esa-sceva/satcom-chunk-collection huggingface dataset:
# Product Demand Prediction with Spatial Graph Neural Networks
## 3 Methodology
### Training Objective  
Building upon the foundational understanding that the predictive accuracy of our model depends on its ability to intricately model the spatial dynamics and attention-driven interactions within the retail network, the training objective of our predictive model takes on a crucial role. The loss function, \(\mathcal{L}\), serves as a quantifiable measure of the model's performance, estimating the discrepancy between the predicted demands \(\hat{\mathbf{y}}\) and the actual demands \(\mathbf{y}\) across the graph. The choice of a mean squared error (MSE) loss function is careful, emphasizing the importance of penalizing larger errors more severely to refine the model's predictive accuracy:  
\[\mathcal{L}(\mathbf{y},\hat{\mathbf{y}})=\frac{1}{|\mathcal{V}|}\sum_{i=1}^{| \mathcal{V}|}(y_{i}-\hat{y}_{i})^{2}. \tag{4}\]  
This mathematical formulation is underpinned by a deeper strategy aimed at capturing the nuanced interplay of factors influencing product demand. The propagation of features across the layers of the Graph Neural Network is not merely a process of data transformation but a methodical approach to distill both the local and global spatial dependencies that are critical to understanding and predicting demand accurately. These dependencies are revealed through the model's attention mechanisms and the spatial adjacency matrix, enabling a comprehensive analysis of how geographic proximity and contextual relevance between retail locations influence demand patterns.  
By meticulously calibrating the parameters of the function \(f\), our objective transcends the minimization of prediction error. We try to shape a predictive model that not only excels in capturing the complex dynamics governing demand across the retail network but also demonstrates an exceptional ability to generalize across diverse nodes and fluctuating demand scenarios. This involves a careful balancing act of ensuring the model remains sensitive to the subtleties of spatial and contextual information while avoiding overfitting to the training data, thereby ensuring its applicability and robustness in real-world settings.  
The training phase, therefore, becomes a critical juncture where theoretical concepts and empirical data converge, guiding the model towards achieving a deep, contextual understanding of demand predicting. Through this process, we aim to equip stakeholders in the online marketplace with a powerful analytical tool, capable of handling the intricacies of demand prediction with enough precision and insight. This endeavor not only advances the frontier of research in e-commerce demand prediction but also offers obvious benefits for retailers seeking to optimize their strategies in response to the ever-evolving landscape of consumer preferences and market conditions.
# Traffic Light Detection and Recognition Method Based on YOLOv5s and AlexNet
## 3 Experiment
### Traffic Light YOLOv5s Detection Network Model Building  
Traffic light detection mainly includes video frame extraction, image preprocessing, and image annotation, as well as YOLOv5s-based model training and target detection. The specific process is shown in Figure 5.  
As the target detection part only needs to determine the location of the traffic lights and does not require image recognition, only one class of image needs to be annotated, which greatly reduces the time spent on the annotation process and also reduces the number of samples required.  
The Epoch of the YOLOv5s network was set to 100 for this experiment. When the YOLOv5s network model was finished training, a nighttime intersection image, as well as a foggy intersection image, were selected and fed into the trained YOLOv5s network model, and the output images are shown in Figures 6 and 7.  
Figure 5: Traffic light detection flow chart.  
The area checked by the box in both images is the detected traffic signal area. Analysis of the detection results of the two images shows that the network model is still very accurate in detecting traffic lights in relatively complex traffic scenes and has a high confidence level.  
When a traffic light is detected, the network will also output the coordinates of the four points where the traffic light is located. Next, we will perform a series of image processing operations on the target based on the coordinates of these four points to meet the requirements that can be fed into AlexNet for final image recognition.
## 3 Results  
The sample included 136 attendants divided into four unbalanced groups, with a maximum of 52 students in onsite group 1 and 23 in online group 1 for the basic level. The average age ranged between 29.5 (25, 35.25) years in onsite group 1 and 34 (28.5, 40.5) in the Online 1 group, with a generally higher percentage of women in all groups (Table 1). The average score ranks for the Kahoot_basic test ranged from 6 (5.5, 7) points in the Onsite 1 group to 7 (6.5, 7.5) points in the Online 1 group compared to scores of 6 (5, 6.5) points in the Onsite 2 group and 7 (6.5, 8.5) points in the Online 2 group of the Kahoot_advanced test (Table 2). The IPTW model stabilized with 3000 iterations in all cases and, in the online groups, this convergence was more accentuated. There was no overlap between the coefficients of each group compared to the rest, which seems to indicate the presence of a large number of extreme values outside the common support area (Figure 1). The propensity score weighting of the covariates between the groups and baseline variables clearly diminished the differences between them against the unweighted values (Figure 2). In the Kahoot_basic scores, the model detected a positive, although not very high, difference between the Online 1 and Onsite 1 groups (0.6708, SE = 0.2680) and a significant (t = 2.502, \(p=0.0149\)) and negative and small difference between the Onsite 1 group and the total effect (\(-0.3354\), SE = 0.1340) and a significant difference (t = \(-2.502,p=0.0149\)) with a low effect size (R\({}^{2}_{\text{adjusted}}=0.103\)). In the Kahoot_advanced scores, the model detected a positive difference between the Online 2 and Onsite 2 groups (1.1832, SE = 0.5425) and a significant (t = \(2.181,p=0.0353\)) and small negative difference between the Onsite 2 group and the total effect (\(-0.3354\), SE = 0.1340) as well as a significant difference (t = \(-2.502,p=0.0149\)) with a low effect size (R\({}^{2}_{\text{adjusted}}=0.125\)). \begin{table}
\begin{tabular}{c c c c c} \hline  & **Onsite 1** & **Onsite 2** & **Online 1** & **Online 2** \\ \hline \hline \multirow{2}{*}{
\begin{tabular}{c} Kahoot\_basic (median (IQR)) \\ \end{tabular} } & \multirow{2}{*}{6 (5.5, 7)} & \multirow{2}{*}{-} & \multirow{2}{*}{7 (6.5, 7.5)} & \multirow{2}{*}{-} \\ Kahoot\_advanced (median (IQR)) & - & & 6 (5, 6.5) & - & 7 (6.5, 8.5) \\ Hollow\_structure (median (IQR)) & 4.6 (4.1, 5.2) & 4.5 (4, 4.95) & 4.7 (4.4, 4.9) & 4.9 (4.62, 5.18) \\ Solid\_structure (median (IQR)) & 3.8 (3.6, 4) & 3.8 (3.6, 4) & 4.5 (4, 3.7) & 4.45 (3.9, 4.7) \\ Between\_structures\_distance (median (IQR)) & 11.6 (11, 12.1) & 11.3 (11.07, 11.75) & 12.1 (11.83, 12.65) & 12.2 (11.8, 12.5) \\ \hline \end{tabular} Data expressed with median and interquartile range (IQR) or with absolute and relative values (\%).  
\end{table} 
Table 2: Scores obtained in the different tests.
## 4 Mission S-Net
### S Band Isl System
#### 4.3.1 Functional Organization  
To maintain adaptivity for various missions, it is important to clearly define the functional interface between the ISL terminal and the controller, represented by the SNC. A suitable interface description is recommended in the Proximity-1 protocol, which was proposed by CCSDS for planetary missions where autonomous communication terminals are used between the lander and orbiter [24]. The S-NET mission has adopted the suggested functional distribution. The ISL radio contains a physical layer (OSI layer 1) and a data link layer (OSI layer 2). The mission-specific network and routing algorithm (network layer 3), which is based on [PERSON]'s algorithm, is implemented within the SNC. The responder ID of the \(L_{1}\) layer (see, also, Section 3.5) is checked, at this instance, to perform proper routing and transportation of the message. To support the memoryless routing rule, the network layer must also determine the geometric relations within the network, normally done by an orbit propagator (e.g., Simplified General Perturbations (SGP)).  
Two kinds of data units are used for data exchange between the SNC and the ISL terminal. One data unit has a variable frame length and is used for the exchange of unspecified service data provided by the data service user. The other unit is reserved for the exchange of user and control information, typically generated by the network layer (see Figure 10).  
Figure 10: Flow of data and messages between transceiver and controller.
## 4 Discussion
### Spatial Distribution Pattern of SOC and Controlling Factors  
The spatial distribution pattern could be explained by environmental covariates [1]. Figures 5 and 6 showed climate was the dominant control factor, which was consistent with previous studies in broad region scales [66; 67]. That is because in broad region scales, attainable SOC is the vital factor to cause differences in SOC distribution, and climate determines the process and intensity of the weathering of parent materials and carbon input by vegetation [68; 69]. In our study, several variables on behalf of temperature play a crucial role in climate, i.e., Tavg, LSTD, BIO09, and LSTN, which are more important than covariates related to precipitation in the study area. That may be because in croplands,the effect of precipitation can be partly offset by irrigation regulation, thus temperature which affects the microbial decomposition process is more representative of the impact of nature's climate on soil [1]. The position on behalf of the spatial information in data was the second important environmental covariate. This indicates a spatial trend of SOC in the area, especially at 105\({}^{\circ}\), which is generally consistent with the direction of the monsoon in the area [34]. We found NCRoNPPa and NCRoNPpv contributed the most in organism covariates. That is because NPP is the direct factor of vegetation productivity and determines the amounts of plant litter, which influences the input and decomposition processes of SOC [1]. Additionally, the lower variability of elevation (100-200 m) and soil types limits the contribution of relief, soil, and parent material to the models.  
For vertical scales, due to the relatively incomplete mineralization, SOC was overall lower in the compacted layers. Notably, we found the downward trend was relatively flat in the intensive cropland area (e.g., the middle of Songnen Plain). That may be because there is large-scale mechanized operation and a relatively long-term cultivation history in flat terrain, causing a strong disturbance in the two layers and a decrease in differences [5].
## 4 Proposed Model
### Open Challenges, Limitations, and Issues
#### 4.2.2 Credential Privacy and Preservation Challenges  
Accreditation record management and storage generate logs of duplicates and redundancy [37]. Privacy and preservation are the main agendas that require maintenance and traceability. A distributed storage structure needs to be arranged so that it cannot create additional slots and focuses on the management of meta record storage [38; 39]. Thus, maintaining the integrity and confidentiality of records and consistency of distributed educational applications during sharing is a complex task for an immutable storage structure with scalability and availability. The key distribution is another type of challenging aspect in accordance with the random generation in a blockchain hyperledger sawtooth-enabled distributed system.
## 1 Introduction
### Transbuccal Drug Delivery Systems: Subtypes  
The spectrum of TDSSs currently available is wide and varied, and includes both chemical/physical biotechnology and a mechatronic approach (Figure 1). This review does not aim to provide a detailed description of each device and its functioning, but rather, to describe the current and potential application of TDSSs in the field of PD and AD. The list below contains the most common TDDSs available for PD and AD patients:  
* Buccal tablets: a small flat disc to insert into the buccal vestibule, designed to dissolve slowly while releasing the active ingredient.
* Buccal patches: made of silicone foil, consisting of an impermeable carrier layer and a reservoir containing the drug that is released in a controlled manner over an extended period of time.
* Buccal spray: a uniform dispersion of active ingredient in a solvent along with propellant to generate a uniform spray during application.
* Therapeutic mouth rinse: viscous liquids used to coat the buccal surface either as protectors or as vehicles for drug delivery.
* Ointments: a semisolid dosage form in which the active ingredients are dispersed in a bioadhesive ointment base that is applied to the oral mucosa.
* Nanoparticle drug delivery systems: a multicomponent delivery system that includes a drug and biodegradable nanoparticles with bioadhesive properties that is able to penetrate regions that might be inaccessible by other delivery systems, such as periodontal pocket areas below the gingival margin. They can generally be incorporated into any suitable oral hygiene product, such as gels, chewing gum, toothpaste, and mouthwash [59].
* IntelliDrug (ID): ID is the most promising delivery system. Along with new therapeutic targets such as \(\alpha\)-Klotho protein (KL), they could become alternative future therapeutic approaches in the field of PD and neurodegenerative diseases overall [60]. IntelliDrug is driven by electronics, mechanics, and computer science (mechantronic approach) and it has been designed to deliver the maximum amount of drug solution with a minimum amount of energy. It is implanted in the oral cavity--built on a prosthetic crown or incorporated within a larger prosthesis--and includes an osmotic pump for liquid propulsion and a normally-closed microvalve [61]. It is also equipped with a flow sensor and a fill-level sensor to monitor the dispensed amounts. The speed of drug release is regulated by the onboard electronics, which changes the duty cycle related to valve openings. The users can control the entire device via infrared remote control, which allows the operator to program the dosage and monitor the status of the device, such as the remaining drug level. The biggest disadvantage of ID is that the patient needs to have two missing adjacent teeth [62].
# Nomological Deductive Reasoning for Trustworthy, Human-Readable, and Actionable AI Outputs
## 3 Current XAI State of the Art
### Explanator Formats
#### 3.1.5 Mixed (Multiformat) Explanations  
Mixed explanations combine two or more of the aforementioned formats in a hybrid approach aimed at providing richer, multi-dimensional insights into the model's behavior by combining the strengths of various formats, as illustrated in Figure 5.  
The key methods in this class include, but are not limited to, the following:  
**LIME and SHAP**: Both methods are inherently model-agnostic and allow for explanations that integrate numerical/graphical and textual components. Specifically, LIME explains a model's decision by approximating it with a simple, interpretable surrogate model, which can be accompanied by a textual explanation of the prediction along with graphical visualizations of feature importance.  
**Interactive Dashboards**: Some systems such as L2X (Learning to Explain) [90], Partial Dependence Plots (PDP) [75], and DeepLIFT (Deep Learning Important FeaTures) [46] use interactive visualizations that combine graphical components, like feature importance graphs, with textual summaries or natural-language explanations. These hybrid methods are increasingly being used in domains like healthcare, where practitioners require a combination of quantitative insights and verbal explanations to make informed decisions.  
**Counterfactual Explanations**[91]: These explanations involve presenting a user with an alternative scenario, i.e., what would need to change in the input to alter the prediction. These can be accompanied by both textual and graphical explanations showing the differences between the original and counterfactual instances.  
There exist other XAI methods in this category, including, but not limited to, Image Caption Generation with Attention Mechanism [92], Bayesian Teaching [93], Neighborhood-based Explanations [94], ExpIAIner [95], Sequential Bayesian Quadrature (SBQ) [96], and Pertinent Negatives [97].  
Shortfall in Conveying Structured Human-Readable Explanations That Make Transparent the Reasoning Process  
Despite the emergence of over 200 XAI techniques and various explanation formats, current methods often fail to deliver cognitively accessible and structured explanations that clearly reveal the model's reasoning process. This shortfall undermines user trust  
Figure 5: Illustration of an SP-LIME algorithm calculated from 500 candidate explanations sampled from the data uniformly at random. Red indicates a contribution to non-diabetes and green indicates a contribution to diabetes. Source: [89].  
and hampers their adoption in critical fields such as healthcare, finance, and autonomous systems [98; 99; 100].
## 2 Methods
### Indoor Humidity Measurement  
Temperature and humidity sensors were installed in all three experimental zones for each space. Except for the subjects staying in the study building for the experiment, the building was empty and no additional heating or cooling equipment was operated. In each space, two sensors were installed on interior bedroom walls not directly exposed to outdoor air, and an additional two sensors were placed on both the interior and exterior surfaces of the living room and exterior walls, using the MX2301A (Onset Computer) sensor capable of simultaneously measuring and recording temperature and humidity in real time (Figure 2).  
The temperature range of the sensor is \(-40\) to \(70\)\({}^{\circ}\)C with an accuracy of \(\pm 0.25\)\({}^{\circ}\)C and a resolution of \(0.02\)\({}^{\circ}\)C, while the relative humidity range is 0-100% within the same temperature range, with an accuracy of \(\pm 1\)%. Measurements were taken over the course of 8 months by recording average values at 4 h intervals. The indoor humidity sensor was positioned at the center of the wall. The outdoor humidity was also measured at the center of the wall with a window. The temperature and humidity measurement sensors were placed at heights of 1.2 and 1.8 m (ASHRAE 2023). Based on the measured temperature and humidity data, the indoor and outdoor equilibrium moisture content (EMC) was calculated using the Hailwood-Horrobin Equation (1), where T is temperature (\({}^{\circ}\)C), h is relative humidity (%/100), EMC is moisture content (%), and W, K, K\({}_{1}\), and K\({}_{2}\) are coefficients of an adsorption model developed by [PERSON] [14]. This allowed for the evaluation of hygrothermal stability of indoor air under changing outdoor conditions and an analysis of occupant comfort in relation to indoor humidity.  
\[\text{EMC}=\frac{1800}{\text{W}}(\frac{\text{Kh}}{1-\text{Kh}}+\frac{\text{K }_{1}\text{Kh}+2\text{K}_{1}\text{K}_{2}\text{K}^{2}\text{h}^{2}}{1+\text{K} _{1}\text{Kh}+\text{K}_{1}\text{K}_{2}\text{K}^{2}\text{h}^{2}}) \tag{1}\]  
\[\text{W}=349+1.29\text{T}+0.0135\text{T}^{2}\]  
\[\text{K}=0.805+0.000736\text{T}-0.00000273\text{T}^{2}\]  
\[\text{K}_{1}=6.27\,-\,0.00938\text{T}\,-\,0.00030\text{T}^{2}\]  
\[\text{K}_{2}=1.91+0.0407\text{T}-0.000293\text{T}^{2}\]
# A 12 dBm B1 dB N-Path Notch Filter for Transmitter Leakage Suppression in Wideband Receiver
## 6 Conclusions  
This paper presents a blocker N-path notch filter for TX leakage suppression in a wideband receiver. The transmission line achieves an impedance flip within the block band to improve the linearity of blocker suppression. The adjustable CLC transmission line minimizes insertion loss shortfall in the tuning range. The filter achieves >25 dB rejection for a 12.3 to 13.6 dBm TX leakage blocker and 12.8 to 14 dBm P1 dB. At the same time, the passband IIP3 of up to 22 to 25.2 dBm is realized, maximizing the dynamic range of the receiving channel. The design of the adjustable transmission line structure is analyzed to reduce the insertion loss in terms of impedance and to achieve 1.7 to 2 dB insertion loss shortfall over 300 to 600 MHz. Due to its high linearity, the TX leakage can be effectively filtered out in the front end of a broadband receiver, improving the receiver sensitivity.  
Conceptualization, X.L.; methodology, X.L. and S.Q.; software, X.L. and S.X.; validation, X.L., S.Q. and S.X.; formal analysis, X.L.; investigation, X.L.; resources, Q.X., G.W. and L.Z.; data curation, X.L. and H.Z.; writing--original draft preparation, X.L.; writing--review and editing, X.L.; visualization, X.L.; supervision, G.W. and L.Z.; project administration, L.Z.; funding acquisition, G.W. and L.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The datasets produced and/or analyzed in the present study are available from the corresponding author upon reasonable request.  
The authors assert that they do not have any conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Reference** & **[**19**]** & **[**20**]** & **[**21**]** & **[**22**]** & **[**12**]** & **[**16**]** & **This Work** \\ \hline Technology & 65 nm CMOS & 65 nm CMOS & 65 nm CMOS & 65 nm CMOS & 45 nm SOI & 45 nm SOI & 130 nm SOI \\  & N-path & N-path & LNTA + & \begin{tabular}{c} CS-LNA \\ BPF+ \\ \end{tabular} & \begin{tabular}{c} N-path \\ BPF \\ \end{tabular} & \begin{tabular}{c} N-path \\ BPF \\ \end{tabular} & \begin{tabular}{c} N-path \\ BPF \\ \end{tabular} & \begin{tabular}{c} N-path \\ BPF \\ \end{tabular} \\ Topology & \begin{tabular}{c} Adjustable Inv. \\ Shunt \\ Notch \\ \end{tabular} & \begin{tabular}{c} Adjustable Inv. \\ Shunt Notch \\ \end{tabular} & \begin{tabular}{c} Adjustable Inv. \\ Shunt Notch \\ \end{tabular} \\ RF & 1 to 1.6 & 0.2 to 1 & 0.7 to 2.2 & 1.35 to 2.7 & 0.2 to 3.6 & 0.9 to 1.1 & 0.3 to 0.6 \\ Range/GHz & & & & & & & 3.5 to 4 \\ BW/MHz & N/A & \begin{tabular}{c} 28 \\ (20 dB BW) \\ \end{tabular} & N/A & \begin{tabular}{c} N/A \\ \end{tabular} & \begin{tabular}{c} \textgreater{}48 \\ \end{tabular} & \
\begin{tabular}{c} \textgreater{}80 \\ \end{tabular} &
\begin{tabular}{