esa-sceva/satcom-chunk-collection huggingface dataset:
# Functional Magnetic Resonance Imaging in Research on Dog Cognition: A Systematic Review
## 4 Discussion
### Research Articles Considering Somatosensory Stimuli and Multi-Stimuli  
Somatosensory stimuli studies confirm fMRI's effectiveness in studying neurocognitive processing in dogs and enhance our understanding of touch processing in mammals. In a study by [PERSON] et al., dogs were touched on their flanks with a wooden rod during scanning to identify areas related to somatosensory processing in dogs using fMRI. Researchers found activation in primary and secondary somatosensory areas, with contralateral hemisphere bias. Additional activations occurred in the cingulate cortex, right cerebellum, vermis, and Sylvian gyri, indicating the roles of motor control and cognitive processing [53]. Additionally, by using BOLD fMRI, studies enabled the differentiation of neuronal responses related and unrelated to pain during acupuncture stimulation. Following acupuncture stimulation post anesthesia, increased activation was observed in the left somatic afferent areas, right visual and auditory association areas, and the descending reticular activating system in the brainstem. Notable differences were found in the left olfactory peduncle and the brainstem's descending reticular activating system when comparing pre-anesthesia and post-anesthesia states. These findings suggest that specific neural pathways activated during acupuncture are unrelated to pain responses [55]. Finally, a study by [PERSON] et al. used visual stimuli. However, it focused on somatosensory processing and anticipated actions. It found that dogs showed greater neural activation in response to objects associated with mouth actions than those associated with paw actions, indicating a connection between sensory processing and anticipated actions. Mouth objects had maximal activation in the region of the cruciate sulcus, corresponding to dogs' primary sensorimotor cortex [54].  
Multi-stimuli studies added insights into behavioral and learning mechanisms in dogs. To assess reward-related learning of visual, olfactory, and verbal stimuli in dogs, activation was measured in the caudate nucleus, amygdala, and parietotemporal cortex. Results indicated that neurobiological learning curves, reward-related caudate activation, and habituation-related amygdala activation should be generated. Visual and olfactory stimuli led to the fastest learning, while verbal commands were the least effective, suggesting they are a less efficient dog training method [51]. The second multi-stimuli study localized frontal brain regions responsible for response inhibition in dogs and explored their relationship with behavioral control. The study established parallels between human and canine self-control mechanisms and laid the groundwork for future comparative studies on their functions and dysfunctions [52].
# Effect of Systemic Zoledronic Acid Dosing Regimens on Bone Regeneration in Osteoporotic Rats
## 2 Materials and Methods
### Surgical Procedure for Bone Graft Placement and Study Groups  
Twelve weeks after ovariectomy, all animals underwent surgery under general anesthesia for bone graft placement in the left femoral condyle, as described previously [43]. Once anesthetized, the left hind limb of the rats was shaved and disinfected with a Povidone-iodine 10% solution (Alphadine; Riyadh Pharma, Riyadh, Saudi Arabia). The knee capsule was incised longitudinally, and the patellar ligament was gently elevated laterally. At the femoral intercondylar notch, a bone defect (3 mm in diameter and depth) was prepared using a surgical bur in a slow-speed rotary drill (800 rpm) (Elcomed 100, W&H Dentalwer Burmoos GmbH, Austria). The bone defect was grafted with particulate anorganic bovine cancellous bone graft granules (particle size: 0.25-1 mm (InterOss(r), SigmaGraft Inc., Fullerton, CA, USA)). No membrane was used to cover the bovine-bone granules into the bone defect. Following the placement of the bone graft, the soft tissue lay  
Figure 1: Experimental animal groups and timeline of intravenous injections, surgical procedures, and euthanasia. OVX; ovariectomized, IV, intravenous, BG, bone graft, ZA, zoledronic acid.  
ers and skin were closed with VICRYLT(tm) (4-0) polyglactin 910 resorbable Sutures (Ethicon, Johnson & Johnson, New Brunswick, NJ, USA). After surgery, the animals were allowed to move unrestrictedly.  
Zoledronic acid injection (0.04 mg/kg body weight) (Zometa 4 mg/5 mL, Novartis, Basel, Switzerland) was prepared by mixing 0.05 mL of the drug with 0.95 mL of normal saline (0.04 mg zoledronic acid) [44]. The prepared solution was injected IV into the tail vein, as per animal weight and according to the study protocol. This dose of zoledronic acid was selected to mimic the cumulative clinical dose administered to human patients treated for osteoporosis [44]. Animals in the control group received 1 mL of saline solution through tail vein injection.
# ML-Based Maintenance and Control Process Analysis, Simulation, and Automation--A Review
## 3 Results
### ML-Based Predictive Maintenance System Architecture  
The key components of an ML-based predictive maintenance architecture include the following:  
* The data ingestion layer collects and aggregates data from various sources, including sensors, maintenance logs, and external databases. It supports real-time streaming and batch data transfers, ensuring that all relevant information is available for processing;
* The data storage layer is a robust and scalable storage solution, often a combination of relational databases, data lakes, and distributed file systems, and is used to store massive amounts of raw and processed data. This layer supports both structured and unstructured data formats;
* Before analysis with ML, the data need to be cleansed, normalised, and transformed with a data preprocessing module, capturing missing data, extracting features, and reducing dimensionality to prepare them for model training and real-time prediction;
* The feature engineering layer creates meaningful features from the raw data, which can increase the predictive power of machine learning models (through time series decomposition, aggregation, and anomaly detection);* ML models form the core of a system, comprising various ML learning algorithms, including supervised, unsupervised, and deep learning models trained on historical data to predict potential failures or maintenance needs;
* Model training and validation involve splitting the data into training, validation, and test sets, with cross-validation and hyperparameter tuning used to optimise performance, ensuring that the model generalises well to unseen data;
* Once the model is trained, a real-time inference engine is deployed to make predictions in real or near real-time: it continuously analyses incoming data, comparing it with the model's predictions to identify potential issues;
* When the predictive model detects an anomaly or predicts a potential failure, a control, communication, alerts, and notifications system sends alerts to maintenance teams or triggers automated actions via dashboards, emails, or integrated maintenance management systems;
* The feedback loop continuously monitors model performance and incorporates new data, allowing the model to be retrained and updated for improved accuracy over time;
* The user interface and dashboard visualise machine health and provide insights into forecasts and maintenance schedules, enabling operators and managers to make informed decisions based on real-time and historical data.  
The method of completing these components and data flows may vary depending on the complexity of the supervised system (Figure 10).
## 3 Issues in Implementing URLLC
### Beamforming and mmWave Frequency Communications  
The next-generation mobile networks will operate with mmWave frequencies in order to increase bandwidth. In mmWave communications, beamforming between the UE and the BS becomes an important aspect, as shown in Figure 7.  
\begin{table}
\begin{tabular}{c c} \hline
**User** & **Speed** \\ \hline Normal vehicle & 120 km/h \\ Drones & 160 km/h \\ High-speed vehicle & 250 km/h \\ Trains & 500 km/h \\ \hline \end{tabular}
\end{table}
Table 4: Mobility requirements for URLLC [22].  
Figure 6: Signaling procedure for downlink data transmission. ACK/NACK, acknowledgment/ negative acknowledgment.  
The process of beam selection can affect the E2E performance and the QoS [28]. In order to counter the key issues of achieving precise beamforming for performance enhancement [29], the 3 GPP NR standard included new MAC and physical layer (PHY) features. The new MAC and PHY features support directional communications [30], inter-network, and multi-network mechanism for LTE [31]. Despite the additional standards, some of the issues related with the directional communications and multi-connectivity are still unsolved. In the directional link, the requirement for precise beamforming can affect E2E performance. On the other hand, the provided solution [31] for multi-connectivity improves the mmWave network's E2E performance by merging a reliable sub-6 GHz link using LTE [32]. In 3 GPP, the deployment of NR networks can be in standalone (SA) and non-standalone (NSA) modes. In the SA mode, the NR core and radio access network (RAN) are included, while LTE evolved packet core (EPC) and the LTE RAN are used in the NSA mode. However, the practical implementation of such systems to support beam management with SA and NSA is still an open issue [30].
## Abbreviations  
ANOVA analysis of variance  
AWG american wire gauge  
Ca calcium  
Ca/P ratio calcium phosphorus ratio  
DC direct current  
EDS energy-dispersive X-ray spectroscopy  
EDTA ethylenediamininetetraacetic acid  
Fe iron  
FE-SEM field emission scan electron microscopy  
Fe\({}_{3}\)O\({}_{4}\) iron oxide  
Gauss unit of magnetic field density  
IONP iron oxide nanomagnet particles  
mL/min milliliters per minute  
\(\mu\)m micrometer  
mm millimeter  
MNPs magnetic nanoparticles  
MSI magnetic field, endodontic needle, and IONP  
MUI magnetic field, ultrasound device, and IONP  
NaOCI sodium hypochlorite  
nm nanometer  
P phosphorus  
rpm revolutions Per Minute.  
SPIONs superparamagnetic iron oxide nanoparticles solution  
UN ultrasound device and normal saline  
UI ultrasound device and IONPs  
V voltage  
VHN vicker hardness number
# Performance Comparison of NB-Fi, Sigfox, and LoRaWAN
###### Abstract  
LPWANs are a promising solution for wireless sensor networks. To compete with such widespread technologies as LoRaWAN and Sigfox, recently a new LPWAN technology called NB-Fi has been developed. In a short time, many NB-Fi networks have been deployed in various countries. Although NB-Fi, Sigfox, and LoRaWAN have been designed for similar applications, they implement different approaches. However, no detailed comparisons of them are present in academic literature. This paper aims to fill this gap by analyzing and comparing NB-Fi, Sigfox, and LoRaWAN focusing on performance evaluation results rather than just nominal parameters declared by the developers. Specifically, the paper evaluates the packet loss rate, packet error rate, and average delay in these networks in different scenarios. The results are used to provide guidelines to decide which technology to use under which conditions. Specifically, Sigfox performs best in scenarios when devices transmit small pieces of data without repetitions and acknowledgments, and LoRaWAN is the most reliable for transmitting bigger pieces of data, while NB-Fi is best suited for acknowledged transmissions of small pieces of data.  
Keywords:NB-Fi, Sigfox; LoRaWAN; LPWAN; ultranarrow band; IoT; sensor networks; performance evaluation +
Footnote †: journal:  
efficiently. Specifically, we compare how the packet loss rate (PLR), packet error rate (PER), and the average delay depend on the load if the amount of available frequency resources is the same. The obtained values of PLR and PER are directly linked to the network capacity, which is an essential metric for LPWANs and determines the maximal number of devices that can concurrently operate in a network with an acceptable service.  
The rest of the paper is organized as follows: In Section 2, we review related papers. Section 3 introduces NB-Fi, Sigfox, and LoRaWAN. Section 4 describes the considered scenario and the problem statement. Section 5 presents and discusses numerical results. The conclusions are given in Section 6.
# Research of Personalized Recommendation Technology Based on Knowledge Graphs
## 5 Experiment
### TransE Model before and after Improvement
#### 5.1.2 Analysis of Results  
We compare the TransE algorithm with our improved TransE algorithm for analysis. In the experiment, we set the generated vector dimension of the two models to 100. The learning rate is set to 0.01. The remaining parameters mentioned in this paper are set to the default values. We first observed the changes in the loss functions of the two models and found that the loss function of the TransE model became stable after about 130 iterations, while the improved TransE model became stable after about 160 iterations. At the same time, the loss function value of the improved TransE model is significantly smaller than the that of the TransE model. However  
Under normal circumstances, the better the model effect, the smaller the MeanRank value and the larger the corresponding hits@10 value. It can be seen from Figure 11 that when the embedding dimension is low, the MeanRank value is relatively large, that is, the correct triples are lower in the sorting. As the dimension increases, the MeanRank value keeps decreasing, and reaches the minimum when the dimension is 120. The minimum value is about 229. After that, it keeps getting bigger.  
It can be seen from Figure 12 that when the embedding dimension is low, the value of hits@10 is smaller. As the dimension increases, the value of hits@10 becomes larger. When the dimension is 120, it reaches the maximum value of about 0.82. After that, it keeps getting smaller.  
At the same time, we also did experiments similar to the above process on the improved TransE model. After multiple experiments under different parameter combinations, we determined the optimal parameter combination according to the MeanRank value and hits@10 value. In the model training process, the number of iterations in is 200. The best combination we get is that the embedding dimension of entities and relations is 100, the learning rate is 0.01, and the margin \(\gamma\) is 1. We calculated the MeanRank value and hits@10 value of the improved TransE model based on the above parameter combination. The experimental results are shown in Table 2 and Figure 13.  
Figure 12: Hits@10 values under different embedding dimensions.  
Figure 13: Comparison of MeanRank value and hit@10 value before and after improvement.  
It can be seen from the above table that the MeanRank value and hits@10 value of the improved TransE model are about 189 and 0.86, respectively. Compared with the transE model, both of them have been significantly improved. The MeanRank value is reduced by about 30. The value of hits@10 increased by approximately 0.04.  
It can be seen that the TransE algorithm performs better in the one-to-one relational mapping type, but does not perform well in the many-to-one and many-to-many relational mapping types. Therefore, this will be analyzed specifically in the future.
# ECC: Passenger Counting in the Elevator Using Commodity WiFi
## 4 Results
### Impact of Different Feature Dimensions  
Since the multi-dimensional feature is used, the impact of different feature dimensions should be evaluated. Figure 10 shows the classification precision when using various features under different passenger numbers. When a single feature is used, the overall average prediction precision can reach about only 30% and 47%, while the precision can effectively improve when 2 features are used simultaneously. The estimated results are 58% (AOA and PEM), 64% (AOA and TVs), and 72% (PEM and TVS), respectively. These results also prove that the precision is greatly improved when the TVS feature is joined, but the effect is still not ideal.  
Figure 10: Precision comparison of different features.  
Figure 9: Performance comparison of different classifiers.  
When 3 features are used, the precision improves to more than 94%. To sum up, there is a monotonically increasing relationship between prediction precision and the number of features. Moreover, the highest prediction precision group from each feature combination is selected, and the cumulative distribution function (CDF) of crowd counting errors is calculated, as shown in Figure 11. More than 97% of estimation errors are less than 2 persons when using 3 features, while the CDF value can only reach 60\(\sim\)75% in other cases. It is because the CSI is susceptible to external factors when the features are few, resulting in poor prediction. In addition, the small estimation error for the crowd number can improve the estimation accuracy of the actual load range and lay a good foundation for the counterweight adjustment of the elevator.
# Minimum Variance Distortionless Response--Hanbury Brown and Twiss Sound Source Localization
## 3 Simulation Analysis
### Distance to Sound Source Target  
The simulation parameters were set as before, with the only difference being a change in the position of the sound source. The simulation results obtained using MVDR-HBT for different source positions at an SNR of \(-5\) dB are shown in Figure 5; from these, the influence of different positions on the localization accuracy can be further studied. As can be seen from Figure 5, the localization results of MVDR-HBT at positions (5, 5) and (8, 10) were (5.1, 5) and (8.2, 10), with relative errors of localization of 1.41% and 1.56%, respectively, indicating that MVDR-HBT can still accurately locate the position at low SNR. These results were compared with the localization results of HBT, summarized in Table 2. It can be inferred from Table 2 that, with increasing distance, the localization error gradually increased under low-SNR conditions below 0 dB. In comparison, MVDR-HBT maintained good localization performance under these conditions. Therefore, the MVDR-HBT algorithm exhibits higher localization accuracy when compared with HBT. In brief, MVDR-HBT can accurately localize a sound source target in a low-SNR environment.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**SNR (dB)**} & \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Position (m)**} & \multirow{2}{*}{**Results (m)**} & \multirow{2}{*}{**Errors (m)**} & **Relative** & **Percentage** \\  & & & & & **Positioning** & **Decrease in** \\ \multirow{3}{*}{\(-10\)} & \multirow{3}{*}{MVDR-HBT} & (5, 5) & (5.2, 5) & (0.2, 0) & 2.83\% & 9.98\% \\  & & & (7.6, 10.6) & (\(-\)0.4, 0.6) & 5.63\% & 3.88\% \\ \multirow{3}{*}{\(-5\)} & \multirow{3}{*}{MVDR-HBT} & (8, 10) & (8.1, 9.8) & (0.1, \(-\)0.2) & 1.75\% & 3.88\% \\  & & & (5.3, 4.7) & (0.3, \(-\)0.3) & 6.00\% & 4.59\% \\ \multirow{3}{*}{\(-5\)} & \multirow{3}{*}{MVDR-HBT} & (5, 5) & (5.1, 5) & (0.1, 0) & 1.41\% & 4.59\% \\ \multirow{3}{*}{\(0\)} & \multirow{3}{*}{HBT} & (8, 10) & (7.6, 9.6) & (\(-\)0.4, \(-\)0.4) & 4.42\% & 2.86\% \\ \cline{1-1}  & & & (8.2, 10) & (0.2, 0) & 1.56\% & 2.86\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the simulation for the same sound target at different positions.  
Figure 4: Simulation results for a sound source at (5, 5): (a) \(-10\) dB HBT; (b) \(-10\) dB MVDR-HBT.
## 2 Open Challenges When SS Meets ISAC
### Spectrum Sharing and Dynamic Access  
ISAC must intelligently allocate spectrum resources for both functions, which requires advanced spectrum- sensing techniques to detect available frequencies in real time. Coexistence with legacy systems such as Wi-Fi and 5G networks poses spectrum management challenges.
## 4 Results and Discussions
When only the traffic data from rainy conditions are plotted, the correlation coefficient is 0.95 with the function Queue Discharge Rate = 100.59 (Speed in Congestion) \(-\) 1062.2 (Figure 4). In the same figure, when the speed in congestion is plotted against the queue discharge rate under clear weather conditions only, the coefficient of correlation is 0.978 with the function Queue Discharge Rate = 106.66 (Speed in Congestion) \(-\) 1319.7. All of the functions follow a linear fit. This shows that weather is not directly an influencing factor of the queue discharge rate but of the speed in congestion alone. This exhibits that speed in congestion is still the strong explaining factor of the queue discharge rates regardless of the weather condition. It displays that traffic management techniques controlling the speed in congestion to increase capacity recovery can be used under different rainfall rates. This important finding can also be used in extending traffic flow theory, such as using and extending the kinematic wave model and simulation setups. Figure 3: Relationship between queue discharge rate and speed in congestion (all data points). For the first time, we have also shown a linear relationship between the speed in congestion and the capacity drop, as shown in Figure 5. As the speed in congestion increases, the percentage of the capacity drop dramatically decreases with a linear relationship. The capacity drop Percentage = 1.8362 (Speed in Congestion) + 104.45 with a coefficient of correlation of 0.9151. This is further evidence that traffic management techniques to control the speed in congestion, such as variable speed limits (VSL), can mitigate the capacity drop phenomenon. This can be used in presenting a new macroscopic model for VSLs that will have the capability of modeling different capacities and critical densities at any weather condition, including further analysis of the fundamental diagram in any weather event.
## 4 Methodology
### Global Transition Flow Graph
#### 4.2.1 Direction-Aware Graph Encoder  
To extract semantically rich transition-aware representations from \(A_{T}\), we propose a direction-aware graph encoder. Unlike standard GCNs, which treat edges as undirected, this encoder explicitly models the asymmetry in user movement behavior. For each location node \(l_{i}\), we separately aggregate features from its incoming and outgoing transitions to learn a direction-sensitive embedding.  
Formally, let \(H\in\mathbb{R}^{|L|\times d}\) denote the current embedding matrix of locations. We define the directional aggregation from source and target directions as follows:  
\[H_{\text{out}} =\sigma\Big{(}D_{s}^{-1}A_{T}HW_{\text{out}}\Big{)}, \tag{7}\] \[H_{\text{in}} =\sigma\Big{(}D_{t}^{-1}A_{T}^{\top}HW_{\text{in}}\Big{)}, \tag{8}\]  
where \(W_{\text{out}}\) and \(W_{\text{in}}\) are learnable weight matrices for outgoing and incoming transitions, respectively, \(D_{s}\) and \(D_{t}\) are diagonal normalization matrices, and \(\sigma(\cdot)\) is a non-linear activation function such as ReLU.  
The final node representation is obtained by fusing the two directions:  
\[H^{\prime}=\text{Fuse}(H_{\text{out}},H_{\text{in}})=\alpha H_{\text{out}}+(1- \alpha)H_{\text{in}}, \tag{9}\]  
where \(\alpha\in[0,1]\) is a learnable parameter or a fixed balancing factor. While other fusion strategies, such as concatenation, attention-based fusion, and MLP-based fusion, could be applied, we observed that the weighted combination method offered the best trade-off between model performance and complexity.  
This dual-path aggregation helps encode both \"where users come from\" and \"where they are likely to go,\" which is essential for modeling temporal dependencies and user intent in next-location prediction.