esa-sceva/satcom-chunk-collection huggingface dataset:
# Compact Sub 6 GHz Dual Band Twelve-Element MIMO Antenna for 5G Metal-Rimmed Smartphone Applications
## 5 MIMO Channel Capacity  
The ergodic channel capacity refers to the average capacity observed in a multipath Rayleigh fading environment. In cases where a transmitter lacks channel state information (CSI) and its power is evenly distributed among each transmit antenna unit, the ergodic channel capacity can be calculated using the following formula [25]:  
\[C=E\{\log_{2}[det(I+\frac{S}{n_{t}}HH^{\dagger})]\} \tag{1}\]  
where \(E\) is the expectation concerning different channel realizations, \(I\) is the identity matrix, det is the determinant, \(s\) is the signal-to-noise ratio (SNR), \(n_{t}\) is the number of transmit antennas, \(H\) is the wireless channel matrix, and \(0^{\dagger}\) is the Hermitian transpose of the matrix.  
Figure 18 illustrates the results of the calculated ergodic capacity at a 20 dB signal-to-noise ratio (SNR). The performance of the proposed 5G twelve-antenna array in free space is compared to an ideal 12 \(\times\) 12 MIMO system and a single-input single-output (SISO) system. In the comparison, the ideal transmitting and receiving antennas possess 100% efficiency, null ECC, and independent and identically distributed (i.i.d.) channels with Rayleigh fading. To ensure convergence to specific values, the capacity results are obtained by averaging 1,000 channel realizations at each frequency point. In the free space scenario, the capacity within the 3300-3600 MHz range ranged from 51.48 to 52.58 bit/s/Hz, while in the 4800-5000 MHz range, it ranged from 53.92 to 55.11 bit/s/Hz. Notably, the highest capacity value in the 3300-3600 MHz band was 10 bit/s/Hz lower than the capacity of the 12 \(\times\) 12 MIMO i.i.d. channels. The proposed twelve-antenna array demonstrates a  
Figure 17: (**a**) Simulated and (**b**) measured antenna efficiency for the twelve-antenna system.  
significantly larger capacity compared to a SISO system, making it a promising solution for future 5G laptop applications.
# Long-Distance Measurements Using a Chromatic Confocal Sensor
## 1 Introduction
The key advantage of this sensor, compared to conventional chromatic confocal sensors, is its ability to operate at longer working distances without altering the optical configuration of an existing industrial machine. This makes the sensor particularly suitable for applications involving the measurement of thickness and depth in extended samples through axial motion. Additionally, a Python-based simulator has been created to assist in the design process and predict the sensor's performance, with the source code available for public use.
## 3 Results and Discussion
### Synthesis of the Metal Complexes
Figure 5: The representation of suggested coordination binding sites for 6-metyl-2-thiouracil and 6-propyl-2-thiouracil. The coordination binding sites in the CuL2 complex proposed in this study was similar to that reported by [PERSON] et al. for Pt(II) and Pd(II) complexes with 6-_tert_-butyl-2-thiouracil [11]. These results demonstrated the bonding of ligand through sulfur in all complexes [11]. Our suggested coordination binding site for 6-propyl-2-thiouracil in CuL2 was similar to that presented by [PERSON] et al. for Cu(I) complexes [14]. Various metal complexes exhibited a monodentate coordination mode with 2-thiouracil derivatives, binding through an S atom (Ru(II), Cu(I), Sn(IV), Pt(II), and Pd(II) complexes) [11; 14; 23; 29] (see Scheme 1, middle). For the Cu(II)L1 complex, we proposed monodentate coordination through an S atom and/or monodentate coordination through an O atom. The coordination binding sites of the abovementioned complex are a combination of structures shown in Scheme 1 (middle). The coordination geometry of Cu(II)L1 and CuL2 complexes is tetrahedral/planar square and octahedral, respectively. The coordination binding sites of PdL1 and PdL2 complexes proposed here are consistent with the data reported by [PERSON] et al. [43] for Pd(II) complexes with sodium 4-(2-methoxyphenyl)piperazine-1-carbodithioate and diphenyl-p-tolylphosphine or tri-p-tolylphosphine. The S-containing ligands acted as a bidentate ligand, coordinating with the metal ion through the two sulfur atoms [43]. In our study, we proposed that one of the ligands participate in coordination via N1 and S2 atoms, and that the other ligand participates in coordination via N3 and O4 atoms. The coordination binding sites of PdL1 and PdL2 complexes are likely a combination of structures A and C shown in Scheme 1. Complexes with Pt(II), Pd(II), Ru(II), and Zn(II) display coordination mode (A) in which the ligand acted as a bidentate chelate through an N1 atom and a S2 atom as it is described [12; 13; 28; 33].
# The MADAG Strategy for Fault Location Techniques
## 5 Discussion
### Fault Localization  
We applied the test cases selected by the MADAG strategy to three typical similarity coefficients of the SBFL technique (i.e., Jaccard [17]; Tarantula [18; 33]; and Ochiai [1; 2] in our experiment. Similar techniques, which calculate the suspiciousness of program elements based on program spectrum, have been proposed, such as [PERSON] [39; 40], Dstar [5], a novel model for fault localization [41], and others (e.g., [PERSON] et al. [42]; [PERSON] et al. [43]). [PERSON] and [PERSON] [14] proposed a nearest-neighbor fault localization method that contrasts a failed test with another successful test and reports the most ambiguous locations in the program. [PERSON] and [PERSON] [44] applied Delta Debugging to identify the portion of one failed test that eventually causes failure. Some other researchers focused on generating tests to locate faults, such as [PERSON] and [PERSON] [45], who proposed a technique that generates a bug report by comparing passed execution and a failed execution. [PERSON] et al. [46] applied probability theory concepts to guide test case generation using entropy. Our strategy, MADAG, selects test cases without knowing whether they have passed or failed, other than the initial failed test case. Furthermore, when picking up new test cases applied to the SBFL technique in order to calculate each suspicious statement, we merely inspect the result (passed or failed) of the selected tests. This strategy could reduce the tedious manual effort and the cost of test cases.
# A Split Ring Resonator-Based Metamaterial for Microwave Impedance Matching with Biological Tissue
## 4 Experimental
The SIW antenna covered by the metamaterial layer has been characterized with the antenna measurement system in anechoic chamber, StarLab SATIMO, shown in Figure 12. The measured gain of the antenna with and without the metamaterial is illustrated in Figure 13, we can observe that SRR superstrate produces an improvement of the gain in the frequency range over \(f=11\) GHz with an increa  
Figure 11: Measured scattering parameter modulus \(|S_{11}|\) versus frequency of SIW cavity-backed patch antenna with (continuous line) and without (dashed line) the metamaterial superstrate that irradiates in the biological tissue. Figure 10: Measured scattering parameter modulus \(|S_{11}|\) versus frequency of SIW cavity-backed patch antenna with metamaterial lens, that irradiates in biological tissue (continuous line) and in air (dashed line). Even if the aim of the paper is the optimization of the impedance matching with the biological tissue, we performed the simulation of the SIW cavity-backed patch antenna covered by the metamaterial and loaded with a biological tissue, i.e., a slab of skin-fat-muscle, whose electromagnetic parameters at the \(f=11\) GHz frequency are reported in Table 3, in order to compare the proposed device with the literature. A comparison with other microwave applicators is complex, due to the large variety of parameters characterizing the investigations reported in literature, among which the kind of antenna, frequency, kind of metamaterial, applied microwave power and obtained performances characterized in terms of Heating Zone Dimension (HZD), SAR zone, maximum temperature at focusing depth, etc. For the proposed applicator, the peak SAR averaged over the mass \(m=1\) g and over the mass \(m=10\) g, the 50% SAR zone, the 42 \({}^{\circ}\)C HZD, for an input power \(P=1\) W at \(f=11\) GHz frequency are evaluated, in the case of the microwave applicator placed at distance \(D=10\) mm from the skin surface of the human tissue. Table 4 shows that the obtained results are interesting if compared with those from the literature. The reduced HZD zone and high SAR peak evidence the strong focusing obtained via the designed SRR metamaterial. This paves the way for promising novel applications. Figure 12: SIW cavity-backed patch antenna with metamaterial lens in the anechoic chamber StarLab SATIMO. Figure 13: Measured gain versus frequency of SIW cavity-backed patch antenna with and without metamaterial.
# Enhancing the Quality of Sampled Phase-Only Hologram (SPOH) Based on Time-Division Comb Filtering
## 2 Sampled Phase-Only Hologram (SPOH)
### Effect of Down-Sampling in the Frequency Space  
Consider an arbitrary row of image \(I(m)\) comprising of \(M\) pixels, and its down-sampled version \(I_{D}(m)\). The Fourier spectrum of \(I_{D}(m)\) is represented by \(\overline{I}_{D}(\omega)\). The frequency axis is bounded within the range \([0,2\pi)\)  radians, and discretized into \(M\) samples uniformly spaced at \(\frac{2\pi}{M}\) radians. In other words, \(\omega=\frac{2\pi k}{M}\Big{|}_{0\leq k<M}\), where \(k\) is an integer. After down-sampled by a factor \(\tau\), its frequency spectrum is given by  
\[\overline{I}_{D}(\omega)=\sum_{t=0}^{\tau-1}\overline{I}(\omega)exp\left[i \left(\omega-\frac{2\pi q}{\tau}\right)\right] \tag{8}\]  
where \(q\) is an integer. The above equation shows that after down-sampling, the frequency spectrum contains \(\tau\) replicas of the baseband signal \(\overline{I}(\omega)\), which are uniformly distributed along the frequency axis at locations \(\left.\omega_{q}=\frac{2\pi q}{\tau}\right|_{0\leq q<\tau}\). The separation between adjacent replicas is \(\frac{2\pi}{\tau}\) radians. We assume that the bandwidth of the signal is limited to within \(\frac{2\pi}{\tau}\) radians, so that the replicas are non-overlapping with each other. If this is not the case, a low-pass filter can be used to band-limit the signal before down-sampling. The result of down-sampling is illustrated with the spectrum of the one-dimensional, band-limited signal in Figure 3a. After down-sampled by two times (i.e., \(q=[0,1]\)), the new signal spectrum is shown in Figure 3b. We observe that the spectrum of the down-sampled signal with \(\tau=2\) is comprising of two copies of the base-band spectrum at \(\omega=0\), and \(\pi\) radians. For down-sampling factor of four, there are four replicas at \(\omega=0\), \(\frac{\pi}{2}\), \(\pi\), and \(\frac{3\pi}{2}\) radians, as shown in Figure 3c.  
The above analysis can be directly applied to down-sampling of a 2-D image \(I(m,n)\) along the horizontal, and vertical directions. From Figure 3, it can be seen that the sparsity of a down-sampled signal can be decreased by reducing the number of replicas spectrum, which is equivalent to lowering the down-sampling factor.  
\begin{table}
\begin{tabular}{c c} \hline  Wavelength of light (\(\lambda\)) & 532 nm \\ Pixel size of hologram and image (\(\delta\)) & 6.4 um \\ Axial distance between hologram and image & 0.16 m \\ Down-sampling factor \(\tau\) & 8, 12, and 16 \\ \hline \end{tabular}
\end{table}
Table 1: Optical settings for generating the Fresnel hologram.  
Figure 2: Cont.
# Design of Advanced Human-Robot Collaborative Cells for Personalized Human-Robot Collaborations
## 4 User-Aware Collaboration
### Integrated Task and Motion Planning
#### 4.2.3 Reactive Speed Modulation Module  
The reactive speed modulation module modifies the nominal speed during the execution of each trajectory. This is necessary to meet safety requirements and avoid unnecessary safety stops. In general, reactive motion planners are shifting from simple yet conservative strategies such as safety zones to optimized methods that adapt the robot motion continuously [46; 47].  
In this work, we adjust the robot's speed according to the safety requirements imposed by safety specifications. In particular, the technical specification ISO/TS 15066 (_Robots and robotic devices_--_Collaborative robots_) [48] defines speed reduction rules for collaborative operations with and without admissible contact between robots and humans. For example, if _speed and separation monitoring_ is applied, the human-robot distance \(S\) must not fall below a protective distance \(S_{p}\).  
To satisfy the safety requirement without jeopardizing the smoothness of the collaboration, we adopt a continuous modulation of the robot speed. The robot nominal velocity is scaled at high rate by a speed override factor \(s_{\text{ovr}}\in[0,1]\) according to the following rule:  
\[s_{\text{ovr}}=\min\left(\frac{v_{\text{max}}}{v^{th}},\;1\right), \tag{5}\]where \(v^{th}\) is the human-robot relative speed and \(v_{\max}\) is the maximum allowed relative velocity, derived from (6) by using the measured user position and velocity in the cell, that is:  
\[v_{\max}=\sqrt{v_{h}^{2}+(a_{s}T_{r})^{2}-2a_{s}\big{(}C-S(t_{0})\big{)}}-a_{s}T _{r}-v_{h}, \tag{6}\]  
where \(S(t_{0})\) is the human-robot relative distance at the current time instant, \(v^{r}\) is the robot velocity toward the human, \(v_{h}\) is the human velocity toward the robot, \(a_{s}\) is the maximum Cartesian deceleration of the robot toward the human, \(T_{r}\) is the reaction time of the robot, and \(C\) is a parameter accounting for the uncertainty of the perception system.
## 5 Discussion
### Challenges  
The methodology employed in this study for developing the proposed RS yielded satisfactory outcomes at a small scale. However, some challenges necessitate attention and resolution.  
* The utilization of datasets for training ML algorithms is characterized by a notable limitation in terms of size. In contrast, RSs usually rely on larger datasets, typically consisting of no less than one thousand data points. As a result, the scalability of the proposed RS has not been adequately tested.
* The utilization of the Passive House database was necessary due to the limited availability of BIM data for buildings. Hence, the accessibility of BIM data has the potential to enhance automation and reduce the reliance on manual efforts in the data preparation process.
* The dataset was manually prepared for the first use case due to the absence of a feature in the Passive House database that allows for data retrieval in a tabular format. From a practical point of view, this could potentially present a challenge for developers who intend to create this kind of RS.
* The Passive House database encompasses a comprehensive range of climatic conditions across Europe. Yet, it is less widely adopted outside Europe, resulting in limited availability of climate-specific construction details for regions beyond Europe. This database constraint directly influences the proposed RS.
* Using the Passive House database for training ML algorithms yields recommendations for constructing energy-efficient building envelopes. Moreover, it is important to note that passive house constructions tend to incur higher costs, which may pose a potential challenge despite their high energy efficiency.
* The current SRI framework is characterized by subjectivity, which consequently impacts the recommendations provided by the proposed or any RS. Nevertheless, it is imperative to acknowledge that this issue cannot be ignored unless certain modifications are made to the official SRI framework.  
The significance of this study lies in the development of a novel algorithm for the proposed RS as well as its demonstration of the potential benefits of integrating SRI and BIM data. The combination of BIM data with data from the Passive House database allows for covering a wide variety of building types and climates within the EU. This integration can provide highly individualized and comprehensive recommendations for improving the energy efficiency of buildings. The use of SRI data in this study can assist researchers with devising innovative algorithms and investigating the potential for creating more valuable solutions targeted at enhancing building energy efficiency. Moreover, this study highlights the necessary steps for leveraging the SRI framework to relevant stakeholders through the creation of a valuable tool that may be upgraded by increasing the SRI data accessibility.
## 6 Results of the Experimental Setup  
This section discusses the deployment of sensor nodes and the gateway in a real-time environment. We also present the sensor data recorded on the cloud server and compare previous studies with the proposed research in detail. To evaluate the coverage of LoRa, we deployed the sensor nodes in a testbed located in our university center. The gateway was placed at a distance of 1 km from the sensor nodes. To analyze the customized sensor node and gateway for crop field monitoring, the sensor node was deployed in the crop field, as shown in Figure 16a,b. The sensor node was embedded with sensors for temperature, humidity, soil moisture, and fire. A 433 MHz-based LoRa was embedded in the sensor node and gateway. LoRa is a form of transceiver communication. The LoRa-based gateway was positioned 1 km from the two sensor nodes. The gateway showed effective results in terms of accurately receiving the sensory data. The gateway was also embedded with an 8266 Wi-Fi modem to communicate the data over the cloud server through the Internet. The sensory data is visible in Figure 16c.  
Figure 16: Real-time experimental setup of the sensor node and gateway.  
Moreover, a hand-held device (or portable device) was also designed and implemented in real-time scenarios, shown in Figure 16d. This hand-held device will assist farmers in checking the field from a remote location and is easy to use. A hand-held device integrates LoRa communication and an ESP 8266 Wi-Fi module. These two communication modules assist in receiving the crop data regarding from the sensor node and gateway. The hand-held device is used to visualize the data of the crop field on a color LCD. The hand-held device shows the sensory data, including temperature, humidity, soil moisture, and fire detection. The gateway node logs the data on the cloud server through the Internet. Here, we used the Blynk cloud server to record the sensory data of the sensor node. The data regarding temperature and humidity can be seen in the Blynk dashboard, and is presented in Figure 17.  
A comparative analysis of previous research on LoRa link budgets is depicted in Table 9. Evaluation parameters are the microcontroller unit (MCU), the communication protocol used, customized hardware designed for the sensor node, the gateway, proof of concepts, and simulation-based analysis used to validate the proposed study. The proposed study has the advantages of a communication protocol that uses both LoRa and Wi-Fi. This means the data can be collected on a local platform and the cloud platform. When designing sensor nodes, each sensor node and gateway were custom designed and built as per the requirements. Customization helped us to reduce the dimensions and architecture of the device. A simulation-based analysis was carried out to validate the proposed study, and further validated on the LoRa testbed.  
Figure 17: Blynk dashboard.
# Assessment of Daytime HONO Emission Source from Asphal Surface to Urban Air
###### Abstract  
Existing studies suggest various potential daytime sources of atmospheric nitrous acid (HONO), including photolysis surface reactions and photo-enhanced NO\({}_{2}\) conversion on organic surfaces. However, the understanding of daytime HONO sources is still inadequate. In this study, we report the HONO formation on asphalt surfaces under various NO\({}_{2}\), VOCs (toluene and hexane), and UV irradiance conditions using a continuous flow chamber. Although no HONO formation was found without light exposure, the light threshold for HONO formation on the asphalt surface was very low, with a total UV (TUV) of 0.7 W m\({}^{-2}\). HONO formation on the asphalt surface was linearly dependent on NO\({}_{2}\) up to 300 ppb in the presence of VOCs, but no HONO formation was observed with humidified air and NO\({}_{2}\). HONO production was saturated at high hydrocarbon concentrations and light intensities. The calculated first-order NO\({}_{2}\) conversion rate to HONO on the asphalt surface was 1.2 \(\times\) 10\({}^{-4}\) s\({}^{-1}\). The observed mean HONO emission flux was 1.3 \(\times\) 10\({}^{9}\) molecules cm\({}^{-2}\) s \({}^{-1}\) with a similar range of those on other urban covered surfaces. The calculated vertical HONO profile using the measured HONO emission flux and 1-D steady state model revealed that the asphalt surface may account for 13% of daytime HONO in the elevated on-road pollutant concentrations in Seoul. However, we show that its HONO contribution could be much higher on real-life road surfaces directly exposed to much higher NO\({}_{2}\) emissions from vehicle exhaust.  
HONO; asphalt surface; UV; VOCs; NO\({}_{2}\) +
Footnote †: journal: Physics Letters B
## 2 Materials and Methods
### Protocol  
Before data collection, each participant's preferred running speed and preferred step rate were determined while running on a treadmill (Body Power, Motorised treadmill, model no: Sprint T700, Sport-Tiedje GmbH, Schleswig, Germany) after a 5-min warm-up. Participants were instructed to adjust the speed as needed over this period until they identified a speed that was representative of their typical moderate-intensity run. This speed was then employed for all three SR trials. SR was visually calculated over a 30-s period by counting the number of right foot strikes and multiplying by four. When calculating steps, we used a round number of steps, ignoring the half/part of the step. The process was repeated to guarantee accuracy with the average value used.  
Vicon(r) motion capture system (Oxford, UK), including 6 MX T20 cameras and 10 Vantage 5 cameras, was used to collect movement data. The lower limb body model (Plug-in-Gait) with 24 reflective markers was placed on each subject, and the kinematic data was recorded at 400 Hz during all running conditions, as shown in Figure 2.  
According to the requirement of the Vicon system and model, a \"T-pose\" calibration trial was performed to establish joint centres, body segment coordinate systems, segment lengths and the local positions of tracking markers with Vicon Nexus 2.8.1 capture software.  
Participants were then asked to run at their preferred running speed under the three SR trials: preferred step rate (PSR) = 152-176 SPM (step per min or cadence), 10% below preferred step rate (10% below PSR) = 137-159 SPM, 10% above-preferred step rate (10% above PSR) = 167-194 SPM. The order of the SR trials was randomised for each participant, with 30 s of data collection for each condition and 30 s of rest between SR trials. Subjects ran in time with a digital audio metronome to facilitate the correct SR. Data collection did not begin until the participants could maintain the prescribed SR for a minimum of 1 min determined by visual inspection. There was 2 min of rest between different step rates.  
Figure 2: Marker placement in lower limb model.  
The iPad application \"Pro Metronome\" by EUM Lab for IOS (Figure 3a) was utilised in conjunction with a Voombox Portable Bluetooth speaker (model: Voombox-Travel, Divoom(r), Shenzhen, China) (Figure 3b) to help participants maintain their step rate throughout the study. Once the step rate was determined by the participant and researcher, the metronome would produce the sound with the step rate, and then the participant maintained the step rate according to the sounds. The 10% increase and decrease to pre-SR were adjusted according to this way.