esa-sceva/satcom-chunk-collection huggingface dataset:
# Structural Monitoring of a Drawbridge in Operation: Signal Analysis
## 3 Signal Processing
### Triggering and Event Acquisition  
As previously mentioned, the developed system works on a ring-buffer basis, with the goal of only storing relevant data and thus limiting the storage requirements to a more reasonable level.  
Thus, it is of the utmost importance that good and performant trigger conditions are defined. As these conditions are checked live on every single data point for multiple sensors, their simplicity is extremely important to avoid slowdowns and system instability. As with the final recorded signals, the application of the triggers require compensating for the temperature for the incoming data points, which further limits the complexity of the trigger checks.  
Thus, four simple trigger conditions were defined:  
* One ascending, where an event is triggered if a transition is detected from below a threshold to above it, while comparing the current point to the point 3 s before it in the ring buffer.
* One descending, where an event is triggered if a transition is detected from above a threshold to below it, under the same conditions.
* Two simple thresholds: larger than and smaller than, where an event is triggered if values above or below a threshold, respectively, are detected.  
These were developed to be configurable, and some are more suited to a particular type of data. For example, acceleration data are well-suited to a simple threshold-type trigger.  
After an event detection, the system acquires continuously all sensors for a predefined event duration, after which it returns to the ring-buffer procedure. During the first three seconds after this switch, the ascending and descending trigger conditions are compared  
Figure 3: Diagram of the data flow and processing.  
with the first point in the buffer, as this is the oldest one available in the ring buffer at that point in time.  
After acquisition, in order to better understand the behavior of the structure, the signals are post-processed as follows.
# Wireless Energy Harvesting with Cooperative Relaying under the Best Relay Selection Scheme
## 3 System Model
### Energy Status in EH Relaying  
The best relay is selected to forward the data from the source to the destination by using its harvested energy. In this case, all other relay nodes will be in silent mode and the selected relay will be active. The other relays will not consume their energy, so their energy can be stored in the batteries. The selected relay that minimizes total energy consumption is selected only if at least \(E^{th}\) is stored in the batteries. The WEH relay can be active or silent at different time instants. Assume symbol duration \(T_{s}\), then the EH relay consumes \(P_{s}T_{s}\) to forward a frame of \(\Lambda\) symbols. The EH relay is called active if it has at least \(PT/2\) energy which is named \(E^{th}\), where \(T=2\Lambda T_{s}\).
# Fog-Based Two-Phase Event Monitoring and Data Gathering in Vehicular Sensor Networks
## 4 TPEG Framework
### Threshold Adjustment  
When an RSU initiates an event-checking procedure, more detailed and accurate sensed data would be uploaded and gathered. These data are processed by the central ITS system, and the final decision about the events would be made either by more intelligent analysis or human intervention. There are two aspects where TPEG can reduce unnecessary message transmissions:  
* Events are assumed to be uncommon phenomenon. When events do not occur, the network should avoid unnecessary event-checking procedures.
* Unlike some monitoring and detection scenarios where events are transient, events at VANETs would usually last for a period of time. Therefore, when an event occurs, it is important for nodes not to be triggered by HCS mode and not to upload the redundant data to the RSU.  
The pavement surface deformation example belongs to the permanent monitoring scenarios because it would need some time (days or weeks) for road administrations to repair them. Car accidents such as slight car crashes belong to the transient monitoring case. Usually, the accident would be handled in several minutes. TPEG adopts a mechanism called \"two-level threshold adjustment\" (2 LTA) to avoid unnecessary event-checking and data upload. 2 LTA adaptively adjusts the thresholds at the node and the RSU levels to suppress unnecessary sensing and data upload operations, while at the same time detecting as many events as possible.
## 5 Discussion
### Dependence of Image-segmentation Models on Illumination  
In the field, the light intensity changes constantly. Unlike single plants grown in pots in greenhouse facilities, segmenting vegetation in a field-grown plot is complex. Factors such as changing weather conditions, and the solar radiation angle that evolves during the day, affect the results of the image segmentation.  
To study how the method responds to different illumination conditions, we used the image brightness function of Photoshop CS6 to adjust the luminance components. The original image brightness was used as the central value for the brightness adjustment, to produce five images of varying luminance (two brighter and two darker than the central value).  
The color lines in Figure 5 show how the indices vary as a function of the illumination conditions when using the proposed improved RF method and the four comparison methods. Specifically, almost all these methods have the lowest \(Q_{seg}\) and the highest \(E_{s}\) for Level 1 light intensity, which indicates that low illumination intensity seriously degrades the image-segmentation accuracy. The improved RF method and DeepLabv3+ method produce the highest \(Q_{seg}\) and \(S_{r}\) for Level 2 light intensity, whereas the three other methods do the same for Level 3, which indicates that the moderate light intensity tends to improve the image-segmentation accuracy. Furthermore, the improved RF and DeepLabv3+ methods performed well for all evaluation indicators (with larger values and smaller SD), indicating that they are less sensitive to varying illumination intensity. The accuracy should be further improved by expanding the training dataset, introducing more pixel-based features, and in particular, by adding images under different illumination conditions for training.  
Figure 5: Results of image segmentation models for different illumination conditions. The color lines in each box represent how the indexes vary as illumination conditions when using different methods. Panels 1–5 show the results for light intensities ranging from dark to bright.
applied sciences Article 1-g Shaking Table Test Study on the Influence of Soil-Caisson Dynamic Interaction (SCDI) on the Caisson Foundation Motion  
Yicheng [PERSON]  
CaiSSon foundations are commonly used as the tower foundations in many long-span bridges. However, the seismic performance analysis of bridge structures using caisson foundations typically assumes that the tower is fixed at the base, applying the free-field ground acceleration to the base. Consequently, the impact of soil-caisson dynamic interaction (SCDI) on the caisson foundation motion is not considered. To investigate the SCDI effects on the motion of the caisson foundation, two different systems of 1 g shaking table model tests were carried out: a free-field system model test and a soil-caisson system model test. The test results show that an increase in the peak acceleration of the table input seismic wave is associated with a greater influence of SCDI on the motion of the caisson foundation. Compared with the free-field ground motion, the SCDI effects reduce the amplitude of the horizontal acceleration of the caisson foundation motion but introduce a significant rotational component. Additionally, both effects are frequency-dependent and become more significant with increasing frequency. The shaking table test study presented in this paper reveals several crucial features of SCDI that influence the motion of the caisson foundation, enhancing the comprehension of the mechanism of SCDI and providing essential data support for subsequent theoretical and numerical simulation studies.  
bridge caisson foundation; soil-caisson dynamic interaction (SCDI); 1 g shaking table test; caisson foundation motion; rotational component
# Collision Avoidance Using Finite Control Set Model Predictive Control for Unmanned Surface Vehicle
## 3 Collision Avoidance Based on the Finite Control Set Model Predictive Control Method
### Control Behaviors and Prediction of USV Trajectory  
With the FCS-MPC method, the optimal control behavior can be obtained under the constraints of the environment and the dynamics. In order to predict vessel motion under different control behaviors, the 3-DOF planar dynamics model \(f_{p}\{X,S\}\) of the USV is adopted, and the state quantity \(X\) of the USV is:  
Figure 3: The CAS based on FCS-MPC.  
\[X=[x,y,\psi,u,v,r]^{T}. \tag{5}\]  
In order to improve the system performance, the finite control set \(S\) should be as wide as possible in the computing time. In the USV system, the thruster speed \(n\) and propulsion angle \(\delta_{r}\) form the finite control set \(S\). The performance of the actuator of the USV is limited, such as the maximum propulsion angle \(\delta_{m}\), the maximum thruster speed \(n_{m}\) and the change rate \(\dot{\delta}\) and \(\dot{n}\) of the propulsion angle and thruster speed. At time \(t_{k},\dot{\delta}\) and \(\dot{n}\) are used to calculate the range of finite control set \(S\) within discrete time \(\Delta t\) as:  
\[S_{m}=\left\{\delta_{r}\in[-\delta_{m},\delta_{m}],n\in[0,n_{m}]\right\} \tag{6}\]  
The maximum number of control input combinations is \(c=\left(2\delta\Delta t/\delta_{d}\right)\times\left(2\dot{n}\Delta t/n_{d}\right)\), where \(\delta_{d}\) and \(n_{d}\) are the discrete propulsion angle and thruster speed. That is to say, the CAS has the most \(c\) kinds of effective control behaviors within discrete time \(\Delta t\).  
When reducing the value of \(\delta_{d}\) and \(n_{d}\), the control behavior will be more detailed, but this will cause too much computation. Therefore, there is a balance between the discrete number of control behavior and the discretization of high fidelity time, prediction horizon, ship model complexity, control update interval and computation waiting time. In addition, a very useful feature of these control behaviors is that they represent meaningful actions when the control behavior remains constant throughout the prediction horizon.
## Materials and Methods
### Removal Experiments for Heavy Metals and Turbidity  
The removal efficiency of the system was evaluated on prepared samples containing heavy metals and turbidity. First, the ceramic membrane was used to confirm the removal of As(V), Zn(II), and turbidity according to solution pH. Heavy metal contaminants As(V) and Zn(II) were prepared at initial concentrations of 50 mg/L, and 2 g/L kaolin was used to create turbidity [21]. The pH values in the experimental solutions were adjusted to 2, 4, 6, and 8 using HCl and NaOH. The removal experiments were carried out with an internal submerged dead-end flow regime membrane system at a flow rate of 10 mL/min using a peristaltic pump (BT100-2J, Longer, China), and the change in pollutant concentration of the solution passed through the ceramic membrane was analyzed.  
The second set of experiments was conducted to evaluate As(V) removal by the ion-exchange fabric. To test the effects of solution pH on As(V) removal, the pH of the solutions was adjusted from 2 to 12 using HCl and NaOH. The test was carried out in a 50 mL solution containing a fabric dose of 2.0 g/L at an initial As(V) concentration of 10 mg/L. The solution was shaken for 6 h using a shaking incubator (HS330, MTOPS, Korea) at 150 rpm and 25 \({}^{\circ}\)C, and the samples were collected after the reaction to measure the residual As(V) concentration. The effects of reaction time were evaluated under the same experimental conditions with different reaction times of 5, 10, 15, 30 60, 120, 240, and 360 min. By varying the concentration of As(V) to 10, 50, 100, 200, and 500 mg/L, the effects of the initial contaminant concentration were examined, and 500 mg/L As(V) solution was used for the comparative experiment with other ion-exchange fibers. Regeneration experiments were carried out using 0.1 M NaCl, 0.1M NaOH, and 0.1 M HCl regeneration solutions for 3 h after the removal test with 10 mg/L As(V) solution for 6 h.  
Finally, the ion-exchange fabric/ceramic membrane hybrid system was used to remove As(V), Zn(II), and turbidity according to solution pH. Heavy metal contaminants As(V) and Zn(II) were prepared at initial concentrations of 50 mg/L, and 10 g/L kaolin was used to create turbidity. The pH values of the experimental solutions were adjusted to 4, 6, 8, and 10 using HCl and NaOH. The removal experiments were carried out at a flow rate of 60 mL/min using a peristaltic pump, and the flow measurements and sampling were conducted every 10 min. Backwashing was performed for 3 min every 30 min using a peristaltic pump (77 mL/min), and the experiment was conducted for a total of four cycles. The transmembrane pressure (TMP) measured by a pressure sensor (PX409-015 GUSBH; Omega Engineering Inc., Stamford, CT, USA) was below 1 bar during the whole experimental process.
# BL0K: A New Stage of Privacy-Preserving Scope for Location-Based Services
## 4 BL0K Technique
### BLOK Algorithm  
Zero Knowledge with Bloom Filter Scenario (BL0K)  
Step 1: Initialization of ZKP as shown in Algorithm 1:  
Prover has an ID\({}^{\text{A}}\) that can calculate J(A). It also has a secret ID. Before step 2, we have two files to update: one for BF updating and this function updates the BF with the data in a text file \"DataListforBF.txt,\" and one for SNS backing store with the data and its locations as (SHA32 bit) code.  
```
1 INPUT: generates primes p and q mod n;
2 OUTPUT: LOCATION;
3 (l) Zero knowledge phase:
4 Initialization-ZKP;
5 Prover P;
6 Verifier V;
7 P has an ID\({}^{\text{A}}\);
8 P starts challenge/response phase;
9 if (V check: P = verified?) then
10 return V send the Query to P;
11 else
12 return server is (Malicious);
13 (2) Bloom Filter phase:
14 After the prover received the data;
15 data send to p;
16 if (data = Bloom filter)? then
17 return LBS send\" LOCATION\" to V;
18 else
19 return LBS \"NO data found\";
20 end
```  
**Algorithm 1**BL0K Algorithm.  
Step 2: [PERSON] has an ID\({}^{\text{A}}\) that can calculate J(A) = \(f\)(Id(A)) mod \(n\). It also has a secret ID and the secret data (A) = J(A)\({}^{-s}\).  
Step 3: [PERSON] generates primes p and q such that p \(\equiv\) 3 mod 4 and q \(\equiv\) 3 mod 4. [PERSON] computes n = pq and sends n to [PERSON].  
Step 4: The actual protocol BL0K begins from this step. [PERSON] sends its identity ID J(A) after [PERSON] selects a random secret \(r\) and \(x=r^{p}\) mod \(n\) to [PERSON].  
Step 5: [PERSON] sends a random challenge to [PERSON] after [PERSON] selects a random challenge \(e\) in \(\{1,2,...,p\}\).  
Step 6: [PERSON] can compute then sends the following response to [PERSON]: \(y\)= \(r\cdot\text{secret(A)}^{e}\) mod \(n\).  
Step 7: [PERSON] receives \(y\), constructs J(A) = \(f\)(Id(A)) mod \(n\), computes \(z\) = J(A)\({}^{e}y^{e}\), and accepts this round if \(z=x\) mod \(n\).  
Step 8: The (GQ) zero-knowledge protocol has complete its work.  
Step 9: Verifier checks if the Prover is verified or not. If verified, the query send data query to Prover to obtain the location if data is present in bloom filter; otherwise, send a message \"data not found\"!  
Step 10: The BL0K protocol is completed.  
Note, the rest of the code is used for result collection. Figure 7 shows the BLOT sequences methods for querying friends' locations within LBS. Figure 8 shows the improvement with the BL0K approach.
# Encapsulation of Blackberry Phenolics and Volatiles Using Apple Fibers and Disaccharides
## 1 Introduction
Blackberries are known as good sources of phenolics with high antioxidant potential, which are responsible for diverse health benefits [20; 21]. In addition to their appealing color, blackberries are also known for their pleasant flavor [22; 23]. Even with the existence of many different encapsulation techniques [24; 25], and although higher unit costs for freeze-drying are a disadvantage, this technique is still widely used to obtain high-value food products, especially when the goal is to have higher polyphenol retention [26; 27]. The conditions of the selected encapsulation technique, the chemical and physical properties of the carriers, and active ingredients play a major role in encapsulation efficiency [24]. In order to improve the encapsulation of the phenolics and volatiles of blackberry juice with apple fibers by freeze-drying, two disaccharides were selected, namely sucrose and trehalose. As far as we know, this combination of carriers has not been studied. Sucrose is a commonly used saccharide in fruit product formulations, while the use of trehalose in formulations of fruit products is increasing in frequency. The positive influence of trehalose on volatiles and phenolics in different freeze-dried fruit products has been proven in several studies [28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39], therefore we wanted to determine whether a similar effect could be achieved in combination with apple fibers. An additional benefit of trehalose is its slow digestion, which results in a lower glycemic index, with a lower insulin release compared to sucrose [40]. Likewise, trehalose has substantially lower cariogenic potential compared to sucrose, and it does not have laxative effects similar to the other low-cariogenic bulk sweeteners [41]. The results of our previous research [42] showed that apple fibers could be good carriers of blackberry polyphenols and volatiles. The aim of this study was to evaluate the effect of the addition of disaccharides on the encapsulation of the phenolic and volatile compounds of blackberry juice with apple fiber. In order to investigate whether disaccharides can improve the encapsulation of the mentioned compounds with apple fiber, sucrose and trehalose were used in fiber:disaccharides ratios of 1:0.5, 1:1, and 1:2. All formulated microparticles were evaluated for total phenolics, proanthocyanidins, individual phenolic content, antioxidant activity, color parameters, and flavor profile.
# A Collaborative Domain Adversarial Network for Unlabeled Bearing Fault Diagnosis
###### Abstract  
At present, data-driven fault diagnosis has made significant achievements. However, in actual industrial environments, labeled fault data are difficult to obtain, making the industrial application of intelligent fault diagnosis models very challenging. This limitation even prevents intelligent fault diagnosis algorithms from being applicable in real-world industrial settings. In light of this, this paper proposes a Collaborative Domain Adversarial Network (CDAN) method for the fault diagnosis of rolling bearings using unlabeled data. First, two types of feature extractors are employed to extract features from both the source and target domain samples, reducing signal redundancy and avoiding the loss of critical signal features. Second, the multi-kernel clustering algorithm is used to compute the differences in input feature values, create pseudo-labels for the target domain samples, and update the CDAN network parameters through backpropagation, enabling the network to extract domain-invariant features. Finally, to ensure that unlabeled target domain data can participate in network training, a pseudo-label strategy using the maximum probability label as the true label is employed, addressing the issue of unlabeled target domain data not being trainable and enhancing the model's ability to acquire reliable diagnostic knowledge. This paper validates the CDAN using two publicly available datasets, CWRU and PU. Compared with four other advanced methods, the CDAN method improved the average recognition accuracy by 7.85% and 5.22%, respectively. This indirectly proves the effectiveness and superiority of the CDAN in identifying unlabeled bearing faults.  
bearing fault diagnosis; domain adversarial networks; unlabeled; transfer learning +
Footnote †: journal: Applied Sciences
## 3 Results  
### Patient Demographics and Clinical Characteristics  
A total of 467 patients who underwent UL or RIRS for upper ureteral stones between March 2018 and May 2022 were included in the analysis. Among them, 244 patientsreceived treatment during the pre-pandemic period (March 2018-March 2020), and 223 during the post-pandemic period (April 2020-May 2022) (Tables 1 and 2). The median age of the overall cohort was 61 years (IQR: 21.0), with no statistically significant difference observed between the pre-pandemic and post-pandemic groups (61 vs. 60 years, \(p\) = 0.442). Females accounted for 35.1% of the total population, with comparable distributions in both groups (\(p\) = 0.602). The median BMI was 24.5 kg/m\({}^{2}\) (IQR: 5.5), showing no significant intergroup variation (\(p\) = 0.200). Medical histories, including diabetes mellitus (18.9% vs. 21.5%, \(p\) = 0.472) and hyperparathyroidismism (1.6% vs. 3.6%, \(p\) = 0.300), were similar between the two cohorts. Likewise, the proportion of patients on anticoagulant/antiplatelet therapy (7.4% vs. 6.7%, \(p\) = 0.784) and corticosteroid use (2.0% vs. 3.6%, \(p\) = 0.467) remained comparable. A statistically significant shift in referral pathways was observed between the two periods (\(p\) = 0.045). As illustrated in Figure 2, referrals from primary care clinics declined from 39% to 27%, whereas referrals from emergency departments (15% to 20%) and secondary/tertiary-care hospitals (22% to 27%) increased. \begin{table}
\begin{tabular}{l c} \hline \hline
**Variables** & **Total (N = 467)** \\ \hline Sex (Male/Female), \(n\) (\%) & 303 (64.9)/164 (35.1) \\ Age (years), median (IQR) & 61 (21.0) \\ BMI (kg/m\({}^{2}\)), median (IQR) & 24.5 (5.5) \\ ECOG-PS \(\geq\) 2, \(n\) (\%) & 14 (3.0) \\ Medical history, \(n\) (\%) & 269 (57.6) \\ Prior ureolithiasis & 94 (20.1) \\ Diabetes mellitus & 12 (2.6) \\ Hyperparathyroidismism & 12 (2.6) \\ Medication use, \(n\) (\%) & \\ Anticoagulant/antiplatelet agents & 33 (7.1) \\ Cortic steroid, \(n\) (\%) & 13 (2.8) \\ Upper ureteral stones: & \\ Max stone size (mm), median (IQR) & 11.9 (10.5) \\ Laterality (right/left/bilateral) & 211 (49.2)/230 (45.2)/26 (5.6) \\ Renal stones, \(n\) (\%) & 88 (18.8) \\ Preoperative SIRS, \(n\) (\%) & 63 (13.5) \\ Preoperative stenting, \(n\) (\%) & 169 (36.2) \\ Hospital stay (days), median (IQR) & 5 (2) \\ Surgical waiting day (days), median (IQR) & 33 (29) \\ Operative time (min), median (IQR) & 70 (49) \\ Postoperative fever, \(n\) (\%) & 32 (6.9) \\ Postoperative SIRS, \(n\) (\%) & 27 (5.8) \\ Stone free rates, \(n\) (\%) & 291 (62.3) \\ Referral patterns: & \\ Emergency department at our institution, \(n\) (\%) & 82 (18) \\ From primary care clinics, \(n\) (\%) & 157 (34) \\ From secondary/tertiary care hospitals, \(n\) (\%) & 112 (24) \\ Others, \(n\) (\%) & 116 (18) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Baseline characteristics of the study population.