esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Analysis of Enzymatic Characteristics  
The characterization of Man26E was investigated with 0.5% locust bean gum as the substrate. Three replicates were set in all experiments independently. So as to obtain the optimal pH value, the enzyme reaction solution was incubated for 10 min at different pH (pH 3-10) conditions and 50 \({}^{\circ}\)C, and the enzyme activity was quantified according to the standard method. To test pH stability, the enzyme solutions were preincubated in the range of pH 4 to 10 for 1 h at 37 \({}^{\circ}\)C, and then the residual activities were measured. The buffers with different pH values included 100 mmol L\({}^{-1}\) sodium citrate (pH 3-6), 100 mmol L\({}^{-1}\) sodium phosphate (pH 7-8) and 100 mmol L\({}^{-1}\) glycine-NaOH (9-10). To examine the optimal temperature, the enzyme activities were determined after the reaction mixtures were incubated at 40, 45, 50, 55, 60, 65, and 70 \({}^{\circ}\)C for 10 min, respectively. The thermostability of Man26E was investigated by preincubating the enzyme in 100 mmol L\({}^{-1}\) citric acid buffer (pH 6.0) at 40, 50 and 60 \({}^{\circ}\)C respectively for different time (15, 30, 45 and 60 min), then the residual enzyme activities were determined. For investigating the action of metal cations on the purified \(\beta\)-mannanase activity, the enzyme solutions were preincubated in 100 mmol L\({}^{-1}\) citric acid buffer (pH 6.0) containing 1, 5 and 10 mmol L\({}^{-1}\) various ions for 5 min at 40 \({}^{\circ}\)C, separately, the enzyme activities were assayed at 50 \({}^{\circ}\)C and pH 6.0. The reaction group without adding any ions was used as a control. In this experiment, the metal ions included Ni\({}^{2+}\), Zn\({}^{2+}\), Mg\({}^{2+}\), Co\({}^{2+}\), Mn\({}^{2+}\), Cu\({}^{2+}\), K\({}^{+}\), Fe\({}^{2+}\), and Ca\({}^{2+}\).
## 1 Introduction
These sensors are sensitive to the changes in conductivity, which make them ideal to detect or control fertilisers or other compounds that are conductive [PERSON] et al. (2018). In the same context, a low-cost sensor array based on planar electromagnetic sensors to determine the contamination level of two common fertiliser components--nitrate and sulphate in water sources--is developed [PERSON] et al. (2018). Thus, although no specific physical systems for monitoring OF is found, the use of electromagnetic sensors, such as inductive coils, have been demonstrated for monitoring other compounds in water. In this paper, the design, calibration, and verification of an inductive sensor to control the quantity of organic fertiliser in water, are presented. The sensors we included in this study are based on prototypes previously developed to detect inorganic fertiliser. In this context, the sensors are going to be applied to measure OF [PERSON] et al. (2018), which has a different characteristic than inorganic fertiliser. Moreover, we are going to test a new procedure to power the prototypes, considering the polarity of the generated magnetic field. The proposed sensors are based on two copper coils attached to the same structure. The prototypes used are those that have the best configuration based on previous studies [PERSON] et al. (2018). Our hypothesis is that the induced magnetic field is sensible to the changes of OF concentration. To test our hypothesis, we use nine samples distributed in the range between 0 g/L and 20 g/L. The first step before testing the sensors in the laboratory is to perform several simulations using COMSOL Multiphysics [PERSON] et al. (2018) for all three prototypes and different mediums: air, pure water, seawater, and three OF samples. To find the Working Frequency (WF) of the prototypes in the four different configurations, we follow the procedure indicated in [PERSON] et al. (2018). We use six samples to find the WF and calibrate the prototypes, and another three to verify them. Finally, we perform an ANOVA procedure to verify if the found differences are statistically significant or not, and verify our hypothesis. The rest of the paper is structured as follows. In Section 2, the state of art (method) and the background are described. Then, the material used in the experiment and the selected methodology to perform the simulations and the tests are detailed in Section 3. Following, Section 4 presents the obtained results of the different prototypes. Finally, the conclusion and future work are outlined in Section 5.
## 3 Deep Learning Algorithms
### Faster-RCNN Algorithm  
The Faster-RCNN algorithm is a typical representative example of a two-stage algorithm for target detection. The Faster-RCNN model consists of four components: a Backbone, Region Proposal Networks (RPN), ROI (Region of Interest) Pooling, and Classifier. The Backbone extracts a feature map that is used for candidate detection area extraction and classification. The RPN further refines the candidate detection areas based on the initial feature map, which may contain the target features. These refined areas are then used for further classification and localization. The ROI Pooling fine-tunes the candidate detection areas based on their candidate box coordinates. Finally, the Classification component uses the proposals and feature maps to determine the category of the proposal and regress the candidate detection frames to obtain their final precise locations.  
The network architecture of Faster-RCNN is illustrated in Figure 3. Firstly, an arbitrary input image (\(\mathbf{P}\times\mathbf{Q}\)) is resized to a standard image (\(\mathbf{M}\times\mathbf{N}\)) and then fed into the network. The backbone (e.g., VGG and ResNet, etc.) extracts features from the \(\mathbf{M}\times\mathbf{N}\) image, followed by convolution and pooling operations, resulting in feature maps for this input. These feature maps contain information about different scales and semantics, enabling the detection of objects with various scales and shapes in the image. In the Region Prediction Network (RPN), the RPN network performs a 3 \(\times\) 3 convolution to generate Positive Anchors and the corresponding Bounding Box Regression offsets. It then calculates Proposals, which are utilized by the ROI pooling layer to extract the Proposals from the Feature Maps. The Proposal Feature is further processed through fully connected and softmax networks for classification.  
Figure 2: A road map of object detection models (modified from [23]).
# Study on Compaction Characteristics and Compaction Process of an Unsaturated Silt Based on PFC3D
## 4 Research on the Rolling Compaction Process
### On-Site Compaction Process Analysis  
The test area selected for the roadbed filling test is the second section of the main project of the reconstruction and expansion project from the Dezhou (Luji boundary) to the Qihe section of the Jingtai Expressway. The selected soil filler is a low liquid limit unsaturated silt in the soil field on the west side of the plain service area. At the engineering site, 3% cement was added to the unsaturated silt with a 15% water content, and its filler was modified. Then, the SEM8222 vibratory roller was used for strong vibration and weak vibration rolling, to determine the best compaction process. Table 5 is the changing table of the compaction degree under this condition.  
Figure 12: Relationship curve between the compaction times under static and dynamic loads and the compaction degree.  
According to Table 5, it can be seen that improving the compaction effect of the soil samples by strong vibration or weak vibration alone is not obvious, and the compaction effect is better when only static rolling once + strong vibration four times. Compared with strong vibration rolling four times, the compaction degree decreases when the strong vibration rolling is carried out five times. The main reason for this phenomenon is that with the increase in the number of strong vibration rolling, the porosity of the soil sample gradually decreases, the water content increases, and the upper surface soil is bonded to the rolling wheel, which leads to the phenomenon of 'peeling' in the subgrade soil, leading to the decrease in the compaction degree.  
The compaction effect of combined rolling is stronger than that of strengthening vibration and weak vibration alone. For combined rolling, both types of combinations can meet the compaction requirements of the first-class expressway, especially static rolling once + strong vibration twice + weak vibration twice, and the maximum compaction degree (98.1%) is reached when rolling five times. Considering the economic and environmental protection construction requirements, for an unsaturated silt with the best water content of 15%, the best compaction process is static pressure once + strong vibration twice + weak vibration twice, which is consistent with the discrete element simulation results.
## 7 Conclusions  
In this paper we have introduced a solution for constrained nodes' _service management_ that improves the exploitation of _network and node_ resources for large resource representation transmissions. The main idea is to keep the communication for the most constrained nodes at the _Network level_ so that the information is purified from transport and application data. This is done by basing the proposed solution on a very essential service characterization model, which foresees that the access to constrained node services is done by means of very short packets.  
Following an analysis on the effects of the CoAP packet sizes on the lower layers in terms of fragmentation, a simulative study is conducted in a COOJA environment. It aimed at comparing the performance of the proposed approach with the traditional CoAP approach in terms of energy waste. The obtained results testified to a better performing behavior of the proposed transmission method (when compared to standard CoAP) _for large resource representations transmission_. While, for short resource transmissions it does not lead to any visible _energy_ improvement. In particular, the energy benefits have been shown by means of an analysis for gradual increments of the CoAP packet size. This testified to a significant dependency of the _transmission_ (Tx), _processing_ (CPU), and _reception_ (Rx) energy components from the amount of sent bytes. Being, indeed, the proposed model based on a Service ID code (identifying the _type of service_), this does not make the service representation increases (like for the CoAP case).  
However, in any case (both _large_ and _short_ service representations), the proposed method has the advantage to enable a unique _global service identifying scheme_. This lets programmers, who want to write back-end applications or middleware IoT solutions, to just refer to any _IoT_ service by referring to its Service ID without the need to first discover the particular resource representation of the underlying object domain. Moreover, the proposed solution is backward-compatible, scalable, and independent from the link layer technology.
## A Survey on M2M Systems for mHealth: A Wireless Communications Perspective  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 1,2, [PERSON] 3, [PERSON] 4, [PERSON] 1 and [PERSON] 2**  
\({}^{1}\) Department of Signal Theory and Communications (TSC), Technical University of Catalonia (UPC-BarcelonaTECH), [PERSON] 7, Castelldefels 08860, Barcelona, Spain;  
E-Mails: [EMAIL_ADDRESS] (A.S.L.); [EMAIL_ADDRESS] (L.A.)  
\({}^{2}\) Telecommunications Technological Centre of Catalonia (CTTC), Av. Gauss 7, Castelldefels 08860, Barcelona, Spain; E-Mails: [EMAIL_ADDRESS] (A.A.); [EMAIL_ADDRESS] (C.V)  
\({}^{3}\) WEST Aquila slr, [PERSON] 18, L'Aquila 67100, Italy;  
E-Mail: [EMAIL_ADDRESS]  
\({}^{4}\) Laboratory of Signals and Systems (L2S), CNRS - SUPELEC - Univ. Paris-Sud XI, 3 Rue Joliot-Curie, 91192 Gif sur Yvette, France; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +34-934-137-084.  
External Editor: [PERSON]  
_Received: 29 July 2014; in revised form: 5 September 2014 / Accepted: 17 September 2014 / Published: 26 September 2014_  
In the new era of connectivity, marked by the explosive number of wireless electronic devices and the need for smart and pervasive applications, Machine-to-Machine (M2M) communications are an emerging technology that enables the seamless device interconnection without the need of human interaction. The use of M2M technology can bring to life a wide range of mHealth applications, with considerable benefits for both patients and healthcare providers. Many technological challenges have to be met, however, to ensure the widespread adoption of mHealth solutions in the future. In this context, we aim to provide a comprehensive survey on M2M systems for mHealth applications from a wireless communication perspective. An end-to-end holistic approach is adopted, focusing on different communication aspects of the M2M architecture. Hence, we first provide a systematic review of Wireless Body Area Networks (WBANs), which constitute the enabling technology at the patient's side, and then discuss end-to-end solutions that involve the design and implementation of practical mHealth applications. We close the survey by identifying challenges and open research issues, thus paving the way for future research opportunities.  
Machine-to-machine (M2M); mHealth; eHealth; healthcare; WBAN; end-to-end communications; IEEE 802.15.6
# A Fault-Signal-Based Generalizing Remaining Useful Life Prognostics Method for Wheel Hub Bearings
## 2 Fault Factors and Indicators of Wheel Hub Bearings
### Fault Indicators of Wheel Hub Bearings
#### 2.2.2 Theoretical Fault Characteristic Frequency of Wheel Hub Bearings  
When the inside of the bearing fails, such as the rolling element, inner ring, or outer ring, it causes a corresponding vibration, and its corresponding frequency is called fault characteristic frequency. This type of vibration may also be contingent. The vibration frequency of each component of the wheel hub bearing is different, resulting in different damage degrees of the bearing components. The theoretical fault characteristic frequency is obtained from the relative motion relationship between the components of the bearing.  
The fault characteristic frequency of the inner raceway is obtained by Equation (4).  
\[f_{bpfo}=\frac{r}{60}\times\frac{1}{2}\times n\times\left(1-\frac{d}{D}\cos \alpha\right) \tag{4}\]  
The fault characteristic frequency of outer raceway is obtained by Equation (5).  
\[f_{bpfi}=\frac{r}{60}\times\frac{1}{2}\times n\times\left(1+\frac{d}{D}\cos \alpha\right) \tag{5}\]  
The fault characteristic frequency of the rolling element is obtained by Equation (6).  
\[f_{bsf}=\frac{r}{60}\times\frac{1}{2}\times\frac{D}{d}\left(1-\left(\frac{d}{D }\cos\alpha\right)^{2}\right) \tag{6}\]The fault characteristic frequency of the cage is obtained by Equation (7) when the cage collides with the inner ring.  
\[f_{c}=\frac{r}{60}\times\frac{1}{2}\times\frac{1}{n}\times\left(1+\frac{d}{D} \cos\alpha\right) \tag{7}\]  
The fault characteristic frequency of the cage is obtained by Equation (8) when the cage collides with the outer ring.  
\[f_{c}=\frac{r}{60}\times\frac{1}{2}\times\frac{1}{n}\times\left(1-\frac{d}{D} \cos\alpha\right) \tag{8}\]  
The hub bearing speed \(r\) is \(r=\mid\)\(f_{o}-f_{i}\)\(\mid\)\(\times\)60, \(f_{o}\) is the outer ring rotation frequency, \(f_{i}\) is the inner ring rotation frequency, \(n\) is the number of rolling elements, \(d\) is the diameter of the rolling element. \(D\) is the diameter of the bearing and \(\alpha\) is the contact angle.
# Effects of Movement for High Time-Bandwidths in Batched Pulse Compression Range-Doppler Radar
## 5 Influence of Linear Movement within the Signal Bandwidth due to Doppler
### Spread over Multiple Range-Doppler Cells  
Every point scatterer that moves with an effective velocity \(v_{\text{eff}}\) will extend in a range-Doppler analysis in range and frequency. This will become visible if the motion is strong compared to tiny achieved processing resolution as it had been described in Section 3.2. If it is considered that the actual range cell spacing depends on the sampling rate \(f_{\text{s}}\) and the totally covered timespan \(T_{\text{cov}}\geq T_{\  
will be challenging for CA-CFAR detection based algorithms because these would need to incorporate the broadening for increased velocity spans. Otherwise, the actual target detection may be missed if the extension around an assumed confined target was mistreated as an increased clutter level. A modification of the CA-CFAR would therefore be required to avoid the spread return in high effective velocity ranges being dropped.
# Nonlinear Dynamics and Machine Learning for Robotic Control Systems in IoT Applications
## 1 Introduction
The diagram in Figure 1 illustrates the four-layer architecture of an IoT system [38], with each layer serving specific functions in the IoT ecosystem:  
Sensing layer: This layer showcases the use of embedded devices, such as sensors and actuators, tasked with data acquisition. These devices collect environmental data. Network layer: This employs a range of communication technologies including WiFi and Bluetooth for local connectivity. For applications requiring extended range, technologies like Zigbee, cellular networks (4G/5G), and LoRaWAN are also incorporated. This layer may also integrate security measures such as encryption and authentication to safeguard data exchanges. Figure 1: Architecture of the IoT and a robot. Data processing layer: Located primarily in cloud-based platforms, this layer handles the heavy lifting of data analysis. It processes incoming data streams to extract actionable insights and supports advanced computational tasks. Technologies in use include data management systems and machine learning algorithms, often within structures like data lakes that store vast amounts of unprocessed data. Application layer: This is a simulator that control applications and highlights where the data become actionable through various applications. It is potentially developed in programming environments like MATLAB R2024a, Python 3.10, or Java 19.0.1.
# Real-Time Farm Surveillance Using IoT and YOLOv8 for Animal Intrusion Detection
## 2 Methodology and Implementation
### Intrusion Detection
#### 2.1.7 Versions of YOLO Used
The YOLOv8 architecture highlights the significance of using multi-scale processing, feature fusion, and specific detection algorithms. It is a sophisticated and effective network built for fast, precise object recognition. Because of its sophisticated architecture, YOLOv8 can perform at the cutting edge of object detection tasks.
# Integrating Autonomous Vehicles (AVs) into Urban Traffic: Simulating Driving and Signal Control
## 5 Discussion
### Infrastructure and Policy Implications  
The integration of AVs into urban traffic systems will likely require significant modifications to existing infrastructure, particularly at intersections. Studies by [21; 23; 45] have emphasized the need for AV-ready infrastructure, such as enhanced traffic signal systems, dedicated AV lanes, and communication networks to support the efficient coordination of AVs. Our study's findings further highlight the importance of infrastructure readiness, particularly in supporting platooning AVs, which showed the greatest benefits in terms of traffic efficiency and environmental outcomes.  
From a policy perspective, the results of this study underscore the need for regulatory frameworks that can guide the integration of AVs into urban traffic systems. This includes the development of safety standards for AV driving behaviors, as well as guidelines for optimizing traffic signal control in AV environments. Research by [8] has similarly highlighted the importance of developing policies that balance the trade-offs between safety and efficiency in AV deployment, particularly as AV technology becomes more widespread.
## 4 Results
### Case I: Network Performance Analysis for DDoS Attacks (24 Nodes)
#### 4.1.2 Step 2: Route Construction Phase  
The routing information generated in Step 1.2.2 was used as the input dataset of the PSO model. The important characteristics of the PSO model were as follows: (1) The population of the particle was set to the number of packets collected for DDoS attacks; (2) the loop executed 100 generations and updated route-searching rules for each loop; (3) the initial value of \(w_{1}\) (weighting factor) was 0.8; (4) \(c_{1}\) was equal to \(c_{2}\), and they were set as 2.0 in Equation (4); and (5) the number of subswarms was 4.  
**Scenario 1:** The routing costs of all routes are the same (res_cost = 1).  
In the experiments, res_cost = numpy.array (\([x,y]\)) was used to set the routing cost for paths \(r1\)-\(r5\)-\(r7\)-\(r2\) and \(r1\)-\(r5\)-\(r10\)-\(r2\) (Figure 13). Figure 14 indicates that all paths had the same routing cost and weighting. The higher the routing cost, the lower was the routing priority. The larger the weight \(w\), the larger was the bandwidth. The shorter the routing distance for a path, the higher was the QoS on that path.  
Figure 14: Static routing cost settings for paths \(r1\)-\(r5\)-\(r7\)-\(r2\) and \(r1\)-\(r5\)-\(r10\)-\(r2\).  
Figure 12: Routing routes for Distributed Denial-Of-Service (DDoS) attacks from host0–host2.  
The system had 118 routes between \(n_{0}\) (the attack node) and \(n_{11}\) (the victim). This information was obtained using the all_simple_paths API call in the _network_ component of ns-3, which is described in Table 3.  
Using the A* search algorithm  
The A* algorithm [19] is an improved [PERSON]'s algorithm. It can be used to identify the shortest path between any two end nodes in the search space. This study compared the performance of both the A* and MSPSO algorithms for solving the IPTBK problem.  
First, we used the A* (networkx suite) algorithm for the attack nodes \(n_{0}\), \(n_{1}\) and \(n_{2}\) attacking \(n_{11}\). The smallest cost of the attack path in this case is as follows:  
Route 1: [\"\(n_{0}\),'sw1', 'r1', 'r5', 'r7', 'r2','sw2', \"\(n_{11}\)\"],  
Route 2: [\"\(n_{1}\),'sw1', 'r1', 'r5', 'r7', 'r2','sw2', \"\(n_{11}\)\"],  
Route 3: [\"\(n_{2}\),'sw1', 'r1', 'r5', 'r7', 'r2','sw2', \"\(n_{11}\)\"]  
Using the MSPSO algorithm  
We used the MSPSO algorithm to analyse the possible attack path for the test case (\(n_{0}\to n_{11}\)).  
Through the reconstruction of the attack path for the attack case (\(n_{0}\to n_{11}\)), we applied the subgroup searching strategy to obtain the optimal solution using the MSPSO-TPBK algorithm. Particles travelled around all the paths and back to the attack origins according to the local and global updating rules presented in Equations (6)-(11). After 100 generations had been executed, the results revealed three possible attack paths for conducting model performance analysis.  
Route 1: [\"\(n_{0}\),'sw1', 'r1', 'r5', 'r7', 'r2','sw2', \"\(n_{11}\)\"]  
Route 2: [\"\(n_{0}\),'sw1', 'r1', 'r4', 'r7', 'r2','sw2', \"\(n_{11}\)\"]  
Route 3: [\"\(n_{0}\),'sw1', 'r1', 'r3', 'r8', 'r2','sw2', \"\(n_{11}\)\"]