esa-sceva/satcom-chunk-collection huggingface dataset:
# Ball Tracking Based on Multiscale Feature Enhancement and Cooperative Trajectory Matching
## 4 Experiments
### Ablation Experiments  
#### 4.3.1 Analysis of the Effectiveness of the Heuristic Composite Sampling Strategy  
First, we conducted ablations on the components of the Heuristic Compound Sampling Strategy. In the actual video of the ball team sports competition, there are long-shot videos, close shots, and close-up videos. Even in the same long shot or panoramic shot, as the position of the ball changes in the court, the position between the ball and the camera will also change. Therefore, the relative size of the balls changes as the camera moves or the ball itself moves. We not only want to detect and track smaller balls in the distance but also capture larger balls in the distance. Secondly, in actual sports videos, there is only one valid ball object by default. Therefore, after the original image is randomly cropped to the size of the network input, there is a large gap in the number of positive samples containing the ball object and negative samples that do not contain the object, resulting in positive and negative samples being severely imbalanced. Finally, there are often unusual situations such as occlusion and motion blur in team ball sports scenes. In order to verify the effectiveness of the heuristic composite sampling strategy, we constructed different sampling strategies for ablation experiments: (a) HMMATrack random cropping and random sampling, and other structures remain unchanged; (b) HMMATrack (w/o tilling): the tilling sampling mechanism is not used, and the detection and tracking network remains unchanged; (c) HMMATrack (w/o heuristic sampling): heuristic sampling is not used, and other structures remain unchanged; (d) HMMATrack (w/o hard sample mining): uses  
Figure 7: Examples of comparisons of results with other methods: (**a,b**), respectively, show partial screenshots of the tracking results of Deepsport and HMMATrack on frames 29 to 36 of episode 4 of the basketball test set; (**c,d**), respectively, show partial screenshots of the tracking results of UNINEXT and HMMATrack on frames 352 to 359 of episode 1 of the football test set; (**e,f**), respectively, show partial screenshots of the tracking results of OSTrack and HMMATrack on frames 97 to 104 in episode 1 of the volleyball test set.  
random sampling without distinguishing between hard samples and ordinary samples, and other structures remain unchanged; and (e) HMMATrack: complete sampling, detection, and tracking pipeline. From the results of Figure 8, it can be seen that the tracking effect obtained by using the complete sampling strategy is the best.
# Energy Storage Overflow-Aware Data Delivery Scheme for Energy Harvesting Wireless Sensor Networks
## 4 Performance Evaluation
### Simulation Environment  
In the simulation, 100 nodes are randomly deployed in a square with size of 200 \(\times\) 200 m\({}^{2}\). We randomly choose a pair of nodes as the source node and the sink, respectively. Then, multiple disjointed paths between the two nodes are generated using the SMR protocol presented in [25]. As in our previous study [2], we assume the nodes harvest solar energy and adopt the solar irradiation data for Chicago O'Hare International Airport from the National Solar Radiation Data Base, which was published by The U.S. Department of Energy [26]. We consider periods of 4:00-5:00, 7:00-8:00, and 11:00-12:00, which are referred to as low, medium, and high energy harvesting periods, respectively. The average solar radiation received on a global horizontal surface in the low, medium, and high energy harvesting periods are 5.1, 296, and 623 Wh/m\({}^{2}\), respectively [26]. Thus, we assume a node can averagely harvest energy with low energy-harvesting rate (LER) of 5.1/400, medium energy-harvesting rate (MER) of 296/400, and high energy-harvesting rate (HER) of 623/400 W. These rates correspond to the energy harvested by a device which captures solar energy with a square of 5 \(\times\) 5 cm\({}^{2}\) i.e., 1/400 of 1 m\({}^{2}\). We performed the simulation with these three energy-harvesting rates.
## 5 Application Case  
### Geological and Engineering Background  
#### 5.1.1 Geology  
The Yanqianshan Iron Mine is located in Anshan City, Liaoning Province, China (Figure 9(a)). The rock masses in the mining area are composed mainly of mixed granite, phyllite, and quartzite. The quartzite is hard and constitutes the east-west side of the slope. The granite rock is a massive structure.  
The outcropped strata in the mining area include Anshan Formation, Liaohe Formation, and Quaternary metamorphic rocks, although the rock strata are mainly composed of Anshan metamorphic rocks. The Anshan Formation has a thickness of more than 400 m and is divided into three sections, with quartzite in the middle and phyllite on either side. There is an uncomformable contact face between the two groups. The Anshan and Liaohe Formations have dip angles of approximately 70\({}^{\circ}\) and 50\({}^{\circ}\), respectively. In addition, in the Quaternary strata of the mining area, the thickness of the artificial accumulation layer is the highest, and the thickness of the slide rock layer is the lowest.  
The basic structural pattern of the mining area is a steep monoclinic structure trending in the direction of 270\({}^{\circ}\)\(\sim\)300\({}^{\circ}\), with a dip direction in the northeast or southwest and a dip angle in the range of 70\({}^{\circ}\)\(\sim\)88\({}^{\circ}\)[30]. The faults in the mining area can be divided into strike-slip, oblique, and transverse faults. The interior of the ore body contains a host of strike-slip faults, causing significant damage to the ore body. The middle of the faults is mostly filled with neutral rocks. Oblique faults are the most widely developed faults in mining areas, and they often intersect with strike-slip faults. Most of them are filled with phyllite, causing significant damage to the rock mass and the ore body. The transverse faults intersect strike-slip and oblique faults.  
Figure 9: Comparison of the displacement before and after removing the landslide body.
In order to improve the crack resistance and toughness of the above mortar, some scholars improve the tensile strength and deformation ability of the mortar by adding fiber, rubber particles, and polymer modifications [19; 20; 21; 22; 23; 24; 25; 26]. Polymer mortar has gradually become applied in reinforcement engineering because of its characteristics of rapid curing, strength in the early stages, and good bonding properties. The most commonly used polymer mortar in engineering mainly includes epoxy resin mortar and polyurethane mortar. Epoxy resin mortar has been widely used in the reinforcement project, but the toughness of the current commonly used epoxy resin mortar is insufficient, and its ultimate tensile strain is generally below 1500 \(\mu\epsilon\)[27], which is lower than the design value of the yield strain (2000 \(\mu\epsilon\)) and the ultimate tensile strain (10,000 \(\mu\epsilon\)) of ordinary steel bars in a concrete structure. Before the tensile reinforcement of the original structure yields, the epoxy mortar has cracked and is withdrawn from work. For the existing epoxy resin whose toughness is insufficient, existing research mainly considers how the macro epoxy resin mortar joins iron filings, fibers, high-strength steel wire mesh, and other reinforcing materials that include physical toughening methods [28; 29; 30] where the epoxy mortar is essentially used as a bonding material. The epoxy mortar will still crack before the original structural tensile reinforcement yields. Based on the epoxy resin mortar's characteristics of having a high strength and a stable performance in the early stages, this study develops a new type of high-strength and high-toughness epoxy mortar by adding chemical toughening materials to the resin substrate to improve the brittleness of the resin substrate by forming an interpenetrating network structure on a microscopic level, in response to the problem of its lack of toughness. The material has a high tensile strength (19.4 MPa) and ultimate tensile strain (10,100 \(\mu\epsilon\)). As a result, it will not prematurely crack and result in the stopping of the work due to the lack of toughness in the reinforced concrete structure. The reinforcement material participates in the structure of the whole process of stress to effectively improve the structural load bearing capacity and to inhibit any structural cracking. This paper carries out a four-point bending test on two comparative beams and two reinforced beams; studies the influence of the new mortar-reinforced thin layer on the failure form, crack distribution, load-deflection curve, and bearing capacity of the material, and obtains the calculation formula of the bending bearing capacity of the new mortar thin layer reinforced RC beam based on the experimental research results.
###### Abstract  
The use of tunnel boring machines (TBMs) in deep hard rock tunnels disrupts the original stress equilibrium of the rock mass, often resulting in the aggregation and release of a large amount of elastic strain energy, and even leading to rockburst. Under extremely high rockburst proneness conditions, advance stress release boreholes (ASRBs) deployed behind the TBM cutter head can be used to reduce stress concentration levels. However, there is a lack of scientific design methods for the parameters of the ASRB program for TBM tunnels, leading to poor stress release and difficulty in mitigating high-intensity rockburst hazards. This study proposes a parameter design method for ASRBs in the potential rockburst seismic source area of deeply buried hard rock TBM tunnels, including test scheme establishment methods, parameter selection methods, and parameter space relationship and evaluation index establishment methods. A deep tunnel in southwest China was used as an engineering case study to explore the effect of stress release and energy dissipation under different ASRB layout schemes. The results show that the sensitivity of the five important design parameters of ASRBs to the stress release effect is, in descending order, \"aperture\", \"inclination\", \"included angle\", \"spacing\", and \"length\". A parameter control law for ASRBs is proposed, which confirms their effectiveness in preventing, controlling, and reducing rockburst disasters in deep hard rock TBM tunnels.  
TBM tunnel; rockburst; advance stress release borehole; parameter design method; orthogonal test Article
## 3 Results
### Histopathological Changes
#### 3.13.2 Histopathological Changes in the Spleen  
In the CPA control group, atrophy was characterized by a marked decrease in the lymphocytes within the white medullary portion of the spleen compared to the control group. The significant (\(p<0.01\)) decreases in spleen thickness and white medullary diameter and number, and reductions in the number of CD3+, CD4+, CD8+, TNF-\(\alpha\)+, IL-1\(\beta\)+, and iNOS+ immunoreactive cells were confirmed. Meanwhile, in all test substance administered groups, including EAP\({}_{200}\), spleen thickness, white medullary diameter and number, number of CD3 +, CD4 +, CD8 +, TNF-\(\alpha\)+, IL-1\(\beta\) +, and iNOS + cells were significantly (\(p<0.01\)) higher than those in the CPA control group. An increase in the number of immunoreactive cells was observed. In particular, the AR-administered groups exhibited an inhibitory effect on spleen atrophy, which was characterized by a dose-dependent decrease in lymphocytes. In addition, the AR\({}_{100}\) group showed EAP (200 mg/kg) comparable inhibitory effects on CPA-induced splenic lymphocyte reduction and related atrophy (Table 8, Figures S2-S5).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Groups**} & \multicolumn{2}{c}{**Controls**} & \multicolumn{2}{c}{**Reference**} & \multicolumn{2}{c}{**Test materials**} \\ \cline{2-6}  & **Intact** & **CPA** & **EAP\({}_{200}\)** & **AR\({}_{400}\)** & **AR\({}_{200}\)** & **AR\({}_{100}\)** \\ \hline NF-\(\kappa\)B & 1.00 \(\pm\) 0.07 & 0.22 \(\pm\) 0.06 \({}^{\rm{c}}\) & 0.42 \(\pm\) 0.11 \({}^{\rm{cd}}\) & 0.69 \(\pm\) 0.19 \({}^{\rm{cd}}\) & 0.56 \(\pm\) 0.12 \({}^{\rm{cd}}\) & 0.42 \(\pm\) 0.09 \({}^{\rm{cd}}\) \\ IFN-\(\gamma\) & 1.00 \(\pm\) 0.08 & 0.22 \(\pm\) 0.05 \({}^{\rm{a}}\) & 0.40 \(\pm\) 0.07 \({}^{\rm{ab}}\) & 0.62 \(\pm\) 0.13 \({}^{\rm{ab}}\) & 0.50 \(\pm\) 0.12 \({}^{\rm{ab}}\) & 0.40 \(\pm\) 0.09 \({}^{\rm{a}}\) \\ TNF-\(\alpha\) & 1.00 \(\pm\) 0.08 & 0.20 \(\pm\) 0.04 \({}^{\rm{c}}\) & 0.37 \(\pm\) 0.08 \({}^{\rm{cd}}\) & 0.54 \(\pm\) 0.12 \({}^{\rm{cd}}\) & 0.48 \(\pm\) 0.17 \({}^{\rm{cd}}\) & 0.36 \(\pm\) 0.09 \({}^{\rm{cd}}\) \\ IL-1\(\beta\) & 1.00 \(\pm\) 0.05 & 0.24 \(\pm\) 0.03 \({}^{\rm{c}}\) & 0.43 \(\pm\) 0.07 \({}^{\rm{cd}}\) & 0.66 \(\pm\) 0.11 \({}^{\rm{cd}}\) & 0.57 \(\pm\) 0.11 \({}^{\rm{cd}}\) & 0.43 \(\pm\) 0.09 \({}^{\rm{cd}}\) \\ IL-6 & 1.00 \(\pm\) 0.06 & 0.28 \(\pm\) 0.06 \({}^{\rm{a}}\) & 0.48 \(\pm\) 0.10 \({}^{\rm{ab}}\) & 0.70 \(\pm\) 0.13 \({}^{\rm{ab}}\) & 0.59 \(\pm\) 0.13 \({}^{\rm{ab}}\) & 0.48 \(\pm\) 0.09 \({}^{\rm{ab}}\) \\ IL-12 & 1.00 \(\pm\) 0.09 & 0.22 \(\pm\) 0.03 \({}^{\rm{c}}\) & 0.37 \(\pm\) 0.07 \({}^{\rm{cd}}\) & 0.59 \(\pm\) 0.14 \({}^{\rm{cd}}\) & 0.47 \(\pm\) 0.08 \({}^{\rm{cd}}\) & 0.38 \(\pm\) 0.07 \({}^{\rm{cd}}\) \\ \hline \end{tabular}
\end{table}
Table 7: Spleen cytokine mRNA expression in intact and CPA-induced immunosuppression mice.
# Detection of Defects Using Spatial Variances of Guided-Wave Modes in Testing of Pipes
## 5 Results
### Second Set of Tests
#### 5.3.3 Method Three  
Figure 19 shows the results achieved using method three. These are the same as before in the tests where the average detection values are in between the minimum and maximum thresholds; namely, the tests using 37 kHz as the excitation frequency are the tests where the defects can be automatically detected without any outliers when using this method. Same as the other methods, using 30 kHz for detection of Defect 2 is only possible when outliers are also reported. For the case of 34 kHz, when using the average value, it is not possible to detect Defect 2 without any outliers. However, it can be seen when using the second version of the thresholding value that the minimum detection value is significantly closer to the average value, which clearly illustrates the advantage of this version.
## 4 Discussion  
### Discussion on Error Calibration Results  
This section discusses the results obtained in Section 3.2.1.  
It can be seen from Figure 9 that the correction values are the same among different satellites of the same frequency because the receiver's power-on clock error is related to the receiver but has nothing to do with the satellite. The test results are consistent with reality. The data in Table 1 provide clearer evidence of this conclusion. The frequency of both GL1 and B1C is the same, while their frequency does not coincide with that of B1I. Therefore, the power-on clock error of GL1 and B1C are the same but different from that of B1C. This is consistent with the derivation of Formula (21), and the power-on clock error is related to the signal frequency. Figure 10 shows the statistical results of the mean value of \(Fr(c\Delta dt_{ij})\) of all satellites among multiple receivers over a period of time.  
Figure 19: Changes in the direction of satellites before and after spoofing.  
Figure 18: The detailed changes in the direction of G14 and G21 before and after spoofing.  
The fractional part \(Fr(\eta)\) of the hardware delay error includes cable delay error, multipath error, and antenna phase center error. In this paper, the correction of this error is carried out by using the method of long-term calculation and drawing the error sky map. In Figures 11-13, the antenna \(A\) is selected as the reference antenna, which is located at the origin of the ENU coordinate system. Ignoring the elevation angle and considering the influence of the azimuth angle, the relative positions of the four antennas in the sky map can be obtained. It can be seen that the sky diagrams of the true and calculated values of carrier difference are approximately symmetrical along the baseline \(AB\). Additionally, the values in the diagram along the baseline \(AB\) vary across integers as the elevation angle changes. This feature is more prominent in Figure 12. When the satellite flies above the \(AB\) baseline, as the ambiguity difference between stations changes, the corresponding calculated value also changes across integers. This is the reason for the change of crossing integers. The number of times of crossing integers is related to the length of the baseline, and in Figures 11 and 12, it is consistent with the length of the \(AB\) baseline. The blank part within the azimuth angle of 330\({}^{\circ}\) to 30\({}^{\circ}\) in Figure 12 is caused by the fact that there is no sub-satellite point in the north of the test area. The results of \(AC\) and \(AD\) are similar to those of baseline \(AB\) and will not be repeated here.
## 5 Stability Analysis  
### Stability of the Controller  
Define:  
\[y_{r}=\beta_{r}-\alpha_{r} \tag{51}\]  
Figure 3: The block diagram of the padded propulsion USV path following.  
It is clear that \(e_{i}\dot{e}_{i}=-K_{ei}e_{i}^{2}-\frac{|i_{e}\cdot\Delta\tau_{i}|+0.5\Delta\tau_{ i}^{2}}{|\sigma_{i}^{2}|}\cdot e_{i}^{2}+\Delta\tau_{i}\cdot e_{i}\) and \(\Delta\tau_{i}\cdot e_{i}\leq\frac{1}{2}\Delta\tau_{i}^{2}+\frac{1}{2}e_{i}^{2}\), where \(i=u,r\). It is obvious that the final equation can be reached.  
\[\dot{V}_{2}\leq -(k_{1}-1)\psi_{e}^{2}-(k_{ue}-\frac{1}{2})u_{e}^{2}-(k_{re}-\frac{ 1}{2})r_{e}^{2}-\frac{1}{2}\kappa_{u}\phi_{u}^{2}-\frac{1}{2}\kappa_{r}\phi_{r} ^{2}-(A_{r}-\frac{\sigma}{2}-\frac{1}{4})y_{r}^{2}-(K_{eu}-\frac{1}{2})e_{u}^{2}\] \[-(K_{or}-\frac{1}{2})e_{r}^{2}+\frac{\chi_{r}^{2}}{2\sigma}+1+ \frac{1}{2}i_{u}^{2}+\frac{1}{2}r_{r}^{2}+\frac{1}{2}\kappa_{u}\varphi_{u}^{2 }+\frac{1}{2}\kappa_{r}\varphi_{r}^{2} \tag{59}\]  
Define \(\lambda_{1}=k_{1}-1>0\), \(\lambda_{2}=k_{ue}-\frac{1}{2}>0\), \(\lambda_{3}=k_{re}-\frac{1}{2}>0\), \(\lambda_{4}=\frac{1}{2}\kappa_{u}\), \(\lambda_{5}=\frac{1}{2}\kappa_{r}\), \(\lambda_{6}=A_{r}-\frac{\sigma}{2}-\frac{1}{4}>0\), \(\lambda_{7}=K_{eu}-\frac{1}{2}>0\), \(\lambda_{8}=K_{or}-\frac{1}{2}>0\), \(\Lambda=\frac{\chi_{r}^{2}}{2\sigma}+1+\frac{1}{2}\tilde{e}_{u}^{2}+\frac{1} {2}\kappa_{r}\varphi_{u}^{2}+\frac{1}{2}\kappa_{r}\varphi_{r}^{2}\). Then:  
\[\dot{V}_{2}\leq -\lambda_{1}\psi_{e}^{2}-\lambda_{2}u_{e}^{2}-\lambda_{3}r_{e}^{2 }-\lambda_{4}\phi_{u}^{2}-\lambda_{5}\phi_{r}^{2}-\lambda_{6}y_{r}^{2}- \lambda_{7}\varepsilon_{u}^{2}-\lambda_{8}\varepsilon_{r}^{2}+\Lambda \tag{60}\]  
Define \(\lambda:=\min\{\lambda_{1},\lambda_{2},\lambda_{3},\lambda_{4},\lambda_{5}, \lambda_{6},\lambda_{7},\lambda_{8}\}\); then, it follows from (60) that:  
\[\dot{V}_{2}\leq-2\lambda V_{2}+\Lambda \tag{61}\]  
Solving Inequality (61) gives:  
\[V_{2}\leq(V_{2}(0)-\frac{\Lambda}{2\lambda})e^{-2/t}+\frac{\Lambda}{2\lambda} \leq V_{2}e^{-2/t}+\frac{\Lambda}{2\lambda},\forall t>0 \tag{62}\]  
The above inequality means that \(V_{2}\) is eventually bounded by \(\frac{\Lambda}{2\lambda}\). Thus, all the error signals are UUB. When appropriate control parameters are selected, the quantity \(\frac{\Lambda}{2\lambda}\) can be made arbitrarily small. The tracking error can also be very small, and then, a good path-following effect is achieved.
# Sillcom: A Communication-Efficient Privacy-Preserving Scheme for Indoor Localization
## 5 Sillcom\({}_{3R}\): Basic Design with Three Online Rounds
### Secure Selection via Secure Shuffle \(\Pi_{Stuffle}\)  
The remaining challenge is how to obliviously select the top-\(k\) distances from the secret-shared ranks. Inspired by [49], we also adopt the shuffle-then-reveal paradigm. The shuffle-based secure selection operates on the principle that, given a secret-shared distance set \((d_{1},\ldots,d_{m})\), applying a secure shuffle before ranking prevents an adversary from linking specific distances to their corresponding ranks. As a result, the ranks of the secret distances can be safely revealed, facilitating the identification of the \(i\)th nearest neighbor and the data points eligible for pruning based on their public ranks, while keeping the actual distances and associated payloads (coordinates) confidential. As discussed in Section 4, recent results [50; 51] have demonstrated that three-party secure shuffling can be performed in two rounds with low computation, making this approach practical and efficient. We provide details on sthe ecure shuffle Algorithm A1 in Appendix A.
# Enhancements and Challenges in CoAP--A Survey
## 4 Enhancements in CoAP
### Enhancement in CoAP Security
#### 4.3.2 Enhanced Security with Hashing  
The CoAP protocol is enhanced in terms of security in [43] where authors have focused on the integrity of CoAP messages. Instead of using DTLS security protocol, hash functions are employed as DTLS is not very efficient and requires high cost and computational power compared to the proposed hashing method. Hash function encryption provides one-way encryption. Similarly, the message that is hashed to some hash value cannot be decrypted back [44]. The hash functions are usually employed in the authentication of message for enhancing the security for example Message Authentication Code (MAC) [45]. The proposed method tests three hash functions including SHA-1, SHA224, and SHA256. The method is implemented on a home automation model consisting of sensors and controller. Keeping the same message format, the CoAP payload in CoAP messages is attached with sensor sequence-id value. The hash value is then calculated and attached with the payload and the message is sent to controller. On the controller side, the hash value of message is calculated where controller is synchronized with sensor's clock. If the hash values match, further action is taken otherwise, message is discarded. The drawback of the proposed enhancement is that performing complex computation on sensors will eventually drain the sensors' energy. Therefore, the energy consumption should also be considered.  
Another method to secure privacy and data protection in lightweight IoT devices is proposed in [46]. Several symmetric key block ciphers for lightweight devices such as Advanced Encryption Standard (AES), Rivest Cipher 6 (RC6), Twofish, SPECK, ChaCha20-Poly1305, and Lightweight Encryption Algorithm (LEA) are evaluated in GCM (Galois/counter mode) mode with all supported key sizes. The evaluation is performed by measuring the metrics such as execution times, battery drainage and throughput. The test results exhibit the best performance of hardware-accelerated AES compared to all other algorithms with very good encryption throughput of 426.964 MiB/s with a 128-bit key; however, it is not suitable for devices with memory constraints. ChaCha20-Poly1305 is therefore a good adoption for lightweight devices with block ciphers such as SPECK and LEA according to the test results in [46].