esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Evaluation of the Platform and Discussion
### Impact and Usability Evaluation
#### 4.4.1 Impact Questionnaire  
The impact of the system on the users was evaluated through a small questionnaire composed of five questions, where each question could be evaluated from 1 to 5 points, with 1 being strongly disagree and 5 being strongly agree. The questions were written to know the impact of this system on the habit of the user, including questions about their user awareness and privacy. Concretely, the questions are listed below.  
1. Do you think the energy AR application can influence in your habit to reduce the energy consumption?
2. Do you think the energy AR application would increase the understandability of your energy environment?
3. Do you think the energy AR application would bring significant benefits to your everyday life?
4. Do you think the energy AR application would threaten your privacy?
5. What is your general opinion about the prototype application?  
In order to have feedback from the maintenance team, the results from the test were processed through statistics like mean and standard deviation (Std). They are shown in Table 1. On average, most questions were positively answered (above 4 out of 5), except for question 4, in which the score is 3.06. Focusing on the results, interviewed users consider the system will positively influence their habits to reduce energy consumption (89 scores). Furthermore, the energy AR system will also help them to understand better their energy environment (81 score). Finally, the scoring of question 3 also reveals a positive impact in life of the users. However, the question about privacy reveals a certain degree of concern; it is not a very negative scoring, but it indicates that the privacy aspects must be improved. The standard deviation is low, meaning that the answers are close to the mean, except for question about privacy, in which users do not agree as in the rest of the questions. In general terms, as question 5 shows, users are happy with the proposed AR energy system and with the positive impact it will have in their work routine.
# Simulation Framework for Detection and Localization in Integrated Sensing and Communication Systems
## 4 Sensing Algorithms Generation: Target Detection and Localization
### Position Estimation Algorithm  
After obtaining the estimated angle and distance of the target from Algorithm 1, we can estimate the position by applying basic geometry. Specifically, if the height of the target (i.e., \(z^{\text{UE}}\)) and the coordinates of the BS, \((x^{\text{BS}},y^{\text{BS}},z^{\text{BS}})\), are assumed to be known, the estimated UE coordinates can be calculated by solving for \(\hat{x}^{\text{UE}}\) and \(\hat{y}^{\text{UE}}\) from the following equations:  
\[\hat{\theta}=\text{acos}\left(\frac{x^{\text{BS}}-\hat{x}^{\text{UE}}}{\sqrt{ \left|x^{\text{BS}}-\hat{x}^{\text{UE}}\right|^{2}+\left|y^{\text{BS}}-\hat{y }^{\text{UE}}\right|^{2}}}\right), \tag{12}\]  
\[\hat{d}=\sqrt{\left(x^{\text{BS}}-\hat{x}^{\text{UE}}\right)^{2}+\left(y^{ \text{BS}}-\hat{y}^{\text{UE}}\right)^{2}+(z^{\text{BS}}-z^{\text{UE}})^{2}}. \tag{13}\]  
Thus, the estimated coordinates can be given as  
\[\hat{x}^{\text{UE}}=x^{\text{BS}}+\cos(\hat{\theta})\sqrt{\hat{d}^{2}-(z^{ \text{BS}}-z^{\text{UE}})^{2}}, \tag{14}\]  
\[\hat{y}^{\text{UE}}=y^{\text{BS}}+\sin(\hat{\theta})\sqrt{\hat{d}^{2}-(z^{ \text{BS}}-z^{\text{UE}})^{2}}. \tag{15}\]
# Amplitude Modeling of Specular Multipath Components for Robust Indoor Localization
## 3 Experiment
### Experiment Pre-Processing  
For the experiment, the exact positions of the mobile agent and the VAs were unknown. Thus, in a pre-processing step, we used the SLAM algorithm [38; 45] and CIRs from the measurement to estimate the trajectory positions \(\{\mathbf{p}\}\) and the locations of the VAs \(\{\mathbf{a}_{k}\}\) and PAs [43].  
Different VAs can be distinguished from different traces of the path lengths, as illustrated in Figure 2.  
Compared with [43], more VA positions are explored in this paper. The estimated trajectory and VAs are plotted in Figure 1. VAs 2, 3, 4, and 6 represent single reflections on the EPB, WW, SW, and NGW respectively. VAs 7, 8, 10, 11, 12, 13, 16, 17, and 19 represent double reflections on EPB then WW, EPB then SW, EPB then NGW, WW then EPB, WW then SW, SW then NGW, SW then WW, NGW then SW, and NGW then WW respectively.  
Given the estimated positions of VAs \(\{\mathbf{a}_{k}\}\), and the receiver positions \(\{\mathbf{p}\}\), the expected delays \(\tau_{k}(\mathbf{p})\) are computed. Next, the SMC amplitudes \(\hat{a}_{k}(\mathbf{p})\) are estimated from the CIR using (8). Finally, the distance dependence will be removed from the SMC amplitudes,  
Figure 3: Equipments used for the experiment: (**a**) Ilmsens UWB M-sequence sounder and Ilmsens power supply, (**b**) coin antenna.  
yielding the normalized amplitude data \(\psi(\phi_{k}(\mathbf{p}))\), where \(\phi_{k}(\mathbf{p})=\angle(\mathbf{a}_{k}-\mathbf{p})\) is the direction angle. Several dataset \(\mathcal{D}_{k}=\{\phi_{k}(\mathbf{p}),\psi(\phi_{k}(\mathbf{p}))\}\) are then obtained.  
On the basis of these data sets, the GPM is learned, using the built-in Matlab function _fitrgp_ (Matlab version R2018a), yielding the hyper-parameters \(\theta_{k}^{\text{abs}}=\left[\hat{a}_{k}^{\text{abs}},\sigma_{k}^{\text{abs} },\sigma_{\
u k}^{\text{abs}},\hat{P}_{k}^{\text{abs}}\right]^{T}\). From these and the data, prediction of the SMC amplitudes is performed using the function _predict_. Other spatial field indicators, such as SINR, PEB are then deduced, c.f. (4) and (6). The resulting REM will be discussed in Section 4, and exploited in Sections 5 and 6.
# Recent Advances in Flexible Resistive Random Access Memory
## 3 Material System of Flexible Resistive Memory
### Flexible Substrate Materials
#### 3.3.2 Flexible Metal Substrates and Other Flexible Substrates  
Metal materials used as flexible substrates include titanium foil [115], copper foil [94], tungsten foils [89], Ni-Cr foil [83], stainless steel foil [132; 133] and so on. Polymer materials generally do not withstand high temperatures, so metal foil is a good substitute for polymer substrates because of their high-temperature resistance and chemical stability. However, metal materials are less flexible than polymer materials and almost opaque. Meanwhile, they will affect the work of the functional layer of the device because of their conductivity. Moreover, the density of metal materials is generally high, so the weight is heavy, which is not conducive to the development of lightweight devices.  
Other flexible substrates include flexible mica [84; 85; 100; 134], flexible glass [135], SiO\({}_{2}\)[65; 90] and so on. Flexible mica is a natural transparent crystalline material with high flexibility due to the layered framework structure of aluminosilicate. It also has a low cost, light weight, high transmittance (>90%), high-temperature resistance (\(\sim\)600 \({}^{\circ}\)C) and low coefficient of thermal expansion (\(10^{-6}\) per centgrade). According to the report by [PERSON] [134], the performance of ITO electrode depositing on flexible mica has no obvious change after being bent \(10^{3}\) times. However, the compatibility between flexible mica and metal electrodes is poor due to its unique structure. For flexible RRAM, flexible metal materials are the most commonly used electrode materials, so flexible mica is not widely used as substrate material. Flexible glass and SiO\({}_{2}\) were used only as substrate material under special circumstances. In the vast majority of cases, the substrate material obviously has better choices. All in all, flexible substrates, as the carriers of the flexible RRAM memory, need to be considered comprehensively and chosen carefully.
## 3 Results Analysis
### Experimental Results for Time Measurements
#### 3.1.1 Idle System Test Results  
Initially, all measurements were performed in idle state to compare the results with heavy loads on the next stages. As shown in Figure 4, median values on Host OS, Xen, QEMU, and ESXi are almost the same in the _TSC_ timers case (bold red lines). In all cases, _TSC_ timers show better performance than the _System Call_. As expected, the Host OS has the lowest time overhead (Figure 4a), with a median about 17.2 ns when using the _TCS_ timer respective 27 ns in the _System Call_ case. The next hypervisor by time overhead is Xen, shown in Figure 4b, which has 18.6 ns median values with _TSC_ timer; however, the _System Call_ shows quite high costs with a median at 119 ns, which is the second highest value, below Virtual Box. VMware ESXi (Figure 4e) has slightly higher median values of 20.1 ns for _TSC_ and a fairly low median value of 34 ns with the _System Call_. QEMU, shown in Figure 4d, has an insignificantly higher median value for _TSC_ with 21.5 ns and also 57 ns median values for the _System Call_. Thus, even virtual monitor based virtualization can pass timing requests very efficiently to the underlying hardware. However, another representative of virtual monitors, Virtual Box (Figure 4c), has a much greater median overhead in comparison to Xen, ESXi, QEMU virtualized OS and Host OS: 2.12 us for _TSC_ timer and almost the same 2.18 us for the _System Call_. Additionally, Virtual Box has the largest spread of values, up to 1 ms for the max value and 1 ns for the minimum value. Considering the CCDFs of given results presented in Figure 5, it can be assumed that data from all sources, except for Virtual Box (blue and red lines), have quite similar distributions and their _TSC_ timer without load can guarantee overhead not higher than 27 ns with a probability of 0.999. Comparing the results of forwarding the _RDTSC_ instruction on Xen, VMware ESXi and QEMU, Xen shows less overhead on the translation. Considering Virtual Box, it has the longest tails, up to 1 ms for _System Call_ and up to 2 ms for _TSC_ timer.  
Figure 4: Measurements of timer cost for the Idle testbed on: (**a**) Host OS; (**b**) Xen; (**c**) Virtual Box; (**d**) QEMU; and (**e**) VMware ESXi.
## 3 Performance Analysis of the MPS Beamformer
### Performance of MPS Beamformer with the Convergence Control Factor \(\beta\)  
The convergence control factor is one of the most important parameters for achieving the convergence towards deep SLL in MPS beamformer design. The cumulative addition of multibeams has some parasitic secondary sidelobes due to the multibeams sidelobes which may result in severe corruption in the overall array factor due to the divergence occurred in Equation (10). If \(\beta\ll 1\), then the convergence is assured but with very slow rate while higher values of \(\beta\) leads to faster SLL reduction until a critical value at which divergence occurs. To examine the MPS beamformer performance with \(\beta\), consider three different square array sizes of \(10\times 10\), \(20\times 20\) and \(30\times 30\) elements using \(48\), \(108\) and \(168\) multibeams according to Equation (9), respectively, at \(\alpha=3\). The SLL learning curves at different values of \(\beta\) are shown in Figure 4 during \(100\) iterations, where divergence occurs at values of \(\beta\) greater than \(0.1\) especially when deep SLL below \(-45\) dB are required. The performance of the MPS beamformer for the three array sizes has some consistent convergence properties especially the critical value of \(\beta\) at which divergence occurs and the speed of convergence where the values of \(\beta\) that are greater than \(0.1\) provide short convergence curves and provides limited deep SLL values. On the other hand, the learning behavior is stable at values of \(\beta<0.1\), however it becomes slower due to the weak reduction in the SLL after each cycle. Therefore, there is a compromise between how deep the required SLL and the processing time. For most array sizes, SLL of \(-30\) dB or less can be achieved faster by using \(\beta=0.2\), while deep SLL values near \(-45\) dB can be achieved at \(\beta\leq 0.1\), so, the optimum value of \(\beta\) regarding processing speed depends on the required SLL and the array size.  
Figure 3: Subtracted multibeam and the resulting array normalized power patterns in dB, respectively: (**a**,**b**) after one reduction cycle, (**c**,**d**) after \(16\) reduction cycles, and (**e**,**f**) after \(68\) reduction cycles.  
Figure 4: MPS beamformer learning curves variation with the number of iterations of reduction at different values of \(\beta\) and for array sizes: (**a**) \(10\times 10\), (**b**) \(20\times 20\), and (**c**) \(30\times 30\).
# Experimental Study on the Seismic Performance of Recycled Concrete Hollow Block Masonry Walls
## 4 Test Analysis
### Ductility  
The ductility coefficient and shift angle are important parameters to assess the seismic performance of the structure, and they also are the essential characteristics to evaluate the deformation ability of the specimen.  
Nowadays, the calculation methods for ductility are different, and each method has its characteristics. In this paper, the displacement ductility coefficient (\(\mu=\frac{\Delta u}{\Delta y}\)) is used for representing the ductility, where \(\Delta y\) is the yield displacement, which is obtained by using the equivalent energy method [33], \(\Delta u\) is the displacement corresponding to the lateral load of 85% of the ultimate load. The Shift angle is calculated as \(\theta=\frac{\Delta u}{H}\), where H is the distance from the top surface of the bottom beam to the lateral loading point. The ductility coefficient and shift angle of each wall are shown in Table 5.  
Figure 10: Skeleton curves of specimens: (a) W1, W2, and W3; (b) W4 and W5.  
As can be seen from Table 5, the mean values of ductility coefficient \(\upmu\) of bare walls are between 1.191 and 3.123, and the mean values of ductility coefficient of W2 are smallest. Compared to W2, the increase in ductility was 42.6% and 162.2% for the specimens W1 and W3, indicating that increasing the compression stress could decrease the ductility of specimens, but increasing the aspect ratio could increase the ductility of specimens.  
The ductility coefficients of reinforced walls were higher than 3, meeting the specification requirements of GB 50003-2011 [34] (\(\upmu\geq 3\)), which indicated that the structural columns enhanced the deformation ability of both walls, and decreased the brittleness of specimens. According to the results of Table 5, the ductility coefficient and the lateral shift angle of W4 were higher than that of W5, which reflected that the ductility of the ordinary concrete structural columns was better than that of the RAC structural columns.
## 6 Evaluation and Experimental Results
### Minimization of HIP DEX Handshake Packets' Sizes  
In this section, we present the performance of the HIP DEX packet header compression discussed in Section 3.4. In Table 5, we present the handshake packets' sizes before and after the compression. We deduce that our novel proposed compression scheme in LC-DEX achieves reduction gains of all HIP DEX packets sizes' about _2 times_ comparing with results obtained in our previous work in the conference paper [9]. For this aim, we give a comparison of the amelioration in packets' sizes performed by our work in [9] in comparison with standard HIP DEX and obtained results of LC-DEX comparing with standard HIP DEX.  
These reductions have a direct consequence in minimizing the computational and communication overhead of the overall HIP DEX handshake. Our solution gives better results than other existing solutions (see Table 6). We compare our results with HIP-based solutions and other solutions based on Extensible Authentication Protocol (EAP) and Authentication and Key Agreement (AKA) protocols in the context of IoT. LC-DEX performs an excellent reduction in total exchanged bytes during the handshake that amount to 90% comparing with PANA/EAP-TLS. Comparing with D-HIP, LC-DEX need the half of exchanged bytes during the handshake. An 80% of reduction is performed in comparison with D-HIP.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Layer** & **Protocol** \\ \hline Application & CoAP \\ Transport & TCP \\ Network & 6 LowPAN-RPL \\ Link & IEEE 802.15.4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Used protocols on the network layers.  
Figure 8: Adopted 6 LoWPAN-based IoT architecture.
## Nomenclature  
The following abbreviations are used in this manuscript:  
Symbols  
e  average annual electricity and heat co-generation ratio  
\(e_{\rm CO2}^{\rm CZ}\)  
carbon dioxide emission ratio for natural gas (Mg/MWh)  
\(e_{\rm CO2}^{\rm CY}\)  
carbon dioxide emission ratio for coal (Mg/MWh)  
E  the amount of electricity produced (MWh/a)  
\(\rm E_{CO2}\)  
annual CO2 emission (Mg/a)  
\(\rm E_{P}\)  
annual consumption of primary energy (MWh/a)  
EER  energy efficiency ratio  
m  mass flow rate (Mg/h)  
P  pressure (Pa)  
Q  the amount of useful heat produced (MWh/a)  
\(\rm Q_{ch,r}\)  
annual cooling energy production (MWh/a)  
SEER  seasonal energy efficiency ratio  
\(\rm SEER_{0}\)  
seasonal energy efficiency ratio for a compression water chiller  
\(\rm SEER_{1(2)}\)  
seasonal energy efficiency ratio for an absorption water chiller  
T  temperature (\({}^{\circ}\)C)  
V  volumetric flow rate (m\({}^{3}\)/h)  
Greek symbols  
\(\eta_{EC}\)  
average annual energy efficiency  
\(\eta_{TEL}\)  
efficiency of electricity production and transmission in the Polish energy system  
\(\eta_{TOT,0}\)  
total efficiency of electricity production, transmission, and utilization  
total efficiency of steam production, transmission, and consumption  
in the CHP plant in the facility  
\(\eta_{W}\)  
efficiency of energy utilization in the facility  
Abbreviations  
AWCh  absorption water chillers  
CCHP  combined cooling, heating, and power  
CHP  combined heating and power  
COP  coefficient of performance  
CWCh  compression water chillers  
ETC  evacuated tube collectors  
GTBS  gas turbine-based trigeneration system  
KLACC  Kalina lithium bromide absorption chiller cycle  
KPCC  Kalina power-cooling cycle  
PCM  phase change materials  
PTC  parabolic trough collectors  
TDA  thermodynamic analysis
# Survey of Blockchain-Based Applications for IoT
## 12 Industry-Specific Applications of Blockchains in the IoT
### Supply Chain Management  
One of the most prominent applications of blockchain technology in the IoT is supply chain management. Blockchain-based IoT systems enable end-to-end visibility in supply chains by securely recording data from IoT sensors during the logistics journey. Each transaction, from manufacturing to delivery, is logged immediately to ensure traceability and transparency. Smart contracts automate payments upon delivery or quality confirmation, thereby reducing manual intervention and disputes. Additionally, smart contracts can be utilized to track IoT-enabled devices and monitor specific conditions such as temperature, humidity, or location during the shipment process. If predefined conditions are not satisfied (e.g., if a temperature-sensitive product exceeds its acceptable range), the smart contract can automatically trigger notifications or penalties. This ensures compliance with agreed-upon terms, enhances accountability, and minimizes losses caused by negligence or environmental factors.  
The blockchain also serves as the ground truth for shipment tracking and condition monitoring. It acts as a decentralized ledger in which all stakeholders, including manufacturers, logistics providers, and customers, can access reliable tamper-proof records. These records provide a single source of truth for disputes, claims, and audits, offering unparalleled transparency regarding the movement and handling of goods. Figure 17 below illustrates how blockchains and the IoT enhance transparency, automation, and security in supply chains.
# A Range-Extension Method for an Indoor Standard Device for Large-Scale Length Measurement
## 3 Range Extension of the Indoor Standard Device for Large-Scale Length Measurement
### Verification of the Range-Extension Experiment  
To further validate the reliability of the range-extension data within the 52 m measuring range and assess the feasibility of our proposed range-extension method, we conducted a separate experiment at a range of 50 m. A comparison was made between the measurement results obtained without using corner reflectors and those obtained with the corner reflectors.  
Table 2 provides the verification results of the range-extension experiment. The three left columns represent the values directly measured at a range of 50 m, while the three right columns present the results upon the range extension at a measuring range of 25 m. The measurement results indicated that the maximum indication error in direct measurement was \(-4.8\)\(\upmu\)m at a distance of 18 m. In contrast, the maximum indication error in range-extension measurement was 12.6 \(\upmu\)m at the actual distance of 15 m. Both error results were within the required indication error range of \(\pm 0.5\)\(\upmu\)m/m.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Nominal** & **Measured** & **Measurement** & **Nominal** & **Measured** & **Measurement** \\
**Value (mm)** & **Value (mm)** & **Error (\(\upmu\)m)** & **Value (mm)** & **Value (mm)** & **Error (\(\upmu\)m)** \\ \hline  
0 & 39,999.7070 & 39,999.7087 & 1.7 \\ \hline \end{tabular}
\end{table}
Table 2: Verification results of the range-extension experiment.  
Figure 7: A diagram of measurement indication error in the range extension at a measuring range of 52 m.  
Figure 8 describes the indication errors before and after the range extension. The green circles indicate the measurements obtained directly within the 50 m range, while the red squares represent the indication errors produced upon the range extension at the measuring range of 25 m. Both approaches enabled measurements within the 50 m range. The indication error of measurement based on the range extension still met the calibration requirements of the laser interferometer and, more importantly, justified the feasibility of the proposed range-extension method. To facilitate a deeper understanding of the measurements, we also carried out a data analysis of the measurement uncertainty following the range extension.
# Joint Design of Transmit Waveforms for Object Tracking in Coexisting Multimodal Sensing Systems
###### Abstract  
We examine a multiple object tracking problem by jointly optimizing the transmit waveforms used in a multimodal system. Coexisting sensors in this system were assumed to share the same spectrum. Depending on the application, a system can include radars tracking multiple targets or multiuser wireless communications and a radar tracking both multiple messages and a target. The proposed spectral coexistence approach was based on designing all transmit waveforms to have the same time-varying phase function while optimizing desirable performance metrics. Considering the scenario of tracking a target with a pulse-Doppler radar and multiple user messages, two signaling schemes were proposed after selecting the waveform parameters to first minimize multiple access interference. The first scheme is based on system interference minimization, whereas the second scheme explores the multiobjective optimization tradeoff between system interference and object parameter estimation error. Simulations are provided to demonstrate the performance tradeoffs due to different system requirements.  
Keywords:multiple object tracking; waveform design for active sensing; spectrum sharing; pulse-Doppler radar; multiuser wireless communications; multiobjective optimization +
Footnote †: journal: Sensors