esa-sceva/satcom-chunk-collection huggingface dataset:
# Combining Dilution of Precision and Kalman Filtering for UWB Positioning in a Narrow Space
## 2 Methods  
The positioning framework is shown in Figure 1. Firstly, the distance between the UWB base stations and the tag was measured by double-sided two-way ranging to complete the initial data acquisition. Based on the ranging data, the WLS method was used to calculate the initial coordinate of the tag, which was considered as the observation values of KF. Secondly, the position dilution of precision (PDOP) value of the positioning scene was acquired by the DOP numerical analysis model, and it was used as the reference value of the observation noise covariance of KF. Finally, the state equation of the system was constructed by Taylor expansion to complete the positioning algorithm.  
Figure 1: Framework of the positioning algorithm.
# Electrical Equalization Analysis of PAM-4 Transmission in Short-Reach Optical Systems
###### Abstract  
Inclusive and intensive performance analysis of electrical equalizers in a short-reach optical system using four-level pulse amplitude modulation (PAM-4) is presented in this paper. Two equalizers are used--a feedforward equalizer and decision feedback equalizer using the least mean square algorithm. The sensitivity to cut-off frequency for the transmitter and receiver filters, fiber length and number of equalizers taps in the means of the bit error rate vs. optical input power are shown. The analysis reveals the considerable impact of the filters' bandwidth, particularly in the receiver, on the equalizer performance. These results and their reasons are analyzed and broadly discussed.  
chromatic dispersion; inter-symbol interference; feedforward equalizer; decision feedback equalizer; least mean square
## 5 Solving Method
```
1 Initialization: Let \(k\)=0, \(\delta^{0}\)=0 ;
2 forall\(\omega\in\Omega\)do
3 Solve \((x_{\omega}^{1},y_{\omega}^{1})=\underset{x_{\omega},y_{\omega}}{\arg\min} \left(c^{T}x_{\omega}+d_{\omega}^{T}y_{\omega}\right):(x_{\omega},y_{\omega}) \in\mathcal{Q}_{\omega})\)
4 end for
5k:=k+1;
6 forall\(\omega\in\Omega\)do
7\(\hat{x}^{k}=\sum\limits_{\omega\in\Omega}\Pr(\omega)x_{\omega}^{k}\)
8\(\hat{\delta}_{\omega}^{k}=\hat{\delta}_{\omega}^{k-1}+\rho\left(x_{\omega}^{k- 1}-\hat{x}^{k-1}\right)\)
9\(x_{\omega}^{k+1}=\min\limits_{x_{\omega},y_{\omega}}\left\{c^{T}x_{\omega}+d _{\omega}^{T}y_{\omega}+\delta^{T}x_{\omega}+\rho/2\parallel x_{\omega}-\hat{ x}\parallel_{2}^{2}\right\}\)
10 if\(k>k_{max}\;\sigma\sqrt{\sum\limits_{\omega\in\Omega}\Pr(\omega)\left(x_{ \omega}^{k+1}-\hat{x}\right)^{2}}<\epsilon\)then
11 Return \(\hat{x}^{k},x_{\omega}^{k+1},y_{\omega}^{k+1}\)
12 end if
13
14 end for
```  
**Algorithm 1**Progressive Hedging Algorithm  
The PHA essentially utilizes a scenario-based decomposition method, and it starts by solving the values of the first- and second-stage variables under every scenario in the first iteration in Step 2. During each iteration after the first, the expected value of \(x_{\omega}^{k}\) (i.e., \(\hat{x}^{k}\)) and the multiplier \(\hat{\delta}_{\omega}^{k}\) are updated in Steps 7 and 8, and then, the value of \(x_{\omega}^{k+1}\) can be calculated in Step 9. The algorithm will terminate if the maximum number of iterations or termination threshold \(\epsilon\) is reached. The term \(\sqrt{\sum\limits_{\omega\in\Omega}\Pr(\omega)\left(x_{\omega}^{k+1}-\hat{x} \right)^{2}}<\epsilon\) means the convergence is guaranteed, i.e., all first-stage decisions are non-anticipative.
## 1 Introduction  
### Mobile and Electronic Environment  
Current technological and economic advancements are exerting a tremendous influence on the environment, to the extent of raising severe concerns about climate change and pollution. Human activities have an undeniable and ever-increasing impact on the climate system, along with recent developments that are unprecedented and currently acknowledged by the Intergovernmental Panel on Climate Change [1]. Environmental monitoring in this context refers to an Internet of Things (IoT) system where sensors are used to collect useful data about the ecosystem, leading to further discoveries and a better and more comprehensive understanding, to execute specific actions in mitigating and addressing the degradation of the environment [2]. Environmental monitoring in indoor environments is another related field that is now gaining popularity. This has proved essential not only for the building's or housing's residents [3] but also in terms of lowering greenhouse gas emissions [4]. Temperature, humidity, rainfall, atmospheric pressure, light intensity, and air quality, which are impacted by pollutants such as carbon dioxide (CO\({}_{2}\)), carbon monoxide (CO), sulfur oxide (SO\({}_{x}\)), volatile organic compounds, and many more are among the most commonly measured parameters. CO is a gas that is colorless and odorless but can cause serious harm to the human population and to the environment. When a substance is burned, smoke and fumes are released containing CO. SOx is a group of sulfur chemicals that cause serious harm to the environment. Therefore, monitoring of the environment and pollutants is required to achieve a safe and healthy environmental ecosystem [5].  
To achieve the goal of a more distributed environmental ecosystem, algorithmic analysis of a large quantity of data [6] will train models that regularly monitor environmental parameters and notify or respond to anomalies in real-time. For the improvement of environmental monitoring, it is crucial to develop efficacious algorithms and models at a continued pace within the environmental community; fostering confidence in these methods requires publicly validated and verifiable processes. Within this system, devices can receive and submit data in a federated manner, with over-the-air updates as the shared algorithms, and models are enhanced with time.  
The data used to train models must be tamper-proof and dependable, and the technique must be secure. This might help acquire the confidence of environmentalists and environmental officials, as well as make data collection for investigations easier. Environmentalists will have to look beyond their present methods to achieve this verified future. The rise of distributed ledger technology has the potential to close this gap. Therefore, we need to evaluate and analyze the performance of distributed ledger systems such as IOTA to securely exchange environmental data [7] and establish trust among environmental professionals.
# Spread Spectrum Image Watermarking Through Latent Diffusion Model
## 4 Conclusions  
In this paper, we propose an image watermarking framework that achieves spread spectrum watermarking in latent diffusion models, thus guaranteeing watermark imperceptibility and robustness. Essentially, the proposed design delivers comparable visual quality to the state-of-the-art methods, but outperforms in terms of resisting most recent attacks stemming from generative models like image editing and regeneration. In future work, we will further address the challenge from image cropping, rotation, and explore solutions for improving the watermark capacity in diffusion models.  
Figure 6: (**a**) PSNR and BER under different strength factors; (**b**) Range of BER under different attacks.  
Figure 7: Parameter analysis about \(\lambda\).  
**Author Contributions:** Conceptualization, H.W. and X.L.; methodology, H.W. and X.L.; software, H.W.; validation, X.L.; formal analysis, G.T.; writing--original draft preparation, H.W. and X.L.; writing--review and editing, G.T; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Fundamental Research Funds for the Central Universities grant number ZQN-1112 and the National Natural Science Foundation of China under Grant 62002124.  
**Institutional Review Board Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Using A Rotary Spring-Driven Gripper to Manipulate Objects of Diverse Sizes and Shapes
## 2 Materials and Methods
### Parts and Specification of the Mechanism
#### 2.1.2 Material Selection  
The grasping mechanisms are very vulnerable to external factors. Therefore, proper material selection for the prototype of this mechanism, which faces various phenomena such as bending, friction, and uneven force distribution, is critical. Based on that, acrylonitrile butadiene styrene (ABS) plastic, because of its specific property, was considered for most parts of the prototype. ABS plastic filament is mostly used in 3D printing, and it is one of the strongest among other materials that are light in weight. This material is more feasible for this design in terms of the geometric structure that the machine must print. It also has sufficient tensile strength and can be printed with high precision. It can be sanded to desired smoothness without deformation, unlike some polylactic acid (PLA) plastics that tend to melt down during machine sanding. While the whole design is printed with ABS plastic, the pistons are made of steel. They are off-the-market screws that are stiff and perfect for the size requirement.  
The total weight of the gripper and its actuation system is crucial with regard to the maximum payload of the robotic arm it is attached to. As the prototype of the design is made of ABS plastic, the weight-to-strength ratio remains decent for the design. Also, using ABS plastic provides quick prototyping abilities with decent remodeling and 3D printing. However, plastic parts bring frictional factors to their peak compared to fine-surface machine parts. Also, the use of plastic restricts the possibility of making it compact, leaving the prototype big and less appealing. The total weight of the system without the control/electronics system is 3.1 kg.  
For the industrial version of this product, among all alternative materials with a higher stress tolerance and lower friction coefficient, 6061 aluminum alloy can be the most appropriate option based on similar research and material data presented in [28]. The final choice of material is made to be 6061 aluminum alloy, but only for high friction-prone parts, so that the overall weight of the system remains pretty much unchanged. Some of the specifications for these three materials are shown in Table 2.
## 3 A Case Study  
A case study is presented in this section to demonstrate the application of the extended approach. The relevant parameters and values in the case study shown in [11] were adopted in this case study. Detailed conditions of this case study are as follows:  
It is assumed that a continuous miner was used to develop a roadway in an underground coal mine. The roadway consists of three sections as shown in Figure 2. The total height of the roadway was 3000 mm. The roof rock and the floor rock were both sandstone with the same material properties as shown in Table 1 in [11].  
The continuous miner was equipped with a 2-start drum which has a tip-to-tip diameter of 1000 mm. The rotational speed and the advance speed of the drum were respectively 50 rpm and 2.35 m/min. In a full face cutting cycle, the drum first cut into the roof of the roadway with a sump-in depth of 500 mm, then sheared down 2000 mm, and finally cut back for 150 mm. The rock breakout during all the cutting processes was effective. According to Equations (8) and (17), it can be calculated that the number of drum revolutions for the sump-in of 500 mm is 11 and for the shearing-down of 2000 mm it is 44.  
A full set of TSDC tipped picks were installed on the drum. These picks were identical to those TSDC tipped picks studied in [11]. The bottom diameter of the tips was 12 mm, and the arm length for the bending force acting on the tips was 4.6 mm. The attack and tilt angles of the picks in consideration were 55 degrees and 0 (zero) degree respectively. Each pick cuts one line.  
In addition, the following assumptions which are made in the case study in [11] are also adopted in this case study:  
* Only sudden failures caused by excessive bending force are considered;
* Tip failure of a pick is independent of its body failure;
* Each small segment covers a central angle of 10 degrees.  
Given that the influences of drum operating parameters, pick geometry, and the material properties of pick and rock have been analyzed in [11], and rock variations and drum advance directions are two new factors introduced in this extended approach, this study focused on the investigation of the influences of these two new factors on the pick failure probability. To this end, only average TSDC tips with the mean allowable force of 9.745 kN were analysed. Furthermore, a series of what-if analyses were carried out based on the following three scenarios:  
Scenario 1: The thickness of the roof rock can be represented by two mean values, (410 mm, 460 mm), with their corresponding likelihoods of (35%, 65%). The thickness of the floor rock can be represented by three mean values, (450 mm, 500 mm, 610 mm), with their corresponding likelihoods of (25%, 50%, 25%).  
Scenario 2: The thickness of the roof rock and the floor rock can be represented by the same set of two mean values, (410 mm, 460 mm), with their corresponding likelihoods of (35%, 65%).  
Scenario 3: The thickness of the roof rock and the floor rock can be represented by a single value of 442.5 mm (=410 \(\times\) 35% + 460 \(\times\) 65%), which is the expected value of the random variable \"rock thickness\" in Scenario 2.
## 3 Results and Discussion  
Targeted Analysis and First Attempt to Evaluate the Impact of Cork Treatment Using Principal Component Analysis  
As already mentioned in Section 2.4, the aqueous cork macroates were analyzed by a sensory panel, and the main conclusion was an increase in biscuit and sweet aromatic notes in the treated samples.  
A list of compounds contributing to the volatile signature of cork was established from a survey of data from the literature (Table 1), and a solution containing all the compounds and the IS was extracted and analyzed by the proposed method (Section 2.2). Retention times were collected and used for the identification of the compounds in the samples.  
Some of the target compounds were identified in the set of studied samples both before and after the washing cycle. It was expected that the washing cycle would contribute to the elimination not only of TCA but also of other volatiles present in the cork disc. An inspection of the area ratios (see supplementary Table S1) has shown that the maximum signal was reduced for some of the volatiles, including TCA, but no clear trend in the decrease in the values for washed cork samples was observed.  
Further analysis was performed using PCA, giving a plot as shown in Figure 1. No useful information could be extracted. For instance, in Figure 1 the first two components only collect 32.4% and 19.8% of the total variance, respectively. The first principal component shows maximum squared loadings (ranging from 0.16 up to 0.29) for four compounds (eucalypt, fenchol, camphor and carpineol), whereas the second component is mainly defined by guaiacol and borneol (squared loadings of 0.44 and 0.37, respectively). The contribution of TCA in both components is almost zero. Similar results to those shown in Figure 1 did not help to identify a compound or set of compounds that were responsible for the differences found in the olfactory evaluation between treated and non-treated samples.  
It was not possible to evaluate the influence of the treatment and to explain the differences observed in the sensory analysis from this first attempt. The need is therefore seen for a different,  
Figure 1: Typical results obtained during the first study by means of PCA. No clear separation of samples (non-treated 0 and treated 1) was obtained.  
objective approach to the evaluation the effect of the treatment and for the identification of a set of compounds that can explain the olfactory characteristics described by the panel.
## 4 Results and Discussions
### The Influence of Input Parameters and Their Interactions
It is observed that the interactions between \(u_{t}\) and some input parameters such as AE (\(u_{t}\)*\(AS_{1}\)), AF (\(u_{t}\)*\(AS_{2}\)), AG (\(u_{t}\)*\(AS_{3}\)), AK (\(u_{t}\)*\(C_{g}\)), and AL (\(u_{t}\)*\(C_{g}\)) in both values of 30 and 100 has the most significant influence on the \(u_{2}\) response, while the stable tendency is observed for the interactions between \(u_{t}\) and remaining input parameters, e.g., AB (\(u_{t}\)*\(X_{h\alpha 1}\)), AC (\(u_{t}\)*\(X_{h\alpha 2}\)), AD (\(u_{t}\)*\(X_{h\alpha 3}\)), AH (\(u_{t}\)*\(T_{out}\)), and AJ (\(u_{t}\)*\(C_{gh}\)). Similarly, we can realize the interactions which have significant influence on the \(u_{2}\) response but are lesser than the ones of \(u_{t}\) like BF (\(X_{h\alpha 1}\)*\(A_{S2}\)), BE (\(X_{h\alpha 1}\)*\(A_{S1}\)), CL (\(X_{h\alpha 2}\)*\(C_{s}\)), CK (\(X_{h\alpha 2}\)*\(C_{g}\)), CF (\(X_{h\alpha 2}\)*\(AS_{2}\)), FH (\(AS_{2}\)*\(T_{out}\)), FG (\(AS_{2}\)*\(AS_{3}\)), FL (\(AS_{2}\)*\(C_{s}\)), FK (\(AS_{2}\)*\(C_{g}\)), FL (\(AS_{2}\)*\(C_{gh}\)), FL (\(AS_{2}\)*\(C_{gh}\)), HK (\(T_{out}\)*\(C_{g}\)), GL (\(AS_{3}\)*\(C_{gh}\)), GJ (\(AS_{3}\)*\(C_{gh}\)), and GH (\(AS_{3}\)*\(T_{out}\)). Referring to the case of the response \(u_{3}\) (cf. Figure 5b), it is visualized that the interactions JK (\(C_{gh}\)*\(C_{g}\)), JL (\(C_{gh}\)*\(C_{s}\)), GK (\(AS_{3}\)*\(C_{g}\)), GL (\(AS_{3}\)*\(C_{s}\)), FK(\(AS_{2}\)*\(C_{g}\)), FL(\(AS_{2}\)*\(C_{s}\)), KL (\(C_{g}\)*\(C_{s}\)), BH (\(X_{h\alpha 1}\)*\(C_{s}\)), BL (\(X_{h\alpha 1}\)*\(C_{s}\)), DG (\(X_{h\alpha 3}\)*\(AS_{3}\)), EG (\(AS_{1}\)*\(AS_{3}\)), and EK (\(AS_{1}\)*\(C_{g}\)) have significant influences on the \(u_{3}\) response. Figure 6 presents the Normal Plot of the standardized effects in which the relationship between the responses (\(u_{2}\) and \(u_{3}\)) and the input parameters as well as their interactions are exposed. Based on the results presented in the figure, it is seen that \(u_{t}\) and \(AS_{2}\) have the greatest influence on the response \(u_{2}\) as previously documented. Furthermore, it is realized that, in addition to single input parameters as early presented (\(u_{t}\), \(AS_{2}\), \(C_{s}\), \(C_{g}\), \(AS_{1}\), and \(AS_{3}\)) the interactions of some input parameters also have both positive and negative impacts on the response of \(u_{2}\). For instance, the increase in the interactions of AK, EL, AJ, AF, BH, HK, and GK leads to the augment of the \(u_{2}\) response. Conversely,  
Figure 4: Main effects plot for (**a**) \(u_{2}\) and (**b**) \(u_{3}\).
## 2 Material and Methods
### Seedling Trait Responses to Treatments
#### 2.4.3 Physiological Traits  
Seedling growth rate was measured using the leaf marking method [47]. At the beginning of the experiment, leaves were perforated close to the seed using a needle. At the end of the experiment, the elongation of the leaf, known as the length from the base of the leaf to the mark, was measured. Leaf SA growth rates were obtained by multiplying the leaf elongation by the leaf width and dividing the result by the number of days since the seagrass leaves were marked. Leaves without holes were considered new leaves and were measured from the leaf base to the tip of the leaves.  
Responses of photosynthetic performance were measured at the end of the experiment using a PAM-2500 chlorophyll fluorometer (Walz, Germany) with rapid light curves (RLC).  
The PAM optical cable was attached to the mid part of the second leaf of the seedling by using a leaf clip, and \(\sim\)3 mm in distance from the tissue. This position was chosen since it represents similar distances from the water surface (and, thus, from the light source) among plants with different leaf lengths [48]. Before measurements, the leaves were adapted in the dark for 5 min while maintained in NSW. The RLC consisted of 12 saturating light pulses (separated by 30-s intervals) increasing photosynthetic active radiation (PAR) between pulses until 3001 umol photons m\({}^{-2}\) s s\({}^{-1}\). From the RLC data, several parameters were calculated. The maximum quantum yield (\(Fv/Fm\)), the relative maximum electron transport rate (\(rETR_{\text{max}}\)), the light saturation coefficient (Ek), and the slope of the light limited part of the curve (\(\alpha\)) calculated using Phytotools [49] under R software [50], following the model in Reference [51]. Efficient utilization of \(Fv/Fm\) was calculated by following Equation (1) [52].  
Maximum quantum yield:  
\[(Fv/Fm)=\frac{(Fm-F_{o})}{Fm} \tag{1}\]  
where \(Fm\) is the maximum dark-adapted fluorescence and \(F_{o}\) is the minimum dark-adapted fluorescence. The relative electron transport rate (\(rETR\)) was calculated for each step of the curve following Equation (2) [53].  
\[rETR=\frac{Fm^{\prime}-F^{\prime}}{Fm^{\prime}}*\frac{PAR}{2} \tag{2}\]  
where \(F_{m^{\prime}}\) is the light-adapted maximum fluorescence, \(F^{\prime}\) is the fluorescence yield at a particular light level, and \(PAR\) is the photosynthetic active radiation. The maximum \(rETR\) value (\(rETR_{\text{max}}\)) was calculated as the inflection point of the fitted \(rETR\) curve.
# UnifiedFace: A Uniform Margin Loss Function for Face Recognition
## 1 Introduction
Furthermore, both approaches have achieved significant performance. Multiplicative and additive margins share a common goal of improving intra-class compactness and inter-class separability by introducing large margins to make decision boundaries tighter. Intuitively, larger margins and stricter decision bounds result by introducing both multiplicative and additive margins. Figure 1: Visualization of the different layers of VGG-16, where similar features are annotated with the boxes of the same color: (**a**) 5 th layer of VGG-16 and (**b**) 10 th layer of VGG-16. To effectively unify the additive and multiplicative margins, we propose a unified margin loss function called UnifiedFace. Specifically, the additive and multiplicative margins jointly influence \(\theta_{y}\) so that it becomes smaller while reaching the same value as \(cos(\theta_{y})\). We introduce a multiplicative margin in the non-target angle activation function and an additive margin in the target angle activation function. As far as we know, this is the first time that both additive and multiplicative margins have been introduced in the loss function with this idea. Meanwhile, the additive and multiplicative margins are no longer independent but are related. We refer to this approach as UnifiedFace. Our contributions are summarized as follows:  
(1) We propose a new efficient and lightweight model called GhostFaceNet based on ghost module, which significantly reduces the number of parameters and the amount of computation without noticeably degrading performance. (2) We propose the frame of a unified margin by introducing both additive and multiplicative margins. This frame can effectively introduce larger margins and achieve tighter decision bounds. Moreover, the additive and multiplicative margins are no longer independent of each other but are related. (3) In this framework, we design a new margin-based loss function called UnifiedFace. UnifiedFace introduces a multiplicative margin in the non-target angle activation function and an additive margin in the target angle activation function. UnifiedFace achieves more stable training and superior generalization. Meanwhile, we give a clear geometric interpretation of UnifiedFace. The structure of the paper is as follows. Section 2 briefly reviews the work related to deep face recognition and lightweight networks frameworks. Section 3 describes our proposed approach in detail, including the GhostFaceNet and UnifiedFace. Experimental results and related discussions are given in Section 4 and the conclusion is presented in Section 5.