esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 A Case Study  
### Working Face Conditions  
The 3301 working face is the first extraction zone of the 3\({}^{-1}\) coal seam in Hanjiawan Coal Mine. The coal seam is buried at a depth of 155 m, with an average thickness of 2.9 m. The working face is 260 m wide and equipped with 152 sets of ZY12000/18/33D hydraulic supports. The main roof comprises fine-grained sandstone with a thickness of 8.5 m, while the immediate roof and floor both consist of siltstone with thicknesses of 1.5 m and 3.5 m, respectively. The goaf of the 2\({}^{-2}\) coal seam lies above the working face, with a spacing of 30 m between the two coal seams. The pre-driven longwall single retracement roadway method is employed for movement and face retracement. The RR is 40 m away from the 3\({}^{-1}\) coal return air roadway, having a rectangular shape of 4.5 m in width and 3.0 m in height. The measuring point arrangement of the RR at the end of L\(W\) 3301 is depicted in Figure 1.  
Figure 1: The measuring point arrangement of the RR at the end of LW 3301.
## 3 The End-to-End Performance Analysis of the System  
For the multi-hop parallel hybrid FSO/RF cooperative system based on the DF scheme, the equivalent SNR \(\gamma_{eq_{i}}\) of the ith path can be expressed as [34]:  
\[\gamma_{eq_{i}}=\min(\gamma_{i,1}^{SC},\gamma_{i,2}^{SC},\cdots,\gamma_{i,M}^{SC}) \tag{11}\]  
Considering the identically and independently distributed hybrid FSO/RF system, the CDF of the equivalent SNR \(\gamma_{eq_{i}}\) of the i-th path can be expressed as [34]:  
\[F_{\gamma_{eq_{i}}}(\gamma)=1-\left[1-F_{\gamma_{t_{ij}}^{SC}}(\gamma)\right]^ {M} \tag{12}\]  
Based on the best path selection scheme, the signal with the largest SNR of the \(\gamma_{eq_{i}}\) (\(i=1,\ldots,N\) represent \(i\)-th path) and \(\gamma_{s,d}^{SC}\) (the instantaneous SNR of the direct hybrid link between the source and the destination) is selected. Therefore, the equivalent output SNR \(\gamma_{eq}\) at the destination node can be expressed as:  
\[\gamma_{eq}=\max(\gamma_{s,d}^{SC},\gamma_{eq}^{\prime}) \tag{13}\]  
where \(\gamma_{eq}^{\prime}=\max\limits_{i=1,\ldots,N}\gamma_{eq_{i}}\).  
Assuming that FSO and RF links in all relay links have the same average SNR--that is, \(\overline{\gamma}_{t,ij}^{SC}=\overline{\gamma}^{FSO}\) and \(\overline{\gamma}_{t,ij}^{RF}=\overline{\gamma}^{RF}\), the CDF of \(\gamma_{eq}^{\prime}\) is:  
\[\begin{split} F_{\gamma_{eq}^{\prime}}(\gamma)&= \left[F_{\gamma_{eq_{i}}}(\gamma)\right]^{N}=\left[1-\left[1-F_{\gamma_{t,ij}^{ SC}}(\gamma)\right]^{M}\right]^{N}\\ &=\left[1-\left[1-\frac{B_{ij}}{\Gamma(m^{RF})}C_{0,0:1:2,3,7}^{0 :0:1:1:61}\bigg{(}\begin{array}{c}1\\ m^{RF},0\end{array}\bigg{|}\begin{array}{c}C_{ij}\\ \overline{\gamma}^{RF}\end{array},\frac{A_{ij}\gamma}{\overline{\gamma}^{FSO}} \bigg{)}\right]^{M}\right]^{N}\end{split} \tag{14}\]  
Therefore, according to Equation (10) and Equation (14), the CDF \(F_{\gamma_{eq}}\) of the output SNR of the destination node is  
\[\begin{split} F_{\gamma_{eq}}(\gamma)&=F_{\gamma_{s,d}^{ SC}}(\gamma)\times F_{\gamma_{eq_{i}}}(\gamma)=F_{\gamma_{s,d}^{SC}}(\gamma)\times \left[1-\left[1-F_{\gamma_{t,ij}^{SC}}(\gamma)\right]^{M}\right]^{N}\\ &=\frac{B_{sd}}{\Gamma(m^{RF})}C_{0:0:1:2:37}^{0:0:1:1:61}\bigg{(} \begin{array}{c}1\\ m^{RF},0\end{array}\bigg{|}\begin{array}{c}C_{sd}\\ \overline{\gamma}_{sd}^{RF}\end{array},\frac{A_{sd}\gamma}{\overline{\gamma}_{ sd}^{\rm FD}}\bigg{)}\\ &\times\left[1-\left[1-\frac{B_{ij}}{\Gamma(m^{RF})}C_{0:0:1:2:37}^{0:0:1:1 :61}\bigg{(}\begin{array}{c}1\\ m^{RF},0\end{array}\bigg{|}\begin{array}{c}C_{ij}\\ \overline{\gamma}^{RF}\end{array},\frac{A_{ij}\gamma}{\overline{\gamma}^{FSO}} \bigg{)}\right]^{M}\right]^{N}\end{split} \tag{15}\]
# Secure Encapsulation Schemes Using Key Recovery System in IoMT Environments
## 5 Proposed Scheme-II (Key Recovery System Using Proxy Re-Encryption)
### Setup Phase  
In this phase, the KGC takes the security parameter \(1^{\lambda}\) as an input and generates public parameters.  
* **Step 1:**\(KGC\) selects \(\lambda\)-bit large prime \(q\) and group \(\mathbb{G}\) of prime order \(q\). In addition, a random generator \(g\in\mathbb{G}\) is selected.
* **Step 2:**\(KGC\) randomly selects master secret key \(s\in\mathbb{Z}_{q}^{*}\), and compute \(S=g^{s}\).
* **Step 3:**\(KGC\) selects Hash function \(H_{1},H_{2},H_{3},H_{4},H_{5},H_{6}\).
* **Step 4:** The message space \(\mathcal{M}\) and public parameters \(params=(\mathbb{G},I_{1},I_{2},q,g,S,H_{1},\)\(H_{2},\)\(H_{3},\)\(H_{4},H_{5},H_{6})\) are published.
## 5 Performance Measures of PAS
### Expected Profit Analysis  
It is always necessary to calculate the expected profit that a system will generate in the times to come. Therefore, the author calculated the expected profit from the public address system. This system is basically used to attract people so that the organizations may sell more and more products on the market by making attractive announcements to customers. The function below can be used to estimate the profit of the system in time interval [0, _t_).  
\[E_{P}(t)=K_{1}\int_{0}^{t}Av(t)dt-K_{2}t \tag{36}\]  
Here, the expression \(\int_{0}^{t}Av(t)dt\) gives the total uptime of the system, and t gives the total time. The parameter \(K_{1}\) represents the revenue generated by the system per unit of  
Figure 7: Sensitivity of the PAS with respect to reliability.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Time (h)** & \(\frac{\partial R(t)}{\partial A_{\text{init}}}\) & \(\frac{\partial R(t)}{\partial A_{\text{init}}}\) & \(\frac{\partial R(t)}{\partial A_{\text{amp}}}\) & \(\frac{\partial R(t)}{\partial A_{\varphi}}\) \\ \hline
0 & 0 & 0 & 0 & 0 \\ \hline
2000 & \(-1515.7837\) & \(-1515.7837\) & \(-1515.7837\) & \(-350.2872\) \\ \hline
4000 & \(-2236.2496\) & \(-2236.2496\) & \(-2236.2496\) & \(-877.8675\) \\ \hline
6000 & \(-2426.9383\) & \(-2426.9383\) & \(-2426.9383\) & \(-1239.5462\) \\ \hline
8000 & \(-2307.7433\) & \(-2307.7433\) & \(-2307.7433\) & \(-1385.1423\) \\ \hline
10000 & \(-2034.6505\) & \(-2034.6505\) & \(-2034.6505\) & \(-1362.5954\) \\ \hline
12000 & \(-1707.2641\) & \(-1707.2641\) & \(-1707.2641\) & \(-1237.2927\) \\ \hline
14000 & \(-1383.1641\) & \(-1383.1641\) & \(-1383.1641\) & \(-1063.6464\) \\ \hline
16000 & \(-1091.5924\) & \(-1091.5924\) & \(-1091.5924\) & \(-878.7942\) \\ \hline
18000 & \(-844.1453\) & \(-844.1453\) & \(-844.1453\) & \(-704.6366\) \\ \hline
20000 & \(-642.2939\) & \(-642.2939\) & \(-642.2939\) & \(-551.9623\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Sensitivity of the PAS with respect to reliability.  
time and \(K_{2}\) represents the expenditure of the system per unit of time. Now, setting \(K_{1}\) = 10 and varying the value of \(K_{2}\) and t, one can easily obtain the expected profit that the public address system can generate, indicated in Table 6 and Figure 8.
## 3 P-MAC Design Integrated with Routing
### Duration of Each Period  
An analysis on the duration of each period in the P-MAC cycle follows:  
(1) SEND/RECEIVE DATA period: As shown in Figure 2, the duration of the _SEND DATA_ period in node \(S\) is identical to the duration of the _RECEIVE DATA_ period in node \(A\), if node \(S\) and \(A\) are involved in the current data transmission. The maximum time of the _SEND/RECEIVE DATA_ period, \(T_{SR}\), is calculated as follows:  
\[T_{{}_{SR}}=2 CW+2\mathrm{DIFS}+2\mathrm{SIFS}+durRTS+durCTS+durDATA+durACK, \tag{1}\]  
where _durRTS_, _durCTS_, _durDATA_ and _durACK_ are the transmission duration of RTS, CTS, DATA and ACK, respectively.  
(2) SLEEP period: In P-MAC, the duration of the _SLEEP_ period is determined by the following equation:  
\[T_{{}_{SLEEP}}=sleep\_\ factor\cdot T_{{}_{S/R}}, \tag{2}\]  
where _sleep_factor_ is a positive integer. The whole cycle duration is given by:  
\[T_{{}_{cycle}}=(sleep\_\ factor+2)\cdot T_{{}_{S/R}}=\tau\cdot T_{{}_{SR}}, \tag{3}\]  
where \(\tau\) is called _cycle coefficient_. Next we discuss how to determine the value of _sleep_factor_.  
Figure 3 shows an example used for analysis. For simplicity, the RTS/CTS handshake process is not shown. There are four nodes, where node 0's grade is zero (it is the sink node), node 1's grade is one,  
Figure 2: P-MAC integrated with routing. Node \(S\) has data to send to the sink.  
and so on. Node 3 has data to send to node 0. \(T_{d}\) is the actual duration time for a node to finish its one-hop data transmission process. Note that \(T_{d}\leq T_{SR}\), since the node may not select the last slot in CW. Thus the node's actual sleep time is \(\Delta T+T_{SLEEP}\), where \(\Delta T=T_{SR}-T_{d}\). Anyway, each node can sleep for at least \(T_{SLEEP}\) time in each cycle.  
If _sleep_factor_ is a positive integer, for example, \(sleep\_factor=1\), when the \(i\)th (\(i>0\)) grade nodes wake up, the (\(i-1\))th grade nodes are still in the _SLEEP_ period, and the (\(i+2\))th grade nodes will go to sleep for at least \(sleep\_factor\)\(\cdot\)\(T_{SR}=T_{SR}\) time. Therefore, the communications between the \(i\)th grade nodes and the (\(i+1\))th grade nodes will not be interfered with by the (\(i-1\))th and (\(i+2\))th grade nodes. Considering that the interference range is about two times the transmission range, the value of \(sleep\_factor\) needs to be at least 2. Figure 3 shows the case of \(sleep\_factor=2\).
# An Overview on Push-Based Communication Models for Information-Centric Networking
## 3 Push-Based Approaches in ICN  
ICN pull-based communication models such as the ones supported by CCNx [12] and NDN attain a two-way delay which is disruptive for situations where devices emit data periodically (e.g., monitoring reports), or for situations where devices generate data in response to a trigger (e.g., alert on machine malfunctioning). Such cases benefit from the one-delay that push-based models attain.  
To assist this reasoning, Figure 3 illustrates a generic IoT scenario where sensors (P1, P2, P3) are collecting data in the context of a Smart Home environment. Sensors P1, P2, P3 are located in different rooms of a house.  
Figure 1: Broker-based publish-subscriber model currently used by message-oriented protocols in IoT.  
Figure 2: ICN publish-subscriber model.  
The sensed data is sent to an application in [PERSON]'s device (C3) via an IoT gateway (C2), which [PERSON] can use to remotely control her Smart Home environment. The data is sent as well over the cloud to registered applications which [PERSON]'s family use for the same purpose. In this scenario, hierarchical naming can be defined to best assist data distribution. For instance, a temperature sensor data can be identified by a name prefix such as \"/homenumber123/room/temperature\", and the IoT gateway or any other router in between can assist in data aggregation based on specific data categorization rules.  
The different \"things\" (e.g., sensors) are therefore _publishers_, while the gateway and the different control/management applications are _subscribers_.  
In these environments producers are usually resource-constrained devices which are often in Idle mode. Furthermore, in IoT scenarios information needs to be frequently updated (e.g., temperature may be updated every minute; in a factory environment, polling may occur within milliseconds). A publish/subscribe pull-based approach helps in getting data in a distributed and asynchronous manner, which is relevant in large-scale environments where topology is variable. However, it may overload the network with Interest packets when IoT applications generate data in shorter time periods. Network performance may also suffer from the high volume of meta-data (e.g., Interest-Data handshake) that needs to be generated to get small transient IoT data.  
Such limitations lead us to investigate different ICN/NDN push-based communication models to IoT.
# MIDOM--A DICOM-Based Medical Image Communication System
## 2 Background
### Cbpc
#### 2.2.2 Entropy Coder  
The CBPC compression method implements more entropy coding methods, namely Huffman, Arithmetic, and Golomb-Rice. The latter is being used as a primary method as it  
Figure 1: An example of the prediction context—processed element shaded gray.  
provides satisfactory performance while retaining ease of implementation. This method encodes only positive values, implying the necessity to mirror negative elements to the positive part of the set. Every code word produced by the Golomb-Rice coder consists of two parts separated by the separator. The first or unary part is a series of bits with length equal to the result of integer division of the value being coded and parameter \(m\). In the current implementation, the first part is a series of logical 1 s, making 0 the separator. The second or binary part of the code word is a binary representation of the modulo of the aforementioned integer division, with length \(k\). Prior to the coding process, parameters \(m\) and \(k\) need to be determined, such that the relation (2) is satisfied.  
\[m=2^{k} \tag{2}\]  
While the length of the binary part is constant, the length of the unary part is variable and is growing with the increase in the value being coded. This implies that the most frequent values will be coded with the shortest coding word, thus making it particularly suitable for coding prediction errors which converge towards zero.  
The original value is decoded by counting the number of 1 s prior to the separator, multiplying it by \(m\) and adding decade value of the next \(k\) bits. If \(n\) denotes the original value, \(u\) number of 1s in the unary part, \(b\) decade value of the binary part, original value reconstruction can be achieved by the relation (3).  
\[n=u\times m+b \tag{3}\]
# Shear-Actuation and Vibrometer Reception of Penetrating Ultrasonic Guided Wave Modes in Human Tibia
## 3 Results  
### Experimental Results  
The time-history data obtained sequentially from each of the 10 sensing locations on the reflective tape (shown in Figure 4a) were recorded. The two key features observed in the A-scans recorded at locations 1, 4, 7, and 10 (shown in Figure 4b) are the multiple wave packets and substantial attenuation of the signal. Prior experience with guided waves in long bones agrees with these observations [8; 26; 27]. A 2D-FFT was applied to the 10 time-series to extract the phase velocity spectrum, which was overlaid on the phase velocity dispersion curves for frequencies from 100 to 300 kHz obtained from the cross-section at the mid-span of a tibial diaphysis.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mid-Span Cross-Section** & **Synthetic Bone** & **Simulation (From Ct-Scan)** \\ \hline \hline \
\begin{tabular}{c} Antero-posterior diameter, \(\mathrm{D_{AP}}\) \\ \end{tabular} & 22.41 & 21.95 \\ \hline \hline
\begin{tabular}{c} Medio-lateral diameter, \(\mathrm{D_{ML}}\) \\ \end{tabular} & 32.97 & 31.12 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of tibial cross-section dimensions (in mm) at the diaphyseal mid-span.  
Figure 3: The cross-section at the mid-diaphyseal region of a human tibia showing the finite element mesh used to determine dispersion curves.  
The highest-intensity phase velocities from the 2D-FFT are in the vicinity of 2 km/s, and are enclosed by an ellipse for emphasis in Figure 4c, which is enlarged in the inset image. The propagating modes are nearly non-dispersive at the low phase velocities in this frequency range. The existence of non-dispersive propagating modes is not a surprise, but their generation by a shear transducer is not a surprise.  
Figure 4: LDV results measured on a synthetic tibia: (**a**) measurement points on the reflective tape; (**b**) A-scans recorded at points 1, 4, 7, and 10; (**c**) phase velocity spectrum obtained from 2D-FFT of data recorded at 10 points overlaid on dispersion curves obtained from finite element simulation.  
and reception via a normal LDV measurement is not intuitive. The next subsection will investigate the displacement components of the modes having these phase velocities to answer the question of why waves generated by an omni-directional shear transducer are detected by particle displacements normal to the surface.
## 4 Data Transformation Impact on Gray Values Compression
### Analysis of the EEDC-RANGE(BWT) Algorithm  
In this subsection, the new algorithm is tested at five compression ratios on the standard test image _horse_. Table 7 shows the values of the average absolute error for five encoders: JPEG, JPEG 2000, EEDC, and EEDC-RANGE (BWT).  
By carefully observing Table 7, it can be seen that the novel algorithm beats its predecessor in all compression ratios. _EEDC-RANGE(BWT)_ is better than the JPEG at 0.1, 0.2, and 0.4 bpp while it is only better than JPEG 2000 at 0.05, 0.1, and 0.2 bpp. The graphical representation is shown on Figure 10.  
Figure 11 shows a series of reconstructed images at all compression ratios for all compression methods. Looking at the images shown, it is clear that the differences are more pronounced at higher compression ratios. The JPEG method was never able to produce a reconstructed image at 0.05 bpp. At 0.1 and 0.2 bpp, this new method is obviously better than other methods. At 0.4 and 0.8 bpp, it is difficult to see the differences between the tested methods. The analysis and the results obtained in this section have shown that the compression of grey values directly affects the quality of the reconstructed image. The improvement is due to the introduction of the Burrows-Wheeler transform and range coding. Still, this method is no better than JPEG 2000 at 0.4 and 0.8 bpp. The question arises, \"Is it possible to achieve better results than JPEG 2000 at all compression ratios?\", and the answer to this question follows in the next section.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**bpp** & **JPEG** & **JPEG 2000** & **EEDC** & **EEDC-RANGE (BWT)** \\ \hline
0.8 & 1.65 & 1.39 & 2.08 & 2.02 \\
0.4 & 2.91 & 2.29 & 2.67 & 2.52 \\
0.2 & 6.20 & 3.78 & 3.80 & 3.61 \\
0.1 & 10.96 & 6.75 & 5.94 & 5.65 \\
0.05 & / & 12.52 & 9.31 & 8.68 \\ \hline \end{tabular}
\end{table}
Table 7: AAE values comparison.  
Figure 10: AAE comparison for image _horse_.
## 3 Study Framework  
The SUMO [33] microscopic traffic simulation model was used to obtain and model the vehicle trajectories, simulated speeds, simulated delays, and simulated traffic conflicts. In this analysis, the most popular car-following model in the literature is the Wiedemann-99 model (e.g., [21; 23; 25; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53], etc.), which was adapted to this analysis. A full 90 min, or 5400 s, of traffic were simulated. Ten simulation runs, each using a different random seed, were applied for each scenario, with a 15 min warm up. Only the simulation results of the middle 60 min are used, after removing the 15 min warming up and the final 15 min.  
The road network with traffic and geometric characteristics can be used to determine the average delay and traffic speed after the simulation has run, and each simulation step can yield the trajectories of every vehicle. The number of simulated conflicts is then obtained by transforming the individual vehicle trajectories into pairs of vehicles for a specific interaction type (i.e., leading and trailing vehicles for rear-end interaction and head-on interactions). Additionally, car locations with velocity and acceleration profiles were obtained every 0.50 s, and an action-step length of 0.50 s was used. Vehicle lengths of 5.00 m and 18.00 m were used for both passenger cars (PCs) and heavy vehicles (HVs) or tractor-trailers (TTs), respectively.  
Figure 1 illustrates a general framework for calculating traffic delays and conflicts. The first step involves simulating all vehicle movements (PCs and HVs) on the highway network using SUMO. The TLTW or 2+1 geometry, number of lanes, and lane configuration represent the input data. The combinations of traffic volumes and the percentage of HVs with the speed-limit strategy (i.e., USL or DSL) are displayed in Table 1.  
Figure 1: Study framework.  
Figure 2 shows the geometry of the highway networks, with the following assumptions: (a) lane width is 3.65 m and (b) alternating lane with 1.0 km length for both 2+1 designs.
# Ensemble-Based Knowledge Distillation for Video Anomaly Detection
## 2 Related Works
### Supervision Factor  
With respect to the supervision factor, generally, we can classify the anomaly detection task into three main branches, which are unsupervised, weakly supervised, and supervised-based models [19] :  
1. **Unsupervised Models:** These models do not require any pre-annotated label or ground-truth information. They focus on intrinsic feature patterns of video data samples and the classification process is considered as outlier detection. An outlier pattern is assumed as its intrinsic features are not similar enough to other normal intrinsic features.
2. **Supervised Models:** In supervised models, frame level annotations or bounding box level annotations are used in the training process as ground-truth information besides video level annotations.
3. **Weakly Supervised Model:** These types of models use a weak supervision method, where both normal and abnormal labels are provided as video-level annotations in the training process. Frame-level labels or bounding box-level labels for localization are not provided to these models in the training process.  
Our proposed method uses a weak supervision model in the training process. Therefore, the studies using weak supervision are focused on within the scope of this study.
## 4 Discussion
### Developing Design Method for Constant-Amplitude Fatigue
#### 4.2.2 Microscopic Fracture Morphology Analysis  
The current work analyzed six typical fatigue fractures at different stress amplitudes using scanning electron microscopy (SEM) to further understand the high-strength bolt's fatigue failure mechanism. The first stage of fatigue failure started from the fatigue source, which was always an impurity or a small notch on the surface of the bolt. For example, Figure 12a,c,d shows the fatigue source region starting from the notch on the surface of the bolt. The crack propagated from the notch to the periphery of the bolt. There are apparent inclusions in Figure 12b,e,f, showing radial patterns around the inclusions.  
The second stage of the fatigue failure was the stable propagation of fatigue cracks, and the propagation paths were perpendicular to the direction of the principal stress. Figure 13a,b demonstrates many apparent fatigue striations on the fracture surface, almost parallel. Analyzing the fatigue striations in Figure 13c,d shows that the directions of the striations were not uniform; they were formed by the intersection of cracks propagated from different planes.  
The third stage of the fatigue failure was the transient fracture. A mixed pattern of fatigue striations and dimples appeared at the junction of the crack instability propagation  
Figure 12: The microscopic morphology of the fatigue source at different stress amplitudes.  
Figure 13: The microscopic morphology of the fatigue propagation area under different stress amplitudes.  
area and the fracture area, as presented in Figure 14a,b. Many tear ridges and dimples could be observed in the transient fracture area (see Figure 14c-f), which implied that the material strength was high and the plastic deformation was small, indicating that the final fracture mode of the bolt was a brittle fracture.  
The current fatigue test methods were mainly based on constant amplitude fatigue tests, but the fatigue problems encountered in practice were always repeated random loads. This type of load would not cause constant amplitude fatigue stress on components, and almost all fatigue stresses were in variable amplitude periodic. Therefore, it was significant to carry on the variable amplitude fatigue tests. In future works, the fatigue performances of M60 high strength bolts under variable amplitude loads would be further studied.