esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
Some solutions offer adaptations of paper-based tests to evaluate specific cognitive domains [28] (e.g., the Trail-Making Test or Simple and Complex Reaction Time) or multiple cognitive domains [27] (e.g., MoCA [29], MMSE [30], or SAGE [31]), while other solutions (e.g., Memoro [32], the NutriNet-Sante Cognitive Test Battery (NutriCog) [33], or the Cambridge Neuropsychological Test Automated Battery (CANTAB) [34]) were specifically developed to be applied using electronic means. In recent years, several innovative solutions have been developed for diagnosing, monitoring (e.g., artificial intelligence applied to radiomics analysis [35]), and managing cognitive impairment (e.g., digital solutions to support self-management in older people with cognitive impairment [36]). Furthermore, the scientific literature reports the development of new instruments that are able to monitor individuals in their residential environments without the presence of a health professional [27]. This possibility maximizes flexibility and widens people's access to cognitive assessment at lower costs [27]. In this respect, smart devices (e.g., smartphones, smartwatches, or smart-home devices) may collect data on individuals' habits and patterns, which can be analyzed to detect subtle changes that may indicate a decline in cognitive performance [37]. Moreover, serious games and virtual reality are alternative approaches to cognitive screening and may also reduce feelings of test anxiety [37; 38]. The research question to be addressed in this systematic review and meta-analysis is: how accurate are digital solutions for detecting both the presence and absence of cognitive impairment in individuals aged 18 years old and over? The primary goal of the current review is to synthesize the evidence on digital solutions' diagnostic ability to screen for cognitive impairment and their accuracy. A secondary goal is to distinguish whether the ability to screen cognitive impairment varies as a function of the type of digital solution: (1) based, in essence, on pre-existing traditional tests, named paper-and-pencil tests (abbreviated as paper-based digital solutions throughout this article); (2) developed from their inception to be applied by electronic means (abbreviated as innovative digital solutions throughout this article).
# Energy Communities Design Optimization in the Italian Framework
## 3 Case Study  
The study reported in this paper considers a typical building in the north of Italy, which features nine apartments occupied by both residential (six) and commercial (three) users; hence, it entails a total of ten PODs: one for each apartment and a condominium one. In particular, the residential apartments are supposed to be occupied by two couples of elderly people, two couples of young workers, and two families with children; the commercial users are represented by offices. Reasonable reference values of annual demands for each category of users and the estimate of their daily profiles have been collected from previous papers and studies [16; 17; 18; 19; 20; 21] (see Table 2). Some hypotheses about occupants' lifestyle have been introduced to differentiate such daily profiles among the same typologies of occupants and therefore consider a more realistic building energy consumption. Some of the considered typical lifestyle types are: different wake-up, lunch, and or sleep time; out-of-home vs. smart/at-home place of work; possibility for someone to come home for the lunch break; different intensity of \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Surface** & **Electricity** & **Space Heating** & **DHW** & **Cooling** \\  & **[m\({}^{2}\)]** & **[kWh]** & **[kWh]** & **[kWh]** & **[kWh]** \\ \hline
**Old couple 1** & 80 & 2700 & 4744 & 1160 & 1205 \\
**Old couple 2** & 80 & 2700 & 4744 & 1160 & 1205 \\
**Young couple 1** & 80 & 2400 & 4744 & 1115 & 1194 \\
**Young couple 2** & 80 & 2400 & 4744 & 1262 & 1194 \\
**Family 1** & 120 & 3200 & 7116 & 1673 & 1801 \\
**Family 2** & 120 & 3200 & 7116 & 1809 & 1801 \\
**Office 1** & 120 & 2923 & 6982 & 588 & 1448 \\
**Office 2** & 100 & 2338 & 6277 & 470 & 1158 \\
**Office 3** & 140 & 3507 & 8379 & 705 & 1738 \\
**Condinium** & - & 531 & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Floor area and total yearly electricity, space heating, domestic hot water (DHW), and cooling demands for each apartment. the loads' usages; different opening/closing hours for the offices; different sizes of the offices (i.e., three-room vs. double-room). These aspects affect both mid and peak load values throughout the day, ramps up/down in the evening and in the morning as well. The resulting profiles are reported in Figure 1, whereas the daily peaks and total values for the electricity, heating, and cooling demand are reported in Tables 3-5, respectively. As already stated in Section 2.1, yearly demands of electricity, heating power, and cooling power are represented by means of \"typical day\" profiles. Typical days have been selected in order to consider the seasonality of the demands during the year: \"Typical day 1\" represents wintertime, \"Typical day
## 4 Discussion
### Energy Convergence of Our Results with Respect to the System Size  
Although full energy convergence is achieved in each QMD simulation, the accuracy of our results in regard to the system size must also be ensured. We checked that the full energy convergence of our results with respect to the system size is achieved for  
Figure 9: The same as in Figure 3 for U\({}_{1-x}\)Pu\({}_{x}\)O\({}_{2}\), \(0<x<1\). Only the running averages are shown in either case.  
both UO\({}_{2}\) and PuO\({}_{2}\). Since all the MOX systems were studied using supercells of exactly the same size as that of both UO\({}_{2}\) and PuO\({}_{2}\), namely \((1-x)\) UO\({}_{2}+x\) PuO\({}_{2}\), full energy convergence must have been achieved in each case. Specifically, in the case of UO\({}_{2}\), a supercell of 324 atoms \((3\times 3\times 3)\) is enough to achieve the energy convergence of up to 1 meV/atom, or uncertainty of \(\sim\)10 K as regards the value of \(T_{m}\), which is an order of magnitude less than the uncertainty of the \(Z\) method itself. Indeed, direct comparison of total energies as the outcomes of the 324- and 768-atom \((4\times 4\times 4)\) runs, for both UO\({}_{2}\) and PuO\({}_{2}\), clearly shows that the 1 meV/atom energy convergence was already achieved. Figures 10 and 11 demonstrate the (ambient) melting run of the 768-atom UO\({}_{2}\) system. In this case \(T_{m}\approx 3200\) K, which is consistent with its 324-atom counterpart of \(\approx\)3150 K within the uncertainty of the method itself. Similarly, Figures 12 and 13 demonstrate the (ambient) melting run of the 768-atom PuO\({}_{2}\) system. In this case \(T_{m}\approx 3100\) K, which is also consistent with its 324-atom counterpart of \(\approx\)3050 K within the uncertainty of the method itself. It is clearly seen in these figures that \(P_{m}\) has also converged, just as \(T_{m}\).
## Materials and Methods
### Modelling Spatiotemporal Crop Productivity  
The GRAMI-rice model was formulated to integrate remotely sensed data, allowing agricultural system modellers to reproduce and monitor potential crop production information [18]. The GRAMI model can receive remote sensing data as an input to execute the 'within-season' calibration procedure [14]. In this process, simulated crop canopy growth (LAI or VIs) is compared with corresponding measured values to allow agreement with the measurement with a minimal error based on parameterization of specified parameters. Four different coefficients (\(L_{0}\), \(a\), \(b\), and \(c\)) are employed in the current model to describe growth processes of rice. Parameter values were obtained through a parameterization process using the Bayesian method with a prior distribution chosen according to the estimates from the previous reports. The relationships between five VIs and LAI were framed using the log-log linear regression models as previously described.  
A Crop Information Delivery System (CIDS) was designed earlier as an extended version of the GRAMI-rice model [18] to project pixel-based geospatial crop growth and yield, based on the integration with remote sensing images (Figure 3a). The CIDS employs pixel-based remote sensing data and climate data as the system inputs. The CIDS takes climate data, either from a single weather station or multiple weather stations (pixels) depending on the situation. The GRAMI-rice model is then implemented to simulate crop growth in each pixel using both types of input data.  
In the CIDS, the GRAMI model simulates a crop in each pixel using both remote sensing data and climate data as inputs [18]. The new model design was evaluated using paddy field datasets obtained in 2012 at Chonnam National University, Gwangju and in 2014 at Gimje and Gyewha, Chonbuk Province, ROK. The CIDS was then applied using RapidEye satellite images obtained from paddy fields of interest in Chonbuk province, ROK in 2014.  
Figure 3: Diagram of the integrated crop modelling system to project spatiotemporal crop productions using the GRAMI model (**a**) and observed (O) and simulated leaf area index (LAI) being matched with a minimal error using the within-season calibration (**b**). GDD = growing degree day; VI = vegetation index.
## 4 ANN Modeling Procedure
### Assisted-ANN Modeling Approach
In addition to the overall lead crack SIF correction factor (\(\beta_{a}\)), which is used in the second assistance level, the overall SIF correction factor for the adjacent MSD crack (\(\beta_{\ell}\)) is also used. The \(\beta_{\ell}\) mainly accounts for the cracks interaction effect and the hole effect (as seen in Equation (6)) and its values are also given in the tables. * _Fourth assistance level_: here, instead of using the SIF correction factors, the residual strength value calculated by the linkup model (Equation (4)) is used as the assistance parameter. As can be seen form the linkup model equation, it includes the SIF geometric correction factors as well as the lead and MSD crack lengths, the ligament length and the material's yield strength. Therefore, the inclusion of the linkup model's residual strength predictions provides the highest level of assistance since it combines the effects of many of the input parameters together. It is important to be noted here that the exact same input parameters used in the traditional approach (7 inputs) are also used in the assisted approach, and the different assistance parameters are included as additional inputs. From the theoretical stand point, one might argue that the inclusion of these assistance parameters among the ANN model inputs eliminates the need for some of the direct geometric input parameters. For instance, for the stiffened panels, the overall lead crack SIF correction factor (\(\beta_{a}\)) accounts for the finite width effect and the complete effect of the stiffeners (their cross-sectional area and spacing, fasteners, etc.). Accordingly, it might be rational to eliminate some of the related direct inputs in such case (such as the panel width and stiffeners cross-sectional area). Nevertheless, in the work presented here, all the traditional direct inputs are kept the same in the four different assistance levels. The purpose of keeping all the direct inputs is mainly to demonstrate the advantage of adding the assistance parameters on the ANN model accuracy. These assistance parameters are basically intended to assist the ANN learning/prediction process, and not to replace any of the direct input parameters. The values of the assistance parameters used at the different levels of assistance for each of the different panel configurations are given in Tables 11-13. The SIF geometric correction factors, which are being used here as assistance parameters, are widely used in fracture mechanics for both fatigue and static fracture calculations. Accordingly, the SIF correction factors for many geometric configurations are readily available and can be found in the literature. Most of the SIF correction factors used in this study are obtained from literature [43; 56; 57; 58] while for the lap-joint panels, the SIF correction factors are obtained using FEA [29].
## 4 Security Threats and Attacks
### Attacks on Physical Layer  
Physical systems transmit a control signal and sensor data through the network. When an attacker accesses the network, it can modify the control-related data on the network which results in divergence from the physical state. Attackers can manipulate it in three ways: the physical layer, firstly it manipulates sensor data, secondly, it manipulates control signals, and thirdly the attacker manipulates both sensor data and control signals.  
During a sensor attack, sensor movement is manipulated to deceive the computing system in performing state estimation which causes computational faults in input data of the actuating process. Then, this faulty input creates malfunctioning of the physical system. In the physical layer, sensor attacks require more attention such as pole dynamic attack (PDA) is a famous sensor attack where attackers use matrices of the physical system and then swiftly change its state which creates difficulty in detecting PDA by traditional detecting methods. The controller attack is another type of physical layer attack which is the modification of the input signal. Attackers or malicious users destabilize the physical system by unpredicted control signals. Controller attacks affect the dynamics and operation of the physical system. In the cyber study, controller attacks on input signals are called zero dynamics attacks (ZDA). During ZDA, the internal state of the physical system diverges toward infinity which is not detected by sensor data due to the property of zero dynamics. A combined attack is a combination of a sensor attack and a controller attack; it launches on both channels. Therefore, it is difficult to launch a combined attack by using sensor and control signals both. However, the detection of combined attacks requires more resources in handling both types of attacks. Attackers have complete knowledge of the physical dynamics of the system in launching a covert attack that is a combined attack. Hackers or malicious users use complete information in generating control input signals from the sensors.
## 2 Theoretical Aspects
### A Nonlinear Hybrid Control Strategy Combining Dry Friction and Active Control Force
#### 2.2.2 Control Law Design in the Second State Space  
The control law is designed in a second, new state space by choosing  
\[\mathbf{Y}_{\mathbf{1}}=\mathbf{Y}, \tag{9}\]  
\[\mathbf{Y}_{\mathbf{2}}=\dot{\mathbf{Y}}_{\mathbf{1}}=\dot{\mathbf{Y}}. \tag{10}\]  
Let us note  
\[\mathbf{F}_{\mathbf{nl}_{\mathbf{m}}}=-\beta|\mathbf{Y}_{\mathbf{2}}|^{a}\text{sign}(\mathbf{Y}_{\mathbf{2 }}), \tag{11}\]  
where \(|\mathbf{Y}_{\mathbf{2}}|^{a}=\left|\dot{\mathbf{Y}}\right|^{a}=\left[\begin{array}{c} \left|\dot{y}_{1}\right|^{a}\\ \left|\dot{y}_{2}\right|^{a}_{x}\\ \left|\dot{y}_{3}\right|^{a}\end{array}\right]\);  
\(\text{sign}(\mathbf{Y}_{\mathbf{2}})=\left[\begin{array}{c}\text{sign}(\dot{y}_{1}) \\ \text{sign}(\dot{y}_{2})\\ \text{sign}(\dot{y}_{3})\end{array}\right]\);  
\(\beta=\left[\begin{array}{cc}\beta_{11}&0&0\\ 0&0&0\\ 0&0&0\end{array}\right]\).  
This control law was proposed to amplify the state space velocity with an exponential function, so this control law is denoted as \"nonlinear velocity compensation control\" in the following text.  
The active control force in the state-space is defined by  
\[\mathbf{F}_{\mathbf{c}_{\mathbf{m}}}=-\mathbf{C}\mathbf{Y}_{\mathbf{2}}=-\mathbf{C}\dot{\mathbf{Y}}, \tag{12}\]  
where \(\mathbf{C}\) is the coefficient matrix of active control force, which is positive definite.  
By utilizing the transformation matrix, the active control force physical space is given by  
\[\mathbf{F}_{\mathbf{c}_{\mathbf{p}}}=\mathbf{P}^{-1}\mathbf{F}_{\mathbf{c}_{\mathbf{m}}}. \tag{13}\]  
In this study, the active control force was applied on the dry friction surface; that is to say, the active control force was applied on \(m_{1}\) and \(m_{2}\). Thus, the distribution matrix \(\mathbf{D}_{\mathbf{p}}\) is defined as Equation (14) as follows:  
\[\mathbf{F}_{\mathbf{c}}=\mathbf{D}_{\mathbf{p}}\cdot\mathbf{F}_{\mathbf{c}_{\mathbf{p}}}, \tag{14}\]  
where \(\mathbf{D}_{\mathbf{p}}=\left[\begin{array}{ccc}1&0&0\\ 0&1&0\\ 0&0&0\end{array}\right]\).
## 3 Results and Discussion
### The Failure Pattern under Compression and Brazilian Testing
#### 3.1.1 The Acoustic Emission Characteristics of Frozen SRM Samples  
The AE energy signals and stress-strain curves for the compression and Brazilian tests are depicted in Figure 7. As can be seen, the AE energy curves of the two types of tests were obviously different in the post-peak stage. The AE time-energy curve showed a unimodal type under the Brazilian test; however, the AE time-energy curve showed a multimodal type under the uniaxial compression test, which is similar to the results of the report by [PERSON] and [PERSON] [40,41]. At the beginning stage of loading, both of the AE activity types could be attributed to the compaction and the breakage of ice crystals during minor stress condition [41]. In the linear elastic stage of the loading curve, only a few acoustic emission signals with low amplitudes could be detected. In the pre-peak region, in which the curves showed an apparent non-linear deformation, both evolutions of AE signals showed a similar response, where the AE signals increased with the increase of the axial stress or the vertical force. Subsequently, in the post-peak stage, the AE signal sharply decreased to a relatively small value under the Brazilian test. Nevertheless, the apparent AE signal was observed in the post-peak stage under the uniaxial compression test and the amplitude was obvious.  
Acoustic emission signals effectively reflect the mechanical deformation of geomaterials [42, 43, 44]. According to the comparison of the mechanical and acoustic responses of the FSRMs subjected to the uniaxial compression and Brazilian tests, the differences in the AE signal responses can be attributed to the motion and friction of internal components, especially the rock blocks during loading. For the Brazilian test, the samples mainly experienced radial tensile stress, especially in the central surface. The separation between the rock and soil matrix and the cracks propagated into the soil matrix mainly occurred in the central surface of the cylindrical disk, which triggered obvious acoustic emission. The obvious acoustic emission activity stopped when the crack had fully propagated throughout the disk sample. Nevertheless, the whole acoustic emission activity pattern was inconclusive under uniaxial loading. Being a mixture composed of soil particles, rock blocks, and ice granules with widely contrasting physical properties, from the inspection of the samples after uniaxial loading testing, obvious surface cracks typically occurred in the post-peak  
Figure 6: Failure patterns of FSRMs under Brazilian testing: (**a**) radial force–displacement curves; (**b**) failure patterns; (**c**) the rock fragments in the tensile cracks; (**d**) fracture morphology of a rock fragment.  
stage. It may be inferred that the movement of rock blocks was relatively active in the post-peak stage, thereby triggering evident AE signals. In the pre-peak stage, the creeping and friction of soil particles and ice granules could be assumed rather than the obvious movement of the rock blocks.
## 2 Port Cranes Overview
### Evolution Timeline of Port Cranes
#### 2.2.3 Early 20 th Century (1900-1950s)  
* Electric Crane Adoption (early 1900s)--At the dawn of the 20 th century, electric motors began to replace steam engines in cranes. This transition offers numerous advantages including smoother operation, higher reliability, and easier control. Electric cranes reduce the maintenance costs associated with steam engines such as boiler maintenance and coal handling. By 1910, many major ports in developed countries had started to incorporate electric-powered cranes into their operations, leading to increased productivity and a more stable power source for lifting heavy loads [2].
* Bulk Handling Innovations (1920s-1940s)--The 1920s-1940s saw significant advancements in bulk cargo handling. Hydraulic systems and grab buckets were introduced, and revolutionizing materials such as coal, ores, and grains were loaded and unloaded. These innovations increased the throughput of bulk cargo at ports, as hydraulic systems provided more precise control over the lifting and dumping of materials. For example, hydraulically operated grab buckets can be adjusted to pick up different amounts of bulk materials and improve the efficiency of handling various types of cargo [2].
## 4 NR Navigation System
### Transmitting and Reception System Block Diagram
To avoid the noise interference and achieve stable tracking time delay, a second-order DLL tracking loop is utilized and it contains a first-order filter [2]:  
\[\tau_{\text{f}}=\tau_{\text{f,pre}}+\omega_{n}\cdot 1.414\left[\tau_{\text{meas }}-\tau_{\text{meas,pre}}\right]+\frac{T_{\text{coh}}}{2}\omega_{n}^{2}\left[ \tau_{\text{meas}}+\tau_{\text{meas,pre}}\right] \tag{14}\]  
where \(\tau_{\text{meas,pre}}\) and \(\tau_{\text{f,pre}}\) represent the measured and filtered delay from the previous integration period, respectively. The natural frequency \(\omega_{n}\) critically affects the tracking accuracy and dynamic response of the NELP. \(\omega_{n}\) is calculated by the DLL noise bandwidth \(B_{\text{L,DLL}}\):  
\[\omega_{n}=\frac{B_{\text{L,DLL}}}{0.53} \tag{15}\]  
Next, the time delay estimate \(\tau_{\text{est}}\) is derived according to the previous estimate \(\tau_{\text{est,pre}}\) and the filter output \(\tau_{f}\):  
\[\tau_{\text{est}}=\tau_{\text{est,pre}}+T_{\text{coh}}\cdot\tau_{f} \tag{16}\]  
FLL operates on the prompt branch, and the frequency offset \(f_{e}\) of the received signal is measured according to the phase change. The phase can be obtained from correlation value \(r_{P}\), which is integrated correlation between the IF signal \(s_{IF}(t)\) and local replica signal:  
\[r_{P} =\int_{0}^{T_{\text{coh}}}s_{\text{IF}}(t)s_{\text{RS,P}}^{*}(t)dt \tag{17}\] \[=\int_{0}^{T_{\text{coh}}}[s_{\text{RS}}(t)+s_{\text{COM}}(t)]e^{ -i(2\pi f_{f}t+\varphi)}s_{\text{RS,P}}^{*}(t)dt\] \[=R_{P}(\tau)\text{sinc}(f_{e}T_{\text{coh}})e^{i\varphi}+n_{\text{ COR}}\]When the correlation noise \(n_{\text{COR}}\) is negligible, the phase variation between consecutive integration periods becomes measurable:  
Figure 3:0  
where \(r_{P,\text{pre}}\) and \(\varphi_{\text{pre}}\) denote the correlation value and phase offset in previous integration period, respectively. Hence, the frequency offset is calculated as follows:  
Figure 3:1  
Like DLL, the FLL employs a low-pass filter to eliminate high-frequency noise from \(f_{\text{meas}}\). The filtered output updates the local carrier frequency \(f_{\text{local}}\) to stably track the frequency of the received signal. Different from the conventional communication receiver, the time-delay configuration of the local replica signal is a new requirement for the NR opportunity navigation receiver. The total delay \(\tau\) decomposes into integer and fractional components relative to the sampling interval \(T_{s}\):  
Figure 3:2  
Integer delay \(\tau_{\text{int}}\) is implemented through discrete signal shifting, while fractional delay \(\tau_{\text{frac}}\) is achieved by precise phase adjustment of subcarriers. For example, the \(k\)-th subcarrier accomplishes early and late phase shifts \(\tau_{\text{frac}}\) by multiplying modulated symbol \(c_{n,k}\) with complex exponential functions:  
\[\left\{ \begin{array}{l}c_{n,k,E}=c_{n,k}e^{-\beta\pi t\tau_{\text{frac}}}/K \\ c_{n,k,L}=c_{n,k}e^{i[2\pi kt\tau_{\text{frac}}}/K\end{array} \right. \tag{21}\]
## 5 Numerical Example
### Example 1
In this case, the effect of the truncation error is sufficiently small. In addition, only one communication channel is required. Thus, communication channels used in control can be reduced by using block-sparse reconstruction.