esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Development of the EMS and Obtained Results for a Specific Case Study
### Non-Optimal EMS  
#### 3.4.1 Rb Ems  
Once the BESS is characterized, the RB algorithm described in Section 2.8.2 has been used in parallel with the EVCS profile optimized in Section 3.2 in order to act on the BESS power. To understand the influence of the BESS on the new EMS, Figure 9 shows the obtained curves related to the EVCS power demand, in blue, and the grid power, in green, with the used price profile. They are similar to those of Figure 8, but the influence of the BESS integration is visible. When \(\epsilon_{G}\) is high, \(P_{EVCS}\) is greater than \(P_{G}\), i.e., the BESS contributes to bus charging. Inversely, the BESS is charged when \(\epsilon_{G}\) is low. This behaviour is confirmed by the curves in Figure 10.  
Figure 9: EVCS and grid power profiles obtained with the RB EMS. The left y-axis is used for the blue and green lines, which refer to the EVCS power consumption and the consumption from the grid, respectively, and the right y-axis is used for the red line, which refers to the electricity price at each moment.  
In addition, to study the influence of the \(DOD\) on the operation cost, it has been reduced from 90 to 60%, so that the BESS is operating at 80 to 20% of its capacity. The \(SOC\) evolution of the case can be seen in Figure 10's left windows, where in the lower window the maximum and minimum \(SOC\) are 0.8 and 0.2, respectively.  
Compared to the original charging, the operation cost is reduced by 13% with the RB EMS and a \(DOD\) of 90%, and by 13.6% when the \(DOD\) is 60%. The degradation of the BESS is related to the \(DOD\). Thus, even if with a reduced \(DOD\) the available power is lower, it can be interesting from an economical point of view.
# An Overview of Biopolymers for Drug Delivery Applications
## 3 Other Polymers Used in DDSs
### Dendritic Polymers  
Dendritic polymer architectures show promising therapeutic properties, with potential applications in drug-delivery systems. Also, the dendritic polymer architecture is more advantageous for delivery applications than linear polymers, having mono-dispersity, high symmetricity, and surface polyvalency [124].  
Depending on the mechanism, a dendrimer can capture a drug through empty spaces (molecular capture), through branching points (hydrogen bonds), or external surface groups (charge-charge interactions). The most used dendrimers for drug delivery systems are PAMAM dendrimers (Poly(amidoamine) dendrimers), commercially available with amine, hydroxyl, carboxylate, and pyrrolinone surfaces, along with corresponding generations [125].  
Also, core-shell architectures of easily accessible hyperbranched polymers have been reported. Thus, based on commercially available dendritic structures (polyglycerol and poly-(ethylene imine)), pH-sensitive shells were attached through acetal or imine bonds, obtaining pH-sensitive nanocarriers [126]. Such dendritic structures are dendritic polyglycerol-co-polycaprolactone (PG-co-PCL)-derived block copolymers used for gemcitabine delivery for pancreatic ductal adenocarcinoma therapy [127].  
Functional dendritic polymer architectures as stimuli-responsive nanocarriers for the delivery of bioactive agents were obtained using several chemical linkers that respond to external stimuli [128].  
[PERSON] et al. developed a fourth-generation poly-lysine dendritic nanocarrier to target epidermal growth factor receptor-overexpressed breast cancer for methotrexate delivery. The methotrexate was incorporated into the nanocarrier using a cathepsin B cleavable spacer (glycine-phenylalanine-leucine-glycine). The in vitro analysis showed that developed DDSs were highly effective. The efficacy analysis using NOD-SCID mice also demonstrated that the DDS reduced tumor volume. The mice treated with the DDSs developed gained weight faster than those treated with the free drug, which allowed for a conclusion that dendrimer is more tolerated by mice than the free drug [129].
# An Improved Visual SLAM Method with Adaptive Feature Extraction
## 1 Introduction
It can also be understood that the feature point method has no rigid requirements for environmental conditions; therefore, it is highly adaptable, so it holds a mainstream position in visual odometry. In SLAM, back-end optimization is not performed in real time. To obtain high-precision pose estimation, the local or global drift error is eliminated by only activating it when a frame is chosen as a key frame. Therefore, the feature extraction speed has a significant impact on the SLAM's real-time performance. Additionally, the majority of feature extraction thresholds used in existing SLAM feature detection are set manually. This could result in a lot of corner clusters and the generation of too many feature points, which would not be able to meet the requirements of various images for feature point extraction and increase the complexity of subsequent image alignment. Furthermore, the robustness of SLAM is more strongly influenced by the robustness, orientation invariance, and scale invariance of extracted feature points. However, the majority of feature extraction techniques use an image pyramid to give the features scale invariance while simultaneously increasing computation. Many features that are faster to extract mostly lack scale invariance. How to balance them is also an important issue. This work proposes an adaptive feature extraction approach to address the above issues while also increasing the real-time speed and robustness of SLAM. The following is a summary of this paper's main contributions:  
1. This paper designs the detection thresholds for AGAST based on the image's overall grayscale information, which can be adjusted to varied situations and extract an appropriate amount of feature points with high quality. Image pyramids are adaptively built for images of various sizes, and feature detection is accomplished by utilizing the adaptive meshing method and AGAST algorithm. This could adapt to various inputs and decrease unnecessary calculations. 3. Following the detection of feature points, we utilize the Non-Maximum Suppression (NMS) method to filter the feature points and combine the quadtree algorithm to homogenize them, which can avoid corner point clustering. We calculate the feature point orientation using the intensity centroid method to provide orientation invariance for it, which is conducive to subsequent feature matching.
## 4 Data
### Transportation and Processing  
Although four means of transport exist (air, sea, road and rail), only rail and road are considered. The conveyance by airplane is too dangerous given the amounts that would be transported and the possibility of a crash, either because of technical failure or also due to a terrorist attack, meaning the hazardous waste could freely escape into the atmosphere. Sea transportation does not seem like a valid option in the U.S. where little rivers exist for an east-west connection; as most of the reactors considered are located at the East Coast, whereas the final repositories, that will be depicted later in this chapter, are either in Nevada or in New Mexico. This deliberation is congruent with representative routes evaluated by the NRC [37].  
The data taken as a basis for the costs for rail and road transport are depicted in Table 2. They are based on [PERSON] et al. [38] and adjusted to the assumed cask size.  
Reprocessing is a way of reducing waste and therefore a potential option for waste management (if the proliferation risk is not considered) if the final storage space is not sufficient. Shielded nuclear radiation containment chambers (hot cells) are necessary for reprocessing nuclear waste [11]. They are also needed for recasking the waste if no pool is available, e.g., at shut down reactor sites [11]. Our model includes these hot cells at two locations: one is near New York City in West Valley, close to the old commercial reprocessing facility and one is close to WIPP in Carlsbad (New Mexico), as there is already a hot cell for dealing with LLW and ILW at WIPP. For simplicity reasons a possible reprocessing facility is co-situated with a hot cell. Other reprocessing options abroad are not considered in the model due to political reasons. The diminution of weight is factored in through a fixed percentage of 4% [39]. As the process of reprocessing demands for the debris to be removed from the cash it conforms with the procedure of recasking. It is much more expensive though, the value being around $5,850,000 per cask (based on [40]), not including installation costs of reprocessing facilities which have a limited capacity of 170 casks per year each (Capacity taken from the largest operating reprocessing facility in France, which has a capacity of 1700 MTU per year [12]).
# Revisiting Label Smoothing Regularization with Knowledge Distillation
## 3 Preliminaries
### Teacher-Free Knowledge Distillation  
The cross-entropy loss in Equation (4) of LSR can be written as a KD-loss, similar to Equation (6):  
\[\begin{split} L_{LSR}(p,q)&=L_{CE}(p(1),q^{\prime} )\\ &=(1-\alpha)\times L_{CE}(p(1),q)+\alpha\times L_{KL}(u||p(1)), \end{split} \tag{7}\]  
which means that LSR can be regarded as a special case of KD with a uniform distribution teacher and \(\tau=1\). Based on the above observation, [PERSON] et al. [27] proposed the Teacher-free Knowledge Distillation (Tf-KD) (Figure 1e) with the following loss function:  
\[L_{Tf-KD}(p,q)=(1-\alpha)\times L_{CE}(p(1),q)+\alpha\times\tau^{2}\times multiplier \times L_{KL}(p^{\prime}(\tau_{1})||p(\tau_{2})). \tag{8}\]  
where \(\tau_{1}\) and \(\tau_{2}\) are different hyperparameters. The uniform distribution teacher \(u\) in Equation (7) is substituted as below [27]:  
\[p^{\prime}_{m}=\left\{\begin{array}{ll}\gamma,&m=c\\ \frac{1-\gamma}{M-1},&m\
eq c\end{array}\right. \tag{9}\]  
where \(c\) is the correct class and \(\gamma\) is the probability of class c. Therefore, a 100% correct teacher is obtained by combining a uniform distribution and the ground truth label information.  
Tf-KD is a promising substitution of LSR and an effective alternative of KD methods without extra cost for teacher model training and forward propagation. However, the Tf-KD method may suffer from the multiple hyperparameters: \(\alpha\), _multiplier_, \(\gamma\), \(\tau_{1}\), and \(\tau_{2}\). For different networks, the optimal parameter set may change dramatically. The proportion of two losses needs to be tuned by two parameters (\(\alpha\) and _multiplier_) to guarantee the performance. These troublesome parameter tunings of Tf-KD may cost more computational resources to tune than other traditional KD methods with external knowledge. This disadvantage makes Tf-KD less attractive to researchers.
## 3 Results and Analysis
### Response of the NDVI in Shanxi Section of the Yellow River Basin to Climatic Factors
From Figure 6a, it can be observed that the proportion of significantly positive correlated regions is minimal, mainly located in the southeastern part of the Linfen Basin. Figure 6: Annual cumulative precipitation (**a**) and annual average temperature (**b**) in the Shanxi section of the Yellow River Basin. The partial correlation coefficients between the NDVI and annual cumulative precipitation in the Shanxi section of the Yellow River Basin range from \(-0.67\) to \(0.88\). The graded statistical results indicate that the relationship between the NDVI and annual cumulative precipitation in the Shanxi section of the Yellow River Basin is primarily positive, accounting for \(94.52\%\) of the total. Among these, the areas with no significant positive correlation, significantly positive correlation, and highly significant positive correlation account for \(63.24\%\), \(18.93\%\), and \(12.35\%\), respectively. Spatially, they are mainly distributed in the southern, northwestern, and central-western regions. The negative correlation regions only make up \(5.48\%\) of the total. Within the Liulin mining area and the Hebopiao mining area, there is a significant proportion of highly significant positive correlation areas, and the Xishanguijiao mining area also has a considerable presence of significantly positive correlation areas. Figure 7: Partial correlation coefficient between the NDVI and temperature and distribution of its significance level: (**a**) Partial correlation coefficient between the NDVI and temperature; (**b**) Distribution of significance level, divided into \(6\) grades by significance test using the t statistic: extremely significant positive correlation (r \(>0\), \(p\leq 0.01\)), significant positive correlation (r \(>0\), \(0.01<p\leq 0.05\)), insignificant positive correlation (r \(>0\), \(p>0.05\)), insignificant negative correlation (r \(<0\), \(p>0.05\)), significant negative correlation (r \(<0\), \(0.01<p\leq 0.05\)), and extremely significant negative correlation (r \(<0\), \(p\leq 0.01\)).
## 2 Related Studies
The sleep/wake algorithm was applied to reduce power consumption. Moreover, the sensor node in the system was configured to transmit data for 48 s every 24 h (i.e., DC is equal to 3.3%). Consequently, battery life is prolonged to 4408 h. The total current consumption of the system is 0.544 mA relative to that in traditional operation, where the sensor node consumes 46.145 mA with a power efficiency of more than 90%. [PERSON] et al. (2016) tested and developed a smart irrigation system based on WSN and solenoid electrovalves. The smart irrigation system was implemented in 14 farms. The power consumption of the WSN was improved by the proposed sleep/wake strategy by using periodic sleeping cycles. The power consumption of the system is extremely low in sleep mode. The power consumption of the XBee Pro S2 module of the sensor node is 177 mA in active mode, 3.5 \(\upmu\)A in sleep mode, and 7 mA in active mode for the soil moisture sensor. Power consumption is improved and battery life is extended through the power-down mode of XBee. All the aforementioned studies are summarized in Table 1. The table highlights the improved power consumption and the related power reduction technique or scheme in each work. The table also provides the hardware, including wireless protocol, microcontroller or processor, climate condition sensors, battery type and capacity, and power and voltage of the solar panel, adopted in each study. The future of the agriculture industry can be revolutionized by relying on computerized systems, advanced sensors, and energy-efficient wireless networks instead of using the traditional agriculture system that is proven to be inefficient, labor intensive, and has low productivity.
## 1 Introduction  
The demand for Nd-Fe-B permanent magnets continues to increase due to their superior magnetic properties, despite the relatively high price of Nd and its restricted supply [1; 2; 3; 4; 5; 6; 7]. The low cost and availability of Ce have driven interest in developing Ce-based permanent magnets to replace expensive Nd\({}_{2}\)Fe\({}_{14}\)B magnets in certain applications [8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19]. This has resulted in investigation of Ce\({}_{2}\)Fe\({}_{14}\)B and its alloys [20; 21; 22; 23; 24; 25; 26]. Ce\({}_{2}\)Fe\({}_{14}\)B has intrinsic magnetic properties [3; 4; 5; 6; 7] that are inferior to those observed for the Nd\({}_{2}\)Fe\({}_{14}\)B [4; 6; 7], but they can be improved using a fourth element to replace some of the Fe in Ce\({}_{2}\)Fe\({}_{14}\)B. Studies of Nd\({}_{2}\)(Fe\({}_{14-x}\)T\({}_{x}\))B, where T = Al, Ni, and Si have shown that Ni and Si increase the Curie temperature (\(T_{\rm C}\)) and Al significantly increases coercivity [27; 28; 29; 30; 31]. Homogeneity ranges for Ce\({}_{2}\)(Fe\({}_{14-x}\)Ni\({}_{x}\))B, Ce\({}_{2}\)(Fe\({}_{14-x}\)Si\({}_{x}\))B, and Ce\({}_{2}\)(Fe\({}_{14-x}\)Al\({}_{x}\))B solid solutions have been found to be (\(0\leq x_{\rm Ni}\leq 1.5\)), (\(0\leq x_{\rm Si}\leq 2.33\)), and (\(0\leq x_{\rm Al}\leq 2.5\)), respectively, at 900 \({}^{\circ}\)C, and the details of the phase equilibria in these systems are reported elsewhere [32]. The dopant concentrations used throughout this paper are the actual values substituting for Fe in the Ce\({}_{2}\)Fe\({}_{14}\)B phase as determined by scanning electron microscopy (SEM) combined with wavelength-dispersive X-ray spectroscopy (WDS). Ce\({}_{2}\)(Fe\({}_{14-x}\)T\({}_{x}\))B solid solutions, where T = Al, Ni, and Si, show visible magnetic domains using magnetic force microscopy (MFM) analysis, and more details can be found in [32]. The aim of this work is to quantify the changes in the intrinsic magnetic properties of Ce\({}_{2}\)Fe\({}_{14}\)B when Fe is replaced by various amounts of Al, Ni, and Si.
## 4 Experimentations
### Clustering Performance Evaluation
#### 4.3.3 V-Measure  
With access to the true class assignments of the samples, it becomes feasible to establish a meaningful metric through the examination of conditional entropy. Specifically, [PERSON] and [PERSON] (1976) delineated two commendable goals for any cluster allocation:  
* Homogeneity: Every cluster is comprised solely of members belonging to a singular class.
* Completeness: Every member belonging to a particular class is assigned to one cluster.  
The V-measure, which is their harmonic mean, is calculated using the following formula, where we use \(\beta\) by its default value 1:  
\[V=\frac{(1+\beta)\times\text{homogeneity}\times\text{completeness}}{\beta \times\text{homogeneity}\,+\text{completeness}} \tag{10}\]  
Rand Index, Mutual Information Score, and V-measure metrics address the inherent complexity of clustering tasks by robustly evaluating the similarity between obtained clusters and true classes. They accommodate challenges such as noise and varying cluster shapes by focusing on structural correspondence rather than absolute label values. Thus, we used these metrics to compare the clusters with the classification that is proposed in the datasets. For the Product Space, each HS code (the nodes), belong to a sector of activity in the sense of the HS nomenclature. For the Cora dataset, each publication is attached to a potential scientific field.
# Centrifuge Model Investigation of Interaction between Successively Constructed Foundation Pits
## 1 Introduction  
When constructing large-scale underground spaces, many foundation pit projects are typically required. In this context, many group pit and adjacent foundation pit projects have been constructed. Many scholars have studied foundation pit engineering, but most of them have focused on the characteristics of a single foundation pit [1, 2, 3, 4, 5, 6] and their impact on the surrounding environment [7, 8, 9, 10, 11, 12, 13, 14]. Research on foundation pit groups is less common, and to the best of our knowledge, only a few of these studies using numerical analyses and case studies are available.  
On the contrary, theoretical research and experimental research on foundation pit groups [15, 16, 17, 18, 19] are even rarer. [PERSON] [20] studied the mechanical deformation characteristics of adjacent retaining structures by describing the interaction of the soil between pits using theoretical derivation and numerical simulation. [PERSON] [21] studied the deformation mechanism of two adjacent foundation pits in a cross construction using field measurement and numerical analysis. [PERSON] [22] compared the construction responses of a single foundation pit and a double foundation pit group via a numerical analysis, and the impact of the adjacent foundation pit on the foundation pit constructed first was analyzed. Geotechnical centrifuge tests have also been widely used to study ultradeep and ultra-large-scalefoundation pits due to their unique advantages [23; 24; 25; 26; 27]. [PERSON] et al. [28] studied the stress and deformation of deep foundation pits near metro hubs through centrifuge model tests and three-dimensional finite element numerical simulations. [PERSON] et al. [29] investigated the deformation of retaining structures, changes in earth pressure, and the surface settlement of ultradeep foundation pits under two groups of working conditions using centrifugal model tests based on an ultradeep foundation pit project with a depth of 38 m in Shanghai. Due to the complex centrifuge test process and the limited size of the centrifuge model box, there are few reports of studies using centrifuge tests to investigate the interaction within a foundation pit group. In this study, a centrifuge test was performed to simulate the sequential construction of two adjacent foundation pits often encountered in engineering, and the interaction between the two foundation pits was analyzed.
# Research and Development of Automatic Monitoring System for Livestock Farms
## 1 Introduction
[17] have used network technology of wireless sensing to monitor the hives and collect information of their surroundings. [PERSON] et al. have also used wireless sensing for animal tracking and animal behavior analysis [18; 19]. [PERSON] et al. [20] have used a network of wireless nodes technology to monitor the vital signs of the sheep and goats. [PERSON] et al. [21] have proposed a set of WSN sensors for monitoring the water quality and fish behavior in aquaculture tanks during the feeding process. [PERSON] et al. [22] have described a WSN node designed to monitor the habit of pink iguanas at the Galapagos Islands. [PERSON] et al. [23] have also developed a WSN based traceability system for recirculation aquaculture and so forth. Livestock farms are often located in sparsely populated remote areas. Due to the environmental considerations such as high cost, difficulty in erecting power lines, or signal attenuation caused by long lines, it's not possible to widely apply wired remote data transmission. Therefore, this study has applied radio frequency (RF) technology for remote wireless data transmission and remote control for monitoring the environment parameters of livestock farms. The signal transmitters emit electromagnetic waves through the antennas, and the signal receiver side can receive the messages through the receivers and demodulation process. This study firstly set up sensors for readings such as temperature, humidity, wind speed and illuminance in the dairy barns, and combined with programmable logic controllers (PLC), analog-to-digital converter modules and human-machine interfaces (HMI) to collect environmental data of dairy farms. Through RFU-400 CR wireless communication transmitting device produced by ICP DAS CO., LTD. (Hinshu, Taiwan), the collected environmental data were dumped into to a remote office's database and instantly playback on the human-machine interface or displays. Finally, the heat stress and apparent temperature of the dairy cows were analyzed based on the temperature-humidity index [24], wind speed and illuminance according to the data from the database to investigate the influence of environmental factors on the lactation yield and physiological status of dairy cows and accurately grasp the optimized environmental parameters to provide the most comfortable environments for dairy cows. By controlling the system to the optimized environmental condition, the milk production of dairy cows and the appetite were both improved, thereby improving the quality of the animal products. The process and results in this research are expected to contribute to the livestock industries.