esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
The distributed Time Division Multiple Access (TDMA)-based MAC protocol was employed to mitigate data collisions in vehicular ad hoc networks [30]. The channel reservation mechanism through Request-to-Send (RTS) and Test-to-Transmit handshake was proposed for a secured communication in an intelligent 6G network [31]. The link delay aware MAC protocol based on TDMA and Frequency Division Multiple Access was designed to improve energy consumption in wireless sensor networks [32]. TDMA schedule-based MAC protocols were used for energy harvesting in cognitive wireless sensor networks [33; 34]. The hybrid CSMA/TDMA MAC protocol was presented to satisfy a variety of QoS requirements in wireless sensor networks [35]. The TDMA-based hybrid MAC protocol was proposed to optimally control a power-saving mechanism to reduce energy consumption in wireless sensor networks [36]. The reservation-based MAC protocol through the scheduling of RTS frames was employed to reduce data collisions and energy consumption for wireless sensor networks [37]. The reservation-based MAC protocol through the random transmissions of RTS frames used Nash equilibrium theory to improve the throughput of wireless sensor networks [38]. In the case of wireless sensor networks based on non-802.11 networks like Bluetooth networks, the research efforts are divided into contention-based, contention-free or reservation-based approaches, and recently, more research efforts have focused on the development of contention-free or reservation-based MAC protocols for wireless sensor networks. We can summarize the research trend on wireless sensor networks based on Wi-Fi networks and non-802.11 networks, as shown in Table 1.  
The MAC protocol's performance significantly degrades as the number of sensors in wireless sensor networks increases due to the interference among sensors. When it comes to wireless sensor networks based on non-802.11 networks like Bluetooth networks, where sensors have a short transmission range, this degradation of network performance can be multiplied as data go through multi-hop links. This may lead to the early adoption of contention-free or reservation-based MAC protocols for wireless sensor networks based on non-802.11 networks. Research on contention-free or reservation-based MAC protocols for wireless sensor networks based on Wi-Fi networks is crucial for enhancing global network performance in a 6G society. Therefore, it is necessary to develop a contention-free MAC protocol that allows many Wi-Fi sensors to transmit their uplink data with minimal transmission overhead to utilize the radio spectrum more efficiently in Wi-Fi sensor networks.
# China's Water Intensity Factor Decomposition and Water Usage Decoupling Analysis
## 4 Conclusions and Implications  
This research focused on the investigation of driving factors behind water usage intensity in China from 2000 to 2020, and the identification of decoupling status between water usage and economics. The LMDI model and Tapio model were applied jointly. The results show that:  
(1) from 2000 to 2020, the technological effect, industrial structure effect, and regional scale effect are \(-376.54\), \(-89.85\), and \(20.66\), respectively. The technical effect is from \(-59.91\) to \(-4.05\), and the industrial structure effect is from \(-17.11\) to \(1.62\), indicating these two effects constrained the increase of water usage intensity. The regional scale effect was stable with values between \(-1\) and \(2\). From the perspectives of different industries, each effect has the greatest impact on primary industry, followed by secondary industry, and finally tertiary industry. (2) From the perspective of different provinces, the development of technology and the adjustment of industrial structure have promoted the decline of water intensity. The technological effect varies in different provinces. For example, Tianjin has the value of \(-1.20\), while Xinjiang has values of \(-34.05\). The industrial structure effect is smaller, with the largest value of \(-0.21\) in Qinghai and the smallest being \(-10.35\) in Jiangsu. The technology effect is greater than the industrial structure effect, except for in Anhui Province. When the effects in each industry in different provinces were explored, the technical effect is largest in primary industry in most areas, and the industrial structure effect of primary industry is negative, with positive values in tertiary industry. (3) The decoupling status for most provinces in China have gradually improved, from weak decoupling to strong decoupling. The technical effect is the main factor towards promoting the decoupling effect, followed by the industrial structure effect. Therefore, two implications could be put forward. Firstly, technological innovation is the most efficient effect on the reduction of water usage intensity in China with the proliferation of water-saving facilities, and it is still the most efficient policy in China in the
# Measurements of Operating Parameters of a Metal Hydride Compressor with a Heat Pump
## 3 A Thermal Analysis of the MH Tank during the Hydrogen Absorption (A \(\approx\) B)  
Equation (1) describes the amount of the heat removed during the isothermal hydrogen absorption into the metal alloy. If the real amount of the removed heat differs from the predefined value, the average temperature of the tank changes. The heating power generated during the hydrogen absorption \(P_{\rm G}\), plus the heating power received from the surrounding environment through the insulation \(P_{\rm ins}\), is removed by the heat exchanger \(P_{\rm he}\) (Figure 5). If a difference between the cooling power of the heat exchanger and the supplied power (\(P_{\rm G}+P_{\rm ins}\)) is not zero, the tank temperature changes while respecting the calorimetric equation.  
The power balance of the tank for an average temperature of the entire tank \(t\) at the time \(\tau\) is described by the following equation:  
\[P_{\rm G}+P_{\rm ins}-P_{\rm he}=\sum(m\cdot c)\cdot\frac{\rm d\,t}{\rm d\tau} \hskip 14.226378 pt(\rm W) \tag{8}\]  
The individual powers may be identified using the following equations:  
\[P_{\rm G}=Q_{\rm abs\ H_{2}}\cdot q_{\rm abs\ H_{2}}\hskip 14.226378 pt(\rm W) \tag{9}\]  
\[P_{\rm ins}=k\cdot A\cdot(t_{\rm a}-t)\hskip 14.226378 pt(\rm W) \tag{10}\]  
\[P_{\rm he}=Q_{\rm mw}\cdot c_{\rm w}\cdot(t_{\rm w-out}-t_{\rm w-in})\hskip 14.226378 pt (\rm W) \tag{11}\]  
where \(Q_{\rm mw}\) is the mass flow rate of the heat-transfer fluid through the heat exchanger (\(\rm kg\cdot s^{-1}\)); \(c_{\rm w}\) is the heat capacity of the heat-transfer fluid (\(\rm\rm\cdot kg^{-1}\).\(\rm K^{-1}\)); \(t_{\rm w\text{-out}}\) is the fluid temperature at the outlet from the heat exchanger (\({}^{\circ}\)C); and \(t_{\rm w\text{-in}}\) is the fluid temperature at the inlet to the heat exchanger (\({}^{\circ}\)C). This mathematical model was used to calculate the current tank temperatures over time with the known operating parameters. Rewriting Equation (8) from a differential form to a difference form facilitated the calculation of the average difference in the tank temperature \(\Delta t\) with the known time difference \(\Delta\tau\).  
\[\Delta t=\frac{Q_{\rm abs\ H_{2}}\cdot q_{\rm abs\ H_{2}}+k\cdot A\cdot(t_{\rm a }-t)-Q_{\rm mw}\cdot c_{\rm w}\cdot(t_{\rm w-out}-t_{\rm w-in})}{\sum(m\cdot c)} \cdot\Delta\tau\hskip 14.226378 pt(^{\circ}\rm C) \tag{12}\]  
The power balance described above was an essential precondition for achieving an optimal design of the MH compressor. Its suitability and accuracy were verified during the measurements of the operating parameters, as described in the next chapter.
## 1 Introduction  
The Limboto Lake--Pentado area is located in the Gorontalo Province, Indonesia, on the northern arm of the Sulawesi Island. The northern arm of Sulawesi Island has a unique geological condition that is characterized by the tectonic activity of the Sulawesi Sea subduction, the Gorontalo Fault, the Molucca Sea double subduction, and the Palu-Koro Fault [1]. The Gorontalo Fault is one of the major block-bounding structures in Sulawesi, and it consists of several segments, including branching segments in the west-east (W-E) direction that are approximately 30 km long and are located to the south and north of the area [2].  
Limboto Lake is shallow and has undergone extensive sedimentation and area reduction. In 1932, the Limboto Lake area was approximately 7000 ha and 30 m deep. However, in 1999, the area was approximately 1900-3000 ha and 2-4 m in depth [3]. Currently, this area is included in the development of the Gorontalo Geopark [4] and geo-tourism [5] with respect to its geological condition and geothermal potential. Geothermal surface manifestations, such as the complex of hot springs near Limboto Lake, may be attributed to the presence of a geothermal system beneath the area. Prior to our study, there were no geophysical surveys covering the Limboto Lake--Pentado area; therefore, no geophysical work has been reported. In this study, we conducted geophysical surveys using magnetotelluric (MT) and gravity methods along with geological observations in Limboto Lake, Pentadio.  
The use of the MT method in geoscience studies and geophysical exploration has increased significantly over the last few decades. This method has been widely applied in various applications, such as volcanic system studies [6; 7; 8; 9]; mineral exploration [10]; groundwater surveys [11]; fault structure investigations [12]; geothermal exploration [13; 14]; and subduction zone, lithospheric structure, and orogenic research [15; 16; 17]. This method identifies subsurface structures based on their electrical resistivity properties. The MT method employs natural electromagnetic signals that are highly sensitive to contrasts in subsurface resistivity structures, thus characterizing geothermal system components, particularly the presence of hydrothermal fluids and alteration clay [18; 19; 20]. In this study, a three-dimensional (3D) MT data inversion was conducted to provide more realistic information regarding the resistivity structure beneath the Limboto Lake--Pentadio area and the possibility of an associated geothermal system.  
The gravity method has been used to determine subsurface geological structures that characterize a geothermal system based on the interpreted subsurface density distribution (e.g., [21; 22; 23; 24]). The interpretation of the MT method is frequently paired with that of the gravity method to create complementary geothermal conceptual models, which in turn improves the geophysical constraints of the conceptual model. This study involved data processing and the interpretation of the MT and gravity methods to obtain information on the geothermal system beneath the Limboto Lake--Pentadio area.
# Isothermal Drying Process and its Effect on Compressive Strength of Concrete in Multiscale
## 3 Results
### Compression Test  
#### 3.2.1 Compression Strength  
In this study, the drying effect on compressive strength is represented by the relative compressive strength, defined as the ratio of compressive strength in dry state to that in saturated state. As evident from Figure 4, the compressive strength of dry specimens was higher 23.91%\(\sim\)25.73%, 13.11%\(\sim\)15.45%, and 4.23%\(\sim\)6.18% than that of saturated specimens, respectively, for cement paste, mortar, and concrete. The strengthening effect of isothermal drying on compressive strength is significant and differs at three scales. Water/cement ratio seems to have little effect on this effect.  
Figure 3: Water-losing rate of cement paste, mortar, and concrete.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Water/Cement Ratio**} & \multirow{2}{*}{**Material**} & \multirow{2}{*}{**Mass Fraction of Paste/\%**} & \multicolumn{2}{c}{**Time Required to Completely Dry State/h**} & \multicolumn{2}{c}{**Water Loss (by Mass)/\%**} & \multicolumn{2}{c}{**Maximum Water-Losing Rate/(kg\(\cdot\)m\({}^{-2}\)-h\({}^{-1}\))**} \\  & & & & & \\ \hline \multirow{4}{*}{0.35} & cement paste & 1.0 & 180 & 16.832 & 0.304 \\  & mortar & 0.403 & 120 & 6.817 & 0.144 \\  & concrete & 0.200 & 80 & 2.950 & 0.111 \\  & cement paste & 1.0 & 172 & 18.268 & 0.338 \\
0.38 & mortar & 0.408 & 104 & 7.289 & 0.178 \\  & concrete & 0.203 & 72 & 3.135 & 0.111 \\  & cement paste & 1.0 & 164 & 19.483 & 0.411 \\
0.41 & mortar & 0.413 & 96 & 7.978 & 0.222 \\  & concrete & 0.207 & 68 & 4.204 & 0.154 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Drying results of cement paste, mortar, and concrete.
# Deep Learning-Based Automatic Summarization of Chinese Maritime Judgment Documents
## 4 HybridSumm: A Hybrid Summarization Model for Maritime Judgment Documents
### Nezha-UniLM-CC: An Abstractive Summarization Model
#### 4.2.1 Nezha Pre-Trained Model  
Nezha (neural contextualized representation for Chinese language understanding), proposed by Huawei Noah's Ark Lab in 2019, is a BERT-based pre-trained language model for Chinese NLP tasks [67]. It achieved state-of-the-art performance across multiple Chinese NLP benchmarks at the time. Similar to the contemporaneous RoBERTa-wwm-ext model, Nezha adopts the whole word masking strategy to enhance semantic modeling of Chinese lexical units. Additionally, Nezha optimizes the original BERT architecture in the following three aspects:  
1. Functional relative positional encoding. To address the limitations of BERT's absolute positional encoding in handling long sequences (e.g., exceeding the 512-token length constraints during pre-training), Nezha introduces a novel sinusoidal-based relative positional encoding mechanism. By mapping relative positional relationships between tokens into a fixed-dimensional vector space using periodic sinusoidal functions--where each dimension corresponds to a specific sinusoidal frequency--the model dynamically generates positional embeddings. This approach not only overcomes sequence length limitations, but also effectively captures long-range dependencies through its periodic characteristics, significantly improving the modeling capability for ultra-long texts.
2. Mixed-precision training acceleration. To enhance training efficiency, Nezha employs an FP16/FP32 mixed-precision training strategy [68]. This method maintains FP32-format master weights as the parameter baseline while converting them to FP16 for forward and backward computations. Gradients are then updated back to the FP32 master weights. By leveraging the computational advantages of GPU tensor cores for half-precision operations while ensuring numerical stability, this strategy achieves a 1.5-3x training speed improvement.
3. Large-scale distributed training optimization. To mitigate the performance degradation of traditional optimizers under large-batch training, Nezha pioneers the use of the LAMB (layer-wise adaptive moments optimizer for batch training) optimizer. LAMB enables stable convergence even when scaling batch sizes to tens of thousands through layer-wise adaptive learning rate adjustments, providing critical technical support for distributed training of ultra-large language models.  
In the maritime judgment document summarization task, where input texts frequently exceed 512 tokens in length, we employ Nezha as the base encoder. By leveraging its optimized relative positional encoding mechanism, Nezha effectively extracts deep semantic features from long documents. The resulting dynamic contextualized vectors are then fed into the unified language model (UniLM) decoder to generate abstractive summaries.
## 2 Materials and Methods
### Identification of PV Power Stealing Based on Interval Prediction
#### 2.3.1 Judgment of Suspected Short-Term Power Stealing  
The judgement of short-term power-stealing suspicion is mainly on the daily scale. The judgement includes two layers. The first layer screening adopts the prediction results at the 95% confidence level. At this time, the coverage rate of the interval is highest, and the average width of the interval is the widest. So, it is suitable for screening the most obvious power-stealing behaviors of users, such as in the increasing voltage method and increasing current method. The first layer of power-stealing identification mainly uses two moments in the morning and evening (6:00-8:00 and 16:00-18:00). No matter what the weather type is, the solar radiation at sunrise and sunset is relatively weak, so theoretically, the power generation of the photovoltaic array is very small. However, for some users who steal electricity, the photovoltaic output in these two periods will be significantly higher than the actual output. Therefore, the photovoltaic power generation in these two periods can be used to determine whether the user has the behavior of stealing power. If the user is found to be three or more times outside the prediction interval in these two periods or fails to fall within the prediction interval for more than five times in a day, it is directly determined that the user is suspected of power stealing.  
The second layer of screening is for users who cannot be determined in the first layer, and the key is to calculate the deviation of the points that fall outside the prediction interval.  
Figure 2: QRLSTM prediction flowchart.  
To this end, a normalized average deviation (NAD) indicator is introduced. The expression is as shown in Equations (9) and (10).  
\[e_{NAD}=\frac{1}{N_{intract}}\sum_{i=1}^{N_{intract}}\gamma_{i}, \tag{9}\]  
\[\gamma_{i}=\left\{\begin{array}{ll}\frac{y_{i}-U_{i}}{\frac{1}{N_{intract}} \sum\limits_{i=1}^{N_{intract}}(U_{i}-U_{i})}&y_{i}>U_{i}\\ \frac{1}{N_{intract}}\sum\limits_{i=1}^{N_{intract}}(U_{i}-U_{i})&y_{i}<L_{i} \end{array}\right., \tag{10}\]  
where \(e\) represents the deviation and \(y_{i}\) represents the actual output value. The larger the \(e_{NAD}\) is, the farther the non-falling point is from the prediction interval, and a reasonable upper limit of deviation is set by referring to the identification of experts and comparing the specific samples of historical photovoltaic power-stealing users to screen out the suspected power-stealing users. If the value of the \(e_{NAD}\) is more than 20% on sunny and cloudy days or more than 30% on rainy days, the user is suspected of power stealing. The second layer of screening uses the prediction results at the 90% confidence level. Here, the average width of the interval is slightly smaller than that at the 95% confidence level, which is suitable for screening the less obvious power-stealing user behavior, such as in the photovoltaic simulator method.
# Optimised Tuning of a PID-Based Flight Controller for a Medium-Scale Rotorcraft
## 2 System Overview and Mathematical Modelling
### Reference Frames  
The rotorcraft dynamics are obtained using the Newton-Euler approach. To make this possible, two reference frames are essential. An Earth-fixed reference frame \(F_{E}=\{RO,x,y,z\}\) is used to represent an inertial reference frame. The second frame of reference is a body-fixed reference frame \(F_{B}=\{R_{B}O_{B},x_{B},y_{B},z_{B}\}\). The centre of this reference frame \(O_{B}\) is assigned to coincide with the rotorcraft's centre of gravity (CG). In this case, \(\zeta=[x\;y\;z]^{T}\) is the position of the rotorcraft's CG with respect to the Earth-fixed reference frame. The rotational angles (i.e., Euler angles) are \(\eta=[\phi\;\theta\;\psi]^{T}\) of the body-fixed reference frame with respect to the Earth-fixed reference frame. The translational and rotational (angular) velocities of the moving body-fixed reference frame are given by \(\mathbf{v}^{B}=[u\;v\;w]^{T}\) and \(\omega^{B}=[p\;q\;r]\), respectively.
# BVLOS Unmanned Aircraft Operations in Forest Environments
## 5 Taking a Risk-Based Approach to BVLOS Operations in Forest Environments  
While there are common considerations worldwide, in terms of following airspace requirements, ensuring separation from other aircraft, and minimising risk to people, property, and terrain, these points can be addressed in many different ways, and different countries may have preferred methods. The following sections describe some of the technologies, operational considerations, and other considerations that should be taken into account when formulating an organisation-specific approach to safely conducting a BVLOS operation. Some considerations will be more or less relevant depending on the exact nature of the operation.
# Game Theory-Based Load-Balancing Algorithms for Small Cells Wireless Backhaul Connections +
## 2 Materials and Methods
### Modeling the Load Balancing on a Set of Tunnels as a Multiplayer Game  
We consider that our communication system must transmit \(K\) input flows and there are available \(N\) tunnels. We denote by \(\mathbf{s}=(\mathbf{K_{1}},\mathbf{K_{2}},\ldots,\mathbf{K_{N}})\) the surjective mapping of the \(K\) flows on the tunnels. \(\mathbf{K_{j}}\) denotes the set of input flows mapped to tunnel \(j\), and we have the conditions: \(\mathbf{K_{1}}\cup\mathbf{K_{2}}\cup\ldots\cup\mathbf{K_{N}}=\mathbf{K}\) and \(\mathbf{K_{i}}\cap\mathbf{K_{j}}=\varnothing\); \(\forall i\
eq j\). We denote by \(\mathbf{S}\) the set of all flow-tunnel mapping possibilities and the system response function \(r:\mathbf{S}\rightarrow\mathbb{R}\); \(\mathbf{s}\in\mathbf{S}\)  
Figure 4: Networking scenario with several APs deployment: (**a**) 2 AP deployment; (**b**) 3 AP deployment.  
gives the system's efficiency if mapping \(\mathbf{s}\) is used. The objective of the LB algorithm is to identify a mapping that maximizes the system efficiency [35], which can be defined as:  
\[arg_{s}\text{max}r(\mathbf{s}):=\{\mathbf{s}|\forall x\in\mathbf{S}:r(x)\leq r( \mathbf{s})\,\} \tag{17}\]  
The LB problem defined above can be modeled as a game in which players are agents representing the flows and the tunnels [36]. The strategies of the flow agents consist of the selection of the tunnels established through the heterogeneous network. The objectives of the flow agents are the maximization of the flows' throughput which is equivalent with the minimization of the delay suffered in the system by the packets of the flows (private objective). The strategies of the tunnel agents consist of the selection of a mapping \(\mathbf{s}\), so that the total rate of the flows mapped on a tunnel should be equal to or less than the instantaneous capacity \(C_{i}(t)\) of the tunnel represented by the agent.