esa-sceva/satcom-chunk-collection huggingface dataset:
# Implementation and Security Test of Zero-Knowledge Protocols on SSI Blockchain
## 2 Background
### Zero-Knowledge Proof  
Now that the concept of SSI and the technology that makes its implementation possible today have been explored, what remains to be understood are the cryptographic mechanisms that allow users to prove that their credentials are legitimate without revealing more information about their identity than is strictly necessary.  
A Zero-Knowledge Proof, or Zero-Knowledge protocol, is the method proposed by [PERSON], [PERSON], and [PERSON] in 1989 [PERSON] et al. (1989) by which an actor (the Prover) can prove to another (the Verifier) the veracity of a statement about a secret, without revealing the secret itself. After the execution of the ZKP protocol, the Verifier will not have gained any additional knowledge about the secret than they had to start with, other than the fact that the statement is true.  
A ZKP test must meet three basic characteristics:  
* Completeness: If the statement is true, the verifier can be convinced of it by the prover.
* Solvency: If what you want to prove is false, the verifier cannot be deceived.
* Zero-Knowledge: The verifier will not have gained additional information except the truth of the statement.  
The first two attributes are required by any verification system; it is zero knowledge that characterizes a ZKP method.  
A simple example of how a ZKP works would be the following situation: imagine [PERSON] wants to prove to [PERSON], who is colorblind, that she is able to distinguish between two pens, one red and one blue, without giving him any information about which is which. To perform this, [PERSON] could ask [PERSON] to take a pen in each hand, hide them behind his back, and choose to switch hands or not. [PERSON] then shows [PERSON] the pens again and she will tell him if he has switched them or not. At first, even if [PERSON] got it right, [PERSON] might think it was a fluke, since [PERSON] has a 50% chance of getting it right whether she really knows the answer. However, if they repeat the protocol numerous times, [PERSON] will become more and more convinced that [PERSON] is telling the truth, until after n repetitions the probability that [PERSON]'s statement is false is so small (1 in 2\({}^{n}\)) that [PERSON] should be convinced.  
Now that we have seen a rather metaphorical example of a ZKP protocol, it is interesting to study a real protocol that can be implemented in a computer system.
# Prediction of Ship-Unloading Time Using Neural Networks
## 3 Related Methods  
### BP Neural Network  
BP is a multilayer neural network whose effectiveness has been demonstrated in numerous studies [17]. Typically, BP consists of one input layer, one or more hidden layers, and one output layer, with full connectivity between adjacent layers. The BP learning algorithm operates iteratively. The network parameters, which include weights and biases, are propagated forward through the network and adjusted backward to minimize error.  
Given a group of samples, \(\left\{(x_{i},y_{i}),\;i=1,\ldots,N,\;x_{i}\in R^{d},y_{i}\in R^{l}\right\}\), assuming there is one hidden layer in the BP (Backpropagation) network. containing \(j\) nodes, denoted as \(v_{1},v_{2},\ldots,v_{J}\). The input weights, biases for the hidden nodes, output weights of the hidden nodes, and thresholds for the output nodes are \(w_{ij},b_{j},w_{jk},\theta_{k},i=1,\ldots,d,\;j=1,\ldots,J\), \(\text{k}=1,\ldots,l\), respectively. Then,  
1. Hidden layer outputs \[v_{j}=f_{1}(\sum\limits_{i=0}^{d}w_{ij}x_{i}),j=0,1,\ldots,J,\] (1) \(f_{1}\) serves as the activation function for hidden node \(j\), \(x_{0}=-1\), \(w_{0j}=b_{j}\).
2. Output layer results \[\bar{y}_{k}=f_{2}(\sum\limits_{j=0}^{J}w_{jk}v_{j}),\;k=0,1,\ldots,l,\] (2) \(f_{2}\) serves as the activation function for output node \(k\), \(v_{0}=-1\), \(w_{0k}=\theta_{k}\).
3. Updating of weights \[\begin{array}{l}\Delta w_{jk}=\eta g_{k}v_{j},\\ j=0,\ldots,J,\;k=1,\ldots,l,\\ g_{k}\triangleq\;\bar{y}_{k}(1-\overline{y}_{k})(y_{k}-\overline{y}_{k}).\\ \Delta w_{ij}=\eta e_{j}x_{i},\\ i=0,\ldots,d,\;j=1,\ldots,J,\\ e_{j}\triangleq-v_{j}(1-v_{j})\sum\limits_{k=1}^{l}w_{jk}g_{k},\;g_{k} \triangleq\overline{y}_{k}(1-\overline{y}_{k})(y_{k}-\overline{y}_{k}).\\ \end{array}\] (3)  
However, the Backpropagation (BP) algorithm is inherently gradient based, which means there is a possibility of converging to local minima. Nevertheless, it remains a robust method due to various strategies designed to mitigate the risk of local optimality. These strategies include the momentum method, adaptive learning rates, and the use of first-order approximations, such as the Newton and quasi-Newton methods, instead of relying solely on the gradient method. The BP algorithm is detailed in Algorithm 1.
# Self-Reporting Technique-Based Clinical-Trial Service Platform for Real-Time Arrhythmia Detection
## 4 Discussion
### Limitations  
This study has several limitations. First, the effectiveness of arrhythmia detection in this platform was not evaluated. This limitation confines the scope of this study in terms of clinical evaluation. An improved algorithm with excellent detection capabilities is required, and its performance should be evaluated with further clinical trials.  
Second, the efficacy of the self-reporting technique for diagnosing arrhythmias has not been validated. To be used in the medical field, its robust efficacy should be proved with a large amount of clinical data. Clinical trials in various environments are necessary to validate its efficacy. The multicenter case study currently in preparation would be the first step for validity assessment, and additional clinical trials need to be designed.  
A third limitation relates to Bluetooth communication. A stable Bluetooth connection is crucial to gather real-time ECG and self-reported data without losses. The communication protocol was improved with the index information, and significantly lower loss rates were obtained. However, the possibility of communication failure remains unresolved. The VP-100 does not send any alarm signal regarding the problem, nor does it automatically reconnect Bluetooth. Thus, clinicians must recognize the problem early and reconnect Bluetooth themselves.  
The fourth limitation relates to the artifact-prevention methods. Baseline wandering and 50/60 Hz power-line interference can be eliminated by applying the band-pass filter. However, ECG signals can be corrupted by artifacts caused by muscle contraction, motion artifacts, and other factors, which could generate uninterpretable ECG data. An improved artifact-prevention method is likely required for effective clinical use.
# Developing a Fire Monitoring System Based on MQTT, ESP-NOW, and a REM in Industrial Environments
## 4 Experimental Implementation
### Data Collection for Environment Mapping  
To ensure high accuracy in data collection, Received Signal Strength Indicator (RSSI) data are gathered using the TurtleBot3's built-in Wi-Fi module. The iwconfig tool extracts detailed network interface information, such as SSID, signal frequency, and link quality. Synchronization of the collected data is critical, and timestamps are meticulously applied to align the RSSI data with the robot's X and Y coordinates for precise localization. The experiment's initial setup uses an IPTime wireless LAN card (A30000 UA-2) to act as a local AP. This setup creates a baseline signal distribution, which is later scaled to include four relay nodes strategically placed to increase the network's complexity. These nodes are crucial in extending the communication range, ensuring that even the most remote parts of the workspace have adequate signal coverage. An ES32 module is used as a relay node to expand network coverage. Figure 5 illustrates the radio environment map (REM) generated during the experimental phase, showcasing the spatial distribution of signal strength within the industrial environment. The REM plays a critical role in guiding the strategic placement of ESP32 relay nodes to ensure robust and reliable communication across the network and provides a comprehensive two-dimensional map of signal strength (RSSI) values across different regions of the workspace. The REM also provides a baseline for re-evaluating and re-optimizing node placements in response to changes undergone in industrial environments, ensuring the network remains adaptive and resilient.
# Performance of Eco-Friendly Zero-Cement Particle Board under Harsh Environment
## 3 Results
### Thickness Swelling  
The thickness swelling of the alkali-activated particle board submerged in water for 2 and 24 h is illustrated in Figure 5. According to the guidelines set by the ASTM standard D1037-03 [29], the maximum allowable thickness swelling is 8%. As a result, all of the tested specimens met the requirement for thickness swelling. The results showed that 25 FT200 and NC200 had the most significant changes in thickness, which were 5.12% and 4.65% after 24 h of submersion in water, respectively. However, the lowest change in thickness was exhibited by NC400, which was 2.17% after 2 h of submersion in water. This can be attributed to the ratio of agro-waste to slag, indicating that a higher slag content decreases thickness swelling. The widely accepted fact is that when the ratio of agro-waste to slag in particle boards rises, it leads to a notable increase in the thickness swelling, resulting in a decline in the boards' dimensional stability [11].  
Figure 4: The density of the particle board with different slag contents and curing conditions.  
On the other hand, the thickness swelling was increased when the particle boards were cured in freezing and thawing or drying and wetting conditions. For instance, it can be shown that specimens 25 FT300 and 25 WD300 had more changes in comparison with NC300, which were 4.11% and 4.20%, respectively. Also, it can be shown that curing in drying and wetting conditions has a greater effect than curing in freezing and thawing conditions. For instance, specimen 25 WD400 had more thickness swelling changes than 25 FT400. This can be attributed to the increase in the amount of pores when the specimens are cured in these conditions.
# Deep Learning for Point-of-Care Ultrasound Image Quality Enhancement: A Review
## 3 Results  
### Study Selection  
The systematic literature research identified 457 articles from the two databases after duplicate removal. Snowballing did not identify any additional relevant articles. The initialscreening based on the title and abstract resulted in 69 articles being selected for full-text review [6; 14; 15; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92; 93; 94; 95; 96]. Following an initial analysis focusing on quality enhancement aspects, 15 articles were identified that addressed multiple quality enhancement aspects. These articles were selected for further descriptive and quantitative assessment. Finally, six articles were selected for a meta-analysis based on their reported outcomes. The study selection process is illustrated in Figure 1.
## 4 Mathematical Model
### Numerical Model Verification  
The reliability of the numerical model was verified by means of PIV experiments. Due to the high particle volume fraction and impeller speed, the application of optical measurement technology would be affected if the number of particles was too large. Therefore, the experimental and simulated velocity vectors were compared at 240 rpm (\(Re\) = 95,385.6) and solids volume fraction of 2%. Figure 6 shows that the liquid phase mean velocity fields of the simulations and PIV experiments were similar. From r/R = 0.3 to r/R = 0.4, the main circulation vortex was formed near the blade tip. At the same time, the fluid flowed out obliquely downward from the tip at a maximum speed of \(u_{tip}\) = 1.8 m/s. After contacting the bottom of the kettle, 80% of the fluid upwards participated in the main circulation of the entire stirred tank, and the rest formed an inverted cone-shaped circulation induction zone downwards.
# MAC Protocols for mmWave Communication: A Comparative Survey +
## 6 Challenges & Future Research Directions
### Large Scale Integration  
Since most of the previous mmWave studies were conducted before deploying 5G mmWave on a large scale in real-world settings, most resource allocation techniques in wireless communication are becoming outdated. In addition, large-scale scalability will create enormous overheads. In addition, ACK packages, which are the fundamental controls in most MAC protocols, also produce a significantly large overhead. The connected smart devices and the IoT modules also amount to large data traffic. Researchers are already concepting cloud radio access network (C-RAN) to break the base station into baseband unit (BBU) and remote radio head (RRH) and coordinating the BBUs and RRHs from a different site and the mechanism in cloud servers in next-generation 5G mmWave networks to reduce the complexity of serving hundreds of devices through the BS. Research on MAC protocols for C-RAN is still in the rudimentary stage. Consequently, it may be necessary to establish new, more robust protocols for minimizing the control overheads in large-scale cellular networks and WPANs.
## 2 Discrete-Time Volterra Series Model for TIADC
### Frequency Domain Analysis of the Discrete-Time Volterra Series Model for a Four-Channel TIADC
For the system composed of weak memory nonlinear devices such as ADCs, the influence of memory nonlinearity on the overall system decreases with the increase in order.
## 4 Quantum Integrated Photonic Devices
### Fabrication and Characterization of a Quantum Project
Next, we choose the directional coupler \((s,l)=(6.0,1.5)\), which is very close to a \(X_{\pi/4}\) coupler and therefore is a projector of quantum states \((1/\sqrt{2})(|1_{1}\rangle\pm i|1_{2}\rangle)\). Alternatively, the device formed by an optical grating introducing a \(\pi/2\) phase plus a directional coupler \(X_{\pi/4}\) can be formally regarded as a projector for states \((1/\sqrt{2})(|1_{1}\rangle\pm|1_{2}\rangle)\). Note that if we use an active optical grating implemented by a spatial light modulator introducing \(0\) or \(\pi/2\) in a random way we have a quantum states detection system (Bob) of random bases. In Figure 17, the experimental results (image of waveguide outputs) for these projections are shown, with the corresponding input coherent states. Note that the bimode coherent states \(|\alpha/\sqrt{2}\,\,\pm i\alpha/\sqrt{2}\rangle\) are projected in states \(|\alpha 0\rangle\) and \(|0\alpha\rangle\), therefore single photon states \((1/\sqrt{2})(|1_{1}\rangle\pm i|1_{2}\rangle)\), will be projected in states \(|1_{1}\rangle\) and \(|1_{2}\rangle\), respectively. On the other hand, the following two-mode coherent states \(|\alpha/\sqrt{2}\,\,\pm\,\alpha/\sqrt{2}\rangle\) do not undergo transformations, therefore the single photon states \((1/\sqrt{2})(|1_{1}\rangle\pm|1_{2}\rangle)\) also do not, and consequently it is also experimentally proved that they are eigenstates. This last case is highly interesting to implement a quantum random number generator. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Series of Couplers**} & \multicolumn{4}{c}{_l (mm)_} \\ \cline{2-6}  & & **0.5** & **1.0** & **1.5** & **2.0** \\ \hline \multirow{3}{*}{\(s\) (\(\mu\)m)} & 3.0 & (17.8)\(|\)\(|\)\(82.2\) & (93.6) & 6.4) & (54.9)\(|\)\(45.1) & ( 2.5)\(|\)\(97.5) \\  & 4.5 & (35.9)\(|\)\(64.1) & ( 5.9)\(|\)\(94.1) & ( 7.4)\(|\)\(92.6) & (40.9)\(|\)\(59.1) \\ \cline{1-1}  & 6.0 & (80.7)\(|\)\(19.3) & (63.8)\(|\)\(36.2) & (49.3)\(|\)\(50.7) & (26.8)\(|\)\(73.2) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Normalized powers \(P_{1}|P_{2}\) at coupler’s outputs \(1\) and \(2\), respectively. In short, the experimental results achieved with the directional coupler confirm that a \(X_{\pi/4}\) transformation is implemented, and therefore a projection operation can be made. A similar experimental study has been made with the \(X_{\pi/2}\) coupler. Therefore, when several couplers \(X_{\pi/4}\) and \(X_{\pi/2}\) are combined, that is, couplers \((s,l)=\{(6.0,1.5),(3.0,2.0)\}\) fabricated simultaneously, the devices described in Section 4.2 can be fabricated and also more general projectors (high-dimensional projectors, Bell states projectors,...) by using ion exchange in glass.
## 2 Study Area and Data Analysis
### WorldView-3 Triplet  
The WorldView-3 Digital Globe's very high-resolution optical sensor was launched in August 2014. Operating at an altitude of 617 km with an orbital period of 97 min, the sensor provides 0.31 m panchromatic resolution.  
According to the metadata, the tri-stereo WorldView-3 images for our study were acquired in spring 2018, on 8 April, within 37 s. The corresponding base has a value of 279.69 km and the intersection angle of rays on the ground is 23\({}^{\circ}\). Even though both sensor platforms fly at approximately the same speed (~7.5 km/s), the WorldView-3 B/H ratios have higher values, because of the lower altitude and increased acquisition time, compared to the Pleiades triplet. Hence, the B/H ratio is 0.20 for both forward-nadir and nadir-backward images, whereas for forward-backward it is 0.40. Each image is pan-sharpened, with four spectral bands, i.e., red, green, blue, and near-infrared, with 16-bit depth and zero cloud coverage. Depending on the viewing direction, the mean GSD values vary from 0.31 m to 0.32 m. The images were delivered as eight, two, and two tiles for close-to-nadir, forward, and backward, respectively. As a pre-processing step, the tiles for each image were mosaicked  
Figure 2: Detail of Pleiades (first row) and WorldView-3 (second row) tri-stereo satellite images on the same built-up area, acquired with forward-, nadir- and backward-looking.  
accordingly. For both image triplets, Pleiades and WorldView-3, auxiliary data including the third-order rational function model (RFM) coefficients are provided as separate files.  
Figure 3 shows a visual comparison between the pan-sharpened (near) nadir images acquired with Pleiades (left) and WorldView-3 (right) over a small area, in order to highlight the effects of the two different resolutions of 0.70 m and 0.31 m. The same objects can be distinguished in both images: streets, buildings, cars, and trees. The higher resolution of WorldView-3 provides a more detailed image with clear visible cars, tree branches, and building roof edges.
# Research on Gangue Detection Algorithm Based on Cross-Scale Feature Fusion and Dynamic Pruning
## 4 Experimental Validation and Analysis
### Evaluation Indicators  
To validate the improved coal gangue detection algorithm's capability in detecting coal and coal gangue, this study primarily evaluates and analyzes precision (P), recall (R), mean Average Precision (mAP), model weight, and detection speed.  
\(P\) refers to the proportion of coal or gangue correctly detected in the overall detection results, as shown in Equation (14).  
\[P=\frac{TP}{TP+FP} \tag{14}\]  
where \(TP\) represents the number of correctly detected coal or gangue instances, and \(FP\) represents the number of instances falsely identified as the target by the model.  
\(R\) refers to the proportion of correctly predicted coal or gangue in the overall detection results, as shown in Equation (15).  
\[R=\frac{TP}{TP+FN} \tag{15}\]  
where \(FN\) represents the quantity of coal or gangue not detected by the model.  
The \(mAP\) is the average precision across multiple categories, and the calculation formula is provided in Equations (16) and (17).  
\[AP=\int_{0}^{1}P(r)dr \tag{16}\]  
\[mAP=\frac{\sum_{q=1}^{Q}AP(q)}{Q} \tag{17}\]  
where \(AP\) is the average, and \(Q\) is the number of categories.
## 2 Materials and Methods  
### Experiment Design, Plant Material and Growing Conditions  
The pot experiment was carried out at the Department of Agronomy of the Poznan University of Life Sciences, Poznan, Poland (52.482854, 16.900465). Lettuce (_Lactuca sativa_) was grown in pots in greenhouse conditions. The experimental factors were a) fertilization and b) water conditions. Four levels of fertilization were used: control (no fertilization), manure 10 (10 g of granulated cattle manure applied to each pot) and two variants with frass applications: HI frass 10 (10 g granulated frass from _Hermetia illucens_ per pot) and Hi frass 12.5 (12.5 g granulated frass from _Hermetia illucens_ per pot). The dose was determined on the basis of a preliminary study, which indicated the optimal amount of fertilization. The water regime had two levels labeled as watered and drought. Half of the plants were irrigated as needed until harvest (watered). In the other half of the plants, a drought effect was induced by ceasing watering six days before harvesting (drought). The experiment was carried out in 4 replications.  
The soil used for the experiment was ordered from Biobizz Worldwide SL (Industrial Systems s.r.o., Prague, Czech Republic). According to the manufacturer's information, it contained the following concentration of nutrients: min. 2.8% nitrogen, min. 2.8% phosphorus, min. 2.0% potassium and minimum 0.8% magnesium. The organic matter content was at least 60%, and the soil had a pH of 6.2. Before filling the pots, this soil was mixed.  
After filling the pots with soil mixed with fertilizers, they were placed on a table and flooded with water for 48 h in the amount of 100 mL/pot, after which lettuce seeds were sown. Further irrigation was carried out using a hand sprinkler to maintain constant soil moisture. Pots with plants were placed in a greenhouse where light and thermal conditions were controlled during the experiment. The length of the day was 16 h and was provided by natural light and additionally by light from a 400 W sodium lamp (Elektro-Valo Oy Netafim, Avi: 13473, HPS, Ussikaupunki, Finland). The temperature during the experiment was 22 \({}^{\circ}\)C.