assessment of femoro-popliteal lesions that have undergone PTA allows for a more accurate definition of technical success compared to angiography.
# Integrating Visual Cryptography for Efficient and Secure Image Sharing on Social Networks
## 3 Proposed Method  
Image encryption is crucial for image security, often making images unrecognizable, thereby safeguarding visual privacy and sensitive information. This is vital in contexts including the transmission and storage of medical images, personal photos, commercial secrets, and military images. However, traditional image encryption methods present significant challenges. Encrypted images often exhibit notable randomness and confusion, which can attract the curiosity of unauthorized users when transmitted over open channels like the Internet. Even with encrypted content, the mere existence of the image poses a potential security threat, as attackers might attempt to decipher it for curiosity or other motives.  
The main ideas behind the privacy-preserving framework are resize-preprocessing and pixel expansion elimination.
# NUDIF: A Non-Uniform Deployment Framework for Distributed Inference in Heterogeneous Edge Clusters
## 3 Non-Uniform Deployment Framework
### Framework Overview  
Aiming at the issues of resource idleness and speed imbalance between stages caused by single-instance deployment in heterogeneous edge clusters, particularly in the context of batch processing, this paper proposes a non-uniform deployment distributed inference framework (NUDIF). As shown in Figure 1, through a multi-instance deployment strategy and an optimization mechanism for processing rates between stages, NUDIF divides the deep neural network (DNN) or large language model (LLM) into multiple sub-models. It differentiates the configuration of the number of instances of each sub-model withinthe heterogeneous device cluster, forming a throughput-adaptive distributed inference pipeline. The core process of the framework is divided into three stages:  
In the first stage, heterogeneous resource profiling is conducted to quantify the hardware characteristics of the cluster and the execution features of sub-models, thereby constructing the dataset required for optimization decisions. This involves collecting device attributes, such as memory capacity, peak computing power, and communication bandwidth. Additionally, sub-model requirements, including memory footprint, output data volume, and single-computation latency on different devices, are measured. The transmission latency across stages is estimated based on device bandwidth and output data of sub-models, allowing for the calculation of communication costs.  
In the second stage, the non-uniform deployment is modeled as a mixed-integer nonlinear programming problem, with core design principles including resource adaptation, rate balance, and elastic expansion. Resource adaptation ensures that the total memory requirement of sub-model instances on each device does not exceed its capacity. Rate balance adjusts the number of instances for each sub-model to align the processing capabilities of adjacent stages. Elastic expansion allows multiple instances of the same sub-model to be deployed across various devices, enabling low-performance devices to participate in lightweight sub-tasks by stacking instances. The optimization solver outputs the optimal deployment configuration for each sub-model instance. Communication delays between devices are considered a cost factor to prevent potential bottlenecks in cross-stage transmission.  
Finally, in the inference execution stage, the system executes tasks in a pipeline mode. Input data are split into batches and allocated to idle instances of the first stage. Once the instance cluster of each stage completes processing, intermediate results select the optimal transmission path based on the real-time load status of downstream instances, balancing computational and communication overheads through a batch processing mechanism.  
Figure 1: Framework of NUDIF.
## 3 Results
### Correlations
#### 3.2.3 Male Group Correlation  
Table 4 illustrates the correlation between the pairs of parameters in the male group. A very strong correlation was identified between the lateral and line-1 longitudinal (\(p\leq 0.05\); \(r=0.93\)) (Figure 6) and between the medial and line-1 longitudinal (\(p\leq 0.05\); \(r=0.92\)) (Figure 7). A weak negative correlation was identified between age and medial, and also between age and line-1 longitudinal. The other measurements showed no correlation with male patients' age (\(p>0.05\) or \(p\leq 0.05\)\(r\leq 0.03\)).  
Figure 5: Correlation between the pairs of parameters (lateral and line-1 longitudinal) in female group.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Parameters** & **Age** & **Lateral** & **Medial** & **Rostral** & **Caudal** & **Area** & \
\begin{tabular}{c} **Line-1** \\ **Longitudinal** \\ \end{tabular} &
\begin{tabular}{c} **Line-2** \\ **Transverse** \\ \end{tabular} \\ \hline age & & 0.36 & 0.27 & & & 0.31 & \\ lateral & 0.36 & & 0.71 & & 0.40 & 0.57 & 0.91 & 0.46 \\ medial & 0.27 & 0.71 & & & 0.49 & 0.89 & 0.40 \\ rostral & & & & & 0.59 & 0.30 & 0.89 \\ caudal & & 0.40 & & & 0.59 & 0.57 & 0.63 \\ area & & 0.57 & 0.49 & & 0.59 & & 0.57 & 0.63 \\ line-1 longitudinal & 0.31 & 0.91 & 0.89 & & 0.30 & 0.57 & & 0.40 \\ line-2 transverse & & 0.46 & 0.40 & 0.49 & 0.89 & 0.63 & 0.40 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: The r-value for the female group correlations (\(r\)-value presented only for pairs showing statistically significant correlation, \(p\leq 0.05\)).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Parameters** & **Age** & **Lateral** & **Medial** & **Rostral** & **Caudal** & **Area** & \
\begin{tabular}{c} **Line-1** \\ **Longitudinal** \\ \end{tabular} &
\begin{tabular}{c} **Line-1** \\ **2 Transverse** \\ \end{tabular} \\ \hline age & & \(-0.30\) & \(-0.38\) & & & & \(-0.45\) & \\ lateral & \(-0.30\) & & 0.81 & & 0.58 & 0.76 & 0.93 & 0.60 \\ medial & \(-0.38\) & 0.81 & & 0.56 & 0.82 & 0.92 & 0.50 \\ rostral & & & & & & 0.76 \\ caudal & & 0.58 & 0.56 & & 0.63 & 0.56 & 0.49 \\ area & & 0.76 & 0.82 & & 0.63 & 0.83 & 0.66 \\ line-1 longitudinal & \(-0.45\) & 0.93 & 0.92 & & 0.56 & 0.83 & 0.57 \\ line-2 transverse & & 0.60 & 0.50 & 0.76 & 0.49 & 0.66 & 0.57 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: The \(r\)-value for the male group correlations (\(r\)-value presented only for pairs showing statistically significant correlation, \(p\leq 0.05\)).  
Figure 6: Correlation between the pairs of parameters (lateral and line-1 longitudinal) in male group.  
Figure 7: Correlation between the pairs of parameters (medial and line-1 longitudinal) in male group.
## 2 Expected Fatigue Damage for a Random Loading  
Let \(x(t)\), \(0<t<T\), be a time-varying signal as measured at a critical point in a structure, whether the signal is load, stress or strain. Throughout the paper, it will be referred to as a stress-time history (or stress record). The fatigue damage of \(x(t)\) under the Palmgren-Miner linear rule is [5]  
\[D(T)=\sum_{i=1}^{N_{T}}\frac{1}{N_{f,i}(s_{i})} \tag{1}\]  
where \(N_{f,i}(s_{i})\) is the number of cycles to failure at stress amplitude \(s_{i}\) and \(N_{T}\) is the number of rainflow cycles counted in \(x(t)\). Equation (1) is very general as it applies to both stationary and non-stationary loadings, and to any S-N equation. Often, constant amplitude experimental data are best-fitted by a S-N curve \(s_{i}^{k}N_{f,i}(s_{i})=C\), where \(C\) is the fatigue strength coefficient and \(k\) the inverse slope.  
Damage \(D(T)\) depends on the stress-time history \(x(t)\) of duration \(T\) from which it was computed. It has a statistical uncertainty coming from two sources: load (e.g., stress amplitudes \(s_{i}\), number of counted cycles \(N_{T}\)) and material strength (coefficient \(C\) and inverse slope \(k\)). The latter can be taken into account by means of a characteristic S-N line defined for a low probability of failure (e.g., 2.3% or less) [25; 26]. The focus of this paper is therefore on the randomness of the load, which is reflected into the randomness of \(s_{i}\) and \(N_{T}\). Because of this, damage \(D(T)\) is a random variable following a certain probability distribution with expected value \(E[D(T)]\) and variance \(\sigma_{D}^{2}\).  
Intuitively, \(E[D(T)]\) can be thought as an average over an infinite population of damage values, which are computed from an infinite number of load records. This situation is purely theoretical. In real engineering applications, the common situation is indeed that in which only few stress-time histories (if not even only one) with finite time duration \(T\) are available from measurements. In this case, \(E[D(T)]\) remains unknown. A statistical method is needed to draw conclusions on \(E[D(T)]\) based on \(D(T)\).
## 4 Performance Evaluation
### Comparison of Results with State-of-the-Art Models
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**Dataset**} & \
\begin{tabular}{c} **Dimensionality** \\ **Reduction** \\ **Techniques** \\ \end{tabular} &
\begin{tabular}{c} **Classification** \\ **Techniques** \\ \end{tabular} & **Accuracy (\%)** & **Recall (\%)** & **Precision (\%)** & **F1 Score (\%)** \\ \hline
[77] & D1 & IGRF-RFE & MLP & 84.24 & 84.24 & 83.6 & 82.85 \\
[78] & D1 & N/A & Early-fusion & 76.47 & 71.59 & 83.53 & N/A \\
[78] & D1 & N/A & Late-fusion & 77.09 & 69.5 & 86.04 & N/A \\
[78] & D1 & N/A & Late-ensemble & 76.84 & 68.18 & 85.92 & N/A \\
[79] & D1 & N/A & Deep SARSA & 82.62 & 82.6 & 82.4 & 82.49 \\
[80] & D1 & CNN & BLSTM & 76.32 & 76 & 81 & 77 \\  & & Adaptive & & & & \\
[81] & D1 & NRS-SSA &
\begin{tabular}{c} ensemble \\ (DT, KNN, RF, \\ XGBoost) \\ \end{tabular} & 81.54 & 81.54 & 82.7 & 79.7 \\
[82] & D1 & Hierarchical &
\begin{tabular}{c} DTTNVM \\ \end{tabular} & 81.21 & N/A & N/A & N/A \\
[83] & D1 & RF-SPS & ANN & 78.23 & 68.85 & 85.13 & 76.13 \\
[83] & D1 & RF-SPS & GRU & 79 & 63 & 70 & 62 \\
[83] & D1 & RF-SPS & LSTM & 78 & 63 & 70 & 64 \\  & & Ensemble & & & & \\  & & (group & & & \\
[84] & D1 & N/A & convolution & 79.59 & 80.38 & 81.27 & 80.82 \\  & & network & & & & \\  & & snapshot & & & & \\  & & ensemble & & & & \\
[85] & D1 & UMAP & RF & 81.6 & 80 & N/A & 80 \\
**This study** & **D1** & **LogitBoost-ResNet** & **MSE** & **90.29** & **90.29** & **90.45** & **89.87** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance metrics comparison between our proposed model and recent IDS models for multiclass classification on the UNSW-NB15 dataset (bold indicates best results).
## 4 Experiments and Evaluation  
This section presents a comparative analysis of the results obtained using the proposed methodology for firearm detection and evaluates the performance of different variant of proposed firearm detection approach with various state-of-the-art feature extractors, including AE [48], ResNet50 [55], VGG19 [56], NasNetLarge [57], DenseNet201 [58], and MobileNetV2 [59]. First, we present a comparative analysis with existing firearm detection and action recognition frameworks. Then, we evaluate the proposed method in conjunction with various deep feature extraction models and state-of-the-art OCC models, including IFC, SVM, GMM, and MCDE. For a fair comparison, all OCC implementations utilize the default parameter settings provided by the scikit-learn library across all feature extractor-based approaches. Finally, we conduct an ablation study by replacing the NN with a CNN in the Variational VAE, resulting in a Convolutional Variational Autoencoder (CVAE). We also analyze the effect of removing specific components of the VAE model on firearm detection performance and compare the impact of variable patch sizes against a fixed patch size.  
For comparative analysis, this paper utilizes the recently introduced VISILAB benchmark dataset [11]. From this dataset, 20,461 normal frames and 7352 firearm frames are extracted, which are extracted and shuffled from 118 _'no gun'_ videos and 141 _'handgun'_ videos in the dataset, respectively. These frames are used to extract the feature vector \(\vec{v}\) of hand patches, while the corresponding human body landmark features \(\vec{l}\) are obtained using HPE. To prevent sharing of temporal context, scene layout, clothing, and background, the training and testing datasets are shuffled prior to training and inference. During inference, 2000 randomly selected samples from the _'no gun'_ category and 2000 randomly selected samples from the _'handgun'_ category are used to evaluate the performance of the proposed OCC-based firearm detection model, ensuring a fair and balanced assessment.
## 3 Results
### Impact of PCP on CWs Substrate Microbial Community
The most abundant specialist genera in control CWs were _Arenomonas_ (23%)  
Figure 10: (**a**) Abundance of the major phyla, (**b**) classes with abundance \(>\) 1% and (**c**) genera more abundant of bacteria across the different CWs systems substrates, irrigated for eight weeks with Ribeira de Joane water not doped (control) or doped with PCP. Samples collected at the beginning (T0) and at the end of the experiment (T8) (\(n\) = 3). \(<\)1% abund. groups the phyla with abundance lower than 1%; and “No_Relative_NA” groups the phyla without any close relatives. Figure 9: (**a**) Cluster dendrogram analysis of Hellinger transformation using the Bray–Curtis dissimilarity matrix and (**b**) NMDS analysis of data transformed by “phyloseq” function and based on the Bray–Curtis dissimilarity matrix to ordinate microbial community composition of the different CWs systems, irrigated for eight weeks with Ribeira de Joane water not doped (control) or doped with PCP. Samples collected at the beginning (T0) and at the end of the experiment (T8) (\(n\) = 3). The Kruskal–Wallis test was conducted to examine the difference between microbial communities of each sample. and _Liminiphilus_ (10%) while the doped CWs were dominated by _Denitromonas_ (40%) and _Xenococcus_ (33%). Taxonomic profile of the _Archaea_ community (which represent ca. 2% of the total community) showed that they were represented by seven phyla (_Altiarchaeota_, _Asgardaeota_, _Crenarchaeota_, _Diapherotrites_ _Euryarchaeota_, _Nanoarchaeota_, _Thuamarchaeota_) (Figure S4A). At the phylum level, the three most abundant taxa, representing more than 80% of the total community, across all the samples were _Thuamarchaeote_ (abundance between 60-87%), _Crenarchaeota_ (6-19%) and _Asgardaeota_ (3-10%). At the class level, the most abundant taxa across all the samples were _Nitrososphaeria_ (abundance between 55-85%), _Bathyarchaeia_ (5-18%), _Woesarchaeia_ (1-7%) and _Lokiacedaeia_ (2-8%) (Figure S4B). The presence of PCP did not interfere with the _Archaea_ community, however, over time the experimental conditions influenced the _Archaea_ community, with a slight increase of the dominant phylum _Thuamarchaeota_ (_Nitrososphaeria_ class).