esa-sceva/satcom-chunk-collection huggingface dataset:
# Elliptic Curve Signcryption-Based Mutual Authentication Protocol for Smart Cards
## 7 Performance Analysis of the Proposed ECSSP-SC
### Analysis of Computational Cost  
The computational time of a security protocol can be analyzed by first counting the most time-consuming operations, and then multiplying them with the actual time consumed in completing the respective operations. Finally, the time consumed by each operation is added to find out the total time consumed by the protocol. In the analysis of the computational cost, only the time consumed in the authentication phase has been considered, since the registration of the _USR_ and the _SVR_ is a one-time process. [PERSON] et al. [61] mentioned that by equipping a 2.5 GHz Intel i5 CPU holding 8 GB RAM, one ECPM operation consumes 2.501 ms and a single modular exponentiation consumes 3.043 ms. Since the time consumed in an ECPM operation and modular exponentiation operations is very high as compared to the other operations, the time invested in the operations other than ECPM and modular exponentiation has been ignored in the analysis. The count of ECPM and modular exponentiation operations in the smart card security protocols are shown in Table 4. A comparative analysis of the computational cost of the ECSSP-SC and related protocols was performed and the pictorial representation of the computational cost analysis of smart card security protocols are publicized in Figure 9.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & \multicolumn{4}{c}{**Technique Used**} \\ \cline{2-4}
**Protocol** & \multicolumn{2}{c}{**Modular Exponentiation**} & \multicolumn{2}{c}{**Elliptic Curve**} & \multirow{2}{*}{**Signcryption**} \\ \cline{2-2} \cline{4-4}  & **Exponentiation** & & & \\ \hline [PERSON] et al. [7] & \(\times\) & ✓ & \(\times\) \\ [PERSON] [11] & ✓ & \(\times\) & \(\times\) \\ [PERSON] et al. [22] & ✓ & \(\times\) & \(\times\) \\ [PERSON] et al. [27] & \(\times\) & ✓ & \(\times\) \\ [PERSON] et al. [29] & \(\times\) & ✓ & \(\times\) \\ [PERSON] et al. [47] & \(\times\) & ✓ & \(\times\) \\ [PERSON] et al. [49] & \(\times\) & ✓ & \(\times\) \\ [PERSON] et al. [55] & \(\times\) & ✓ & \(\times\) \\ [PERSON] et al. [61] & \(\times\) & ✓ & \(\times\) \\
**ECSSP-SC** & \(\times\) & ✓ & ✓ \\ \hline \hline \end{tabular} \(\checkmark\)—used, \(\times\)—not used.  
\end{table}
Table 3: Technique used in different smart card authentication schemes.
## 3 Computational Methods
### Follicle and Ovary Detection
#### 3.5.2 Loss Function  
The proposed CNNs were trained by using the same loss function as defined in [7]. The loss function was left unchanged intentionally, as we wanted to demonstrate that all the improvements were solely due to the CNN architecture modifications and supervised training. Our final loss function \(L\) is, therefore, a combination of the binary cross-entropy  
Figure 7: New layers of the second U-Net extension (EXT 2) with five scales, as used in our experiments. These layers (on the right) are connected with the basic U-Net (on the left), which is depicted without annotations. See Figure 6 for an explanation of the basic U-Net and used denotations.  
loss (\(L_{Ent}\)), the loss based on the Dice Similarity Coefficient (\(L_{DSC}\)) and the loss based on \(\rho_{1}\rho_{2}\) product (\(L_{\rho_{1}\rho_{2}}\)) [\(Y\)]:  
\[L(y,\hat{y})=L_{Ent}(y,\hat{y})+L_{DSC}(y,\hat{y})+L_{\rho_{1}\rho_{2}}(y,\hat{y })\;, \tag{1}\]  
where \(y\) is the true and \(\hat{y}\) is the estimated object (annotation/segmentation), and the individual losses are defined as  
\[L_{Ent}(y,\hat{y})=-\frac{1}{H\,W\,D}\sum_{i}y_{i}\,log\hat{y}_{i}, \tag{2}\]  
\[L_{DSC}(y,\hat{y})=1-\frac{2\sum_{i}y_{i}\,\hat{y}_{i}}{\sum_{i}y_{i}+\sum_{i} \hat{y}_{i}}, \tag{3}\]  
\[L_{\rho_{1}\rho_{2}}(y,\hat{y})=1-\frac{(\sum_{i}y_{i}\,\hat{y}_{i})^{2}}{\sum _{i}y_{i}\sum_{i}\hat{y}_{i}}. \tag{4}\]  
The \(H\), \(W\), and \(D\) denote data volume size (i.e., \(H\times W\times D\)).  
When the CNN has multiple outputs, which is either in the case of a network for simultaneous detection of follicles and ovaries, and/or when the Deep Supervision is employed, then the loss function, denoted as total loss \(L_{Total}\), is calculated as the sum of losses across all \(n\) outputs:  
\[L_{Total}=\sum_{n}L(y_{n},\hat{y}_{n}). \tag{5}\]
# Towards a Low-Cost Precision Viticulture System Using Internet of Things Devices
## 5 Hardware Infrastructure
### Processing Unit  
As a processing unit, a microcontroller was used. The microcontroller collects the sensor data, processes them and forwards them to the control room. To keep a low energy consumption, a low power microcontroller is required. There are several low-cost microcontrollers in the market. Due to its low power consumption, low-cost and ease of integration with other devices, an Arduino from MKR family was selected [28]. It has available pins to connect up to 8 sensors, for instance 4 for soil moisture and 4 for soil temperature, it is small and has low power requirements, while it can provide different wireless connectivity. An Arduino MKR is shown in Figure 8 along with the specifications in Table 3.
# Error Performance of Amplitude Shift Keying-Type Asymmetric Quantum Communication Systems
## 6 Conclusions
In particular, when \(R_{0}=0\), the AQC system using the quasi-Bell state has the same or a lower error probability than the corresponding system using the TSVS. However, when \(R_{1}=1\), a large average number of photons is required to enable the AQC system using the quasi-Bell state to exceed the performance of the system using the TSVS. Finally, we compared the error probability for the AQC system using the quasi-Bell state, the lower bound on the error probability for the AQC system using the TSVS, and the universal lower bound on the error probability, and found that performance closer to the universal lower bound was achieved using the quasi-Bell state when compared with the system with TSVS in the case when \(R_{0}\) and \(R_{1}\) are very small. As a result, it is expected that the AQC system using the quasi-Bell state will be applicable even in ultra-long distance channels, in which severe attenuation can occur. In this paper, we evaluated the performance based on the error probability results for the AQC system and clarified the basic performance characteristics. In fact, if an eavesdropper Eve was present between Alice and Bob, one of the simplest attack methods for Eve would be to intercept the light corresponding to mode 5, which is reflected from Alice. However, Eve cannot access the light corresponding to mode A, which remains inside Bob, and thus there would be a difference in reception performance between Eve and Bob. This reception performance difference creates the security of the AQC system. In addition, the AQC system discussed in this paper is expected to have various security applications, e.g., quantum cryptographic conferencing [35]. Future work will include security evaluation of this system, including the case of information leakage when an eavesdropper is present, and security enhancement for this system by performing some quantum communication protocols. Conceptualization, T.W. and T.S.U. ; software, T.W. ; validation, T.W. and T.S.U. ; formal analysis, T.W. ; investigation, T.W. ; data curation, T.W. ; writing--original draft preparation, T.W. ; writing--review and editing, T.W. and T.S.U. All authors have read and agreed to the published version of the manuscript. This research was funded by JSPS KAKENHI, grant numbers JP20K20397, JP20H00581, and JP21K04064. Not applicable. This research was funded by the Marubun Research Promotion Foundation and The Nitto Foundation. The authors would like to thank S. Takahira for valuable discussions during this and previous research. We thank David MacDonald from Edanz ([[https://jp.edanz.com/ac](https://jp.edanz.com/ac)]([https://jp.edanz.com/ac](https://jp.edanz.com/ac)), accessed 12 May 2022) for editing a draft of this manuscript. The authors declare no conflict of interest. The following abbreviations are used in this manuscript
###### Abstract  
The electro-mechanical impedance (EMI) change in Piezoelectric (PZT) sensors embedded in steel-fiber-reinforced mortar (SFRM) was investigated to assess the material setting time. The EMI was continuously monitored for 12 h by the PZT sensor embedded in SFRM having fiber volume fraction of 0.5%, 1.5%, and 2.0%. The initial and final setting time of the SFRM were estimated using EMI signal change. The penetration resistance test, a conventional test method for the setting time of cement mortar, was also conducted. In the penetration resistance test, it was observed that the initial and the final setting time of SFRM accelerated as the volume fraction of the steel fiber increased. On the other hand, in the EMI sensing technique, the initial and the final setting time of the SFRM were consistent regardless of the fiber volume fraction.  
electro-mechanical impedance; setting time; steel-reinforced mortar; piezoelectric sensor +
Footnote †: journal: Applied  
18 February 202213 April 202213 April 202214 April 202214 April 202213 April  
sensor due to the converse piezoelectric effect. Using the input and output signal of the PZT sensors, the EMI can be calculated [6; 7; 8].  
In many applications of the EMI sensing technique for cementitious materials, a PZT sensor is adhesively bonded on the surface of host structures to couple the PZT sensor and the structure. Several research efforts have been made to monitor the concrete strength development by measuring EMI signals of PZT attached on the surface of concrete. They tried to corelate the EMI signal change and the strength development of concrete [9; 10; 11; 12].  
Surface bonding is not available for the early hydration stage of cementitious materials since their surface is not hard enough to attach a PZT sensor. For this reason, techniques for measuring EMI by embedding a PZT sensor directly into a cementitious material have been introduced. The EMI sensing techniques using embedded PZT sensor were aimed to monitored concrete strength development at early ages. [PERSON] et al. monitored the early age strength of concrete using a PZT sensor embedded in concrete [13], and [PERSON] and [PERSON] used EMI sensing technique to predict the change in strength of concrete at an early age by embedding PZT sensor into concrete [14].  
Since the electro-mechanical response of the PZT sensor embedded in fiber-reinforced mortar is greatly affected by the cementitious material in direct contact with the PZT sensor, the fibers around the PZT sensor might have little effect on the electro-mechanical response of the PZT sensor. Therefore, the EMI sensing technique using an embedded PZT sensor could be an effective tool for assessing the setting time of fiber-reinforced cementitious materials.  
In this study, the EMI signal of PZT sensors embedded in SFRM was continuously monitored to assess the setting time of the material. The setting times assessed by EMI sensing technique were compared with those from penetration resistance test [3], a conventional test method for the setting time of cement mortar.
## 3 Federated Learning for Space-Air-Ground Integrated Networks
### Device Selection Optimization Strategy Based on Hierarchical Reinforcement Learning
#### 3.4.1 Hierarchical Reinforcement Learning Strategy
To illustrate this hierarchical approach in our experimental environment with 10 terminal nodes (4 ground, 3 aerial, 3 space-based), consider a specific training round: The global layer evaluates network conditions and determines that aerial devices currently offer the optimal balance of coverage, latency, and bandwidth (using Equations (8)-(10)). After selecting the aerial category, the local layer then assesses the 3 UAVs based on their individual computational capabilities, data volumes, and connection status (using Equations (13)-(15)). If UAV-1 has the highest combination of computational power and data quality but unstable connectivity, while UAV-2 has moderate resources but stable connectivity, the local policy might select UAV-2 to ensure reliable model updates. This two-tier decision process efficiently reduces the search space from 10 possible devices to 3, while making context-aware selections.
## 4 Results and Discussion
### Effect of Variables
It was observed that Mn extraction can be described by multiple linear regression for S1 and quadratic regression for S2, with the interaction factor not contributing significantly to the variability of the model. In contrast, for Co extraction, the quadratic time factor and interaction factor are significant for the S1 model, while only the interaction factor is significant in the S2 model. The ANOVA test indicates that the models adequately represent Mn and Co extraction for the sampled values. Furthermore, the models do not require additional adjustments and are validated by the following goodness-of-fit statistics:  
* The \(p\)-values of the models (\(p<0.05\)) confirm their statistical significance. Figure 4: Linear main effect plot for Mn (**a**) and Co (**b**) extraction (\(\%\)) [S1]. Figure 5: Contour plot of Mn (**a**) and Co (**b**) Extraction (\(\%\)) versus ferromanganese crust/Fe(res) and time (min) [S1]. Additionally, the residual normality test indicates that the residuals are normally distributed. The \(p\)-value of the Kolmogorov-Smirnov (KS) test (\(p>0.15\)) exceeds the significance level, and the KS statistics are lower than the critical KS indicator (Table 9). Consequently, the assumption of normality for the regression model residuals cannot be rejected. Gradient analysis of Mn and Co extraction, \(\
abla\)Mn and \(\
abla\)Co extraction (\(x_{1},\ x_{2}\)) (see Table 10), indicates the increases and decreases in the response variable in function of the predictor variables. The response decreases with respect to the variable \(x_{1}\) and increases with respect to the variable \(x_{2}\) for Mn extraction for both samples S1 and S2, for the entire set of sampled variables. However, although the trend is usually maintained for Co extraction, there is an exception in the trend of gradient of \(x_{1}\) levels for lower levels of \(x_{2}\) (for sample S1), where the gradient is positive for lower and medium values of time and negative for higher time values. As shown in previous research [10; 12], in a leaching process conducted in an acidic medium with the addition of iron as a reducing agent, the potential range for manganese dissolution is 1.4 to 1.2 V, with a pH range of 2 to 8. For Co dissolution, the potential range is 0.8 to 0.25 V, and the pH range is 2 to 4.5. Therefore, the results presented in Figure 8 fall within the appropriate potential and pH ranges to facilitate the regeneration of Fe\({}^{3+}\) and Fe\({}^{2+}\) ions, enabling the simultaneous dissolution of both elements. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{4}{*}{**Equation**} & \multicolumn{3}{c}{\(\
abla\)**Mn Extraction (\%) [Equation (2)]**} & \multicolumn{3}{c}{\(\
abla\)**Co Extraction (\%) [Equation (3)]**} \\ \cline{2-9}  & _Levels_\(x_{1}|x_{2}\) & \(-1\) & 0 & 1 & _Levels_\(x_{1}|x_{2}\) & \(-1\) & 0 & 1 \\ \cline{2-9}  & \(-1\) & (6.6; \(-5.5\)) & (6.6; \(-5.5\)) & (6.6; \(-5.5\)) & \(-1\) &
# Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review
## 2 Wireless Communication Technologies for Agriculture
### Long Range Radio (LoRa) Protocol  
LoRa is introduced by the LoRa Alliance as a protocol stack for low power and wide area Internet of Things (IoT) communication technologies that are associated with indoor transmission [43]. The basic network architecture of a LoRaWAN consists of LoRa end devices, LoRa gateways, and a LoRa network server. The LoRa end device communicates with gateways that employ LoRa with LoRaWAN. LoRa gateways pass raw LoRaWAN packets from the end devices to a LoRa network server with a high throughput based on the backhaul interface, which is typically 3G or Ethernet. Accordingly, LoRa gateways act as a bidirectional communication or protocol adapter with the LoRa network server. In this case, the LoRa network server takes charge of decoding the data packets transmitted by the LoRa devices and creating the frames that would be directed back to the devices. LoRa offers a bidirectional solution that matches machine-to-machine (M2M) WiFi or cellular technologies. LoRa presents a cost-effective method for linking batteries or mobile devices to the network or to end devices. The LoRa wireless protocol was used in [44] to monitor bee colonies in rural areas and to ensure a communication between the bee node and the local server that is located in a remote location. The soil moisture and temperature, air temperature and humidity, and light intensity inside greenhouses were also monitored using different sensors, microcontrollers, and the LoRa wireless protocol [8]. The LoRa gateway collects data from LoRa nodes to construct the topology of a star network, and may communicate with a cloud server over a long communication range and with high scalability.  
LoRa was recently utilized in several agriculture research projects developed by the Libelium Company (Zaragoza, Spain) [45], such as improving kiwi production based on a smart irrigation system (Italy), monitoring of green areas by using a smart garden system (Spain), improving fertilization methods of corn yields (Italy), improving production of banana yields (Colombia), increasing tobacco crops by controlling the climate condition (Italy), saving water through a smart irrigation system (Barcelona), monitoring of vineyard crops (Spain), and monitoring of diseases that affect vineyards (Switzerland).
###### Abstract  
In this paper, we propose a method for localizing the optic nerve head and segmenting the optic disc/cup in retinal fundus images. The approach is based on a simple two-stage Mask-RCNN compared to sophisticated methods that represent the state-of-the-art in the literature. In the first stage, we detect and crop around the optic nerve head then feed the cropped image as input for the second stage. The second stage network is trained using a weighted loss to produce the final segmentation. To further improve the detection in the first stage, we propose a new fine-tuning strategy by combining the cropping output of the first stage with the original training image to train a new detection network using different scales for the region proposal network anchors. We evaluate the method on Retinal Fundus Images for Glaucoma Analysis (REFUGE), Magrabi, and MESSIDOR datasets. We used the REFUGE training subset to train the models in the proposed method. Our method achieved 0.0430 mean absolute error in the vertical cup-to-disc ratio (MAE vCDR) on the REFUGE test set compared to 0.0414 obtained using complex and multiple ensemble networks methods. The models trained with the proposed method transfer well to datasets outside REFUGE, achieving a MAE vCDR of 0.0785 and 0.077 on MESSIDOR and Magrabi datasets, respectively, without being retrained. In terms of detection accuracy, the proposed new fine-tuning strategy improved the detection rate from 96.7% to 98.04% on MESSIDOR and from 93.6% to 100% on Magrabi datasets compared to the reported detection rates in the literature.  
c 2020 10 3833; doi:10.3390/app10113833
# Superpixel Segmentation of Hyperspectral Images Based on Entropy and Mutual Information
## 3 Superpixel Segmentation Based on Color Histogram Driving
### Superpixel Segmentation Algorithm Based on Hill Climbing Optimization  
During the segmentation process, the one-by-one superpixel adjustment strategy through gradual local optimization can be used to obtain a superpixel region that is sufficiently segmented and uniform in color. The hill climbing optimization was used as the one-by-one superpixel adjustment strategy in the superpixel segmentation. The hill climbing optimization is an iterative algorithm that starts with an arbitrary solution and then attempts to optimize the solution by incrementally changing a single element of the solution. The segmentation includes two important operations, which are described below [28]:  
(1) The initialization of superpixels  
First, the method proposed in [15] was used to manually set the segmentation amplitude threshold \(U\). Using this method, the maximum number of superpixels was obtained. Then, the side length \(L\) of the initial square superpixel was obtained, as shown in Equation (14).  
\[L=f\Bigg{(}\sqrt{\frac{N}{U}}\Bigg{)} \tag{14}\]  
where \(N\) is the total number of pixels, and \(f(\cdot)\) represents the function to get the upper even number.  
(2) The adjustment of superpixelsFirst, a block adjustment was performed on the square area formed by adjacent pixels centred at the superpixels, which speeded up the segmentation process. Then, the pixels were adjusted by pixel, and the pixels on the adjusted superpixel edge were moved to its neighboring superpixels. Each superpixel was guaranteed to remain unchanged after the modification.  
Through the above two steps, the final segmentation results were obtained. The overall segmentation process of HSIs is shown in Figure 4. The spectral bands were preliminarily selected based on entropy and CMF, and then \(n\) spectral bands were selected to synthesize false color images. Next, the false color images were first segmented based on hill climbing optimization, in which \(E(s)\) was used to evaluate the segmentation results. Subsequently, both block-level and pixel-level adjustments were performed for the pre-set number of iterations, \(N\). Finally, the segmentation result was obtained and output. This aspect might be added in future work since the results might be further improved by a global optimization method. We can refer to the method proposed by [PERSON] et al. for combining conventional HE technology with particle swarm optimization (PSO) algorithm to naturally enhance distorted images [29].