esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Energy-Aware Security Mechanisms for the Internet of Things
## 1 Introduction
### Importance of Security Protection  
IoT devices are widely used in industries such as healthcare, manufacturing, transportation, and smart homes because of the IoT's ability to provide features like intelligence, automation, and real-time monitoring to increase efficiency [9], reduce costs, and enhance the quality of life. However, it is these very same advantages that also make IoT devices a major target for hackers, because IoT devices--being deployed on a large scale--cover a wide range of applications. This nature gives attackers ample opportunities to locate potential targets, so it is of paramount importance to implement cyber security solutions to safeguard these devices.  
Similarly, data security is at the core of information security. In today's world of increasingly advanced intelligence, data-faking techniques based on smart science pose a huge challenge to information security. This challenge is demanding because healthcare devices may contain the sensitive medical data of patients, manufacturing devices may store production processes and trade secrets, and transportation and smart home devices may involve the personal information of users. If an attacker obtains this critical information, and conducts theft, extortion, or other malicious activities leading to the violation of personal privacy, or in more serious cases, even the leakage of confidential information, then offering strong data security is important to securely protect the user information.  
IoT devices play an important role in critical infrastructures, such as medical devices, transportation-control systems, and manufacturing processes. Attackers may cause service disruptions through malicious operations [10], which can have a significant impact on society and the economy. The security of the Internet of Things is more of a precursor to the protection of critical infrastructure, which is directly related to the infrastructure of the economic operation, social order, and public interest, and has a significant impact on the information system, including communications, electric power, water conservancy, transportation, finance, energy, and municipal and other areas of the various networks, as well as information infrastructures and important information systems. Once a critical information infrastructure is damaged, loses its functions, or has a data leakage, it may seriously jeopardize national security, economic operations, and the social order, and even cause widespread social panic. Therefore, safeguarding the security of IoT infrastructures is a top priority for national security and social stability.
## 2 Battery State Space Model
Because the temperature will have a certain impact on the activity of the reactants inside the battery, excessively low activity will reduce the battery's capacity, and excessively high activity will damage the battery, so the experiment was carried out at a constant temperature of 25 \({}^{\circ}\)C. The battery tester used was a Xinwei BTS-5V6A charge-discharge tester. During the experiment, the SoC and OCV values of charge and discharge were calibrated many times at the same time, and the mean values of these values were calculated [27]. It was necessary to perform a few criteria charging and discharging operations on the tester: according to the results of the experiment, a group of open-circuit voltage and state-of-charge records of average charge and discharge were ultimately gained, and the data were fitted using the least-squares method. The fourth-order fitting expression is presented in Equation (7) for further details, and the OCV-SoC fitting curve is shown in Figure 2.  
\[U_{OCV}=-10.32635 oC^{4}+22.4018 SoC^{3}-16.3058 SoC^{2}+5.0034 SoC+3.2026 \tag{7}\]  
where \(U_{OCV}\) stands for the battery's open-circuit voltage.
## 4 Results and Discussion
### RSRP and RSRQ Prediction Models
Figure 15: 3D representation of the proposed logarithmic model for RSRP. Based on the presented results it can be concluded that all the proposed models can predict RSRP and RSRQ values with acceptable accuracy in suburban environments. The selection of a prediction model depends on its application; for example, for cases where computational resources are not the limiting factor and computational accuracy is critical, nonlinear models can be better choices, whereas linear models are simple to interpret and can provide a quick and accurate prediction.
# Optimizing Energy Efficiency in Cloud Data Centers:
## 5 Proposed Reinforcement Learning-Based VM Placement in Cloud Environment Using RF_SOM Classifier
### Reinforcement Learning Model
Unlike simpler heuristics such as first fit or best fit, the Firefly algorithm offers a more global search capability, effectively balancing exploration and exploitation to minimize energy consumption and migration costs while ensuring SLA adherence. Regarding the computational overhead, we ensure that the training of RF and SOM is done offline, with the Q-learning model converging efficiently within about 200 episodes. Once trained, the RF/SOM classification and Q-learning policy application are fast, operating in the millisecond range. The Firefly algorithm's migration phase, though computationally more intensive, is optimized to run within a reasonable time, with the migration overhead accounting for less than 5% of the total simulation time. Figure 3: Combined Q-learning–Firefly–VM classifier process. While the integration of these techniques adds complexity, we argue that this combination enables our approach to outperform baseline algorithms in terms of energy consumption, SLA violations, and VM migrations. In Algorithm 1, we describe the process of allocating virtual machines using reinforcement learning, combining a Q-learning policy with the migration mechanism based on the Firefly algorithm. ```
1:Input: (vmsToMigrate, excludedHosts)
2:Output: Label for any host
3:power = 0, powerLast = 0, newstate = 0, statindex = 0
4:firstFitDecreasing (vmsToMigrate, excludedHosts) \(\triangleright\) Apply the initial mapping
5:init () \(\triangleright\) Initialize action and Q-value
6:for episode in range (NUM_EPISODES) do
7:Restartstate ()
8:for step in range (MAX_STEPS_PER_EPISODE) do
9:currentState = getState ()
10:powlast = powerBeforeconsumption(hostslist)
11:statindex = getStateIndex(currentState)
12:action = exploreExploit (statindex, episode)
13:if action == 0 then
14:vmslist = getVmsToMigrateFromOverHost(host)
15: FireflyAlgorithm(vmslist)
16:endif
17:if action is ==\(-\)1 then
18:vmslist = getVmsToMigrateFromUnderHost(host)
19: FireflyAlgorithm (Listevms)
20:endif
21:pow = PowerAfterconsumption(Hostslist)
22:updateQValue (statindex, action, pow, powlast, currentState, newstate)
23:currentState = newState
24:endfor
25:endfor
```  
**Algorithm 1**Reinforcement Learning for Vm Placement
# Constitutive Characteristics of Rock Damage under Freeze-Thaw Cycles
## 3 Preparation of Samples and Their Associated Physical Characteristics
### Analysis of Strength Loss Rate  
Table 1 shows the average value of saturated uniaxial compressive strength and loss rate of samples with different FTCs. The experimental data show that with the increase in FTC number, the saturated compressive strength of specimens decreases obviously, and the same FTC number has a similar weakening effect on the saturated compressive strength of different types of rocks.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline
**Mean Compressive** & \multicolumn{2}{c}{**0 Times of FTC**} & \multicolumn{2}{c}{**10 Times of FTC**} & \multicolumn{2}{c}{**20 Times of FTC**} & \multicolumn{2}{c}{**30 Times of FTC**} & \multicolumn{2}{c}{**40 Times of FTC**} \\
**Strength at** & \multicolumn{2}{c}{**Surface**} & \multicolumn{2}{c}{**Loss Rate**} & \multicolumn{2}{c}{**Surface Composition**} & \multicolumn{2}{c}{**Loss Rate**} & \multicolumn{2}{c}{**Compressive**} & \multicolumn{2}{c}{**Loss Rate**} & \multicolumn{2}{c}{**Compressive**} & \multicolumn{2}{c}{**Loss Rate**} & \multicolumn{2}{c}{**Saturated**} & \multicolumn{2}{c}{**Loss Rate**} \\
**and** & **Water Com-** & **pressure** & **Thaw** & **pressure** & **Thaw** & **Saturated** & **Thaw** & **Saturated** & **Thaw** & **Saturated** & **Thaw** & **pressure** & **Thaw** \\
**Saturation** & **Strength** & **Strength** & **Strength** & **Strength** & **Water** & **Strength** & **Water** & **Strength** & **Water** & **Strength** & **Strength** & **Strength** \\
**(MPa)** & **(MPa)** & **(\%)** & **(MPa)** & **(\%)** & **(MPa)** & **(\%)** & **(MPa)** & **(\%)** & **(MPa)** & **(\%)** & **(MPa)** & **(\%)** & **(\%)** \\ \hline
125.7 & 119.5 & 0 & 116.9 & 7.0 & 113.2 & 9.94 & 111.7 & 11.14 & 105.6 & 15.99 \\ \hline \end{tabular}
\end{table}
Table 1: Mean saturated strength and strength loss rate of samples after different FTCs.  
Figure 3: The relationship between rock mass loss rate and p-wave velocity with the number of FTCs: (**a**) P-wave velocity; (**b**) Quality damage rate.  
With the increase in the number of FTCs, the mechanical properties of rock blocks basically show a downward trend, but there are also abnormal conditions, e.g., the compressive strength of the granulated rock group after 20 FTCs is greater than that after 10 FTCs, which shows the discrete data and non-uniformity of rock samples during the experiment.
# MEASegNet: 3D U-Net with Multiple Efficient Attention for Segmentation of Brain Tumor Images
## 6 Limitations and Future Perspectives  
MEASegNet achieved a certain improvement in terms of the Dice score and HD95 distance when using the three datasets BraTS2019, BraTS2020, and BraTS2021. However, there are still some challenges in the practical application of the BraTS dataset, which is manually annotated. The BraTS annotation is a widely accepted benchmark; however, differences in ratings between neuroradiologists may introduce label noise. As for how to deal with wrong annotations or inconsistencies, we introduce preprocessing methods such as Z-score normalization, clipping to the minimum bounding box containing non-zero voxels, and intensity values to the 1 st and 99 th percentiles of voxel distribution to solve the problem of outliers, to some extent. In addition, the motion artifact phenomenon of brain tumor images, caused by the patient's own motion and uneven magnetic fields of hardware devices, is not dealt with in the data preprocessing process of MEASegNet, but it is an important factor affecting brain tumor segmentation and recognition. We implemented data augmentation techniques such as affine transformation to simulate motion artifacts. The Dice scores for WT, TC, and ET were 91.37%, 86.06%, and 80.06%, respectively, and the HD95 values were 5.99, 9.04, and 20.50, respectively. The performance worsened, and affine transformation had a certain impact on the final effect. Our subsequent studies will focus on the motion artifacts and on enhancing the generalization of the model, thus improving the ability of the model to be used in clinical applications.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**Dice (\%)**} & \multicolumn{4}{c}{**HD95 (mm)**} \\ \cline{2-9}  & **WT** & **TC** & **ET** & **AVG** & **WT** & **TC** & **ET** & **AVG** \\ \hline Without deep supervision & 91.63 & 85.03 & 82.43 & 86.36 & 5.64 & 11.36 & 17.37 & 11.46 \\ With deep supervision (our) & 92.50 & 87.49 & 84.16 & 88.05 & 4.18 & 7.96 & 14.40 & 8.85 \\ \hline \hline \end{tabular}
\end{table}
Table 10: The results of ablation studies on deep supervision.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**NO**} & \multirow{2}{*}{**Expt**} & \multicolumn{4}{c}{**Dice (\%)**} & \multirow{2}{*}{**FLOPs (G)**} & \multirow{2}{*}{**Parameter (M)**} \\ \cline{3-3} \cline{5-8}  & & **WT** & **TC** & **ET** & & **AVG** & \\ \hline A & Base & 90.83 & 85.93 & 82.54 & 86.43 & **1056.983** & **14.537** \\ B & Base+PCSAB+CRASPP+SLRFB & **92.50** & **87.49** & **84.16** & **88.05** & 1090.586 & 17.856 \\ \hline \hline \end{tabular}
\end{table}
Table 11: The results of parameters and FLOPs in MEASegNet, with the best performance highlighted in bold.
## 1 Introduction  
Combined with a unipolar barrier structure design called XBn [1], a Ga-free InAs/InAsSb type-II superlattice (T2 SL) is currently under development for a high-performance midwave infrared (MWIR, 3-5 \(\upmu\)m) quantum detector suitable for military, medical and spatial applications [2]. Ten years ago, InAs/InAsSb T2 SL was considered as a promising material [3, 4] for MWIR detection with a minority carrier lifetime over 5 \(\upmu\)s at 77 K [5]. Nowadays, thanks to important progress achieved on MWIR detector structures grown by molecular beam epitaxy (MBE) [6, 7, 8, 9, 10], technological processes of devices [11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23] and focal plane array (FPA) [12, 24, 25, 26, 27] with temperature operation higher than 110 K, this Ga-free T2 SL technology strongly competes with its MWIR counterpart imaging systems based on InSb and InAs/GaSb T2 SL [26, 27]. However, to enhance the device performances and therefore its operating temperature over 120 K to satisfy size, weight and power (SWaP) requirements, better knowledge of electrical and electro-optical properties a functions of temperature in the Ga-free T2 SL structure and devices is necessary in the MWIR domain. Lately, specific measurements and studies have been performed to determine fundamental properties [28, 29] or to extract the vertical hole mobility in n-type Ga-free T2 SL structure [19, 30, 31, 32]. Results showed a strong anisotropic transport behavior with hole vertical mobility lower than 10 cm\({}^{2}\)/V at 150 K [31] inducing low vertical minority carrier diffusion length that could penalize the quantum efficiency in the Ga-free T2 SL XBn detector.  
Temperature dependence investigation of dark current measurements and quantum efficiency is an efficient way to characterize an infrared photodetector, and consequently to understand the transport of minority carriers in the T2 SL device. Indeed, in a well-designed XBn detector, the dark current should be diffusion-limited whatever the temperature, and the depletion region must be confined only to the barrier layer. If not, the identification of additional current mechanisms, such as generation-recombination (GR), trap-assistedtunneling (TAT) and leakage currents, is important to determine the reasons for malfunctioning. At a given temperature, the operating voltage (Vop), for which the photoresponse (PR) value saturates, must be as low as possible. In this XBn structure, the Vop value is directly linked to the presence of a valence band offset between the barrier layer (BL) and the absorbing layer (AL), impeding the transport of minority carriers. Moreover, the PR value and its evolution as a function of temperature is essential to determine the optimized AL thickness inducing the highest quantum efficiency (QE).  
We studied the influence of temperature on the electrical and electro-optical performances of a MWIR Ga-free T2 SL XBn detector, grown by MBE on n-type GaSb substrate, in order to improve the design and fabrication of the structure and device, compatible with an operating temperature as high as 150 K.
## 4 Simulation Analysis
### Simulation Results Analysis
#### 4.4.2 Comparison Results Analysis
Figure 10 illustrates the variation in packet arrival rates among the three algorithms under different numbers of link failures while maintaining a constant network load of 3000 data packets. As evidenced by the figure, all three algorithms exhibit declining performance trends as the number of link failures increases. The DR-BM algorithm, employing a centralized routing approach, demonstrates more rapid performance degradation with increasing link failures due to its inherent inability to promptly respond to sudden link disruptions. In contrast, both the conventional Q-Routing algorithm and our proposed MARL-JR algorithm, leveraging distributed architectures, enable satellites to adapt routing strategies based on local information, thereby maintaining better routing performance during link failures. Notably, MARL-JR outperforms Q-Routing through the following two key innovations: (1) incorporation of a residual load factor for congestion-aware routing, and (2) periodic link-state broadcasting for timely topology updates. These enhancements enable MARL-JR to achieve superior robustness, as manifested by more stable packet arrival rates under increasing link failure conditions.
## 3 Proposed CS-DDSG Scheme
### Selection of Parameters  
In this subsection, we investigate the values of the parameters \(\mathbf{r_{t}}\) and \(\mathbf{p_{2}}\). We consider a network with \(\mathbf{N}=\mathbf{400}\) nodes, which are randomly deployed over an area of size \(S=\mathbf{1}\times\mathbf{1}\) in this paper. As described in Section 2, to ensure the network connectivity, \(\mathbf{r_{t}}\) must satisfy the condition in Equation (5). Thus, \(\mathbf{r_{t}}>\mathbf{0.069}\); we set \(\mathbf{r_{t}}=\mathbf{0.075}\).  
In Stage 3 of CS-DDSG, nodes forward their updated packets with a probability \(\mathbf{p_{2}}\) and all neighboring nodes can receive this data. For the sake of an appropriate \(\mathbf{p_{2}}\) that reduces the number of transmissions \(\mathbf{N_{t}}\) and increases the proportion of reception nodes \(\mathbf{P_{r}}\) simultaneously, we simulate \(\mathbf{N_{r}}\) and \(\mathbf{P_{r}}\) versus \(\mathbf{p_{2}}\) by setting \(\mathbf{p_{1}}=\mathbf{0.2}\) and \(\mathbf{r_{t}}=\mathbf{0.075}\) as shown Figure 4, where all normal nodes stop receiving any data after merging one packet. As Figure 4 shows, as \(\mathbf{p_{2}}\) increases, the values of \(\mathbf{N_{t}}\) and \(\mathbf{P_{r}}\) both increase. Furthermore, \(\mathbf{P_{r}}\) increases almost linearly with \(\mathbf{p_{2}}\). Thus, when \(\mathbf{p_{2}}=\mathbf{0.32}\), 98% nodes receive a broadcast packet. Moreover, as \(\mathbf{p_{2}}\) increase beyond 0.32, \(\mathbf{N_{r}}\) increases less, while \(\mathbf{P_{r}}\) increases sharply. Therefore, the appropriate value for \(\mathbf{p_{2}}\) is 0.32, because that value provides a balanced trade-off between the number of transmissions and the percentage of receiving nodes.  
Figure 4: The impact of forwarding probability on the number of transmitting and receiving nodes.
## 1 Introduction
It is an end-to-end network that can decode signals without conventional sampling from images received by a CMOS-based camera, which is different from all existing algorithms for RGB-LED-based RSE-based OCC systems. Since our decoding method does not need conventional sampling, it is not easily affected by sampling offset caused by ISI. Based on our equalizer, we also fuse the function of spatial-wise and time-wise equalization that is for the first time considered by an equalizer for an OCC system. * We propose a header-location-based image segmentation algorithm with satisfactory supportability for precise sub-image demodulation that combines signal tracing processing and gamma correction. * To our knowledge, the performance of the proposed decoding algorithm in data rate and BER is record-high in the RSE-based OCC. We also prove experimentally that the decoding scheme based on CAE-LSTM-Net outperforms those comprised of other existing CNNs when the transmitted symbol is modulated by 8-CSK.
## 1 Introduction
Efforts have been made to reduce the uncertainties of existing precipitation products by developing GPPs with a higher spatial and temporal resolution [32; 33]. However, the validation and evaluation of these products is based on thresholds and scores or is limited to certain regions with higher station densities. The conclusions from such studies might not be realistic for representing regional and larger scale precipitation behavior. Few studies have been conducted to compare the performance of GPPs with in-situ data that are limited to small regions, areas with a low density of station records or over short time periods. In the Swat river watershed in northern Pakistan TRMM products have been used in hydrological modelling but it has been reported that TMPA did not capture the precipitation magnitude and spatial patterns at annual and seasonal scales well enough for hydrological applications [34]. TMPA and APHRODITE products were compared with station data for 1998-2013. It was found that TMPA products slightly overestimated daily, monthly and annual precipitation, with variable error metrics according to the terrain elevation and the climatic region [35]. TRMM 3B43 monthly and reanalysis products were used in the Indus Basin alongside in-situ and river discharge data, significant uncertainties were observed in all GPPs mainly due to the lower number of in-situ records for validation in the complex topography [36]. TMPA and TMPA-RT products for 2005-2008 were compared over the monsoon regions of Pakistan and it was found that these datasets were reliable for monsoon season forecasts and applications [37]. TMPA (3B42 V-6 &V-7) and reanalysis precipitation for 1998-2006 were compared over India and it was found that the improved TMPA V-7 could capture monsoon and post-monsoon precipitation [38]. Monthly TRMM products were used in the Indus Basin for calibration purposes in runoff and water balance studies. It was reported that TRMM products underestimated precipitation over north western and south western coastal regions and overestimated precipitation in mountainous regions [39]. In this study, four GPPs with different resolutions, namely MSWEP, CHIRPS, PERSIANN-CDR and TMPA-3B42 V-7 were compared over a range of climatic zones in Pakistan, with varying land cover and elevation for the time period of 1998-2016. Daily precipitation records from 51 rain gauges were used, which enabled a more detailed analysis in terms of temporal and spatial coverage compared to previous studies in the same study area. The remainder of the paper consists of sections covering the study area, data and methods and results, while Sections 5 and 6 provides a discussion and conclusion.
# An Estimation Method of River Dry Runoff Alteration after Upper New Reservoirs Storage
## 2 Background and Methodology
### Hydrological Characteristics Alteration
#### 2.2.1 Jinsha River
After the construction of the first major dam, Ertan, in the Jinsha River basin, which began impounding in 2000, as mentioned earlier, a noticeable increase in monthly average runoff was observed during the dry period, ranging from 12 to 423 m\({}^{3}\)/s, with the highest values occurring in March. Conversely, a slight attenuation was observed during the flood period, ranging from 157 to 465 m\({}^{3}\)/s, with the highest values in October. Subsequently, with the completion of the Xiluodu, Xiangjiaba, and Jinping 1 st dams in 2014, a greater increase in monthly average runoff was observed during the dry period, ranging from 234 to 1253 m\({}^{3}\)/s. Additionally, a more significant attenuation was observed during the flood period, ranging from 547 to 1574 m\({}^{3}\)/s, compared to the situation before 2000, when no dam construction had taken place, as shown in Figure 3b. It highlights the substantial impact on downstream river hydraulic characteristics resulting from the implementation of cascade reservoirs.