esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The prevalence of food allergies in Europe is increasing rapidly. Currently, in adults, self-reported symptoms after ingesting different varieties of food are reported by 5.7-61.6% of people, and physician-diagnosed hypersensitivity are reported by 0.2-4.2% of people [1]. Diagnosing a suspected food allergy accurately is of great importance, both to prevent severe allergic reactions and to avoid unnecessary dietary restrictions caused by inaccurate diagnosis. The diagnosis of food allergy involves the use of skin pick tests (SPTs), allergen specific immunoglobulin E (slgE), and oral food challenges (OFC) as a gold standard [2]. It is generally agreed that the core diagnostic step for type I mediated allergy, the SPT, should be further standardized, and further studies are necessary to define worldwide standards for allergen extracts [3]. In a recent EAACI position paper about in vivo diagnostic test allergens, the importance of reliable allergens was also stressed [4].  
The evaluation of patients with a possible food allergy starts with an extensive food specific medical history and a physical examination. The focus should be on possible dietary triggers, the quantity and quality of the ingested food, possible facilitating co-factors around the time of the reaction (exercise, illness, use of medication), and the specific symptoms that led to the allergic reaction [5]. Knowledge of cross-reactivity within protein families would help to decide the ensuing pathway.  
The next step in the diagnosis of a food allergy is measuring sensitization to the suspected food allergen by either performing an SPT with the suspected food allergen, and/or measuring serum slgE. SPTs are a quick, reliable, and cheap method to measure sensitization. Although the negative predictive value (NPV) of SPTs often reaches 90% or more [5], false negative SPTs may occur if the used extracts are not standardized or have insufficient quantities of the allergen. In commercially available extracts of fruits and vegetables, the proteins might be destroyed during the manufacturing process, e.g., heating, giving less reliable results [6]. Generally, SPTs with food allergens have high sensitivity but low specificity, and must be interpreted with caution [6], and neither SPT nor slgE are sufficient to diagnose food allergies on their own [7]. [PERSON] et al. (2014) also concluded that SPT and slgE appear to be sensitive but not specific enough for diagnosing IgE-mediated food allergy, although this may differ between foods [8]. The availability of commercial food allergen extracts is limited, which leads to a need for alternative methods for the extraction of food allergens. One of the alternatives for commercial extracts might be to prepare homemade (HM) extracts through standardized protocols, but the quality of these extracts is unknown. Thus, the objective of this study was to compare SPT results of HM food allergen extracts and commercially available extracts.
# TinyML Algorithms for Big Data Management in Large-Scale IoT Systems
## 3 Methodology
### Big Data Challenges and Problems Addressed  
* Volume: Local processing reduces data volume heading to centralized systems.
* Velocity: Raspberry Pis can handle high-frequency data, making real-time requirements attainable.
* Variety: Given their flexibility, Raspberry Pis can be customized to manage a multitude of data formats and types.
* Veracity: They can ensure data quality, filtering anomalies or errors before transmission.
* Value: On-device processing extracts meaningful insights, ensuring only the most relevant data are transmitted to central systems.
## 4 Conclusions  
In practice, the data of short-term traffic volume commonly owns strong nonlinearity and non-stationarity so that it is hard to provide a satisfactory forecasting result through the traditional methods. In order to improve the forecasting performance, a novel hybrid model based on the combination of TVF-EMD algorithm and LSSVM is developed in this study. Two case studies based on measured data from an intersection are provided to evaluate the performance of the proposed method. Several main conclusions are summarized as follows:  
TVF-EMD has a more positive impact than EMD on improving forecasting accuracy. As a newly-improved decomposition method, TVF-EMD can vividly describe the time-varying characteristics (e.g., non-stationarity and nonlinearity) hidden in the data by time-varying filtering technology, where the problems of end effect and model mixing may be well addressed.  
The forecasting accuracy of the hybrid models is higher than those of the single models. Generally, the hybrid model could combine the advantages of different component models. In this paper, the advantages of TVF-EMD in processing data non-stationarity and nonlinearity and the merit of LSSVM's strong ability in addressing the nonlinear problem are combined.  
The innovation of this paper is to introduce a new data processing method TVF-EMD algorithm, which improves the model mixing problem of the original EMD algorithm. To further improve the forecasting performance, some future tasks should be carried out. For example, the combination of the proposed method with probabilistic prediction models should be focused; the multi-step ahead prediction will be developed in the future; the application of the proposed method in other fields, such as wind speed prediction and solar radiation prediction, should also be performed.  
Conceptualization, Y.W. and L.Z.; investigation, Y.X.; methodology, Y.W. and X.W.; software, L.Z.; validation, Y.W., L.Z. and S.L.; writing--original draft, Y.W.; writing--review and editing, Y.W. All authors have read and agreed to the published version of the manuscript. This research was funded by Science and Technology Research Project Fund of Chongqing Education Commission (Grant NO. KJ1705136, KJ1600512). The authors declare no conflict of interest.
# Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications
## 6 Structured Background Models
### Cluster or Mixture-based Models  
Cluster or mixture-based models aim to directly solve the expression (33). They seek the background endmembers and their abundances in the pixel spectrum. As they allow a pixel to be a mixture of the spectra, they are adapted to detect unresolved or sub-pixel targets (anomalies). A plethora of automatic endmember extraction and respective abundance estimation techniques have been developed [114], such as N-FINDR [115] or OASIS [116]. Enforcing the constraints in the LMM (33) leads to the convex hull model (CHM) [117] that provides physically related endmembers [10]. Cluster or mixture models conform to the convex hull model. The detection statistic can be derived as the distance of PUT spectral vector \(\underline{x}\) from a convex hull of the endmembers [82]:  
\[D_{CHM}(\underline{x}|\mathcal{B})=\left\|\underline{x}-\underline{\underline{ U}}\cdot\underline{\delta}\right\|^{2}\begin{array}{c}H_{1}\\ >\\ <\\ H_{0}\end{array} \tag{40}\]  
where vector \(\underline{\delta}\) is the least-squares solution of the equation \(\underline{\underline{U}}\cdot\underline{\delta}=\underline{x}\) with non-negativity and sum-to-one constraints:  
\[0\leq\delta_{i}\leq 1 \tag{41}\]  
and matrix \(\underline{\underline{U}}\) is composed of the background endmembers \(u_{i}\): \(\underline{\underline{U}}=[\underline{u}_{1}\cdots\underline{u}_{L}]\).  
Besides the presented approach, anomaly detection in the LMM sense can be carried out if the background convex hull (determined by the endmembers) is first subtracted from the input hyperspectral image, and some of the RX variants are then run on the residual image.  
Considering that the global or local Gaussian models (RX detectors) have shown inadequate modeling nonhomogeneous backgrounds [11], image clustering or the use of the more complex models is suggested [82]. Therefore, models based on Gaussian-mixture and cluster or segmentation anomaly detection have been developed.
## 3 Results and Discussion
Statistical result for about 17-20 h measurements shows that the level of standard deviation (1-\(\sigma\)) turned out to be basically the same. The recorded BC concentrations  
Figure 3: Comparison of both devices for (**a**) the whole period shown in Figure 1 and (**b**) the period while the battery status of Inst_1 was low. Figure 2: (**a**) Black carbon (BC) concentrations and (**b**) attenuation (ATN) values obtained from both instruments. for Inst_1 and Inst_2 are 702 \(\pm\) 337 ng/m\({}^{3}\) and 604 \(\pm\) 378 ng/m\({}^{3}\), respectively. The normalized numerical derivatives of ATN for Inst_1 and Inst_2 are 0.522 \(\pm\) 0.249 and 0.434 \(\pm\) 0.271, respectively. As for Inst_1, the ratio of the standard deviation to the average BC concentration is calculated to be 0.480 and the ratio of the standard deviation to the average numerical derivative of ATN is to be 0.477. As for Inst_2, the ratio of the standard deviation to the average BC concentration is calculated to be 0.626 and the ratio of the standard deviation to the average numerical derivative of ATN is to be 0.625. It turned out that there was no significant difference between the recorded BC concentration and the derivative of ATN for entire measurement period. However, it was quite effective to take a derivative of ATN signal when the spike noise happened. Truncation error may be added when the number of calculation step increases. The numerical derivative may help reduce the magnitude of spike noise because less truncation errors were involved during the conversion process of \(I\) into BC concentration by directly obtaining the derivative of ATN. One of the recommendations to improve detection limit for the manufacturer might be to introduce a differentiation function on the circuit board of the device and to directly acquire the derivative value as an alternative to the \(\Delta\)ATN.
## 3 Experiments and Comparisons
### Experiment 1  
The first experiment compared the performance depending on the feature combination dimension, using the feature sets shown in Figure 11. Feature set 1 concatenated the first maximum feature map from each layer sequentially (the Maxpo01, fourth convolution, and fifth convolution layers). Feature set 2 concatenated the first and the second maximum feature maps sequentially, and feature set 3 concatenated all the top three feature maps selected from each layer sequentially. The feature set dimensions were 1440, 2880, and 4320, respectively. Detectors were trained by the AdaBoost algorithm using the three feature sets, and the performance was compared using the DET curves, as shown in Figure 12.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Maxpool\_1** & **4 th Convolution Layer** & **5 th Convolution Layer** \\ \hline Max 1 & 45 & 3 & 55 \\ Max 2 & 43 & 38 & 9 \\ Max 3 & 10 & 40 & 28 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Top-three maximum value features selected for each layer.  
Figure 10: Top-three activation feature maps selected: (**a**) Maxpool_1 layer, (**b**) fourth convolution layer, and (**c**) fifth convolution layer.  
The performance did not significantly improve when more feature maps with vehicle information were included. We investigated the negative image visualization results to identify the reason. Figure 13 shows the visualizations of the same feature map sequence for each layer for the positive and the negative image samples. Low-level features extracted from the initial layer showed large differences between the positive and the negative images. The 45 th feature map in the Maxpool_1 layer, where the headlamp information of the vehicle was extracted, was clearly visible. The same edge information was extracted from the other feature maps in the Maxpool_1 layer, but the edge directions differed. High-level features were extracted from both the positive and the negative image samples for the Conv_4 layer, with the high-level features concentrated in a specific area in each image sample; i.e., the difference between the trained feature maps for the positive and negative image samples was not as significant as that for the initial layer. Finally, the Conv_5 layer, which contained the highest-level features, was extracted. The trained feature maps were similar because the high-level feature was extracted over a narrower specific area than the Conv_4 layer. Thus, the detector performance suffered when we increased dimensionality by concatenating several features with the vehicle image information in the feature set. The larger the number of feature maps from the upper layers (Conv_4 and Conv_5) included in the feature set, the more likely it was that these feature maps would act as noise. Therefore, we chose a 1440-dimensional feature set, including just the maximum feature maps from the Maxpool_1, Conv_4, and Conv_5 layers.  
Figure 11: Feature sets used in the experiment.  
Figure 12: Dimensionality effects.
## 4 Results and Discussions
### Validation of the Models  
Previous models with better performances were selected for further validation with comparisons of the measurements and prediction of the surface roughness through independent machining tests. The machining tests were conducted with the same tooling conditions on the milling machine used in the previous tests. The details of the cutting parameters, measured surface roughness, and the predicted values are listed in Table 7. The vibration levels assessed in the machining and the surface morphologies of the workpieces are illustrated in Figure 14.  
Figure 14: Morphologies of the machined surfaces under various cutting parameters.  
Figure 13: Comparison of the target and output values of the surface roughness for all data.  
The statistical values of multiple regression models (#1 and #3) and the ANN models (MLP 3-13-1 and MLP 4-15-1) are presented in Table 8 for performance comparison; they are evaluated based on correlation coefficients, RMSE, and MAPE between the measured and predicted roughness values. As listed in Table 8, the ANN models show better prediction ability and accuracy than the regression models. In particular, the ANN model MLP 4-15-1 predicts the surface roughness with more than 93% accuracy and the least values of RMSE (4.89%) and MAPE (6.86%). This validation clearly indicates that the machining vibration plays a crucial role in affecting the surface quality of the workpiece in the milling operation. The inclusion of the machining vibration as the input variable in the modeling of the surface roughness indeed enhances the prediction performance. As a whole, the ANN model demonstrates superior performance in the prediction of the surface roughness of machined parts when compared with conventional models.
## 3 Results  
A total of 40 participants were enrolled in the study, with 20 assigned to the experimental group and 20 to the control group. At baseline, no significant differences were observed between the groups in demographic characteristics, including age (\(p\) = 0.6156), weight (\(p\) = 0.1930), and gender distribution (\(p\) = 0.2357). The only notable difference was in height, with the control group being significantly taller on average (\(p\) = 0.0385). Baseline measures of cognitive performance and brain activity were also comparable between the groups, with no significant differences observed in MMSE scores, correct answers, total training attempts, or brain activity across the 15 prefrontal regions (\(p\) > 0.05). Therefore, no covariates were included in the analysis (Table 1).  
Post-intervention, the experimental group exhibited significant improvements in cognitive performance. The number of correct answers increased by 10.35 points (\(p\) = 0.0004), and the success rate of task completions improved by 19.44% (\(p\) = 0.0001). Between-group comparisons confirmed the effectiveness of the drumming intervention, with the experimental group outperforming the control group in correct answers (\(p\) = 0.0248) and success rate (\(p\) = 0.0003) (Table 2).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Measure** & **Control Group (n = 20)** & **Experimental Group (n = 20)** & \(p\)**-Value** \\ \hline Sex, n (\%) & & & \\ \hline Male & 6 (30.0\%) & 2 (10.0\%) & \\ \hline Female & 14 (70.0\%) & 18 (90.0\%) & 0.236 * \\ \hline Age, years, mean (SD) & 62.95 (5.76) & 63.75 (4.09) & 0.61563 \# \\ \hline Height, cm, mean (SD) & 164.45 (7.69) & 159.80 (5.91) & 0.0385 \# \\ \hline Weight, kg, mean (SD) & 61.25 (7.89) & 58.25 (6.34) & 0.193 \# \\ \hline Pre MMSE 1 , mean (SD) & 27.25 (2.57) & 27.00 (1.52) & 0.7104 \# \\ \hline \end{tabular}
\end{table}
Table 1: Baseline characteristics of subjects in the experimental group and the control group.  
The fNIRS data revealed significant changes in brain activity in the experimental group, particularly in two key regions of the prefrontal cortex. Channel 1, located in the right dorsolateral prefrontal cortex (DLPFC), showed a near-significant increase in brain activity post-intervention (\(p\) = 0.074). Channel 15, located in the left orbitofrontal cortex (OFC), exhibited a significant increase in brain activity (\(p\) = 0.028) (Table 3).  
A 2 \(\times\) 2 mixed design ANOVA on correct answers revealed significant main effects of Group (F(1,38) = 10.12, \(p\) = 0.003) and Time (F(1,38) = 24.35, \(p\) < 0.001), and a significant Group \(\times\) Time interaction (F(1,38) = 5.67, \(p\) = 0.021), indicating a larger pre- to post-improvement in the drumming group. Similarly, for Success Rate, there were main effects of Group (F(1,38) = 12.48, \(p\) = 0.001) and Time (F(1,38) = 31.02, \(p\) < 0.001), with a significant interaction (F(1,38) = 7.82, \(p\) = 0.008). These findings suggest that drumming-based cognitive and physical training effectively enhances cognitive function and prefrontal cortex activity in older adults.
## 2 Related Works  
Recently, numerous overviews on LoRa technology and LoRaWAN networks have been published [16; 17]. They deal with various research fields such as network lifetime, spreading factor allocation, energy conservation, coverage, connectivity, and performance improvement. LoRa technology has grown in popularity in a variety of industries, including smart metering [18], smart cities [19], fleet tracking [20], and energy management. IoT applications necessitate devices transmitting over long distances, which exposes signals to interference from various sources. Similarly, LoRaWAN coverage and scalability have been studied both indoors and outdoors to achieve a coverage range of 5 to 15 km in an open area with limited interference [16]. However, due to the changing environment, these studies on interference have not fully mitigated the problem to improve the transmission success probability. Unlike the Sigfox communication model, LoRa uses chip spread spectrum (CSS) modulation, which allows for a higher receiver sensitivity and thus a greater resistance to interference. However, there is still a lot of research being conducted on how to reduce cospreading factor interference and collisions caused by two or more LoRa devices transmitting their packets at the same SF [6]. Many other works assume that the SFs are static and are assigned based on the distance between the LoRa devices and their nearest GW, forming a kind of tiered ring structure around each GW. To solve the CSS modulation problems of LoRa communications, there are three research areas: spread factor allocation, clustering, and multihop communication. Thus, we examine the three research areas in the next three subsections in detail, respectively.
# SoK: Delegated Security in the Internet of Things
## 6 Discussion
### Rights Delegation
#### 6.3.1 Confidentiality  
All protocols presented in this domain are secure against \(\mathcal{A}_{weak}\), as they incorporate encryption mechanisms that prevent \(\mathcal{A}_{weak}\) from extracting any sensitive information through passive observation of the communication.  
Most protocols are also secure against \(\mathcal{A}_{middle}\). Despite the presence of internal accomplices, several solutions provide strong confidentiality guarantees. For instance, some rely on blockchain-based designs [50; 51; 52; 53], while others incorporate encryption-based mechanisms [54; 55] that ensure sensitive data can only be decrypted by authorised parties, even if \(\mathcal{A}_{middle}\) has internal access. However, a few protocols [56; 57; 58] are unable to fully resist internal threats, as in certain scenarios, even relatively weak accomplices may gain access to sensitive information.  
Considering \(\mathcal{A}_{strong}\), the majority of protocols can resist attacks, as they incorporate data obfuscation or decentralised control mechanisms, making it difficult even for \(\mathcal{A}_{strong}\) to access sensitive information [50; 53; 59; 60; 61]. However, some protocols remain vulnerable [62; 63; 64], particularly when internal adversaries are able to access delegation tokens or manipulate session data.
# Data Fusion Analysis and Synthesis Framework for Improving Disaster Situation Awareness
## 4 Domain Model for Data Sources
### Data Sources Which Can Be Attached to Geographical Areas  
**Airborne data sources:** UAVs, airplanes, and satellites [3; 25] are considered in this context. They can be used to observe a large area by sending images or videos of the disaster area via a communication link to a ground station.  
In this diagram, we consider three data sources for UAVs: Camera, Sonic, and IR. Optical cameras are especially relevant for estimating the effect of disasters such as earthquakes. They can have a high probability of detection especially during a daytime surveillance mission. Ultrasonic sensors (Sonic) can be used at low altitudes, for example, to measure the level of floods. Infrared sensors (IR) can be used to detect living creatures and moving targets such as vehicles from altitudes of 1 to 5 km.  
Cameras and infrared sensors can also be attached to satellites and airplanes. It is assumed that the gathering time with airplanes is higher than that of UAV. The response time of a satellite depends on many factors, for example, whether it is geosynchronous or not, the location of the satellite, the availability of online tracking by the base station, etc. For brevity, the sub-features of satellites and airplanes are not shown in the figure.  
**Aerial trackers:** Within this context, we consider GPS trackers [47], mobile phone applications [48], and base-stations [6]. These data sources are relevant for estimating the coordinates of persons. The accuracy of detection of positions of persons depends on where the tracked persons are located. A GPS tracker is useful in tracking position, direction, and time of movements. Mobile phone applications are also based on GPS; however, they use the internal sensors of a mobile phone and track position through an application. Base stations track positions based on online information about the registered mobile phones.  
**Registration database:** Every well-organized municipality has a well-maintained list of inhabitants per location. This information can be used to estimate the existence of persons under the rubble after the occurrence of earthquakes. However, the probability of detection depends on many factors. In addition to a normal living pattern, a person may be living in multiple addresses, temporarily away from home, or even not living at that address at all. The probability may also depend on night- or day-time occurrences of the disaster.  
Figure 1: Feature model representing the domain of data sources for detecting the effect of earthquakes.