esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Protocol Using Captive Portals for FIDO2 Network Authentication +
## 5 Prototype Development and Validation
### Captive Portal Integration with OpenNDS  
Once we developed a first working version of the WAWA, it was integrated with captive-portal software: OpenNDS (v9.10.0). This software implements the Provisioning Service, the API Server and the Enforcement Device. First, we explain why we have chosen OpenNDS for the prototype development. Secondly, we explain the integration of the WAWA with the API Server and the Enforcement Device of OpenNDS.  
OpenNDS feature for externalising the User Portal is called Forwarding Authentication Service (FAS) [11], which forwards the redirected requests to a captive portal to the selected external web server. Finally, the OpenNDS Enforcement Device should be integrated with the external web server to identify the authenticated client and authorise its access to the network. Therefore, during this section, the developed WebAuthn Authentication Web Application (WAWA) server was modified to implement an external FAS server compatible with OpenNDS.  
During this section, the captive portal is commonly referenced with the chosen solution name: OpenNDS. On the other hand, our WAWA server is commonly referenced as the FAS server (Forwarding Authentication Service), nomenclature used in the OpenNDS configuration for the User Portal server.
## 3 Results and Discussion  
### Climate and Evaportranspiration Characteristics  
Daily average \(\mathrm{R_{n}}\), \(\mathrm{T_{mean}}\), RH, \(\mathrm{W_{s}}\) (at 2 m height), and \(\mathrm{ET_{a}}\) across the observation stations for the tea plantations were 7.31 MJ m\({}^{-2}\) day\({}^{-1}\), 16.59 \({}^{\circ}\)C, 78%, 1.31 m s\({}^{-1}\), and 2.05 mm day\({}^{-1}\), respectively, during the study period. The average monthly distributions of \(\mathrm{R_{n}}\), \(\mathrm{T_{mean}}\), RH, \(\mathrm{W_{s}}\), and \(\mathrm{ET_{a}}\) are presented in Figure 3. Similar seasonal patterns were observed for \(\mathrm{R_{n}}\), \(\mathrm{T_{mean}}\), and \(\mathrm{ET_{a}}\). Low values of \(\mathrm{R_{n}}\), \(\mathrm{T_{mean}}\), and \(\mathrm{ET_{a}}\) were recorded in January and December, whereas higher values generally occurred in July and August (Figure 3). The maximum \(\mathrm{R_{n}}\), \(\mathrm{T_{mean}}\), and \(\mathrm{ET_{a}}\) reached 18.93 MJ m\({}^{-2}\) day\({}^{-1}\), 34.05 \({}^{\circ}\)C, and 6.9 mm day\({}^{-1}\), respectively, in July during the study period. About 65-75% RH was observed in February, April, and October, whereas a high RH of more than 75% was reported in other months. Low \(\mathrm{W_{s}}\) (0.88-1.05 m s\({}^{-1}\)) were experienced in July, September,and October. By contrast, May, April, and August presented higher W\({}_{\rm s}\) (1.5-1.9 m s\({}^{-1}\)) (Figure 3c).
## 3 Proposed Method
### Color Adjustment  
In Section 2.1 a general color correction method was introduced. However, complicated backgrounds degrade the performance of the method. To achieve satisfactory results, a color adjustment method based on color transfer and k-means clustering is introduced. The main idea is to divide the blood smear image into several parts by using k-means clustering. The color transfer algorithm will be applied for each part of the image, and the synthetic image can be obtained by reunion of the parts.  
The stained blood smear images can be divided into three parts based on the following information: the background, the RBCs, and the WBC nuclei. Each pixel in each part has specific features. K-means clustering is used to segment the background, RBC, and the nucleus.  
The original image in the RGB color space was a \(\mathrm{M}\times\mathrm{N}\times 3\) matrix. The value of each pixel was a \(1\times 3\) vector such as [r g b], where r denotes the R component value of this pixel, g denotes the G component value, and b denotes the B component value. The [r g b] of each pixel was treated as a feature vector; the original \(\mathrm{M}\times\mathrm{N}\times 3\) matrix was reshaped to an \(\mathrm{L}\times 3\) matrix (\(\mathrm{L}=\mathrm{M}\times\mathrm{N}\)), then k-means clustering was used to cluster every pixel into three partitions. Figure 4 shows the partition results.  
After clustering, the template image and the input image are divided into three parts respectively. As shown in Figure 5, the color transfer algorithm is applied to each part of the template image and the  
Figure 3: Flow chart of the proposed method.  
input image. The synthetic image can be obtained by the reunion of the three parts, and the subsequent steps are based on the synthetic image.  
Figure 4: **(a)** Original image; **(b–d)** Partition results after clustering.  
Figure 5: Schematic plot of the proposed color adjustment method.
## 4 Command and Control
### Monitoring  
The monitoring function is performed by means of Prometheus technology [29]. Prometheus is an open-source technology that can be interface with any system to supply monitoring and real time alerting functions. The monitoring function for the C&C is realized as a Prometheus job, i.e., an application-specific configuration for Prometheus identified by a name and HTTP target endpoints where application data are published, and which constitute the data sources for a Prometheus process. Prometheus allows one to implement metrics for a system as a combination of simple built-in metrics, such as count and gauge, on event data acquired from the monitored system and stored as time series. These time series can be queried by means of an internal expression language, useful to realize dashboards, for example by means of Grafana [28]. Dashboards for system performance, as well as displaying metrics, such as latency of functions and database accesses charts and statistics, are common and already exist as add-ons for many application server technologies, and domain-specific metrics can be useful to support ex post assessment, such as safety scenario analysis.  
For the aims of both system performance and application-data analysis, various types of metrics have been put in place for the BCT deployed system. In particular, two groups of application-specific metrics have been implemented: \"Time\" and \"Commuter\" focused metrics. Time metrics compute the overall duration of the commuter detection in various scenarios of normal and threat cases. These have been used to compare the \"virtual\" duration of the trial runs with the real duration, i.e., the time employed by the system for a detection against the direct observation duration. Additionally, the time of MIC sensor-video data fusion operation has been computed to allow measuring the latency of the individual detections. This type of data at the BCT trials has been especially useful to recognize needs for component re-synchronization with the NTP server to adjust time accuracy, which is a necessary requirement for sensor-video data fusion.  
Commuter metrics allow one to summarize events of interest referring to the detection results, as shown in Figure 7. This dashboard is used to obtain an automatic summary of the trials and verification against the planned scenarios.  
In an ex-post analysis, by combining these metrics on the same timeline, it is possible to elaborate time profiles of crowds traversing the monitored area that can be used for a better tuning and for future enhancements of the system.
## 1 Introduction
### Occurrence Probability Calculation
#### 2.2.1 Uncorrelated Buffers (Random)  
To derive output response PDFs, the occurrence probability of each SSO combination should be derived. The occurrence probability of SSO can be calculated as in Equation (1), where \(N_{\text{SSO}}\) is the total number of aggressor SSO buffers, \(N_{T}-1\), excluding the number of victim buffers  
\[P_{\text{m}}(N_{01},N_{10},\ N_{11},N_{00})=\left(\frac{1}{2}\right)^{2}\times \left(\frac{1}{4}\right)^{N_{\text{SSO}}}\times_{N_{00}}C_{N_{0}}\times_{(N_{ \text{SSO}}-N_{00})}C_{N_{01}}\times_{(N_{\text{SSO}}-N_{00}-N_{01})}C_{N_{00}}, \ \text{m}=\ 00,\ 11,\ 01,\ \&\ 10 \tag{1}\]  
In (1), \(\frac{1}{2}\) is the transition probability of the victim buffer and \(\frac{1}{4}\) is the transition probability of each SSO buffer. Since the number of SSO buffers remaining in steady states,  
Figure 3: Output responses for specific buffer combinations are shown. All responses are derived and plotted. Power/ground noise due to SSO buffers degrades slew-rate and causes voltage uncertainty.  
and \(N_{00}\), has negligible impact on the victim buffer, (1) can be rewritten by adding up the probabilities of \(N_{11}\) or \(N_{00}\), which is summarized in Equation (2)  
\[P_{\text{m}}(N_{01},N_{10})\ =\ \sum_{N_{00}\ (\text{or}N_{11})=0}^{N_{00}-N_{ 01}-N_{10}}\ P_{\text{m}}(N_{01},N_{10},N_{11},N_{00}) \tag{2}\]  
In Figure 4, the occurrence probability distributions are given as examples by calculating and plotting the occurrence probability of each response for a case when \(N_{T}=16\) (Figure 4a) and a case when \(N_{T}=32\) (Figure 4b). The probability is highest when both \(N_{01}\) and \(N_{10}\) are near \(\frac{(N_{T}-1)}{4}\). Unless the SSO buffers are coded, cases when all SSO buffers are evenly distributed are the most likely to appear.
# Utilization of By-Products from the Fruit and Vegetable Processing Industry in Pasta Production
## 3 Incorporation of By-Products
### Acceptability  
Acceptability is one of the most concerning aspects when developing products enriched with by-products. It has been shown that one of the main reasons for consumer rejection of these products is their lower organoleptic quality. Unfortunately, most studies on pasta enriched with by-products do not conduct acceptability studies. In the studies that do include acceptability tests, the number of panelists rarely exceeds 60 people, which is somewhat low for this type of research [117]. In general, studies that include sensory analysis report a decrease in the overall quality of pasta enriched with by-products, as well as lower acceptability. In terms of acceptability, some studies highlight the different colors and appearance of the pasta, but also its inferior taste. It is worth noting that people tend to have a negative predisposition toward bitter flavors [118], and the phenolic compounds, which we have already mentioned are abundant in both by-products and enriched pasta, often contribute to these flavors [119].  
Despite the importance of these findings for the potential commercialization of these products, very few studies address ways to improve their acceptability. It is also worth mentioning that, while most studies on pasta quality assess plain, unseasoned cooked pasta, which follows a well-established methodology, sensory acceptability studies often use pasta prepared with different seasonings. While most studies do not incorporate any kind of dressing [58; 82; 84], which is uncommon when pasta is consumed, some use commercial sauces [73], which could influence the results. However, no study has analyzed the impact of these seasonings or cooking methods on product acceptability. It is known that certain bitter flavors can be masked by specific molecules [120], which could improve acceptability and help these products reach the market. Similarly, bitter flavors can be masked using physical methods [121], although this has not been studied in pasta production.  
Furthermore, some changes in the culinary quality of the pasta, such as modifications in texture, can lead to lower acceptability, and this should be examined to explore possible ways to improve it.
## 4 Results of Experiment 1: Recall/Detection Rate
### Individual Performance  
Table 3 shows the percentage of attacks detected for each attack dataset and the average detection rate (row AVERAGE).  
The tests revealed a strikingly low detection rate for the least sensitive configuration (low) of InspectorLog and ModSecurity. Logically, the maximum detection capability is obtained with the most sensitive configuration. InspectorLog(max) scored a detection rate greater than 90% in 4 of the 7 attack datasets used, and 66% in the worst-case dataset, averaging a recall of 83.26%. ModSecurity scored an average recall of 73.7% in its PL4 mode (max), ranging between 89% and 52% with _fwaf-2200_ and _fwaf-bad_ datasets, respectively. Finally, Nemesida exhibited an average recall of almost 64%, scoring a remarkable 96.5% detection rate with the _fwaf-2200_ dataset and a minimum of 38% with the _A-420_ dataset.  
Therefore, we can conclude that the detection capability obtained with different predetermined subsets of the full ruleset in the open-source detectors is very different according to the configuration and dataset tested, ranging from 6-8% (minimum sensitivity configuration) to 73-83% (using the full ruleset). Nemesida scored an average detection rate of 64% in our tests. It is remarkable that, even in the best case, the detection rate for attacks that should be known is lower than expected by the authors--83% at most--and is not enough to effectively protect the web service. This is not claimed in the literature, as a really high detection capability is expected for SIDSs regarding known attacks at least 2 years older than the ruleset.
# Overload Mitigation of Inertial Grid-Forming Inverters Under Frequency Excursions
## 3 Overloading Mitigation Strategies
### Angle Limiter  
#### 3.2.1 Strategy Description  
The angle limiter (AL) strategy prevents inverter overloading by directly limiting the phase angle \(\delta^{\prime}\) between the internal voltage reference \(E\) and the PCC voltage. Because active power in inductive systems is proportional to this phase shift (16), constraining \(\delta^{\prime}\) effectively limits the active power exchange.  
The simplified diagram is shown in Figure 7, where an auxiliary PLL is required to estimate the phase of the PCC voltage. When the measured \(\delta^{\prime}\) exceeds predefined thresholds, the controller saturates the angle reference. This strategy inherently disables frequency regulation, whereas the inertia is determined by the dynamics of the auxiliary PLL.  
\[P=\frac{EV_{pcc}}{\omega_{r}L_{c}}\sin\delta^{\prime} \tag{16}\]  
Following the same procedure as in (5), the synchronization constant \(K_{c}\) can be related to the inverse of the filter inductance \(L_{c}\). The resulting maximum and minimum phase shifts can be approximated as follows:  
\[\left\{\begin{array}{l}\delta^{\prime}_{max}\approx P_{max}/K_{c}\\ \delta^{\prime}_{min}\approx P_{min}/K_{c}\end{array}\right. \tag{17}\]  
As \(L_{c}\) is small, typically ranging [0.05, 0.15] pu, the maximum and minimum phase shifts are constrained to just a few degrees [35]. For instance, the previous inductance range requires a phase shift ranging from 2.9 to 8.7 degrees under \(P\) = 1 pu. As a result, the AL strategy is highly sensitive to delays, such as the one introduced by control discretization, measurements, or PWM. These delays have to be compensated to ensure a proper power limitation. Authors propose to add a phase compensation term, \(\theta_{d}\), to the PLL angle according to (18). This angle (in radians) depends on the number of samples to compensate \(n\), the estimated angular frequency \(\omega_{pll}\), and the controller sampling period \(T_{s}\).  
\[\theta_{d}=\omega_{b}\omega_{pll}nT_{s} \tag{18}\]  
Additionally, when the AL strategy is enabled, the PSL may experience windup issues as it is no longer part of the active control loop. To address this, an anti-windup  
Figure 7: Angle limiter strategy.  
mechanism is implemented (see blue box in Figure 7). The anti-windup is based on a modified virtual power strategy, which adjusts \(P^{*}\) based on the power which is not injected by the inverter. This power is calculated according to the difference between the saturated and unsaturated \(\delta^{\prime}\).
## 2 System Model
### The Received Signal  
The time domain signal will be impacted by the channel's fading as it travels across it. Here, \(h(\tau,v)\) represents the complex base-band response of the channel in the DD domain, where \(\tau,v\) represent the delay and the Doppler component, respectively. Then, \(h(\tau,v)\) represents the following:  
\[h(\tau,v)=\sum_{p=1}^{P}h_{p}\delta(\tau-\tau_{p})\delta(v-v_{p}) \tag{3}\]  
where \(P\) is the multipath number; \(h_{p}\) is the attenuation coefficient of \(p\)-th path; \(\tau_{p},v_{p}\) represent the discretized value of delay and the Doppler of the \(p\)-th path, respectively; and \(\delta(\cdot)\) denotes Dirac delta function. Then, after the time-domain transmitted signal passes through the channel, the receiving signal can represent the two-dimensional convolution of the time-domain transmitted signal and the DD channel, and the expression is as follows:  
\[r(t)=\int\int h(\tau,v)s(t-\tau)\mathrm{e}^{j2\pi v(t-\tau)}d\tau dv+z(t) \tag{4}\]  
where \(z(t)\) is the time domain additive Gaussian white noise. At the receiving end, the Wigner transform is applied to the time domain signal \(r(t)\), and the expression of the discrete signal \(y_{tt}[k,l]\) is obtained:  
\[y_{tt}[l,k]=\int r(t)g_{r}^{*}(t-kT)\mathrm{e}^{-j2\pi l\Delta f(t-kT)}dt \tag{5}\]  
Figure 1: The transmission model of the OTFS system.  
In (5), \(g_{\tau}(t)\) denotes the matching filter function of the receiving end. Similarly, when \(g_{\tau}(t)\) is a rectangular pulse form, Equation (5) is a discrete Fourier transform (DFT). Finally, after the symmetric finite Fourier transform (SFFT) is used, the resource grid \(y_{dd}[m,n]\) of the DD domain is obtained as follows:  
\[y_{\text{dd}}[m,n]=\frac{1}{\sqrt{MN}}\sum_{k=0}^{N-1}\sum_{l=0}^{M-1}x_{\text {tf}}[l,k]\text{e}^{-j2\pi(\frac{nk}{N}-\frac{ml}{M})} \tag{6}\]
## 3 Experimental Results and Discussion  
The suggested GSLEO-AEFA method is compared against six other algorithms, including AEFA [22], particle swarm optimization (PSO) algorithm [5], differential evolution (DE) algorithm [47], JAYA [48], sine-cosine algorithm (SCA) [13] and cuckoo search (CS) [49] to precisely assess its efficiency. 1000 iterations were completed with 30 executions to obtain the average and the standard deviation. The population size is set 30. Each algorithm's parameters were chosen to reflect the original, first-published standard versions by the authors. The parameters for each algorithm are shown in Table 1.  
Figure 3: Flowchart of GSLEO-AEFA. This test set consists of 10 fixed-dimension functions (F14-F23), six multi-modal functions (F8-F13), and seven single-modal functions (F1-F7). The detailed information is given in Table 2.
# Efficient Resource Management for Sum Capacity Maximization in 5G NOMA Systems
## 1 Introduction
### Related Work
in Reference [26] investigated an optimization problem to reduce total power consumptions in HetNet based on NOMA exploiting power multiplexing. They used a relaxation method to transform the non-convex power allocation problem to a convex problem and derived a closed form solution based on Lagrange method. Besides the aforementioned advances, a joint power and code multiplexing scheme was presented in 5G and 5 GB networks to improve the symbol error rate and sum rate of the system [27]. The proposed framework was compared with the conventional power multiplexing and code multiplexing. Their simulation results shown better performance compared to the baseline schemes. Moreover, the authors of References [28; 29] have also provided the rate and power allocation optimization for downlink and uplink transmission in rate splitting multiple access (RSMA) based wireless networks.
## 2 Control and Estimation under Information Constraints
### Coding/Decoding Schemes
#### 2.3.3 Coders with Memory  
The coding/decoding procedure can include the embedded observer, which adds a memory to the coder. The following model of _drive process_ is used:  
\[\hat{x}(t)=Ax(t)+B\varphi(t),\quad y(t)=Cx(t),\quad x(0)=x_{0} \tag{6}\]  
where \(x(t)\in\mathbb{R}^{n}\) is the process state space vector; \(y(t)\) is the scalar measured signal; \(A\in\mathbb{R}^{n\times n}\), \(B\in\mathbb{R}^{n\times 1}\) are given real matrices; and \(\varphi(t)\) is the external input signal which is assumed to be the same both on the transmitter and the receiver sides (so that the equi-memory condition be fulfilled).  
The _quantized observation error_\(\bar{\sigma}[k]\) is defined as a deviation between measured signal \(y(t)\) and its estimate \(\hat{y}(t)\) quantized with given \(M[k]\) as follows:  
\[\bar{\sigma}[k]=q\big{(}y(t_{k})-\hat{y}(t_{k}),M[k]\big{)},\quad t_{k}=kT_{0}. \tag{7}\]  
where the estimate \(\hat{y}(t)\) is generated by the following observer:  
\[\hat{x}(t)=A\hat{x}(t)+B\varphi\big{(}\hat{y}(t)\big{)}+L\bar{\sigma}(t),\quad \hat{y}(t)=C\hat{x}(t), \tag{8}\]  
where \(\hat{x}(t)\in\mathbb{R}^{n}\) is the state estimation vector; \(\hat{y}(t)\) is the estimate of the drive process; \(L\) is \((n\times 1)\)-matrix (the column-vector) of the observer parameters; and the continuous-time observation error \(\bar{\sigma}(t)\) is found as an extension of \(\bar{\sigma}[k]\) over the sampling interval. In the case of the _zero-order extrapolation_, \(\bar{\sigma}(t)\) has a form \(\bar{\sigma}(t)=\bar{\sigma}[k]\) as \(t_{k}\leq t<t_{k+1}\).
# Control of _Francisella tulareis_ Virulence at Gene Level: Network of Transcription Factors
## 7 Response Regulators Involved in Two-Component Systems Work as Transcription Factors
### KdpE  
KdpE, a response regulator transcription factor, interacts with the sensor kinase KdpD in many bacteria, and the KdpE/KdpD system is associated with the cellular regulation of potassium [65]. The presence of KdpE/KdpD was confirmed also in the _F. novicida_ genome, but detailed studies are missing. Mutant strains defective in the _kdpE_ or _kdpD_ genes are attenuated in animal models [47; 65]. In other bacteria, the KdpE/KdpD system is responsible for regulating the bacterial response to a variety of stress stimuli, and the DNA-binding activity of KdpE could play a key role in virulence and pathogenesis [65].