esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Converting a Pyramidal Horn Antenna into a Circularly Polarized Antenna
### Simulation and Measurement Results  
The measurements were carried out inside an anechoic chamber. The probe antenna and the antenna under test were connected to a vector network analyzer after compensating the cable effects by performing a calibration. The probe antenna was a broadband ridged horn operating from 700 MHz to 18 GHz, with a gain varying between 2 and 17 dBi. The distance between the two antennas was set to 2400 mm.  
Figure 12: Fabricated FSS structures: type 5 (a), type 1 (b), type 2 (c) and type 8 (d).  
The polarization pattern of the measured antenna was drawn by rotating the probe antenna in the E-plane with an angular step of 45\({}^{\circ}\), and the radiation pattern by rotating the antenna under test in the H-plane, respectively.  
The measurement setup for antenna gain is presented in Figure 13a, and for antenna polarization in Figure 13b, respectively. Figure 14a shows the radiating system under test consisting of the general purpose, pyramidal horn antenna and different types of FSS structure. The side view of the FSS structure with \" \(\mid\)\" type radiators can be seen in Figure 14b.
# A Brief Review of Formaldehyde Removal through Activated Carbon Adsorption
## 2 Thermodynamics and Kinetics for Adsorption of Formaldehyde on AC
### Thermodynamics and Adsorption Isotherms
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Modification AC** & **Operating Conditions: T** & **Ce, Feed Concentration** & **Qe** & **Ce vs. Qe,** & **Linear/Nonlinear** \\  & (\({}^{\circ}\)C/Humidity (\%)/pH) & **(ppm) of FA Up to** & **(mg/g)** & **(Isotherm Model)** & **Ref.** \\ \hline Ag-AC & 25, 35, 45, 60/-/- & 1000 & \(<\)120 & \begin{tabular}{c} Nonlinear \\ (Langmuir) \\ \end{tabular} & [21] \\ \hline CaO-AC & 25/-/2-10 & 10 & \(<\)20 & \begin{tabular}{c} Nonlinear \\ (Freundlich) \\ \end{tabular} & [22] \\ \hline Granular AC and & 26/-/- & 35 & \(<\)450 & \begin{tabular}{c} Nonlinear \\ (Qi-LeVan) \\ \end{tabular} & [23] \\ \hline MnO\({}_{2}\)-AC & 25/-/- & 0.2 & \(<\)0.1 & \begin{tabular}{c} Nonlinear \\ (Langmuir) \\ \end{tabular} & [24] \\ \hline Potassium-AC & \(28\pm 2/40\pm 2/\)- & 0.9 & \(<\)0.4 & \begin{tabular}{c} Nonlinear \\ (Freundlich and \\ Langmuir) \\ \end{tabular} & [25] \\ \hline \({}^{\star}\) EDA-AC & 25/-/- & 2.45 & \(<\)2.5 & \begin{tabular}{c} Nonlinear \\ (Langmuir) \\ \end{tabular} & [26] \\ \hline Ag-AC & 25/-/- & 14 & \(<\)7 & \begin{tabular}{c} Linear \\ (Langmuir and \\ Freundlich) \\ \end{tabular} & [27] \\ \hline AC adsorptive filter & 28 \(\pm\) 2/40 \(\pm\) 2/- & 0.8 & \(<\)0.75 & \begin{tabular}{c} Nonlinear \\ (Langmuir) \\ \end{tabular} & [28] \\ \hline Ag-AC & Room/-/- & 0.9 & \(<\)35 & \
\begin{tabular}{c} Linear \\ (Langmuir) \\ \end{tabular} & [29] \\ \hline Ag-AC, Cu-AC & 25/-/- & 20 & \(<\)0.6 &
\begin{tabular}{c} Linear \\ (Langmuir and \\ Freundlich) \\ \end{tabular} & [30] \\ \hline \hline \end{tabular} \({}^{\star}\) EDA: Ethylenediamine.  
\end{table} 
Table 2: Adsorption isotherms for formaldehyde on surface-modified AC as reported in the literature. Meanwhile, isotherm models have been evaluated in the literature. [PERSON] et al. [22] investigated the adsorption of formaldehyde over AC-decorated CaO and Fe\({}_{3}\)O\({}_{4}\) nanoparticles, and adsorption behaviors were evaluated using the three isotherms of the Langmuir model, Freundlich model, and Dubinin-Radushkevich model. They found that the Freundlich model was the best fit after comparing the R\({}^{2}\) values of all three models. They concluded that their adsorption process was a physical and multilayer adsorption. In other respects, [PERSON] et al. [23] examined the adsorption behaviors using commercialized activated carbon fiber cloth and granular ACs, and they reported that the adsorptions follow the Type V isotherm, which is different from the normal Type I behavior [23]. Thus, they proposed the Qi-LeVan model as the best fit. They also suggested the following three factors to explain Type V behavior: (1) reduction to a finite, nonzero limit at low partial pressures, (2) description of the adsorption isotherm over the entire range of adsorption considered, and (3) successful fit of both HCHO and water vapor adsorption isotherms.
# Integrated Photonics for IoT, RoF, and Distributed Fog-Cloud Computing: A Comprehensive Review
## 6 Key Enablers of High-Speed Networks Technology
### Quantum and Advanced Computing  
PIC technology plays a foundational role in emerging paradigms such as quantum computing, quantum communication, and neuromorphic computing--each poised to reshape Fog and Cloud architectures. PICs are well-suited to quantum photonic systems due to their low-loss, phase-stable, and scalable optical paths, enabling the integration of sources, beam-splitters, phase shifters, and detectors on a single chip for quantum logic and entanglement distribution [83]. In quantum communication, PICs support secure, high-speed links between Fog nodes and Cloud cores via quantum key distribution (QKD), enhancing data security across distributed systems [84]. Neuromorphic computing, which mimics brain-like parallel processing, also benefits from photonic implementations that enable ultra-fast, energy-efficient edge AI and sensory fusion in Fog environments [67]. These innovations empower distributed infrastructures to handle complex tasks--such as encryption, inference, and real-time learning--locally, while reserving heavier computation for the Cloud. As a result, PICs provide the hardware foundation for scalable, cognitive, and secure Fog-Cloud ecosystems.  
Companies like PsiQuantum and Intel are advancing integrated photonic chips for quantum computing, with major implications for distributed architectures. PsiQuantum leverages photonic circuits and existing semiconductor manufacturing to develop scalable, fault-tolerant quantum computers aimed at deployment in global data centers [85]. Intel is developing silicon-based spin-qubit processors with integrated photonic components for high-fidelity qubit control and readout, supporting compact quantum modules suitable for Fog and Cloud environments [86]. Collectively, these efforts highlight how integrated photonics can enable seamless, high-performance quantum computing across distributed computing networks.  
Table 4 provides an overview of key PIC technologies relevant to Fog and Cloud computing. It highlights the current state of the art and outlines emerging research opportunities across integration, interconnects, energy-efficiency, and advanced computing paradigms.  
Figure 11: Four-channel coherent transmitter module combining hybrid integration concepts at the chip and package levels, taken from [82]. **(a)** Schematic of the multi-chip module consisting of four InP-based HCSELs, silicon–organic hybrid (SOH) modulators, and output single-mode fibres interconnected by photonic wire bonds. **(b)** Top view and schematic cross-section of a single SOH MZM, highlighting the electro-optic material deposition. **(c)** Experimental setup for coherent transmission, showing the AWG-based modulation, transmission over 75 km fibre, and coherent detection. **(d)** Constellation diagrams and measured bit error ratios (BERs) for channels operating at symbol rates of 28 GBd and 56 GBd, demonstrating BER values below the forward-error correction threshold.
###### Abstract  
UHF satellite communication for Internet of Things (IoT) technology is rapidly emerging in monitoring applications as it offers the possibility of lower-costs and global coverage. At the present time, Low Power Wide Area Network (LPWAN) solutions offer low power consumption, but still suffer from white zones. In this paper, the authors propose an UHF frequency reconfigurable Antenna for hybrid connectivity LoRaWAN (at 868 MHz) and UHF satellite communication (Tx at 401 MHz and Rx at 466 MHz) with the Low Earth Orbit (LEO) Kineis constellation. The antenna is based on a meandered line structure loaded with lumped components and a PIN diode to control the antenna resonant frequencies. It resonates at 401 and 868 MHz when the PIN diode is forward-biased (ON state) and 466 MHz in reverse-biased configuration (OFF state). The antenna is designed inside the enclosure with the presence of all the parts of the connected device. The results of EM simulations and parametric studies on the values of the lumped components and the PIN diode equivalent model, which are obtained with HFSS, are presented. The antenna is prototyped and has dimensions of 78 mm \(\times\) 88 mm \(\times\) 1.6 mm. The paper proposes a fast and practical method to reduce time development and compensate the frequency shift between measurement and simulation.  
reconfigurable antenna; PIN diode; UHF; IoT +
Footnote †: journal: Sensors  
1  
[PERSON]; [PERSON]; [PERSON] Design and Realization of an UHF Frequency Reconfigurable Antenna for Hybrid Connectivity LPWAN and LEO Satellite Networks.  
1
# Path Difference Optimization of 5G Millimeter Wave Communication Networks in Malaysia
## 6 Simulation Results
The link margin significantly decreases as the path difference increases, as shown in Figure 11. From this figure, it can be observed that the link margin for the urban scenario without rain fall is high compared to  
Figure 10: NLOS path loss estimation for the urban and highway scenarios with and without rain attenuation. Figure 9: LOS path loss estimation for the urban and highway scenario with and without rain attenuation. In Table 3, \(R^{U}_{WORK}\) and \(R^{U}_{WR}\) are the respective optimal path differences for the urban scenario without and with rainfall attenuation, \(R^{H}_{WOR}\) and \(R^{H}_{WR}\) are the respective optimal path differences for the highway scenario without and with rainfall attenuation, and \(R^{U/H}_{WOR}\) and \(R^{U/H}_{WR}\) are the respective optimal path differences between the urban and highway scenarios without and with rainfall attenuation. From Table 2, it can be noticed that the optimal path difference in the urban scenario is better than that in the highway scenario. In addition, it can be seen that the optimal path difference without rainfall is greater than the optimal path difference with rainfall. These optimal path difference values improve coverage capacity, system performance, and throughput, and can boost data rates by up to 1 Gbps. The impact of rain on connected vehicle applications is reduced by these optimal distances, potentially lowering the number of crashes that occur on the roads when it rains heavily.
## 1 Introduction
This method also draws on the spring-mass model's principles but targets the velocity profile. As the CoM descends during the flight phase, it reaches a minimum vertical velocity at IC, where the leg acts as a spring-damper system [21; 26]. Similar to IMU-based approaches which identify IC events using vertical acceleration zero-crossings [27; 28], we detect IC by finding minima in the vertical velocity derived from GNSS data. Although differentiating positions to obtain the velocity may introduce noise, this method's alignment with a clear biomechanical event could yield more precise \(SF\) and \(SL\) estimates. Thus, this study aims to compare Method 1 (vertical position minima) and Method 2 (vertical velocity minima) to evaluate their accuracy and reliability in estimating running parameters--\(SF\), \(SL\), and _running velocity_--during each step of a 400 m sprint. As describedin the Materials and Methods section, only two subjects were recruited for this study. Therefore, we regard this study as a pilot study. This initial, small-scale approach follows the precedent of other feasibility studies which used limited samples to develop and refine new measurement methodologies [19; 29]. Determining which method provides more accurate and reliable metrics is crucial for optimizing RTK GNSS technology for detailed running performance analyses, especially when testing novel devices in real-world contexts.
## 4 Experiments
### Dataset Description  
We conducted experiments on the proposed HMA Conv-LSTM model on four benchmark datasets [48] with the same experimental setup as in the previous work. Table 2 shows the basic information statistics of the four datasets. Figure 5 shows the distribution of sample categories for the four benchmark datasets.  
**Opportunity** dataset [49] mainly contains daily household and kitchen actions. Subjects recorded data using inertial measurement units (IMUs) such as accelerometers, gyroscopes, and magnetometers at 12 locations on the body. The dataset is annotated for 18 mid-level actions (e.g., opening/closing the refrigerator), with one null category exceeding 76% of the data. It makes the dataset highly unbalanced in terms of the distribution of action categories.  
**PAMP2** dataset [50] mainly contains multiple household actions. A total of nine subjects were instructed to perform 12 actions of daily living. Subjects recorded complete IMU data, temperature, and heart rate data using three wearable sensors located on the hand, chest, and ankle.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameters** & **Value** \\ \hline Optimizer & Adam \\ Loss function & Cross entropy \\ Batch size & 128 \\ Learning rate & 0.001 \\ Learning rate scheduler & Cosine \\ Training epoch & 80 \\ Dropout rate & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hyperparameters of the model trained.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Action** & **Validation** & **Test Subject ID** & **Sampling Rate** & **Downsampling** & **Sensors Used** \\ \hline Opportunity & 18 & 1(Run 2) & 2, 3(Run 4, 5) & 30 Hz & 100\% & A, G, M \\ PAMAP2 & 12 & 105 & 106 & 100 Hz & 33\% & A, G \\ USC-HAD & 12 & 11, 12 & 13, 14 & 100 Hz & 33\% & A, G \\ Skoda & 11 & 1(10\%) & 1(10\%) & 98 Hz & 33\% & A \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the datasets. Here A = Accelerometer, G = Gyroscope, M = Magnetometer.  
**USC-HAD** dataset [51] includes six readings from three-axis accelerometers and gyroscopes worn on the subjects' bodies. It contains 12 different action categories from 14 subjects, including walking, running, elevator up/down, etc. In addition, the sensor locations and division of action categories in this dataset make classification using feature representation learning challenging. For example, it is difficult to discriminate between actions such as walking to the left or right using only accelerometers and gyroscopes.  
**Skoda** dataset [52] mainly consists of 10 actions performed by workers in an automotive production environment, such as opening/closing doors and check steering wheel. It also includes labeled empty categories. It consisted of one subject wearing an accelerometer in several different positions on their arm to perform manual maintenance and quality checks on automotive parts.
# CRCSI: A Generic Block Interleaver for the Next Generation Terrestrial Broadcast Systems
## 4 Simulation Results
### Simulation Parameters  
The main parameter settings for this paper are listed in Table 1, where the LTE-based 5G terrestrial broadcasting setup is used. Rel-16 introduces two CP lengths of 100 \(\upmu\)s and 300 \(\upmu\)s for car-mounted and fixed reception, respectively. The new CP of length 300 \(\upmu\)s adopted in the middle power middle tower (MPMT) scenario supports coverage radius up to 100 km, while the 100 \(\upmu\)s CP length can be used to support the transmission with a maximum speed of 250 km/h in the low power low tower (LPLT) scenario. For the fixed reception, the transmission block size (TBS) is 76208/45352, while for the mobile reception, the TBS is 9912. Tapped delay line (TDL) channel models with 10 MHz bandwidth are utilized to perform the simulation. The channel model TDL-B is adopted for the fixed reception scenarios with a delay spread of 35 \(\upmu\)s for MPMT and the mobile reception with 20 \(\upmu\)s for LPLT, which is obtained according to link-level simulation in [22].
## 2 \(\Phi\)-Otdr Signal Storage Model  
### \(\Phi\)-Otdr  
Unlike traditional monitoring, the \(\Phi\)-OTDR technique regards the optical fiber as an organic whole in the monitoring process. In other words, the optical fiber is considered as a single vibration signal appearing at multiple points on the same line [23; 24; 25]. Owing to the distributed feature, the vibration signal of the \(\Phi\)-OTDR contains three kinds of information--amplitude, time and length--in which the amplitude varies with time and length.  
The optical fiber takes the shape of a long line. The signal at each point on the length axis must be observable from the time axis, and the inverse is also true. The signal is the strongest at the point where the vibration source is vertical to the fiber. From this point, the signal strength gradually decays until reaching the two ends of the fiber. If the length axis is between 4.5 m and 5.9 m, then the strongest signal will appear at 5.2 m. Then, the signal amplitude between 4.4 m and 6.1 m can be measured by time and length (Figure 2).  
Table 1 below provides a detailed explanation of Figure 2.
## 5 Experiments
### Experimental Setup
#### 5.1.4 Evaluation Metric  
For the evaluation metric, we used the mean square error (MSE) as defined in Equation (12) [22; 24; 42].  
\[MSE=\frac{1}{S}\sum_{i}^{S}(\hat{y}_{i}-y_{i})^{2}, \tag{12}\]  
in which \(S\) is the total number of cascades, \(\hat{y}_{i}\) is the predicted value, and \(y_{i}\) is the ground-truth label.  
In the real-life setting, our focus is more on identifying a relatively large cascade rather than the exact value of its size. That is, we should be more concerned about whether the model predicts a higher ranking of relative values when the source tweets have larger cascades. Therefore, in addition to regression metrics, we also use two metrics commonly employed for ranking: normalized discounted cumulative gain on the top \(k\) (\(NDCG@k\)) and hit ratio on the top \(k\) (\(HR@k\)). \(HR@k\) is defined as follows:  
\[HR@k=\frac{Number\ of\ Hits@k}{Total\ number@k}, \tag{13}\]  
in which 'hits' means that if a tweet is predicted to be in the top \(k\), it is also in the top \(k\) of the ground-truth label.  
\(NDCG@k\) is defined as follows:  
\[NDCG@k=\frac{DCG@k}{IDCG@k}, \tag{14}\]  
where \(DCG@k\) and \(IDCG@k\) are defined as  
\[DCG@k= \sum_{i=1}^{k}\frac{rd(i)}{log_{2}(i+1)}, \tag{15a}\] \[IDCG@k= \sum_{i=1}^{|REL|}\frac{rd(i)}{log_{2}(i+1)}, \tag{15b}\]  
in which \(i\) is the index of the \(i\)-th highest predicted label. \(rel(i)\) represents the corresponding true cascade size of the \(i\)-th cascade. \(|REL|\) indicates that the results are sorted in descending order based on relevance, and the set of the first \(k\) results is selected, allowing for enhanced efficiency.  
Both evaluation measures enabled us to examine our model from different perspectives. The MSE determines how accurate our model is in terms of cascade size (when comparing the true and predictive values), while NDCG is used to compare the level of similarity between true and predictive cascade orders in terms of cascade size.  
We varied \(k\) in {1%, 5%, 10%, 15%} since only about 15% of cascades contain retweets. The distribution is shown in Figure 14.
## 2 The Geometric Dilution of Precision and Genetic Algorithm
### Recursive Algorithm  
Suppose that \(m\) satellites are observed, \(\mathbf{G}_{m}\) is their observation matrix, and \(\mathbf{G}_{m}^{T}\mathbf{G}_{m}=\mathbf{G}_{m-1}^{j}\mathbf{G}_{m-1}^{i}+g_{1}^{T}g_{i}\). \(\mathbf{G}_{m-1}^{j}\) is the observation matrix that excludes the \(i^{th}\) satellite from the \(m\) satellites. Thus,  
\[\mathbf{H}_{m}=\left(\mathbf{G}_{m}^{T}\mathbf{G}_{m}\right)^{-1} \tag{16}\]  
according to the Sherman-Morrison formula, which is  
\[\begin{split}\mathbf{H}_{m-1}^{i}&=\left(\mathbf{G}_{m-1}^{ i}\mathbf{G}_{m-1}^{i}\right)^{-1}\\ &=\left(\mathbf{G}_{m}^{T}\mathbf{G}_{m}-g_{i}^{T}g_{i}\right)^{-1}\\ &=\mathbf{H}_{m}+\mathbf{H}_{m}g_{i}^{T}\left(1-g_{i}\mathbf{H}_{m}g_{i}^{T} \right)^{-1}\!\!g_{i}\mathbf{H}_{m}\end{split} \tag{17}\]  
Let scalar \(1-g_{i}\mathbf{H}_{m}g_{i}^{T}=\lambda_{mi}\). The differences in GDOP between the \(m-1\) satellites and the \(m\) satellites are  
\[\begin{split} GDOP_{m-1}^{i}{}^{2}&=\text{trace} \mathbf{H}_{m-1}^{i}\\ &=GDOP_{m}^{2}+\text{trace}\Big{(}\mathbf{H}_{m}g_{i}^{T}g_{i}\mathbf{H} _{m}/\lambda_{mi}\Big{)}\\ GDOP_{m-1}^{i}{}^{2}&-GDOP_{m}^{2}=\text{trace} \Big{(}\mathbf{H}_{m}g_{i}^{T}g_{i}\mathbf{H}_{m}/\lambda_{mi}\Big{)}\end{split} \tag{18}\]  
The change in \(GDOP^{2}\) caused by the \(i^{th}\) satellite is \(\text{trace}\left(\mathbf{H}_{m}g_{i}^{T}g_{i}\mathbf{H}_{m}\lambda_{mi}\right)\). Let \(\Delta\mathbf{G}_{i}=\text{trace}\big{(}\mathbf{H}_{m}g_{i}^{T}g_{i}\mathbf{H}_{m}/\lambda _{mi}\big{)}\). The larger \(\Delta\mathbf{G}_{i}\) is, the larger the \(i^{th}\) satellite's contribution to \(GDOP_{m}\) is. This suggests that the \(i^{th}\) satellite should not be excluded. We can design a Satellite Selection Algorithm by iteratively removing a satellite with the smallest contribution. The recursive algorithm process is as follows:  
1. Calculate the current satellites' positions through ephemeris data.
2. Calculate all visible satellites' contribution values \(\Delta\mathbf{G}_{i}\).
3. Rank the visible satellites in the order of \(\Delta\mathbf{G}_{i}\).
4. If the \(\Delta\mathbf{G}_{i}\) of each visible satellite is larger than or equal to a threshold, the selection result is all visible satellites. Otherwise, proceed to the next step.  
Remove the satellites whose \(\Delta G_{i}\) value is smaller than the threshold, and the rest of the satellites are selected [3].
## 3 Results and Discussion
### Empirical Models  
A multiple regression method was harnessed to develop empirical equations enabling the estimation of biogas and methane yields in methane fermentation. It was found that biogas and methane yields were statistically significantly affected by such dependent variables as OLR as well as TOC concentrations in the supernatant. The postulated model of biogas yield (5) is characterized by an estimation error of \(\pm\)8.7437 and reflects ca. 91.99% of changes in the process of biogas production (R\({}^{2}\) = 0.9199). The methane yield model (6) reflects ca. 91.38% changes in the process of its production (R\({}^{2}\) = 0.9138) with an estimation error of \(\pm\)13.384. Figure 7 shows the forecasted impact of the COD concentration in the supernatant and OLR on biogas and methane yields.  
\[\text{BIOGAS}=0.0079\text{TOC}-12.35\text{OLR}+365.9324 \tag{5}\]  
\[\text{METHANE}=0.0084\text{TOC}-10.1389\text{OLR}+211.9114 \tag{6}\]  
BIOGAS--biogas yield, cm\({}^{3}\)/gCOD;  
METHANE--methane yield, cm\({}^{3}\)CH\({}_{4}\)/gCOD;  
TOC--TOC concentration in the supernatant, mgO\({}_{2}\)/dm\({}^{3}\); and  
OLR--initial organic load rate (OLR) of anaerobic respirometers, gCOD/dm\({}^{3}\)