esa-sceva/satcom-chunk-collection huggingface dataset:
# Device Management and Data Transport in IoT Networks Based on Visible Light Communication
###### Abstract  
LED-based Visible Light Communication (VLC) has been proposed as the IEEE 802.15.7 standard and is regarded as a new wireless access medium in the Internet-of-Things (IoT) environment. With this trend, many works have already been made to improve the performance of VLC. However, the effectively integration of VLC services into IoT networks has not yet been sufficiently studied. In this paper, we propose a scheme for device management and data transport in IoT networks using VLC. Specifically, we discuss how to manage VLC transmitters and receivers, and to support VLC data transmission in IoT networks. The proposed scheme considers uni-directional VLC transmissions from transmitter to receivers for delivery of location-based VLC data. The backward transmission from VLC receivers will be made by using platform server and aggregation agents in the network. For validation and performance analysis, we implemented the proposed scheme with VLC-capable LED lights and open sources of oneM2M. From the experimental results for _virtual museum_ services, we see that the VLC data packets can be exchanged within 590 ms, and the handover between VLC transmitters can be completed within 210 ms in the testbed network.  
Keywords:Visible Light Communication; Internet of Things; device management; data transport; experimentation; analysis +
Footnote †: journal: Sensors
## 4 Discussion
### Future Studies  
The present systematic review of the literature provided valuable insights into the efficacy of nonpharmacological treatments for TDs in individuals aged < 18 years, supporting the effectiveness of CBIT, its reduced version, HRT, and ERP treatments for tic reduction, also in samples with children and adolescents [12].  
Areas for future studies are also highlighted. Research into the treatment of TDs should continue to evaluate the effectiveness of nonpharmacological interventions (especially on vocal tics), while also identifying those treatment components which are most essential to drive change: these includes, among others, the role of parental involvement [20; 30] and stress management techniques. Effectiveness could be further explored by enhancing the understanding of predictors and moderators of response. Interventions' efficacy should be investigated in different age groups (preschool, school-age, and adolescents) [15; 18; 19; 20; 21; 29; 31], as well as in different settings and modalities (individual and group-based). Group interventions, for example, might increase cost-effectiveness, reducing waiting times [30].  
Moreover, while clinical and self-reported measures have been valuable in evaluating the efficacy of nonpharmacological interventions, future studies would benefit from incorporating objective, continuous measurement methods to capture the dynamic nature of tic activity. These tools, e.g., electromyography (EMG) or electroencephalography (EEG), would provide a more nuanced understanding of how tics evolve and respond to treatment, offering insights beyond episodic assessments, such as the YGTSS. For instance, EMG has been successfully applied in assessing motor function after stroke [53], and EEG has been widely used in epilepsy research [54]. Incorporating such methodologies into TDs research could enhance the precision of treatment evaluations and broaden the implications for future interventions.  
The use of wider samples and rigorous methodologies will enhance the generalizability of results. Future research should aim to enroll larger cohorts, and include both short- and long-term follow-up assessments [19; 20; 31] to have a better understanding of the maintenance of treatment gains over time and provide clarity on long-term outcomes.  
Regarding CBIT-VoIP treatments, it seems reasonable to investigate the relationship between computer- and Internet-related variables, such as access to technology, digital literacy, and treatment outcomes, in order to determine whether these factors influence treatment efficacy [19].
## 2 Materials and Methods  
### Experimental Design  
The experiment was carried out between March and June of 2018 in a controlled growth chamber at the Rise Environmental Risk Assessment Facility (RERAF) phytotron (Technical University of Denmark (DTU), Roskilde campus, Denmark), with a ground area of 24 m\({}^{2}\) and height of 3.1 m (e.g., [63; 64]). Air temperature in the chamber was set to 25 \({}^{\circ}\)C for daytime and 19 \({}^{\circ}\)C for night-time. The relative humidity (RH) was kept at 50% during day hours and 70% during night time. To promote rapid growth, illumination conditions simulated 16 hours of daytime. Light racks were composed by 28 high-pressure mercury lamps (1000 W) and 14 halogen lamps (400 W). CO\({}_{2}\) concentration was set constant at 400 ppm, similar to the global average. Air temperature, relative humidity, light intensity, and CO\({}_{2}\) concentration were automatically recorded using a climate control computer.  
In the experiment, two types of crop seeds were sown: _Zea mays_ cv. NK Arma (maize) and _Glycine max_ (L) cv. Merr. Buenos (soybean), provided by SAGEA Centro di Saggio s.r.l. Integrated Solutions for Sustainable Agriculture ([[https://www.sagea.com](https://www.sagea.com)]([https://www.sagea.com](https://www.sagea.com))). These crops were selected as they are staple crops produced under similar climatic conditions and present different photosynthetic pathways (C\({}_{3}\) for soybean and C\({}_{4}\) for maize) and hydraulic strategies. Soybean seeds were sown on 23 March and maize seeds on 9 April in 9 L pots with 4 kg of soil. Two plants were grown in each pot. Table 1 shows days after sowing (DAS) and days of measurement (DoM) of each crop. Along with this document, DoM was used for visually more precise figures and as reference for phenology. The soil used was a pre-fertil soil extracted from peat blocks and nutrient enriched (Pindstrup substrate no. 6, Pindstrup Mosebrug A/S, Ryomgaard, Denmark, [[https://www.pindstrup.com](https://www.pindstrup.com)]([https://www.pindstrup.com](https://www.pindstrup.com))). This soil type has been previously used in other experiments in the same growth chamber [63; 64] and presents a total water holding capacity (WHC) of 0.625 kg water/kg soil. Three soil water levels were established to maintain: (i) 100% WHC (control/wet), (ii) 70% WHC (mid), and (iii) 40% WHC (dry), later referred to as soil drought or water stress. Irrigation was applied every three days until the 24 April and after that, every two days. For each WHC group and crop, six replicates were arranged for a total of 36 pots (2 crops \(\times\) 3 WHC \(\times\) 6 replicates).
# Dynamic Controllability of Processes without Surprises
## 7 Checking Semi-Dynamic Controllability with STNUs
### Mapping to STNUs  
Several previous works, e.g., [17], have shown how to check whether a process model such as the process in Figure 1 is dynamically controllable by mapping it into a temporally equivalent STNU (Simple Temporal Network with Uncertainty) [10]. The idea is that a process model is dynamically controllable if and only if its equivalent STNU is dynamically controllable.  
To be self-contained, we repeat here the most important aspects of STNUs, and refer the reader to [10] for details. Essentially, an STNU is a directed graph, with nodes representing time points and edges representing binary constraints between time points. A special time point \(Z\), or \(zero\), marks the reference in time, after which all other time points occur. Edges in an STNU can be non-contingent or contingent. Non-contingent edges represent constraints that must be enforced by the execution environment by assigning appropriate values to the time points. Contingent edges (also called links), instead, represent constraints that are guaranteed to hold, but the corresponding time point assignments can only be observed, not controlled by the execution environment.  
In line with prior work, we consider the usual notation \((A,B,\delta)\) for non-contingent edges from \(A\) to \(B\) with weight \(\delta\), which require that \(B\leq A+\delta\); and \((A^{C},l,u,C)\) for contingent links between \(A^{C}\) and \(C\), which state that \(C\) occurs some time between \(l\) and \(u\) after \(A^{C}\).  
We also briefly recap the rules for mapping a process model \(P\) into a temporally equivalent STNU \(S\) here:  
* Each \(n.i\in P.N^{e}\) is mapped into a node in \(S\);
* Each \((n1,n2)\in P.E\) is mapped into a non-contingent edge \((n2.s,n1.e,0)\) in \(S\); \((start.s,\)\(zero,0)\) is added to \(S\);
* Each duration constraint \(d(n,n_{min},n_{max},[sc|nc])\in P.\mathcal{C}\) is mapped into a pair of non-contingent edges \((n.s,n.e,n_{max})\) and \((n.e,n.s,-n_{min})\) in \(S\);
* Each duration constraint \(d(n,n_{min},n_{max},c)\in P.\mathcal{C}\) is mapped into a contingent link \((n.s,n_{min},n_{max},n.e)\) in \(S\);
* Each constraint \(ubc(a,b,\delta)\in P.\mathcal{C}\) is mapped into a non-contingent edge \((a,b,\delta)\) in \(S\);
* Each constraint \(lbc(a,b,\delta)\in P.\mathcal{C}\) is mapped into a non-contingent edge \((b,a,-\delta)\) in \(S\);
* Process deadline \(P.\Omega\) is mapped into a non-contingent edge \((start.s,end.e,\Omega)\).  
Figure 11 shows the STNU derived by applying the above mapping rules to the process model of Figure 1. In the figure, we adopted the usual graphical notation for STNUs, with contingent edges dashed and inverted with respect to non-contingent edges, and labeled with the name of the contingent time point. For a more compact presentation and without loss of temporal information, in the figure we did not include nodes resulting from the mapping of the par-split and par-join.
# AxP: A HW-SW Co-Design Pipeline for Energy-Efficient Approximated ConvNets via Associative Matching
## 4 Experimental Results
### Input Activations Profiling  
Figure 10 (top) shows the hit rate trends as function of the number of input and activation patterns stored \(N_{in}\). As expected, the hit rate increases with \(N_{in}\), yet with different rates depending on the task. IC and KS reach higher hit rates for instance (71-81%). The best case for IC gets close to 75%, whereas KS goes even higher to 81% when \(N_{in}=64\). The direct implication of this observation is that the FPMul workload decreases up to 80%, which means a mere 20-30% of the overall input patterns are handled as classical arithmetic operations. The results get slightly different for the OD task where the hit rate ranges are limited between 25% and 36%. This might be due to the higher variance in input data. Similar behavior is depicted when scaling bit-width (FP16). Here, the hit rate for OR and KS slightly increases (up to \(\approx\)3%) due to the natural reduction of the bit-width. The peak performance is recorded for the KS benchmark, where the hit rate ranges from 76% to 89%.  
However, an increment in the hit rate due to a higher number of clusters has a side effect on energy consumption. Higher \(N_{in}\) means larger associative memory indeed, and hence more energy consumption. This behavior motivates the need to balance those contrasting metrics (hit rate and energy consumption) through extensive co-design exploration.
## 3 Proposed Personalized HRTF Estimation Method
### Extraction of Ear Images  
The ear images in the CIPIC database have different resolutions. Thus, a 32 \(\times\) 32 region of interest (ROI) is applied to each image in this paper because image processing for the ear image requires a consistent resolution. Moreover, color ear images are converted to grayscale ones because skin color has no impact on sound propagation.  
In general, the more complex a neural network model is, the more data it requires; furthermore, the use of insufficient data can cause severe performance degradation due to the overfitting problem. In particular, this problem occurs more frequently in regression models [30]. For example, the National Institute of Standards and Technology (MNIST) challenge, which recognizes handwritten digits, provides 60,000 training images, and 10,000 test images [31]. Meanwhile, only 31 ear images are included in the CIPIC database, and such insufficient data can lead to an overfitting problem. Thus, instead of using features extracted from the images, edge-detected images are directly used as the input features of Sub-network B. Therefore, the first layer of CNN performs filtering to extract low-level features, such as edges and lines.  
Figure 5 illustrates an example of an original color image from the CIPIC database as well as the edge-detected ear image. The image size of the edge-detected ear image, as shown in Figure 5b, is 32 \(\times\) 32 pixels, where the ear image corresponds to the boxed area of Figure 5a. The Canny edge detection algorithm [32] is used here, which is as follows:  
1. Apply a Gaussian filter to the image to remove noise and unwanted details
2. Find the intensity gradients of the image
3. Apply non-maximum suppression to remove spurious responses to edge detection
4. Apply double thresholds with hysteresis by suppressing all edges that are weak and not connected to a strong edge.  
Figure 5: Illustration of (**a**) the ear image of subject 010 in the Center for Image Processing and Integrated Computing (CIPIC) database and (**b**) its edge-detected ear image with region of interest (ROI).
# Energy Harvested and Cooperative Enabled Efficient Routing Protocol (EHCRP) for IoT-WBAN
## 5 Energy Harvested and Cooperative-Enabled Routing Protocol (EHCRP) Scheme
### Scheduling Phase  
When all sensors nodes are registered with the CNC, then they need to be schedule for normal communication. In this stage the CNC assigns time slot to each SN. The time slots are provided considering for either normal or emergency data transmission. Where the later gets a special certain time slot in Contention Free Period (CFP) from MAC super frame. This ensures quick and efficient transfer of emergency data. CNC doesn't sense its own data but collects data from SNs and forward it to PDA.
# Near Real-Time Freeze Detection over Agricultural Plots Using Sentinel-1 Data
## 5 Discussion
### Effect of the Temperature Filter  
While false detections during the freezing period over our study sites (between November and February) did not exceed most of the time 10% across the land cover classes; false detections were mostly present outside of this period, and especially after rainfall events (November, and February through April), and a combination of rainfall, and irrigation events from May onwards. Therefore, the inclusion of in situ air temperature filter was found to be particularly relevant to entirely discard these false detections. In this study, we chose an air temperature threshold of 3 \({}^{\circ}\)C, over which we removed any freezing detection. In reality, freezing occurs when soil temperature, not air temperature, is equal to or below 0 \({}^{\circ}\)C. However, due to several reasons--(1) the tight coupling between air temperatures and that of top soil [65]; (2) The distance between our study sites and their corresponding weather stations and the difference in morphologies; (3) the fact that soil temperatures are generally lower than air temperatures [17]--we believe an air temperature threshold of 3 \({}^{\circ}\)C would have a better likelihood of the air temperature representing actual frozen conditions [60].
## 4 Results and Discussion
### Influence of Separate Columns on the Output Performance  
Currently, the number of uniformly arranged separate columns is set to 24. Here we change the number of separate columns to 4, 8, 12, 16, 20, 24, 32, and 40, and the resultant load current is shown in Figure 23. The inductance of a single separate column is \(\sim\)126.7 nH. When multiple separate columns are used for the current return path and mechanical support, they are connected in parallel, and the equivalent inductance of these separate columns is further reduced. Compared to the magnetizing inductance of the FLT cavity of \(\sim\)10 uH, the structural inductance of the separate columns is very small. Therefore, the number of separate columns has negligible influence on the output performance of FLT cavity, as can be indicated from Figure 23. Moreover, the diameter of the separate columns has been changed for parametric sweep simulations and it also has negligible influence on the load current of the FLT cavity. Therefore, the 24-separate columns can replace the outer cylindrical wall without influencing the output electrical performances of FLT.  
Figure 21: Cross-sectional view of the magnitude and vector diagram of electric field intensity at the instant of maximum load voltage.  
Figure 22: Cross-sectional view of the magnitude and vector diagram of magnetic field intensity at the instant of maximum load current.
## 2 Case Study: Closely Spaced mmWave MIMO Systems  
This section describes two different design configurations of an MIMO system of two closely spaced monopole antennas. Both designs operate in the millimeter wave band. The primary point of interest is a reduction in mutual coupling between the antenna elements, with the specific FSS-based technique proposed and elaborated on later in the paper.
## 7 Simulation Results
### Security Performance  
Figure 6 shows the safety periods for all the four protocols grow as \(h\) grows. This is because as \(h\) increases, the phantom locations are farther from the source and consequently the number of random directed paths increases. The safety period for SLP-E increases by 147.68%, 91.58% and 15.58% on average when compared to phantom single-path, PRLA and SLP, respectively.  
Figure 7 shows the safety periods for all protocols grow as \(H\) grows. This is because as the distance from the source to the base station grows, the attacker has to take more hops to reach the source. In particular, we observe the safety period for SLP-E increases by 145.44%, 114.94% and 7.71% on average when compared to phantom single-path, PRLA and SLP, respectively.  
Figures 6 and 7 show that SLP-E performs best and SLP outperforms PRLA in terms of safety period. The phantom single-path has the lowest security performance. The average safety period for SLP is  
Figure 5: Communication cost _vs._ source-sink distance.  
Figure 6: Safety period _vs._ random directed hops.  
significantly higher than with PRLA. In particular, the average safety period of SLP increases that of PRLA by nearly an order of magnitude. Figures 6 and 7 also show that SLP-E provides a better security source location protection than SLP. This is because SLP-E can completely avoid inefficient path generation and therefore further improve source location security.
# New Approximation Methods Based on Fuzzy Transform for Solving SODEs: [PERSON]*, [PERSON], [PERSON], [PERSON]
## 3 FzT for Solving SODEs
### Numerical Scheme I for SODEs
Therefore, we will use the same trick as in [6] and replace functions by their FzT components:  
\[\hat{F}_{k}[f]=\frac{\int_{t_{1}}^{t_{n}}f(t,X_{k},Y_{k})\ A_{k}(t)\ dt}{\int _{t_{1}}^{t_{n}}A_{k}(t)\ dt},\ \hat{G}_{k}[g]=\frac{\int_{t_{1}}^{t_{n}}g(t,X_{k},Y_{k})\ A_{k}(t)\ dt}{\int _{t_{1}}^{t_{n}}A_{k}(t)\ dt},\ k=1,\ldots,n-1. \tag{14}\]  
Thus, the components of FzT of \(x\) and \(y\) can be approximated from the following Scheme I:  
\[X_{k+1}=X_{k}+h\hat{F}_{k},\qquad X_{1}=\alpha,\quad|\quad Y_{k+1}=Y_{k}+h \hat{G}_{k},\qquad Y_{1}=\beta,\ k=1,\ldots,n-1. \tag{15}\]  
Finally, the approximate solution of (5) can be obtained using the inverse FzT as follows:  
\[x_{n}(t)=\sum_{k=1}^{n}X_{k}\ A_{k}(t),\ \ y_{n}(t)=\sum_{k=1}^{n}Y_{k}\ A_{k}(t), \tag{16}\]  
where \(A_{k}(t)\), \(k=1,2,\ldots,n\) are given basic functions. The proposed method is similar to the well-known Euler method and under similar assumptions. It has the same degree of accuracy. In the next theorem, we obtain an error estimate in the context of a fuzzy partition and error analysis of numerical Scheme I. **Theorem 1**. : _Let \(f,g:[t_{1},t_{n}]\to\mathbb{R}\) be twice continuously differentiable on \([t_{1},t_{n}]\). Let, moreover, \(f,g:[t_{1},t_{n}]\times\mathbb{R}\times\mathbb{R}\to\mathbb{R}\) be Lipschitz continuous with respect to \(x\) and \(y\), i.e. there exists a constant \(L\in\mathbb{R}\), such that for all \(t\in[t_{1},\ t_{n}]\) and \(x,x^{\prime},y,y^{\prime}\in\mathbb{R}\),_  
\[|f(t,x,y)-f(t,x^{\prime},y^{\prime})| \leq L(|x-x^{\prime}|+|y-y^{\prime}|),\] \[|g(t,x,y)-g(t,x^{\prime},y^{\prime})| \leq L(|x-x^{\prime}|+|y-y^{\prime}|). \tag{17}\]  
_Assume that \(\{A_{k}\ |\ k=1,\ldots,n\}\), \(n\geq 2\), is a uniform fuzzy partition of \([t_{1},t_{n}]\). Then, the local (global) error of Scheme I (14)-(15) is of the order \(h^{2}\) (\(h\))._  
Proof. : Let us choose and fix some \(k\), where \(2\leq k\leq n\), and assume that \(X_{k}\ (Y_{k})\) is the \(k\)-th FzT component of \(x\ (y)\). We consider the SODEs (5) and their FzT representation by the system of equations (11).
# Low Radiation Dose Implications in Obese Abdominal Computed Tomography Imaging
## 2 Materials and Methods  
### Phantom Preparation  
Organ doses and image quality were assessed using male adult anthropomorphic phantoms (RANDO, Alderson Research Laboratories, Stamford, CT, USA). To define the patient size classifications (i.e., normal-weighted and obese) for the anthropomorphic phantom, demographic (age, gender) and anatomical (weight (kg), height (m), body mass index (BMI)) data were retrospectively collected from medical records of 177 patients after obtaining institutional ethical approval. On each CT scan, the anterior-posterior (AP) abdominal diameter was measured using the level of the upper pole of the right kidney as a landmark. Data analysis showed that obese (BMI \(\geq\) 30 kg m\({}^{-2}\)), overweight (BMI: 25-29.9 kg m\({}^{-2}\)), and normal-weighted (BMI: 18.5-24.9 kg m\({}^{-2}\)) patients' AP diameters were 28 cm \(\pm\) 4.2, 24 cm \(\pm\) 2.4, and 22 cm \(\pm\) 2.9, respectively.  
Two phantom structures were then built to simulate normal-weighted (i.e., AP diameter = 22 cm) and obese (i.e., AP diameter = 28 cm) phantoms using beef fat. Each fat layer was vacuum packed. Single circumferential animal (beef) fat packs measuring 3 cm in thickness forming a subcutaneous fat layer were used to mimic obese patients. The layers were adherently attached to the abdominal area (Figure 1).  
Additionally, the CT attenuation property of the applied fat was checked to ensure it had similar Hounsfield unit (HU) values to that of human adipose fat. The fat was scanned several times using different tube potential (kVp) settings of 100 and 120 kVp, with a fixed tube current (mA) of 150. The HU values (\(-89\) and \(-88\), respectively) were within the typical human fat attenuation range, as shown by CT images (\(-195\) to \(-45\) HU) [11].