esa-sceva/satcom-chunk-collection huggingface dataset:
# Vision Transformer-Based Tailing Detection in Videos
## 4 Experiments and Analysis
### Experimental Settings
#### 4.2.3 Method 3: Proposed TSViT  
We used TSViT configuration based on ViT [34], which is summarized in Table 2. The \"Base\", and \"Large\" models are adopted from ViT, while we added a variant called \"Small\" in our experiments. In Tables 2 and 3, B, L and S stand for Base, Large and Small, while the number written next to the model name, i.e., 256 and 512, represent the input frame size 512 \(\times\) 512.  
* Model variants.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & & \multicolumn{2}{c}{**Tailing**} \\ \cline{3-5}
**Model** & **Params (M)** & **Accuracy (\%)** & **Precision** & **Recall** \\ \hline Simple-CNN & 229 & **63.54** & **0.7224** & 0.6741 \\ Simple-ResNet & 584 & 59.37 & 0.7148 & 0.5794 \\ ResNet-50/512 & 1591 & 60.24 & 0.6255 & 0.9025 \\ ResNet-101/512 & 2410 & 62.32 & 0.6371 & **0.9192** \\ ResNet-152/512 & 3399 & 61.63 & 0.6462 & 0.8496 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Tailing detection performance comparison among different CNN architectures.  
* Training configurations. We trained all TSViT for 100 epochs using AdamW [47] with \(\beta_{1}=0.9\), \(\beta_{2}=0.999\), weight decay = 0.01, and a batch size of 64. We performed five ordered random sampling per video in each epoch. We performed grid search with a learning rate in [0.0025, 0.1]. We used a learning rate with gradual warmup [48] up to 20 epochs and then cosine annealing decay [49] for the remaining epochs. For data augmentation, we only used random horizontal flipping.  
The overall comparison of Tables 1 and 3 reveals that the proposed TSViT method outperform all CNN-based models. Transformer-based \"Base\" model TSViT-B/512 exhibits the best accuracy of 76.56% in comparison with the 63.54% best accuracy of simple-CNN, a 13.02% improvement. Similarly, the recall is improved from 91.92% of ResNet-101 to 98.05% of the TSViT-B/512 model, even with the smaller model size (1092 M vs. 2410 M parameters). This shows that the transformer can have better inductive biases compared to CNN by using temporal information in a better way of embedding time-series data frame by frame. Third, the performance of Transformer-based models increases as the input frame size is increased. This is because the self-attention layer in the transformer embeds positional information from the input image [50], and thus, higher resolution translates to more accurate positional embedding.  
The proposed TSViT-based tailing detector shows a processing time of 7.69 milliseconds per sample (a sample consists of 9 frames), while the object detector exhibits a delay of 23 milliseconds. Overall, the tailing decision is made within 30.69 milliseconds using one GPU.
###### Abstract
The detection of abrupt faults is comparatively easy because they show sudden changes in the normal behavior for a given input. However, the detection of gradual faults is difficult because some vital parameters or patterns must be observed continuously to identify such faults; thus, the detection of these faults is comparatively challenging [21]. Fault diagnosis carried out conventionally is becoming challenging because of the specific needs for accuracy and efficiency; hence, there is a need for intelligent diagnosis to meet the demands of the current industry. Data-driven and model-based techniques with the concepts of information fusion have laid an efficient path for diagnosing faults in industrial valves [22; 23; 24; 25]. One of the data-driven approaches for fault analysis is machine learning along with its subset; deep learning can be applied to different types of valves [26; 27; 28; 29]. The use of reliable data acquisition systems in real time generates huge volumes of data, which marked the beginning of the usage of dimensionality reduction techniques such as Canonical Variate Analysis (CVA) and crimcipal component analysis (PCA) for the detection of various faults [30; 31]. The most frequent incipient flaw in process industries is the static friction provided by the movement of the control valve stem. The continuous movement of the stem and the environmental conditions can cause wear and tear in a control valve. This static friction, also  
Figure 1: Pneumatic actuator fault categories. referred to as \"stiction\", describes the situation in which a change in the controller output causes the valve to stick or resist moving [32]. One such fault is the presence of stiction, which causes oscillations in the control loops, which is an undesired behavior. These continuous oscillations decrease the performance and health of the valve, thus reducing its life. Oscillations increase operating costs roughly in proportion to the deviation [33]. This causes instability in the control system and speeds up equipment wear, leading to inconsistency in product quality. This instability or nonlinearity is mainly referred to as a stuck valve. Stiction is generally denoted by plotting the manipulated variable, that is, the valve position, for a change in controller output.
# Sep-RefineNet: A Deinterleaving Method for Radar Signals Based on Semantic Segmentation
## 4 Experimental Results and Discussion
### Experimental Results
#### 4.2.3 Verify the Anti-Noise Ability  
Our method is effective for different PRI modulation modes. The aliasing pulses and missing pulses are both set to 15%, and the experiments on constant PRI, staggered PRI, jittered PRI and their mixed pulses are completed. Pulse streams from the test are set to complete deinterleaving and to obtain their F1-Score. The average results are shown in Table 6.  
Table 6 shows that the proposed method has an accurate sorting result for different modulation PRI. For the pulses with only constant PRI, the recall is 0.993, the precision can reach 0.971, and the F1-Score is 0.982. The sorting result of the jittered PRI will be relatively poor, but the recall rate is still 0.885, the precision is 0.859, and the F1-Score is 0.872. This is because the pulse jitter will cause the FCM to be affected during encoding, destroying the semantic features between PRI, and the jittered PRI is most affected. The reason why the recall rate is slightly higher than the precision is the position decoding is affected by the FCM difference order and the harmonics. If the Sep-RefineNet predicts the same wrong results at the symmetrical position, it will lead to error sorting, making the number of predicted pulses more than the labels. In order to verify the anti-noise ability of the proposed method, pulse streams under different interference rates were selected from the test set, and different deinterleaving algorithms were compared when the aliasing pulses and missing pulses were 5%, 10%, 15%, and 20%, respectively. The calculated F1-Score and the obtained results are shown in Table 7.  
Under different pulse interference rates, the proposed deinterleaving algorithm still has a good sorting effect. When the aliasing pulses and missing pulses are 5%, the F1-Score is 0.946. When the noise rate is 10%, the F1-Score is 0.931. When the noise rate is 20%, the F1-Score can still reach above 0.9. Compared with the traditional algorithm, the proposed radar signal deinterleaving algorithm is less affected by the aliasing pulses and missing pulses and has better anti-noise interference ability.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**PRI** & & & & \\
**Modulation** & **Constant** & **Staggered** & **Jittered** & **Constant + Staggered** & **Constant + Jittered** \\ \hline F1-Score & 0.982 & 0.885 & 0.872 & 0.933 & 0.917 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The F1-Score of different PRI modulation modes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Aliasing and** & & & & \\
**Missing Rate** & **5\%** & **10\%** & **15\%** & **20\%** \\ \hline
**F1-Score** & & & & \\ \hline SDIF & 0.894 & 0.878 & 0.841 & 0.782 \\ CDIF & 0.875 & 0.860 & 0.836 & 0.774 \\ PRI transform & 0.836 & 0.814 & 0.753 & 0.683 \\ Sep-RefineNet & 0.946 & 0.931 & 0.918 & 0.904 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The F1-Score under different pulse interference rates.
## 4 Removal of Pharmaceuticals during Wastewater Treatment
### Sorption Process for the Removal of Pharmaceutical Products
#### 4.4.3 Biochar  
Biochar has been used as an adsorbent of pharmaceutical compounds by [PERSON] et al. [1]. They demonstrated that many factors affect pharmaceutical adsorption on biochar that are common to most sorbents: pH, temperature, feedstock, preparation conditions, presence of ions, humic acid, pharmaceutical structures, and functional groups present both in the biochar's surface and within its solid structure. Biochar-based materials have been used by [PERSON] et al. [85] for the removal of antibiotic sulfonamides and tetracyclines in aquatic environments. They showed that in contrast to active carbon (AC), biochar is considered a successful adsorbent; even with low surface area, it can have excellent potential as an adsorbent due to its ability to swell in water leaving more room for adsorbents [86]. Therefore, the adsorption of pharmaceuticals onto AC often results in both high removal efficiencies and capacities without generating toxic products. Hydrophobic pharmaceuticals tend to reach equilibrium with AC faster than hydrophilic compounds [1].
# Experimental Study on ELID Grinding of Silicon Nitride Ceramics for G5 Class Bearing Balls
## 2 Materials and Methods  
Surface defects that occur during the ceramic grinding process are a result of brittle fracture caused by an increase in cutting force. Therefore, it is necessary to perform surface grinding at a low and constant cutting force to eliminate these defects. The gradual increase in cutting force during the grinding process is caused by the loading phenomenon. Hence, performing periodic dressing to remove chips before loading occurs is essential. However, the time loss incurred by dressing work is substantial, and determining the periodicity of the dressing cycle proves to be highly challenging due to its variation depending on the grinding conditions. ELID grinding is well suited for surface machining of brittle materials such as ceramics because it maintains a low and constant cutting force, preventing the occurrence of the loading phenomenon and eliminating the need for separate dressing work due to the continuous dressing effect during grinding [21; 22; 23; 24]. In this study, separate ELID grinding experiments were conducted to analyze both the impact of silicon nitride ceramics' properties on the ground surface and the influence of grinding conditions on the ground surface. The evaluation of the ground surface was divided into two categories: a quantitative evaluation, achieved through the measurement of surface roughness, and a qualitative evaluation, performed by analyzing surface photographs using a surface magnifying microscope [28; 29; 30; 31] and FE-SEM (Field Emission Scanning Electron Microscope) device.
## 3 Results and Discussion
### Retrofitted Joints
#### 3.2.3 Joint T3 (Brace Local Buckling)  
The axial load-displacement curve of the non-retrofitted T3 joint is shown in Figure 11. The buckling happens when the vertical displacement of the brace end reaches to about 2 mm, where the first wrinkle is observed near the chord-brace intersection. This local buckling leads to a sudden significant reduction of about 72% in the load-bearing capacity. By increasing the brace end displacement, successive minor hardenings appear, which are stopped by successive wrinkles above the first local buckling. This process causes a  
Figure 8: The axial load-displacement curve of the T2 joint (the brace overall buckling mode).  
Figure 9: The effect of (**a**) ply distributions, and (**b**) retrofitted brace length, on the axial load-displacement curve of the retrofitted T2 joint.  
vibration shape in the load-displacement curve shown in Figure 11. The final deformed shape of the local buckling, including the successive wrinkles, is depicted in Figure 12.  
In order to improve or prevent the brace local buckling, the FRP patches were wrapped just around the brace member. In the first step, various ply distributions were considered to show their effects; for this purpose, two plies were used in all cases. As represented in Figure 13a,b, just two plies of FRP can prevent the sudden decrease of the load bearing due to the local brace buckling and change the governing buckling mode to the chord ovalization. If the fibers are oriented in the brace longitudinal axis, i.e., (90/90) distribution, the FRP patches start to carry the load by applying the axial load, hence the FRP failure happens faster than when the fibers are oriented in the brace circumferential distribution. Hence, the (90/90) distribution leads to the higher ultimate load while the (0/0) distribution shows higher durability. From Figure 14b, the retrofit of the brace with more number of  
Figure 11: The axial load-displacement curve of the T3 joint (the brace local buckling mode).  
Figure 10: Failure of the FRP patches in the retrofitted T2 joint (the brace overall buckling mode). Composite failure modes: (**a**) fiber compression; (**b**) fiber tension; (**c**) matrix compression; (**d**) matrix tension.  
The possible composite failures are shown in Figure 14. The critical zone for the composite failures is near the intersection of the brace and the chord. It is revealed that the fiber and matrix compression modes are more critical than the other modes.
# Improved Genetic Algorithm for Solving Green Path Models of Concrete Trucks
## 2 Fuel Consumption Models
### Fuel Consumption of Stirring Tank  
The engine power of the concrete mixer truck is divided into two parts: one part is used to drive the vehicle, while the other part drives the stirring tank to rotate. Specifically, the mechanical energy is initially transformed into hydraulic energy by the hydraulic pump, and this hydraulic energy is then transmitted to the hydraulic motor, which converts it back into mechanical energy; the resulting mechanical energy is utilized to drive the stirring tank's rotation, after the torque is amplified by the reducer. From the above analysis, it can be seen that the stirring tank will also affect vehicle fuel consumption in the process of vehicle distribution. Ignoring the loss during energy conversion, the energy consumption of the stirring tank can be approximately replaced by the output work of the hydraulic motor. To derive the fuel consumption model for the stirring tank, the fuel efficiency and fuel calorific value are combined, as shown in Equation (5). (W\({}_{k}\) (MJ): energy consumed by vehicle k stirring tank, and F\({}_{k}\): fuel consumption of vehicle k stirring tank.) Table 1 describes the key parameters in the fuel consumption model.  
\[\mathrm{F_{k}} =W_{k}/\gamma_{f}C_{f}\rho_{f} \tag{5}\]
## 4 Results
### Evaluation Metrics  
UGS segmentation can be regarded as a binary classification instance segmentation task. Following the practices outlined in [41; 42; 43], we used five metrics to evaluate model performance, including precision (Pre), the F1-score (F1), intersection over union (IoU), overall accuracy (OA), and the number of model parameters (Params). These metrics are defined as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{6}\]  
\[Recall=\frac{TP}{TP+FP} \tag{7}\]  
\[F1=2\times\frac{precision\times recall}{precision+recall} \tag{8}\]  
\[IoU=\frac{TP}{FP+TP+FN} \tag{9}\]  
\[OA=\frac{TP+TN}{FP+TP+FN+TN} \tag{10}\]  
where \(TP\), \(FP\), \(TN\), and \(FN\) represent True Positive, False Positive, True Negative, and False Negative, respectively. Params describes the total number of trainable parameters in the model.
# Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks
## 4 The Proposed Clustering Protocol
### Data Transmission (DT) phase  
The data transmission phase starts after the time slots are allocated to every associated \(C_{MNs}\) in the network. This phase is the most important phase in the lifetime of the network because nodes in this phase send their sensed data to their relevant CH which aggregates and forwards this correlated  
Figure 8: (**a**) The complete description of phases in a round of model-based optimal cluster heads (MOCHs) and the association process of non-cluster member for data forwarding; (**b**) CH data collection during a round in slots.  
data to the BS. The total energy consumed by all the nodes for transmitting and receiving a full-length data packet containing \(F\) frames is calculated in a similar way as in [11]:  
\[E_{DT}=K\bigg{[}E_{CH-DT}+\bigg{(}\frac{N}{K}-1\bigg{)}E_{N-DT}\bigg{]}\times F \tag{25}\]  
After computing the energy consumed in each phase of a round, finally, we calculate the energy consumed by all the nodes during a complete round as:  
\[E_{round}=E_{CF}+E_{SP}+E_{DT}\times F \tag{26}\]  
In recent cluster-based protocols [9; 10; 12; 27], the CHs convey their data to the BS through relay nodes. Most of the times the CHs towards the BS are chosen as relay nodes. In this case, the CHs sensing their area and also working as the head nodes. The CHs are ordinary nodes with no extra resources and when these ordinary nodes are working as CHs their energy consumption increase. However, when these nodes are selected as relay nodes to forward the data of backward CH nodes, these nodes exhaust their power resources very quickly. The CH nodes, which are closer to the BS are most of the time run out of batteries making the network unstable much earlier. In MOCHs, the CHs collect all the information from their \(C_{MNs}\) and then they directly convey collected information to the BS.  
All the sensed data packets in the network sensed by the \(C_{MNs}\) arrive at the CHs.  
Proof.: The root nodes, which receives a CH status message _Adv-\(O_{CHs}\)-Msg_, send their _Joint-Req-Msg_ to the concern CHs. The CHs then accept their request and these root nodes become the \(C_{MNs}\). After cluster formation, the CH sends a TDMA slot to each node individually in which \(C_{MNs}\) send their packets to the CHs. So, each member node in the network sends one data packet to the CH. Hence, all the nodes in the network send all the sensed data packets to the CHs.
# A Method for the Rapid Propagation of Emergency Event Notifications in a Long Vehicle Convoy
## 3 Simulation Environment and the Convoy VANET System
### Convoy VANET System
#### 3.4.3 Routing Protocol
Note that the SM list does not include the entire SM but merely an identifying value for each one. This is to reduce the amount of data added to the VS message. The received SM list is used by other nodes as a confirmation of reception. If the VS's sender is behind the receiving node, then this confirmation is also proof of rearward propagation. Hearing a rearward node's retransmission of an SM is the other method of confirming rearward propagation. Every packet received by a node passes through the input routing function. While the input routing function handles both VS and SM packets, VS packets are simply stripped of their SM lists and then handed off to the receiver (see Section 3.4.2). Received SM lists are handled by Algorithm 1. Received SMs are handled by Algorithm 2. Note, in Algorithm 1, the for-each loop uses the set difference operation in its check, meaning that it is executing for every SM we have received thus far but not including every SM the sending node has listed as received. ```
\(L\leftarrow\) SM list from sending node \(P\leftarrow\) position of sending node, if known \(R\leftarrow\) SM list for ego node ifP is known and rearwardthen foreach\(s\in L\)do  Mark \(s\) as having propagated rearward  Cancel any cancellable scheduled retransmissions of \(s\)  end foreach\(m\in R\setminus L\)do ifP is knownthen \(r\leftarrow\) number of retransmissions such that there is at least a 90% chance of at least one being received, up to a maximum of 6 else \(r\gets 4\)  end  Schedule \(r\) uncancelable retransmissions of \(m\) with delays based on \(P\)  end foreach
```  
**Algorithm 1**Handler Function for Received SM List  
```
\(S\leftarrow\) received SM ifS is an SM we have not seen beforethen  Add \(S\) to our list of SMs end if ifS has not propagated rearwardthen  Set S.PRTX to our choice of PRTX ifwe are the original PRTX forSthen  Retransmit \(S\) immediately  end  Schedule 3 retransmissions of \(S\) with delays based on the original PRTX location end if ifS has propagated rearward AND we have not recorded this factthen  Record \(S\) as having propagated rearward  Cancel any cancellable scheduled retransmissions of \(S\)  end if
```  
**Algorithm 2**Route Input Function for SM
## 7 Lessons Learned and Limitations
### Lessons Learned  
Several findings from the implementation and evaluation process may guide future applications:  
* Modularity Supports Resilience: Dynamic reconfiguration and the hot-swapping of modules proved feasible, even under active operation. This validated the modularity concept of the architecture.
* Protocol Heterogeneity is Manageable: The mixed use of OPC UA and native protocols did not cause system instability, confirming that the architecture's semantic abstraction layer worked as intended.
* User Feedback is Crucial: Early-stage operator feedback significantly influenced UI layout and system messaging, underscoring the importance of human-centric design, even in highly technical environments.
# Satellite Attitude Determination and Map Projection Based on Robust Image Matching
## 4 Results  
### Improvement of Feature Matching with Robust Estimation  
All the four variants for outlier rejection in our proposed scheme, RANSAC (the baseline RANSAC), MSAC, MLESAC, and PROSAC, were examined with Kanto, Yosemite, and Kyushu, and they all selected the same sets of inlier pairs in all the three cases. Figure 5 shows the surviving feature pairs after the proposed method. All of the irregular lines in Figure 4 were successfully eliminated. The averaged \(\theta\)s estimated from the correct pairs alone were approximately \(0.02^{\circ}\), much smaller than those estimated from all pairs (Table 3) and comparable to the accuracy of typical STTs. Interestingly, although fewer correct pairs were extracted from the cloudy image (Figure 4c) than from the other cases, the magnitudes of the averaged \(\theta\)s were the same in all three images. This indicates that the proposed scheme can robustly selected the correct feature pairs under the geometric condition introduced in Section 3.1, even when many spurious pairs were found in the rough matching stage. The distributions of \(\theta\)s from the incorrect pairs were clearly separated from those of the correct pairs, and their magnitudes far exceeded the threshold (Figure 6). This separation of the distribution is considered the key to achieving consistent outlier rejection results with the four different algorithms in the RANSAC family.  
It should be noted that the errors in satellite position may add systematic errors to satellite attitude determination, because the viewing angle from a satellite to a target changes linearly according to the displacement of the satellite position. For the UNIFORM-1 case (624 km altitude), a 1-km position error (from TLE) corresponds to a \(0.1^{\circ}\) error in determining the satellite attitude. If we use more accurate position information, e.g., from GPS, systematic errors will be reduced.