esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 AI-Empowered Communication with Perceptual Constraint
### AI-Empowered Semantic Communication  
Driven by advances in machine learning, there has been a significant increase in interest in semantic communication systems [98; 106; 107], with intelligent network architectures being progressively integrated into existing communication standards [108; 109; 110; 111; 112; 113]. Broadly, the objective of communication is to transmit meaningful information. In the context of generative models, the term \"meaningful\" is contingent upon the semantic meaning of the information, which represents a fundamentally different global property compared to traditional average distortion measures, such as Hamming distance and MSE. The notions of semantic and pragmatic communication problems are addressed by drawing parallels to linguistics [107]. By integrating machine learning algorithms, particularly generative modeling, into the widely deployed digital communication infrastructure, semantic communication aims to deliver content with higher perceptual quality more robustly over wireless channels, which are frequently influenced by environmental factors such as weather conditions, buildings, and device activities.
# AI-Based Intelligent Monitoring System for Estrus Prediction in the Livestock Industry
### The Changing State of the Livestock Industry  
In the Korean case, Hanwoo is a representative breed of Korean native cattle common in the Korean livestock industry. According to the National Statistics Korea (KOSIS--Korean Statistical Information Service, 2020), the number of Hanwoo being bred in Korea has steadily increased from about 1.5 million in 2005 to 2.8 million in 2018. The number of large livestock farms with 100 or more animals has increased more than five times over the last two decades, from 1155 households in 2005 to 6001 households in 2020 (Figure 1).  
Thus, livestock farms are gradually becoming larger. However, compared to the growing scale, the growth rate in the number of professional workers participating in breeding in the livestock industry, who are essential at livestock sites, is low [1,2]. Professionals (such as veterinarians or experts in food safety and productivity) concerned about the management of livestock have an interest in the abnormal livestock behaviors exhibited during specific periods (e.g., estrus, disease, feeding, change of living environment). Hence, the meanings of specific behaviors of target livestock are continuously being researched.  
Exectives in the livestock industry know that the construction of healthy and safe breeding environments for livestock and work environments for the workers are crucial for the development of the industry. The livestock experts and workers of large-scale livestock farms are involved in various tasks for the efficient management of individual livestock. In most small farms that have a small number of livestock, the workers can easily predict the estrus of target livestock by observation and experience. Moreover, they can easily perform artificial insemination and management procedures based on these observation results. However, the optimal timing to artificially inseminate cattle is around 20 h after the start of specific behaviors (estrus signs) such as mounting activity. Thus, in large-scale livestock farms, it is difficult to perform various observations and management tasks for individual livestock because of the large number of target livestock that workers are responsible for. In particular, although the scale of breeding environments is gradually increasing, the number of employees in the livestock industry is decreasing. Therefore, proper breeding management of target livestock that is required in the livestock production and consumption process without increasing the tasks of workers in the livestock industry has been established as an important element [3].  
To adapt to this trend in the livestock industry, livestock farms are increasingly applying ICT, which requires them to solve various problems related to workers, exectives, target livestock, and many other factors [4].  
Figure 1: Changes in Hanwoo breeding farms and the number of Hanwoo in Korea.
# Discretly Exploiting Inter-Session Information for Session-Based Recommendation
## 5 Experiments
### Experiment Settings
#### 5.1.1 Baselines  
To demonstrate the comparative performance of DEISI, several representative and/or state-of-the-art models have been chosen as our baselines. They can be categorized into three types: (1) **Non-GNN models**: FPMC, GRU4 Rec, NARM, and STAMP; (2) **GNN models only considering intra-session information**: SR-GNN, Disen-GNN, and TAGNN; and (3) **GNN models considering both inter- and intra-session information**: GCE-GNN, DHCN, IGN, and COTREC.  
We briefly introduce them as follows.  
* **FPMC**[23] utilizes Markov chain to make recommendations. It only focuses on the sequential relationship between interacted items and does not construct the user's latent interest.
* **GRU4 REC**[9] adapts a Gated Recurrent Unit (GRU) from NLP to SBR. As an RNN-based model, it only cares about sequential relationships between items.
* **NARM**[10] combines attention mechanism with GRU to consider both global relationships and sequential relationships to make recommendations.
* **STAMP**[11] emphasizes a novel model for short-term attention and memory prioritization, which effectively captures the user's general interests by leveraging the long-term memory.
* **SR-GNN**[12] introduces GGNN to obtain item embeddings by information propagation and also employs a soft-attention mechanism to obtain the session embedding for making recommendations.
* **Disen-GNN**[14] deploys DRL into SBR to learn the latent factor-level embeddings. Then use GGNN in factor-level sub-channels to learn factor-level session embeddings.
* **TAGNN**[13] is an improved variant of SR-GNN. It replaces the original session encoder of SR-GNN with a target-aware attentive network.
* **DHCN**[18] facilitates the learning of inter-session information for items by leveraging the power of a novel framework of contrastive learning.
* **GCE-GNN**[30] aggregates information acquired from the global item graph to augment the session graph's information for recommendation.
* **I3 GN**[19] simultaneously learns inter-session and intra-session information and synthesizes the information learned from it through an attention layer.
* **COTREC**[17] is an improved variant of DHCN. It integrates the idea of co-training into CL by adding divergence constraints to DHCN's CL module to comprehensively learn inter- and intra-session information.
# Experimental Study of the Air Demand of a Spillway Tunnel with Multiple Air Vents
## 3 Results and Discussion  
### Effects of Water Velocity and Flow Depth on Air Demand  
Increased pump flow elevates water velocity (\(U_{w}\)), thereby increasing air discharge (\(Q_{a}\)) (Figure 2). This coupling mechanism originates from two synergistic effects: (1) U w-driven air acceleration at the interface, and (2) turbulence-enhanced air entrainment. These findings align with [PERSON] et al.'s experimental observations [23]. Increasing the flow depth leads to a reduction in the space above the free surface and an increase in the air velocity (\(U_{A}\)) in the tunnel. Because the effect of increasing the air velocity on the air discharge is more significant than that of decreasing the space above the free surface at small gate openings, the air discharge increases with increasing flow depth, as shown in Figure 2. However, as the gate opening increases, the free-surface flow transforms into foamy flow [12] and the effect of increasing the air velocity becomes less significant than that of decreasing the space above the free surface, resulting in decreasing air discharge. This suggests a local peak in air discharge with a given tunnel height (\(h\)), which can be verified by prototype observations [24]. The pressure (\(p_{i}\)) at the connection of the air vent and the tunnel decreases with increasing water velocity (\(U_{w}\)) and water depth (\(h_{w}\)) due to increasing air discharge, and the pressure (\(p_{1}\)) closer to the gate is the lowest, as shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Case** & **Condition 1** & **Condition 2** & **Comment** \\ \hline (1) & \(U_{w}=6,7,8,9,10,11,12\) m/s & \(h_{w}=4,6,8,10\) cm & \(a_{1}=a_{2}=a_{3}=56\) cm\({}^{2}\) \\  & & \(L_{1}=L_{2}=2\) m, \(L_{3}=4\
# IEEE 802.11 ax OFDMA Resource Allocation with Frequency-Selective Fading
## 4 Problem Statement
Therefore, the problem of maximizing the utility function can be written as  
\[\max_{c\in C}\max_{X\in H}\sum_{(s,r)\in X}\lambda(s,r,c). \tag{2}\]  
Please note that RUs within an allocation \(X\in H\) can differ in size (i.e., include a different number of tones). All RUs differ from each other due to frequency selective fading. Therefore the solution of (2) requires a full search for all \(X\in H\), which is highly computing-intensive. A common way to solve hard optimization problems is to use a greedy heuristic, e.g., as it is done in [31]. Therefore, in this paper, we propose a greedy procedure for an approximate solution of (2). \begin{table}
\begin{tabular}{c l} \hline \hline
**Symbol** & **Description** \\ \hline \(add(s,r)\) & Increment of the fading estimation for STA \(s\) in RU \(r\) \\ \(C\) & Set of MCSs \\ \(c_{best}\) & The fastest MCS at which the STA can transmit in entire channel \\ \(count_{success}(s,r)\) & Number of consecutive successful transmissions from STA \(s\) in RU \(r\) \\ \(count_{fail}(s,r)\) & Number of consecutive failed transmissions from STA \(s\) in RU \(r\) \\ \(D(s)\) & Remaining amount of data at STA \(s\) \\ \(Fading(s,r)\) & Fading of the signal from STA \(s\) in RU \(r\) \\ \(F(s,r)\) & Estimated values of fading component from STA \(s\) in RU \(r\) \\ \(f_{c}\) & Carrier frequency \\ \(\lambda(s,r,c)\) & The gain of the utility function if the scheduler assigns a STA \(s\) to an \\  & RU \(r\) at an MCS \(c\) \\ \(M\) & Number of RUs in the frequency band \\ \(N\) & Number of STAs \\ \(H\) & Set of all RU allocations that satisfy the restriction (1) \\ \(p_{TX}^{STA}\) & STA transmission power \\ \(P(r_{size})\) & Required power for successful reception in RU \(r_{size}\) \\ \(P_{sup}\) & Power supplement: the value by which the STA should increase its \\  & transmission power \\ \(PL\) & Path-loss \\ \(Q(s)\) & Average service rate of the STA \(s\) \\ \(R\) & Maximal allowed distance between an AP and an associated STA \\ \(R_{242}\) & Set of 242-tone (i.e., 20 MHz-wide) RUs of the channel \\ \(r_{j}\) & Resource Unit \(j\) \\ \(r_{entire}\) & RU which occupies the entire channel \\ \(rate(r,c)\) & Transmission rate in RU \(r\) at MCS \(c\) \\ \(s_{i}\) & STA \(i\) \\ \(s_{dist}\) & Distance from the STA to the AP \\ \(S\) & Set of STAs that have data for transmission \\ \(T_{fd}\) & Period of fading estimation update \\ \(Target_{RSSI}\) & desirable power level of the transmitted signal at the AP \\ \(\tau_{max}\) & 5484 μs, maximal transmission time \\ \(X\) & Set of pairs \((s_{i},r_{j})\) which indicates the resource allocation \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Notations.
# A New Maximum Likelihood Estimator Formulated in Pole-Residue Modal Model
## 2 Structure of the Combined LSCF-MLE-PMM
### The MLE-PMM (2 rd Step)
#### 2.2.1 Fast Estimation of the Perturbations on Modal Parameters  
From Equation (17), the perturbation on the coefficients \(\Delta\theta_{k}\) (i.e., the perturbations on the real and imaginary parts of the modal residues and on the real and imaginary parts of the upper and lower residuals) can be written as a function of the perturbation on the poles, \(\Delta\theta_{\lambda}\), as  
\[\Delta\theta_{k}=-R_{k}^{-1}\left(\mathrm{Re}\left(Y_{k}^{H}E_{k}\right)+S_{k }\Delta\theta_{\lambda}\right) \tag{18}\]  
By making use of Equation (18), the perturbations \(\Delta\theta_{k}\) can eliminated from the normal Equation (17), yielding  
\[\Big{(}\sum_{k=1}^{N_{k}}T_{k}-S_{k}^{T}R_{k}^{-1}S_{k}\Big{)}\Delta\theta_{ \lambda}=\sum_{k=1}^{N_{\varrho}N_{\varrho}}S_{k}^{T}R_{k}^{-1}\mathrm{Re} \left(Y_{k}^{H}E_{k}\right)-\mathrm{Re}\left(X_{k}^{H}E_{k}\right) \tag{19}\]  
or in a more compact form  
\[M_{1}\Delta\theta_{\lambda}=M_{2} \tag{20}\]  
with  
\[\begin{split} M_{1}&=\sum_{k=1}^{N_{\varrho}N_{ \varrho}}T_{k}-S_{k}^{T}R_{k}^{-1}S_{k},\\ M_{2}&=\sum_{k=1}^{N_{\varrho}N_{\varrho}}S_{k}^{T }R_{k}^{-1}\mathrm{Re}\left(Y_{k}^{H}E_{k}\right)-\mathrm{Re}\left(X_{k}^{H}E_ {k}\right)\end{split} \tag{21}\]  
This elimination decreases the memory, as well as the time required to run the algorithm. As for the MLE-CDM [21], an efficient implementation of the MLE-PMM is only possible if the variances are taken into account in the cost function (6). Also, because it uses the noise information as a weighting in its cost function, the MLE-PMM can handle measurements with a large dynamical range. Once the perturbations on the natural frequencies and damping ratios are calculated by means of Equation (19), then perturbations on the modal residues and on the upper and lower residuals are computed using Equation (18).  
It is worth highlighting that, although the derivation of the reduced normal equations as in Equation (20) follows a strategy similar to those in References [1; 14], the resulting system of equations is different. This follows from the fact that the system of equations represented by Equation (20) is formulated with the Pole-residue Modal Model with upper and lower residuals (4). The derivation of the reduced normal equations as in Equation (21) is described in Appendix B.
## 3 Flexible Substrate Antenna Design
### Material Selection  
To comply with the flexibility requirements of the antennas, the materials selected for the conductor, in general, and the substrate more specifically, need to be highly flexible and mechanically robust. Detailed analysis on the conductive and substrate materials was demonstrated in [1], which describes the range of properties for suitable polymer substrate materials. In the case of the conductive material, which is the ground plane and radiating element, copper is selected due to its qualities of being highly conductive and possessing very low resistivity. Moreover, copper is highly flexible, withstands a high level of bending and crumpling, has high tensile strength with an ability to withstand repeated pressure and deformations, and is resistant to corrosion and oxidization. See Table 1 for the properties of pure copper.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Properties of Pure Copper [20]**} \\ \hline \multicolumn{2}{c}{**Physical**} & \multicolumn{2}{c}{**Electrical**} & \multicolumn{2}{c}{**Mechanical**} & \multicolumn{2}{c}{**Thermal**} \\ \hline \multirow{2}{*}{Melting Point} & \multirow{2}{*}{Density} & \multirow{2}{*}{Conductivity} & Temperature & Tensile & \multirow{2}{*}{Modulus} & Thermal & \multirow{2}{*}{CTE} \\  & & & Coefficient & Strength & & Conductivity & \\ \hline \multirow{4}{*}{1083 °C} & 8.96 g/cm\({}^{3}\) & 58 MS/m & 0.0043/K & & & 401 & 17.0 \(\times\) 10\({}^{-6}\) \\  & at & at & at & 224-314 MPa & & 137.8 & W/m-K & m/m-K \\ \cline{1-1} \cline{2-6}  & 20 °C & 20 °C & 0–100 °C & & & at & \\ \cline{1-1} \cline{2-6}  & & & & 0–100 °C & & 0–100 °C \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of pure copper.  
Figure 3: Five steps in the design procedure for a flexible substrate antenna.  
The selection of an appropriate polymer substrate is very important for flexible antennas. It should be highly flexible, easy to integrate with a conductive material, have a high tolerance level for bending or crumpling, and have a high repeatability endurance. To corroborate the impact of curvature on different polymer substrates, PET (Melinex 401), PTFE Teflon, and flexible PVC were chosen. The selection of these three dielectric polymers was based on the analysis of their physical, electrical, and thermal properties, which showed their suitability for bending applications [1; 21].
# Sensor Technologies for Transmission and Distribution Systems: A Review of the Latest Developments
## 3 Key Transmission and Distribution System Components
### Overhead Line Power Conductors
Temperature sensors such as air and solar temperature sensors are being replaced by surface acoustic wave SAW temperature sensors, since they have the advantage of high temperature sensitivity and high temperature coefficients with low delay time [74]. Optical fiber-based sensors called Sagometers [20] are also used for temperature measurements in low and medium voltage power conductors. An increase in temperature and thermal stress could also lead to an increase in sagging of conductors, which if unchecked can lead to the damage and breakage of power conductors. Fiber Briggs grating-based optical sensors have been developed that can check the sag by monitoring the tension and the strain on the power conductors [75]. Optical and optoelectronics-based vibration sensors have also been used to detect the vibrations of the power conductors. The frequency of vibration below a threshold value signifies the increase in sag in power conductors above the permissible limit. Currently, unmanned aerial vehicle-based sag measurement based on image processing techniques are being used [76]. Robots with multiple sensors are also used, especially in live high voltage transmission lines, to monitor the sag as well as temperature, vibration, humming sounds, etc. Examples are a semi-automated robot system named Line ReconRobot and a power line inspection robot named ROSETLineBot. [77]. Another relevant device called overhead transmission line monitoring (OTLM) [78], which is embedded with multiple sensors, can monitor temperature, wind speed, inclination, current and humidity and can calculate sag and tension measurements with the help of the sensed measurements. These sensors can monitor high voltage power conductors with voltage levels of 500 kV and transmit the sensed parameters along with GPS timestamps through cellular, LoRa, Wi-Fi or satellite networks to the utility server. Finally, the installation of dynamic line rating (DLR) systems [79] requires knowledge of the real time thermal rating of the overhead power conductors to optimize the ampacity and establish dynamic limits on the conductors according to real time weather variations. For this reason, these systems utilize various temperature sensors such as stick-on wireless temperature sensors, ThermalRateTM systems, TLMTM conductor monitors, power donutTM, SAW-based temperature sensors, thermal infrared cameras, etc. [80; 81; 82]. Additionally, DLR systems are also known to employ various distributed sensors for online monitoring of conductor tension, sag, clearance-to-ground, and humidity. Thus, multiple sensor technologies that have been developed to detect various faults in the transmission line and distribution line power conductors have been discussed here.
## 6 Case Study
### Case study--Usability  
After laying the foundation and setting an initial framework considering all technical and functional requirements, it is time to proceed to the usability study. Our case study's design process began while the company already had a first functional prototype, but usability issues were not considered during the development process. FULE was used to develop the second generation of the product to improve user interaction with it. We started this phase by examining the functional prototype in search of possible failures and risks. Failures were encoded on a scale between 1-4, where the lowest rank is 1 = user frustration, and the highest is 4 = physical or mental injury.  
Among the findings were deficiencies in both the design of the system itself and of its disposable units. The buttons were all aligned, and were the same size, shape, and color, with no hierarchy, and without distinguishing between urgent and non-urgent actions. In addition, the connectors were placed without considering the wiring direction, linking the various lines, and tangling them.  
In the next step, we conducted observations at Rambam Hospital, monitoring the system in action and at two other hospitals (Assaf Harofeh, Be'er Ya'akov, Israel and Rabin Medical Center, Petah Tikva, Israel) watching nurses' regular work where there was no similar device. In addition, we interviewed the nurses and other medical staff to understand their needs, expectations, and requirements for the future product.  
The next stage was creating usage scenarios for each possible human-technology interaction. Every scenario included all possible failures that might occur due to inappropriate usage. These usage scenarios indicated that the system's dimensions and weight made its mobility in the hospital almost impossible, and it often was in the staff's way. Nurses claimed it was often difficult to cope with the complex assembly of the system (before connecting it to a new patient), as it required gathering various components located in different rooms that were sometimes unavailable when needed. The assembling process itself appeared to be confusing for the staff, potentially causing user mistakes. It seems that at this stage, rather than making the task easier, the system was a burden for the staff.  
Regarding the required information, all nurses agreed that the system should display only the most essential information (cuff pressure and critical alarms). Observations and interviews' outcomes were used to help us design three different concepts of the new device. The top three suggestions for the system location were: (1) attached to patient's bed, (2) connected to the back wall, and (3) using a designated pole. We used [PERSON]'s profile [PERSON] (1966) to help us visualize each design concepts' strengths and weaknesses according to our declared requirements (Figure 10).  
Using this four-scale scoring with a graphic representation helped us easily compare between our three options (derived from conducted interviews and observations) and we chose to design a designated pole that fit the design requirements in the best way.
# A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting
## 3 The Architecture of Deep Learning Algorithms
### Generative Adversarial Networks (GAN)  
The GAN, which was invented by the authors of [120], and its versions, including convolutional GAN (Conv-GAN), which was developed by the authors of [121], demonstrate significant potential in producing pictures that are incredibly similar to a given collection of training images. GANs are a robust category of generative models that perform their tasks by implicitly modeling high-dimensional data distributions [122]. In image processing, GAN has shown superiority relative to other generating approaches in terms of its capacity to create realistic synthetic pictures [121; 123; 124]. In terms of applicability to our study, GAN can be utilized to understand the distribution of meteorological data and augment it. This work has the possibility of supplementing training specimens with a wide range of samples to increase the generalization of deep learning algorithms. The training technique in GAN involves building two antagonistic networks that compete with each other, including the two main neural networks, which are typically the generator (G) and the discriminator (D), that may be trained using a traditional backpropagation approach.  
The two networks collaborate to enhance each other, albeit in an aggressive manner. During the training phase, G attempts to learn and make \"fake\" specimens of input noise Z to trick D, while D endeavors to identify \"fake\" or \"genuine\" inputs accurately. The model converges until the discriminator no longer distinguishes between the specimens. It is worth noting that the input noise Z typically keeps track of a Gaussian distribution across G to provide the sample I = G (z), where Z = N (\(\mu,\sigma^{2}\)), and D is a fundamental neural network filter for bilateral categorization. This process provides an excellent opportunity to supplement training specimens with a broad range of data to increase the generalization of deep learning algorithms.