esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The unique physical and chemical properties of spinels have made them highly suitable electrocatalysts in oxygen evolution reaction and oxygen reduction reaction (OER & ORR). Zinc-air batteries (ZABs), which are safer and more cost-effective power sources than commercial lithium-ion batteries, hinge on ORR and OER. The slow kinetics of the air electrode reduce its high theoretical energy density and specific capacity, which limits its practical applications. Thus, tuning the performance of the electrocatalyst and cathode architecture is vital for improving the performance of ZABs, which calls for exploring spinel, a material that delivers improved performance. However, the structure-activity relationship of spinel is still unclear because there is a lack of extensive information about it. This study was performed to address the promising potential of spinel as the bifunctional electrocatalyst in ZABs based on an in-depth understanding of spinel structure and active sites at the atomic level.  
spinel; oxygen reduction reaction; oxygen evolution reaction; zinc-air battery; bifunctional electrocatalysts +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Mobile Application to Monitor the Data Transmitted by the Chest Band  
The breathing parameters may be consulted on the patient's smartphone by a custom Android application. As described above, the chest band integrates a BLE module that transmits the acquired parameters to a host device (smartphone or tablet) to allow remote data monitoring. Figure 6 shows a screen from a Bluetooth serial monitor with the data transmitted by the chest band.  
The latter comprises two screens: a _Connection_ screen where the user can choose the device to be connected and a _Monitoring_ screen where the user can track the data acquired from the wearable device. The first step is to connect the device to the RR sensor by Bluetooth connection: on the application's main page, the user must push the _Scan_ button to begin the Bluetooth device's scan (Figure 7a). The scanning results are shown on a listview window, sorted according to the signal strength.  
Figure 6: Breathing parameters measured by the chest band shown by the Bluetooth serial monitor application.  
Afterwards, the user selects the band (JDY-23) and clicks on the _Connect_ button to establish the connection. Once the connection is established, a second page is loaded and displays the respiration rate data received. Specifically, the last acquired value is displayed by a doughnut chart; in addition, the time trend of the acquired values is depicted by a line graph on the screen's lower section (Figure 7b). In the lower section of the _Monitoring_ screen, a tabular array with four textboxes is placed, reporting the last received TI, TE, IER, and V values. Finally, a _Back_ button can be used to terminate the connection, leading the application to the _Connection_ screen.  
In this case, the security of the link between the chest band and smartphone is of primary importance for preserving the exchanged data. The BLE link is exposed to several threats (e.g., MITM, eavesdropping, denial of service (DoS), Bluesnarfing, Bluejacking) [53]. Among the layers of the BLE stack, the Security Manager manages the security of communication links, including the generation and management of encryption keys, verification of the authenticity of other BLE devices, and obscuring the device's personal information. In [53], [PERSON] et al. performed vulnerability analyses in three study cases involving commercial wearable devices communicating through BLE links.
## 2 Method and Principle
To ensure the computational accuracy of the steel belt-anchor hole pose visual measurement system, it is essential to calibrate the external parameters between the camera and the three laser pointers. This calibration involves determining the coordinates of the laser pointers' centers and the direction vectors of the laser rays in the camera's coordinate system. Specifically, the coordinates of the camera's optical center \(O_{C}\), the laser pointing centers \(P_{i}(i=1,2,3)\), and points \(Q_{i}(i=1,2,3)\) on the laser rays can be measured using a total station. By converting these measurements into the camera's coordinate system, the coordinates of \(P_{i}\) and \(Q_{i}\) can be obtained, from which the direction vectors \(s_{L_{i}}\) of the laser rays \(L_{i}\) can be further derived. In summary, the method described in this paper includes the following steps:  
1. A monocular camera is used to capture images of the laser spots and the steel belt. To address the issue of poor spot extraction accuracy, a two-dimensional arctangent function fitting method is proposed for extracting the precise pixel coordinates of the laser spot center. Additionally, image processing methods are employed to achieve the rapid identification and precise positioning of the anchoring holes;
2. Based on the calibration of the camera and laser pointer parameters, the visual positioning model is constructed. Using the spot coordinate information, the rotation  
Figure 1: Composition of the visual pose measurement system for anchor holes on the steel belt: (**a**) components of the system; (**b**) pose measurement unit for anchor holes. matrix and translation vector between the anchoring hole's coordinate system and the camera's coordinate system are calculated. Finally, through coordinate transformation, the position and orientation of the anchoring holes in the camera's coordinate system are obtained, thus achieving anchoring-hole pose detection.
###### Abstract  
This study aimed to optimize the osmotic dehydration process of strawberry slices by examining the effects of glycerol concentration, immersion time, and temperature on water loss and solid gain. Additionally, the study explored the use of chokeberry infusion to enhance the total phenolic content of the strawberries, thereby increasing their nutritional value. Using the Box-Behnken design and response surface methodology, the study identified optimal conditions to maximize water loss and solid gain. The findings revealed that high glycerol concentration (60% \(w/w\)) and temperature (50 \({}^{\circ}\)C), combined with optimal immersion times, significantly influenced water loss and solid gain. Experimental validations confirmed the model's predictions, showing high prediction accuracy (87.37% to 87.69%) for water loss determination but moderate prediction accuracy (42.80% to 64.72%) for solid gain. The immersion of strawberry slices in a hypertonic osmotic chokeberry infusion for 170-220 min maximized the migration of natural antioxidants. Moreover, the osmotic dehydration process effectively reduced water activity and moisture content, enhancing the strawberries' shelf stability. Furthermore, the addition of calcium chloride (CaCl\({}_{2}\)) helped maintain the strawberries' firmness and color during dehydration. Overall, the optimized osmotic dehydration process preserved the structural integrity and improved the nutritional profile and preservation quality of the strawberries, suitable for preparation of various fruit-based products.  
osmotic dehydration; strawberry; process optimization; response surface methodology; total phenolic content (TPC); physicochemical characteristics; antioxidant enrichment
## 3 Numerical Example 1
### Numerical Optimization Results--SA Method  
Optimal MTMD parameters, which are discussed below, were obtained as a result of computations. Table 2 shows the end values of the \(J1\) and \(J2\) objective functions, after the completion of the SA optimization process, using the \(J1\) and \(J2\) objective functions. In addition, it also specifies the percentage change of the objective function value, relative to a 1 TMD system. Computation accuracy was adopted at a level of \(10^{-10}\).  
Tables 3 and 4 show optimal MTMD parameters obtained with optimized \(J1\) and \(J2\) objective functions. A tabular presentation of the 20 TMDs was omitted due to the high number of the results. Results for these cases are shown as drawings in the next section. Figure 6a shows a graph of the FRF module for an equivalent system \(\left|\overline{H}_{41,41}\right|\), for a different number of TMDs, and with applied \(J1\) objective function, while Figure 6b shows the same for the \(J2\) objective function.
# An In-Depth Survey of Bypassing Buffer Overflow Mitigation Techniques
## 3 Hardware-Based Mitigation Techniques
### Exploiting the NX Bit Mitigation Technique
#### 3.2.2 Step by Step Procedure of Bypassing NX Bit Mitigation  
An attacker cannot launch stack-based buffer overflow attacks with standard techniques like code-injection attacks or return-to-libc in NX's presence. But with the help of ROP [34], we can perform stack overflow with NX bit enabled. Figure 9 shows the flow of strategy that we have used to bypass NX bit.  
**Phase 1:** The testbed system on which this research have tested and executed the vulnerable program and related exploits as a proof of concept used a x86-64 processor computing machine running Kali Linux 2020 with Kernel version 5.9.0. It was complemented with tool chain includes gcc 10.2.0, python 2.7.18, pwntools 20.1.1, and gdb-peda plugin version 10.1.90. Figure 10 shows the sample C program having BOF vulnerability that reads input from the user and copies in a temporary buffer.  
As shown in Figure 11, compile the vulnerable program by disabling different protection mechanisms other than NX bit. Address Space Layout Randomization (ASLR) is also disabled by placing zero (0) in the randomize_va_space file residing in /proc/sys/kernel directory.  
Figure 9: Bypassing NX bit.  
**Phase 2:** The second step is the preparation of the payload that was passed as an input to the executable. The payload over here, are the three ROP gadgets. We first load the vulnerable program in gdb with peda. The address of first ROP gadget \"pop rdi; ret\", can be found using the asmsearch command of gdb-peda. The address of second ROP gadget \"system\" can be found by using the print command of gdb, which will print the base address of system() inside libc. Finally, to get the address of \"/bin/sh\" first, we need to find the starting and ending address of libc in gdb using the info proc map command. After getting those addresses, we can find the \"/bin/sh\" location in that specific range using the searchmem command. Figure 12 shows the summary of phase 2.  
**Phase 3:** Now we have all the required addresses to create a payload in python. Figure 13 shows the crafted python script having all the gadget addresses. In order to calculate the number of bytes after which the return address is saved, we have used the pattern_create and pattern_offset commands of gdb_peda, which shows that the offset of saved return address is 22 bytes. Thus, in Figure 13 at line # 5, we have placed 22 As, and after that we have placed the addresses of the ROP gadgets. Once the python script shown in Figure 13, is executed, it generates our payload, which when passed to the vulnerable program will spawn a shell as shown in Figure 13.  
Figure 11: Program Compilation with mitigation disabled.  
Figure 12: Finding Gadget Addresses.
## 3 Results
### Model Selection Results  
Firstly, the model evaluation is performed using different metrics, as shown in Table 2. For _NSE_, the MMM model outperforms the other two models, with an extremely narrow advantage over the SVM, with a difference of only 0.01. For _MAPE_, the SVM model outperforms the other two models, with a MAPE of 102.18%, which is significantly better than those of the ENN model (160%) and the MMM model (120%). For _RMSE_, the MMM model has the best prediction effect, which is 2.87 mm lower than ENN's RMSE and slightly superior to SVM's _RMSE_ by 0.55 mm. Regarding the _MAE_ values, the SVM model has the best prediction ability, and the overall error for all data is significantly smaller than the prediction errors of ENN and MMM. In terms of _QR_, the predictive power of SVM is much higher than ENN and MMM, being 0.12 and 0.06 higher than both, respectively. In conclusion, the simulation effect of ENN is the worst by all aspects of evaluation, while further calculations and discussion on the simulation effect of SVM and MMM are needed to find out which is better.  
Secondly, the comprehensive rating index method was applied to rank the evaluation indexes of different models, and the calculation results of \(r_{i}\) are shown in Table 3. Based on  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & _NSE_ **(mm)** & _MAPE_ & _RMSE_ & _MAE_ **(mm)** & _QR_ \\ \hline SVM model & 0.607 & 102.18\% & 52.89 & 34.31 & 0.54 \\ Elman model & 0.572 & 160.07\% & 55.21 & 38.54 & 0.42 \\ MMM model & 0.615 & 123.95\% & 52.34 & 35.34 & 0.48 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation of model performance results through five metrics.  
Figure 12: Taylor Diagram.  
the above scoring rules, the final scoring results of the three models were calculated and shown in Table 4. The results show that the SVM model is the best runoff simulation model with a score of 0.53, the MMM model is the next best with a score of 0.47, and the ENN model has the worst simulation with a score of only 0. Therefore, combining these results, the SVM model has the highest evaluation score among the three models and is the model with the best simulation effect.
# Soil Hg Contamination Impact on Earthworms'
## 3 Results
### Beta Diversity in Earthworms Gut Parts (Experiment 2)  
In experiment 2, similar to in experiment 1, both bacterial and fungal community structures were impacted by the Hg treatment (Figure 6, Table 3). However, the fungal community was affected by the Hg-contaminated soil in only a few individual gut parts (UgT, UgS and LgT) and the bacterial community was not impacted in any individual gut part. However, the structure of the bacterial community differed between the pot soil and earthworm gut parts. The structure of the lower gut bacterial community (LgT and LgS) was different from that of the soil in the pot, the stomach, and the tissue and soil from the upper gut (UgT and UgS; Supplementary Table S3). Moreover, the relative abundance of the Gammaproteobacteria in the lower gut parts increased drastically in the Hg-contaminated soil (Figure 7). The structure of the fungal community in the earthworm gut parts differed from that in the soil in the pot, but no differences among gut parts were observed (Supplementary Table S3).  
Figure 6: Beta diversity of the bacterial (**A**) and fungal (**B**) communities in experiment 2, presented in a canonical analysis of principal coordinates (CAP) ordination with the Hg treatments and the soil in the pot and earthworm gut parts as constrained factors. Sto = stomach, UgT = upper gut tissue, UgS = upper gut soil, LgT = lower gut tissue, LgS = lower gut soil.  
The majority of the bacterial OTUs that responded significantly to the Hg treatment had a significantly increased abundance in the Hg-contaminated treatment in comparison to the uncontaminated one, i.e., they were Hg-tolerant (Figure 8). Moreover, a Verminophorbacter sp. OTU and two Mollicutes OTUs were particularly abundant and tolerant to Hg treatment in all gut parts. Only a few fungal taxa responded significantly to the Hg-contaminated treatment. Two fungal OTUs, Yarrowia sp. and Penicillum sp., were observed to be particularly abundant and tolerant to the Hg treatment. Fungal OTUs that were sensitive to the Hg treatment included a Penicillum sp. OTU and an Agaricus sp. OTU, whose abundance in particular decreased in the soil in the pot, stomach and the upper gut parts, and a Coprinopsis sp. OTU, which was mostly affected in the lower gut part (Figure 8).  
Figure 7: Relative abundance of bacterial (**A**) and fungal (**B**) phyla and classes across the treatments (Hg-contaminated, uncontaminated) and the sample types (soil, earthworm and cast) in experiment 2.
## 5 Experiment, Results and Discussion
### Experimental Results and Discussion
#### 5.2.2 Localization System Test Results and Discussion  
In the static experiment, we collected about 70 data sets at three positions, respectively. Figures 16 and 17 show the results and localization errors of this static experiment. Since Kalman filter parameter initialization requires a certain number of filtering times, the localization results and the calculation results of removing the first five groups of data by localization error are shown in Figures 16 and 17.  
As can be seen from Figure 16, most of the localization results are beside the corresponding real positions. For Figure 17, we define the error as the distance between the real position and the position calculated by the localization system. For T1, T2, and T3, the average errors of our system localization are 0.47 m, 0.54 m, and 0.42 m, respectively. The reason why the error reaches 1 M or even 2 m is that NLOS is wrongly identified as LOS. The error in ranging cannot be eliminated in the linear model, so the error will be transferred to the positioning process in the subsequent process, resulting in a large location error. As T2 and T3 are located on one side of all anchors, the distance errors cannot cancel each other, and their location errors are larger than T1.  
Figure 16: Localization results of the static experiment.  
Figure 17: Error of localization results. T1 corresponds to the tag of the position of the red five-pointed star in the static experiment, T2 corresponds to the position of the blue five-pointed star, and T3 corresponds to the position of the green five-pointed star.  
Figure 18 shows the results of the dynamic experiment. Since the real position of the tag at each moment could not be determined during the dynamic experiment, we could not calculate the error and could only compare the real track of tag with the track formed by the location result of our location system. As can be seen from Figure 18, the location at the first few moments has a large error, which is mainly due to the slow initialization of the Kalman filter parameters. When the parameters of the Kalman filter are gradually stabilized, the localization accuracy of the system begins to improve, and the movement track of the tag calculated by the system is almost consistent with that of the real tag. The reason for the large deviation of the location track lies in the error of the NLOS/LOS classification of the tag and one of the three anchors, which leads to the location deviation.  
According to the results of static and dynamic experiments, the accuracy of our localization system can reach decimeter level in complex tunnel environment. It can not only get the position of motionless personnel, but also track and locate the moving personnel in real time. When the line-of-sight condition is occasionally misidentified, the positioning result will have a large error. However this error is rare and permitted in the coal mine environment.
## 4 Model Formulation
### Lower-Layer Model
#### 4.3.3 CAV Queue-Following Model  
Given the simplification of the three-stage trajectory planning in this study, for the sake of simulation, the CAV queue-following model only needs to consider vehicle performance. Therefore, the NGSIM vehicle-following model in [PERSON]'s paper [46] is adopted and described as following:  
\[x^{\omega}(t+\Delta t)=max\Big{\{}x_{\omega}^{U}(t+\Delta t),x_{\omega}^{L}(t+ \Delta t)\Big{\}} \tag{42}\]  
where \(x_{\omega}^{U}(t)\) and \(x_{w}^{L}(t)\) represent the upper and lower limits of \(x^{\omega}(t)\), respectively.  
\(x_{\omega}^{U}(t+\Delta t)\) can be stated using the following equation:  
\[\begin{array}{rl}x_{\omega}^{U}(t+\Delta t)=&min\Big{\{}x^{\omega^{*}}(t+ \Delta t-\tau^{\omega})-l^{\omega^{*}}-d_{jam}^{\omega},\ x^{\omega}(t)+\varphi^ {\omega}(t)\Delta t\\ &+a^{U}\Delta t^{2},x^{\omega}(t)+v_{max}\Delta t,\ \ x^{\omega}(t)+\Delta x^{ \omega}(t+\Delta t)\Big{\}}\end{array} \tag{43}\]  
\[\begin{array}{rl}\Delta x^{\omega}(t+\Delta t)\\ =\Delta t\Bigg{(}a^{L}\tau^{\omega}+\sqrt{(a^{L}\tau^{\omega})^{2}-2a^{L} \bigg{(}x^{\omega^{*}}(t)-x^{\omega}(t)-l^{\omega^{*}}-d_{jam}^{\omega}-\frac {(x^{\omega^{*}}(t))^{2}}{2a^{L}}\bigg{)}}\Bigg{)}\end{array} \tag{44}\]  
where the CAV convoy's lead vehicle is \(\omega^{*}\), CAV \(\omega^{*}\)s reaction time is \(\tau^{\omega}\), the distance between CAV \(\omega\) and other CAVs is \(d_{jam}^{\omega}\), \(l^{\omega^{*}}\) is the vehicle length of CAV vehicle \(\omega^{*}\), \(a^{U}\) is the maximum CAV acceleration, the greatest headway safety time distance that CAV \(\omega\) must travel to avoid a collision at time \(t+\Delta t\) is \(\Delta x^{\omega}(t+\Delta t)\), and \(a^{L}\) is the maximum CAV deceleration.  
The lower limit distance \(x_{\omega}^{L}(t)\) is related to the current position that prevents the CAV from backing up and the maximum acceleration/deceleration of the vehicle, so that \(x_{\omega}^{L}(t+\Delta t)\) can be expressed as follows:  
\[x_{\omega}^{L}(t+\Delta t)=max\Big{\{}x^{\omega}(t),x^{\omega}(t)+\varphi^{ \omega}(t)\Delta t+a^{L}\Delta t^{2}\Big{\}} \tag{45}\]  
Figure 12: Two illustrative paths with the same travel distance.
# Secure Quantum Communication Technologies and Systems: From Labs to Markets
## 6 Industrial Roadmap
* A high point in European research towards practical application of quantum cryptography was achieved in 2004-2008 with the SECOQC FP6 project, which involved several academic as well as industrial partners. However, some European companies seem to have now reduced their engagement. In light of the section on the application domain, we suggest which segments could be affected in the medium and short term. We consider not only the previously discussed technology aspects, but also the interest and investment capacity of the markets. The deployment in the short term is likely to be led by the absence of repeaters (limiting the link distance to tens of kilometers) with simple network topologies to avoid complex system coexistence. The space segment is expected to grow faster, considering the sensitiveness of data transferred and the amount of investment in the field. The higher reachable distance (achievable without repeaters), the unguided medium, and the simple network topology (without switching, routing, or amplifying) will further help. Inter-database connections are characterized typically by simple network topologies, short distances, and a low number of network nodes, providing a good opportunity for short-term impact. Other market segments may be found with similar characteristics, for example where short-reach optical networks exist, as in the radio access network (RAN), connecting mobile user equipment to base stations. Here, the low cost is an important requirement, however, integrated hardware is also required. Special critical ad-hoc infrastructure (e.g., links between security agencies or banks) may be deployed and realized soon. For large-scale wide area network (WAN) deployment, further effort concerning the integration of quantum systems in commercial networks is needed and efficient techniques to increase the distance are required.
# Sparse Representation-Based SAR Image Target Classification on the 10-Class MSTAR Data Set
## 2 Sparse Representation-Based Classification
After the optimal solution \(x\) is settled, the class of the test sample \(y\) will be worked out by the minimal reconstruction error:  
\[\operatorname*{argmin}_{i}\left|\right|y-\hat{y}_{i}\left|\right|\right.= \operatorname*{argmin}_{i}\left|\right|y-\phi\hat{\delta}_{i}\left(x\right) \left|\right| \tag{7}\]  
where \(\delta_{i}\left(\cdot\right)\) is an operator that selects the weight coefficients belonging to class \(i\) and sets all the other coefficients to be zero. So \(\hat{y}_{i}\) is a linear combination of the training samples belonging to class \(i\). Finally, the \(i\) meeting the objective function is the class label of \(y\).