esa-sceva/satcom-chunk-collection huggingface dataset:
# Mars to Earth Data Downloading: A Directory Synchronization Approach
###### Abstract  
This paper aims to present a possible alternative to direct file transfer in \"challenged networks\", by using DTNbox, a recent application for peer-to-peer directory synchronization between DTN nodes. This application uses the Bundle Protocol (BP) to tackle long delays and link intermittency typical of challenged networks. The directory synchronization approach proposed in the paper consists of delegating the transmission of bulk data files to DTNbox, instead of modifying source applications to interface with the API of a specific BP implementation, or making use of custom scripts for file transfers. The validity of the proposed approach is investigated in the paper by considering a Mars to Earth interplanetary environment. Experiments are carried out by means of Virtual Machines running ION, the NASA-JPL implementation of DTN protocols. The results show that the directory synchronization approach is a valid alternative to direct transfer in interplanetary scenarios such as that considered in the paper.  
delay-/disruption-tolerant networking; inter-planetary internet; bundle protocol; directory synchronization +
Footnote †: journal:
# Wheat Quantity Monitoring Methods Based on Inventory Measurement and SVR Prediction Model
## 3 Principle of SVR Prediction Model
### Evaluation Indicators  
To objectively evaluate the effectiveness of SVR predictions, two metrics, the mean squared error \(E\) and the coefficient of determination \(R^{2}\), are used to evaluate the performance of SVR prediction models [36; 37]. The smaller the mean squared error \(E\), the better the performance of the SVR prediction model. The coefficient of determination is in the range of [0,1], and the closer \(R^{2}\) is to the value 1, the better the SVR prediction model. Otherwise, the performance of SVR prediction model is worse. If the resulting SVR regression model does not satisfy the set requirement, the SVR regression model is reconstructed by modifying the model parameters or changing the kernel function type until the set requirement is satisfied. The mean square error \(E\) and the coefficient of determination \(R^{2}\) to be determined are calculated as follows:  
\[E=\frac{1}{m}\sum_{i=1}^{m}(\hat{y}_{i}-y_{i})^{2} \tag{21}\]  
\[R^{2}=\frac{\big{(}\text{m}\sum_{i=1}^{m}\hat{y}_{i}\text{y}_{i}-\sum_{i=1}^{m }\hat{y}_{i}\sum_{i=1}^{m}\text{y}_{i}\big{)}^{2}}{\Big{(}\text{m}\sum_{i=1}^{m }\hat{y}_{i}^{2}-\big{(}\sum_{i=1}^{m}\hat{y}_{i}\big{)}^{2}\Big{)}\Big{(}\text{ m}\sum_{i=1}^{m}\text{y}_{i}^{2}-\big{(}\sum_{i=1}^{m}\text{y}_{i}^{2}\big{)}^{2} \Big{)}} \tag{22}\]where \(m\) is the number of samples in the whole test set; \(y_{i}\) is the true value of the \(i\) th sample; and \(\hat{y}_{i}\) is the predicted value for the \(i\) th sample.
# Potential of Sentinel-1 Radar Data for the Assessment of Soil and Cereal Cover Parameters
## 3 Results and Discussions
### Radar Signal Analysis  
#### 3.2.1 Relationship between Radar Signal and Soil Moisture  
In order to minimize the influence of vegetation heterogeneity, the sensitivity of the radar signals to soil moisture was analyzed for three classes of NDVI. The first of these is defined by NDVI \(<\) 0.25, corresponding (in general) to bare or poorly covered soils. The second class is defined for 0.25 \(<\) NDVI \(<\) 0.5, corresponding to a medium vegetation density, and the third class is defined for high NDVI values (NDVI \(>\) 0.5). The NDVI threshold defined at 0.5 corresponds to an LAI equal to \(\sim\)1. Figure 6 plots the VV and VH radar signals as a function of soil moisture for the three NDVI classes. The sensitivity of radar signals to soil moisture can be seen to decrease with increasing NDVI. The vegetation component, which attenuates the signal scattered by the soil, thus influences the radar sensitivity to variations in soil moisture. As an example, for the VV polarization the sensitivity decreases from 0.27 dB/vol. % for the weak NDVI values (NDVI \(<\) 0.25), and to 0.12 dB/vol. % for the highest NDVI values (NDVI \(>\) 0.5). The signal strength sensitivity is clearly higher in the VV than in the VH polarization, for all three classes of NDVI. As an example, in the first NDVI class the sensitivity is 0.27 dB/vol. % for VV and 0.18 dB/vol. % for VH. In the VH polarization, the lower soil moisture sensitivity can be explained by the higher sensitivity of the radar signals to the volume component of medium scattering [35]. For the two highest NDVI classes, the correlation between moisture and radar signals is the lowest, due to the disruptive effect of increasing vegetation cover and the absence of field data at low values of soil moisture.  
Figure 5: Relationship between LAI ground measurements and normalized difference vegetation index (NDVI) derived from optical images.  
Figure 6: _Cont._The behaviors found for S-1 are generally consistent with those reported in previous studies, in the case of data acquired in the C-band [30; 54].
## 5 Evaluation
### Impacts of Experimental Parameters  
In this section, we investigate the impact of experiment parameters on the model's performance by changing one parameter and keeping another two fixed. To visualize the location-estimation errors, we use bar charts: each bar's middle line is the mean squared loss (MSE) [80], and its height equals two times the standard deviation over the squared loss of all concerned testing points. Most of the errors are within this interval, which could be used to observe the prediction stability of each method. Corresponding numerical results (both MSE and variance) are provided in tables.
## 5 Discussion
#### Future Work  
In future research work, the following aspects can be explored:  
* Consider performing feature extraction at intervals of every \(\mathsf{S}\) data packet, and explore the effectiveness and performance of feature extraction under this approach.
* In the machine-learning model part, select a model with a wider recognition ability to reduce the model's space consumption and improve its operational efficiency.
* Conduct in-depth research and introduce more advanced feature extraction techniques. Meanwhile, explore the technology for automatically selecting statistical features in different environments to enhance the system's adaptability in complex environments.
# Augmented MIMO: Body-Mounted Antennas for Tiny Wearable Devices
## 4 Augmented MIMO Throughput Evaluation
### Non-Line-of-Sight Environment  
Measurements for another scenario, in a non-line-of-sight (NLOS) environment, were conducted in a larger laboratory room in the same university building, as shown in Figure 8. The distance between the WLAN router and the client was increased to 8.8 m. The direct path was blocked by a 1.8 m \(\times\) 1.0 m whiteboard. Due to the furniture in the room, including desks and stools, it was a highly scattered environment.  
The PCs, WLAN router/dongle, and IPerf settings used were the same as in the LOS environment experiment.  
Figure 7: (**a**) A schematic diagram of experimental system for measuring the throughput of Augmented MIMO. (**b**) An overview of the experimental system of chip-to-AP link. (**c**) The watch antennas are connected to the external antenna ports of a WLAN dongle on the IPerf client PC. (**d**) An overview of Augmented MIMO evaluation system. (**e**) In the Augmented MIMO system, the cuff antenna PCB is stacked onto the watch PCB.
###### Abstract  
The rapid growth of the ITS (intelligent transport system) development requires us to realize new kinds of applications, such as the winter road surveillance system. However, it is still necessary to consider the network difficulty areas for LTE (long-term evolution) or 3G transmissions when one visits sightseeing spots such as ski resorts or spas in mountain areas. Therefore, this paper proposes a winter road surveillance system in the local area based on vehicular delay-tolerant networks. The adaptive array antenna controlled by image recognition with the Kalman filter algorithm is proposed as well to the system in order to realize higher delivery rates. The implementations of the prototype system are presented in this paper as well, and the effectivity of the radio transmission in the prototype system is realized by vehicular image recognition methods and the asynchronous voltage controls for antenna elements for the rapid directional controls of the radio transmission. The experimental results showed that the radio directional controls by the prototype system for the target vehicle can proceed within one second, and that the simulation with the GIS (geographic information system) map pointed out the delivery rates of the proposed method--which are better than those of the epidemic DTN (delay-tolerant networking) routing by the nondirectional antenna. The experiments of the proposed methods indicate a higher efficiency of the data transmissions--even in the mountain area. Furthermore, future research subjects are discussed in this paper.  
delay-tolerant networks; adaptive array antenna; road surveillance system +
Footnote †: journal:  
0000-0001-6993-3300][PERSON]  
0000-0001-6993-3300][PERSON]
# GNSS Profile from the Greenland Korth Expeditions in the Context of Satellite Data
## 4 Greenland Korth Expeditions and Satellite Altimetry
The current classification of the glacier zones (facies) distinguishes five basic types: dry snow, percolation, wet snow, superimposed ice and ablation zones [31]. The majority of the Greenland North Expeditions measurement sites are located in the percolation zone, only a few peripheral sites (C5, C6, C35) are possibly located within the wet snow zone, and there is no Greenland North Expeditions site within the dry snow zone [32,33]. As for the slopes [34], in the case of the Greenland North Expeditions measurement sites shown in Figure 7d,e, all of the surface slopes are below \(0.5^{\circ}\), except for site C5 with a slope of \(1.2^{\circ}\) and site C6 with a slope of \(0.8^{\circ}\).
# Activity Prediction Based on Deep Learning Techniques
## 2 Materials
### Details on the Dataset  
Four subject persons were given IDs, ID\(\_\)01, ID\(\_\)02, ID\(\_\)03, and ID\(\_\)04, respectively. Figure 3 shows the overall plots of the environmental and trajectory-related data for the ID\(\_\)4 during the whole observation period as an example. The plots include profiles of activities and environmental information measured while the subject person is on the move. However, the location, altitude, and speed information of the subject is omitted to focus more on the relationship between the activity pattern and the environmental information. Each piece of data was plotted with respect to the observation time when it was collected, and the chosen data were used as features for the classifier to predict the activity pattern for the next observation time. Activity patterns are enumerated for the convenience of simulation and visualization as follows: Commuting with a bus as 1, Commuting with a car as 2, Commuting with a subway as 3, Commuting with a train as 4, Commuting with an elevator as 5, Home-BBQ Pan-Frying as 6, Home-SHS as 7, Staying inside home as 8, Staying inside work place as 9, Staying outside as 10, Visiting other commercial place as 11, Visiting restaurant-cafe as 12, and Walking at outside as 13.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Specifications** & **Range** \\ \hline Temperature range & \(-\)40\(-\)185 °F \\ Effective PM\({}_{2.5}\) range & 0–500 \(\upmu\)g/m\({}^{3}\) (\(\geq\)1000 \(\upmu\)g/m\({}^{3}\) for max) \\ Accuracy tolerance for relative humidity & \(\pm\)3\({}^{\circ}\) \\ GPS accuracy & 2.5 m \\ Altitude range & \(<\)18,000 m \\ Velocity range & \(<\)515 m/s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sensor specifications.  
Figure 1: Sample trajectory (in red) of a subject person for a day.  
Samples of activity profiles are depicted in Figure 4, where activity patterns during the morning and evening periods are very dynamic compared to those of other time periods. As shown on the left, the activity pattern changes from 13 (Walking outside) to 1 (Commuting with a bus), which indicates that the subject moves from home to working place or other outdoor activities. Meanwhile, in the evening hours, most people come home after work or meet others outside, and corresponding activity patterns can be observed on the right-hand side of Figure 4. From Figure 4, we can conclude that the data collection  
Figure 3: Sample data profiles during an observation period.  
Figure 2: A snapshot of the dataset. It contains several environmental data such as temperature (Temp), relative humidity (RH), particulate matter2.5 (pm2_5), as well as GPS data (Latitude and Longitude), altitude, and speed data that relate to human trajectory, plus pre-defined labels for the 13 activity patterns (activity13).  
process was properly executed, and the collected data show a contextual connection for each activity.
## 2 Material and Methods
### Plant Material and Compounds Extraction  
The fruit samples of _C. australsica_ L. were supplied by MonCitric in October 2018, and were collected in an organic experimental field located in Cullera (Valencia, Spain, 39\({}^{\circ}\)13\({}^{\prime}\)0.2\({}^{\prime\prime}\) \(\mathrm{N}\) 0\({}^{\circ}\)14\({}^{\prime}\)5.9\({}^{\prime\prime}\) W), a well-known citrus-growing area. Fruits (2 kg) were collected from different trees representative of the entire field.  
The samples were frozen and transported to Teagasc (Ashtown, Ireland). The samples were then cleaned with Milli-Q water. Peel and caviar-like pulp from the fruits (Figure 1) were carefully separated, freeze dried and milled into a fine powder before their analysis. Exhaustive extraction was carried out in 80% aqueous methanol aided by ultrasonication, as outlined in Figure 2. Briefly, 1 g of the powdered material (peel or pulp) was extracted with 20 mL of methanol:water (80:20 (\(v/v\))) in an ultrasound bath for 30 min at room temperature. This process was repeated three times to ensure the total extraction of phenolic compounds. Then, the three extractions were pooled, centrifuged and the methanolic solvent was evaporated in a rotavapor at 35 \({}^{\circ}\)C. Thereafter, the remaining water fraction (\(\sim\)12 mL) was successively liquid-liquid partitioned (\(\times\)3) with 15 mL of \(n\)-hexane, ethyl acetate or butanol to obtain four different fractions for each matrix (peel and pulp).
# User Mobility Modeling in Crowdsourcing Application to Prevent Inference Attacks
###### Abstract  
With the rise of the Internet of Things (IoT), mobile crowdsourcing has become a leading application, leveraging the ubiquitous presence of smartphone users to collect and process data. Spatial crowdsourcing, which assigns tasks based on users' geographic locations, has proven to be particularly innovative. However, this trend raises significant privacy concerns, particularly regarding the precise geographic data required by these crowdsourcing platforms. Traditional methods, such as dummy locations, spatial cloaking, differential privacy, k-anonymity, and encryption, often fail to mitigate the risks associated with the continuous disclosure of location data. An unauthorized entity could access these data and infer personal information about individuals, such as their home address, workplace, religion, or political affiliations, thus constituting a privacy violation. In this paper, we propose a user mobility model designed to enhance location privacy protection by accurately identifying Points of Interest (POIs) and countering inference attacks. Our main contribution here focuses on user mobility modeling and the introduction of an advanced algorithm for precise POI identification. We evaluate our contributions using GPS data collected from 10 volunteers over a period of 3 months. The results show that our mobility model delivers significant performance and that our POI extraction algorithm outperforms existing approaches.  
location protection; personal data preservation; security breach; crowdsourcing; geolocated data; privacy; inference attack +
Footnote †: journal:  
0000-0001-4697-3309][PERSON]. [PERSON]
## 1 Introduction  
Nutritional intake is the basis for human growth and health, and the intake of different types of nutrients and micronutrients can affect health. Most diseases are inextricably linked to diet. Diabetes, cardiovascular diseases (hypertension, hyperlipidemia), gout, peptic ulcers, and gastroenteritis are all diet-related diseases that are increasing in prevalence every year, while the age group of those suffering from these diseases is gradually decreasing. The development of the Internet has made it possible to conduct online nutrition surveys through large-scale food and nutrition databases linked to automated dietary records, and there are now a growing number of software, platforms, and applications for nutrition surveys [1].  
The most common technologies used for dietary recording are web-based or online tools, mobile apps, camera-based image analysis tools, wearable sensors, etc., while traditional methods rely on the use of Food Frequency Questionnaires (FFQs) or 24 h dietary recording methods. However, past techniques have suffered from a lack of accuracy in recording, as recall methods may not accurately record the food consumed or have difficulty estimating portion sizes or limited food ingredient lists [2].  
The coding and translation of food records from nutrition surveys into nutrient analyses are labor-intensive and time-consuming, meaning that it is more difficult to collect detailed information regarding food intake in large scale population studies. Such studies rely on answers to food frequency questionnaires, and the accuracy of this data is dependent on the expertise of the interviewer compared to other self-reported measures [3,4].  
Innovative technological tools have evolved with the development of various IT technologies, including natural language analysis of text, speech analysis, and image processing. The popularity of smartphones, tablets, and computers has increased the acceptance of using IT for nutritional intake assessments [5, 6, 7, 8].  
This study develops an artificial intelligence model for a precision nutrient analysis, which allows users to enter the name of a dish and serving size to assess a total of 24 nutrients. The recipes can be modified by the user, which allows the model to be used in all countries and all contexts, thus improving interoperability and accuracy of the analysis.
## 4 Result and Discussion
### Dynamic Multi-Stage Path Planning Experiments  
To evaluate the generalization capability of the PER-D3 QN-EDAgger model and its adaptability to dynamic multi-level transportation tasks in complex production environments, we conduct simulation experiments in different scenarios, using the traditional centralized planning method CBS, ID A*, and modern distributed planning method PRIMAL2 as a reference. The centralized planning methods re-plan every steps to avoid dynamic obstacles.  
The experimental environment is represented on a larger warehouse-like simulated map, that is shown in Figure 7. The map is generated based on the aforementioned layout of buildings, roads, and shelf areas, by controlling the number of AGVs and the shelf density parameter. The shelf density parameter primarily regulates the obstacle density in the environment, ranging from 0.05 to 0.9 in increments of 0.05. During environment initialization, if the random number for a shelf node exceeds the shelf density parameter, a shelf is placed. The number of AGVs controls the scale of agents and dynamic obstacles, with 90% of the AGVs acting as system-controlled agents and 10% acting as dynamic obstacles that move randomly without conflicts. The AGV count ranges from 10 to 200 in increments of 10. Upon initialization, each AGV is randomly placed on the roads.  
These two parameters can generate \(18\times 20=360\) different environments; in each environment, 10 sets of experiments are performed, with each experiment randomly generating 20 transportation tasks in the context of inter-warehouse relocations. Notably, 10 of them are two-stage transportation tasks; each two-stage task has two subtasks, and each subtask has two key waypoints. This results in a total of 200 tasks or 300 subtasks.  
Figure 6: The average rewards of different algorithms at different training iterations—the higher, the better.  
Key metrics such as task completion rate, path length, and response time are recorded. The response time is defined as the time taken from when an AGV is assigned a transport task to when it takes its first action in response; this metric reflects real-time planning capability. For centralized planning, it measures the total path planning time; for distributed planning, it measures the action selection time of an individual AGV; for the hybrid method, it is the sum of the two.  
The maximum path length is set to 256 steps and the maximum response time is set to 60 s. If the cumulative path length or response time exceeds the limit during path planning, the task is considered failed. In such cases, the unfinished task is recorded as incomplete and the cumulative path length and response time are assigned their respective upper limits. The key experiment results are shown in Table 5.
# Recognition of American Sign Language Gestures in a Virtual Reality Using Leap Motion
## 2 Methods
### Network Service Gesture Identification System  
The amount of data needed to recognize the gesture can grow exponentially depending upon the number of gestures. For ten or more gestures, algorithms of the gesture recognition system require a considerable amount of time, on average about half a minute. However, the alphabet of ASL has thirty two different gestures (signs). As it requires large computation resources for online retraining, the system was implemented using a cloud-based network service. For implementation, we have employed microservices--a case of the service-oriented architecture (SOA) that defines an application as a stack of loosely coupled services [39].  
The connection of the gesture recognition system to the network service (Figure 2), has made it possible to easily allocate the resources necessary for the system between several computers. All gesture identification data are stored in the remote gesture database. By launching several services which send requests to the same database, it has been possible to manage algorithmic training. At the same time, it is possible to record new gestures, carry out research and teach algorithms to recognize new gestures.  
The network service provides an easy access to the gesture recognition system from different environments. The Leap Motion device can be used in games created using Unity or Unreal Engine gaming engines and easily integrated into any Windows application or web page. The gesture recognition system launched as a network service allows very many different systems to communicate with it. For the time being, due to the easier installation, the Simple Object Access Protocol (SOAP) was used but the system can easily be expanded to accept the Representational State Transfer (REST) data requests. This functionality would allow the gesture recognition system to be accessed from any environment. Data recorded with the Leap Motion device is stored in the Microsoft Structured Query Language (MS SQL) database, which allows for creation of personalized gesture collections as in Reference [40].