esa-sceva/satcom-chunk-collection huggingface dataset:
# A Bimodal Lognormal Distribution Model for the Prediction of COVID-19 Deaths
## 4 Validation
### Retrospective Predictions  
To use a larger dataset to assess the accuracy of the model predictions, a second validation study has been carried out. In this case, the model has been used to predict retrospectively the (known) number of deaths as of September 2020. Different dates were chosen for the end of the fitting range from 30 June to 15 September 2020. Table 2 shows the actual and forecast number of deaths in the six continents and the World. Percentages in parentheses represent the errors of the theoretical predictions with respect to the actual data, calculated as \(\left[D(t_{\text{forecast}})-F(t_{\text{forecast}})\right]/F(t_{\text{forecast}})\).  
Figure 2: Actual vs. predicted total deaths: (**a**) main continents, (**b**) World.  
The largest percent errors are observed for Oceania. In particular, the three-month forecasts (as of 30 June and 15 July 2020) are largely in defect because they do not take into account the effects of the second wave occurred in this continent since the end of July 2020. Oceania, however, carries a marginal weight in the global balance. Relatively large errors can be noted also in the three-month predictions for South America, Africa, and Asia. Actually, such predictions are computed on a relatively little amount of data, which causes poor quality of data fitting and extrapolation (as of June 2020, the epidemic was still at an early stage in many countries of these three continents). Three-month forecasts for North America and Europe are the most accurate, thanks to the large amount of data fitted (as of June 2020, the first peaks of the epidemic were already overcome). Two-month predictions (as of 31 July and 15 August 2020) and one-month predictions (as of 31 August and 15 September 2020) show absolute errors less than about 15% and 10%, respectively, for all of the continents (apart from Oceania).  
Interestingly, in the World summary, plus and minus errors seem to compensate and a much more stable trend of predictions is observed. The three-, two-, and one-month forecasts are affected by errors less than about 20%, 10%, and 1%, respectively.  
Based on the above, it can be concluded that the model is capable of capturing future trends with reasonable accuracy. Larger errors are expected for countries and continents with little amount of fitted data and where the epidemic is still at an early stage. Predictions for single continents may be affected by larger errors with respect to the World summary.
# Monitoring System Enhancing the Potential of Urban Bekeeping
## 3 Results
### Sensors  
The following characteristics of multi-sensor devices were studied: (1) the capability to reflect the levels and the temporal variation of the measured parameters inside and outside beehives, (2) the measurement data collection efficiency, (3) the percentage of outliers in the measurement data, and (4) the occurrence of the systematic change of individual sensor response.  
1.1 Capability to Reflect the Levels and the Temporal Variation of the Measured Parameters Inside and Outside Beehives  
An example of the results of measurements performed by multi-sensor devices is shown in Figure 6. In charts, we compared the data of the monitoring of air inside the beehive and outside, in the apiary, close to the beehive.  
Based on Figure 6, we see that the multi-sensor devices are capable of reflecting the actual values of the parameters of air inside the beehives as well as outside them. As shown, beehive air has a different composition in terms of the content of compounds, such as CO\({}_{2}\), VOCs, and PM, as compared with the ambient air in the apiary. The beehive environment is also featured by different thermal conditions (temperature and relative humidity) compared with its surroundings. As shown in Figure 6, the measurements done with multi-sensor devices also allow observation of the temporal variation of the parameters of air inside the beehives and outside them. For the majority of parameters, like T, RH, PM, and TVOC(2), the diurnal variation was well pronounced. However, other parameters, like CO2, NOx, and TVOC(1), seemed not to follow this kind of pattern of temporal change. In our future work, the data collected by our monitoring system will be  
Figure 5: Measurement frame with the multi-sensor device and endoscopic camera: (**a**) originally, and (**b**) after comb formation around the camera.  
extensively investigated regarding the mutual relationship between the ambient air and the beehive atmosphere in urban conditions.
# Design and Lateral Stability Analysis of an Attitude Adjustment Tractor for Moving on Side Slopes
## 5 Case Study and Experiment Variation
### Manufacture and Test of a Prototype
#### 5.2.2 Bench Tests
Therefore, the lateral stability method developed in Section 4 is accurate enough to bring a potential model to the actual solution. Results show that a tractor is able to increase its lateral stability by properly adjusting its configuration/posture, to avoid fatal accidents related to lateral overturns. In addition, the designed tractor can actively adjust its configuration/roll angle, to enhance the lateral overturn stability. More specifically, the configuration is changed by adjusting the rotation angle of the slewing bearing on both downhill and uphill sides.
# ICENET: An Information Centric Protocol for Big Data Wireless Sensor Networks
## 2 Related Works
### Information-Centric Protocols
If the receiver node has the requested content, it broadcasts a content object. Data is only sent in response to an interest and consumes the interest message. Therefore, periodic data sending ('convergecast') is not provided. Moreover, as in DD, data return along the reverse path of the interest propagation and a simple caching mechanism is adopted. Consequently, CCN-WSN still inherits some features that make it less appropriate for WSNs. Recently, the Content Centric Routing (CCR) protocol [35] was proposed for efficient in-network data aggregation and retrieval in IoT networks. The authors developed an objective function for the RPL protocol that enabled routing based on content, such that data messages with different content types were forwarded through different routes. To this end, the nodes execute the objective function in order to select the relay node for each traffic content. First, the node broadcasts a message informing the outgoing traffic content types, the corresponding traffic volume, and a candidate selection criteria. Candidate nodes respond with a message containing their node id and their estimated lifetime, if they are chosen to forward the data. Then, the source node ranks the candidates using the proposed objective function, and assigns a relay node to the corresponding traffic. Thus, nodes maintain a different routing entry for each piece of content. Moreover, received data are locally processed and aggregated. As a consequence, the CCR protocol has several advantages: Avoiding transmission of redundant network traffic, providing energy efficiency, and prolonging the network lifetime. However, only multi-point to point data collection scenarios are considered by [35], while the forwarding of data requests to data sources was not investigated.
# Hyperspectral Imaging to Evaluate the Effect of Irrigation Water Salinity in Lettuce
## 3 Results and Discussion
### Effect of Salinity on Spectral Features
#### 3.2.2 First Derivative of the Spectra  
Figure 4A shows the first derivative mean spectra of each saline treatment, where the maximum value corresponds with the red edge. A magnification of the red edge region can be seen in Figure 4B.  
A displacement to the right in the peak, though slight and progressive, can be observed in the S3 treatment (Figure 4B). However, numerous authors found that a displacement to the left of the red edge typically occurs when plants suffer environmental conditions of stress (water, nutritive, or salt stress) [21; 32; 38]. This displacement to the left is typically related to the loss of activity and the degradation of chlorophyll [39]. [PERSON] et al. [28] confirmed this fact in spinach leaves during shelf life and attributed it to aging processes and the loss of cell structure and chlorophyll. However, the effect of aging in harvested leaves over time is not similar to the effect of saline stress in freshly harvested leaves.  
[PERSON] et al. [26] performed a test with castor bean seedlings under salinity conditions of 0, 100, 200, and 300 mM of NaCl. They observed a displacement to the right of the red edge at 100 mM NaCl and displacements towards the left edge for higher concentrations. This salt concentration (100 mM) is in the range of the highest concentrations of the present research. The salinity may initially cause a displacement of the red edge to the right until reaching a certain level of salt concentration, subsequently shifting to the left when the salinity stress increases; this concurs with [26].  
Although it has been reported that an increment in the soil salinity generally tends to induce a decrease in leaf reflectance in the NIR spectral region [40], in this study in that region (800-1000 nm), it can be observed a minimal effect of the salinity. As high near infrared reflectance values are associated with proper development of the plants, it could be interpreted that the levels of salinity applied in the present study are not enough to induce change in the NIR, i.e. are not enough to cause the displacement of the red edge towards lower wavelengths.
## 2 Materials and Methods
### Statistics  
The statistical analysis was performed using the statistical software package SPSS for Windows (Version 23, SPSS Inc., Chicago, IL, USA). In order to compare the two methods, precision and accuracy were evaluated. Precision was indicated as the standard deviation (SD) of the repeated measurements. Accuracy was defined as the bias, which was described as the average difference between the measured and true values. Box plots to visualize data distribution or outliers were plotted, as well as Bland-Altman plots [28] to assess the interchangeable applicability of the 2D\({}_{\text{wear}}\) next to the golden standard 3D\({}_{\text{wear}}\) method. Within these scatter plots, the calculated differences for detected wear by both methods were plotted against their average value. The limit of agreement (LoA) was set to mean \(\pm\) 1.96 standard deviations of the measured differences, representing approximately 95% of all measured values. The influence of pelvic position was statistically proven using a paired t-test or Wilcoxon matched-pair signed-rank test, which was applied between each paired wear position depending on whether the difference between the two positions conformed to a normal distribution. The level of significance was adjusted to \(p\) < 0.05.
# GNSS-R Observations of Marine Plastic Litter in a Water Flume: An Experimental Study
## 2 Materials and Methods
### GNSS-R-Like System
#### 2.1.2 Reception System  
The transmitted signal was then simultaneously received by two antennas placed at a 1 m height with respect to the water level: a RHCP one looking upwards to acquire the direct GPS signal, and a dual polarization one (RHCP and LHCP) looking downwards to pick the GPS signal scattered on the water surface.  
The up-looking antenna was connected to a Universal Software Radio Peripheral (USRP) model B200 mini-i from Ettus Research [8], which down-converted and sampled the received signal at a rate of \(f_{\mathrm{s}}=2.5\) MSps. This signal is used as the reference one.  
Figure 1: GNSS-R transmitters and receivers mounted at Deltares Atlantic Basin premises. In the middle of the basin the elevation transmitters and receivers are hanging from the upper corridors, while in the lateral side three azimuthal transmitters are located. The 60\({}^{\circ}\) elevation antenna is not seen [[[https://www.deltares.nl/en/facilities/atlantic-basin-3/](https://www.deltares.nl/en/facilities/atlantic-basin-3/)]([https://www.deltares.nl/en/facilities/atlantic-basin-3/](https://www.deltares.nl/en/facilities/atlantic-basin-3/)), accessed on 18 October 2022].  
The down-looking antenna was connected to an ADALM-PLUTO (rev C) SDR [7], which down-converted and sampled synchronously the two received signals at both polarizations at a rate of \(f_{s}\) = 2.046 MSps.
# A Review of the Scheduling Problem within Canadian Healthcare Centres
## 4 Hospital Scheduling in Depth
### Operating Room Scheduling
#### 4.3.2 Papers
(2018) look into how to use historical data to predict patient arrivals. Their work was spurred by the effects of the COVID-19 pandemic, which has created a severe backlog of patients whose surgeries were cancelled. The goal was to determine how many surgeries require scheduling and how many additional surgeries a operating room could handle in attempts to whittle down the list. The data was gathered from six different sources:  
* The Province of Ontario Wait Times Information Systems. * The Canadian Institute for Health Information National Ambulatory Care Reporting System. * The Canadian Institute for Health Information Discharge Abstract Database. * The CorHealth Ontario Cardiac Registry. * The Trillium Gift of Life Network Organ and Tissue Allocation System. * The Surgery Efficiency Target Program. From these sources it was estimated that there were around approximately 148,364 surgeries in the backlog that needed to be scheduled. This estimate comes from the predicted vs. observed surgeries using historical data from 15 March to 13 June. As surgeries were cancelled this led to a drop in surgeries being performed and with this data we can extrapolate an approximate number of cancellations. The next step was to estimate how much additional stress the system can withstand given new surgeries. Time series models were used to forecast surgeries to determine both how long it would take to address the issues and what resources would be needed. Probabilistic sensitivity analysis was used to account for uncertainty. It was found that on average it would take 9 ICU beds, 265 ward beds, 719 OR hours every week for 84 weeks to eliminate the backlog. This would allow the operating rooms to allow an additional 717 patients through. The overall goal of this data collection is to pass it onto Regional Leads who in turn can use this data to more efficiently and effectively schedule ORs.
# FM-UWB: Towards a Robust, Low-Power Radio for Body Area Networks
## 2 Review of FM-UWB Transceivers
### Summary  
The above sections described the most important power reduction techniques applied in the FM-UWB transceivers. These techniques, combined with technology scaling, led to sub-milliwatt power levels in today's implementations. The evolution of power consumption over the past eight years is illustrated in Figure 5 for both transmitters and receivers.  
Although there has been a decrease by a factor of 20, the narrow-band receivers still have the edge, at least when it comes to power consumption. Proposed wake-up receivers found in literature consume from 100 \(\mu\)W [26] all the way down to 100 nW [27]. FM-UWB can hardly compete with such low levels, a simple consequence of the fact that wide-band circuits require more power to achieve the same performance in terms of gain and noise figure. On the other hand, the FM-UWB brings higher resilience to interferers without off-chip components such as SAW filters, better performance in frequency selective channels and more potential for miniaturization. All of these are very favorable capabilities that could assure a place for FM-UWB in short-range applications such as Body Area Networks.
## 1 Introduction
The cloud server is responsible for control plane operations and necessary management settings related to transmission security, such as key exchange, with various endpoint devices (i.e., fog nodes). Each endpoint device conducts routine processing tasks like data compression/decompression, encryption/decryption, and transmission/reception in the data-plane. This approach significantly reduces the storage and computational burden on the server. In terms of system implementation, the design and management functionalities need to be updated and flexible in the control plane, so development will primarily utilize a software-centric approach. Additionally, in the data-plane, considering the routine, repetitive, and cyclic nature of data-flow processing, Field-Programmable Gate Array (FPGA) will be employed for necessary hardware data stream processing pipelines to ease the computational load on the device's micro-processor (\(\upmu\)P). In addressing the security requirements of zero-trust IoT devices in future 6G communication, the primary contribution of this paper is the attainment of theoretical unbreakability based on one-time pad [15] through the proposed secure mechanism. A one-time pad is one in which a key can only be used once (i.e., non-repeating) for data encryption, which is proven by [PERSON] [16] to possess theoretical un-breakability. Nevertheless, the computational intensity of the non-repeating check poses a challenge for conventional pure software implementations. Consequently, the proposed secure mechanism incorporates purpose-designed hardware accelerators to offload the computational intensity from the software executed by the micro-processor. As a result, the integration approach of the micro-processor and hardware accelerators in the FPGA, encompassing system design architecture, execution flow, and bus components of the micro-processor, accelerators, and other essential controllers, will be elucidated in the Section 4 \"Design Implementation\". This paper is structured as follows: Section 2 provides an explanation of the background of zero-trust networks, IoT security architecture, and the proposed mechanism. Section 3 introduces the research methodology, encompassing the applied cipher algorithm, security method, and operational process. Section 4 delves into the system design and implementation. In Section 5, experimental results are analyzed. Finally, Section 6 presents a discussion and the conclusion.
## 2 Establishment of LIM Dynamic Model and Preliminaries
### Fuzzy Logic System (FLS)  
In fact, all the parameters of the system cannot be correctly estimated because of many reasons. Firstly, when measuring the values of resistance, inductance and capacitance, we cannot guarantee the accuracy of the measuring tool. Secondly, when the LIM works, the temperature and other environmental factors in the system will cause the parameters to change greatly or slightly. Thus, so as to weaken or even eliminate the effects of uncertain nonlinear functions and parameters on speed control, FLS is applied whose fuzzy rules are deduced as [11]:  
\[\text{IF }x_{1}\text{ is }F_{1}^{j}\text{ and }\cdots\text{ and }x_{n}\text{ is }F_{n}^{j}\text{ Then }y\text{ is }A^{j}\]  
where \(j=1,2,\cdots,N\); \(x=[x_{1},x_{2},\cdots,x_{n}]^{T}\in R^{n}\) is the input FLS while \(y\in R\) is the output; \(F_{i}^{j}\) and \(A^{j}\) are the fuzzy sets. Thus, we can get the output of the FLS as:  
\[y\left(x\right)=\frac{\sum\limits_{j=1}^{N}w_{j}\prod\limits_{i=1}^{n}\mu_{i} ^{j}\left(x_{i}\right)}{\sum\limits_{j=1}^{N}\prod\limits_{i=1}^{n}\mu_{i}^{j} \left(x_{i}\right)} \tag{11}\]  
where \(w_{j}\) is defined as the parameter of the consequent part \(A^{j}\), \(\mu_{i}^{j}\left(x_{i}\right)\) the fuzzy membership function. Let \(b_{j}\left(x\right)=\frac{\prod\limits_{j=1}^{n}\mu_{i}^{j}\left(x_{i}\right)}{ \sum\limits_{j=1}^{N}\prod\limits_{i=1}^{n}\mu_{i}^{j}\left(x_{i}\right)}\), \(B(x)=[b_{1}(x),b_{2}(x),\cdots,b_{N}(x)]^{T}\) is fuzzy basis function vector, so the FLS could be overwritten as:  
\[y\left(x\right)=W^{T}B\left(x\right) \tag{12}\]  
The following lemma can be guaranteed once all the fuzzy membership functions belong to Gaussian functions:  
**Lemma 1**.: _[_11_]_ _: If function \(g(x)\) is on a compact set \(\Lambda\), then the FLS gets the following property for any \(\varsigma>0\):_  
\[\sup_{y\in\Lambda}\left|g\left(x\right)-y\left(x\right)\right|\leq\varsigma \tag{13}\]
# A New Observation in Spiral and Curl Antenna Configurations above the Ground Plane
## 3 Curl Antenna
### Direct Feed  
Figure 5 shows the configuration of a curl antenna. The antenna is made of a wire of radius \(\rho\), which is bent at height \(h\) and curled above the ground plane. The antenna consists of vertical (\(o\)-\(o^{\prime}\)), horizontal (\(o^{\prime}\)-\(a\)), and curled (\(a\)-\(b\)) parts. The Archimedean spiral function defines the curled part, with its center being at point \(o^{\prime}\). The length of the horizontal part (\(o^{\prime}\)-\(a\)) is given by \(a_{\rm s}\cdot\phi_{\rm st}=L_{\rm f}/2\). The curl's outer circumference and adjacent arm distance are given by \(2\pi a_{\rm s}\cdot\phi_{\rm end}=C\) and \(2\pi a_{\rm s}=2\ d\), respectively. Note that the definitions (\(C\), \(d\), \(L_{\rm f}\)) used for the two-arm spiral antenna shown in Figure 1 apply to the present curl antenna. The antenna is fed by a coaxial line at the lower end of the vertical wire, at point \(o\).  
Preliminary calculations show that, when the configuration parameters are \(C\), \(d\), \(L_{\rm f}=1.19\lambda_{0}\), \(0.02\lambda_{0}\), \(0.22\lambda_{0}\), respectively, a curl antenna at \(h=0.15\lambda_{0}\) radiates a CP wave with an axial ratio of less than 0.1 dB. These parameters are found to be almost the same as those for a low-profile spiral antenna at \(h=0.15\lambda_{0}\), as described in Section 2: \(C\), \(d\), \(L_{\rm f}=1.18\lambda_{0}\), \(0.02\lambda_{0}\), \(0.20\lambda_{0}\), respectively. From the similarity in the configuration parameters for CP radiation, it can be said that the curl antenna corresponds to a low-profile spiral antenna.  
Figure 4: Input impedance versus antenna height \(h\).  
Figure 3: CP wave bandwidth versus antenna height \(h\).  
Now, we reduce the antenna height of the curl from \(h=0.15\lambda_{0}\). The left side of Figure 2, denoted as \"curl antenna\", shows the optimized parameters (\(C\), \(d\), \(L_{\text{f}}\)) for CP radiation with an axial ratio criterion of 0.1 dB. It is emphasized that the outer circumference C and the adjacent wires at distance \(d\) smoothly vary in the antenna height range of the curl and the spiral.  
The left part of Figure 3 shows the 3 dB axial ratio bandwidth versus height \(h\). A bandwidth of 8% at \(h=0.15\lambda_{0}\) decreases to 3% at \(h=0.10\lambda_{0}\). It should be noted that the curl antenna with \(h=0.10\lambda_{0}\) can be constructed, but the previous two-arm spiral antenna with \(h=0.10\lambda_{0}\) cannot be constructed due to the extremely small adjacent wires at distance \(d\). The curl, therefore, has an advantage over the spiral with respect to reducing antenna height.  
The left part of Figure 4 depicts the input impedance \(Z_{\text{in}}=R_{\text{in}}+j\;X_{\text{in}}\) for a smaller antenna height than \(h=0.15\lambda_{0}\). The curl has an input impedance of \(80-j\;200\;\Omega\) at \(h=0.10\lambda_{0}\), which gives a high VSWR (of 12 to a 50-\(\Omega\) coaxial line). In Section 3.2, impedance matching is considered.
# A CMOS-Based Terahertz Reconfigurable Reflectarray with Amplitude Control: Design and Validation
## 4 Experimental Verification  
To validate the performance of the terahertz amplitude-control RRA element, the split-ring resonator structure of the element is fabricated into an array employing the standard 65 nm CMOS processes, followed by tape-out, interconnects, and packaging, to construct a terahertz on-chip amplitude-control RRA prototype. Unit-level performance characterization is conducted on the prototype to extract its amplitude and phase response characteristics.