esa-sceva/satcom-chunk-collection huggingface dataset:
# Transfer Learning-Based Object Detection Model for Steel Structure Bolt Fastening Inspection
## 3 Methodology
### Transfer Learning  
Transfer learning is a technique that enhances performance by utilizing pre-trained parameters from extensive data in related domains [11]. In transfer learning, the process involves removing the classifier from the original model, adding a new classifier tailored to the specific objective, and training the modified model. When working with a small dataset, transfer learning leverages the feature extraction layer's parameters from a model trained on a large-scale image dataset, and fine-tunes the weights using the available dataset, which only has parameters related to the final classification layer. Alternatively, when dealing with a large dataset, it is possible to update parameters across all pre-trained layers through the training process. Figure 1 illustrates three strategies for transfer learning. Strategy 1 entails training the model from scratch, utilizing only the pre-trained model's architecture, which requires a substantial dataset and high-performance computing systems. In Strategy 2, low-level layers extract general features, while high-level layers focus on capturing specific features, thus determining the extent of retraining the neural network parameters. This strategy proves useful when working with a large dataset or a smaller model. Strategy 3 involves freezing the convolutional base and solely training the classifier. This method is suitable for scenarios with limited computing power or small datasets. Therefore, the third strategy was applied in the case study to address the issues of a small training dataset.  
* Strategy 1: retrain the entire model;
* Strategy 2: freeze some layers of the convolutional base and retrain the remaining layers and classifier;
* Strategy 3: freeze the convolutional base and retrain only the classifier.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Algorithms**} & **Minimum** & **Maximum** & **Minimum** & **Maximum** \\  & **Average Precision** & **Average Precision** & **Inference Time** & **Inference Time** \\  & **(\%)** & **(\%)** & **per Image (ms)** & **per Image (ms)** \\ \hline YOLOv3 & 28 & 33 & 23 & 50 \\ RetinaNet & 34 & 38 & 55 & 115 \\ Faster & 39 & 41 & 70 & 145 \\ R-CNN & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of performance among algorithms trained with COCO dataset.  
Figure 1: Three strategies of transfer learning.
## 5 Experiments and Analysis
### Performance Analysis
#### 5.4.1 Performance Variation of Algorithms Under Varying Total Task Volumes  
As illustrated in Figure 5, the performance of the MATD3-TORA and MADDPG algorithms was evaluated as the task volume increased from 100 Mbits to 200 Mbits, with measurements taken at 20 Mbit intervals. The results demonstrate that both MATD3-TORA and MADDPG significantly outperformed the Local-Only and Edge-Only strategies in  
Figure 4: The convergence of the MATD3-TORA algorithm under varying weights (\(w\)).  
terms of weighted delay and energy consumption. Notably, the rate of increase in weighted delay and energy consumption for MATD3-TORA was lower than that of Local-Only and Edge-Only algorithms. This advantage stems from MATD3-TORA's ability to determine optimal offloading ratios, thereby validating its effectiveness in task allocation. Although the convergence results of MADDPG and MATD3-TORA show minimal numerical differences, this is expected, since MATD3-TORA is an enhanced version of MADDPG, which is primarily designed to improve learning efficiency and stability rather than significantly altering the final outcomes. This analysis underscores the superior performance of MATD3-TORA in handling varying task volumes, highlighting its capability to achieve efficient and stable task offloading in dynamic environments.  
As seen in Figure 6, the proposed MATD3-TORA algorithm demonstrated superior adaptability and efficiency compared to MAPPO and QMIX when handling diverse task volumes. While existing methods struggle to maintain optimal offloading decisions under increasing computational loads, MATD3-TORA dynamically adjusts its resource allocation strategy through its hybrid decentralized-centralized architecture. This enables more effective balancing of latency and energy consumption across the UAV network. In contrast, MAPPO exhibits policy divergence under heavy task loads due to its unconstrained policy updates, while QMIX suffers from rigid value decomposition that fails to adapt to varying computation requirements. MATD3-TORA's twin-delayed learning mechanism and task-aware optimization allow it to maintain stable performance across different workload intensities, whereas competing methods show significant degradation as task volumes scale.  
Figure 5: The performance variation of algorithms (MATD3-TORA, Edge-only, and MADDPG) under varying total task volumes.
## 3 Results
### Characterization of Gingipains
#### 3.2.2 Gelatin Zymography  
Further characterization of catalytic transparent bands that observed on gelatin zymogram gel was done by incorporating selective inhibitors, Leueptin inhibitor and Cathepsin B inhibitor II for identifying the arginine-specific gingipain (Rgp) and lysine-specific gingipains (Kgp), respectively [21; 29; 30]. Polyacrylamide gel electrophoresis of the partially purified gingipains under non-denaturing conditions showed two apparent bands with the molecular weight ranging from 48 kDa to 75 kDa and a band above 245 kDa (Figure 4a).  
In the gelatin zymogram with leupeptin inhibitor, the bands with a molecular weight of approximately 50 kDa and 245 kDa were not observed (Figure 4b). All bands were observed in the gelatin zymogram without the inhibitor and were similarly observed in the gel for the gelatin zymogram with Cathepsin B inhibitor II (Figure 4c). In gelatin zymogram with TLC inhibitor, there was a significant reduction of bands intensity with the molecular weight in the range of 48 kDa to 75 kDa (Figure 4d). The catalytic bands observed in gelatin zymogram for current study were consistent with those described in [PERSON] et al. (1995); the major catalytic zone of HG66 strains was found on the very top of the gel and in the molecular weight range between 48 kDa to 52 kDa area the gel. From this, the catalytic zones that were observed on the very top and between the molecular weight range of 48 kDa to 52 kDa in the gelatin zymogram were strongly indicated as arginine-specific gingipains (RgpA and RgpB), which are also suggested as gelatinase.  
Figure 3: SDS-PAGE of crude protein samples after acetone precipitation. The fractions were boiled in reducing solubilizing sample buffer and electrophoresed on 10% SDS polyacrylamide gel at 110 V for 90 min. Lane 1 was BLUeye pre-stained protein standards and Lane 2 was the crude gingipains sample.
# Spindle Error Movements and Their Measurement
## 5 Publications Related to the Measurement of Spindle Error Movements
### Spindle Error Movement Measurements Taken during Machining
#### 5.5.2 Determination of Error Movement of the End Mill  
The method can be used at high spindle speeds. Contrary to the methods that use the pattern (target and sphere) to determine error movements at low rotational speeds, in the case of high spindle speeds, the tool, its mass, misalignment, and unbalance affect the size of error movement. The article of [37] describes a practical method of measuring error movement for the spindle in the radial direction of the end mill at high rotational speeds,without the need to use a ball standard. This method is based on measuring the size of the machining marks left by the face of the end mill. The rotating cutter blades leave circular marks, and their diameter is associated with errors in the movement of the spindle/tool system. As shown in Figure 43, the tip of the cutting blade forms a path on the workpiece. Figure 43a shows a \(\frac{1}{4}\) revolution of the cutter and Figure 43b shows multiple revolutions.  
In this method, the depth of cut is small (e.g., 0.012 mm) so that the cutting forces are small and the tool remains sharp. Marks left by the tool at different rotational speeds are shown in Figure 44.  
The authors prove that the marks left at different rotational speeds contain information about the radial error of the spindle/tool system for a given rotational speed. The conducted research proves that the measurements carried out with this method have the required accuracy, repeatability, and resolution. The proposed measurement and calculation technique can be used to determine the influence of the spindle's rotational speed, the level of mass unbalance, and the spindle stiffness on the value of the error movement in the radial direction. However, the authors admit that the proposed method of measuring the machining marks cannot replace the conventional polar plot method because there is no information related to the angular position of the spindle and the time. It is recommended to perform both tests, e.g., with the Lion Precision set and the tool mark measurement, as it gives a better picture of the behaviour of the tool/spindle system at high rotational speeds.  
Figure 44: Machining marks at different rotational speeds [37].  
Figure 43: Machining marks after (**a**) one revolution and (**b**) multiple revolutions [37].
## 3 Results and Discussion
### Modeling of Sorption Isotherms--Influence of pH
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**CRZ**} & \multirow{2}{*}{**Initial**} & \multirow{2}{*}{\(\boldsymbol{q_{e,exp}}\), \(\mu\)g/g} & \multicolumn{6}{c}{**Pseudo-First-Order**} & \multicolumn{6}{c}{**Pseudo-Second-Order**} \\ \cline{3-10}  & & & \(q_{e,calc}\), \(\mu\)g/g & \(k_{1}\), min\({}^{-1}\) & \(R^{2}\) & \(\boldsymbol{q_{e,calc}}\), \(\mu\)g/g & \(k_{2}\), g/ug min & \(R^{2}\) \\ \hline \multirow{2}{*}{suspended TiO\({}_{2}\)} & 5 & 15.88 & 8.00 & 0.0059 & 0.9460 & 15.77 & \(9.87\times 10^{-3}\) & 0.9997 \\  & 15 & 46.32 & 7.91 & \(4\times 10^{-5}\) & 0.6230 & 46.30 & \(1.13\times 10^{-3}\) & 0.9996 \\  & 25 & 67.39 & 10.21 & \(5\times 10^{-5}\) & 0.7200 & 68.03 & \(4.12\times 10^{-4}\) & 0.9989 \\ \hline \multirow{3}{*}{TiO\({}_{2}\)-GF} & 5 & 42.39 & 2.86 & 0.0002 & 0.4222 & 42.55 & \(7.15\times 10^{-4}\) & 1.000 \\  & 15 & 120.57 & 5.72 & 0.0002 & 0.7360 & 121.95 & \(5.37\times 10^{-4}\) & 0.9996 \\  & 25 & 207.93 & 8.99 & 0.0004 & 0 The linear isotherm showed the best fit to the obtained data for all TiO\({}_{2}\) experiments at different pH values (\(R^{2}\) > 0.99) suggesting the domination of partition interactions [40]. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Isotherm** & \multicolumn{3}{c}{**Suspended**} & \multicolumn{3}{c}{**TiO\({}_{2}\)-GF**} & \multicolumn{3}{c}{**TiO\({}_{2}\)/CNT-GF**} \\ \hline \multirow{3}{*}{_Linear_} & **5** & **7** & **9** & **5** & **7** & **9** & **5** & **7** & **9** \\ \cline{2-10}  & \(K_{d}\) (mL/g) & 162.2 & 213.8 & 672.3 & 22.1 & 36.4 & 160.5 & 75.4 & 82.3 & 113.2 \\ \(R^{2}\) & 0.9960 & 0.9980 & 0.9910 & 0.9930 & 0.9970 & 0.9910 & 0.9900 & 0.9900 & 0.9920 \\ \hline \multirow{3}{*}{_Langmuir_} & \multirow{3}{*}{5.0} & \multirow{3}{*}{5.0} & \multirow{3}{*}{20} & \multirow{3}{*}{5.0} & \multirow{3}{*}{1.1} & \multirow{3}{*}{5.0} & \multirow{3}{*}{0.05} & \multirow{3}{*}{0.051} & \multirow{3}{*}{0.049} & \multirow{3}{*}{0.059} \\ \(k_{L}\) (mL/g) & 0.5 & 0.4 & 0.07 & 0.004 & 0.15 & 0.03 & 0.051 & 0.049 & 0.059 \\ \(R^{2}\) & 0.8896 & 0.9199 & 0.9989 & 0.9990 & 0.9190 & 0.9900 & 0.9430 & 0.9540 & 0.9440 \\ \hline \multicolumn{2}{c}{_[PERSON]_} & \multirow{3}{*}{3.0} & \multirow{3}{*}{2.6} & \multirow{3}{*}{1.4} & \multirow{3}{*}{0.95} & \multirow{3}{*}{1.88} & \multirow{3}{*}{0.91} & \multirow{3}{*}{0.64} & \multirow{3}{*}{0.68} & \multirow{3}{*}{0.66} \\ \((\text{ug}/\text{g})\text{(mL}/\text{ug})^{1/n}\) & & & & & & & & \\ \(R^{2}\) & 0.9750 & 0.9990 & 0.9990 & 0.9980 & 0.9690 & 0.9910 & 0.9720 & 0.9770 & 0.9680 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Sorption isotherm parameters for IMT as an influence of pH.
# The Response of Vegetation Dynamics to Climate in Xinjiang from 1991 to 2018
## 3 Results  
### Temporal and Spatial Variations of Vegetation Characteristics  
#### 3.1.1 NDVI Change Trend  
The annual average NDVI value of vegetation in Xinjiang generally indicated a characteristic of being higher in the north and lower in the south, ranging from 0 to 0.48 (Figure 3). The high-value areas of annual NDVI (>0.23) were primarily located in the western and northern Tianshan Mountains, Altai Mountains, Ili River Valley, mountainous areas around the Junggar Basin, and regions surrounding the Tarim Basin. The distribution and variation of the annual NDVI values were mainly influenced by vegetation conditions during the growing season (May to October). The maximum NDVI value reached 0.78, mainly distributed in the western Tianshan Mountains and Ili River Valley during the growing season. The high-value NDVI (>0.12) areas during the non-growing season were similar to those of the growing season, mainly distributed in the western and northern Tianshan Mountains and areas surrounding the Tarim Basin. Influenced by topography and meteorological factors, vegetation in the arid southern Xinjiang region was primarily distributed around the Tarim Basin and in high-altitude mountainous areas during different seasons. The agricultural areas in southern Xinjiang were concentrated in areas with high NDVI values.  
Overall, the multi-year average NDVI value was 0.28, ranging from 0.04 to 0.78. Vegetation showed a gradually increasing trend during the study period (_slope_ = 0.52, R\({}^{2}\) = 0.58), indicating an improvement of ecological conditions. In terms of NDVI trend distribution, the NDVI values of desert vegetation mostly showed a decreasing trend (>\(-\)0.05). Areas with a significant declining trend (<\(-\)0.2) were mainly located in the central Tianshan Mountains, Ili River Valley, and Junggar Basin. Regions with an increasing NDVI trend greater than 0.2 were mainly distributed in the northern Tianshan Mountains and areas around the Tarim Basin. These regions were mainly dominated by grasslands and croplands.
# Performance Assessment of the Medium Frequency R-Mode Baltic Testbed at Sea near Rostock
## 4 Mf R-Mode System Accuracy: Results and Discussion
### Night-Time Results
(**a**) Reference RTK (red circles) and R-Mode (blue crosses) tracks. (**b**) Scatterplot of the R-Mode positioning error in the east and north. (**c**) Histogram of the estimated receiver clock bias. \begin{table}
\begin{tabular}{|c c c c c c c c c c c|} \hline \multicolumn{2}{|c|}{**Hoburg**} & \multicolumn{2}{c|}{**Rozewie**} & \multicolumn{2}{c|}{**Holmsjo**} & \multicolumn{2}{c|}{**Helgoland**} & \multicolumn{2}{c|}{**Hammerodde**} & \multicolumn{2}{c|}{**Gros Mohrdorf**} \\ \hline CW1 & CW2 & CW1 & CW2 & CW1 & CW2 & CW1 & CW2 & CW1 & CW2 & CW1 & CW2 \\ \hline uh & uh & uh & uh & h & h & h & uh & uh & h & uh \\ \hline \end{tabular}
\end{table} 
Table 3: Night-time signals used for the position and time estimation in green with the “h” flag. Signals in red with the “uh” flag were not used. The sky-wave impact is remarkably visible for Helgoland (Figure 9c), located at a distance of 267 km. It can be noted how the night-time distribution spreads much more than the daytime distribution. In detail, the standard deviation is 6.0 m for the day and 15.3 m for the night. In Figure 9, we can observe in the lower plots the distribution of the error for the stations which were not used in the navigation solution, i.e., Hoburg (Figure 9d), Holmsjo (Figure 9e) and Rozewie (Figure 9f). It is important to note that a different scale is used on the horizontal axis with respect to the upper plots. It appears clear that the error at night is significantly larger than during the day. We can deduce that the sky-wave also depends on the distance between the transmitter and the receiver. In general, at larger distances the ground-wave is more attenuated than the sky-wave, which introduces larger errors. Moreover, the attenuation of the ground-wave depends on the propagation path, as explained in Section 3.1. Therefore, a signal which travels mostly over land will be attenuated more than a signal travelling over water. This effect explains the larger difference in range accuracy between Hammerodde and Helgoland. Despite the fact that the distance from each of them to the receiver is similar, the signal from Helgoland travels mostly over land, whereas the one from Hammerodde travels mostly over water, as is visible in Figure 6.
# Trade-offs between Risk and Operational Cost in SDN Failure Recovery Plan
## 2 System Model and Preliminaries
#### Flow Operations and Flow Conservation  
It can be assumed that in order to configure the full network path for a new flow, the SDN controller will add or remove flow entries to/from the SDN switches along that path. If this operation involves rerouting a flow, some flow entries may also have to be removed from flow tables on the SDN switches. The process of removing or adding a single flow entry from the flow table of an SDN switch is called a flow operation [1; 29; 30]. Hereafter, we use the term _operational cost_ to refer to the number of add/remove operations.  
Consider a flow that takes a high-risk path \(\{r_{i,j}\}\). We wish to reroute this flow from path \(\{r_{i,j}\}\), for which \(\beta(\{x_{i,j}\})>\tau\), to a lower-risk path \(\{x_{i,j}\}\). The new path \(\{x_{i,j}\}\) is defined in the same fashion as in Equation (1). The flow connectivity conditions must be satisfied at every node on the new path \(\{x_{i,j}\}\), i.e.,  
\[\sum_{j:(i,j)\in E}x_{i,j}-\sum_{j:(j,i)\in E}x_{j,i}=b_{i} \tag{5}\]  
where \(b_{i}\) is the net flow out of node \(i\)[31]. For a flow between nodes \(s\) and \(t\), we have  
\[b_{i}=\left\{\begin{array}{c}1\ i=s\\ -1\ i=t\\ 0\ i\
otin\{s,t\}\end{array}\right. \tag{6}\]  
It is proven in [10] that the operational cost of rerouting a flow from \(\{r_{i,j}\}\) to \(\{x_{i,j}\}\), i.e., \(\phi(\{x_{i,j}\})\) is given by:  
\[\phi(\{x\ i\,\ j\})=\sum_{(i,j)}f_{i,j}x_{i,j} \tag{7}\]  
where  
\[f_{i,j}\ =1-r_{i,j}\ =\left\{\begin{array}{c}0\ e_{i,j}\ \text{is traversed by}\ \{r_{i,j}\}\\ 1\ \text{otherwise}\end{array}\right. \tag{8}\]
## 5 Simulations
### Simulation Results
#### 5.2.2 Performance of DecantFed by Varying \(\tau\)  
The deadline \(\tau\) plays a critical role in determining the performance of DecantFed. A smaller value of \(\tau\) enables more tiers in the system, resulting in fewer clients assigned to a tier. Hence, a smaller \(\tau\) makes DecantFed behave more like asynchronous FL, eventually becoming asynchronous when each tier only has one client. Conversely, a larger \(\tau\) results in DecantFed behaving more like FedAvg, i.e., all the clients are assigned to a tier, and the FL server has to wait for the last client to upload its local model if \(\tau\rightarrow\infty\). Figure 4 shows the learning curve for different deadline settings by using CIFAR-10 when \(\beta=0.1\) and \(\beta=1\). As shown in Figure 4a,c, a larger \(\tau\) not only accelerates convergence over iterations but also stabilizes the training process. This is because a larger \(\tau\) allows for more low-index-tier clients to contribute to each global update. Additionally, a larger \(\tau\) helps global model stability. For example, there are several noticeable sudden drops in test accuracy when \(\tau=10\), which is owing to the fact that there is only an average of 1 client in the first tier, likely providing a biased local model update. Conversely, when \(\tau=80\), the training curve is more stable due to the fact that more clients are in the lower tiers. However, a larger \(\tau\) leads to slower convergence over time owing to the longer duration of a global iteration. Table 3 shows final model accuracy (i.e., when the model is converged) by applying different values of \(\tau\) when \(\beta=0.1\). Note that DecantFed with \(\tau=2.5\) s mirrors asynchronous FL with dynamic workload optimization, where each tier has only one client, and the FL server updates the global model immediately upon receiving a local model from any client. From the table, we can observe that choosing the appropriate deadline (e.g., \(\tau=10\) s) is critical to optimize the tradeoff by maximizing the final model accuracy and maximizing the convergence rate over time in DecantFed.  
Figure 4: Test accuracy of DecantFed with different deadlines for CIFAR-10 with \(\beta=0.1\) and \(1\).  
Figure 3: Test accuracy of different algorithms for CIFAR-10 with \(\beta=0.1\) and \(1\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Deadline \(\tau\) (s)** & **2.5** & **5** & **10** & **20** & **40** & **80** \\ \hline Test accuracy (\%) & 69.07 & 72.59 & 73.48 & 73.45 & 73.03 & 72.61 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test accuracy over \(\tau\) (seconds) under non-IID with \(\beta=0.1\).
# Local Dependency-Enhanced Graph Convolutional Network for Aspect-Based Sentiment Analysis
## 3 Proposed Approach/ Methodology
### Semantic Feature Extraction  
Attention is a commonly used method to grasp the interaction between aspects and the surrounding contexts [25]. The semantic feature extraction module comprises a two-layer multi-head attention mechanism. Multi-head attention (MHA) is a variation of attention. The input sequence undergoes multiple linear transformations and is mapped to several vector representations, including query, key and value. Each new query and key-value pair undergoes an independent attention operation, referred to as a head. The attention values generated by different heads are concatenated to obtain the final output value. The mathematical calculation is as follows:  
\[\text{Attention}(K,Q)=\text{softmax}\Bigg{(}\frac{QW^{Q}\times\big{(}KW^{K} \big{)}^{\text{T}}}{\sqrt{d}}\Bigg{)}K \tag{2}\]  
where matrices \(Q\) and \(K\) are the query vector and the key vector, respectively, while \(W^{Q}\) and \(W^{K}\) are learnable weight matrices and \(d\) is the dimensionality of the input node features.  
\[\text{MHA}(K,Q)=\Big{[}head^{1}\oplus head^{2}\oplus\ldots\oplus head^{n} \Big{]}W_{m} \tag{3}\]  
\[head^{i}=\text{Attention}^{i}(K,Q) \tag{4}\]  
where \"\(\oplus\)\" represents the concatenation of vectors and \(W_{m}\in R^{h_{im}}\) is a learnable weight matrix. In the first attention layer, the semantic feature extraction module utilizes self-attention with \(K=Q=H^{c}\) to learn the correlation within the sequence and obtain the introspective feature representation, \(H^{cs}\). In addition, it employs interactive attention with \(K=H^{c}\) and \(Q=H^{a}\) to acquire the aspect-aware representation, \(H^{as}\), with a primary focus on capturing the interactions between aspects and contexts. The second attention layer exploits a MHA with \(K=H^{cs}\) and \(Q=H^{as}\) to obtain a richer semantic representation, \(H^{sem}\):  
\[H^{cs}=\text{MHA}(H^{c},H^{c}) \tag{5}\]  
\[H^{as}=\text{MHA}(H^{c},H^{a}) \tag{6}\]  
\[H^{sem}=\text{MHA}(H^{cs},H^{as}) \tag{7}\]