esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Path Data Distribution Mechanism Based on RPL for Energy Consumption and Time Delay
###### Abstract  
The RPL (Routing Protocol for LLN) protocol is a routing protocol for low power and lossy networks. In such a network, energy is a very scarce resource, so many studies are focused on minimizing global energy consumption. End-to-end latency is another important performance indicator of the network, but existing research tends to focus more on energy consumption and ignore the end-to-end delay of data transmission. In this paper, we propose a kind of energy equalization routing protocol to maximize the surviving time of the restricted nodes so that the energy consumed by each node is close to each other. At the same time, a multi-path forwarding route is proposed based on the cache utilization. The data is sent to the sink node through different parent nodes at a certain probability, not only by selecting the preferred parent node, thus avoiding buffer overflow and reducing end-to-end delay. Finally, the two algorithms are combined to accommodate different application scenarios. The experimental results show that the proposed three improved schemes improve the reliability of the routing, extend the lifetime of the network, reduce the end-to-end delay, and reduce the number of DAG reconfigurations.  
Keywords:data distribution; multi-path; RPL; wireless sensor network +
Footnote †: journal: Information Information
# A Real-Time Detection of Pilot Workload Using Low-Interference Devices
## 2 Literature Review
### Main Methods for Detecting Workload  
#### 2.2.1 Subjective Measurement of Workload  
Subjective measurement techniques, initially introduced by [PERSON] [37], provide a holistic measure of workload. These measures typically assess the psychological workload induced by tasks and their behavioral performance through structured self-report scales. This method has become widely used by scholars in many studies [38; 39], demonstrating good psychometric properties. Popular subjective workload scales include the Cooper-Harper single-factor rating scale [40], the NASA task load index (NASA-TLX) [41], and the subjective workload assessment technique (SWAT) [42], all known for their reliability. NASA-TLX was originally designed to offer a comprehensive assessment of workload in tasks, especially in the aviation and aerospace sectors. The subsequent extensive research has optimized the selection of its subscales and the weighted averaging approach. Over the past two decades, it has demonstrated both ease of use and consistent responsiveness to key experimental manipulations [43].  
The main advantages of subjective measurement techniques are their sensitivity and simplicity. However, due to the simplicity and limited number of direct questions, they only provide an overall measure of workload and cannot distinguish between different types of workloads. This method is not a real-time measurement; rather it is conducted after the operator's task is completed. This subjective and offline approach, asynchronous with the subject's actual perception processes, can potentially lead to discrepancies between the assessment results and the subject's true condition [15].
## 5 Discussions
### Challenges and Future Research  
In our daily lives, a fall might occur while doing complex tasks like cycling, therefore it is not as straightforward as a fall while walking. The dataset of complicated activities is multi-dimensional, containing dependent and independent information in many dimensions with different formats. The edge, fog, and cloud layers of an IoT architecture provide processing, storage, data management, and decision for fall cases. Three steps have been proposed for a diagnostic system of falls [46]: prediction, prevention, and detection mode, with each stage determining which layer is appropriate. Stages should be implemented on each layer utilizing protocols, energy efficiency, and device-to-device and layer-to-layer transmission techniques, as well as specialized learning algorithms for each layer. A smartphone, for example, can detect falls on the edge and in the fog computing layers.  
Because a wearable processor, such as a wristwatch, is currently too weak to execute advanced learning algorithms effectively, data are sent to another device situated in fog. Without the need of additional layers, high-performance processors could be proposed to identify the falls by processing measured data and authenticating fall occurrences at the edge. Smart sensors are more complicated than regular sensors because they contain small microprocessors, noise filters, transducers, and amplifiers. Thousands or millions of them are spread in an IoT environment, and they must be logically connected in order to supply energy, communicate with one another, increase sensor lifetime, and overcome the shortage of processing and storage capacities. Even though many research studies have been carried out on sensor placement, which might be static or dynamic, this aspect still remains one of the most difficult subjects in the field.  
Another method for monitoring is the implanted sensor that is static and within the body [89]. Sensors should be protected from unexpected impact, have a network connection, and be pleasant to wear in both static and dynamic environments. Prediction is crucial in determining when a fall event will occur. The importance of abnormal trunk, leg standing duration, and body histograms in predicting fall occurrences has been examined. Future studies might look into dizziness, epilepsy, hypertension, and cramping (muscle contraction). The most significant component of the prediction system is understanding fall risks, based on physiological parameters such as heart rates and blood pressure of elderly population [21; 40]. The predictions stage's outputs are utilized to create smarter IoT-based settings.
## 5 Scheme Construction
### System Initialization  
The following three algorithms are used for system setup:  
**GlobalSetup(\(\lambda\)) \(\rightarrow\) GPP:** The algorithm is called to initialize the system. It uses the input \(\lambda\) and generates GPP (global public parameters). Then, it chooses two bilinear groups \(G\) and \(G_{T}\) of order \(O=p_{1}p_{2}p_{3}\) and mapping \(e:G\ X\ G\ G\ G\). Let the subgroup in \(G\) with the order \(p_{i}j\)be denoted as \(G_{p_{i}}\). \(g\) is a randomly chosen element from \(G_{p_{1}}\). \(I_{3}\) is a generator of \(G_{p_{3}}\). The GPP are made available as \(=\) (\(O\), \(e\), \(g\), \(I_{3}\), \(\Omega_{Sign}\)), where \(\Omega_{Sign}\)= (GenKey,Sign,Verify) is a secure signature scheme and will be used to counter any collusion attempts.  
**CASetup(GPP) \(\rightarrow\) (MPK, MSK):** The CA runs the GenKey algorithm of \(\Omega_{Sign}\) to obtain signature key MSK and verification key MPK. All the AAs use MPK.  
**AASetup(GPP, k, Uk) \(\rightarrow\) (APK\(\mathrm{{}_{k}}\), ASK\(\mathrm{{}_{k}}\)):** The attribute authority \(\mathrm{AA_{k}}\) runs the algorithm. Let us assume attribute universe \(\mathrm{AU_{k}}\) consists of n attribute names e.g., (a\({}_{1}\), a\({}_{2}\),..., a\({}_{n}\)), and each attribute a\({}_{i}\) has n\({}_{i}\) attribute values, e.g., (\(a_{i1}\), \(a_{i2}\),..., \(a_{i,n}\)). Each A\({}_{k}\) choosesa random exponent \(t_{k,n_{i}}\in Z_{N}\) and computes \(T_{k,n_{i}}=g^{t_{k,n_{i}}}\). It also chooses two random exponents \(\alpha_{k},\ \beta_{k}\in Z_{N}\) and computes the public keys of \(\mathrm{AA_{k}}\) as:  
\[PK_{k,1}=e(g,g)^{\alpha_{k}} \tag{1}\]  
\[PK_{k,2}=g^{\beta_{k}} \tag{2}\]  
Hence, the cumulative public key of \(\mathrm{AA_{k}}\) is:  
\[APK_{k}=\left\{e(g,g)^{\alpha_{k}},g^{\beta_{k}},g^{t_{k,n_{i}}}\forall a_{k,n_ {i}}\in U_{k}\right\} \tag{3}\]  
And the cumulative secret key of \(\mathrm{AA_{k}}\) is:  
\[ASK_{k}=\left\{\alpha_{k},\beta_{k},t_{k,n_{i}},\ \forall a_{k,n_{i}}\in U_{k}\right\} \tag{4}\]
## 2 Signal Model and Problem Formulation  
We explore the upstream broadcast of a SC-FDMA system, where \(U\) users are in simultaneous communication with a single base-station (BS), as illustrated in Figure 1. The different notations are explained in Table 1. Each user is allocated a portion of the total number of accessible subcarriers, \(\mathcal{N}\), denoted by \(\Phi(u)\), where \(u\) is the user index. The \(u\)th user has exclusive usage of the subset \(\Phi(u)\), rather than sharing it with any other users. This fulfills that \(\bigcup_{u=0}^{U-1}\Phi(u)=\{0,1,\cdots,\mathcal{N}-1\}\) and \(\Phi(u)\bigcap\Phi(u^{\prime})=\phi\) for \(u\
eq u^{\prime}\), and \(\phi\) is the empty set. In this context, we impose no restrictions on the modulation, coding, or interleaving settings employed by each user.
## 3 Background
### Word Embedding Techniques
#### 3.2.4 BERT  
BERT, standing for Bidirectional Encoder Representations from Transformers, was introduced in [18] at Google. It is based on the transformer architecture that was introduced in 2017 by [PERSON] et al. [17]. The transformer architecture introduced the concept of self-attention, in which each input embedding is represented as three vectors, namely the query, key, and value vectors. These vectors are then used to calculate attention scores among the different units and combined to produce a final sum. This was a key improvement over other models as it meant that we could now capture long-range dependencies without having fixed-size windows. Like ELMo, BERT uses self-attention and also produces contextualized word embeddings.  
The architecture of BERT consists of just a stack of encoder models, twelve in number to be precise. The BERT model produces embeddings by following a two-step procedure of pre-training and fine-tuning. During pre-training, the model is trained on a very huge amount of text data and also undergoes training on a couple of specific tasks which are next-sentence prediction (NSP) and masked language modeling (MLM). In MLM, some of the words in the input are masked, and the model is tasked with predicting the original masked word which helps the model learn robustly. In NSP, the model is tasked with deciding which among two of the input sentences precedes the other in a text. This helps the model learn relationships between sentences. Following all this, fine-tuning of these pre-trained parameters is carried out. This fine-tuning is based on the task to be performed and incorporates training on a labeled dataset to optimize the parameters generated in pre-training.
# Spectra-Based Selective Searching for Hyperspectral Anomaly Detection
## 2 Materials and Methods
### Hyperspectral Dataset  
The first hyperspectral dataset is named AVIRIS San Diego airport in this manuscript and was collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), mainly covering the urban scene where San Diego Airport is located in California, USA. The spatial resolution of the dataset was 3.5 m, and the number of spectral channels was 224, spanning the wavelength range of 370 nm to 2510 nm. After removing the corresponding water absorption regions and low signal-to-noise ratio bands (1-6, 33-35, 107-113, 153-166, and 221-224), 189 bands of the raw dataset were reserved for the experiments. The top-left 100 \(\times\) 100 pixels were chosen as the test image. The land-cover type in this scene is mainly ground, road, roof, and shadow, as shown in Figure 2a. There are three aircraft which are the anomaly targets to be detected. The corresponding ground-truth map is shown in Figure 2b.  
The second hyperspectral dataset is named HYDICE Urban in this manuscript, obtained from the Hyperspectral Digital Imagery Collection Experiment (HYDICE) remote sensor. This dataset mainly covered an urban scene consisting of some plant areas, several roofs, and some roads with vehicles. The spatial and spectral resolutions of the image are 1 m and 10 nm, respectively. This dataset contained 210 spectral channels from 400 nm to 2500 nm. Low signal-to-noise ratio and water vapor absorption bands (1-4, 76, 87, 101-111, 136-153, and 198-210) were eliminated, retaining 162 bands for the experiments. The ground truth defined that the anomalous targets were the vehicles embedded in different backgrounds. The test image considered an 80 \(\times\) 100 subscene containing the anomaly pixels. Figure 3a,b show the false color image and the corresponding ground truth map of the dataset, respectively.  
Figure 2: First real-world data set: AVIRIS San Diego airport scene. (**a**) False color image; (**b**) ground-truth map.
# A New Approach to Privacy-Preserving Multiple Independent Data Publishing
## 4 Merging Method
### Formulation
#### 4.1.3 Tuple Separation  
This entails generating different subsets of \(T\), such that each tuple is assigned to one subset. Every subset of tuples is called an equivalence class. Specifically, let there be \(e\) equivalence classes \(E_{1},E_{2},...,E_{e}\) such that \(\bigcup_{i=1}^{e}E_{i}=T\) and for any \(1\leq i_{1}\
eq i_{2}\leq e\); \(E_{i_{1}}\bigcap E_{i_{2}}=\emptyset\).  
The tuple separation operation divides the records horizontally into a number of partitions, called buckets or equivalence classes. The Mondrian [44] algorithm is applied, and it follows the top-down approach without generalization feature to separate tuples in the equivalence classes. Within each equivalence class, the values in each column are randomly permuted to break the cross-column associations. Therefore, the tuple separation will minimize the linkage between the sensitive values with the \(Ql\) values which will reduce the adversary's confidence in linking with the sensitive values.
# Boosting Safety Protocol Compliance with Eye-Tracking in Chemical Plants
## 2 Materials and Methods
### Participants  
The study was approved by the Institutional Review Board of City University of Macau (Ref-202504251421). Participants were recruited from the chemical industry and local vocational institutions to ensure diversity in operational background and prior training exposure. A total of 59 individuals were initially enrolled through stratified sampling to balance experience levels and demographic representation. One participant withdrew due to scheduling conflicts, leaving a final sample of 58 participants. Among them, 30 were classified as experienced operators (\(\geq\)3 years of work experience in chemical production, range: 3-22 years, M = 6.00, SD = 5.16), and 28 were classified as inexperienced (minimal exposure to formal industrial safety training). Participants ranged in age from 21 to 53 years (M = 29.21, SD = 9.46), with a gender distribution of 72.88% male (\(n\) = 43) and 27.12% female (\(n\) = 16), representative of the broader industrial workforce. To control the influence of prior exposure to XR-based technologies, participants were grouped according to their familiarity with immersive simulation tools. The experimental design further ensured an even allocation of participants to two training conditions: a VR-only environment and a VR system with real-time eye-tracking feedback to monitor and guide visual attention. This design allowed for a demographically and experientially balanced sample and minimized potential selection bias in performance assessment. Participants were randomly assigned using a computer-generated sequence. Post-training performance data were evaluated by a research assistant who was blinded to group assignments.
## 3 Problem Formulation
To maintain the encirclement-interception formation, the \(q_{di}\) is connected with the other interceptors:  
\[\dot{r}_{i} =v_{T}\cos(q_{i}-\theta_{T})-v_{i}\cos(q_{i}-\theta_{i}) \tag{9}\]2  
where \(q_{s}\) denotes the LOS angle error of the encirclement interception. Then, the cooperative-guidance model changes as follows:  
\[ \begin{array}{l}\dot{x}_{1i}=x_{2i}\\ \dot{x}_{2i}=x_{1i}x_{4i}^{2}-u_{ri}+w_{ri}\\ \dot{x}_{3i}=x_{4i}\\ \dot{x}_{4i}=-2\frac{x_{2i}}{x_{1i}}x_{4i}-\frac{u_{qi}}{x_{1i}}+\frac{w_{qi} }{x_{1i}}-\dot{q}_{j}\end{array}  \tag{17}\]  
where \(\dot{q}_{j}=-2\frac{x_{2j}}{x_{1j}}x_{4i}-\frac{u_{qi}}{x_{1j}}+\frac{w_{qi}}{ x_{1j}}\). During the interception process, the time-to-go of the interceptor can be approximated by:  
\[t_{goi}=-\frac{r_{i}}{\dot{r}_{i}} \tag{18}\]  
By taking the derivate of (18), we obtain the following:  
\[\dot{t}_{goi}=-1+\frac{{x_{1i}}^{2}{x_{4i}}^{2}}{{x_{2i}}^{2}}-\frac{{x_{1i} }}{{x_{2i}}^{2}}u_{ri}+\frac{{x_{1i}}}{{x_{2i}}^{2}}w_{ri} \tag{19}\]  
A new state-variable flight time (\(t_{f_{i}}\)) is introduced as follows:  
\[t_{fi}=t+t_{goi} \tag{20}\]  
Define the error of the flight time:  
\[e_{ii}=t_{fi}-t_{fj}i,j\in N \tag{21}\]Taking the derivative of (21) yields:  
\[ \begin{array}{l}\dot{e}_{\rm{ti}}=t_{fi}-t_{fj}=t+t_{gai}-t+t_{goj}\\ =\sum\limits_{j=1}^{N}a_{ij}(\frac{x_{1j}^{2}x_{3i}}{x_{2i}!}^{2}-\frac{x_{1j} ^{2}x_{3i}!}{x_{3j}!}^{2})-\sum\limits_{j=1}^{N}a_{ij}(\frac{x_{1j}}{x_{2i}!}u_ {ri}-\frac{x_{1j}}{x_{2j}!}u_{rj})+\\ \sum\limits_{j=1}^{N}a_{ij}(\frac{x_{1i}}{x_{2i}!}w_{ri}-\frac{x_{1j}}{x_{2j}!} w_{rj})\end{array}  \tag{22}\]  
To realize simultaneous arrival, we introduce the new state variable (\(e_{t}\)), and the cooperative-guidance model for the followers (17) can be rewritten as:  
\[ \begin{array}{l}\dot{e}_{\rm{ti}}=\sum\limits_{j=1}^{N}a_{ij}(\frac{x_{1i}^{ 2}x_{3i}!}{x_{2i}!}^{2}-\frac{x_{1j}^{2}x_{3i}!}{x_{3j}!}^{2})-\sum\limits_{j =1}^{N}a_{ij}(\frac{x_{1i}}{x_{2i}!}u_{ri}-\frac{x_{1j}}{x_{2j}!}u_{rj})+\\ \sum\limits_{j=1}^{N}a_{ij}(\frac{x_{1i}}{x_{2i}!}w_{ri}-\frac{x_{1j}}{x_{2j}! }w_{rj})\\ \dot{x}_{3i}=x_{4i}\\ \dot{x}_{4i}=-2\frac{x_{2i}}{x_{3i}!}x_{4i}-\frac{u_{gi}}{x_{4i}}+\frac{w_{gi }}{x_{4i}}-\dot{q}_{di}\end{array}  \tag{23}\]  
**Remark 1**: _Our cooperative-guidance law aims to arrange the followers around the leader to realize the encirclement attack. The separation distance between different vehicles is designed by the LOS angle error (\(q_{s}\)). Unlike the traditional cooperative-guidance law with the static LOS-angle constraint in [22; 23; 24; 25], our guidance law sets the various LOS angle constraints for the followers, which adaptively adjust to the LOS angle of the leader and the LOS angle error. This cooperative-encirclement-guidance strategy brings a new differential term: \(\dot{q}_{di}\). This term can be obtained by the communication with the other interceptors._
### Challenges  
The most challenging problem is dealing with occlusion as there are usually many objects in a dense location in an agricultural setting [17; 23; 24; 29]. Another important challenge that must be considered when designing an object detection system in agriculture is illumination [23; 24; 29]. Due to most operations occurring in an outside environment, variance in natural lighting can greatly affect the performance of the object detection algorithm. The authors in [23] explained that the distance from the object of interest is important as the smaller the object is when presented to the camera, the less information can be extracted. Other challenges consist of similar appearance, such as shape and colour, as well as multi-fruit detection cases. The authors in [20] experienced issues with differentiating between ripe and immature tomatoes in some cases due to the similarities in shape and colour [24].  
Occlusion and illumination are the two challenges that this paper looks to address as they were the most relevant in terms of the environment used for the experiment. For occlusion, data from both sides of the apple tree is taken which will be used to average the data to decrease the effect of occlusion on the resulting fruit flower count. For the case of this experiment, illumination was overcome by being constant with the time of day for observation as well as the amount of sunlight for data collection to ensure proper detection of the fruit flowers. This paper presents a new fruit flower cluster detection strategy based on machine vision and learning which could be used as farmers work towards the concept of smart agriculture.  
The remaining information of the paper will be presented as follows. Section 2 explains the experimental setup and the process and methods used for obtaining the fruit flower count. Section 3 presents results that were found from the object detection model, such as the flower count, precision and F1-Score, average error and distribution of flower count of the detected and hand-counted. Section 4 discusses the main findings and results. Section 5 concludes the paper.
# Correlation Analysis of Microbiota and Volatile Flavor Compounds of Caishiji Soybean Paste
## 4 Discussion
During the fermentation process, the relative abundance of _Bacillus_ increased, while that of _Staphylococcus_ decreased or even disappeared completely. _Tetragonococcus_ is closely related to the production of biogenic amines, which directly affect the flavor and safety of soybean paste; therefore, regulation of _Tetragonococcus_ content is essential [1]. _Aspergillus_ was the predominant fungal genus in CSP samples; it is found in soy sauce, douchi, and vinegar [33]. Compared with the other genera identified in this study, _Aspergillus_ exhibits a higher tolerance to dryness. The analysis of microbial dynamics revealed that bacteria were involved in the decomposition of materials in the CSP fermentation, whereas most of the fungi were presented in the late stages of fermentation. The fermentation of soybean paste takes place mostly in anaerobic and high-humidity environments. Compared with bacteria, fungi may disrupt metabolic activity under dry and aerobic conditions; thus, fungal communities play a more important role in fermented bean paste. The relationships between microbiota and VFCs in traditional fermented foods have been researched; however, such studies have produced limited relevant data. In the present study, we analyzed the complex connections between the microbiota and 12 characteristic VFCs during the CSP fermentation. As indicated in Figure 4, the Pearson's correlation coefficients (r) and significance (\(p\)) were calculated, and the bacteria and fungi with the 10 highest abundances were obtained. The 2-Methylbutyraldehyde was significantly positively correlated with _Bacillus_ and _Orenia_ (\(r>0.7\)) but significantly negatively correlated with _Tetragonococcus_ and _Staphylococcus_. _Kocuria_ had a positive correlation with vanillin, and _Halomonas_ was negatively correlated with isobutyric acid and ethyl butyrate (Figure 3a). The 2-Methylbutyraldehyde, 3-methylthiopropionaldehyde, nonanaldehyde, and decyl aldehyde were significantly negatively correlated with _Aspergillus_, but they were positively correlated with other fungi, such as _Debaryomyces_, _Nigrospora_, _Curularia_, and _Penicillium_. Vanillin was positively correlated with _Aspergillus_ but negatively correlated with _Cladosporium_, _Curvalaria_, and _Cutaneousproon_ (Figure 3b). Through correlation analysis, microbiatas were determined to be the main factors affecting the generation of flavor compounds during the CSP fermentation. For example, _Bacillus_ and _Orenia_ may produce undesirable flavor compounds during CSP fermentation, whereas _Apergillus_, _Etragonocccus_, and _Staphylococcus_ may produce flavor-enhancing compounds. Through detection and analysis, the connections between microbiota and characteristic VFCs were determined. In follow-up studies, these correlations should be verified in application and potentially used to further advancements in \"omics\" technologies.