esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Effects of Phytochemicals on the Intestinal Microbiome, Obesity and Metabolic Syndrome
Increases in high-density lipoprotein (HDL) and urinary T and HT were also observed. Urinary T and HT increased in a dose-dependent manner with the phenolic content of the olive oils consumed. There is increasing evidence that the Mediterranean diet, in which olive oil is the main source of fat, has a beneficial impact on diseases associated with oxidative damage. Phenolic compounds are antioxidant agents contained in several foods of the Mediterranean diet, among which virgin olive oil is the most important. HT is considered to be a powerful antioxidant in in vitro studies [45]. A 3-week double-blind, cross-over, randomized controlled trial, estimated the impact of daily consumption (60 mL) of high-polyphenolic extra virgin olive oil (HPOO), in comparison to low-polyphenolic olive oil (LPOO), on oxidative status and on inflammatory biomarkers. Olive oil polyphenols have many benefits for cardiovascular health due to their antioxidant and anti-inflammatory properties. Plasma total antioxidant capacity (TAC) is inversely related to chronic disease risk. The consumption of unrefined extra virgin olive oil with a high content of polyphenols led to an important increase in TAC and to a reduction in plasma-oxidized low-density lipoproteins (ox-LDLs). Both oxidative stress and inflammation may cause damage to endothelial cells, boosting a pro-inflammatory response, as shown by the increased expression of endothelial dysfunction markers such as adhesion molecules and cytokines IL-6 and TNF-\(\alpha\). IL-6 and TNF-\(\alpha\) are responsible for the secretion of CRP. Ox-LDL is a very immunogenic particle of great importance to the initiation and progression of atheromatic plaque formation within the arterial wall [46]. Table 4 summarizes the effects of polyphenols in agreement with the above recent studies. \begin{table}
\begin{tabular}{|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|} \hline
**Study Type** & **Study Sample/Duration** & **Participants** & **Protocol** & **Summary of Results** & **Study Reference** \\ \hline Randomized, double-blind, crossover & 30 obese Participants & 18–65 y /30 days & Curcuminoids (1 g/day), or placebo. & Curcuminoid supplementation (1 g/day for 30 days) reduced triglycerides in concentrations in serum. & [41] \\ \hline A double-blind, placebo-controlled, cross-over study & 22 participants /4 weeks & Overweight and obese adults. & 2150 mg of a water-soluble tomato extract rich in polyphenols pac day or placebo (malodextrin for 4 weeks with a 6-week wash-out between interventions. & [42] \\ \hline A double-blind pilot randomized parallel group placebo-controlled study & 28 participants/35-days & Obese men with MetS & Resveratrol 1 g orally twice daily or placebo while consuming a western-style diet. & Improvement in insulin sensitivity and glucose tolerance, significant increase in _A. muciniphia._ & [43] \\ \hline \end{tabular}
\end{table} 
Table 4: Summary results of the effects of polyphenols on the human microbiome related to obese and MetS.
# Improved Performance of High-Voltage Vertical GaN LEDs via Modification of Micro-Cell Geometry
###### Abstract  
Vertical-type high-voltage light-emitting diodes (HV-LEDs) with 2 \(\times\) 2 micro-cells were fabricated on Cu substrates, and the micro-cell geometry was modified to enhance the optoelectronic performance. The current spreading in micro-cell is most dominantly affected by the distance between electrode and edge of chip. When square cells were combined in a HV-LED, the device performance was poor due to an obvious current-crowding phenomenon that occurred near the electrodes. This was attributed that the electrodes in these four square micro-cells were all far away from the edges, resulting in the severe current-crowding phenomenon. On the contrary, as the HV-LED was prepared with four rectangle, triangle, or L-shaped micro-cells, the electrodes were close to the edges of micro-cells and the current spreading effect can be easily improved. Although a HV-LED connected with L-shaped cells possessed a better current spreading effect and a lower surface temperature, the light extraction was relatively low because of an electrode-shading loss effect. When triangular cells were used to prepare the HV-LED, the device achieved a superior optoelectronic performance compared with that of other cells because of a lower current-crowding effect and a more uniform light emission. After an epoxy package process, a lower forward voltage of 14.9 V and a higher output power of 353.2 mW were obtained using this HV-LED at an injection current of 80 mA. Additionally, the wall-plug efficiencies of this device at 20 and 80 mA were 41.1% and 29.7%, respectively. The results confirm that the design of triangular cell is beneficial for enhancing the optoelectronic performance of HV-LEDs. Furthermore, the fabrication processes of vertical LEDs have high potential for HV-LED applications.  
vertical light emitting diodes; high-voltage light emitting diodes; micro-cell geometry; current crowding effect; spatial distribution image +
Footnote †: journal: Applied Physics Letters
# Substation Operation Sequence Inference Model Based on Deep Reinforcement Learning
## 6 Operation Sequence Inference Module
### Procedure Real-Time Evaluation Module  
The evaluation of operational steps is mainly accomplished through the reward and punishment function. In the value-based Dueling DQN model, the reward and punishment value is the immediate feedback obtained by the agent after the action selection. In order to set the reward and punishment function reasonably, this paper sets the operation evaluation function \(O(s)\). According to the requirements in the process of operation ticket formation, a reward function \(R(s)\) is set in this paper to guide the model towards the destination direction, and a penalty function \(P(s)\) is set in order to avoid maloperations in the operation sequence or make the environment enter a bad state. The environment state function is the accumulation of reward function and punishment function, as follows:  
\[O(s)=\sum R(s)+\sum P(s) \tag{4}\]  
The task of the reverse operation is to realize the operation of the conversion between the four basic operating states of the task equipment. According to the updating rules of the operating state of the task equipment, the reward function \(R(s)\) can be divided into the switching device state reward \(R_{S}(s)\) and the task device state \(R_{D}(s)\) reward, where:  
\[R_{s}(s)=\begin{cases}1&S_{s}=\sigma_{1},\sigma_{2},\sigma_{3},\sigma_{4}\\ 0.5&else\end{cases} \tag{5}\]  
\[R_{D}(s)=\begin{cases}0.5&S_{D}=\sigma_{1},\sigma_{2},\sigma_{3},\sigma_{4} \\ 1&S_{D}=S_{aim}\end{cases} \tag{6}\]  
\[R(s)=R_{S}(s)+R_{D}(s) \tag{7}\]  
In the formula, \(S_{s}\) represents the running state of the switch group, \(S_{D}\) represents the running state of the task device, and \(S_{aim}\) represents the target state of the task device.  
For the penalty function \(P(s)\), according to the safety requirements in the switching operation, this paper divides the penalty function \(P_{I}(s)\) in the bad state and \(P_{O}(s)\) in the wrong operation sequence, where:  
\[\begin{cases}P_{I}(s)=-2&S_{s}\;or\;S_{D}=\sigma_{6}\\ P_{O}(s)=-1.5\end{cases} \tag{8}\]  
Figure 6: Model of the duel network.  
The wrong operation sequence refers to that the isolation switches in the same switch group do not operate in the sequence of load side followed by power side in the case of power failure and power side followed by load side in the case of power supply.
# A Finite State Machine-Based Improved Cryptographic Technique
## 5 Conclusions  
There are four factors that are crucial to the success of the proposed methods: key length, key order, block size, and intermediate cypher generation. We use a larger key than the methods proposed by previous studies, with a key length of 128-512 bits, a key matrix order of 4 \(\times\) 4 to 6 \(\times\) 6, and block sizes of 16 bytes to 36 bytes. Compared to existing solutions, the proposed approaches are superior since their encryption and decryption times scale linearly with the key size. Guessing the Plaintext takes a lot longer and becomes almost impossible without knowing the intermediate encryption. Based on the comparative analysis provided in the previous chapter, the following is provided. The Plaintext can be concealed using the proposed techniques; however, recovering the Plaintext without knowledge of the intermediate encryption and key is incredibly challenging due to the large key size, the high order of the key matrix, and the large block size of the Plaintext. Future work on image encryption applications may benefit from the frequent binary to decimal translation, which increases the strength of the technique. Over time, we want to enhance this algorithm's protection of text as well as audio, video, and still photos. Other statistical methods will be used to assess the keys' degree of unpredictability.
# Real-Time Path Planning of Driverless Mining Trains with Time-Dependent Physical Constraints
## 3 Problem Description
### Train Model  
Let \(M=\{1,2,\cdots,m\}\) be the set of real trains travelling in the physical network \(G^{*}\). For each train \(m\in M\), we will get the following set of inputs.  
* \(loSeq^{m}\): Sequence of scheduled load-outs to visit;
* \(depTime^{m}\): Scheduled departure time; and
* \(depLoc^{m}\): Scheduled departure location.  
As we have a cyclical network and the train changes direction (turns back) after going to a load-out, in order to model this behaviour we will break down the train journey into different parts. Each part will be represented by a different model train. Thus, if a train \(m\) goes to \(n\) load-outs, its whole journey will be represented by a set of \(n+1\) model trains, called \(T_{m}\), where each model train represents a specific segment of train \(m\)'s journey. Accordingly, one business rule will be added so that any model train can depart only after its predecessor has finished its journey.  
For example, considering the case of a train \(m\) starting from station A and going to two load-outs G1 and G2, its journey will be modelled as follows in Table 1.  
From now on, we will use the term \"train\" to refer to model train only, unless specified otherwise. Thus, for each train \(t\in T\), we will have the following set of information.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Train** & **Departure Node** & **Destination Node** & **Predecessor Train** \\ \hline \(t_{m1}\) & A & G1 & null \\ \(t_{m2}\) & G1 & G2 & \(t_{m1}\) \\ \(t_{m3}\) & G2 & A & \(t_{m2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model train example.  
Figure 1: Railway network sample.  
\(depQ_{l}\): Scheduled departure time; \(dep_{t}\): Scheduled departure location node; \(dest_{t}\): Final destination node of the train, after which it will disappear from the network; and \(t_{pre}\): Predecessor train of train \(t\).
## 3 System Model
### PDR Review  
#### 3.5.1 EKF  
In the domain of PDR, the extended Kalman filter (EKF) is extensively employed for state estimation and error correction [19].  
\[\delta X_{k}=F_{k-1}\cdot\delta X_{k-1}+\omega_{k-1} \tag{9}\]  
\[\delta Z_{k}=H_{k}\cdot\delta X_{k}+v_{k} \tag{10}\]  
where \(\delta X_{k}\) denotes the state error vector--typically including position, velocity, and attitude angles--at time \(k\), \(F_{k-1}\) represents the state transition matrix, describing the system dynamics from time \(k-1\) to time \(k\), \(\delta X_{k-1}\) represents the state error vector at the previous time \(k-1\), \(\omega_{k-1}\) denotes the process noise vector, representing the uncertainties introduced during the state transition, \(\delta Z_{k}\) denotes the observation error vector at time \(k\), \(H_{k}\) denotes the observation matrix, mapping the state space to the observation space, \(\delta X_{k}\) denotes the state error vector at time \(k\), as defined in the state prediction equation, and \(v_{k}\) represents the observation noise vector, representing the uncertainties introduced during the observation process.
# Capturing Features and Performing Human Detection from Human Gaits Using RFID
## Materials and Methods
### Preliminaries
#### 2.2.2 Preliminary Experiment and Analysis  
In order to better show the correlation between user step length and height and the influence of multiple users, we conducted two preliminary experiments. The five tags and antennas are deployed on both sides of the corridor. The labels are placed in a row parallel to the antenna. The width of the corridor is 2 m, the label spacing is 0.7 m and the label is about 0.3 m above the ground. The receiving range of the antenna is about 120\({}^{\circ}\). In this experiment, height cannot be used as the evaluation standard for RF-Detection. Therefore, we combine the relationship between height and step length, and grouped step length to take it as the detection standard. Compared with single tag, multi-tag placement can largely cover all signal ranges for a long corridor, and more effectively capture the behavior characteristics of users with different heights and different step lengths. In the first experiment, we first analyzed the relationship between user height and step length. According to a large amount of data, different walking speed, stride frequency and walking type may have a certain impact on the relationship between height and step length, which  
Figure 1: Signal propagation path.  
Figure 2: The signal decomposition model.  
is clearly shown in Challenge 2, where detailed calculation methods are given. In the experiment, since the walking speed of the experimenter was maintained in a relatively stable condition (i.e., normal walking), the linear relationship between the height and the step length in [32] was adopted for experimental analysis. We had two volunteers with different height-steps walk through a corridor sensing area. Figure 3 depicts the preprocessed phase and amplitude data of the two volunteers. The detailed processing process is shown in Section 2.3. We can see that different tags have different phase and amplitude changes, especially when volunteers pass through tag 3, where the change is most obvious. In addition, the volunteers who were out of sync also caused changes in phase and amplitude. These findings indicate that the phase and amplitude of tags imply physical features such as spatio-temporal correlation and behavioral features, which can be used for human detection.  
In order to detect multiple users, in the second experiment we had two volunteers with different step lengths and two volunteers with the same step length continuously walk through the corridor detection area. Figure 4 shows two different sets of phase and amplitude readings. From the two groups of different phase values, it can be seen that the phase data of the two volunteers present a continuous state, and this state can be identified by segmentation, which proves that the RF-Detection system has good detection results in dealing with continuous person detection.  
Figure 3: Phase and amplitude readings of two asynchronous volunteers under all five tags: (**a,b**) are the phase and amplitude of volunteer 1; (**c,d**) are the phase and amplitude of volunteer 2.
# A Vision-Based Method for Simultaneous Instance Segmentation and Localization of Indoor Objects
## 3 System Overview and Method
### System Overview  
The system for binocular instance segmentation and simultaneous localization is depicted in Figure 1, encompassing six sequential steps: (1) Image acquisition through the binocular camera; (2) Camera calibration to derive internal and external parameters followed by image correction based on a distortion model; (3)Targeted semantic segmentation and recognition can be achieved through a multi-task semantic segmentation network that incorporates an attention mechanism, enabling the identification and segmentation of specific targets. (4) Feature extraction and matching is accomplished using a multi-feature point fusion algorithm that combines edge detection with FAST corner detection. (5) Binocular parallax positioning is employed to compute the three-dimensional coordinates of an object in the camera coordinate system by analyzing the parallax of feature points present in both left and right images. (6) The output comprises target location information along with category identification.
## 3 Results  
The results regarding the percentage of enamel surface hardness loss (%SHL) in pH-cycled specimens are presented in Figure 1. Statistically significant differences were detected among the groups (ANOVA; F = 23.07, \(p\) < 0.0001). The D group demonstrated a significantly lower %SHL (75.5% \(\pm\) 7.88) than the other groups (\(p\) < 0.05). E1 (86.3% \(\pm\) 5.33)was statistically similar to NC (81.8% \(\pm\) 4) but lower than E2. E2 showed the highest %SHL (91.6% \(\pm\) 3.64) compared to the other groups.  
In Figure 2, the SEM images of the enamel surfaces reflect the differences in surface hardness loss (%SHL) observed among the treatment groups. The D group, which demonstrated the lowest %SHL (75.5% \(\pm\) 7.88), shows less demineralization in the SEM images, with a relatively smoother surface and more defined enamel prism structures compared to the other groups.  
Figure 1: Mean (\(\pm\)SD, \(n\) = 15/group) of the percentage of enamel hardness loss after application of the treatments. NC/negative control; experimental varnishes containing the combination of \(tt\)-farnesol, quercetin, and theobromine at concentrations of 1.5% (E1) and 4.5% (E2); and Duraphat(tm) NaF 5%/gold standard—D. Different letters indicate statistically significant differences (\(p<\) 0.05).  
Figure 2: Scanning electron microscopy images of dental enamel surface. NC—**(A1)** control area (prior to pH cycling), **(A2)** area post cycling; E1—**(B1)** control area without cycling, **(B2)** area post cycling; E2—**(C1)** control area (prior to pH cycling), **(C2)** area post pH cycling; D—**(D1)** control area (prior to pH cycling), **(D2)** area post pH cycling.  
In contrast, the E2 group, which exhibited the highest %SHL (91.6% \(\pm\) 3.64), shows the most pronounced surface damage in the SEM images. The enamel appears highly porous with visible mineral loss, and the prism structures are less distinct.  
The E1 group (86.3% \(\pm\) 5.33) and NC group (81.8% \(\pm\) 4.0), which had intermediate %SHL values, display moderate surface demineralization. The prism structures are visible, but the surface appears rough and somewhat porous, reflecting the partial loss of mineral content. Although the surface damage is less severe than in the E2 group, it is still more pronounced than in the D group.
## 3 Results
### Molecular Weight (MW)  
Based on the analysis in Table 4, different types of natural and thermally modified starch exhibit significant variations in molecular weight, polydispersity index (PDI), and characteristics after thermal modification, directly influencing their potential applications in  
Figure 4: SEM images of various starches: CSN—cassava native starch samples, CSM—cassava modified starch samples, PSN—potato native starch samples, PSM—potato modified starch samples, WSN—wheat native starch samples, WSM—wheat modified starch samples, CoSN—corn mative starch samples, CoSM—corn modified starch samples, PeSN—pea native starch samples, PeSM—pea modified starch samples, RSN—rice multiple starch samples. biodegradable films. Starches with high molecular weight and low PDI values, such as cassava and corn starch, maintain stable molecular structures, and after thermal modification, they exhibit a slightly reduced molecular weight but retain low PDI values [50]. These properties ensure excellent mechanical strength and humidity resistance, making them ideal for long-term biodegradable films, such as agricultural much films. Medium-molecular-weight starches, such as potato and pea starch, show minor reductions in molecular weight and PDI after thermal modification, resulting in more uniform molecular distribution and improved mechanical properties. These starches are suitable for films requiring moderate strength and flexibility, especially for short-term or seasonal applications. Starches with low molecular weight and a high PDI, such as wheat and rice starch, naturally degrade quickly due to their smaller molecular size. After thermal modification, their PDI decreases moderately, indicating more stable molecular distribution, while retaining high biodegradability. These starches are ideal for disposable or short-term biodegradable films. Thermal modification significantly enhances the stability and moisture resistance of starch while optimizing its mechanical performance. By adjusting thermal modification processes, the application scope of starch in biodegradable materials can be further expanded.
# An Analysis of Cloud Security Frameworks, Problems and Proposed Solutions
## 2 Literature Review
### Cloud Security Problems and Solutions
#### 2.2.1 Cloud Security Problems  
_Data Privacy and Leakage:_ Data privacy concerns arise due to the shared nature of cloud environments. Unauthorized access or data breaches can lead to sensitive information leakage [16].  
_Data Location and Jurisdiction:_ The dynamic nature of cloud infrastructures can lead to uncertainty about the physical location of data, potentially causing conflicts with data protection regulations and legal jurisdictions [17].  
_Multi-tenancy Risks:_ Multi-tenancy introduces the risk of resource and data co-residency, where data from different customers may reside on the same physical hardware, potentially leading to data exposure [18].  
_Inadequate Access Control_: Ensuring proper access control mechanisms to prevent unauthorized users from accessing resources and data is challenging in cloud environments with complex access requirements [18].