esa-sceva/satcom-chunk-collection huggingface dataset:
# A Continuous Music Recommendation Method Considering Emotional Change
## 1 Introduction
Among content types, lyrics and audio information, which are elements of music, are used to analyze the emotions of music [30; 32; 33; 34; 35; 36]. There are also studies analyzing the emotions evoked by paintings [37] or movies [38]. To analyze a user's emotions, research has been conducted to infer emotions using social factors, facial expressions, voices, and brainwave data [39; 40; 41] and to predict emotions based on other people's emotions or actions [26; 42; 43]. Existing research on predicting emotions [26] predicted other people's emotions through a mental model integrated with a questionnaire on emotional transitions. This model is limited because it cannot account for personalized emotional changes, applying instead the average value of the emotional transition probability for the entire user group. To address this, this study proposes a personalized mental model. We attempted to classify music emotions using a multiple regression model based on music emotion data. Additionally, we aimed to predict changes in the user's emotions using Markov chain and interpolation methods based on the user's emotional history data. In this study, we propose a continuous music recommendation method that considers emotional changes. This is as follows:  
* First, we created an emotion vector for the music using valence and arousal values, elements of [PERSON]'s emotion model. Using a multiple regression model, we analyzed the relationships between valence, arousal, and the four emotions to generate emotion vectors for the music. * Second, we generated the user's emotion vector based on their emotional changes. The rate of emotional change for each user perspective was used to create these vectors. Each time, we generated an individual's emotional state transition matrix using a Markov chain, defining the user's individual mental model. The user's emotion vector was then generated by predicting the emotional state at time \(t_{0}+n\), where \(t_{0}\) is the current time. * Third, we calculated the similarity between the music emotion vector and the user emotion vector to generate a recommendation list according to the emotional state at time \(t_{0}+n\). For continuous recommendation, interpolation was used to estimate the user's emotional state between times \(t_{0}\) and \(t_{0}+n\), creating a continuous recommendation list based on user emotions. We expect the proposed method to recommend content that is more suitable for users. The structure of this paper is as follows. Section 2 describes existing emotion-based recommendation methods, emotion models, and emotion analysis methods as research related to this paper. Section 3 details the generation of music emotion vectors proposed in this paper, Section 4 describes the creation of user emotion vectors according to emotional changes, and Section 5 presents the continuous music recommendation method. Section 6 discusses the experiment and evaluation results of the proposed method. Finally, Section 7 provides the conclusion.
## 4 Proposed Method
### Focal Modulation Network
SA captures local contextual information around the query tokens through a fixed-size window, whereas Focal Modulation encodes spatial contextual information at different levels of granularity and adaptively fuses this information according to the query content, making the interaction and aggregation process more lightweight and efficient. Figure 2: This is a description of the framework for focal modulation, and we compare it with both Self Attention and the SPPF module to show the differences between the three in a more graphic way. ```
1:Input/Output Shape:\((B,H,W,C)\)\(\triangleright\) Batchsize \(B\); height \(H\), width \(W\), dim \(C\)
2:Focal Levels:\(L\); Conv Kernel Size at Level \(\ell\):\(k^{\ell}\)
3:functioninit
4:\(pj\_in\), \(pj\_ext\leftarrow\) Linear\((C,2*C+(L+1))\), Conv2d\((C,C,1)\)
5:\(hc\_layers\leftarrow\) [Sequential(Conv2d\((C,C,k^{\ell}\), groups = \(C\)), GeLU()) for \(\ell\) in range\((L)\)]
6:\(pj\_out\leftarrow\) Sequential(Linear(C,C), Dropout())
7:endfunction
8:functionForward(\(x,m=0\))
9:\(x\gets pj\_in(x).permute(0,3,1,2)\)
10:\(q,z,gate\leftarrow\) split\((x,(C,C,L+1),1)\)
11:for\(\ell\) in range\((L)\)do
12:\(z\gets hc\_layers[\ell](z)\)\(\triangleright\) Equation (4), hierarchical contextualization
13:\(m=m+z\times\) gate\([:,:,\ell:\ell+1]\)\(\triangleright\) Equation (5), gated aggregation
14:endfor
15:\(m=m+\) GeLU\((z.mean(\texttt{dim}=2,3))\times\) gate\([:,:,:L]\)\(\triangleright\) Equation (6), focal modulation
16:\(x\gets q+pj\_ext(m)\)
17:return\(pj\_out(x.permute(0,2,3,1))\)
18:endfunction
```  
**Algorithm 1** Pseudo code for Focal Modulation
## 2 Background
### Conjunctive Query Programs (CQ-Programs)  
The integration of ontologies and rules can be classified as heterogeneous or homogeneous [22]. Heterogeneous integration for hybrid reasoning is further distinguished as being loosely or tightly coupled. Description Logic Program (DLP) and Semantic Web Rule Language (SWRL) are examples of homogeneous integration, but they lack non-monotonic reasoning capabilities for policy exceptions handling. Therefore, we adopt one of the loosely-coupled approaches, _i.e._, conjunctive query programs (cq-programs), which is an extended version of a description logic program (dl-program), to design and enforce semantic legal policies [23].  
The semantic legal policies are expressed as a _cq-program_, e.g., a pair \((\mathcal{T},\mathcal{P})\), where \(\mathcal{T}\) is the DL-based ontology and \(\mathcal{P}\) consists of a finite set of non-monotonic datalog rules. A _cq-rule_ has the form:  
\[a\longleftarrow b_{1},...,b_{n},not\;\;b_{n+1},...,not\;\;b_{m} \tag{1}\]  
where \(a\) is a rule predicate and any \(b_{1},...,b_{m}\) may be a DL predicate or a rule predicate.  
The cq-program combines datalog rules with negation under stable model (or answer set) semantics with OWL DL. The difference between stable model semantics and well-founded semantics is that between two-valued (true or false) and three-valued (true, false, or unknown) logic. In practice, the two semantics coincide in the stratified logic program. Negation-as-failure (NAF) for weak negation (\(\sim\)) in the closed world assumption (CWA) and explicitly negative knowledge for strong negation (\(\
eg\)  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Acronym** & **Full spelling** & **Acronym** & **Full spelling** \\ \hline TLD & Trusted Legal Domain & TVD & Trusted Virtual Domain \\ SPD & Super-Peer Domain & sp & super-peer \\ GAV & Global-As-View & XaaS & Everything-as-a-Service \\ GLAV & Global-Local-As-View & LAV & Local-As-View \\ CQ & Conjunctive Query & VMs & Virtual Machines \\ \hline \hline \end{tabular}
\end{table}
Table 1: An acronym table for frequently used terms.  
are allowed in the cq-program. In fact, Reiter-style default logic and CWA can be implemented in cq-programs to support non-monotonic reasoning of description logics [11].
## 4 Parameter Sensitivity Analysis
### The Impact of Initial Saturation on Frost Heave and Thaw Settlement Displacement  
Simulations of one year of freeze-thaw cycles were conducted for four initial saturation conditions: 0.2, 0.4, 0.6, and 0.8, as shown in Figure 14. The results indicate that as the initial saturation of the soil increases, both the surface and pipeline surrounding frost heave displacements significantly increase. This suggests that frost heave in the soil is primarily caused by the volume change induced by the expansion of pore water during freezing. When the soil's initial saturation is higher, there is more moisture in the pore spaces, and during the freezing process, the moisture migrates towards the cold end and forms ice crystals, resulting in significant volume expansion. This phenomenon also exerts a greater impact on the deformation of the soil surrounding the pipeline. As shown in the stress contour map (see Figure 15), frost heave under high saturation exerts higher pressure on the pipeline structure, increasing the risk of pipeline deformation.  
Figure 14: Time-dependent variation of surface and pipeline surrounding displacements for different initial saturation levels.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 6 Efficient Power and Data Transfer in BackCom
### Scheduling and Resource Allocation
#### 6.3.4 Optimization  
The efficient use of resources in BackCom improves the efficiency of a system. This type of scenario was considered in backscatter sensor networks. Authors focused on data optimization using a Markov decision process framework, and a state action reward state action (SARSA) algorithm, to achieve optimal policy [139]. Similarly, [PERSON] et al. focused on the optimization of channel-training energy and energy allocation weights for multiple energy beams [127]. The performance optimization  
Figure 34: RF-powered cognitive radio network with Amb-BackCom. problem for cognitive radio networks with an RF energy harvesting ability was studied for secondary users [156]. This work also showed a system model of secondary users that can increase the throughput of the complete network by identifying a group of ordinary channels. [PERSON] et al. considered link optimization for wireless power transfer [157]. This work further suggested a method for designing and optimizing multi-sine waveforms for wireless power transfer. Moreover, [PERSON] et al. proposed energy efficiency maximization for WPCNs [158]. In this case, users can harvest energy from a dedicated energy source even before communicating with the station. [PERSON] et al. proposed the design for two optimal transmission strategies for a buffer-aided Amb-BackCom system [159]. This system can minimize energy consumption while sending data. The throughput can be maximized with a certain amount of energy. [PERSON] et al. proposed an efficient method of obtaining an optimal solution to the maximum common throughput optimization problem [160]. **Discussion:** Table 10 demonstrates the summary of scheduling and resource allocation, and the critical points of scheduling and resource allocation are enumerated below. 1. There are two types of scheduling in BackCom. The energy scheduling [95] and data scheduling [139]. 2. The allocation of resources is carried out depending on different parameters, such as time [100], throughput [143], and energy [101]. 3. Optimal allocation policies for time are shown for BackCom [143]. 4. The cognitive radio-based allocation of resources is considered for Amb-BackCom, and this depends on the availability of channels [152]. 5. The SARSA algorithm performs well for the optimization of resources [139].
## 3 Underwater-Air Channel Model  
Given the high cost and complexity of field experiments, the channel modeling and performance evaluation of laser transmission links are considered vital preparatory steps before constructing an operational system [10; 11; 12; 20]. On the basis of the photon weighting method, this study employs the ray tracing technique and Monte Carlo simulations to assess the energy prediction of laser rays within channels.  
This simulation process focuses on determining the ray paths through three key channels: the seawater channel, the sea surface, and the air channel. Each path calculation relies on the probability distributions associated with these media, which include factors such as the minimum path length of the laser rays, the scattering direction, and the optical properties of each medium. Finally, the properties of the laser beam at the receiving plane are counted to gather information about the light field. To highlight the impact of the complex sea surface on laser uplink transmission, several aspects of the seawater and air channels are simplified, and the background radiation is ignored.
## 5 Scanning the Coverage Area with Directional BS Antennas (LOS Channels)  
Based on the misdetection probability rates for the omnidirectional antenna in Figure 4, we realize that to keep the misdetection rate \(P_{M}\) low for each slot of search time, the SNR level received at the UEs should be at least \(-13\) dB. Directional antennas can be used to compensate for the path loss, and improve the cell search procedure. Figure 5 demonstrates the concept of using a directional antenna at BS to focus the main beam to a specific location on the ground. We will consider two scanning schemes in this paper: random and deterministic.  
Both random scanning and deterministic scanning schemes have been proposed in the past work shown in [21; 22; 23; 24]. Authors used an \(8\times 8\) array antenna at BS to scan randomly in [21]. Exhaustive and iterative searching methods were employed in [22; 23; 24] to address the cell searching problem deterministically. We employ an adaptive array antenna in our analysis. Depending on our scanning scheme, we choose between three possible configurations: \(4\times 4\), \(8\times 8\) and \(12\times 12\) element arrays. Table 2 shows the different adaptive array configurations we employ, and their relevant antenna properties.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Antenna Configuration** & **Gain (dB)** & **HPBW** \\ \hline \(4\times 4\) & \(12\) & \(27^{\circ}\) \\ \(8\times 8\) & \(18\) & \(13^{\circ}\) \\ \(12\times 12\) & \(21.5\) & \(9^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Gain values and half-power beamwidth (HPBW) of different adaptive array configurations considered.  
Figure 5: Demonstration of BS antenna illuminating the ground at a specific time slot.  
We will investigate two scenarios as indicated in Table 3. For the random scanning algorithm, we employ an \(8\times 8\) element array similar to [21]. Our deterministic approach utilizes all three configurations as indicated in Table 2. The specific beam positions for the array configurations are calculated so as to minimize any gaps; i.e., SNR less than \(-13\) dB in the coverage area, for the deterministic algorithm. We will explain in detail the methodology we use in identifying these beam positions in the following subsection. For both random and deterministic algorithms, we first consider a LOS channel. Later on, we demonstrate how the same concepts can be applied to NLOS channels, and the key factors to be considered in adapting these ideas to more realistic NLOS scenarios.
## 4 Importance of Electrode Configurations for FECG Measurement
### Electrode Configurations
The accuracy of the method used for NIFECG monitoring to determine the fetal heart rate is entirely dependent on the number of electrodes used for monitoring, placement of electrodes, gestation week, and parameters such as sampling frequency, resolution bits, and the gain. Furthermore, in a long-term monitoring, use of the conventional hydrogel electrodes would also significantly affect the accuracy since the hydrogel electrodes would dry out over time, thus affecting skin-electrode impedance.
# Advancing Digital Twin-Based Collision Avoidance:
## 1 Introduction  
Industrial safety functions are critical in preventing accidents, often relying on wired connections to ensure reliability. Redundant connections are employed in high-risk environments to enhance fail-safety. However, in 2022, Germany reported 19,758 industrial truck accidents, eight of which were fatal [1]. In the US, 70 fatal forklift-related accidents were reported [2], highlighting the ongoing challenges in safety.  
Truck collisions are a major contributor to accidents, prompting the development of safety functions. While wired communication is the norm due to its perceived reliability, there is a need for innovative solutions, especially for scenarios where wired connections are impractical.  
The integration of wireless technologies into safety functions introduces a new dimension, requiring a robust theoretical foundation for assessing their performance level (PL). Existing standards lack specific criteria for wireless communication, creating a gap in safety evaluations. This study addresses this gap by proposing a theoretical approach centered on packet loss rates. Despite inherent simplifications, the approach provides a valuable framework for assessing wireless safety systems.  
Our research involves an in situ measurement study in a real industrial environment, focusing on a forklift truck safety system. We validate the theoretical framework through real-world measurements, emphasizing the need for nuanced evaluations in dynamic industrial settings.  
The dynamic nature of industrial environments, marked by constant changes in warehouses, challenges the accuracy of simplified metrics. Our study advocates for a more comprehensive framework that considers warehouse dynamics and other influencing factors. This research contributes to the secure implementation of wireless technologies in safety-critical applications within the evolving landscape of Industry 4.0.  
In summary, this paper makes a threefold contribution:  
* We propose a safety control system for detecting truck collisions.
* We provide real-world measurement results from an industrial site, demonstrating the system's performance.
* We introduce the first method to determine a PL for wireless safety functions.  
The remainder of this paper is structured as follows. In the next section, we present variations of a system for collision avoidance. We then provide traffic measurement results from our in situ study. In Section 4, we discuss how to determine dangerous outages for systems and how to derive a performance level from measurement results. Finally, we discuss our results, what they mean for the industry and what the next steps are.
###### Abstract  
The Dixi area of the Junggar Basin has favorable petroleum geological conditions, with the Cretaceous system representing one of the principal hydrocarbon-bearing strata. However, the genetic origin and mixing characteristics of natural gas across different tectonic zones remain insufficiently understood. In this study, a total of 65 natural gas samples were analyzed using molecular composition and stable carbon isotopic data to determine gas origins and quantify the contributions of different source rocks. A novel multivariate mathematical analysis method was developed and applied to convert compositional and isotopic data into quantitative parameters, enabling the accurate estimation of end-member mixing ratios in natural gas. This methodological innovation addresses the challenge of interpreting multi-source gas systems under complex geological conditions. The results show that the Cretaceous natural gas in the Dixi area is derived from three main sources, comprising both oil-type gas from Permian lacustrine source rocks and coal-type gas from Carboniferous coal-measure source rocks. The calculated mixing proportions exhibit significant spatial variation: in the northern Dixi area, coal-type gas dominates (67.8-84.3%), while the southern zone presents a broader mixture (25.6-68.4% coal-type gas). In the Dongdaohaizi Depression, oil-type gas is predominant, accounting for 89.4-97.7%. This study not only clarifies the genetic classification and mixing dynamics of natural gas in the Dixi area but also provides a quantitative framework for evaluating accumulation processes and source contributions in multi-source gas reservoirs. The proposed method offers valuable guidance for assessing resources and optimizing exploration strategies in the Junggar Basin and other similar basins.  
Junggar Basin; natural gas origin; multivariate mathematical analysis; gas-source correlation; stable carbon isotopes 2025 15 7130 1
## 2 Materials and Methods  
### Physiology of Respiratory Split in S2  
The usual explanation attributes the varying splitting to the changes of heart hemodynamics induced by respiration [11]. In inspiration, the extension of the pleura and thorax increases the volume of the thoracic cavity and lowers the pressure inside. Hence, the pressure gradient from the extra-thoracic regions to the right atrium increases, as illustrated in Figure 1. This increased gradient leads to increased blood filling of the right ventricle (RV). The increased RV end-diastolic volume (EDV) leads to an increased RV stroke volume (SV) via the Frank-Starling mechanism. The dilated RV causes the left ventricle (LV) to become less compliant by physical compression. The interventricular septum thus moves leftward, which results in reduced LV filling. Simultaneously, the distending lungs and their circulatory volume tend to reduce the pressure gradient and flow from the pulmonary veins to the LV, and the transmural diastolic aortic pressure, which is called the LV afterload, increases. These additive effects induced by respiration result in decreased LV-SV. Due to the effect of hemodynamic changes in inspiration, the aortic valve closes early and the pulmonary valve closes delay. Therefore, the splitting between the two components increases. The opposing process occurs during expiration in which RV-EDV and RV-SV decrease, while LV-EDV and LV-SV increase. So, the two components are closer, or even fused in expiration. With the opposite changes of heart hemodynamics in inspiration and expiration, the characteristics of respiratory variation in splitting is illustrated in Figure 2a. The morphology changes of heart sounds due to respiration have been investigated in recent years [12; 13; 14; 15]. One real example of these variations of a healthy subject is shown in Figure 2b, where the S2s are sorted by respiratory phase. The aortic components are aligned in time domain to facilitate observation of the split. The respiratory variation is clearly observed.  
In view point of physiology, the split in S2 reflects the effect of respiration to heart hemodynamics. So, it is important to measure the split quantitatively for monitoring hemodynamic response to respiration. This paper proposes a method to measure the respiratory split of S2. The findings show that the respiratory split variations can be accurately monitored and the variations of the split reflect the physiological relation between respiration and heart hemodynamic response.
# Advancing TinyML in IoT: A Holistic System-Level Perspective for Resource-Constrained AI
## 3 Key Challenges in AI for Constrained Devices
### Data Security and Privacy  
Although on-device inference can enhance privacy by limiting data exposure, many security vulnerabilities still exist. Edge devices deployed \"in the wild\" are physically accessible and can be tampered with by adversaries who might extract sensitive information from stored models or training parameters [50]. Lightweight encryption and secure boot processes become essential for protecting both firmware integrity and any locally stored data. In more advanced scenarios, secure enclaves or hardware-based trusted execution environments (TEEs) can isolate sensitive computations from other system components [12].  
Moreover, privacy is not solely a matter of data encryption. Complex, intelligent systems can inadvertently leak information through side channels or by revealing model outputs [60]. Federated learning, for instance, transmits model updates or gradients rather than raw data, but these gradients can sometimes be reversed or manipulated to expose sensitive details about the local dataset [61]. As a result, implementing techniques like differential privacy or homomorphic encryption in resource-constrained settings has become an active area of research. Balancing robust security with minimal overhead is challenging, as cryptographic operations themselves can be computationally expensive, consuming both energy and processing cycles that are already at a premium [62].