esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Structural Behavior of Hysteretic Steel Dampers under Cyclic Loading
## 4 Discussion  
### Energy Dissipation  
Figure 7 shows the energy dissipation of the specimens for each cycle. In this study, the energy dissipation was the inner area of the load-displacement relationship curve. In the first cycle, since the elastic and plastic phases existed together, the value was slightly lower than those of the other cycles. The cumulative energy dissipation of Specimens SD2 and SD3 showed almost proportional increase, as shown in Figure 7a.  
SD1 showed an energy dissipation capacity of 1154.5 kN-mm in the first cycle, as shown in Figure 7b. Afterward, the energy dissipation area increased by 11.4% to 1286.3 kN-mm in the second cycle, and the total accumulated energy dissipation capacity was 2440.8 kN-mm. As shown in Figure 7b, SD2 had an energy dissipation capacity of 1386.6 kN-mm, 20.1% higher than that of SD1, in the first cycle. In the second cycle, the energy dissipation capacity of SD2 increased by 10.5% from the first cycle, a similar result to that obtained from SD1. The final fifth cycle ended with a 12.5% increase from the first cycle. The experimental results confirmed that the energy dissipation capacity of SD2 remained almost unchanged after the second cycle.  
SD3 had an energy dissipation capacity of 4417.4 kN-mm in the first cycle. This was 3.8 times higher than that of SD1, which meant that even though only the lower horizontal member was added, the energy dissipation capacity was greatly improved. SD3 showed 12.8% and 14.9% higher energy dissipation capacities from the first cycle in the second and fifth cycles, respectively, as shown in Figure 7b. In other words, there was no decrease in energy dissipation capacity during five cycles, such as SD2. From the experimental results, it was confirmed that SD3 has excellent energy dissipation capacities.
# Enhanced Tuna Detection and Automated Counting Method Utilizing Improved YOLOv7 and ByteTrack
## 4 Experiment and Results
### Analysis of Experimental Results
#### 4.2.3 Contrast Experiment  
To further validate the reliability of the YOLOv7-Tuna model for tuna detection, we conducted comparative experiments with Faster-RCNN and YOLOv5, popular current object detection algorithms. The experimental results are shown in Table 3, and the performance advantages of YOLOv7-Tuna can be concluded in comparison with these algorithms.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Test** & **DySnakeConv** & **CoordConv** & **DyHead** & **Precision** & **Recall** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** \\ \hline
1 & \(\times\) & \(\times\) & \(\times\) & 91.1\% & 95.8\% & 98\% & 58.5\% \\
2 & \(\surd\) & \(\times\) & \(\times\) & 92.9\% & 99\% & 98.5\% & 63.5\% \\
3 & \(\times\) & \(\surd\) & \(\times\) & 95.9\% & 97.4\% & 98.3\% & 66.8\% \\
4 & \(\times\) & \(\times\) & \(\surd\) & 94.6\% & 98.5\% & 98.7\% & 65.6\% \\
5 & \(\surd\) & \(\surd\) & \(\surd\) & 96.3\% & 98.9\% & 98.5\% & 68.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation Experiment.  
Comparing the detection performance with Faster R-CNN, YOLOv5, and YOLOv7, it can be observed that YOLOv7-Tuna excels in precision, recall, and [EMAIL_ADDRESS] across all aspects. In terms of precision, it surpasses Faster R-CNN by 14.7%, which is 5.6% higher than YOLOv5, and 5.2% higher than YOLOv7. In terms of recall, it is 15.5% higher than Faster R-CNN, 3.7% higher than YOLOv5, and 3.1% higher than YOLOv7. In terms of [EMAIL_ADDRESS], it is 11.8% higher than Faster R-CNN, 0.4% higher than YOLOv5, and 0.5% higher than YOLOv7. Therefore, compared to other object detection algorithms, YOLOv7-Tuna exhibits significant advantages in terms of precision, recall, and mAP, leading to a noticeable improvement in the accuracy of tuna detection.
## 5 Communicating Feedback
### Psychometric Interpretation  
Before students can be supported to interpret assessment results, assessment developers must accurately interpret the results from a psychometric perspective. DCMs assume that the latent variables predicting the item responses (i.e., the attributes) are discrete variables. For each attribute, two groups are distinguished: those who master the attribute and those who do not. With DCMs, one can estimate the mastery probability for each attribute (expected a posteriori estimates). Classification as master or nonmaster is based on these marginal mastery probabilities. For example, probabilities above 0.5 result in classification as master and below 0.5 as nonmaster (although some contexts may call for a different cut-off value).  
The mastery probability is not an estimate of the amount of mastery, nor of a student's progress from nonmastery to mastery. Rather, it indicates the (un)certainty of the classification, which is determined by students' consistency in providing either correct or incorrect answers to different items measuring an attribute [58]. The closer the mastery probability is to 0 or 1, the more certainty there is about the classification, whereas a probability of 0.5 reflects complete uncertainty. Although these models do not allow to break the probability scale into more than two groups to indicate more than two mastery levels, it is possible to break the scale to indicate uncertainty. One could for example not classify students on attributes with mastery probabilities between 0.4 and 0.6, but instead add an \"undetermined\"  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(\mathbf{M_{2}}\) & \(\mathbf{RMSEA_{2}}\) & \(\mathbf{SRMS}\) & **Max Transformed Correlation \({}^{*}\)** & **Max Log-Odds Ratio \({}^{*}\)** \\ \hline Assessment 1 & 591.4 (\(p<0.001\)) & 0.025 & 0.042 & 0.128 (\(p=0.10\)) & 0.982 (\(p=0.19\)) \\ Assessment 2 & 212.1 (\(p=0.06\)) & 0.014 & 0.038 & 0.104 (\(p=0.76\)) & 0.850 (\(p=0.49\)) \\ \hline \hline \end{tabular} \({}^{*}\) To control for multiple comparisons, \(p\)-value adjustments according to the Holm–Bonferroni method are conducted [43].  
\end{table}
Table 4: Absolute fit statistics of the final models.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Assessment 1**} & \multicolumn{3}{c}{**Assessment 2**} \\
**Attribute** & **A** & **B** & **C** & **D** & **E** & **F** & **G** & **H** & **I** \\ \hline Accuracy & 0.89 & 0.83 & 0.90 & 0.89 & 0.82 & 0.82 & 0.82 & 0.84 & 0.88 \\ Reliability & 0.87 & 0.69 & 0.89 & 0.87 & 0.68 & 0.67 & 0.67 & 0.76 & 0.85 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Attribute-level classification accuracy and reliability.  
category for these uncertain situations (as proposed by [58], and as will be demonstrated below), or one could flag uncertain classifications when reporting results.
###### Abstract  
Palladium catalysts supported by a mesoporous form of sulfonated poly-divinylbenzene, Pd/\(\mu\)S-pDVB10 (1%, \(w/w\)) and Pd/\(\mu\)S-pDVB35 (3.6% \(w/w\)), were applied to the direct synthesis of hydrogen peroxide from dihydrogen and dioxygen. The reaction was carried for 4 h out in a semibatch reactor with continuous feed of the gas mixture (H\({}_{2}\)/O\({}_{2}\) = 1/24, \(v/v\); total flow rate 25 mL-min\({}^{-1}\)), at 25 \({}^{\circ}\)C and 101 kPa. The catalytic performances were compared with those of a commercial egg-shell Pd/C catalyst (1%, \(w/w\)) and of a palladium catalyst supported by a macroticular sulfonated ion-exchange resin, Pd/mS-pSDVB10 (1%, \(w/w\)). Pd/\(\mu\)S-pDVB10 and Pd/C showed the highest specific activity (H\({}_{2}\) consumption rate of about 75-80 h\({}^{-1}\)), but the resin supported catalyst was much more selective (ca 50% with no promoters). The nanoparticles (NP) size was somewhat larger in Pd/\(\mu\)S-pDVB10, showing that either the reaction was structure insensitive or diffusion limited to some extent over Pd/C, in which the support is microporous. The open pore structure of Pd/\(\mu\)S-pDVB10, possibly ensuring the fast removal of H\({}_{2}\)O\({}_{2}\) from the catalyst, could also be the cause of the relatively high selectivity of this catalyst. In summary, Pd/\(\mu\)S-pDVB10 was the most productive catalyst, forming ca 375 mol\({}_{H_{2}\mathrm{O}_{2}}\)-kg\({}_{\mathrm{Pd}}\)\({}^{-1}\)-h\({}^{-1}\), also because it retained a constant selectivity, while the other ones underwent a more or less pronounced loss of selectivity after 80-90 min. Ageing experiments showed that for a palladium catalyst supported on sulfonated mesoporous poly-divinylbenzene storage under oxidative conditions implied some deactivation, but a lower drop in the selectivity; regeneration upon a reductive treatment or storage under strictly anaerobic conditions (dry-box) lead to an increase of the activity but to both a lower initial selectivity and a higher drop of selectivity with time.  
hydrogen peroxide; support morphology; mesoporous poly-divinylbenzene; palladium; ageing +
Footnote †: journal: Physics Letters B
## 5 Conclusions  
Energy efficiency is one of the major concerns of BSNs. Many energy efficient MAC protocols have been proposed. This paper considers adopting multi-hop communication in mobile swallowable BSNs  
Figure 8: _Cont._  
for the first time. The paper evaluates multi-hop communication though the human body based on the IEEE 802.15.6 body channel model. The results showed that multi-hop communication saves energy consumption compared with single-hop communication when the circuitry power is small enough. Based on the result, the TDMA schedules for medium access are given in detail. The coordinator collects the neighboring information from sensors and calculates the routing and transmission power for each sensor. The sensors follow the schedules to finish data transmission. The proposed protocol avoids the idle listening, overhearing problem and most of the processing work is shifted to the coordinator. Simulation results showed the proposed TDMA protocol gives better performance than IEEE 802.15.6 in terms of energy consumption when the circuitry power becomes low. The main reason is that multi-hop communication makes the transmit power smaller and the corresponding adaptive power control. As the circuitry power becomes smaller due to new technologies, the proposed TDMA MAC protocol could be a good reference for energy efficient solution of BSN MAC standard. The future work would focus on slot assignment algorithm and hardware experiments.
# Privacy-Preserving Solution for European Union Digital Vaccine Certificates
## 6 Security Analysis
### Communication Protocol  
The communication between the user application and the verifier application is performed using four fixed messages corresponding to the data structures described above: see Figure 7. We use the BLE communication protocol to transmit data between the user and the verifier. The verifier's terminal is acting as a beacon. The user application is able to search for a device whose advertisement name includes the string covidpass-terminal. When the user selects one of the available verifier's devices, the first message is sent by the user device. To ensure the reliability of the communication, a simple acknowledgment of the sent chunks has been created.  
The first message carries the string STARTVERIFY. In response to receiving this message, the verifier's terminal responds with a message of the data type NonceRequest. The message thus carries a _nonce_ challenge, a list of attributes to reveal, and the verifier's certificate. The verifier's certificate is used to verify the identity of the verifier, verify what attributes are to be disclosed to the verifier, and whether the verifier has the right to query those attributes. The certificate is verified upon receipt of the NonceRequest message, and the requested attributes are compared against the allowed attributes. Once the user decides to proceed with the verification process, the cryptographic proof is generated and sent to the verifier along with the revealed attributes. The sending of this message is signaled by including the string VERIFYREQUEST at the beginning of the message before the proof in the form of the data structure VERIFYREQUEST. When the message is received by the verifier  
Figure 7: Implemented communication model.  
device, the proof is verified. Based on the result of this verification, the final user-verifier communication message is produced. The final message may take the form VERIFY_OK if the authentication is successful. If the verification fails, a message VERIFY_FAILED or BAD_VERIFYREQUST is sent. The first message indicates that the verification failed. The second message indicates that a communication error occurred and the user is prompted to resend the third message of the protocol.
## 8 Introduction to Proprietary and Standards-based Solutions
### Standards-Based Technologies
#### 8.2.7 EC-GSM-IoT  
Enhanced coverage-global system for mobile Internet of Things (EC-GSM-IoT), developed by 3 GPP, is one of the promising candidates for low power, long range cellular IoT (cloT) for providing similar coverage and battery life as NB-IoT [37]. It is based on enhanced GPRS and designed for scalable, low complex LPWAN applications. As the majority of current cloT devices are based on GPRS/EDGE to connect to the Internet, EC-GSM-IoT provides an easy path to improve energy efficiency and a 20 dB coverage improvement. It is optimized and improved by means of software upgrades to GPRS/EDGE networks and also to support new devices. The traffic of legacy GSM devices and EC-GSM-IoT is multiplexed on the same physical channels without much compromise of the performance of the legacy traffic. The bandwidth of the EC-GSM-IoT channel is 200 kHz. Like GSM, it is FDMA + TDMA + FDD technology, supporting peak data rates of 70 kbps and 240 kbps based on GMSK and 8 PSK, respectively. EC-GSM-IoT provides multi-fold improvement in the coverage for low rate applications. It also has an ability to reach challenging locations such as deep indoor basements [38], where many smart meters and parking sensors are installed, or remote areas in which sensors are deployed for agriculture or infrastructure monitoring use cases [39]. The important features of the physical layer include new logical channels, repetitions to provide necessary robustness to support up to 164 dB maximum coupling loss, and the use of overlay CDMA to increase cell capacity.
# Anti-Fouling Properties of Phosphonium Ionic Liquid Coatings in the Marine Environment
## 3 Result and Discussion
### Preparation of Phosphonium Ion Gel Coatings
#### 3.1.2 Casting of Phosphonium Ion Gel Coatings  
Mixtures of monomer and free phosphonium docusate ionic liquids combined with crosslinking agent divinylbenzene, photoinitiator (2-hydroxy-2-methylpropiophenone), and biocide copper(II) oxide (if required) were able to be cast without a solvent. The resulting solutions were easily coated uniformly onto stainless steel coupons that had been pre-treated with a commercial epoxy coating for corrosion inhibition.  
The resulting gels were crosslinked through radical polymerization under an inert environment following coating. This method has been used to produce similar phosphonium ion gels for gas separations and ionic conductivity [42]. While it is difficult to scale it industrially, this approach to producing robust crosslinked films was judged sufficient for this proof-of-concept work. The polymerization was confirmed through FT-IR spectra, which showed that the C=C stretch between 1640-1610 cm\({}^{-1}\) was missing after curing (Supplementary Sections S3 ans S7).  
The exact compositions of the coatings are summarized in Tables 1 and 2. The rationale behind selecting a wide variety of compositions was to test the marine anti-fouling effects of hydrophobicity via alkyl substituent length on the phosphonium cation (either C4 or C8), the liquid or gel-like morphology via the loading of free ionic liquid, and the biocide concentration via copper(II) oxide).
## 3 Results and Discussions
### Signal-to-Noise Ratio Analysis  
The ultimate tensile strength, percentage elongation, and microhardness of the fabricated AA5083/Silicon carbide and AA5083/Coal composite joints were assessed using the Taguchi methods to find their optimal parameters. [PERSON]'s approach, a single-response optimization strategy, uses the signal-to-noise ratio to evaluate each parameter's effect on response factors. To optimize the response, the S/N ratio in this investigation was determined using the 'larger-the-better' criteria. The impact of each control factor on the response, the means, and S/N ratios were computed. The following formula can be used to compute the \"larger-the-better\" criteria [43]:  
\[\frac{S}{N}=-10\text{log}\left(\frac{1}{n}\sum_{i=1}^{3}\frac{1}{y^{2}}\right) \tag{1}\]  
where \(n\) is the number of experimental repeats and \(y\) is the response factor. The desirable output value is represented by 'Signal (S)', while the undesired value is represented by 'Noise (N)'.  
The S/N ratio for parameter combinations of the AA5083/Silicon carbide composite is shown in Table 6, while the S/N ratio for parameter combinations of the AA5083/Coal composite is shown in Table 7.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**TS** & **RS** & **TA** & **MH** & **UTS** & **PE** & **S/N Ratio** & **S/N Ratio** & **S/N Ratio** \\
**(mm/min)** & **(rpm)** & **(\({}^{\circ}\))** & **(Hv)** & **(MPa)** & **(\%)** & **for HV** & **for UTS** & **for PE** \\ \hline
30 & 600 & 1 & 94.8 & 71.5 & 9.88 & 38.89 & 37.09 & 19.89 \\
45 & 600 & 1.75 & 89.6 & 114 & 11.8 & 39.04 & 41.14 & 21.40 \\
60 & 600 & 2 & 89.6 & 88 & 11.2 & 39.04 & 38.89 & 21.01 \\
30 & 900 & 1.75 & 89.4 & 210 & 29.0 & 39.03 & 46.44 & 29.25 \\
45 & 900 & 2 & 94.8 & 145 & 18.5 & 39.37 & 43.23 & 25.32 \\
60 & 900 & 1 & 92.9 & 141 & 19.9 & 39.37 & 42.98 & 25.98 \\
30 & 1200 & 2 & 90.6 & 243 & 29.5 & 39.14 & 47.71 & 29.39 \\
45 & 1200 & 1 & 93.7 & 132 & 18.7 & 39.22 & 42.41 & 25.44 \\
60 & 1200 & 1.75 & 89.6 & 121 & 12.1 & 39.04 & 41.66 & 21.64 \\ \hline \end{tabular}
\end{table}
Table 6: AA5083/Silicon carbide composite experimental results with relevant S/N ratio.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**TS** & **RS** & **TA** & **MH** & **UTS** & **PE** & **S/N Ratio** & **S/N Ratio** & **S/N Ratio** \\
**(mm/min)** & **(rpm)** & **(\({}^{\circ}\))** & **(Hv)** & **(MPa)** & **(\%)** & **for HV** & **for UTS** & **for PE** \\ \hline
30 & 600 & 1 & 93.6 & 167 & 11.9 & 38.66 & 44.45 & 21.55 \\
45 & 600 & 1.75 & 88.3 & 109 & 7.00 & 38.92 & 40.75 & 16.90 \\
60 & 60
## 3 Proposed Method
### Frame Synchronization Process  
Figure 4 shows the proposed frame synchronization process. The watermarked signal is segmented into \(K\) frames. LP residue \(r_{k-1}(n)\) in the \((k-1)\)th frame is calculated from the watermarked signal \(y_{k-1}(n)\) in the \((k-1)\)th frame. The expected value is obtained by the following equation to multiply the obtained LP residue \(r^{\prime}_{k-1}(n)\) in the \((k-1)\)th frame by the watermarked signal \(y_{k}(n)\) in the \((k)\)th frame.  
\[F_{k}(s) = E\{y_{k}(n-s)r^{\prime}_{k-1}(n-s)\}. \tag{10}\]  
Substituting \(y_{k}(n-s)=x_{k}(n-s)+am(n-s)r_{k-1}(n-s)\) into Equation (10) yields  
\[F_{k}(s) = E\{x_{k}(n-s)r^{\prime}_{k-1}(n)\}+E\{am(n-s)r_{k-1}(n-s)r^{\prime }_{k-1}(n)\}, \tag{11}\]  
where \(x_{k}(n-s)\), \(y_{k}(n-s)\), and \(r_{k-1}(n-s)\) are assumed to be ergodic. The LP residue has the statistical properties \(E\{r_{k-1}(n-s)\}=0\) and \(E\{r_{k-1}(n-s)\}=1\). We then multiply the watermarked signal \(y_{k}(n-s)\) by the LP residue \(r^{\prime}_{k}(n-s)\) and obtain the expected value \(E\{y_{k}(n-s)r^{\prime}_{k-1}(n-s)\}\) by Fourier transform as follows:  
\[F_{k}(s) = E\{am(n-s)r_{k-1}(n-s)r^{\prime}_{k-1}(n-s)\}, \tag{12}\]  
where \(s\) is the sample shift: \(s=-N\), \(-N+1\), \(\cdot\cdot\cdot\), \(-1\), \(0\), \(1\), \(\cdot\cdot\cdot\), \(N-1\), \(N\). The subscript \(k\) is the reference frame that is defined as frames of watermark signal \(y(n)\): \(k=2,4,6,\cdot\cdot\cdot\), \(K-2\), \(K\). The \(r_{k-1}(n-s)\) is the LP residue in the \((k-1)\)th frame used to embed the security code. By finding the expectation value as in Equation (9), only the second term in Equation (11) remains. Then, the watermarked signal \(y_{k}(n-s)\) in the \((k)\)th frame is shifted by one sample and the expected value is calculated in the same way. This is done until \(s=-N\sim N\) samples. The expected value is processed as an absolute value, and the arithmetic mean is calculated using the correlation values for the number of reference frames, as  
\[\bar{F}(s) = \frac{2}{K}\sum_{k=2,k\in\text{even}}^{K}|F_{k}(s)|. \tag{13}\]  
Figure 3: Block diagram of proposed blind-detection method. Security code embedding in (**a**) front-side and (**b**) back-side forms. Security code detection in (**c**) front-side and (**d**) back-side form.  
The synchronized position \(\hat{s}\) is determined from the maximum value of \(\bar{F}\) obtained from the arithmetic mean, as  
\[\hat{s}=\arg\max_{s}\bar{F}(s). \tag{14}\]
## 5 Description of the Selected VGs and Related \"Scientific Virtual Geotours\"
### Dikes on Santorini's Northern Caldera Cliff  
A well-exposed local dike swarm is visible on the northern caldera cliffs and represents the volcanic activity in the area during the time span 530-522 ka [43,55,56]. In detail, five main volcanic phases can be recognized: (i) the Peristeria volcanic activity (530-530 ka), (ii) the Simandiri shield (172 ka), (iii) the Megalo and Kokkino Vouno cinder cones (125-180 ka), (iv) the Skaros shield (70-54 ka), and the Therasia dome complex (54-22 ka). These phases formed a succession that consists mainly of breccia, hyalocaldsite, tuff, lava flows, and scoria deposits. Moreover, the host rock is dissected by normal and strike-slip faults [43,56].  
Virtual Geosite 2 (VG2) encompasses an area of 730 \(\times\) 300 m in the NE section of the cliffs (view towards the NE, Figure 8a) of the northern caldera wall, where an active fault zone (_pilmark 1_, Figure 8b) is observed [85], along with other inactive faults (_pilmark 2_, Figure 8b) into which disks are preferentially deflected [56].  
Figure 7: Three-dimensional model of the 1570–1573 AD volcanic crater on Nea Kameni volcano (view towards the northeast) (_pilmark 1_–3).  
In detail, the model shows a panorama of the northern caldera cliff, cut by several subvertical dikes (extensional magma-filled fractures) (_pinmark 3_, Figure 8b). The one to the east (right) strikes about NE-SW (_pinmark 4_, Figure 8c) while a silicic, 8 m-thick dike (left) lies between the fault zones (_pinmark 5_, Figure 8b). The dikes are found to follow different pathways while they were emplaced on the caldera wall, as a consequence of the differences in material properties of the crustal segments through which magma propagated (highly heterogeneous and anisotropic). The contrasting stiffness and variable thickness of the layers, the mechanical properties of the pre-existing fractures (e.g., faults), and the geometrical parameters of the host rock can deviate a dike's path towards the surface, resulting in deflected, arrested, or feeder dikes [55; 56].