esa-sceva/satcom-chunk-collection huggingface dataset:
# PCONet: A Trainable Feedback Scheme of Precoder for the Uplink Multi-User MIMO Systems
## 3 The Proposed PCQNet
### Testing of PCQNet
#### 3.3.2 Baseline2: The Lloyd-Max Quantization Scheme  
We apply the Lloyd-Max quantizer to reduce the dimension of the feedback matrices which is labeled as LloydMax in our simulation. An optimized Lloyd-Max quantizer minimizes the mean square quantization error (distortion) as much as possible. The Lloyd-Max quantization scheme stores the designed codebooks and partitions at the UEs and the BS, thus the BS only needs to transmit the indexes to specify the precoding matrices.  
In the offline stage, we first acquire the datasets generated by Algorithm 1. The empirical probability distribution function of the real and the imaginary parts of the precoding matrices is obtained for the design of the Lloyd-Max quantizer in [13; 28; 29]. Then, we develop a Lloyd-Max-based quantizer under different SNRs and channel models. The codebooks and partitions are specifically optimized by the Lloyd-Max algorithm in [30; 31].  
In the online stage, the UEs can readily recover the precoding matrices utilizing the received indexes with the prestored Lloyd-Max quantization partitions and codebooks. Each element in the precoding matrices has to be quantized individually, thus the minimum number of feedback bits is \(N_{k,t}\times N_{k,s}\times 2\) for each precoding matrix \(\mathbf{F}_{k}\in\mathbb{C}^{N_{k,t}\times N_{k,s}}\). (e.g., \(N_{k,t}=4\), \(N_{k,s}=2\), \(\beta=1\), \(m=16\)). It is necessary to further reduce the feedback overhead and design a more SNR-adaptive feedback scheme to combat channel variations.
## 2 Materials and Methods
### In Vitro Cytotoxicity Assay  
2.4.1. Cell Cultures  
Human liver hepatocarcinoma cells (HepG2), human breast adenocarcinoma cells (MCF-7), human cervical cancer cells (HeLa), human lung adenocarcinoma (A549) and African green monkey kidney cells (Vero) were cultured in Dulbecco's Modified Eagle Medium (DMEM) and minimum essential medium (MEM), supplemented with 10% foetal bovine serum (FBS) and 1% gentamicin solution. The cells were grown in 5% CO\({}_{2}\) at 37 \({}^{\circ}\)C in a humidified atmosphere.  
2.4.2. The 3-(4,5-Dimethyltetrazolzolium Bromide) (MTT) Reduction Assay  
Cytotoxicity was evaluated using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) reduction assay described by [27], with minor modifications. Cells (100 \(\mu\)L) were seeded at 1 \(\times\) 10\({}^{5}\) cells/mL density in 96-well microtitre plates and incubated at 37 \({}^{\circ}\)C in a 5% CO\({}_{2}\) incubator for 24 h to allow attachment of the cells. Extracts and fractions were resuspended in acetone to 100 mg/mL and the compounds were resuspended in DMSO to 20 \(\mu\)g/mL, then serial dilutions of the samples were prepared in DMEM (10% FBS and 1% gentamicin solution). After the incubation period, 100 \(\mu\)L of each sample were added to the wells containing cells. Doxorubicin was used as a positive control. Negative controls with the same volume of solvents were also included, and the plates were further incubated for 48 h in a CO\({}_{2}\) incubator. Following incubation, medium in each well was removed from the cells, which were then washed with approximately 150 \(\mu\)L of PBS. The PBS was aspirated, and fresh medium (200 \(\mu\)L) was added to all the wells. Finally, 30 \(\mu\)L of MTT (5 mg/mL in PBS) was added to each well and the microtitre plates were incubated at 37\({}^{\circ}\)C for 4 h. Following 4 h incubation, the medium was aspirated from the wells, and 50 \(\mu\)L of DMSO added to solubilize the resulting formazan crystals. The absorbance was read using a microplate reader (Bio-Tek Synergy, Instruments Inc, Santa Clara, CA, USA) at 570 nm with a reference wavelength of 630 nm. The IC\({}_{50}\) values were calculated as the concentration of the tested samples, resulting in a 50% reduction of absorbance compared to untreated cells. The relative safety of each sample was assessed using the selectivity index, which was calculated as follows:  
% viability = (absorbance of sample treated cells/absorbance of control cells) \(\times\) 100.  
All experiments were performed in triplicate and mean values were calculated.  
2.4.3. Selectivity Index (SI)  
The selectivity index (SI) indicates the degree of cytotoxic selectivity of the tested sample against cancer cells versus normal cells (Vero) and was calculated by dividing the IC\({}_{50}\) of the tested sample in normal cells by the IC\({}_{50}\) of cancer cells.  
The SI values were calculated by applying the formula:  
\[\text{SI}=\text{IC}_{50}\text{ normal cell/IC}_{50}\text{ cancer cell.}\]
## 4 Results and Discussion  
### Experimental Results  
#### 4.1.1 Watermark Encryption  
The watermark image utilized in this paper to create zero watermarks is a binary image with a size of 64 \(\times\) 64 pixels (Figure 4a) that provides relevant copyright information 'SUST'. Arnold scrambling algorithm [39] is used to encrypt it to improve security. The scrambling period of the watermark image is 48 times when all parameters in the Arnold  
Figure 3: Procedure of watermark extraction and copyright authentication.  
formula are set to 1 (See Reference [39] for the specific formula of the Arnold scrambling algorithm). Figure 5 shows the original watermark image and the status of the image at the 15 th, 25 th, 35 th, and 48 th perturbations, where the perturbed watermark image at the 15 th, 25 th, and 35 th perturbations is similar to a random noise map that is difficult to identify and has good concealment and reverts to the original state at the 48 th perturbation. We chose the 15 th disrupted image as the encrypted state's watermark information and obtained its binary watermark sequence for the construction of zero watermarks.
# 5G Support for Industrial IoT Applications--Challenges, Solutions, and Research Gaps
## 3 A Brief Overview of 5G  
### Requirements  
The requirements and the current technological capabilities delineate the sensibly reachable targets for 5G. The recent recommendation on the \"Service requirements for the 5G system\" is described in TS 22.261 [3] by 3 GPP. It formulates 32 different basic capabilities, defines the performance requirements, as well as security and charging aspects.  
Clearly not all the listed service requirements are related to IoT, i.e., charging aspects are vaguely defined; and those are anyway outside of the scope of this paper, although the performance and security related ones are all relevant; as well as some basic capabilities. Those capabilities that are majorly relevant for IoT are the following.  
* Network slicing,
* resource efficiency (management for IoT, bulk operations for IoT),
* efficient user plane,
* priority, QoS and policy control,
* network capability exposure,
* energy efficiency,
* QoS monitoring (especially for URLLC, vertical automation communication, and eV2X services),
* non-public networks,
* positioning services, and
* cyberphysical control applications in vertical domains.  
The performance-related requirements are addressed from various aspects throughout the article. According to TS 22.261 [3], these include high traffic density, low latency, high reliability, high accuracy positioning, high availability IoT traffic, and Key Performance Indicators (KPIs) for the User Equipment (UE) to network relaying in 5G system. The actual target measures and KPIs are detailed in the various tables of TS 22.261 [3].  
High reliability is especially important for 5G, since it is a key distinguishing feature when compared to architectures that use non-licensed radio spectrum, or legacy, evolutionary-engineered heterogeneous networks.  
A vast body of research on 5G high reliability tackles every aspect of the well-known network performance problems: reliability, latency, and bandwidth. Providing high reliability for novel 5G services is actually even greater at the advent of a geographically distributed system. The difficulty stems from the fact that wide-area networking and all its well-known issues come into play and affect the performance of the applications. When addressing these issues, the authors of [8; 9] considered the replica and virtual function placement, to achieve lower migration time. As a further step, authors of [10] investigated the fog resource provisioning problem for deadline-driven IoT services to minimize the cost considering the probability of resource failures. They considered that VM failures are temporary and recoverable.
## 3 Results and Discussion
### Discussion
#### 3.2.1 Relative Importance of Various System Components  
A key theme of this research is the development of quantitative, internally consistent estimates of the relative roles of watershed components in the retention and release of nitrogen over seasonal timescales. The overall goal is the exploration of the temporally varying relationship between these components--vegetation, soils, hydrology, and the aquatic environment--in regulating the release of nitrogen from the system. In the hydrologic literature, much work in this direction has focused on the concept of hydrologic flushing [48]. At the same time, it is often assumed in both the soils and forest ecology literature (e.g., [49]) that the temporal variation in net mobilization is ultimately responsible for the availability of any nitrogen that might be flushed out of the system by the hydrology. This mobilization potential is particularly relevant in regions, like the PNW, where atmospheric deposition remains low. All the while, the role of the stream channel, as well as the riparian vegetation [47] in immobilizing significant amounts available nitrogen from the aquatic system, and hence modifying cross-weir export measurements, frequently remains unnoticed in catchment studies. And perhaps even more importantly in systems where nitrogen is strongly retained, the particulate export of dissolved organic nitrogen, over which the aquatic system exerts significant control, is often of a similar magnitude, if not considerably larger, than the export of dissolved nitrogen [30].  
Our modeling work indicates that hydrologically-mediated fluxes are much smaller in magnitude (DON + DIN export of 22.1 mg/m\({}^{2}\)-year) than the mobilization fluxes that occur within the vegetation (747.7 mg/m\({}^{2}\)-year N mobilization, decomposition-uptake) or within the root zone SOM (3838.3 mg/m\({}^{2}\)-year) N mobilization. This finding is consistent with the wide variety of observational work at HJA [29] and has also been demonstrated at other forested watersheds [43]. However, the hydrologically-mediated fluxes are larger in magnitude to the immobilization potential of the aquatic environment (3.42 mg/m\({}^{2}\)-year) (Figures 6-10). These observations provide a useful means of understanding the system and ranking system components as to their role in the regulation of nitrogen cycling. In addition, the model results provide data that can be evaluated at finer seasonal time scales.
## 2 Materials and Methods  
### Process Data  
The experimentation dataset used in this work has been collected from a press machine of a stamping production line, facilitated by a private company. Press machines are machine tools that create metal-formed components by applying pressure, forming input material with cuts and deformations. Figure 1 shows an image of press machine and its main components.  
This use case's press machine has a conventional electrical drive, composed by an electric engine connected to a flywheel and clutch-brake mechanism, and crank and connecting rod system that turns cinematic chain rotary movement into slide linear movement. The slide moves perpendicular to the floor, going from the top dead center (\(0^{\circ}\)) to bottom dead center (\(180^{\circ}\)), and then back to top dead center (\(360^{\circ}\)), to complete the stamping cycle of \(360^{\circ}\). The slide forms input material by stamping in the die, which is a two-part element specifically designed to address the requirements of finished blank presented in right part of Figure 1.  
The press machine used in this work is a transfer press. This contains working stations that perform different forming processes simultaneously with the same stroke. The input material is formed in all working stations, and there is a transfer element that advances components from one working station to the next one in the time frame between strokes. Once the component is formed in all the working stations, it exits the process.  
The data were collected by sensors for each stroke, containing both single measure and evolution variables. Single measures like the die identifier or stroke number contain just one value that gives information about the stroke. Moreover, evolution variables refer to the data collected from sensors during each stroke in time-series format. The dataset is composed by the following variables: 2 speed sensors, 2 power consumption sensors, 6 force sensors, and 2 position sensors. These sensors are generic and have been integrated by the manufacturing company. A PLC collects the sensor data, synchronizes it and uploads it to the cloud, providing access to the synchronized data through a web API. Due to an NDA, no further details about the sensors/sensor placement or the API can be provided.  
With the objective of reducing industrial data variability to facilitate data analysis, data was grouped by similar environmental and operational conditions. This grouping was achieved by filtering data by stroke identifier and workorders.
# Beyond Classical AI: Detecting Fake News with Hybrid Quantum Neural Networks
## 4 Discussion  
### Interpretation of HQDNN Model Performance  
The experimental findings demonstrate that the proposed HQDNN architecture exhibits a clear advantage in recall performance across both of its variants: the TF-IDF-based HQDNN and the DistilBERT-enhanced HQDNN. These results highlight the model's strong ability to detect fake news, even with differing input representations. In the TF-IDF-based HQDNN model, a recall score of 92.58% was achieved with the test set. This result confirms the model's high sensitivity to the positive class (i.e., fake news), which is essential in minimizing the number of false negatives. In contrast, the DistilBERT with Quantum variant attained an even higher recall of 94.40%, suggesting that contextualized embeddings further enhance the model's capacity to capture subtle semantic cues associated with fake news. However, both the models exhibit relatively low precision--58.08% for the TF-IDF version and 57.80% for the DistilBERT variant--indicating a shared tendency to misclassify some true news as fake. This outcome reflects a deliberate high-recall-low-precision trade-off. In many real-world scenarios, such as misinformation detection, cybersecurity, and regulatory oversight, prioritizing the identification of harmful content over the risk of false alarms can be a strategic design choice.  
It is particularly noteworthy that these recall-oriented results are achieved with compact model architectures. The TF-IDF variant relies on a minimal number of classical parameters, while the DistilBERT-based version benefits from pretrained transformer representations combined with a lightweight quantum layer. These findings suggest that hybrid quantum-classical models can deliver a robust performance with minimal computational overhead, especially in natural language processing tasks where fine-grained class separation is critical.  
Taken together, both the configurations of the HQDNN demonstrate that quantum-assisted modeling can be a viable and efficient solution in resource-constrained or safety-critical applications, offering a compelling balance between sensitivity and generalization.
# Inversion of a Thunderstorm Cloud Charging Model Based on a 3D Atmospheric Electric Field
## Materials and Methods
### Principles of Measurement and Calibration
The distance between the observation point \(A\) and charge \(q\) and its image charge \(q_{1}\) were \(R_{0}\) and \(R_{1}\), respectively. The electric field of region 1 was generated by charge \(q\) and the image charge \(q_{0}\) of region 0, and the distance of the image charge \(q_{0}\) from the observation point \(A\) was also \(R_{0}\). The potentials at any point above the surface (\(z>0\)) and inside the earth (\(z<0\)) are shown in Equations (6) and (7), respectively:  
\[\varphi_{0}=\frac{1}{4\pi\varepsilon_{0}}\Big{(}\frac{q}{R_{0}}+\frac{q_{1}}{ R_{1}}\Big{)}=\frac{1}{4\pi\varepsilon_{0}}\Bigg{(}\frac{q}{\sqrt{\left(x-x_{0} \right)^{2}+\left(y-y_{0}\right)^{2}+\left(z-z_{0}\right)^{2}}}+\frac{q_{1}}{ \sqrt{\left(x-x_{0}\right)^{2}+\left(y-y_{0}\right)^{2}+\left(z+z_{0}\right) ^{2}}}\Bigg{)} \tag{6}\]  
\[\varphi_{1}=\frac{1}{4\pi\varepsilon_{1}}\bigg{(}\frac{q_{0}+q}{R_{0}}\bigg{)} =\frac{1}{4\pi\varepsilon_{1}}\Bigg{(}\frac{q_{0}+q}{\sqrt{\left(x-x_{0} \right)^{2}+\left(y-y_{0}\right)^{2}+\left(z-z_{0}\right)^{2}}}\Bigg{)} \tag{7}\]  
The boundary conditions at \(z=0\) are shown in Equations (8) and (9):  
\[\varphi_{0}=\varphi_{1} \tag{8}\]  
\[\varepsilon_{0}\frac{\partial\varphi_{0}}{\partial z}=\varepsilon_{1}\frac{ \partial\varphi_{1}}{\partial z} \tag{9}\]  
Finally, the image charges can be obtained from Equations (6)-(9):  
\[q_{0}=\frac{\varepsilon_{1}-\varepsilon_{0}}{\varepsilon_{1}+\varepsilon_{0}}q \tag{10}\]  
\[q_{1}=-\frac{\varepsilon_{1}-\varepsilon_{0}}{\varepsilon_{1}+\varepsilon_{0}}q \tag{11}\]  
By substituting Equations (10) and (11) into Equation (6), one can obtain the potential \(\varphi_{0}\) at the observation point \(A\), as shown in Equation (12):  
\[\varphi_{0}=\frac{1}{4\pi\varepsilon_{0}}\Big{(}\frac{q}{R_{0}}+\frac{q_{1}}{ R_{1}}\Big{)}=\frac{1}{4\pi\varepsilon_{0}}\Bigg{(}\frac{q}{\sqrt{\left(x-x_{0} \right)^{2}+\left(y-y_{0}\right)^{2}+\left(z-z_{0}\right)^{2}}}-\frac{ \varepsilon_{1}-\varepsilon_{0}}{\varepsilon_{1}+\varepsilon_{0}}\frac{q}{ \sqrt{\left(x-x_{0}\right)^{2}+\left(y-y_{0}\right)^{2}+\left(z+z_{0}\right) ^{2}}}\Bigg{)} \tag{12}\]  
The potential scalar function was used to derive the partial derivatives of \(x\), \(y\), and \(z\). The value \(z=0\) was used to obtain the electric field component produced by the charged \(q\) of thunderstorm cloud \(B\) (\(x_{0},y_{0},z_{0}\)) at the ground observation point; that is, the installation position \(A\) (\(x,y,z\)) of the three-dimensional electric field meter:  
Figure 2:0  
Figure 2:1  
Figure 2:2  
Combined with the polarity of the electric field, the new three-dimensional electric field meter determined the horizontal and elevation angles of the thunderstorm cloud charge relative to the observation point by measuring the dielectric constant and electric field component of the installed surface in real time, so as to realize the thunderstorm cloud location in complex surface environments and different weather conditions.
## 2 Related Work
Many studies also deal with the optimization of localization and positioning of doctors and nurses in hospitals and health care organizations [21; 22]. In this work we demonstrate that, for small problems, our method achieves the optimal value; for larger problems, it guarantees a close to optimal solution with a deviation of 1-3%.
## 2 Materials and Methods
### Post-Operation Morphological Parameter Measurement  
Custom created MATLAB scripts were used to analyze the palpated landmarks, following a workflow divided into sections. Initially, anatomical reference frames were constructed according to ISB standards [61]. Mirroring the healthy pelvis landmarks about the healthy sacrum allowed for the creation of a complete set of landmarks for constructing a simulated healthy pre-operation reference frame of the entire pelvis. Hip joint center of rotation positions and their deviations from the contra-lateral side were calculated. Greater trochanter offset deviations were measured by comparing the greater trochanter offsets of the healthy and affected sides of each pelvis. Lastly, an acetabular inclination analysis involved fitting a plane onto a set of manually palpated ridge points and then measuring the inclination angle between its normal vector projected on the frontal plane and the vertical axis of the pelvis reference frame.
# Propagation of Optical Coherence Vortex Lattices in Turbulent Atmosphere
## 3 Numerical Results
### Beam Wander  
In this subsection, we explore the beam wander of the OCVLs in turbulent atmosphere for several different initial beam parameters.  
Figure 6 shows the beam wander of the OCVLs in turbulent atmosphere for various \(\delta_{0}\), relative distance \(d\), topological charges \(l\), and beam array order \(M\) with \(\lambda=532.8\) nm, \(C_{n}^{2}=10^{-15}\) m\({}^{-2/3}\), _and_\(\omega_{0}~{}=~{}1\) cm. From Figure 6, it is found that the beam wander of the OCVLs in turbulent atmosphere increases as the coherence width \(\delta_{0}\) increases, or as the relative distance \(d\), topological charge \(l\), and beam array order \(M\) decrease, which means that the OCVLs are less affected by turbulence for large topological charge, large beam array order, large relative distance, and small coherence length from the view point of the beam wander.
# Leaking Gas Source Tracking for Multiple Chemical Parks within An Urban City
## 2 Generation of Sensors Data by AERMOD
### Basic Principle of AERMOD  
AERMOD adopts an approach that defines two plume states, one taking building downwash into consideration, and the other just corresponding to a plume that is not influenced by building downwash. The contributions from these two states are combined using a weighting factor as shown in Equation (1).  
\[\chi_{TOTAL}=\gamma\chi_{PRIME}+(1-\gamma)\chi_{AERMOD} \tag{1}\]  
The weighting function, \(\gamma\), is equal to 1.0 within the wake region, and beyond the wake region is calculated as Equation (2):  
\[\gamma=\exp(\frac{-(x-\sigma_{xg})^{2}}{2\sigma_{xg}^{2}})\exp(\frac{-(y- \sigma_{yg})^{2}}{2\sigma_{yg}^{2}})\exp(\frac{-(z-\sigma_{gg})^{2}}{2\sigma _{gg}^{2}}) \tag{2}\]  
where  
x = downwind distance of receptor from upwind edge of the building;  
y = lateral distance of receptor from building centerline;  
z = receptor height above stack base, including terrain and flagpole;  
\(\sigma_{xg}\) = maximum of 15R (the wake length scale and a function of the building dimensions) and the distance to transition from wake to ambient turbulence;  
\(\sigma_{yg}\) = lateral distance from building centerline to lateral edge of the wake at receptor location;  
\(\sigma_{gg}\) = height of the wake at the receptor location.